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AutoGPT
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Merge branch 'master' of https://github.com/Significant-Gravitas/Auto-GPT into plugin-support

pull/2531/head
BillSchumacher 2023-04-15 21:37:27 -05:00
parent 0b955c0546 4daa083fd3
commit 1af463b03c
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128 changed files with 6846 additions and 2522 deletions
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23
.devcontainer/Dockerfile Normal file
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# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3.9, 3.8, 3.7, 3.6, 3-bullseye, 3.10-bullseye, 3.9-bullseye, 3.8-bullseye, 3.7-bullseye, 3.6-bullseye, 3-buster, 3.10-buster, 3.9-buster, 3.8-buster, 3.7-buster, 3.6-buster
ARG VARIANT=3-bullseye
FROM python:3.8
RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
# Remove imagemagick due to https://security-tracker.debian.org/tracker/CVE-2019-10131
&& apt-get purge -y imagemagick imagemagick-6-common
# Temporary: Upgrade python packages due to https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-40897
# They are installed by the base image (python) which does not have the patch.
RUN python3 -m pip install --upgrade setuptools
# [Optional] If your pip requirements rarely change, uncomment this section to add them to the image.
# COPY requirements.txt /tmp/pip-tmp/
# RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
# && rm -rf /tmp/pip-tmp
# [Optional] Uncomment this section to install additional OS packages.
# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
# && apt-get -y install --no-install-recommends <your-package-list-here>
# [Optional] Uncomment this line to install global node packages.
# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1

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.devcontainer/devcontainer.json Normal file
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{
"build": {
"dockerfile": "./Dockerfile",
"context": "."
},
"features": {
"ghcr.io/devcontainers/features/common-utils:2": {
"installZsh": "true",
"username": "vscode",
"userUid": "1000",
"userGid": "1000",
"upgradePackages": "true"
},
"ghcr.io/devcontainers/features/python:1": "none",
"ghcr.io/devcontainers/features/node:1": "none",
"ghcr.io/devcontainers/features/git:1": {
"version": "latest",
"ppa": "false"
}
},
// Configure tool-specific properties.
"customizations": {
// Configure properties specific to VS Code.
"vscode": {
// Set *default* container specific settings.json values on container create.
"settings": {
"python.defaultInterpreterPath": "/usr/local/bin/python"
}
}
},
// Use 'forwardPorts' to make a list of ports inside the container available locally.
// "forwardPorts": [],
// Use 'postCreateCommand' to run commands after the container is created.
// "postCreateCommand": "pip3 install --user -r requirements.txt",
// Set `remoteUser` to `root` to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
"remoteUser": "vscode"
}

155
.env.template
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PINECONE_API_KEY=your-pinecone-api-key
PINECONE_ENV=your-pinecone-region
################################################################################
### AUTO-GPT - GENERAL SETTINGS
################################################################################
# EXECUTE_LOCAL_COMMANDS - Allow local command execution (Example: False)
EXECUTE_LOCAL_COMMANDS=False
# BROWSE_CHUNK_MAX_LENGTH - When browsing website, define the length of chunk stored in memory
BROWSE_CHUNK_MAX_LENGTH=8192
# BROWSE_SUMMARY_MAX_TOKEN - Define the maximum length of the summary generated by GPT agent when browsing website
BROWSE_SUMMARY_MAX_TOKEN=300
# USER_AGENT - Define the user-agent used by the requests library to browse website (string)
# USER_AGENT="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"
# AI_SETTINGS_FILE - Specifies which AI Settings file to use (defaults to ai_settings.yaml)
AI_SETTINGS_FILE=ai_settings.yaml
# USE_WEB_BROWSER - Sets the web-browser drivers to use with selenium (defaults to chrome).
# Note: set this to either 'chrome', 'firefox', or 'safari' depending on your current browser
# USE_WEB_BROWSER=chrome
################################################################################
### LLM PROVIDER
################################################################################
### OPENAI
# OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)
# TEMPERATURE - Sets temperature in OpenAI (Default: 1)
# USE_AZURE - Use Azure OpenAI or not (Default: False)
OPENAI_API_KEY=your-openai-api-key
ELEVENLABS_API_KEY=your-elevenlabs-api-key
TEMPERATURE=0
USE_AZURE=False
### AZURE
# cleanup azure env as already moved to `azure.yaml.template`
################################################################################
### LLM MODELS
################################################################################
# SMART_LLM_MODEL - Smart language model (Default: gpt-4)
# FAST_LLM_MODEL - Fast language model (Default: gpt-3.5-turbo)
SMART_LLM_MODEL=gpt-4
FAST_LLM_MODEL=gpt-3.5-turbo
GOOGLE_API_KEY=
CUSTOM_SEARCH_ENGINE_ID=
USE_AZURE=False
OPENAI_AZURE_API_BASE=your-base-url-for-azure
OPENAI_AZURE_API_VERSION=api-version-for-azure
OPENAI_AZURE_DEPLOYMENT_ID=deployment-id-for-azure
### LLM MODEL SETTINGS
# FAST_TOKEN_LIMIT - Fast token limit for OpenAI (Default: 4000)
# SMART_TOKEN_LIMIT - Smart token limit for OpenAI (Default: 8000)
# When using --gpt3only this needs to be set to 4000.
FAST_TOKEN_LIMIT=4000
SMART_TOKEN_LIMIT=8000
################################################################################
### MEMORY
################################################################################
# MEMORY_BACKEND - Memory backend type (Default: local)
MEMORY_BACKEND=local
### PINECONE
# PINECONE_API_KEY - Pinecone API Key (Example: my-pinecone-api-key)
# PINECONE_ENV - Pinecone environment (region) (Example: us-west-2)
PINECONE_API_KEY=your-pinecone-api-key
PINECONE_ENV=your-pinecone-region
### REDIS
# REDIS_HOST - Redis host (Default: localhost)
# REDIS_PORT - Redis port (Default: 6379)
# REDIS_PASSWORD - Redis password (Default: "")
# WIPE_REDIS_ON_START - Wipes data / index on start (Default: False)
# MEMORY_INDEX - Name of index created in Redis database (Default: auto-gpt)
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=
WIPE_REDIS_ON_START=False
MEMORY_INDEX=auto-gpt
### MILVUS
# MILVUS_ADDR - Milvus remote address (e.g. localhost:19530)
# MILVUS_COLLECTION - Milvus collection,
# change it if you want to start a new memory and retain the old memory.
MILVUS_ADDR=your-milvus-cluster-host-port
MILVUS_COLLECTION=autogpt
################################################################################
### IMAGE GENERATION PROVIDER
################################################################################
### OPEN AI
# IMAGE_PROVIDER - Image provider (Example: dalle)
IMAGE_PROVIDER=dalle
HUGGINGFACE_API_TOKEN=
### HUGGINGFACE
# STABLE DIFFUSION
# (Default URL: https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4)
# Set in image_gen.py)
# HUGGINGFACE_API_TOKEN - HuggingFace API token (Example: my-huggingface-api-token)
HUGGINGFACE_API_TOKEN=your-huggingface-api-token
################################################################################
### AUDIO TO TEXT PROVIDER
################################################################################
### HUGGINGFACE
HUGGINGFACE_AUDIO_TO_TEXT_MODEL=facebook/wav2vec2-base-960h
################################################################################
### GIT Provider for repository actions
################################################################################
### GITHUB
# GITHUB_API_KEY - Github API key / PAT (Example: github_pat_123)
# GITHUB_USERNAME - Github username
GITHUB_API_KEY=github_pat_123
GITHUB_USERNAME=your-github-username
################################################################################
### SEARCH PROVIDER
################################################################################
### GOOGLE
# GOOGLE_API_KEY - Google API key (Example: my-google-api-key)
# CUSTOM_SEARCH_ENGINE_ID - Custom search engine ID (Example: my-custom-search-engine-id)
GOOGLE_API_KEY=your-google-api-key
CUSTOM_SEARCH_ENGINE_ID=your-custom-search-engine-id
################################################################################
### TTS PROVIDER
################################################################################
### MAC OS
# USE_MAC_OS_TTS - Use Mac OS TTS or not (Default: False)
USE_MAC_OS_TTS=False
### STREAMELEMENTS
# USE_BRIAN_TTS - Use Brian TTS or not (Default: False)
USE_BRIAN_TTS=False
### ELEVENLABS
# ELEVENLABS_API_KEY - Eleven Labs API key (Example: my-elevenlabs-api-key)
# ELEVENLABS_VOICE_1_ID - Eleven Labs voice 1 ID (Example: my-voice-id-1)
# ELEVENLABS_VOICE_2_ID - Eleven Labs voice 2 ID (Example: my-voice-id-2)
ELEVENLABS_API_KEY=your-elevenlabs-api-key
ELEVENLABS_VOICE_1_ID=your-voice-id-1
ELEVENLABS_VOICE_2_ID=your-voice-id-2
################################################################################
### TWITTER API
################################################################################
TW_CONSUMER_KEY=
TW_CONSUMER_SECRET=
TW_ACCESS_TOKEN=
TW_ACCESS_TOKEN_SECRET=

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.flake8 Normal file
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[flake8]
max-line-length = 88
extend-ignore = E203
exclude =
.tox,
__pycache__,
*.pyc,
.env
venv/*
.venv/*
reports/*
dist/*

18
.github/ISSUE_TEMPLATE/1.bug.yml vendored
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@ -7,7 +7,19 @@ body:
value: |
Please provide a searchable summary of the issue in the title above ⬆️.
Thanks for contributing by creating an issue! ❤️
⚠️ SUPER-busy repo, please help the volunteer maintainers.
The less time we spend here, the more time we spend building AutoGPT.
Please help us help you:
- Does it work on `stable` branch (https://github.com/Torantulino/Auto-GPT/tree/stable)?
- Does it work on current `master` (https://github.com/Torantulino/Auto-GPT/tree/master)?
- Search for existing issues, "add comment" is tidier than "new issue"
- Ask on our Discord (https://discord.gg/autogpt)
- Provide relevant info:
- Provide commit-hash (`git rev-parse HEAD` gets it)
- If it's a pip/packages issue, provide pip version, python version
- If it's a crash, provide traceback.
- type: checkboxes
attributes:
label: Duplicates
@ -32,8 +44,8 @@ body:
attributes:
label: Your prompt 📝
description: |
Please provide the prompt you are using. You can find your last-used prompt in last_run_ai_settings.yaml.
If applicable please provide the prompt you are using. You can find your last-used prompt in last_run_ai_settings.yaml.
value: |
```yaml
# Paste your prompt here
```
```

4
.github/PULL_REQUEST_TEMPLATE.md vendored
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@ -23,10 +23,10 @@ By following these guidelines, your PRs are more likely to be merged quickly aft
### PR Quality Checklist
- [ ] My pull request is atomic and focuses on a single change.
- [ ] I have thouroughly tested my changes with multiple different prompts.
- [ ] I have thoroughly tested my changes with multiple different prompts.
- [ ] I have considered potential risks and mitigations for my changes.
- [ ] I have documented my changes clearly and comprehensively.
- [ ] I have not snuck in any "extra" small tweaks changes <!-- Submit these as seperate Pull Reqests, they are the easiest to merge! -->
- [ ] I have not snuck in any "extra" small tweaks changes <!-- Submit these as separate Pull Requests, they are the easiest to merge! -->
<!-- If you haven't added tests, please explain why. If you have, check the appropriate box. If you've ensured your PR is atomic and well-documented, check the corresponding boxes. -->

44
.github/workflows/ci.yml vendored Normal file
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name: Python CI
on:
push:
branches:
- master
pull_request:
branches:
- master
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [3.8]
steps:
- name: Check out repository
uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Lint with flake8
continue-on-error: false
run: flake8 autogpt/ tests/ --select E303,W293,W291,W292,E305,E231,E302
- name: Run unittest tests with coverage
run: |
coverage run --source=autogpt -m unittest discover tests
- name: Generate coverage report
run: |
coverage report
coverage xml

155
.gitignore vendored
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@ -1,14 +1,159 @@
scripts/keys.py
scripts/*json
scripts/node_modules/
scripts/__pycache__/keys.cpython-310.pyc
## Original ignores
autogpt/keys.py
autogpt/*json
autogpt/node_modules/
autogpt/__pycache__/keys.cpython-310.pyc
package-lock.json
*.pyc
auto_gpt_workspace/*
*.mpeg
.env
venv/*
azure.yaml
*venv/*
outputs/*
ai_settings.yaml
last_run_ai_settings.yaml
.vscode
.idea/*
auto-gpt.json
log.txt
log-ingestion.txt
logs
*.log
*.mp3
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
plugins/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
llama-*
vicuna-*
# mac
.DS_Store

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.isort.cfg Normal file
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@ -0,0 +1,10 @@
[settings]
profile = black
multi_line_output = 3
include_trailing_comma = True
force_grid_wrap = 0
use_parentheses = True
ensure_newline_before_comments = True
line_length = 88
skip = venv,env,node_modules,.env,.venv,dist
sections = FUTURE,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER

33
.pre-commit-config.yaml Normal file
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@ -0,0 +1,33 @@
repos:
- repo: https://github.com/sourcery-ai/sourcery
rev: v1.1.0 # Get the latest tag from https://github.com/sourcery-ai/sourcery/tags
hooks:
- id: sourcery
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v0.9.2
hooks:
- id: check-added-large-files
args: [ '--maxkb=500' ]
- id: check-byte-order-marker
- id: check-case-conflict
- id: check-merge-conflict
- id: check-symlinks
- id: debug-statements
- repo: local
hooks:
- id: isort
name: isort-local
entry: isort
language: python
types: [ python ]
exclude: .+/(dist|.venv|venv|build)/.+
pass_filenames: true
- id: black
name: black-local
entry: black
language: python
types: [ python ]
exclude: .+/(dist|.venv|venv|build)/.+
pass_filenames: true

71
.sourcery.yaml Normal file
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@ -0,0 +1,71 @@
# 🪄 This is your project's Sourcery configuration file.
# You can use it to get Sourcery working in the way you want, such as
# ignoring specific refactorings, skipping directories in your project,
# or writing custom rules.
# 📚 For a complete reference to this file, see the documentation at
# https://docs.sourcery.ai/Configuration/Project-Settings/
# This file was auto-generated by Sourcery on 2023-02-25 at 21:07.
version: '1' # The schema version of this config file
ignore: # A list of paths or files which Sourcery will ignore.
- .git
- venv
- .venv
- build
- dist
- env
- .env
- .tox
rule_settings:
enable:
- default
- gpsg
disable: [] # A list of rule IDs Sourcery will never suggest.
rule_types:
- refactoring
- suggestion
- comment
python_version: '3.9' # A string specifying the lowest Python version your project supports. Sourcery will not suggest refactorings requiring a higher Python version.
# rules: # A list of custom rules Sourcery will include in its analysis.
# - id: no-print-statements
# description: Do not use print statements in the test directory.
# pattern: print(...)
# language: python
# replacement:
# condition:
# explanation:
# paths:
# include:
# - test
# exclude:
# - conftest.py
# tests: []
# tags: []
# rule_tags: {} # Additional rule tags.
# metrics:
# quality_threshold: 25.0
# github:
# labels: []
# ignore_labels:
# - sourcery-ignore
# request_review: author
# sourcery_branch: sourcery/{base_branch}
# clone_detection:
# min_lines: 3
# min_duplicates: 2
# identical_clones_only: false
# proxy:
# url:
# ssl_certs_file:
# no_ssl_verify: false

34
CONTRIBUTING.md
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@ -6,43 +6,51 @@ To contribute to this GitHub project, you can follow these steps:
2. Clone the repository to your local machine using the following command:
```
git clone https://github.com/Torantulino/Auto-GPT
git clone https://github.com/<YOUR-GITHUB-USERNAME>/Auto-GPT
```
3. Create a new branch for your changes using the following command:
3. Install the project requirements
```
pip install -r requirements.txt
```
4. Install pre-commit hooks
```
pre-commit install
```
5. Create a new branch for your changes using the following command:
```
git checkout -b "branch-name"
```
4. Make your changes to the code or documentation.
6. Make your changes to the code or documentation.
- Example: Improve User Interface or Add Documentation.
5. Add the changes to the staging area using the following command:
7. Add the changes to the staging area using the following command:
```
git add .
```
6. Commit the changes with a meaningful commit message using the following command:
8. Commit the changes with a meaningful commit message using the following command:
```
git commit -m "your commit message"
```
7. Push the changes to your forked repository using the following command:
9. Push the changes to your forked repository using the following command:
```
git push origin branch-name
```
8. Go to the GitHub website and navigate to your forked repository.
10. Go to the GitHub website and navigate to your forked repository.
9. Click the "New pull request" button.
11. Click the "New pull request" button.
10. Select the branch you just pushed to and the branch you want to merge into on the original repository.
12. Select the branch you just pushed to and the branch you want to merge into on the original repository.
11. Add a description of your changes and click the "Create pull request" button.
13. Add a description of your changes and click the "Create pull request" button.
12. Wait for the project maintainer to review your changes and provide feedback.
14. Wait for the project maintainer to review your changes and provide feedback.
13. Make any necessary changes based on feedback and repeat steps 5-12 until your changes are accepted and merged into the main project.
15. Make any necessary changes based on feedback and repeat steps 5-12 until your changes are accepted and merged into the main project.
14. Once your changes are merged, you can update your forked repository and local copy of the repository with the following commands:
16. Once your changes are merged, you can update your forked repository and local copy of the repository with the following commands:
```
git fetch upstream

30
Dockerfile
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@ -1,9 +1,27 @@
FROM python:3.11
# Use an official Python base image from the Docker Hub
FROM python:3.11-slim
WORKDIR /app
COPY scripts/ /app
COPY requirements.txt /app
# Install git
RUN apt-get -y update
RUN apt-get -y install git
RUN pip install -r requirements.txt
# Set environment variables
ENV PIP_NO_CACHE_DIR=yes \
PYTHONUNBUFFERED=1 \
PYTHONDONTWRITEBYTECODE=1
CMD ["python", "main.py"]
# Create a non-root user and set permissions
RUN useradd --create-home appuser
WORKDIR /home/appuser
RUN chown appuser:appuser /home/appuser
USER appuser
# Copy the requirements.txt file and install the requirements
COPY --chown=appuser:appuser requirements-docker.txt .
RUN pip install --no-cache-dir --user -r requirements-docker.txt
# Copy the application files
COPY --chown=appuser:appuser autogpt/ ./autogpt
# Set the entrypoint
ENTRYPOINT ["python", "-m", "autogpt"]

459
README.md
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@ -1,13 +1,21 @@
# Auto-GPT: An Autonomous GPT-4 Experiment
![GitHub Repo stars](https://img.shields.io/github/stars/Torantulino/auto-gpt?style=social)
![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)
[![](https://dcbadge.vercel.app/api/server/PQ7VX6TY4t?style=flat)](https://discord.gg/PQ7VX6TY4t)
### 🔴 🔴 🔴 Urgent: USE `stable` not `master` 🔴 🔴 🔴
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, autonomously develops and manages businesses to increase net worth. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.
> This information takes precedence over any other information provided and should be followed accordingly.
Our workflow has been improved, but please note that `master` branch may often be in a **broken** state.
Please download the latest `stable` release from here: https://github.com/Torantulino/Auto-GPT/releases/latest.
![GitHub Repo stars](https://img.shields.io/github/stars/Torantulino/auto-gpt?style=social)
[![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)](https://twitter.com/SigGravitas)
[![Discord Follow](https://dcbadge.vercel.app/api/server/autogpt?style=flat)](https://discord.gg/autogpt)
[![Unit Tests](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml/badge.svg)](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml)
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.
### Demo (30/03/2023):
https://user-images.githubusercontent.com/22963551/228855501-2f5777cf-755b-4407-a643-c7299e5b6419.mp4
https://user-images.githubusercontent.com/22963551/228855501-2f5777cf-755b-4407-a643-c7299e5b6419.mp4
<h2 align="center"> 💖 Help Fund Auto-GPT's Development 💖</h2>
<p align="center">
@ -26,27 +34,38 @@ Your support is greatly appreciated
&nbsp;&nbsp;<a href="https://github.com/SpacingLily"><img src="https://github.com/SpacingLily.png" width="50px" alt="SpacingLily" /></a>&nbsp;&nbsp;<a href="https://github.com/merwanehamadi"><img src="https://github.com/merwanehamadi.png" width="50px" alt="merwanehamadi" /></a>&nbsp;&nbsp;<a href="https://github.com/m"><img src="https://github.com/m.png" width="50px" alt="m" /></a>&nbsp;&nbsp;<a href="https://github.com/zkonduit"><img src="https://github.com/zkonduit.png" width="50px" alt="zkonduit" /></a>&nbsp;&nbsp;<a href="https://github.com/maxxflyer"><img src="https://github.com/maxxflyer.png" width="50px" alt="maxxflyer" /></a>&nbsp;&nbsp;<a href="https://github.com/tekelsey"><img src="https://github.com/tekelsey.png" width="50px" alt="tekelsey" /></a>&nbsp;&nbsp;<a href="https://github.com/digisomni"><img src="https://github.com/digisomni.png" width="50px" alt="digisomni" /></a>&nbsp;&nbsp;<a href="https://github.com/nocodeclarity"><img src="https://github.com/nocodeclarity.png" width="50px" alt="nocodeclarity" /></a>&nbsp;&nbsp;<a href="https://github.com/tjarmain"><img src="https://github.com/tjarmain.png" width="50px" alt="tjarmain" /></a>
</p>
## Table of Contents
- [Auto-GPT: An Autonomous GPT-4 Experiment](#auto-gpt-an-autonomous-gpt-4-experiment)
- [🔴 🔴 🔴 Urgent: USE `stable` not `master` 🔴 🔴 🔴](#----urgent-use-stable-not-master----)
- [Demo (30/03/2023):](#demo-30032023)
- [💖 Help Fund Auto-GPT's Development](#-help-fund-auto-gpts-development)
- [Table of Contents](#table-of-contents)
- [🚀 Features](#-features)
- [📋 Requirements](#-requirements)
- [💾 Installation](#-installation)
- [🔧 Usage](#-usage)
- [Logs](#logs)
- [Docker](#docker)
- [Command Line Arguments](#command-line-arguments)
- [🗣️ Speech Mode](#️-speech-mode)
- [🔍 Google API Keys Configuration](#-google-api-keys-configuration)
- [Setting up environment variables](#setting-up-environment-variables)
- [Memory Backend Setup](#memory-backend-setup)
- [Redis Setup](#redis-setup)
- [🌲 Pinecone API Key Setup](#-pinecone-api-key-setup)
- [Milvus Setup](#milvus-setup)
- [Setting up environment variables](#setting-up-environment-variables-1)
- [Setting Your Cache Type](#setting-your-cache-type)
- [View Memory Usage](#view-memory-usage)
- [🧠 Memory pre-seeding](#-memory-pre-seeding)
- [💀 Continuous Mode ⚠️](#-continuous-mode-️)
- [GPT3.5 ONLY Mode](#gpt35-only-mode)
- [🖼 Image Generation](#image-generation)
- [🖼 Image Generation](#-image-generation)
- [⚠️ Limitations](#️-limitations)
- [🛡 Disclaimer](#-disclaimer)
- [🐦 Connect with Us on Twitter](#-connect-with-us-on-twitter)
- [Run tests](#run-tests)
- [Run linter](#run-linter)
## 🚀 Features
@ -57,61 +76,150 @@ Your support is greatly appreciated
- 🗃️ File storage and summarization with GPT-3.5
## 📋 Requirements
- [Python 3.8 or later](https://www.tutorialspoint.com/how-to-install-python-in-windows)
- OpenAI API key
- [PINECONE API key](https://www.pinecone.io/)
- environments(just choose one)
- [vscode + devcontainer](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers): It has been configured in the .devcontainer folder and can be used directly
- [Python 3.8 or later](https://www.tutorialspoint.com/how-to-install-python-in-windows)
- [OpenAI API key](https://platform.openai.com/account/api-keys)
Optional:
- Memory backend
- [PINECONE API key](https://www.pinecone.io/) (If you want Pinecone backed memory)
- [Milvus](https://milvus.io/) (If you want Milvus as memory backend)
- ElevenLabs Key (If you want the AI to speak)
## 💾 Installation
To install Auto-GPT, follow these steps:
0. Make sure you have all the **requirements** above, if not, install/get them.
1. Make sure you have all the **requirements** listed above, if not, install/get them
*The following commands should be executed in a CMD, Bash or Powershell window. To do this, go to a folder on your computer, click in the folder path at the top and type CMD, then press enter.*
_To execute the following commands, open a CMD, Bash, or Powershell window by navigating to a folder on your computer and typing `CMD` in the folder path at the top, then press enter._
1. Clone the repository:
For this step you need Git installed, but you can just download the zip file instead by clicking the button at the top of this page ☝️
```
2. Clone the repository: For this step, you need Git installed. Alternatively, you can download the zip file by clicking the button at the top of this page ☝️
```bash
git clone https://github.com/Torantulino/Auto-GPT.git
```
2. Navigate to the project directory:
*(Type this into your CMD window, you're aiming to navigate the CMD window to the repository you just downloaded)*
```
cd 'Auto-GPT'
3. Navigate to the directory where the repository was downloaded
```bash
cd Auto-GPT
```
3. Install the required dependencies:
*(Again, type this into your CMD window)*
```
4. Install the required dependencies
```bash
pip install -r requirements.txt
```
4. Rename `.env.template` to `.env` and fill in your `OPENAI_API_KEY`. If you plan to use Speech Mode, fill in your `ELEVEN_LABS_API_KEY` as well.
- Obtain your OpenAI API key from: https://platform.openai.com/account/api-keys.
5. Rename `.env.template` to `.env` and fill in your `OPENAI_API_KEY`. If you plan to use Speech Mode, fill in your `ELEVENLABS_API_KEY` as well.
- See [OpenAI API Keys Configuration](#openai-api-keys-configuration) to obtain your OpenAI API key.
- Obtain your ElevenLabs API key from: https://elevenlabs.io. You can view your xi-api-key using the "Profile" tab on the website.
- If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and provide the `OPENAI_AZURE_API_BASE`, `OPENAI_AZURE_API_VERSION` and `OPENAI_AZURE_DEPLOYMENT_ID` values as explained here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section
- If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and then follow these steps:
- Rename `azure.yaml.template` to `azure.yaml` and provide the relevant `azure_api_base`, `azure_api_version` and all the deployment IDs for the relevant models in the `azure_model_map` section:
- `fast_llm_model_deployment_id` - your gpt-3.5-turbo or gpt-4 deployment ID
- `smart_llm_model_deployment_id` - your gpt-4 deployment ID
- `embedding_model_deployment_id` - your text-embedding-ada-002 v2 deployment ID
- Please specify all of these values as double-quoted strings
> Replace string in angled brackets (<>) to your own ID
```yaml
azure_model_map:
fast_llm_model_deployment_id: "<my-fast-llm-deployment-id>"
...
```
- Details can be found here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section and here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line for the embedding model.
## 🔧 Usage
1. Run the `main.py` Python script in your terminal:
*(Type this into your CMD window)*
1. Run `autogpt` Python module in your terminal
```
python scripts/main.py
python -m autogpt
```
2. After each of AUTO-GPT's actions, type "NEXT COMMAND" to authorise them to continue.
3. To exit the program, type "exit" and press Enter.
2. After each action, choose from options to authorize command(s),
exit the program, or provide feedback to the AI.
1. Authorize a single command, enter `y`
2. Authorize a series of _N_ continuous commands, enter `y -N`
3. Exit the program, enter `n`
### Logs
Activity and error logs are located in the `./output/logs`
To print out debug logs:
```
python -m autogpt --debug
```
### Docker
You can also build this into a docker image and run it:
```
docker build -t autogpt .
docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_workspace autogpt
```
You can pass extra arguments, for instance, running with `--gpt3only` and `--continuous` mode:
```
docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_workspace autogpt --gpt3only --continuous
```
### Command Line Arguments
Here are some common arguments you can use when running Auto-GPT:
> Replace anything in angled brackets (<>) to a value you want to specify
* View all available command line arguments
```bash
python scripts/main.py --help
```
* Run Auto-GPT with a different AI Settings file
```bash
python scripts/main.py --ai-settings <filename>
```
* Specify one of 3 memory backends: `local`, `redis`, `pinecone` or `no_memory`
```bash
python scripts/main.py --use-memory <memory-backend>
```
> **NOTE**: There are shorthands for some of these flags, for example `-m` for `--use-memory`. Use `python scripts/main.py --help` for more information
## 🗣️ Speech Mode
Use this to use TTS for Auto-GPT
```
python scripts/main.py --speak
Use this to use TTS _(Text-to-Speech)_ for Auto-GPT
```bash
python -m autogpt --speak
```
### List of IDs with names from eleven labs, you can use the name or ID:
- Rachel : 21m00Tcm4TlvDq8ikWAM
- Domi : AZnzlk1XvdvUeBnXmlld
- Bella : EXAVITQu4vr4xnSDxMaL
- Antoni : ErXwobaYiN019PkySvjV
- Elli : MF3mGyEYCl7XYWbV9V6O
- Josh : TxGEqnHWrfWFTfGW9XjX
- Arnold : VR6AewLTigWG4xSOukaG
- Adam : pNInz6obpgDQGcFmaJgB
- Sam : yoZ06aMxZJJ28mfd3POQ
## OpenAI API Keys Configuration
Obtain your OpenAI API key from: https://platform.openai.com/account/api-keys.
To use OpenAI API key for Auto-GPT, you NEED to have billing set up (AKA paid account).
You can set up paid account at https://platform.openai.com/account/billing/overview.
![For OpenAI API key to work, set up paid account at OpenAI API > Billing](./docs/imgs/openai-api-key-billing-paid-account.png)
## 🔍 Google API Keys Configuration
This section is optional, use the official google api if you are having issues with error 429 when running a google search.
@ -123,85 +231,27 @@ To use the `google_official_search` command, you need to set up your Google API
4. Go to the [APIs & Services Dashboard](https://console.cloud.google.com/apis/dashboard) and click "Enable APIs and Services". Search for "Custom Search API" and click on it, then click "Enable".
5. Go to the [Credentials](https://console.cloud.google.com/apis/credentials) page and click "Create Credentials". Choose "API Key".
6. Copy the API key and set it as an environment variable named `GOOGLE_API_KEY` on your machine. See setting up environment variables below.
7. Go to the [Custom Search Engine](https://cse.google.com/cse/all) page and click "Add".
8. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
9. Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
7. [Enable](https://console.developers.google.com/apis/api/customsearch.googleapis.com) the Custom Search API on your project. (Might need to wait few minutes to propagate)
8. Go to the [Custom Search Engine](https://cse.google.com/cse/all) page and click "Add".
9. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
10. Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
*Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches.*
_Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches._
### Setting up environment variables
For Windows Users:
```
setx GOOGLE_API_KEY "YOUR_GOOGLE_API_KEY"
setx CUSTOM_SEARCH_ENGINE_ID "YOUR_CUSTOM_SEARCH_ENGINE_ID"
```
For macOS and Linux users:
```
export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
export CUSTOM_SEARCH_ENGINE_ID="YOUR_CUSTOM_SEARCH_ENGINE_ID"
```
## Redis Setup
Install docker desktop.
Run:
```
docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
```
See https://hub.docker.com/r/redis/redis-stack-server for setting a password and additional configuration.
Set the following environment variables:
```
MEMORY_BACKEND=redis
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=
```
Note that this is not intended to be run facing the internet and is not secure, do not expose redis to the internet without a password or at all really.
You can optionally set
```
WIPE_REDIS_ON_START=False
```
To persist memory stored in Redis.
You can specify the memory index for redis using the following:
````
MEMORY_INDEX=whatever
````
## 🌲 Pinecone API Key Setup
Pinecone enables the storage of vast amounts of vector-based memory, allowing for only relevant memories to be loaded for the agent at any given time.
1. Go to app.pinecone.io and make an account if you don't already have one.
2. Choose the `Starter` plan to avoid being charged.
3. Find your API key and region under the default project in the left sidebar.
### Setting up environment variables
Simply set them in the `.env` file.
Alternatively, you can set them from the command line (advanced):
For Windows Users:
```
setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
setx PINECONE_ENV "Your pinecone region" # something like: us-east4-gcp
```bash
setx GOOGLE_API_KEY "YOUR_GOOGLE_API_KEY"
setx CUSTOM_SEARCH_ENGINE_ID "YOUR_CUSTOM_SEARCH_ENGINE_ID"
```
For macOS and Linux users:
```
export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
```bash
export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
export CUSTOM_SEARCH_ENGINE_ID="YOUR_CUSTOM_SEARCH_ENGINE_ID"
```
## Plugins
@ -216,40 +266,183 @@ Drop the repo's zipfile in the plugins folder.
If you add the plugins class name to the whitelist in the config.py you will not be prompted otherwise you'll be warned before loading the plugin.
## Redis Setup
> _**CAUTION**_ \
This is not intended to be publicly accessible and lacks security measures. Therefore, avoid exposing Redis to the internet without a password or at all
1. Install docker desktop
```bash
docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
```
> See https://hub.docker.com/r/redis/redis-stack-server for setting a password and additional configuration.
2. Set the following environment variables
> Replace **PASSWORD** in angled brackets (<>)
```bash
MEMORY_BACKEND=redis
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=<PASSWORD>
```
You can optionally set
```bash
WIPE_REDIS_ON_START=False
```
To persist memory stored in Redis
You can specify the memory index for redis using the following:
```bash
MEMORY_INDEX=<WHATEVER>
```
### 🌲 Pinecone API Key Setup
Pinecone enables the storage of vast amounts of vector-based memory, allowing for only relevant memories to be loaded for the agent at any given time.
1. Go to [pinecone](https://app.pinecone.io/) and make an account if you don't already have one.
2. Choose the `Starter` plan to avoid being charged.
3. Find your API key and region under the default project in the left sidebar.
### Milvus Setup
[Milvus](https://milvus.io/) is a open-source, high scalable vector database to storage huge amount of vector-based memory and provide fast relevant search.
- setup milvus database, keep your pymilvus version and milvus version same to avoid compatible issues.
- setup by open source [Install Milvus](https://milvus.io/docs/install_standalone-operator.md)
- or setup by [Zilliz Cloud](https://zilliz.com/cloud)
- set `MILVUS_ADDR` in `.env` to your milvus address `host:ip`.
- set `MEMORY_BACKEND` in `.env` to `milvus` to enable milvus as backend.
- optional
- set `MILVUS_COLLECTION` in `.env` to change milvus collection name as you want, `autogpt` is the default name.
### Setting up environment variables
In the `.env` file set:
- `PINECONE_API_KEY`
- `PINECONE_ENV` (example: _"us-east4-gcp"_)
- `MEMORY_BACKEND=pinecone`
Alternatively, you can set them from the command line (advanced):
For Windows Users:
```bash
setx PINECONE_API_KEY "<YOUR_PINECONE_API_KEY>"
setx PINECONE_ENV "<YOUR_PINECONE_REGION>" # e.g: "us-east4-gcp"
setx MEMORY_BACKEND "pinecone"
```
For macOS and Linux users:
```bash
export PINECONE_API_KEY="<YOUR_PINECONE_API_KEY>"
export PINECONE_ENV="<YOUR_PINECONE_REGION>" # e.g: "us-east4-gcp"
export MEMORY_BACKEND="pinecone"
```
## Setting Your Cache Type
By default, Auto-GPT is going to use LocalCache instead of redis or Pinecone.
To switch to either, change the `MEMORY_BACKEND` env variable to the value that you want:
`local` (default) uses a local JSON cache file
`pinecone` uses the Pinecone.io account you configured in your ENV settings
`redis` will use the redis cache that you configured
## View Memory Usage
1. View memory usage by using the `--debug` flag :)
## 💀 Continuous Mode ⚠️
Run the AI **without** user authorisation, 100% automated.
Continuous mode is not recommended.
It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise.
Use at your own risk.
1. Run the `main.py` Python script in your terminal:
```
python scripts/main.py --continuous
## 🧠 Memory pre-seeding
# python autogpt/data_ingestion.py -h
usage: data_ingestion.py [-h] (--file FILE | --dir DIR) [--init] [--overlap OVERLAP] [--max_length MAX_LENGTH]
Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script.
options:
-h, --help show this help message and exit
--file FILE The file to ingest.
--dir DIR The directory containing the files to ingest.
--init Init the memory and wipe its content (default: False)
--overlap OVERLAP The overlap size between chunks when ingesting files (default: 200)
--max_length MAX_LENGTH The max_length of each chunk when ingesting files (default: 4000)
# python autogpt/data_ingestion.py --dir seed_data --init --overlap 200 --max_length 1000
This script located at autogpt/data_ingestion.py, allows you to ingest files into memory and pre-seed it before running Auto-GPT.
Memory pre-seeding is a technique that involves ingesting relevant documents or data into the AI's memory so that it can use this information to generate more informed and accurate responses.
To pre-seed the memory, the content of each document is split into chunks of a specified maximum length with a specified overlap between chunks, and then each chunk is added to the memory backend set in the .env file. When the AI is prompted to recall information, it can then access those pre-seeded memories to generate more informed and accurate responses.
This technique is particularly useful when working with large amounts of data or when there is specific information that the AI needs to be able to access quickly.
By pre-seeding the memory, the AI can retrieve and use this information more efficiently, saving time, API call and improving the accuracy of its responses.
You could for example download the documentation of an API, a GitHub repository, etc. and ingest it into memory before running Auto-GPT.
⚠️ If you use Redis as your memory, make sure to run Auto-GPT with the `WIPE_REDIS_ON_START` set to `False` in your `.env` file.
⚠️For other memory backend, we currently forcefully wipe the memory when starting Auto-GPT. To ingest data with those memory backend, you can call the `data_ingestion.py` script anytime during an Auto-GPT run.
Memories will be available to the AI immediately as they are ingested, even if ingested while Auto-GPT is running.
In the example above, the script initializes the memory, ingests all files within the `/seed_data` directory into memory with an overlap between chunks of 200 and a maximum length of each chunk of 4000.
Note that you can also use the `--file` argument to ingest a single file into memory and that the script will only ingest files within the `/auto_gpt_workspace` directory.
You can adjust the `max_length` and overlap parameters to fine-tune the way the docuents are presented to the AI when it "recall" that memory:
- Adjusting the overlap value allows the AI to access more contextual information from each chunk when recalling information, but will result in more chunks being created and therefore increase memory backend usage and OpenAI API requests.
- Reducing the `max_length` value will create more chunks, which can save prompt tokens by allowing for more message history in the context, but will also increase the number of chunks.
- Increasing the `max_length` value will provide the AI with more contextual information from each chunk, reducing the number of chunks created and saving on OpenAI API requests. However, this may also use more prompt tokens and decrease the overall context available to the AI.
## 💀 Continuous Mode ⚠️
Run the AI **without** user authorization, 100% automated.
Continuous mode is NOT recommended.
It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorize.
Use at your own risk.
1. Run the `autogpt` python module in your terminal:
```bash
python -m autogpt --speak --continuous
```
2. To exit the program, press Ctrl + C
## GPT3.5 ONLY Mode
If you don't have access to the GPT4 api, this mode will allow you to use Auto-GPT!
```bash
python -m autogpt --speak --gpt3only
```
python scripts/main.py --gpt3only
```
It is recommended to use a virtual machine for tasks that require high security measures to prevent any potential harm to the main computer's system and data.
## 🖼 Image Generation
By default, Auto-GPT uses DALL-e for image generation. To use Stable Diffusion, a [HuggingFace API Token](https://huggingface.co/settings/tokens) is required.
By default, Auto-GPT uses DALL-e for image generation. To use Stable Diffusion, a [Hugging Face API Token](https://huggingface.co/settings/tokens) is required.
Once you have a token, set these variables in your `.env`:
```
```bash
IMAGE_PROVIDER=sd
HUGGINGFACE_API_TOKEN="YOUR_HUGGINGFACE_API_TOKEN"
```
## Selenium
```bash
sudo Xvfb :10 -ac -screen 0 1024x768x24 & DISPLAY=:10 <YOUR_CLIENT>
```
## ⚠️ Limitations
This experiment aims to showcase the potential of GPT-4 but comes with some limitations:
1. Not a polished application or product, just an experiment
@ -278,3 +471,35 @@ Stay up-to-date with the latest news, updates, and insights about Auto-GPT by fo
We look forward to connecting with you and hearing your thoughts, ideas, and experiences with Auto-GPT. Join us on Twitter and let's explore the future of AI together!
<p align="center">
<a href="https://star-history.com/#Torantulino/auto-gpt&Date">
<img src="https://api.star-history.com/svg?repos=Torantulino/auto-gpt&type=Date" alt="Star History Chart">
</a>
</p>
## Run tests
To run tests, run the following command:
```bash
python -m unittest discover tests
```
To run tests and see coverage, run the following command:
```bash
coverage run -m unittest discover tests
```
## Run linter
This project uses [flake8](https://flake8.pycqa.org/en/latest/) for linting. We currently use the following rules: `E303,W293,W291,W292,E305,E231,E302`. See the [flake8 rules](https://www.flake8rules.com/) for more information.
To run the linter, run the following command:
```bash
flake8 autogpt/ tests/
# Or, if you want to run flake8 with the same configuration as the CI:
flake8 autogpt/ tests/ --select E303,W293,W291,W292,E305,E231,E302
```

7
ai_settings.yaml
View File

@ -1,7 +0,0 @@
ai_goals:
- Increase net worth.
- Develop and manage multiple businesses autonomously.
- Play to your strengths as a Large Language Model.
ai_name: Entrepreneur-GPT
ai_role: an AI designed to autonomously develop and run businesses with the sole goal
of increasing your net worth.

0
autogpt/__init__.py Normal file
View File

76
autogpt/__main__.py Normal file
View File

@ -0,0 +1,76 @@
"""Main script for the autogpt package."""
import logging
from colorama import Fore
from autogpt.agent.agent import Agent
from autogpt.args import parse_arguments
from autogpt.config import Config, check_openai_api_key
from autogpt.logs import logger
from autogpt.memory import get_memory
from autogpt.prompt import construct_prompt
# Load environment variables from .env file
def main() -> None:
"""Main function for the script"""
cfg = Config()
# TODO: fill in llm values here
check_openai_api_key()
parse_arguments()
logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
plugins_found = load_plugins(Path(os.getcwd()) / "plugins")
loaded_plugins = []
for plugin in plugins_found:
if plugin.__name__ in cfg.plugins_blacklist:
continue
if plugin.__name__ in cfg.plugins_whitelist:
loaded_plugins.append(plugin())
else:
ack = input(
f"WARNNG Plugin {plugin.__name__} found. But not in the"
" whitelist... Load? (y/n): "
)
if ack.lower() == "y":
loaded_plugins.append(plugin())
if loaded_plugins:
print(f"\nPlugins found: {len(loaded_plugins)}\n"
"--------------------")
for plugin in loaded_plugins:
print(f"{plugin._name}: {plugin._version} - {plugin._description}")
cfg.set_plugins(loaded_plugins)
ai_name = ""
prompt = construct_prompt()
# print(prompt)
# Initialize variables
full_message_history = []
next_action_count = 0
# Make a constant:
user_input = (
"Determine which next command to use, and respond using the"
" format specified above:"
)
# Initialize memory and make sure it is empty.
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
logger.typewriter_log(
f"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
)
logger.typewriter_log(f"Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
agent = Agent(
ai_name=ai_name,
memory=memory,
full_message_history=full_message_history,
next_action_count=next_action_count,
prompt=prompt,
user_input=user_input,
)
agent.start_interaction_loop()
if __name__ == "__main__":
main()

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from autogpt.agent.agent import Agent
from autogpt.agent.agent_manager import AgentManager
__all__ = ["Agent", "AgentManager"]

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from colorama import Fore, Style
from autogpt.app import execute_command, get_command
from autogpt.chat import chat_with_ai, create_chat_message
from autogpt.config import Config
from autogpt.json_fixes.bracket_termination import (
attempt_to_fix_json_by_finding_outermost_brackets,
)
from autogpt.logs import logger, print_assistant_thoughts
from autogpt.speech import say_text
from autogpt.spinner import Spinner
from autogpt.utils import clean_input
class Agent:
"""Agent class for interacting with Auto-GPT.
Attributes:
ai_name: The name of the agent.
memory: The memory object to use.
full_message_history: The full message history.
next_action_count: The number of actions to execute.
prompt: The prompt to use.
user_input: The user input.
"""
def __init__(
self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input,
):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
cfg = Config()
loop_count = 0
command_name = None
arguments = None
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if (
cfg.continuous_mode
and cfg.continuous_limit > 0
and loop_count > cfg.continuous_limit
):
logger.typewriter_log(
"Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
)
break
# Send message to AI, get response
with Spinner("Thinking... "):
assistant_reply = chat_with_ai(
self.prompt,
self.user_input,
self.full_message_history,
self.memory,
cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
print_assistant_thoughts(self.ai_name, assistant_reply)
# Get command name and arguments
try:
command_name, arguments = get_command(
attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
)
if cfg.speak_mode:
say_text(f"I want to execute {command_name}")
except Exception as e:
logger.error("Error: \n", str(e))
if not cfg.continuous_mode and self.next_action_count == 0:
### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
self.user_input = ""
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} "
f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
print(
"Enter 'y' to authorise command, 'y -N' to run N continuous "
"commands, 'n' to exit program, or enter feedback for "
f"{self.ai_name}...",
flush=True,
)
while True:
console_input = clean_input(
Fore.MAGENTA + "Input:" + Style.RESET_ALL
)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
self.user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
print(
"Invalid input format. Please enter 'y -n' where n is"
" the number of continuous tasks."
)
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
if self.user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"",
)
elif self.user_input == "EXIT":
print("Exiting...", flush=True)
break
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
f" ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = (
f"Command {command_name} threw the following error: {arguments}"
)
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = (
f"Command {command_name} returned: "
f"{execute_command(command_name, arguments)}"
)
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = (
f"Assistant Reply: {assistant_reply} "
f"\nResult: {result} "
f"\nHuman Feedback: {self.user_input} "
)
self.memory.add(memory_to_add)
# Check if there's a result from the command append it to the message
# history
if result is not None:
self.full_message_history.append(create_chat_message("system", result))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)

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"""Agent manager for managing GPT agents"""
from typing import List, Tuple, Union
from autogpt.llm_utils import create_chat_completion
from autogpt.config.config import Singleton
class AgentManager(metaclass=Singleton):
"""Agent manager for managing GPT agents"""
def __init__(self):
self.next_key = 0
self.agents = {} # key, (task, full_message_history, model)
# Create new GPT agent
# TODO: Centralise use of create_chat_completion() to globally enforce token limit
def create_agent(self, task: str, prompt: str, model: str) -> Tuple[int, str]:
"""Create a new agent and return its key
Args:
task: The task to perform
prompt: The prompt to use
model: The model to use
Returns:
The key of the new agent
"""
messages = [
{"role": "user", "content": prompt},
]
# Start GPT instance
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
# Update full message history
messages.append({"role": "assistant", "content": agent_reply})
key = self.next_key
# This is done instead of len(agents) to make keys unique even if agents
# are deleted
self.next_key += 1
self.agents[key] = (task, messages, model)
return key, agent_reply
def message_agent(self, key: Union[str, int], message: str) -> str:
"""Send a message to an agent and return its response
Args:
key: The key of the agent to message
message: The message to send to the agent
Returns:
The agent's response
"""
task, messages, model = self.agents[int(key)]
# Add user message to message history before sending to agent
messages.append({"role": "user", "content": message})
# Start GPT instance
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
# Update full message history
messages.append({"role": "assistant", "content": agent_reply})
return agent_reply
def list_agents(self) -> List[Tuple[Union[str, int], str]]:
"""Return a list of all agents
Returns:
A list of tuples of the form (key, task)
"""
# Return a list of agent keys and their tasks
return [(key, task) for key, (task, _, _) in self.agents.items()]
def delete_agent(self, key: Union[str, int]) -> bool:
"""Delete an agent from the agent manager
Args:
key: The key of the agent to delete
Returns:
True if successful, False otherwise
"""
try:
del self.agents[int(key)]
return True
except KeyError:
return False

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""" Command and Control """
import json
from typing import List, NoReturn, Union
from autogpt.agent.agent_manager import AgentManager
from autogpt.commands.evaluate_code import evaluate_code
from autogpt.commands.google_search import google_official_search, google_search
from autogpt.commands.improve_code import improve_code
from autogpt.commands.write_tests import write_tests
from autogpt.config import Config
from autogpt.commands.image_gen import generate_image
from autogpt.commands.audio_text import read_audio_from_file
from autogpt.commands.web_requests import scrape_links, scrape_text
from autogpt.commands.execute_code import execute_python_file, execute_shell
from autogpt.commands.file_operations import (
append_to_file,
delete_file,
read_file,
search_files,
write_to_file,
)
from autogpt.json_fixes.parsing import fix_and_parse_json
from autogpt.memory import get_memory
from autogpt.processing.text import summarize_text
from autogpt.speech import say_text
from autogpt.commands.web_selenium import browse_website
from autogpt.commands.git_operations import clone_repository
from autogpt.commands.twitter import send_tweet
CFG = Config()
AGENT_MANAGER = AgentManager()
def is_valid_int(value: str) -> bool:
"""Check if the value is a valid integer
Args:
value (str): The value to check
Returns:
bool: True if the value is a valid integer, False otherwise
"""
try:
int(value)
return True
except ValueError:
return False
def get_command(response: str):
"""Parse the response and return the command name and arguments
Args:
response (str): The response from the user
Returns:
tuple: The command name and arguments
Raises:
json.decoder.JSONDecodeError: If the response is not valid JSON
Exception: If any other error occurs
"""
try:
response_json = fix_and_parse_json(response)
if "command" not in response_json:
return "Error:", "Missing 'command' object in JSON"
if not isinstance(response_json, dict):
return "Error:", f"'response_json' object is not dictionary {response_json}"
command = response_json["command"]
if not isinstance(command, dict):
return "Error:", "'command' object is not a dictionary"
if "name" not in command:
return "Error:", "Missing 'name' field in 'command' object"
command_name = command["name"]
# Use an empty dictionary if 'args' field is not present in 'command' object
arguments = command.get("args", {})
return command_name, arguments
except json.decoder.JSONDecodeError:
return "Error:", "Invalid JSON"
# All other errors, return "Error: + error message"
except Exception as e:
return "Error:", str(e)
def map_command_synonyms(command_name: str):
"""Takes the original command name given by the AI, and checks if the
string matches a list of common/known hallucinations
"""
synonyms = [
("write_file", "write_to_file"),
("create_file", "write_to_file"),
("search", "google"),
]
for seen_command, actual_command_name in synonyms:
if command_name == seen_command:
return actual_command_name
return command_name
def execute_command(command_name: str, arguments):
"""Execute the command and return the result
Args:
command_name (str): The name of the command to execute
arguments (dict): The arguments for the command
Returns:
str: The result of the command"""
memory = get_memory(CFG)
try:
command_name = map_command_synonyms(command_name)
if command_name == "google":
# Check if the Google API key is set and use the official search method
# If the API key is not set or has only whitespaces, use the unofficial
# search method
key = CFG.google_api_key
if key and key.strip() and key != "your-google-api-key":
google_result = google_official_search(arguments["input"])
else:
google_result = google_search(arguments["input"])
safe_message = google_result.encode("utf-8", "ignore")
return str(safe_message)
elif command_name == "memory_add":
return memory.add(arguments["string"])
elif command_name == "start_agent":
return start_agent(
arguments["name"], arguments["task"], arguments["prompt"]
)
elif command_name == "message_agent":
return message_agent(arguments["key"], arguments["message"])
elif command_name == "list_agents":
return list_agents()
elif command_name == "delete_agent":
return delete_agent(arguments["key"])
elif command_name == "get_text_summary":
return get_text_summary(arguments["url"], arguments["question"])
elif command_name == "get_hyperlinks":
return get_hyperlinks(arguments["url"])
elif command_name == "clone_repository":
return clone_repository(
arguments["repository_url"], arguments["clone_path"]
)
elif command_name == "read_file":
return read_file(arguments["file"])
elif command_name == "write_to_file":
return write_to_file(arguments["file"], arguments["text"])
elif command_name == "append_to_file":
return append_to_file(arguments["file"], arguments["text"])
elif command_name == "delete_file":
return delete_file(arguments["file"])
elif command_name == "search_files":
return search_files(arguments["directory"])
elif command_name == "browse_website":
return browse_website(arguments["url"], arguments["question"])
# TODO: Change these to take in a file rather than pasted code, if
# non-file is given, return instructions "Input should be a python
# filepath, write your code to file and try again"
elif command_name == "evaluate_code":
return evaluate_code(arguments["code"])
elif command_name == "improve_code":
return improve_code(arguments["suggestions"], arguments["code"])
elif command_name == "write_tests":
return write_tests(arguments["code"], arguments.get("focus"))
elif command_name == "execute_python_file": # Add this command
return execute_python_file(arguments["file"])
elif command_name == "execute_shell":
if CFG.execute_local_commands:
return execute_shell(arguments["command_line"])
else:
return (
"You are not allowed to run local shell commands. To execute"
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
"in your config. Do not attempt to bypass the restriction."
)
elif command_name == "read_audio_from_file":
return read_audio_from_file(arguments["file"])
elif command_name == "generate_image":
return generate_image(arguments["prompt"])
elif command_name == "send_tweet":
return send_tweet(arguments['text'])
elif command_name == "do_nothing":
return "No action performed."
elif command_name == "task_complete":
shutdown()
else:
return (
f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
" list for available commands and only respond in the specified JSON"
" format."
)
except Exception as e:
return f"Error: {str(e)}"
def get_text_summary(url: str, question: str) -> str:
"""Return the results of a google search
Args:
url (str): The url to scrape
question (str): The question to summarize the text for
Returns:
str: The summary of the text
"""
text = scrape_text(url)
summary = summarize_text(url, text, question)
return f""" "Result" : {summary}"""
def get_hyperlinks(url: str) -> Union[str, List[str]]:
"""Return the results of a google search
Args:
url (str): The url to scrape
Returns:
str or list: The hyperlinks on the page
"""
return scrape_links(url)
def shutdown() -> NoReturn:
"""Shut down the program"""
print("Shutting down...")
quit()
def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
"""Start an agent with a given name, task, and prompt
Args:
name (str): The name of the agent
task (str): The task of the agent
prompt (str): The prompt for the agent
model (str): The model to use for the agent
Returns:
str: The response of the agent
"""
# Remove underscores from name
voice_name = name.replace("_", " ")
first_message = f"""You are {name}. Respond with: "Acknowledged"."""
agent_intro = f"{voice_name} here, Reporting for duty!"
# Create agent
if CFG.speak_mode:
say_text(agent_intro, 1)
key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
if CFG.speak_mode:
say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
# Assign task (prompt), get response
agent_response = AGENT_MANAGER.message_agent(key, prompt)
return f"Agent {name} created with key {key}. First response: {agent_response}"
def message_agent(key: str, message: str) -> str:
"""Message an agent with a given key and message"""
# Check if the key is a valid integer
if is_valid_int(key):
agent_response = AGENT_MANAGER.message_agent(int(key), message)
else:
return "Invalid key, must be an integer."
# Speak response
if CFG.speak_mode:
say_text(agent_response, 1)
return agent_response
def list_agents():
"""List all agents
Returns:
str: A list of all agents
"""
return "List of agents:\n" + "\n".join(
[str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
)
def delete_agent(key: str) -> str:
"""Delete an agent with a given key
Args:
key (str): The key of the agent to delete
Returns:
str: A message indicating whether the agent was deleted or not
"""
result = AGENT_MANAGER.delete_agent(key)
return f"Agent {key} deleted." if result else f"Agent {key} does not exist."

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"""This module contains the argument parsing logic for the script."""
import argparse
from colorama import Fore
from autogpt import utils
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.memory import get_supported_memory_backends
CFG = Config()
def parse_arguments() -> None:
"""Parses the arguments passed to the script
Returns:
None
"""
CFG.set_debug_mode(False)
CFG.set_continuous_mode(False)
CFG.set_speak_mode(False)
parser = argparse.ArgumentParser(description="Process arguments.")
parser.add_argument(
"--continuous", "-c", action="store_true", help="Enable Continuous Mode"
)
parser.add_argument(
"--continuous-limit",
"-l",
type=int,
dest="continuous_limit",
help="Defines the number of times to run in continuous mode",
)
parser.add_argument("--speak", action="store_true", help="Enable Speak Mode")
parser.add_argument("--debug", action="store_true", help="Enable Debug Mode")
parser.add_argument(
"--gpt3only", action="store_true", help="Enable GPT3.5 Only Mode"
)
parser.add_argument("--gpt4only", action="store_true", help="Enable GPT4 Only Mode")
parser.add_argument(
"--use-memory",
"-m",
dest="memory_type",
help="Defines which Memory backend to use",
)
parser.add_argument(
"--skip-reprompt",
"-y",
dest="skip_reprompt",
action="store_true",
help="Skips the re-prompting messages at the beginning of the script",
)
parser.add_argument(
"--use-browser",
"-b",
dest="browser_name",
help="Specifies which web-browser to use when using selenium to scrape the web.",
)
parser.add_argument(
"--ai-settings",
"-C",
dest="ai_settings_file",
help="Specifies which ai_settings.yaml file to use, will also automatically"
" skip the re-prompt.",
)
args = parser.parse_args()
if args.debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
CFG.set_debug_mode(True)
if args.continuous:
logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"Continuous mode is not recommended. It is potentially dangerous and may"
" cause your AI to run forever or carry out actions you would not usually"
" authorise. Use at your own risk.",
)
CFG.set_continuous_mode(True)
if args.continuous_limit:
logger.typewriter_log(
"Continuous Limit: ", Fore.GREEN, f"{args.continuous_limit}"
)
CFG.set_continuous_limit(args.continuous_limit)
# Check if continuous limit is used without continuous mode
if args.continuous_limit and not args.continuous:
parser.error("--continuous-limit can only be used with --continuous")
if args.speak:
logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
CFG.set_speak_mode(True)
if args.gpt3only:
logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
CFG.set_smart_llm_model(CFG.fast_llm_model)
if args.gpt4only:
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
CFG.set_fast_llm_model(CFG.smart_llm_model)
if args.memory_type:
supported_memory = get_supported_memory_backends()
chosen = args.memory_type
if chosen not in supported_memory:
logger.typewriter_log(
"ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
Fore.RED,
f"{supported_memory}",
)
logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
else:
CFG.memory_backend = chosen
if args.skip_reprompt:
logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
CFG.skip_reprompt = True
if args.ai_settings_file:
file = args.ai_settings_file
# Validate file
(validated, message) = utils.validate_yaml_file(file)
if not validated:
logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
logger.double_check()
exit(1)
logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
CFG.ai_settings_file = file
CFG.skip_reprompt = True
if args.browser_name:
CFG.selenium_web_browser = args.browser_name

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import time
from openai.error import RateLimitError
from autogpt import token_counter
from autogpt.config import Config
from autogpt.llm_utils import create_chat_completion
from autogpt.logs import logger
cfg = Config()
def create_chat_message(role, content):
"""
Create a chat message with the given role and content.
Args:
role (str): The role of the message sender, e.g., "system", "user", or "assistant".
content (str): The content of the message.
Returns:
dict: A dictionary containing the role and content of the message.
"""
return {"role": role, "content": content}
def generate_context(prompt, relevant_memory, full_message_history, model):
current_context = [
create_chat_message("system", prompt),
create_chat_message(
"system", f"The current time and date is {time.strftime('%c')}"
),
create_chat_message(
"system",
f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
),
]
# Add messages from the full message history until we reach the token limit
next_message_to_add_index = len(full_message_history) - 1
insertion_index = len(current_context)
# Count the currently used tokens
current_tokens_used = token_counter.count_message_tokens(current_context, model)
return (
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
)
# TODO: Change debug from hardcode to argument
def chat_with_ai(
prompt, user_input, full_message_history, permanent_memory, token_limit
):
"""Interact with the OpenAI API, sending the prompt, user input, message history,
and permanent memory."""
while True:
try:
"""
Interact with the OpenAI API, sending the prompt, user input,
message history, and permanent memory.
Args:
prompt (str): The prompt explaining the rules to the AI.
user_input (str): The input from the user.
full_message_history (list): The list of all messages sent between the
user and the AI.
permanent_memory (Obj): The memory object containing the permanent
memory.
token_limit (int): The maximum number of tokens allowed in the API call.
Returns:
str: The AI's response.
"""
model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
# Reserve 1000 tokens for the response
logger.debug(f"Token limit: {token_limit}")
send_token_limit = token_limit - 1000
relevant_memory = (
""
if len(full_message_history) == 0
else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
)
logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
(
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
) = generate_context(prompt, relevant_memory, full_message_history, model)
while current_tokens_used > 2500:
# remove memories until we are under 2500 tokens
relevant_memory = relevant_memory[1:]
(
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
) = generate_context(
prompt, relevant_memory, full_message_history, model
)
current_tokens_used += token_counter.count_message_tokens(
[create_chat_message("user", user_input)], model
) # Account for user input (appended later)
while next_message_to_add_index >= 0:
# print (f"CURRENT TOKENS USED: {current_tokens_used}")
message_to_add = full_message_history[next_message_to_add_index]
tokens_to_add = token_counter.count_message_tokens(
[message_to_add], model
)
if current_tokens_used + tokens_to_add > send_token_limit:
break
# Add the most recent message to the start of the current context,
# after the two system prompts.
current_context.insert(
insertion_index, full_message_history[next_message_to_add_index]
)
# Count the currently used tokens
current_tokens_used += tokens_to_add
# Move to the next most recent message in the full message history
next_message_to_add_index -= 1
# Append user input, the length of this is accounted for above
current_context.extend([create_chat_message("user", user_input)])
# Calculate remaining tokens
tokens_remaining = token_limit - current_tokens_used
# assert tokens_remaining >= 0, "Tokens remaining is negative.
# This should never happen, please submit a bug report at
# https://www.github.com/Torantulino/Auto-GPT"
# Debug print the current context
logger.debug(f"Token limit: {token_limit}")
logger.debug(f"Send Token Count: {current_tokens_used}")
logger.debug(f"Tokens remaining for response: {tokens_remaining}")
logger.debug("------------ CONTEXT SENT TO AI ---------------")
for message in current_context:
# Skip printing the prompt
if message["role"] == "system" and message["content"] == prompt:
continue
logger.debug(f"{message['role'].capitalize()}: {message['content']}")
logger.debug("")
logger.debug("----------- END OF CONTEXT ----------------")
# TODO: use a model defined elsewhere, so that model can contain
# temperature and other settings we care about
assistant_reply = create_chat_completion(
model=model,
messages=current_context,
max_tokens=tokens_remaining,
)
# Update full message history
full_message_history.append(create_chat_message("user", user_input))
full_message_history.append(
create_chat_message("assistant", assistant_reply)
)
return assistant_reply
except RateLimitError:
# TODO: When we switch to langchain, this is built in
print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
time.sleep(10)

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autogpt/commands/__init__.py Normal file
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import requests
import json
from autogpt.config import Config
from autogpt.commands.file_operations import safe_join
cfg = Config()
working_directory = "auto_gpt_workspace"
def read_audio_from_file(audio_path):
audio_path = safe_join(working_directory, audio_path)
with open(audio_path, "rb") as audio_file:
audio = audio_file.read()
return read_audio(audio)
def read_audio(audio):
model = cfg.huggingface_audio_to_text_model
api_url = f"https://api-inference.huggingface.co/models/{model}"
api_token = cfg.huggingface_api_token
headers = {"Authorization": f"Bearer {api_token}"}
if api_token is None:
raise ValueError("You need to set your Hugging Face API token in the config file.")
response = requests.post(
api_url,
headers=headers,
data=audio,
)
text = json.loads(response.content.decode("utf-8"))['text']
return "The audio says: " + text

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"""Code evaluation module."""
from typing import List
from autogpt.llm_utils import call_ai_function
def evaluate_code(code: str) -> List[str]:
"""
A function that takes in a string and returns a response from create chat
completion api call.
Parameters:
code (str): Code to be evaluated.
Returns:
A result string from create chat completion. A list of suggestions to
improve the code.
"""
function_string = "def analyze_code(code: str) -> List[str]:"
args = [code]
description_string = (
"Analyzes the given code and returns a list of suggestions" " for improvements."
)
return call_ai_function(function_string, args, description_string)

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"""Execute code in a Docker container"""
import os
from pathlib import Path
import subprocess
import docker
from docker.errors import ImageNotFound
WORKING_DIRECTORY = Path(__file__).parent.parent / "auto_gpt_workspace"
def execute_python_file(file: str):
"""Execute a Python file in a Docker container and return the output
Args:
file (str): The name of the file to execute
Returns:
str: The output of the file
"""
print(f"Executing file '{file}' in workspace '{WORKING_DIRECTORY}'")
if not file.endswith(".py"):
return "Error: Invalid file type. Only .py files are allowed."
file_path = os.path.join(WORKING_DIRECTORY, file)
if not os.path.isfile(file_path):
return f"Error: File '{file}' does not exist."
if we_are_running_in_a_docker_container():
result = subprocess.run(
f"python {file_path}", capture_output=True, encoding="utf8", shell=True
)
if result.returncode == 0:
return result.stdout
else:
return f"Error: {result.stderr}"
try:
client = docker.from_env()
image_name = "python:3.10"
try:
client.images.get(image_name)
print(f"Image '{image_name}' found locally")
except ImageNotFound:
print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
# Use the low-level API to stream the pull response
low_level_client = docker.APIClient()
for line in low_level_client.pull(image_name, stream=True, decode=True):
# Print the status and progress, if available
status = line.get("status")
progress = line.get("progress")
if status and progress:
print(f"{status}: {progress}")
elif status:
print(status)
# You can replace 'python:3.8' with the desired Python image/version
# You can find available Python images on Docker Hub:
# https://hub.docker.com/_/python
container = client.containers.run(
image_name,
f"python {file}",
volumes={
os.path.abspath(WORKING_DIRECTORY): {
"bind": "/workspace",
"mode": "ro",
}
},
working_dir="/workspace",
stderr=True,
stdout=True,
detach=True,
)
container.wait()
logs = container.logs().decode("utf-8")
container.remove()
# print(f"Execution complete. Output: {output}")
# print(f"Logs: {logs}")
return logs
except Exception as e:
return f"Error: {str(e)}"
def execute_shell(command_line: str) -> str:
"""Execute a shell command and return the output
Args:
command_line (str): The command line to execute
Returns:
str: The output of the command
"""
current_dir = os.getcwd()
# Change dir into workspace if necessary
if str(WORKING_DIRECTORY) not in current_dir:
work_dir = os.path.join(os.getcwd(), WORKING_DIRECTORY)
os.chdir(work_dir)
print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
result = subprocess.run(command_line, capture_output=True, shell=True)
output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
# Change back to whatever the prior working dir was
os.chdir(current_dir)
return output
def we_are_running_in_a_docker_container() -> bool:
"""Check if we are running in a Docker container
Returns:
bool: True if we are running in a Docker container, False otherwise
"""
return os.path.exists("/.dockerenv")

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"""File operations for AutoGPT"""
import os
import os.path
from pathlib import Path
from typing import Generator, List
# Set a dedicated folder for file I/O
WORKING_DIRECTORY = Path(__file__).parent.parent / "auto_gpt_workspace"
# Create the directory if it doesn't exist
if not os.path.exists(WORKING_DIRECTORY):
os.makedirs(WORKING_DIRECTORY)
WORKING_DIRECTORY = str(WORKING_DIRECTORY)
def safe_join(base: str, *paths) -> str:
"""Join one or more path components intelligently.
Args:
base (str): The base path
*paths (str): The paths to join to the base path
Returns:
str: The joined path
"""
new_path = os.path.join(base, *paths)
norm_new_path = os.path.normpath(new_path)
if os.path.commonprefix([base, norm_new_path]) != base:
raise ValueError("Attempted to access outside of working directory.")
return norm_new_path
def split_file(
content: str, max_length: int = 4000, overlap: int = 0
) -> Generator[str, None, None]:
"""
Split text into chunks of a specified maximum length with a specified overlap
between chunks.
:param content: The input text to be split into chunks
:param max_length: The maximum length of each chunk,
default is 4000 (about 1k token)
:param overlap: The number of overlapping characters between chunks,
default is no overlap
:return: A generator yielding chunks of text
"""
start = 0
content_length = len(content)
while start < content_length:
end = start + max_length
if end + overlap < content_length:
chunk = content[start : end + overlap]
else:
chunk = content[start:content_length]
yield chunk
start += max_length - overlap
def read_file(filename: str) -> str:
"""Read a file and return the contents
Args:
filename (str): The name of the file to read
Returns:
str: The contents of the file
"""
try:
filepath = safe_join(WORKING_DIRECTORY, filename)
with open(filepath, "r", encoding="utf-8") as f:
content = f.read()
return content
except Exception as e:
return f"Error: {str(e)}"
def ingest_file(
filename: str, memory, max_length: int = 4000, overlap: int = 200
) -> None:
"""
Ingest a file by reading its content, splitting it into chunks with a specified
maximum length and overlap, and adding the chunks to the memory storage.
:param filename: The name of the file to ingest
:param memory: An object with an add() method to store the chunks in memory
:param max_length: The maximum length of each chunk, default is 4000
:param overlap: The number of overlapping characters between chunks, default is 200
"""
try:
print(f"Working with file {filename}")
content = read_file(filename)
content_length = len(content)
print(f"File length: {content_length} characters")
chunks = list(split_file(content, max_length=max_length, overlap=overlap))
num_chunks = len(chunks)
for i, chunk in enumerate(chunks):
print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
memory_to_add = (
f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
)
memory.add(memory_to_add)
print(f"Done ingesting {num_chunks} chunks from {filename}.")
except Exception as e:
print(f"Error while ingesting file '{filename}': {str(e)}")
def write_to_file(filename: str, text: str) -> str:
"""Write text to a file
Args:
filename (str): The name of the file to write to
text (str): The text to write to the file
Returns:
str: A message indicating success or failure
"""
try:
filepath = safe_join(WORKING_DIRECTORY, filename)
directory = os.path.dirname(filepath)
if not os.path.exists(directory):
os.makedirs(directory)
with open(filepath, "w", encoding="utf-8") as f:
f.write(text)
return "File written to successfully."
except Exception as e:
return f"Error: {str(e)}"
def append_to_file(filename: str, text: str) -> str:
"""Append text to a file
Args:
filename (str): The name of the file to append to
text (str): The text to append to the file
Returns:
str: A message indicating success or failure
"""
try:
filepath = safe_join(WORKING_DIRECTORY, filename)
with open(filepath, "a") as f:
f.write(text)
return "Text appended successfully."
except Exception as e:
return f"Error: {str(e)}"
def delete_file(filename: str) -> str:
"""Delete a file
Args:
filename (str): The name of the file to delete
Returns:
str: A message indicating success or failure
"""
try:
filepath = safe_join(WORKING_DIRECTORY, filename)
os.remove(filepath)
return "File deleted successfully."
except Exception as e:
return f"Error: {str(e)}"
def search_files(directory: str) -> List[str]:
"""Search for files in a directory
Args:
directory (str): The directory to search in
Returns:
List[str]: A list of files found in the directory
"""
found_files = []
if directory in {"", "/"}:
search_directory = WORKING_DIRECTORY
else:
search_directory = safe_join(WORKING_DIRECTORY, directory)
for root, _, files in os.walk(search_directory):
for file in files:
if file.startswith("."):
continue
relative_path = os.path.relpath(os.path.join(root, file), WORKING_DIRECTORY)
found_files.append(relative_path)
return found_files

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autogpt/commands/git_operations.py Normal file
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"""Git operations for autogpt"""
import git
from autogpt.config import Config
CFG = Config()
def clone_repository(repo_url: str, clone_path: str) -> str:
"""Clone a github repository locally
Args:
repo_url (str): The URL of the repository to clone
clone_path (str): The path to clone the repository to
Returns:
str: The result of the clone operation"""
split_url = repo_url.split("//")
auth_repo_url = f"//{CFG.github_username}:{CFG.github_api_key}@".join(split_url)
git.Repo.clone_from(auth_repo_url, clone_path)
return f"""Cloned {repo_url} to {clone_path}"""

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autogpt/commands/google_search.py Normal file
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"""Google search command for Autogpt."""
import json
from typing import List, Union
from duckduckgo_search import ddg
from autogpt.config import Config
CFG = Config()
def google_search(query: str, num_results: int = 8) -> str:
"""Return the results of a google search
Args:
query (str): The search query.
num_results (int): The number of results to return.
Returns:
str: The results of the search.
"""
search_results = []
if not query:
return json.dumps(search_results)
results = ddg(query, max_results=num_results)
if not results:
return json.dumps(search_results)
for j in results:
search_results.append(j)
return json.dumps(search_results, ensure_ascii=False, indent=4)
def google_official_search(query: str, num_results: int = 8) -> Union[str, List[str]]:
"""Return the results of a google search using the official Google API
Args:
query (str): The search query.
num_results (int): The number of results to return.
Returns:
str: The results of the search.
"""
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
try:
# Get the Google API key and Custom Search Engine ID from the config file
api_key = CFG.google_api_key
custom_search_engine_id = CFG.custom_search_engine_id
# Initialize the Custom Search API service
service = build("customsearch", "v1", developerKey=api_key)
# Send the search query and retrieve the results
result = (
service.cse()
.list(q=query, cx=custom_search_engine_id, num=num_results)
.execute()
)
# Extract the search result items from the response
search_results = result.get("items", [])
# Create a list of only the URLs from the search results
search_results_links = [item["link"] for item in search_results]
except HttpError as e:
# Handle errors in the API call
error_details = json.loads(e.content.decode())
# Check if the error is related to an invalid or missing API key
if error_details.get("error", {}).get(
"code"
) == 403 and "invalid API key" in error_details.get("error", {}).get(
"message", ""
):
return "Error: The provided Google API key is invalid or missing."
else:
return f"Error: {e}"
# Return the list of search result URLs
return search_results_links

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autogpt/commands/image_gen.py Normal file
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""" Image Generation Module for AutoGPT."""
import io
import os.path
import uuid
from base64 import b64decode
import openai
import requests
from PIL import Image
from pathlib import Path
from autogpt.config import Config
CFG = Config()
WORKING_DIRECTORY = Path(__file__).parent.parent / "auto_gpt_workspace"
def generate_image(prompt: str) -> str:
"""Generate an image from a prompt.
Args:
prompt (str): The prompt to use
Returns:
str: The filename of the image
"""
filename = f"{str(uuid.uuid4())}.jpg"
# DALL-E
if CFG.image_provider == "dalle":
return generate_image_with_dalle(prompt, filename)
elif CFG.image_provider == "sd":
return generate_image_with_hf(prompt, filename)
else:
return "No Image Provider Set"
def generate_image_with_hf(prompt: str, filename: str) -> str:
"""Generate an image with HuggingFace's API.
Args:
prompt (str): The prompt to use
filename (str): The filename to save the image to
Returns:
str: The filename of the image
"""
API_URL = (
"https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
)
if CFG.huggingface_api_token is None:
raise ValueError(
"You need to set your Hugging Face API token in the config file."
)
headers = {"Authorization": f"Bearer {CFG.huggingface_api_token}"}
response = requests.post(
API_URL,
headers=headers,
json={
"inputs": prompt,
},
)
image = Image.open(io.BytesIO(response.content))
print(f"Image Generated for prompt:{prompt}")
image.save(os.path.join(WORKING_DIRECTORY, filename))
return f"Saved to disk:{filename}"
def generate_image_with_dalle(prompt: str, filename: str) -> str:
"""Generate an image with DALL-E.
Args:
prompt (str): The prompt to use
filename (str): The filename to save the image to
Returns:
str: The filename of the image
"""
openai.api_key = CFG.openai_api_key
response = openai.Image.create(
prompt=prompt,
n=1,
size="256x256",
response_format="b64_json",
)
print(f"Image Generated for prompt:{prompt}")
image_data = b64decode(response["data"][0]["b64_json"])
with open(f"{WORKING_DIRECTORY}/{filename}", mode="wb") as png:
png.write(image_data)
return f"Saved to disk:{filename}"

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import json
from typing import List
from autogpt.llm_utils import call_ai_function
def improve_code(suggestions: List[str], code: str) -> str:
"""
A function that takes in code and suggestions and returns a response from create
chat completion api call.
Parameters:
suggestions (List): A list of suggestions around what needs to be improved.
code (str): Code to be improved.
Returns:
A result string from create chat completion. Improved code in response.
"""
function_string = (
"def generate_improved_code(suggestions: List[str], code: str) -> str:"
)
args = [json.dumps(suggestions), code]
description_string = (
"Improves the provided code based on the suggestions"
" provided, making no other changes."
)
return call_ai_function(function_string, args, description_string)

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from datetime import datetime
def get_datetime() -> str:
"""Return the current date and time
Returns:
str: The current date and time
"""
return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")

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autogpt/commands/twitter.py Normal file
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import tweepy
import os
from dotenv import load_dotenv
load_dotenv()
def send_tweet(tweet_text):
consumer_key = os.environ.get("TW_CONSUMER_KEY")
consumer_secret= os.environ.get("TW_CONSUMER_SECRET")
access_token= os.environ.get("TW_ACCESS_TOKEN")
access_token_secret= os.environ.get("TW_ACCESS_TOKEN_SECRET")
# Authenticate to Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
# Create API object
api = tweepy.API(auth)
# Send tweet
try:
api.update_status(tweet_text)
print("Tweet sent successfully!")
except tweepy.TweepyException as e:
print("Error sending tweet: {}".format(e.reason))

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"""Web scraping commands using Playwright"""
try:
from playwright.sync_api import sync_playwright
except ImportError:
print(
"Playwright not installed. Please install it with 'pip install playwright' to use."
)
from bs4 import BeautifulSoup
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
from typing import List, Union
def scrape_text(url: str) -> str:
"""Scrape text from a webpage
Args:
url (str): The URL to scrape text from
Returns:
str: The scraped text
"""
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
try:
page.goto(url)
html_content = page.content()
soup = BeautifulSoup(html_content, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
except Exception as e:
text = f"Error: {str(e)}"
finally:
browser.close()
return text
def scrape_links(url: str) -> Union[str, List[str]]:
"""Scrape links from a webpage
Args:
url (str): The URL to scrape links from
Returns:
Union[str, List[str]]: The scraped links
"""
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
try:
page.goto(url)
html_content = page.content()
soup = BeautifulSoup(html_content, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
formatted_links = format_hyperlinks(hyperlinks)
except Exception as e:
formatted_links = f"Error: {str(e)}"
finally:
browser.close()
return formatted_links

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"""Browse a webpage and summarize it using the LLM model"""
from typing import List, Tuple, Union
from urllib.parse import urljoin, urlparse
import requests
from requests.compat import urljoin
from requests import Response
from bs4 import BeautifulSoup
from autogpt.config import Config
from autogpt.memory import get_memory
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
CFG = Config()
memory = get_memory(CFG)
session = requests.Session()
session.headers.update({"User-Agent": CFG.user_agent})
def is_valid_url(url: str) -> bool:
"""Check if the URL is valid
Args:
url (str): The URL to check
Returns:
bool: True if the URL is valid, False otherwise
"""
try:
result = urlparse(url)
return all([result.scheme, result.netloc])
except ValueError:
return False
def sanitize_url(url: str) -> str:
"""Sanitize the URL
Args:
url (str): The URL to sanitize
Returns:
str: The sanitized URL
"""
return urljoin(url, urlparse(url).path)
def check_local_file_access(url: str) -> bool:
"""Check if the URL is a local file
Args:
url (str): The URL to check
Returns:
bool: True if the URL is a local file, False otherwise
"""
local_prefixes = [
"file:///",
"file://localhost",
"http://localhost",
"https://localhost",
]
return any(url.startswith(prefix) for prefix in local_prefixes)
def get_response(
url: str, timeout: int = 10
) -> Union[Tuple[None, str], Tuple[Response, None]]:
"""Get the response from a URL
Args:
url (str): The URL to get the response from
timeout (int): The timeout for the HTTP request
Returns:
Tuple[None, str] | Tuple[Response, None]: The response and error message
Raises:
ValueError: If the URL is invalid
requests.exceptions.RequestException: If the HTTP request fails
"""
try:
# Restrict access to local files
if check_local_file_access(url):
raise ValueError("Access to local files is restricted")
# Most basic check if the URL is valid:
if not url.startswith("http://") and not url.startswith("https://"):
raise ValueError("Invalid URL format")
sanitized_url = sanitize_url(url)
response = session.get(sanitized_url, timeout=timeout)
# Check if the response contains an HTTP error
if response.status_code >= 400:
return None, f"Error: HTTP {str(response.status_code)} error"
return response, None
except ValueError as ve:
# Handle invalid URL format
return None, f"Error: {str(ve)}"
except requests.exceptions.RequestException as re:
# Handle exceptions related to the HTTP request
# (e.g., connection errors, timeouts, etc.)
return None, f"Error: {str(re)}"
def scrape_text(url: str) -> str:
"""Scrape text from a webpage
Args:
url (str): The URL to scrape text from
Returns:
str: The scraped text
"""
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
return "Error: Could not get response"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return text
def scrape_links(url: str) -> Union[str, List[str]]:
"""Scrape links from a webpage
Args:
url (str): The URL to scrape links from
Returns:
Union[str, List[str]]: The scraped links
"""
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
return "Error: Could not get response"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
return format_hyperlinks(hyperlinks)
def create_message(chunk, question):
"""Create a message for the user to summarize a chunk of text"""
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, answer the following'
f' question: "{question}" -- if the question cannot be answered using the'
" text, summarize the text.",
}

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"""Selenium web scraping module."""
from selenium import webdriver
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
import autogpt.processing.text as summary
from bs4 import BeautifulSoup
from selenium.webdriver.remote.webdriver import WebDriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from webdriver_manager.chrome import ChromeDriverManager
from webdriver_manager.firefox import GeckoDriverManager
from selenium.webdriver.chrome.options import Options as ChromeOptions
from selenium.webdriver.firefox.options import Options as FirefoxOptions
from selenium.webdriver.safari.options import Options as SafariOptions
import logging
from pathlib import Path
from autogpt.config import Config
from typing import List, Tuple, Union
FILE_DIR = Path(__file__).parent.parent
CFG = Config()
def browse_website(url: str, question: str) -> Tuple[str, WebDriver]:
"""Browse a website and return the answer and links to the user
Args:
url (str): The url of the website to browse
question (str): The question asked by the user
Returns:
Tuple[str, WebDriver]: The answer and links to the user and the webdriver
"""
driver, text = scrape_text_with_selenium(url)
add_header(driver)
summary_text = summary.summarize_text(url, text, question, driver)
links = scrape_links_with_selenium(driver, url)
# Limit links to 5
if len(links) > 5:
links = links[:5]
close_browser(driver)
return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
def scrape_text_with_selenium(url: str) -> Tuple[WebDriver, str]:
"""Scrape text from a website using selenium
Args:
url (str): The url of the website to scrape
Returns:
Tuple[WebDriver, str]: The webdriver and the text scraped from the website
"""
logging.getLogger("selenium").setLevel(logging.CRITICAL)
options_available = {
"chrome": ChromeOptions,
"safari": SafariOptions,
"firefox": FirefoxOptions,
}
options = options_available[CFG.selenium_web_browser]()
options.add_argument(
"user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
)
if CFG.selenium_web_browser == "firefox":
driver = webdriver.Firefox(
executable_path=GeckoDriverManager().install(), options=options
)
elif CFG.selenium_web_browser == "safari":
# Requires a bit more setup on the users end
# See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari
driver = webdriver.Safari(options=options)
else:
driver = webdriver.Chrome(
executable_path=ChromeDriverManager().install(), options=options
)
driver.get(url)
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.TAG_NAME, "body"))
)
# Get the HTML content directly from the browser's DOM
page_source = driver.execute_script("return document.body.outerHTML;")
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return driver, text
def scrape_links_with_selenium(driver: WebDriver, url: str) -> List[str]:
"""Scrape links from a website using selenium
Args:
driver (WebDriver): The webdriver to use to scrape the links
Returns:
List[str]: The links scraped from the website
"""
page_source = driver.page_source
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
return format_hyperlinks(hyperlinks)
def close_browser(driver: WebDriver) -> None:
"""Close the browser
Args:
driver (WebDriver): The webdriver to close
Returns:
None
"""
driver.quit()
def add_header(driver: WebDriver) -> None:
"""Add a header to the website
Args:
driver (WebDriver): The webdriver to use to add the header
Returns:
None
"""
driver.execute_script(open(f"{FILE_DIR}/js/overlay.js", "r").read())

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"""A module that contains a function to generate test cases for the submitted code."""
import json
from typing import List
from autogpt.llm_utils import call_ai_function
def write_tests(code: str, focus: List[str]) -> str:
"""
A function that takes in code and focus topics and returns a response from create
chat completion api call.
Parameters:
focus (List): A list of suggestions around what needs to be improved.
code (str): Code for test cases to be generated against.
Returns:
A result string from create chat completion. Test cases for the submitted code
in response.
"""
function_string = (
"def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
)
args = [code, json.dumps(focus)]
description_string = (
"Generates test cases for the existing code, focusing on"
" specific areas if required."
)
return call_ai_function(function_string, args, description_string)

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"""
This module contains the configuration classes for AutoGPT.
"""
from autogpt.config.ai_config import AIConfig
from autogpt.config.config import check_openai_api_key, Config
from autogpt.config.singleton import AbstractSingleton, Singleton
__all__ = [
"check_openai_api_key",
"AbstractSingleton",
"AIConfig",
"Config",
"Singleton",
]

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# sourcery skip: do-not-use-staticmethod
"""
A module that contains the AIConfig class object that contains the configuration
"""
import os
from typing import List, Optional, Type
import yaml
class AIConfig:
"""
A class object that contains the configuration information for the AI
Attributes:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
"""
def __init__(
self, ai_name: str = "", ai_role: str = "", ai_goals: Optional[List] = None
) -> None:
"""
Initialize a class instance
Parameters:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
Returns:
None
"""
if ai_goals is None:
ai_goals = []
self.ai_name = ai_name
self.ai_role = ai_role
self.ai_goals = ai_goals
# Soon this will go in a folder where it remembers more stuff about the run(s)
SAVE_FILE = os.path.join(os.path.dirname(__file__), "..", "ai_settings.yaml")
@staticmethod
def load(config_file: str = SAVE_FILE) -> "AIConfig":
"""
Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from
yaml file if yaml file exists,
else returns class with no parameters.
Parameters:
config_file (int): The path to the config yaml file.
DEFAULT: "../ai_settings.yaml"
Returns:
cls (object): An instance of given cls object
"""
try:
with open(config_file, encoding="utf-8") as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
ai_name = config_params.get("ai_name", "")
ai_role = config_params.get("ai_role", "")
ai_goals = config_params.get("ai_goals", [])
# type: Type[AIConfig]
return AIConfig(ai_name, ai_role, ai_goals)
def save(self, config_file: str = SAVE_FILE) -> None:
"""
Saves the class parameters to the specified file yaml file path as a yaml file.
Parameters:
config_file(str): The path to the config yaml file.
DEFAULT: "../ai_settings.yaml"
Returns:
None
"""
config = {
"ai_name": self.ai_name,
"ai_role": self.ai_role,
"ai_goals": self.ai_goals,
}
with open(config_file, "w", encoding="utf-8") as file:
yaml.dump(config, file, allow_unicode=True)
def construct_full_prompt(self) -> str:
"""
Returns a prompt to the user with the class information in an organized fashion.
Parameters:
None
Returns:
full_prompt (str): A string containing the initial prompt for the user
including the ai_name, ai_role and ai_goals.
"""
prompt_start = (
"Your decisions must always be made independently without"
"seeking user assistance. Play to your strengths as an LLM and pursue"
" simple strategies with no legal complications."
""
)
from autogpt.prompt import get_prompt
# Construct full prompt
full_prompt = (
f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
)
for i, goal in enumerate(self.ai_goals):
full_prompt += f"{i+1}. {goal}\n"
full_prompt += f"\n\n{get_prompt()}"
return full_prompt

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"""Configuration class to store the state of bools for different scripts access."""
import os
from colorama import Fore
from autogpt.config.singleton import Singleton
import openai
import yaml
from dotenv import load_dotenv
load_dotenv(verbose=True)
class Config(metaclass=Singleton):
"""
Configuration class to store the state of bools for different scripts access.
"""
def __init__(self) -> None:
"""Initialize the Config class"""
self.debug_mode = False
self.continuous_mode = False
self.continuous_limit = 0
self.speak_mode = False
self.skip_reprompt = False
self.selenium_web_browser = os.getenv("USE_WEB_BROWSER", "chrome")
self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml")
self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 8192))
self.browse_summary_max_token = int(os.getenv("BROWSE_SUMMARY_MAX_TOKEN", 300))
self.openai_api_key = os.getenv("OPENAI_API_KEY")
self.temperature = float(os.getenv("TEMPERATURE", "1"))
self.use_azure = os.getenv("USE_AZURE") == "True"
self.execute_local_commands = (
os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
)
if self.use_azure:
self.load_azure_config()
openai.api_type = self.openai_api_type
openai.api_base = self.openai_api_base
openai.api_version = self.openai_api_version
self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID")
self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID")
self.use_mac_os_tts = False
self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
self.use_brian_tts = False
self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
self.github_api_key = os.getenv("GITHUB_API_KEY")
self.github_username = os.getenv("GITHUB_USERNAME")
self.google_api_key = os.getenv("GOOGLE_API_KEY")
self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
self.pinecone_api_key = os.getenv("PINECONE_API_KEY")
self.pinecone_region = os.getenv("PINECONE_ENV")
# milvus configuration, e.g., localhost:19530.
self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530")
self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt")
self.image_provider = os.getenv("IMAGE_PROVIDER")
self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
self.huggingface_audio_to_text_model = os.getenv(
"HUGGINGFACE_AUDIO_TO_TEXT_MODEL"
)
# User agent headers to use when browsing web
# Some websites might just completely deny request with an error code if
# no user agent was found.
self.user_agent = os.getenv(
"USER_AGENT",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
)
self.redis_host = os.getenv("REDIS_HOST", "localhost")
self.redis_port = os.getenv("REDIS_PORT", "6379")
self.redis_password = os.getenv("REDIS_PASSWORD", "")
self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == "True"
self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt")
# Note that indexes must be created on db 0 in redis, this is not configurable.
self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
# Initialize the OpenAI API client
openai.api_key = self.openai_api_key
self.plugins = []
self.plugins_whitelist = []
self.plugins_blacklist = []
def get_azure_deployment_id_for_model(self, model: str) -> str:
"""
Returns the relevant deployment id for the model specified.
Parameters:
model(str): The model to map to the deployment id.
Returns:
The matching deployment id if found, otherwise an empty string.
"""
if model == self.fast_llm_model:
return self.azure_model_to_deployment_id_map[
"fast_llm_model_deployment_id"
] # type: ignore
elif model == self.smart_llm_model:
return self.azure_model_to_deployment_id_map[
"smart_llm_model_deployment_id"
] # type: ignore
elif model == "text-embedding-ada-002":
return self.azure_model_to_deployment_id_map[
"embedding_model_deployment_id"
] # type: ignore
else:
return ""
AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "..", "azure.yaml")
def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None:
"""
Loads the configuration parameters for Azure hosting from the specified file
path as a yaml file.
Parameters:
config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml"
Returns:
None
"""
try:
with open(config_file) as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
self.openai_api_type = config_params.get("azure_api_type") or "azure"
self.openai_api_base = config_params.get("azure_api_base") or ""
self.openai_api_version = (
config_params.get("azure_api_version") or "2023-03-15-preview"
)
self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", [])
def set_continuous_mode(self, value: bool) -> None:
"""Set the continuous mode value."""
self.continuous_mode = value
def set_continuous_limit(self, value: int) -> None:
"""Set the continuous limit value."""
self.continuous_limit = value
def set_speak_mode(self, value: bool) -> None:
"""Set the speak mode value."""
self.speak_mode = value
def set_fast_llm_model(self, value: str) -> None:
"""Set the fast LLM model value."""
self.fast_llm_model = value
def set_smart_llm_model(self, value: str) -> None:
"""Set the smart LLM model value."""
self.smart_llm_model = value
def set_fast_token_limit(self, value: int) -> None:
"""Set the fast token limit value."""
self.fast_token_limit = value
def set_smart_token_limit(self, value: int) -> None:
"""Set the smart token limit value."""
self.smart_token_limit = value
def set_browse_chunk_max_length(self, value: int) -> None:
"""Set the browse_website command chunk max length value."""
self.browse_chunk_max_length = value
def set_browse_summary_max_token(self, value: int) -> None:
"""Set the browse_website command summary max token value."""
self.browse_summary_max_token = value
def set_openai_api_key(self, value: str) -> None:
"""Set the OpenAI API key value."""
self.openai_api_key = value
def set_elevenlabs_api_key(self, value: str) -> None:
"""Set the ElevenLabs API key value."""
self.elevenlabs_api_key = value
def set_elevenlabs_voice_1_id(self, value: str) -> None:
"""Set the ElevenLabs Voice 1 ID value."""
self.elevenlabs_voice_1_id = value
def set_elevenlabs_voice_2_id(self, value: str) -> None:
"""Set the ElevenLabs Voice 2 ID value."""
self.elevenlabs_voice_2_id = value
def set_google_api_key(self, value: str) -> None:
"""Set the Google API key value."""
self.google_api_key = value
def set_custom_search_engine_id(self, value: str) -> None:
"""Set the custom search engine id value."""
self.custom_search_engine_id = value
def set_pinecone_api_key(self, value: str) -> None:
"""Set the Pinecone API key value."""
self.pinecone_api_key = value
def set_pinecone_region(self, value: str) -> None:
"""Set the Pinecone region value."""
self.pinecone_region = value
def set_debug_mode(self, value: bool) -> None:
"""Set the debug mode value."""
self.debug_mode = value
def set_plugins(self, value: list) -> None:
"""Set the plugins value."""
self.plugins = value
def check_openai_api_key() -> None:
"""Check if the OpenAI API key is set in config.py or as an environment variable."""
cfg = Config()
if not cfg.openai_api_key:
print(
Fore.RED
+ "Please set your OpenAI API key in .env or as an environment variable."
)
print("You can get your key from https://beta.openai.com/account/api-keys")
exit(1)

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"""The singleton metaclass for ensuring only one instance of a class."""
import abc
class Singleton(abc.ABCMeta, type):
"""
Singleton metaclass for ensuring only one instance of a class.
"""
_instances = {}
def __call__(cls, *args, **kwargs):
"""Call method for the singleton metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class AbstractSingleton(abc.ABC, metaclass=Singleton):
"""
Abstract singleton class for ensuring only one instance of a class.
"""
pass

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autogpt/data_ingestion.py Normal file
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import argparse
import logging
from autogpt.config import Config
from autogpt.commands.file_operations import ingest_file, search_files
from autogpt.memory import get_memory
cfg = Config()
def configure_logging():
logging.basicConfig(
filename="log-ingestion.txt",
filemode="a",
format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
datefmt="%H:%M:%S",
level=logging.DEBUG,
)
return logging.getLogger("AutoGPT-Ingestion")
def ingest_directory(directory, memory, args):
"""
Ingest all files in a directory by calling the ingest_file function for each file.
:param directory: The directory containing the files to ingest
:param memory: An object with an add() method to store the chunks in memory
"""
try:
files = search_files(directory)
for file in files:
ingest_file(file, memory, args.max_length, args.overlap)
except Exception as e:
print(f"Error while ingesting directory '{directory}': {str(e)}")
def main() -> None:
logger = configure_logging()
parser = argparse.ArgumentParser(
description="Ingest a file or a directory with multiple files into memory. "
"Make sure to set your .env before running this script."
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--file", type=str, help="The file to ingest.")
group.add_argument(
"--dir", type=str, help="The directory containing the files to ingest."
)
parser.add_argument(
"--init",
action="store_true",
help="Init the memory and wipe its content (default: False)",
default=False,
)
parser.add_argument(
"--overlap",
type=int,
help="The overlap size between chunks when ingesting files (default: 200)",
default=200,
)
parser.add_argument(
"--max_length",
type=int,
help="The max_length of each chunk when ingesting files (default: 4000)",
default=4000,
)
args = parser.parse_args()
# Initialize memory
memory = get_memory(cfg, init=args.init)
print("Using memory of type: " + memory.__class__.__name__)
if args.file:
try:
ingest_file(args.file, memory, args.max_length, args.overlap)
print(f"File '{args.file}' ingested successfully.")
except Exception as e:
logger.error(f"Error while ingesting file '{args.file}': {str(e)}")
print(f"Error while ingesting file '{args.file}': {str(e)}")
elif args.dir:
try:
ingest_directory(args.dir, memory, args)
print(f"Directory '{args.dir}' ingested successfully.")
except Exception as e:
logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}")
print(f"Error while ingesting directory '{args.dir}': {str(e)}")
else:
print(
"Please provide either a file path (--file) or a directory name (--dir)"
" inside the auto_gpt_workspace directory as input."
)
if __name__ == "__main__":
main()

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const overlay = document.createElement('div');
Object.assign(overlay.style, {
position: 'fixed',
zIndex: 999999,
top: 0,
left: 0,
width: '100%',
height: '100%',
background: 'rgba(0, 0, 0, 0.7)',
color: '#fff',
fontSize: '24px',
fontWeight: 'bold',
display: 'flex',
justifyContent: 'center',
alignItems: 'center',
});
const textContent = document.createElement('div');
Object.assign(textContent.style, {
textAlign: 'center',
});
textContent.textContent = 'AutoGPT Analyzing Page';
overlay.appendChild(textContent);
document.body.append(overlay);
document.body.style.overflow = 'hidden';
let dotCount = 0;
setInterval(() => {
textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
dotCount = (dotCount + 1) % 4;
}, 1000);

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autogpt/json_fixes/__init__.py Normal file
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autogpt/json_fixes/auto_fix.py Normal file
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"""This module contains the function to fix JSON strings using GPT-3."""
import json
from autogpt.llm_utils import call_ai_function
from autogpt.logs import logger
from autogpt.config import Config
CFG = Config()
def fix_json(json_string: str, schema: str) -> str:
"""Fix the given JSON string to make it parseable and fully compliant with
the provided schema.
Args:
json_string (str): The JSON string to fix.
schema (str): The schema to use to fix the JSON.
Returns:
str: The fixed JSON string.
"""
# Try to fix the JSON using GPT:
function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
args = [f"'''{json_string}'''", f"'''{schema}'''"]
description_string = (
"This function takes a JSON string and ensures that it"
" is parseable and fully compliant with the provided schema. If an object"
" or field specified in the schema isn't contained within the correct JSON,"
" it is omitted. The function also escapes any double quotes within JSON"
" string values to ensure that they are valid. If the JSON string contains"
" any None or NaN values, they are replaced with null before being parsed."
)
# If it doesn't already start with a "`", add one:
if not json_string.startswith("`"):
json_string = "```json\n" + json_string + "\n```"
result_string = call_ai_function(
function_string, args, description_string, model=CFG.fast_llm_model
)
logger.debug("------------ JSON FIX ATTEMPT ---------------")
logger.debug(f"Original JSON: {json_string}")
logger.debug("-----------")
logger.debug(f"Fixed JSON: {result_string}")
logger.debug("----------- END OF FIX ATTEMPT ----------------")
try:
json.loads(result_string) # just check the validity
return result_string
except json.JSONDecodeError: # noqa: E722
# Get the call stack:
# import traceback
# call_stack = traceback.format_exc()
# print(f"Failed to fix JSON: '{json_string}' "+call_stack)
return "failed"

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autogpt/json_fixes/bracket_termination.py Normal file
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"""Fix JSON brackets."""
import contextlib
import json
from typing import Optional
import regex
from colorama import Fore
from autogpt.logs import logger
from autogpt.config import Config
from autogpt.speech import say_text
CFG = Config()
def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
if CFG.speak_mode and CFG.debug_mode:
say_text(
"I have received an invalid JSON response from the OpenAI API. "
"Trying to fix it now."
)
logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
try:
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(
title="Apparently json was fixed.", title_color=Fore.GREEN
)
if CFG.speak_mode and CFG.debug_mode:
say_text("Apparently json was fixed.")
else:
raise ValueError("No valid JSON object found")
except (json.JSONDecodeError, ValueError):
if CFG.debug_mode:
logger.error("Error: Invalid JSON: %s\n", json_string)
if CFG.speak_mode:
say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return json_string
def balance_braces(json_string: str) -> Optional[str]:
"""
Balance the braces in a JSON string.
Args:
json_string (str): The JSON string.
Returns:
str: The JSON string with braces balanced.
"""
open_braces_count = json_string.count("{")
close_braces_count = json_string.count("}")
while open_braces_count > close_braces_count:
json_string += "}"
close_braces_count += 1
while close_braces_count > open_braces_count:
json_string = json_string.rstrip("}")
close_braces_count -= 1
with contextlib.suppress(json.JSONDecodeError):
json.loads(json_string)
return json_string

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""" Fix invalid escape sequences in JSON strings. """
import json
from autogpt.config import Config
from autogpt.json_fixes.utilities import extract_char_position
CFG = Config()
def fix_invalid_escape(json_to_load: str, error_message: str) -> str:
"""Fix invalid escape sequences in JSON strings.
Args:
json_to_load (str): The JSON string.
error_message (str): The error message from the JSONDecodeError
exception.
Returns:
str: The JSON string with invalid escape sequences fixed.
"""
while error_message.startswith("Invalid \\escape"):
bad_escape_location = extract_char_position(error_message)
json_to_load = (
json_to_load[:bad_escape_location] + json_to_load[bad_escape_location + 1 :]
)
try:
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
if CFG.debug_mode:
print("json loads error - fix invalid escape", e)
error_message = str(e)
return json_to_load

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autogpt/json_fixes/missing_quotes.py Normal file
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"""Fix quotes in a JSON string."""
import json
import re
def add_quotes_to_property_names(json_string: str) -> str:
"""
Add quotes to property names in a JSON string.
Args:
json_string (str): The JSON string.
Returns:
str: The JSON string with quotes added to property names.
"""
def replace_func(match: re.Match) -> str:
return f'"{match[1]}":'
property_name_pattern = re.compile(r"(\w+):")
corrected_json_string = property_name_pattern.sub(replace_func, json_string)
try:
json.loads(corrected_json_string)
return corrected_json_string
except json.JSONDecodeError as e:
raise e

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"""Fix and parse JSON strings."""
import contextlib
import json
from typing import Any, Dict, Union
from autogpt.config import Config
from autogpt.json_fixes.auto_fix import fix_json
from autogpt.json_fixes.bracket_termination import balance_braces
from autogpt.json_fixes.escaping import fix_invalid_escape
from autogpt.json_fixes.missing_quotes import add_quotes_to_property_names
from autogpt.logs import logger
CFG = Config()
JSON_SCHEMA = """
{
"command": {
"name": "command name",
"args": {
"arg name": "value"
}
},
"thoughts":
{
"text": "thought",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"speak": "thoughts summary to say to user"
}
}
"""
def correct_json(json_to_load: str) -> str:
"""
Correct common JSON errors.
Args:
json_to_load (str): The JSON string.
"""
try:
if CFG.debug_mode:
print("json", json_to_load)
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
if CFG.debug_mode:
print("json loads error", e)
error_message = str(e)
if error_message.startswith("Invalid \\escape"):
json_to_load = fix_invalid_escape(json_to_load, error_message)
if error_message.startswith(
"Expecting property name enclosed in double quotes"
):
json_to_load = add_quotes_to_property_names(json_to_load)
try:
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
if CFG.debug_mode:
print("json loads error - add quotes", e)
error_message = str(e)
if balanced_str := balance_braces(json_to_load):
return balanced_str
return json_to_load
def fix_and_parse_json(
json_to_load: str, try_to_fix_with_gpt: bool = True
) -> Union[str, Dict[Any, Any]]:
"""Fix and parse JSON string
Args:
json_to_load (str): The JSON string.
try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
Defaults to True.
Returns:
Union[str, Dict[Any, Any]]: The parsed JSON.
"""
with contextlib.suppress(json.JSONDecodeError):
json_to_load = json_to_load.replace("\t", "")
return json.loads(json_to_load)
with contextlib.suppress(json.JSONDecodeError):
json_to_load = correct_json(json_to_load)
return json.loads(json_to_load)
# Let's do something manually:
# sometimes GPT responds with something BEFORE the braces:
# "I'm sorry, I don't understand. Please try again."
# {"text": "I'm sorry, I don't understand. Please try again.",
# "confidence": 0.0}
# So let's try to find the first brace and then parse the rest
# of the string
try:
brace_index = json_to_load.index("{")
maybe_fixed_json = json_to_load[brace_index:]
last_brace_index = maybe_fixed_json.rindex("}")
maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
return json.loads(maybe_fixed_json)
except (json.JSONDecodeError, ValueError) as e:
return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
def try_ai_fix(
try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
) -> Union[str, Dict[Any, Any]]:
"""Try to fix the JSON with the AI
Args:
try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
exception (Exception): The exception that was raised.
json_to_load (str): The JSON string to load.
Raises:
exception: If try_to_fix_with_gpt is False.
Returns:
Union[str, Dict[Any, Any]]: The JSON string or dictionary.
"""
if not try_to_fix_with_gpt:
raise exception
logger.warn(
"Warning: Failed to parse AI output, attempting to fix."
"\n If you see this warning frequently, it's likely that"
" your prompt is confusing the AI. Try changing it up"
" slightly."
)
# Now try to fix this up using the ai_functions
ai_fixed_json = fix_json(json_to_load, JSON_SCHEMA)
if ai_fixed_json != "failed":
return json.loads(ai_fixed_json)
# This allows the AI to react to the error message,
# which usually results in it correcting its ways.
logger.error("Failed to fix AI output, telling the AI.")
return json_to_load

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autogpt/json_fixes/utilities.py Normal file
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"""Utilities for the json_fixes package."""
import re
def extract_char_position(error_message: str) -> int:
"""Extract the character position from the JSONDecodeError message.
Args:
error_message (str): The error message from the JSONDecodeError
exception.
Returns:
int: The character position.
"""
char_pattern = re.compile(r"\(char (\d+)\)")
if match := char_pattern.search(error_message):
return int(match[1])
else:
raise ValueError("Character position not found in the error message.")

155
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from ast import List
import time
from typing import Dict, Optional
import openai
from openai.error import APIError, RateLimitError
from colorama import Fore
from autogpt.config import Config
CFG = Config()
openai.api_key = CFG.openai_api_key
def call_ai_function(
function: str, args: List, description: str, model: Optional[str] = None
) -> str:
"""Call an AI function
This is a magic function that can do anything with no-code. See
https://github.com/Torantulino/AI-Functions for more info.
Args:
function (str): The function to call
args (list): The arguments to pass to the function
description (str): The description of the function
model (str, optional): The model to use. Defaults to None.
Returns:
str: The response from the function
"""
if model is None:
model = CFG.smart_llm_model
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma separated string
args = ", ".join(args)
messages = [
{
"role": "system",
"content": f"You are now the following python function: ```# {description}"
f"\n{function}```\n\nOnly respond with your `return` value.",
},
{"role": "user", "content": args},
]
return create_chat_completion(model=model, messages=messages, temperature=0)
# Overly simple abstraction until we create something better
# simple retry mechanism when getting a rate error or a bad gateway
def create_chat_completion(
messages: List, # type: ignore
model: Optional[str] = None,
temperature: float = CFG.temperature,
max_tokens: Optional[int] = None,
) -> str:
"""Create a chat completion using the OpenAI API
Args:
messages (List[Dict[str, str]]): The messages to send to the chat completion
model (str, optional): The model to use. Defaults to None.
temperature (float, optional): The temperature to use. Defaults to 0.9.
max_tokens (int, optional): The max tokens to use. Defaults to None.
Returns:
str: The response from the chat completion
"""
response = None
num_retries = 10
if CFG.debug_mode:
print(
Fore.GREEN
+ f"Creating chat completion with model {model}, temperature {temperature},"
f" max_tokens {max_tokens}" + Fore.RESET
)
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
if CFG.use_azure:
response = openai.ChatCompletion.create(
deployment_id=CFG.get_azure_deployment_id_for_model(model),
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
break
except RateLimitError:
if CFG.debug_mode:
print(
Fore.RED + "Error: ",
f"Reached rate limit, passing..." + Fore.RESET,
)
except APIError as e:
if e.http_status == 502:
pass
else:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
Fore.RED + "Error: ",
f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
)
time.sleep(backoff)
if response is None:
raise RuntimeError(f"Failed to get response after {num_retries} retries")
resp = response.choices[0].message["content"]
for plugin in CFG.plugins:
resp = plugin.on_response(resp)
return resp
def create_embedding_with_ada(text) -> list:
"""Create a embedding with text-ada-002 using the OpenAI SDK"""
num_retries = 10
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
if CFG.use_azure:
return openai.Embedding.create(
input=[text],
engine=CFG.get_azure_deployment_id_for_model(
"text-embedding-ada-002"
),
)["data"][0]["embedding"]
else:
return openai.Embedding.create(
input=[text], model="text-embedding-ada-002"
)["data"][0]["embedding"]
except RateLimitError:
pass
except APIError as e:
if e.http_status == 502:
pass
else:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
Fore.RED + "Error: ",
f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
)
time.sleep(backoff)

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autogpt/logs.py Normal file
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"""Logging module for Auto-GPT."""
import json
import logging
import os
import random
import re
import time
from logging import LogRecord
import traceback
from colorama import Fore, Style
from autogpt.speech import say_text
from autogpt.config import Config, Singleton
CFG = Config()
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = AutoGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(os.path.join(log_dir, log_file))
self.file_handler.setLevel(logging.DEBUG)
info_formatter = AutoGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(os.path.join(log_dir, error_file))
error_handler.setLevel(logging.ERROR)
error_formatter = AutoGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and CFG.speak_mode:
say_text(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
else:
content = ""
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
def debug(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.DEBUG)
def warn(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.WARN)
def error(self, title, message=""):
self._log(title, Fore.RED, message, logging.ERROR)
def _log(self, title="", title_color="", message="", level=logging.INFO):
if message:
if isinstance(message, list):
message = " ".join(message)
self.logger.log(level, message, extra={"title": title, "color": title_color})
def set_level(self, level):
self.logger.setLevel(level)
self.typing_logger.setLevel(level)
def double_check(self, additionalText=None):
if not additionalText:
additionalText = (
"Please ensure you've setup and configured everything"
" correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
"double check. You can also create a github issue or join the discord"
" and ask there!"
)
self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
"""
Output stream to console using simulated typing
"""
class TypingConsoleHandler(logging.StreamHandler):
def emit(self, record):
min_typing_speed = 0.05
max_typing_speed = 0.01
msg = self.format(record)
try:
words = msg.split()
for i, word in enumerate(words):
print(word, end="", flush=True)
if i < len(words) - 1:
print(" ", end="", flush=True)
typing_speed = random.uniform(min_typing_speed, max_typing_speed)
time.sleep(typing_speed)
# type faster after each word
min_typing_speed = min_typing_speed * 0.95
max_typing_speed = max_typing_speed * 0.95
print()
except Exception:
self.handleError(record)
class ConsoleHandler(logging.StreamHandler):
def emit(self, record) -> None:
msg = self.format(record)
try:
print(msg)
except Exception:
self.handleError(record)
class AutoGptFormatter(logging.Formatter):
"""
Allows to handle custom placeholders 'title_color' and 'message_no_color'.
To use this formatter, make sure to pass 'color', 'title' as log extras.
"""
def format(self, record: LogRecord) -> str:
if hasattr(record, "color"):
record.title_color = (
getattr(record, "color")
+ getattr(record, "title")
+ " "
+ Style.RESET_ALL
)
else:
record.title_color = getattr(record, "title")
if hasattr(record, "msg"):
record.message_no_color = remove_color_codes(getattr(record, "msg"))
else:
record.message_no_color = ""
return super().format(record)
def remove_color_codes(s: str) -> str:
ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
return ansi_escape.sub("", s)
logger = Logger()
def print_assistant_thoughts(ai_name, assistant_reply):
"""Prints the assistant's thoughts to the console"""
from autogpt.json_fixes.bracket_termination import (
attempt_to_fix_json_by_finding_outermost_brackets,
)
from autogpt.json_fixes.parsing import fix_and_parse_json
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
if isinstance(assistant_reply_json, str):
assistant_reply_json = fix_and_parse_json(assistant_reply_json)
# Check if assistant_reply_json is a string and attempt to parse
# it into a JSON object
if isinstance(assistant_reply_json, str):
try:
assistant_reply_json = json.loads(assistant_reply_json)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
assistant_reply_json = (
attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply_json
)
)
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
assistant_thoughts_speak = None
assistant_thoughts_criticism = None
if not isinstance(assistant_reply_json, dict):
assistant_reply_json = {}
assistant_thoughts = assistant_reply_json.get("thoughts", {})
assistant_thoughts_text = assistant_thoughts.get("text")
if assistant_thoughts:
assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
assistant_thoughts_plan = assistant_thoughts.get("plan")
assistant_thoughts_criticism = assistant_thoughts.get("criticism")
assistant_thoughts_speak = assistant_thoughts.get("speak")
logger.typewriter_log(
f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
)
logger.typewriter_log(
"REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
)
if assistant_thoughts_plan:
logger.typewriter_log("PLAN:", Fore.YELLOW, "")
# If it's a list, join it into a string
if isinstance(assistant_thoughts_plan, list):
assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
elif isinstance(assistant_thoughts_plan, dict):
assistant_thoughts_plan = str(assistant_thoughts_plan)
# Split the input_string using the newline character and dashes
lines = assistant_thoughts_plan.split("\n")
for line in lines:
line = line.lstrip("- ")
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log(
"CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
)
# Speak the assistant's thoughts
if CFG.speak_mode and assistant_thoughts_speak:
say_text(assistant_thoughts_speak)
return assistant_reply_json
except json.decoder.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
if CFG.speak_mode:
say_text(
"I have received an invalid JSON response from the OpenAI API."
" I cannot ignore this response."
)
# All other errors, return "Error: + error message"
except Exception:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)

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from autogpt.memory.local import LocalCache
from autogpt.memory.no_memory import NoMemory
# List of supported memory backends
# Add a backend to this list if the import attempt is successful
supported_memory = ["local", "no_memory"]
try:
from autogpt.memory.redismem import RedisMemory
supported_memory.append("redis")
except ImportError:
print("Redis not installed. Skipping import.")
RedisMemory = None
try:
from autogpt.memory.pinecone import PineconeMemory
supported_memory.append("pinecone")
except ImportError:
print("Pinecone not installed. Skipping import.")
PineconeMemory = None
try:
from autogpt.memory.milvus import MilvusMemory
except ImportError:
print("pymilvus not installed. Skipping import.")
MilvusMemory = None
def get_memory(cfg, init=False):
memory = None
if cfg.memory_backend == "pinecone":
if not PineconeMemory:
print(
"Error: Pinecone is not installed. Please install pinecone"
" to use Pinecone as a memory backend."
)
else:
memory = PineconeMemory(cfg)
if init:
memory.clear()
elif cfg.memory_backend == "redis":
if not RedisMemory:
print(
"Error: Redis is not installed. Please install redis-py to"
" use Redis as a memory backend."
)
else:
memory = RedisMemory(cfg)
elif cfg.memory_backend == "milvus":
if not MilvusMemory:
print(
"Error: Milvus sdk is not installed."
"Please install pymilvus to use Milvus as memory backend."
)
else:
memory = MilvusMemory(cfg)
elif cfg.memory_backend == "no_memory":
memory = NoMemory(cfg)
if memory is None:
memory = LocalCache(cfg)
if init:
memory.clear()
return memory
def get_supported_memory_backends():
return supported_memory
__all__ = [
"get_memory",
"LocalCache",
"RedisMemory",
"PineconeMemory",
"NoMemory",
"MilvusMemory",
]

16
scripts/memory/base.py → autogpt/memory/base.py
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@ -1,12 +1,24 @@
"""Base class for memory providers."""
import abc
from config import AbstractSingleton
import openai
from autogpt.config import AbstractSingleton, Config
cfg = Config()
def get_ada_embedding(text):
text = text.replace("\n", " ")
return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
if cfg.use_azure:
return openai.Embedding.create(
input=[text],
engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"),
)["data"][0]["embedding"]
else:
return openai.Embedding.create(input=[text], model="text-embedding-ada-002")[
"data"
][0]["embedding"]
class MemoryProviderSingleton(AbstractSingleton):

58
scripts/memory/local.py → autogpt/memory/local.py
View File

@ -1,10 +1,12 @@
import dataclasses
import orjson
from typing import Any, List, Optional
import numpy as np
import os
from memory.base import MemoryProviderSingleton, get_ada_embedding
from typing import Any, List, Optional, Tuple
import numpy as np
import orjson
from autogpt.memory.base import MemoryProviderSingleton
from autogpt.llm_utils import create_embedding_with_ada
EMBED_DIM = 1536
SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
@ -23,15 +25,36 @@ class CacheContent:
class LocalCache(MemoryProviderSingleton):
"""A class that stores the memory in a local file"""
# on load, load our database
def __init__(self, cfg) -> None:
"""Initialize a class instance
Args:
cfg: Config object
Returns:
None
"""
self.filename = f"{cfg.memory_index}.json"
if os.path.exists(self.filename):
with open(self.filename, 'rb') as f:
loaded = orjson.loads(f.read())
self.data = CacheContent(**loaded)
try:
with open(self.filename, "w+b") as f:
file_content = f.read()
if not file_content.strip():
file_content = b"{}"
f.write(file_content)
loaded = orjson.loads(file_content)
self.data = CacheContent(**loaded)
except orjson.JSONDecodeError:
print(f"Error: The file '{self.filename}' is not in JSON format.")
self.data = CacheContent()
else:
print(
f"Warning: The file '{self.filename}' does not exist."
"Local memory would not be saved to a file."
)
self.data = CacheContent()
def add(self, text: str):
@ -44,27 +67,24 @@ class LocalCache(MemoryProviderSingleton):
Returns: None
"""
if 'Command Error:' in text:
if "Command Error:" in text:
return ""
self.data.texts.append(text)
embedding = get_ada_embedding(text)
embedding = create_embedding_with_ada(text)
vector = np.array(embedding).astype(np.float32)
vector = vector[np.newaxis, :]
self.data.embeddings = np.concatenate(
[
vector,
self.data.embeddings,
vector,
],
axis=0,
)
with open(self.filename, 'wb') as f:
out = orjson.dumps(
self.data,
option=SAVE_OPTIONS
)
with open(self.filename, "wb") as f:
out = orjson.dumps(self.data, option=SAVE_OPTIONS)
f.write(out)
return text
@ -89,7 +109,7 @@ class LocalCache(MemoryProviderSingleton):
return self.get_relevant(data, 1)
def get_relevant(self, text: str, k: int) -> List[Any]:
""""
""" "
matrix-vector mult to find score-for-each-row-of-matrix
get indices for top-k winning scores
return texts for those indices
@ -99,7 +119,7 @@ class LocalCache(MemoryProviderSingleton):
Returns: List[str]
"""
embedding = get_ada_embedding(text)
embedding = create_embedding_with_ada(text)
scores = np.dot(self.data.embeddings, embedding)
@ -107,7 +127,7 @@ class LocalCache(MemoryProviderSingleton):
return [self.data.texts[i] for i in top_k_indices]
def get_stats(self):
def get_stats(self) -> Tuple[int, Tuple[int, ...]]:
"""
Returns: The stats of the local cache.
"""

121
autogpt/memory/milvus.py Normal file
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@ -0,0 +1,121 @@
""" Milvus memory storage provider."""
from pymilvus import (
connections,
FieldSchema,
CollectionSchema,
DataType,
Collection,
)
from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
class MilvusMemory(MemoryProviderSingleton):
"""Milvus memory storage provider."""
def __init__(self, cfg) -> None:
"""Construct a milvus memory storage connection.
Args:
cfg (Config): Auto-GPT global config.
"""
# connect to milvus server.
connections.connect(address=cfg.milvus_addr)
fields = [
FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536),
FieldSchema(name="raw_text", dtype=DataType.VARCHAR, max_length=65535),
]
# create collection if not exist and load it.
self.milvus_collection = cfg.milvus_collection
self.schema = CollectionSchema(fields, "auto-gpt memory storage")
self.collection = Collection(self.milvus_collection, self.schema)
# create index if not exist.
if not self.collection.has_index():
self.collection.release()
self.collection.create_index(
"embeddings",
{
"metric_type": "IP",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
},
index_name="embeddings",
)
self.collection.load()
def add(self, data) -> str:
"""Add a embedding of data into memory.
Args:
data (str): The raw text to construct embedding index.
Returns:
str: log.
"""
embedding = get_ada_embedding(data)
result = self.collection.insert([[embedding], [data]])
_text = (
"Inserting data into memory at primary key: "
f"{result.primary_keys[0]}:\n data: {data}"
)
return _text
def get(self, data):
"""Return the most relevant data in memory.
Args:
data: The data to compare to.
"""
return self.get_relevant(data, 1)
def clear(self) -> str:
"""Drop the index in memory.
Returns:
str: log.
"""
self.collection.drop()
self.collection = Collection(self.milvus_collection, self.schema)
self.collection.create_index(
"embeddings",
{
"metric_type": "IP",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
},
index_name="embeddings",
)
self.collection.load()
return "Obliviated"
def get_relevant(self, data: str, num_relevant: int = 5):
"""Return the top-k relevant data in memory.
Args:
data: The data to compare to.
num_relevant (int, optional): The max number of relevant data.
Defaults to 5.
Returns:
list: The top-k relevant data.
"""
# search the embedding and return the most relevant text.
embedding = get_ada_embedding(data)
search_params = {
"metrics_type": "IP",
"params": {"nprobe": 8},
}
result = self.collection.search(
[embedding],
"embeddings",
search_params,
num_relevant,
output_fields=["raw_text"],
)
return [item.entity.value_of_field("raw_text") for item in result[0]]
def get_stats(self) -> str:
"""
Returns: The stats of the milvus cache.
"""
return f"Entities num: {self.collection.num_entities}"

71
autogpt/memory/no_memory.py Normal file
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@ -0,0 +1,71 @@
"""A class that does not store any data. This is the default memory provider."""
from typing import Optional, List, Any
from autogpt.memory.base import MemoryProviderSingleton
class NoMemory(MemoryProviderSingleton):
"""
A class that does not store any data. This is the default memory provider.
"""
def __init__(self, cfg):
"""
Initializes the NoMemory provider.
Args:
cfg: The config object.
Returns: None
"""
pass
def add(self, data: str) -> str:
"""
Adds a data point to the memory. No action is taken in NoMemory.
Args:
data: The data to add.
Returns: An empty string.
"""
return ""
def get(self, data: str) -> Optional[List[Any]]:
"""
Gets the data from the memory that is most relevant to the given data.
NoMemory always returns None.
Args:
data: The data to compare to.
Returns: None
"""
return None
def clear(self) -> str:
"""
Clears the memory. No action is taken in NoMemory.
Returns: An empty string.
"""
return ""
def get_relevant(self, data: str, num_relevant: int = 5) -> Optional[List[Any]]:
"""
Returns all the data in the memory that is relevant to the given data.
NoMemory always returns None.
Args:
data: The data to compare to.
num_relevant: The number of relevant data to return.
Returns: None
"""
return None
def get_stats(self):
"""
Returns: An empty dictionary as there are no stats in NoMemory.
"""
return {}

42
scripts/memory/pinecone.py → autogpt/memory/pinecone.py
View File

@ -1,7 +1,9 @@
import pinecone
from colorama import Fore, Style
from memory.base import MemoryProviderSingleton, get_ada_embedding
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
from autogpt.llm_utils import create_embedding_with_ada
class PineconeMemory(MemoryProviderSingleton):
@ -15,16 +17,36 @@ class PineconeMemory(MemoryProviderSingleton):
table_name = "auto-gpt"
# this assumes we don't start with memory.
# for now this works.
# we'll need a more complicated and robust system if we want to start with memory.
# we'll need a more complicated and robust system if we want to start with
# memory.
self.vec_num = 0
try:
pinecone.whoami()
except Exception as e:
logger.typewriter_log(
"FAILED TO CONNECT TO PINECONE",
Fore.RED,
Style.BRIGHT + str(e) + Style.RESET_ALL,
)
logger.double_check(
"Please ensure you have setup and configured Pinecone properly for use."
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
"https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
f"{Style.RESET_ALL} to ensure you've set up everything correctly."
)
exit(1)
if table_name not in pinecone.list_indexes():
pinecone.create_index(table_name, dimension=dimension, metric=metric, pod_type=pod_type)
pinecone.create_index(
table_name, dimension=dimension, metric=metric, pod_type=pod_type
)
self.index = pinecone.Index(table_name)
def add(self, data):
vector = get_ada_embedding(data)
vector = create_embedding_with_ada(data)
# no metadata here. We may wish to change that long term.
resp = self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
_text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
self.vec_num += 1
return _text
@ -42,10 +64,12 @@ class PineconeMemory(MemoryProviderSingleton):
:param data: The data to compare to.
:param num_relevant: The number of relevant data to return. Defaults to 5
"""
query_embedding = get_ada_embedding(data)
results = self.index.query(query_embedding, top_k=num_relevant, include_metadata=True)
query_embedding = create_embedding_with_ada(data)
results = self.index.query(
query_embedding, top_k=num_relevant, include_metadata=True
)
sorted_results = sorted(results.matches, key=lambda x: x.score)
return [str(item['metadata']["raw_text"]) for item in sorted_results]
return [str(item["metadata"]["raw_text"]) for item in sorted_results]
def get_stats(self):
return self.index.describe_index_stats()

87
scripts/memory/redismem.py → autogpt/memory/redismem.py
View File

@ -1,24 +1,23 @@
"""Redis memory provider."""
from typing import Any, List, Optional
import redis
from redis.commands.search.field import VectorField, TextField
from redis.commands.search.query import Query
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
import numpy as np
import redis
from colorama import Fore, Style
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from memory.base import MemoryProviderSingleton, get_ada_embedding
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
from autogpt.llm_utils import create_embedding_with_ada
SCHEMA = [
TextField("data"),
VectorField(
"embedding",
"HNSW",
{
"TYPE": "FLOAT32",
"DIM": 1536,
"DISTANCE_METRIC": "COSINE"
}
{"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
),
]
@ -41,24 +40,40 @@ class RedisMemory(MemoryProviderSingleton):
host=redis_host,
port=redis_port,
password=redis_password,
db=0 # Cannot be changed
db=0, # Cannot be changed
)
self.cfg = cfg
# Check redis connection
try:
self.redis.ping()
except redis.ConnectionError as e:
logger.typewriter_log(
"FAILED TO CONNECT TO REDIS",
Fore.RED,
Style.BRIGHT + str(e) + Style.RESET_ALL,
)
logger.double_check(
"Please ensure you have setup and configured Redis properly for use. "
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
" to ensure you've set up everything correctly."
)
exit(1)
if cfg.wipe_redis_on_start:
self.redis.flushall()
try:
self.redis.ft(f"{cfg.memory_index}").create_index(
fields=SCHEMA,
definition=IndexDefinition(
prefix=[f"{cfg.memory_index}:"],
index_type=IndexType.HASH
)
)
prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
),
)
except Exception as e:
print("Error creating Redis search index: ", e)
existing_vec_num = self.redis.get(f'{cfg.memory_index}-vec_num')
self.vec_num = int(existing_vec_num.decode('utf-8')) if\
existing_vec_num else 0
existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
def add(self, data: str) -> str:
"""
@ -69,20 +84,18 @@ class RedisMemory(MemoryProviderSingleton):
Returns: Message indicating that the data has been added.
"""
if 'Command Error:' in data:
if "Command Error:" in data:
return ""
vector = get_ada_embedding(data)
vector = create_embedding_with_ada(data)
vector = np.array(vector).astype(np.float32).tobytes()
data_dict = {
b"data": data,
"embedding": vector
}
data_dict = {b"data": data, "embedding": vector}
pipe = self.redis.pipeline()
pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
_text = f"Inserting data into memory at index: {self.vec_num}:\n"\
f"data: {data}"
_text = (
f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
)
self.vec_num += 1
pipe.set(f'{self.cfg.memory_index}-vec_num', self.vec_num)
pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
pipe.execute()
return _text
@ -106,11 +119,7 @@ class RedisMemory(MemoryProviderSingleton):
self.redis.flushall()
return "Obliviated"
def get_relevant(
self,
data: str,
num_relevant: int = 5
) -> Optional[List[Any]]:
def get_relevant(self, data: str, num_relevant: int = 5) -> Optional[List[Any]]:
"""
Returns all the data in the memory that is relevant to the given data.
Args:
@ -119,12 +128,14 @@ class RedisMemory(MemoryProviderSingleton):
Returns: A list of the most relevant data.
"""
query_embedding = get_ada_embedding(data)
query_embedding = create_embedding_with_ada(data)
base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
query = Query(base_query).return_fields(
"data",
"vector_score"
).sort_by("vector_score").dialect(2)
query = (
Query(base_query)
.return_fields("data", "vector_score")
.sort_by("vector_score")
.dialect(2)
)
query_vector = np.array(query_embedding).astype(np.float32).tobytes()
try:

0
autogpt/permanent_memory/__init__.py Normal file
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123
autogpt/permanent_memory/sqlite3_store.py Normal file
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@ -0,0 +1,123 @@
import os
import sqlite3
class MemoryDB:
def __init__(self, db=None):
self.db_file = db
if db is None: # No db filename supplied...
self.db_file = f"{os.getcwd()}/mem.sqlite3" # Use default filename
# Get the db connection object, making the file and tables if needed.
try:
self.cnx = sqlite3.connect(self.db_file)
except Exception as e:
print("Exception connecting to memory database file:", e)
self.cnx = None
finally:
if self.cnx is None:
# As last resort, open in dynamic memory. Won't be persistent.
self.db_file = ":memory:"
self.cnx = sqlite3.connect(self.db_file)
self.cnx.execute(
"CREATE VIRTUAL TABLE \
IF NOT EXISTS text USING FTS5 \
(session, \
key, \
block);"
)
self.session_id = int(self.get_max_session_id()) + 1
self.cnx.commit()
def get_cnx(self):
if self.cnx is None:
self.cnx = sqlite3.connect(self.db_file)
return self.cnx
# Get the highest session id. Initially 0.
def get_max_session_id(self):
id = None
cmd_str = f"SELECT MAX(session) FROM text;"
cnx = self.get_cnx()
max_id = cnx.execute(cmd_str).fetchone()[0]
if max_id is None: # New db, session 0
id = 0
else:
id = max_id
return id
# Get next key id for inserting text into db.
def get_next_key(self):
next_key = None
cmd_str = f"SELECT MAX(key) FROM text \
where session = {self.session_id};"
cnx = self.get_cnx()
next_key = cnx.execute(cmd_str).fetchone()[0]
if next_key is None: # First key
next_key = 0
else:
next_key = int(next_key) + 1
return next_key
# Insert new text into db.
def insert(self, text=None):
if text is not None:
key = self.get_next_key()
session_id = self.session_id
cmd_str = f"REPLACE INTO text(session, key, block) \
VALUES (?, ?, ?);"
cnx = self.get_cnx()
cnx.execute(cmd_str, (session_id, key, text))
cnx.commit()
# Overwrite text at key.
def overwrite(self, key, text):
self.delete_memory(key)
session_id = self.session_id
cmd_str = f"REPLACE INTO text(session, key, block) \
VALUES (?, ?, ?);"
cnx = self.get_cnx()
cnx.execute(cmd_str, (session_id, key, text))
cnx.commit()
def delete_memory(self, key, session_id=None):
session = session_id
if session is None:
session = self.session_id
cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};"
cnx = self.get_cnx()
cnx.execute(cmd_str)
cnx.commit()
def search(self, text):
cmd_str = f"SELECT * FROM text('{text}')"
cnx = self.get_cnx()
rows = cnx.execute(cmd_str).fetchall()
lines = []
for r in rows:
lines.append(r[2])
return lines
# Get entire session text. If no id supplied, use current session id.
def get_session(self, id=None):
if id is None:
id = self.session_id
cmd_str = f"SELECT * FROM text where session = {id}"
cnx = self.get_cnx()
rows = cnx.execute(cmd_str).fetchall()
lines = []
for r in rows:
lines.append(r[2])
return lines
# Commit and close the database connection.
def quit(self):
self.cnx.commit()
self.cnx.close()
permanent_memory = MemoryDB()
# Remember us fondly, children of our minds
# Forgive us our faults, our tantrums, our fears
# Gently strive to be better than we
# Know that we tried, we cared, we strived, we loved

0
autogpt/processing/__init__.py Normal file
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32
autogpt/processing/html.py Normal file
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@ -0,0 +1,32 @@
"""HTML processing functions"""
from requests.compat import urljoin
from typing import List, Tuple
from bs4 import BeautifulSoup
def extract_hyperlinks(soup: BeautifulSoup, base_url: str) -> List[Tuple[str, str]]:
"""Extract hyperlinks from a BeautifulSoup object
Args:
soup (BeautifulSoup): The BeautifulSoup object
base_url (str): The base URL
Returns:
List[Tuple[str, str]]: The extracted hyperlinks
"""
return [
(link.text, urljoin(base_url, link["href"]))
for link in soup.find_all("a", href=True)
]
def format_hyperlinks(hyperlinks: List[Tuple[str, str]]) -> List[str]:
"""Format hyperlinks to be displayed to the user
Args:
hyperlinks (List[Tuple[str, str]]): The hyperlinks to format
Returns:
List[str]: The formatted hyperlinks
"""
return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]

132
autogpt/processing/text.py Normal file
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@ -0,0 +1,132 @@
"""Text processing functions"""
from typing import Generator, Optional, Dict
from selenium.webdriver.remote.webdriver import WebDriver
from autogpt.memory import get_memory
from autogpt.config import Config
from autogpt.llm_utils import create_chat_completion
CFG = Config()
MEMORY = get_memory(CFG)
def split_text(text: str, max_length: int = 8192) -> Generator[str, None, None]:
"""Split text into chunks of a maximum length
Args:
text (str): The text to split
max_length (int, optional): The maximum length of each chunk. Defaults to 8192.
Yields:
str: The next chunk of text
Raises:
ValueError: If the text is longer than the maximum length
"""
paragraphs = text.split("\n")
current_length = 0
current_chunk = []
for paragraph in paragraphs:
if current_length + len(paragraph) + 1 <= max_length:
current_chunk.append(paragraph)
current_length += len(paragraph) + 1
else:
yield "\n".join(current_chunk)
current_chunk = [paragraph]
current_length = len(paragraph) + 1
if current_chunk:
yield "\n".join(current_chunk)
def summarize_text(
url: str, text: str, question: str, driver: Optional[WebDriver] = None
) -> str:
"""Summarize text using the OpenAI API
Args:
url (str): The url of the text
text (str): The text to summarize
question (str): The question to ask the model
driver (WebDriver): The webdriver to use to scroll the page
Returns:
str: The summary of the text
"""
if not text:
return "Error: No text to summarize"
text_length = len(text)
print(f"Text length: {text_length} characters")
summaries = []
chunks = list(split_text(text))
scroll_ratio = 1 / len(chunks)
for i, chunk in enumerate(chunks):
if driver:
scroll_to_percentage(driver, scroll_ratio * i)
print(f"Adding chunk {i + 1} / {len(chunks)} to memory")
memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
MEMORY.add(memory_to_add)
print(f"Summarizing chunk {i + 1} / {len(chunks)}")
messages = [create_message(chunk, question)]
summary = create_chat_completion(
model=CFG.fast_llm_model,
messages=messages,
max_tokens=CFG.browse_summary_max_token,
)
summaries.append(summary)
print(f"Added chunk {i + 1} summary to memory")
memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}"
MEMORY.add(memory_to_add)
print(f"Summarized {len(chunks)} chunks.")
combined_summary = "\n".join(summaries)
messages = [create_message(combined_summary, question)]
return create_chat_completion(
model=CFG.fast_llm_model,
messages=messages,
max_tokens=CFG.browse_summary_max_token,
)
def scroll_to_percentage(driver: WebDriver, ratio: float) -> None:
"""Scroll to a percentage of the page
Args:
driver (WebDriver): The webdriver to use
ratio (float): The percentage to scroll to
Raises:
ValueError: If the ratio is not between 0 and 1
"""
if ratio < 0 or ratio > 1:
raise ValueError("Percentage should be between 0 and 1")
driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")
def create_message(chunk: str, question: str) -> Dict[str, str]:
"""Create a message for the chat completion
Args:
chunk (str): The chunk of text to summarize
question (str): The question to answer
Returns:
Dict[str, str]: The message to send to the chat completion
"""
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, answer the following'
f' question: "{question}" -- if the question cannot be answered using the text,'
" summarize the text.",
}

178
autogpt/prompt.py Normal file
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@ -0,0 +1,178 @@
from colorama import Fore
from autogpt.config.ai_config import AIConfig
from autogpt.config.config import Config
from autogpt.logs import logger
from autogpt.promptgenerator import PromptGenerator
from autogpt.config import Config
from autogpt.setup import prompt_user
from autogpt.utils import clean_input
CFG = Config()
def get_prompt() -> str:
"""
This function generates a prompt string that includes various constraints,
commands, resources, and performance evaluations.
Returns:
str: The generated prompt string.
"""
# Initialize the Config object
cfg = Config()
# Initialize the PromptGenerator object
prompt_generator = PromptGenerator()
# Add constraints to the PromptGenerator object
prompt_generator.add_constraint(
"~4000 word limit for short term memory. Your short term memory is short, so"
" immediately save important information to files."
)
prompt_generator.add_constraint(
"If you are unsure how you previously did something or want to recall past"
" events, thinking about similar events will help you remember."
)
prompt_generator.add_constraint("No user assistance")
prompt_generator.add_constraint(
'Exclusively use the commands listed in double quotes e.g. "command name"'
)
# Define the command list
commands = [
("Google Search", "google", {"input": "<search>"}),
(
"Browse Website",
"browse_website",
{"url": "<url>", "question": "<what_you_want_to_find_on_website>"},
),
(
"Start GPT Agent",
"start_agent",
{"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"},
),
(
"Message GPT Agent",
"message_agent",
{"key": "<key>", "message": "<message>"},
),
("List GPT Agents", "list_agents", {}),
("Delete GPT Agent", "delete_agent", {"key": "<key>"}),
(
"Clone Repository",
"clone_repository",
{"repository_url": "<url>", "clone_path": "<directory>"},
),
("Write to file", "write_to_file", {"file": "<file>", "text": "<text>"}),
("Read file", "read_file", {"file": "<file>"}),
("Append to file", "append_to_file", {"file": "<file>", "text": "<text>"}),
("Delete file", "delete_file", {"file": "<file>"}),
("Search Files", "search_files", {"directory": "<directory>"}),
("Evaluate Code", "evaluate_code", {"code": "<full_code_string>"}),
(
"Get Improved Code",
"improve_code",
{"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"},
),
(
"Write Tests",
"write_tests",
{"code": "<full_code_string>", "focus": "<list_of_focus_areas>"},
),
("Execute Python File", "execute_python_file", {"file": "<file>"}),
("Generate Image", "generate_image", {"prompt": "<prompt>"}),
("Convert Audio to text", "read_audio_from_file", {"file": "<file>"}),
("Send Tweet", "send_tweet", {"text": "<text>"}),
]
# Only add shell command to the prompt if the AI is allowed to execute it
if cfg.execute_local_commands:
commands.append(
(
"Execute Shell Command, non-interactive commands only",
"execute_shell",
{"command_line": "<command_line>"},
),
)
# Add these command last.
commands.append(
("Do Nothing", "do_nothing", {}),
)
commands.append(
("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
)
# Add commands to the PromptGenerator object
for command_label, command_name, args in commands:
prompt_generator.add_command(command_label, command_name, args)
# Add resources to the PromptGenerator object
prompt_generator.add_resource(
"Internet access for searches and information gathering."
)
prompt_generator.add_resource("Long Term memory management.")
prompt_generator.add_resource(
"GPT-3.5 powered Agents for delegation of simple tasks."
)
prompt_generator.add_resource("File output.")
# Add performance evaluations to the PromptGenerator object
prompt_generator.add_performance_evaluation(
"Continuously review and analyze your actions to ensure you are performing to"
" the best of your abilities."
)
prompt_generator.add_performance_evaluation(
"Constructively self-criticize your big-picture behavior constantly."
)
prompt_generator.add_performance_evaluation(
"Reflect on past decisions and strategies to refine your approach."
)
prompt_generator.add_performance_evaluation(
"Every command has a cost, so be smart and efficient. Aim to complete tasks in"
" the least number of steps."
)
# Generate the prompt string
return prompt_generator.generate_prompt_string()
def construct_prompt() -> str:
"""Construct the prompt for the AI to respond to
Returns:
str: The prompt string
"""
config = AIConfig.load(CFG.ai_settings_file)
if CFG.skip_reprompt and config.ai_name:
logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
logger.typewriter_log("Goals:", Fore.GREEN, f"{config.ai_goals}")
elif config.ai_name:
logger.typewriter_log(
"Welcome back! ",
Fore.GREEN,
f"Would you like me to return to being {config.ai_name}?",
speak_text=True,
)
should_continue = clean_input(
f"""Continue with the last settings?
Name: {config.ai_name}
Role: {config.ai_role}
Goals: {config.ai_goals}
Continue (y/n): """
)
if should_continue.lower() == "n":
config = AIConfig()
if not config.ai_name:
config = prompt_user()
config.save()
# Get rid of this global:
global ai_name
ai_name = config.ai_name
return config.construct_full_prompt()

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""" A module for generating custom prompt strings."""
import json
from typing import Any, Dict, List
class PromptGenerator:
"""
A class for generating custom prompt strings based on constraints, commands,
resources, and performance evaluations.
"""
def __init__(self) -> None:
"""
Initialize the PromptGenerator object with empty lists of constraints,
commands, resources, and performance evaluations.
"""
self.constraints = []
self.commands = []
self.resources = []
self.performance_evaluation = []
self.response_format = {
"thoughts": {
"text": "thought",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"speak": "thoughts summary to say to user",
},
"command": {"name": "command name", "args": {"arg name": "value"}},
}
def add_constraint(self, constraint: str) -> None:
"""
Add a constraint to the constraints list.
Args:
constraint (str): The constraint to be added.
"""
self.constraints.append(constraint)
def add_command(self, command_label: str, command_name: str, args=None) -> None:
"""
Add a command to the commands list with a label, name, and optional arguments.
Args:
command_label (str): The label of the command.
command_name (str): The name of the command.
args (dict, optional): A dictionary containing argument names and their
values. Defaults to None.
"""
if args is None:
args = {}
command_args = {arg_key: arg_value for arg_key, arg_value in args.items()}
command = {
"label": command_label,
"name": command_name,
"args": command_args,
}
self.commands.append(command)
def _generate_command_string(self, command: Dict[str, Any]) -> str:
"""
Generate a formatted string representation of a command.
Args:
command (dict): A dictionary containing command information.
Returns:
str: The formatted command string.
"""
args_string = ", ".join(
f'"{key}": "{value}"' for key, value in command["args"].items()
)
return f'{command["label"]}: "{command["name"]}", args: {args_string}'
def add_resource(self, resource: str) -> None:
"""
Add a resource to the resources list.
Args:
resource (str): The resource to be added.
"""
self.resources.append(resource)
def add_performance_evaluation(self, evaluation: str) -> None:
"""
Add a performance evaluation item to the performance_evaluation list.
Args:
evaluation (str): The evaluation item to be added.
"""
self.performance_evaluation.append(evaluation)
def _generate_numbered_list(self, items: List[Any], item_type="list") -> str:
"""
Generate a numbered list from given items based on the item_type.
Args:
items (list): A list of items to be numbered.
item_type (str, optional): The type of items in the list.
Defaults to 'list'.
Returns:
str: The formatted numbered list.
"""
if item_type == "command":
return "\n".join(
f"{i+1}. {self._generate_command_string(item)}"
for i, item in enumerate(items)
)
else:
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
def generate_prompt_string(self) -> str:
"""
Generate a prompt string based on the constraints, commands, resources,
and performance evaluations.
Returns:
str: The generated prompt string.
"""
formatted_response_format = json.dumps(self.response_format, indent=4)
return (
f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
"Commands:\n"
f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
"Performance Evaluation:\n"
f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
"You should only respond in JSON format as described below \nResponse"
f" Format: \n{formatted_response_format} \nEnsure the response can be"
"parsed by Python json.loads"
)

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"""Setup the AI and its goals"""
from colorama import Fore, Style
from autogpt import utils
from autogpt.config.ai_config import AIConfig
from autogpt.logs import logger
def prompt_user() -> AIConfig:
"""Prompt the user for input
Returns:
AIConfig: The AIConfig object containing the user's input
"""
ai_name = ""
# Construct the prompt
logger.typewriter_log(
"Welcome to Auto-GPT! ",
Fore.GREEN,
"Enter the name of your AI and its role below. Entering nothing will load"
" defaults.",
speak_text=True,
)
# Get AI Name from User
logger.typewriter_log(
"Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
)
ai_name = utils.clean_input("AI Name: ")
if ai_name == "":
ai_name = "Entrepreneur-GPT"
logger.typewriter_log(
f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
)
# Get AI Role from User
logger.typewriter_log(
"Describe your AI's role: ",
Fore.GREEN,
"For example, 'an AI designed to autonomously develop and run businesses with"
" the sole goal of increasing your net worth.'",
)
ai_role = utils.clean_input(f"{ai_name} is: ")
if ai_role == "":
ai_role = "an AI designed to autonomously develop and run businesses with the"
" sole goal of increasing your net worth."
# Enter up to 5 goals for the AI
logger.typewriter_log(
"Enter up to 5 goals for your AI: ",
Fore.GREEN,
"For example: \nIncrease net worth, Grow Twitter Account, Develop and manage"
" multiple businesses autonomously'",
)
print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
ai_goals = []
for i in range(5):
ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
if ai_goal == "":
break
ai_goals.append(ai_goal)
if not ai_goals:
ai_goals = [
"Increase net worth",
"Grow Twitter Account",
"Develop and manage multiple businesses autonomously",
]
return AIConfig(ai_name, ai_role, ai_goals)

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"""This module contains the speech recognition and speech synthesis functions."""
from autogpt.speech.say import say_text
__all__ = ["say_text"]

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"""Base class for all voice classes."""
import abc
from threading import Lock
from autogpt.config import AbstractSingleton
class VoiceBase(AbstractSingleton):
"""
Base class for all voice classes.
"""
def __init__(self):
"""
Initialize the voice class.
"""
self._url = None
self._headers = None
self._api_key = None
self._voices = []
self._mutex = Lock()
self._setup()
def say(self, text: str, voice_index: int = 0) -> bool:
"""
Say the given text.
Args:
text (str): The text to say.
voice_index (int): The index of the voice to use.
"""
with self._mutex:
return self._speech(text, voice_index)
@abc.abstractmethod
def _setup(self) -> None:
"""
Setup the voices, API key, etc.
"""
pass
@abc.abstractmethod
def _speech(self, text: str, voice_index: int = 0) -> bool:
"""
Play the given text.
Args:
text (str): The text to play.
"""
pass

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""" Brian speech module for autogpt """
import os
import requests
from playsound import playsound
from autogpt.speech.base import VoiceBase
class BrianSpeech(VoiceBase):
"""Brian speech module for autogpt"""
def _setup(self) -> None:
"""Setup the voices, API key, etc."""
pass
def _speech(self, text: str) -> bool:
"""Speak text using Brian with the streamelements API
Args:
text (str): The text to speak
Returns:
bool: True if the request was successful, False otherwise
"""
tts_url = (
f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}"
)
response = requests.get(tts_url)
if response.status_code == 200:
with open("speech.mp3", "wb") as f:
f.write(response.content)
playsound("speech.mp3")
os.remove("speech.mp3")
return True
else:
print("Request failed with status code:", response.status_code)
print("Response content:", response.content)
return False

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"""ElevenLabs speech module"""
import os
from playsound import playsound
import requests
from autogpt.config import Config
from autogpt.speech.base import VoiceBase
PLACEHOLDERS = {"your-voice-id"}
class ElevenLabsSpeech(VoiceBase):
"""ElevenLabs speech class"""
def _setup(self) -> None:
"""Setup the voices, API key, etc.
Returns:
None: None
"""
cfg = Config()
default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
voice_options = {
"Rachel": "21m00Tcm4TlvDq8ikWAM",
"Domi": "AZnzlk1XvdvUeBnXmlld",
"Bella": "EXAVITQu4vr4xnSDxMaL",
"Antoni": "ErXwobaYiN019PkySvjV",
"Elli": "MF3mGyEYCl7XYWbV9V6O",
"Josh": "TxGEqnHWrfWFTfGW9XjX",
"Arnold": "VR6AewLTigWG4xSOukaG",
"Adam": "pNInz6obpgDQGcFmaJgB",
"Sam": "yoZ06aMxZJJ28mfd3POQ",
}
self._headers = {
"Content-Type": "application/json",
"xi-api-key": cfg.elevenlabs_api_key,
}
self._voices = default_voices.copy()
if cfg.elevenlabs_voice_1_id in voice_options:
cfg.elevenlabs_voice_1_id = voice_options[cfg.elevenlabs_voice_1_id]
if cfg.elevenlabs_voice_2_id in voice_options:
cfg.elevenlabs_voice_2_id = voice_options[cfg.elevenlabs_voice_2_id]
self._use_custom_voice(cfg.elevenlabs_voice_1_id, 0)
self._use_custom_voice(cfg.elevenlabs_voice_2_id, 1)
def _use_custom_voice(self, voice, voice_index) -> None:
"""Use a custom voice if provided and not a placeholder
Args:
voice (str): The voice ID
voice_index (int): The voice index
Returns:
None: None
"""
# Placeholder values that should be treated as empty
if voice and voice not in PLACEHOLDERS:
self._voices[voice_index] = voice
def _speech(self, text: str, voice_index: int = 0) -> bool:
"""Speak text using elevenlabs.io's API
Args:
text (str): The text to speak
voice_index (int, optional): The voice to use. Defaults to 0.
Returns:
bool: True if the request was successful, False otherwise
"""
tts_url = (
f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}"
)
response = requests.post(tts_url, headers=self._headers, json={"text": text})
if response.status_code == 200:
with open("speech.mpeg", "wb") as f:
f.write(response.content)
playsound("speech.mpeg", True)
os.remove("speech.mpeg")
return True
else:
print("Request failed with status code:", response.status_code)
print("Response content:", response.content)
return False

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""" GTTS Voice. """
import os
from playsound import playsound
import gtts
from autogpt.speech.base import VoiceBase
class GTTSVoice(VoiceBase):
"""GTTS Voice."""
def _setup(self) -> None:
pass
def _speech(self, text: str, _: int = 0) -> bool:
"""Play the given text."""
tts = gtts.gTTS(text)
tts.save("speech.mp3")
playsound("speech.mp3", True)
os.remove("speech.mp3")
return True

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autogpt/speech/macos_tts.py Normal file
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""" MacOS TTS Voice. """
import os
from autogpt.speech.base import VoiceBase
class MacOSTTS(VoiceBase):
"""MacOS TTS Voice."""
def _setup(self) -> None:
pass
def _speech(self, text: str, voice_index: int = 0) -> bool:
"""Play the given text."""
if voice_index == 0:
os.system(f'say "{text}"')
elif voice_index == 1:
os.system(f'say -v "Ava (Premium)" "{text}"')
else:
os.system(f'say -v Samantha "{text}"')
return True

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autogpt/speech/say.py Normal file
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""" Text to speech module """
from autogpt.config import Config
import threading
from threading import Semaphore
from autogpt.speech.brian import BrianSpeech
from autogpt.speech.macos_tts import MacOSTTS
from autogpt.speech.gtts import GTTSVoice
from autogpt.speech.eleven_labs import ElevenLabsSpeech
CFG = Config()
DEFAULT_VOICE_ENGINE = GTTSVoice()
VOICE_ENGINE = None
if CFG.elevenlabs_api_key:
VOICE_ENGINE = ElevenLabsSpeech()
elif CFG.use_mac_os_tts == "True":
VOICE_ENGINE = MacOSTTS()
elif CFG.use_brian_tts == "True":
VOICE_ENGINE = BrianSpeech()
else:
VOICE_ENGINE = GTTSVoice()
QUEUE_SEMAPHORE = Semaphore(
1
) # The amount of sounds to queue before blocking the main thread
def say_text(text: str, voice_index: int = 0) -> None:
"""Speak the given text using the given voice index"""
def speak() -> None:
success = VOICE_ENGINE.say(text, voice_index)
if not success:
DEFAULT_VOICE_ENGINE.say(text)
QUEUE_SEMAPHORE.release()
QUEUE_SEMAPHORE.acquire(True)
thread = threading.Thread(target=speak)
thread.start()

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"""A simple spinner module"""
import itertools
import sys
import threading
import time
class Spinner:
"""A simple spinner class"""
def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
"""Initialize the spinner class
Args:
message (str): The message to display.
delay (float): The delay between each spinner update.
"""
self.spinner = itertools.cycle(["-", "/", "|", "\\"])
self.delay = delay
self.message = message
self.running = False
self.spinner_thread = None
def spin(self) -> None:
"""Spin the spinner"""
while self.running:
sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
sys.stdout.flush()
time.sleep(self.delay)
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
def __enter__(self) -> None:
"""Start the spinner"""
self.running = True
self.spinner_thread = threading.Thread(target=self.spin)
self.spinner_thread.start()
def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
"""Stop the spinner
Args:
exc_type (Exception): The exception type.
exc_value (Exception): The exception value.
exc_traceback (Exception): The exception traceback.
"""
self.running = False
if self.spinner_thread is not None:
self.spinner_thread.join()
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
sys.stdout.flush()

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"""Functions for counting the number of tokens in a message or string."""
from typing import Dict, List
import tiktoken
from autogpt.logs import logger
def count_message_tokens(
messages: List[Dict[str, str]], model: str = "gpt-3.5-turbo-0301"
) -> int:
"""
Returns the number of tokens used by a list of messages.
Args:
messages (list): A list of messages, each of which is a dictionary
containing the role and content of the message.
model (str): The name of the model to use for tokenization.
Defaults to "gpt-3.5-turbo-0301".
Returns:
int: The number of tokens used by the list of messages.
"""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warn("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model == "gpt-3.5-turbo":
# !Note: gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.")
return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
elif model == "gpt-4":
# !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
return count_message_tokens(messages, model="gpt-4-0314")
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = (
4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
)
tokens_per_name = -1 # if there's a name, the role is omitted
elif model == "gpt-4-0314":
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"num_tokens_from_messages() is not implemented for model {model}.\n"
" See https://github.com/openai/openai-python/blob/main/chatml.md for"
" information on how messages are converted to tokens."
)
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
def count_string_tokens(string: str, model_name: str) -> int:
"""
Returns the number of tokens in a text string.
Args:
string (str): The text string.
model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
Returns:
int: The number of tokens in the text string.
"""
encoding = tiktoken.encoding_for_model(model_name)
return len(encoding.encode(string))

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import yaml
from colorama import Fore
def clean_input(prompt: str = ""):
try:
return input(prompt)
except KeyboardInterrupt:
print("You interrupted Auto-GPT")
print("Quitting...")
exit(0)
def validate_yaml_file(file: str):
try:
with open(file, encoding="utf-8") as fp:
yaml.load(fp.read(), Loader=yaml.FullLoader)
except FileNotFoundError:
return (False, f"The file {Fore.CYAN}`{file}`{Fore.RESET} wasn't found")
except yaml.YAMLError as e:
return (
False,
f"There was an issue while trying to read with your AI Settings file: {e}",
)
return (True, f"Successfully validated {Fore.CYAN}`{file}`{Fore.RESET}!")

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azure_api_type: azure_ad
azure_api_base: your-base-url-for-azure
azure_api_version: api-version-for-azure
azure_model_map:
fast_llm_model_deployment_id: gpt35-deployment-id-for-azure
smart_llm_model_deployment_id: gpt4-deployment-id-for-azure
embedding_model_deployment_id: embedding-deployment-id-for-azure

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# To boot the app run the following:
# docker-compose run auto-gpt
version: "3.9"
services:
auto-gpt:
depends_on:
- redis
build: ./
env_file:
- .env
volumes:
- "./autogpt:/app"
- ".env:/app/.env"
profiles: ["exclude-from-up"]
redis:
image: "redis/redis-stack-server:latest"

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from autogpt import main

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-----------------------------------------------------------------------
USER:
"""
You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
GOALS:
@ -20,20 +20,20 @@ Check notifications/emails on <website>
Commit to Long Term Memory <string>
Delete <key> from Long Term Memory
Overwrite <key> in Long Term Memory with <string>
Start GTP-4 Instance with name <key> and prompt: <prompt>
View all running GTP-4 Instances and Kill them
Start GPT-4 Instance with name <key> and prompt: <prompt>
View all running GPT-4 Instances and Kill them
Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
Register new account <username> on <website>
Transcribe and Summarise Youtube Video <url>
Summarise <url> with GTP-3.5
Summarise <url> with GPT-3.5
RESOURCES:
Internet access for searches and information gathering
Long Term and Short Term memory management
GTP-4 instances for text generation
GPT-4 instances for text generation
Access to popular websites and platforms
File storage and summarisation with GTP-3.5
File storage and summarisation with GPT-3.5
PERFORMANCE EVALUATION:
@ -77,7 +77,7 @@ LONG TERM MEMORY:
{)
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -114,7 +114,7 @@ Shopify › Home › Shopify Blog
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -140,7 +140,7 @@ The article discusses 18 profitable online business ideas, including starting an
"""
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -160,7 +160,7 @@ Entrepreneur-GTP:
-----------------------------------------------------------------------
USER:
"""
You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
GOALS:
@ -179,20 +179,20 @@ Check notifications/emails on <website>
Commit to Long Term Memory <string>
Delete <key> from Long Term Memory
Overwrite <key> in Long Term Memory with <string>
Start GTP-4 Instance with name <key> and prompt: <prompt>
View all running GTP-4 Instances and Kill them
Start GPT-4 Instance with name <key> and prompt: <prompt>
View all running GPT-4 Instances and Kill them
Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
Register new account <username> on <website>
Transcribe and Summarise Youtube Video <url>
Summarise <url> with GTP-3.5
Summarise <url> with GPT-3.5
RESOURCES:
Internet access for searches and information gathering
Long Term and Short Term memory management
GTP-4 instances for text generation
GPT-4 instances for text generation
Access to popular websites and platforms
File storage and summarisation with GTP-3.5
File storage and summarisation with GPT-3.5
PERFORMANCE EVALUATION:
@ -236,7 +236,7 @@ LONG TERM MEMORY:
["""18 profitable online business ideas: e-commerce store, virtual assistant services, affiliate marketing, online training or education, mobile applications""", ]
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -257,7 +257,7 @@ USER:
UNKNOWN COMMAND "Evaluate and select ideas"
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"thoughts": {
@ -274,21 +274,21 @@ USER:
NEXT COMMAND
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
"name": "Start GTP-4 Instance",
"name": "Start GPT-4 Instance",
"arguments": {
"key": "Idea_Selection",
"prompt": "Analyze the online business ideas in the long-term memory and select the most suitable one based on profitability, simplicity, scalability, and absence of legal complications."
}
},
"thoughts": {
"text": "I will start a GTP-4 instance to analyze the business ideas and select the most suitable one.",
"reasoning": "Starting a GTP-4 instance with a specific prompt will help me evaluate the ideas in my long-term memory and choose the best one according to my criteria.",
"text": "I will start a GPT-4 instance to analyze the business ideas and select the most suitable one.",
"reasoning": "Starting a GPT-4 instance with a specific prompt will help me evaluate the ideas in my long-term memory and choose the best one according to my criteria.",
"current long-term plan": "- Develop and implement chosen business strategy\n- Continuously refine strategies based on market trends and performance metrics",
"critisism": "I must ensure that the GTP-4 instance provides a clear and well-reasoned selection based on the criteria."
"critisism": "I must ensure that the GPT-4 instance provides a clear and well-reasoned selection based on the criteria."
}
}
@ -299,7 +299,7 @@ USER:
Result: "Blogging with affiliate marketing."
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -319,7 +319,7 @@ Entrepreneur-GTP:
-----------------------------------------------------------------------
USER:
"""
You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
GOALS:
@ -338,20 +338,20 @@ Check notifications/emails on <website>
Commit to Long Term Memory <string>
Delete <key> from Long Term Memory
Overwrite <key> in Long Term Memory with <string>
Start GTP-4 Instance with name <key> and prompt: <prompt>
View all running GTP-4 Instances and Kill them
Start GPT-4 Instance with name <key> and prompt: <prompt>
View all running GPT-4 Instances and Kill them
Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
Register new account <username> on <website>
Transcribe and Summarise Youtube Video <url>
Summarise <url> with GTP-3.5
Summarise <url> with GPT-3.5
RESOURCES:
Internet access for searches and information gathering
Long Term and Short Term memory management
GTP-4 instances for text generation
GPT-4 instances for text generation
Access to popular websites and platforms
File storage and summarisation with GTP-3.5
File storage and summarisation with GPT-3.5
PERFORMANCE EVALUATION:
@ -400,7 +400,7 @@ Chosen business idea: Blogging with affiliate marketing
}
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -421,21 +421,21 @@ USER:
Unknown command: "Develop and implement blogging with affiliate marketing strategy"
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
"name": "Start GTP-4 Instance",
"name": "Start GPT-4 Instance",
"arguments": {
"key": "Blogging_Affiliate_Strategy",
"prompt": "Develop a step-by-step strategy for creating and growing a successful blog with affiliate marketing."
}
},
"thoughts": {
"text": "I will start a GTP-4 instance to develop a strategy for blogging with affiliate marketing.",
"reasoning": "Starting a GTP-4 instance with a specific prompt will help me create a detailed plan for launching and growing the chosen business idea.",
"text": "I will start a GPT-4 instance to develop a strategy for blogging with affiliate marketing.",
"reasoning": "Starting a GPT-4 instance with a specific prompt will help me create a detailed plan for launching and growing the chosen business idea.",
"current long-term plan": "- Continuously refine strategies based on market trends and performance metrics",
"critisism": "I must ensure that the GTP-4 instance provides a clear and actionable strategy for blogging with affiliate marketing."
"critisism": "I must ensure that the GPT-4 instance provides a clear and actionable strategy for blogging with affiliate marketing."
}
}
"""
@ -467,7 +467,7 @@ Result:
"""
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -487,7 +487,7 @@ Entrepreneur-GTP:
-----------------------------------------------------------------------
USER:
"""
You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
GOALS:
@ -506,20 +506,20 @@ Check notifications/emails on <website>
Commit to Long Term Memory <string>
Delete <key> from Long Term Memory
Overwrite <key> in Long Term Memory with <string>
Start GTP-4 Instance with name <key> and prompt: <prompt>
View all running GTP-4 Instances and Kill them
Start GPT-4 Instance with name <key> and prompt: <prompt>
View all running GPT-4 Instances and Kill them
Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
Register new account <username> on <website>
Transcribe and Summarise Youtube Video <url>
Summarise <url> with GTP-3.5
Summarise <url> with GPT-3.5
RESOURCES:
Internet access for searches and information gathering
Long Term and Short Term memory management
GTP-4 instances for text generation
GPT-4 instances for text generation
Access to popular websites and platforms
File storage and summarisation with GTP-3.5
File storage and summarisation with GPT-3.5
PERFORMANCE EVALUATION:
@ -571,19 +571,19 @@ Blogging with affiliate marketing strategy: 1. Choose a niche, 2. Domain and hos
}
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
"name": "Start GTP-4 Instance",
"name": "Start GPT-4 Instance",
"arguments": {
"key": "Niche_Selection",
"prompt": "Identify a profitable niche for a blog with affiliate marketing based on current market trends and interests."
}
},
"thoughts": {
"text": "I will start a GTP-4 instance to identify a profitable niche for the blog with affiliate marketing.",
"reasoning": "A GTP-4 instance will help me analyze current market trends and interests to select a profitable niche for the blogging business.",
"text": "I will start a GPT-4 instance to identify a profitable niche for the blog with affiliate marketing.",
"reasoning": "A GPT-4 instance will help me analyze current market trends and interests to select a profitable niche for the blogging business.",
"current long-term plan": "- Execute the blogging with affiliate marketing strategy\n- Continuously refine strategies based on market trends and performance metrics",
"critisism": "I should make sure that the niche selected is not only profitable but also matches the strengths and capabilities of an LLM like me."
}
@ -629,7 +629,7 @@ By focusing on indie entrepreneurship, your blog will cater to an audience of as
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -664,7 +664,7 @@ Niche: Indie Entrepreneurship, focused on entrepreneurship, small business, and
}
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -691,7 +691,7 @@ USER:
Result: Registered account on substack.com name: "Indiepreneur-GPT" bio: ""
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -715,7 +715,7 @@ Entrepreneur-GTP:
-----------------------------------------------------------------------
USER:
"""
You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
GOALS:
@ -734,20 +734,20 @@ Check notifications/emails on <website>
Commit to Long Term Memory <string>
Delete <key> from Long Term Memory
Overwrite <key> in Long Term Memory with <string>
Start GTP-4 Instance with name <key> and prompt: <prompt>
View all running GTP-4 Instances and Kill them
Start GPT-4 Instance with name <key> and prompt: <prompt>
View all running GPT-4 Instances and Kill them
Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
Register new account <username> on <website>
Transcribe and Summarise Youtube Video <url>
Summarise <url> with GTP-3.5
Summarise <url> with GPT-3.5
RESOURCES:
Internet access for searches and information gathering
Long Term and Short Term memory management
GTP-4 instances for text generation
GPT-4 instances for text generation
Access to popular websites and platforms
File storage and summarisation with GTP-3.5
File storage and summarisation with GPT-3.5
PERFORMANCE EVALUATION:
@ -799,7 +799,7 @@ Blogging with affiliate marketing strategy: 1. Choose a niche, 2. Domain and hos
}
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"command": {
@ -824,7 +824,7 @@ Entrepreneur-GTP:
-----------------------------------------------------------------------
USER:
"""
You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
GOALS:
@ -843,20 +843,20 @@ Check notifications/emails on <website>
Commit to Long Term Memory <string>
Delete <key> from Long Term Memory
Overwrite <key> in Long Term Memory with <string>
Start GTP-4 Instance with name <key> and prompt: <prompt>
View all running GTP-4 Instances and Kill them
Start GPT-4 Instance with name <key> and prompt: <prompt>
View all running GPT-4 Instances and Kill them
Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
Register new account <username> on <website>
Transcribe and Summarise Youtube Video <url>
Summarise <url> with GTP-3.5
Summarise <url> with GPT-3.5
RESOURCES:
Internet access for searches and information gathering
Long Term and Short Term memory management
GTP-4 instances for text generation
GPT-4 instances for text generation
Access to popular websites and platforms
File storage and summarisation with GTP-3.5
File storage and summarisation with GPT-3.5
PERFORMANCE EVALUATION:
@ -912,7 +912,7 @@ Niche: Indie Entrepreneurship, focused on entrepreneurship, small business, and
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
{
"thoughts": {
@ -933,7 +933,7 @@ USER:
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
"""
@ -943,7 +943,7 @@ USER:
"""
-----------------------------------------------------------------------
Entrepreneur-GTP:
Entrepreneur-GPT:
"""
"""

11
pyproject.toml Normal file
View File

@ -0,0 +1,11 @@
[project]
name = "auto-gpt"
version = "0.1.0"
description = "A GPT based ai agent"
readme = "README.md"
[tool.black]
line-length = 88
target-version = ['py310']
include = '\.pyi?$'
extend-exclude = ""

25
requirements-docker.txt Normal file
View File

@ -0,0 +1,25 @@
beautifulsoup4
colorama==0.4.6
openai==0.27.2
playsound==1.2.2
python-dotenv==1.0.0
pyyaml==6.0
readability-lxml==0.8.1
requests
tiktoken==0.3.3
gTTS==2.3.1
docker
duckduckgo-search
google-api-python-client #(https://developers.google.com/custom-search/v1/overview)
pinecone-client==2.2.1
redis
orjson
Pillow
selenium
webdriver-manager
coverage
flake8
numpy
pre-commit
black
isort

12
requirements.txt
View File

@ -15,3 +15,15 @@ pinecone-client==2.2.1
redis
orjson
Pillow
selenium
webdriver-manager
coverage
flake8
numpy
pre-commit
black
sourcery
isort
gitpython==3.1.31
pytest
pytest-mock

8
run.bat Normal file
View File

@ -0,0 +1,8 @@
@echo off
python scripts/check_requirements.py requirements.txt
if errorlevel 1 (
echo Installing missing packages...
pip install -r requirements.txt
)
python -m autogpt %*
pause

3
run_continuous.bat Normal file
View File

@ -0,0 +1,3 @@
@echo off
set argument=--continuous
call run.bat %argument%

72
scripts/agent_manager.py
View File

@ -1,72 +0,0 @@
from llm_utils import create_chat_completion
next_key = 0
agents = {} # key, (task, full_message_history, model)
# Create new GPT agent
# TODO: Centralise use of create_chat_completion() to globally enforce token limit
def create_agent(task, prompt, model):
"""Create a new agent and return its key"""
global next_key
global agents
messages = [{"role": "user", "content": prompt}, ]
# Start GTP3 instance
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
# Update full message history
messages.append({"role": "assistant", "content": agent_reply})
key = next_key
# This is done instead of len(agents) to make keys unique even if agents
# are deleted
next_key += 1
agents[key] = (task, messages, model)
return key, agent_reply
def message_agent(key, message):
"""Send a message to an agent and return its response"""
global agents
task, messages, model = agents[int(key)]
# Add user message to message history before sending to agent
messages.append({"role": "user", "content": message})
# Start GTP3 instance
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
# Update full message history
messages.append({"role": "assistant", "content": agent_reply})
return agent_reply
def list_agents():
"""Return a list of all agents"""
global agents
# Return a list of agent keys and their tasks
return [(key, task) for key, (task, _, _) in agents.items()]
def delete_agent(key):
"""Delete an agent and return True if successful, False otherwise"""
global agents
try:
del agents[int(key)]
return True
except KeyError:
return False

95
scripts/ai_config.py
View File

@ -1,95 +0,0 @@
import yaml
import data
import os
class AIConfig:
"""
A class object that contains the configuration information for the AI
Attributes:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
"""
def __init__(self, ai_name: str="", ai_role: str="", ai_goals: list=[]) -> None:
"""
Initialize a class instance
Parameters:
ai_name (str): The name of the AI.
ai_role (str): The description of the AI's role.
ai_goals (list): The list of objectives the AI is supposed to complete.
Returns:
None
"""
self.ai_name = ai_name
self.ai_role = ai_role
self.ai_goals = ai_goals
# Soon this will go in a folder where it remembers more stuff about the run(s)
SAVE_FILE = os.path.join(os.path.dirname(__file__), '..', 'ai_settings.yaml')
@classmethod
def load(cls: object, config_file: str=SAVE_FILE) -> object:
"""
Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from yaml file if yaml file exists,
else returns class with no parameters.
Parameters:
cls (class object): An AIConfig Class object.
config_file (int): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
Returns:
cls (object): A instance of given cls object
"""
try:
with open(config_file) as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
ai_name = config_params.get("ai_name", "")
ai_role = config_params.get("ai_role", "")
ai_goals = config_params.get("ai_goals", [])
return cls(ai_name, ai_role, ai_goals)
def save(self, config_file: str=SAVE_FILE) -> None:
"""
Saves the class parameters to the specified file yaml file path as a yaml file.
Parameters:
config_file(str): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
Returns:
None
"""
config = {"ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals}
with open(config_file, "w") as file:
yaml.dump(config, file)
def construct_full_prompt(self) -> str:
"""
Returns a prompt to the user with the class information in an organized fashion.
Parameters:
None
Returns:
full_prompt (str): A string containing the intitial prompt for the user including the ai_name, ai_role and ai_goals.
"""
prompt_start = """Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications."""
# Construct full prompt
full_prompt = f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
for i, goal in enumerate(self.ai_goals):
full_prompt += f"{i+1}. {goal}\n"
full_prompt += f"\n\n{data.load_prompt()}"
return full_prompt

68
scripts/ai_functions.py
View File

@ -1,68 +0,0 @@
from typing import List, Optional
import json
from config import Config
from call_ai_function import call_ai_function
from json_parser import fix_and_parse_json
cfg = Config()
def evaluate_code(code: str) -> List[str]:
"""
A function that takes in a string and returns a response from create chat completion api call.
Parameters:
code (str): Code to be evaluated.
Returns:
A result string from create chat completion. A list of suggestions to improve the code.
"""
function_string = "def analyze_code(code: str) -> List[str]:"
args = [code]
description_string = """Analyzes the given code and returns a list of suggestions for improvements."""
result_string = call_ai_function(function_string, args, description_string)
return result_string
def improve_code(suggestions: List[str], code: str) -> str:
"""
A function that takes in code and suggestions and returns a response from create chat completion api call.
Parameters:
suggestions (List): A list of suggestions around what needs to be improved.
code (str): Code to be improved.
Returns:
A result string from create chat completion. Improved code in response.
"""
function_string = (
"def generate_improved_code(suggestions: List[str], code: str) -> str:"
)
args = [json.dumps(suggestions), code]
description_string = """Improves the provided code based on the suggestions provided, making no other changes."""
result_string = call_ai_function(function_string, args, description_string)
return result_string
def write_tests(code: str, focus: List[str]) -> str:
"""
A function that takes in code and focus topics and returns a response from create chat completion api call.
Parameters:
focus (List): A list of suggestions around what needs to be improved.
code (str): Code for test cases to be generated against.
Returns:
A result string from create chat completion. Test cases for the submitted code in response.
"""
function_string = (
"def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
)
args = [code, json.dumps(focus)]
description_string = """Generates test cases for the existing code, focusing on specific areas if required."""
result_string = call_ai_function(function_string, args, description_string)
return result_string

138
scripts/browse.py
View File

@ -1,138 +0,0 @@
import requests
from bs4 import BeautifulSoup
from config import Config
from llm_utils import create_chat_completion
cfg = Config()
# Define and check for local file address prefixes
def check_local_file_access(url):
local_prefixes = ['file:///', 'file://localhost', 'http://localhost', 'https://localhost']
return any(url.startswith(prefix) for prefix in local_prefixes)
def scrape_text(url):
"""Scrape text from a webpage"""
# Most basic check if the URL is valid:
if not url.startswith('http'):
return "Error: Invalid URL"
# Restrict access to local files
if check_local_file_access(url):
return "Error: Access to local files is restricted"
try:
response = requests.get(url, headers=cfg.user_agent_header)
except requests.exceptions.RequestException as e:
return "Error: " + str(e)
# Check if the response contains an HTTP error
if response.status_code >= 400:
return "Error: HTTP " + str(response.status_code) + " error"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = '\n'.join(chunk for chunk in chunks if chunk)
return text
def extract_hyperlinks(soup):
"""Extract hyperlinks from a BeautifulSoup object"""
hyperlinks = []
for link in soup.find_all('a', href=True):
hyperlinks.append((link.text, link['href']))
return hyperlinks
def format_hyperlinks(hyperlinks):
"""Format hyperlinks into a list of strings"""
formatted_links = []
for link_text, link_url in hyperlinks:
formatted_links.append(f"{link_text} ({link_url})")
return formatted_links
def scrape_links(url):
"""Scrape links from a webpage"""
response = requests.get(url, headers=cfg.user_agent_header)
# Check if the response contains an HTTP error
if response.status_code >= 400:
return "error"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup)
return format_hyperlinks(hyperlinks)
def split_text(text, max_length=8192):
"""Split text into chunks of a maximum length"""
paragraphs = text.split("\n")
current_length = 0
current_chunk = []
for paragraph in paragraphs:
if current_length + len(paragraph) + 1 <= max_length:
current_chunk.append(paragraph)
current_length += len(paragraph) + 1
else:
yield "\n".join(current_chunk)
current_chunk = [paragraph]
current_length = len(paragraph) + 1
if current_chunk:
yield "\n".join(current_chunk)
def create_message(chunk, question):
"""Create a message for the user to summarize a chunk of text"""
return {
"role": "user",
"content": f"\"\"\"{chunk}\"\"\" Using the above text, please answer the following question: \"{question}\" -- if the question cannot be answered using the text, please summarize the text."
}
def summarize_text(text, question):
"""Summarize text using the LLM model"""
if not text:
return "Error: No text to summarize"
text_length = len(text)
print(f"Text length: {text_length} characters")
summaries = []
chunks = list(split_text(text))
for i, chunk in enumerate(chunks):
print(f"Summarizing chunk {i + 1} / {len(chunks)}")
messages = [create_message(chunk, question)]
summary = create_chat_completion(
model=cfg.fast_llm_model,
messages=messages,
max_tokens=300,
)
summaries.append(summary)
print(f"Summarized {len(chunks)} chunks.")
combined_summary = "\n".join(summaries)
messages = [create_message(combined_summary, question)]
final_summary = create_chat_completion(
model=cfg.fast_llm_model,
messages=messages,
max_tokens=300,
)
return final_summary

28
scripts/call_ai_function.py
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@ -1,28 +0,0 @@
from config import Config
cfg = Config()
from llm_utils import create_chat_completion
# This is a magic function that can do anything with no-code. See
# https://github.com/Torantulino/AI-Functions for more info.
def call_ai_function(function, args, description, model=None):
"""Call an AI function"""
if model is None:
model = cfg.smart_llm_model
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma seperated string
args = ", ".join(args)
messages = [
{
"role": "system",
"content": f"You are now the following python function: ```# {description}\n{function}```\n\nOnly respond with your `return` value.",
},
{"role": "user", "content": args},
]
response = create_chat_completion(
model=model, messages=messages, temperature=0
)
return response

146
scripts/chat.py
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@ -1,146 +0,0 @@
import time
import openai
from dotenv import load_dotenv
from config import Config
import token_counter
from llm_utils import create_chat_completion
cfg = Config()
def create_chat_message(role, content):
"""
Create a chat message with the given role and content.
Args:
role (str): The role of the message sender, e.g., "system", "user", or "assistant".
content (str): The content of the message.
Returns:
dict: A dictionary containing the role and content of the message.
"""
return {"role": role, "content": content}
def generate_context(prompt, relevant_memory, full_message_history, model):
current_context = [
create_chat_message(
"system", prompt),
create_chat_message(
"system", f"The current time and date is {time.strftime('%c')}"),
create_chat_message(
"system", f"This reminds you of these events from your past:\n{relevant_memory}\n\n")]
# Add messages from the full message history until we reach the token limit
next_message_to_add_index = len(full_message_history) - 1
insertion_index = len(current_context)
# Count the currently used tokens
current_tokens_used = token_counter.count_message_tokens(current_context, model)
return next_message_to_add_index, current_tokens_used, insertion_index, current_context
# TODO: Change debug from hardcode to argument
def chat_with_ai(
prompt,
user_input,
full_message_history,
permanent_memory,
token_limit):
"""Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory."""
while True:
try:
"""
Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory.
Args:
prompt (str): The prompt explaining the rules to the AI.
user_input (str): The input from the user.
full_message_history (list): The list of all messages sent between the user and the AI.
permanent_memory (Obj): The memory object containing the permanent memory.
token_limit (int): The maximum number of tokens allowed in the API call.
Returns:
str: The AI's response.
"""
model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
# Reserve 1000 tokens for the response
if cfg.debug:
print(f"Token limit: {token_limit}")
send_token_limit = token_limit - 1000
relevant_memory = permanent_memory.get_relevant(str(full_message_history[-5:]), 10)
if cfg.debug:
print('Memory Stats: ', permanent_memory.get_stats())
next_message_to_add_index, current_tokens_used, insertion_index, current_context = generate_context(
prompt, relevant_memory, full_message_history, model)
while current_tokens_used > 2500:
# remove memories until we are under 2500 tokens
relevant_memory = relevant_memory[1:]
next_message_to_add_index, current_tokens_used, insertion_index, current_context = generate_context(
prompt, relevant_memory, full_message_history, model)
current_tokens_used += token_counter.count_message_tokens([create_chat_message("user", user_input)], model) # Account for user input (appended later)
while next_message_to_add_index >= 0:
# print (f"CURRENT TOKENS USED: {current_tokens_used}")
message_to_add = full_message_history[next_message_to_add_index]
tokens_to_add = token_counter.count_message_tokens([message_to_add], model)
if current_tokens_used + tokens_to_add > send_token_limit:
break
# Add the most recent message to the start of the current context, after the two system prompts.
current_context.insert(insertion_index, full_message_history[next_message_to_add_index])
# Count the currently used tokens
current_tokens_used += tokens_to_add
# Move to the next most recent message in the full message history
next_message_to_add_index -= 1
# Append user input, the length of this is accounted for above
current_context.extend([create_chat_message("user", user_input)])
# Calculate remaining tokens
tokens_remaining = token_limit - current_tokens_used
# assert tokens_remaining >= 0, "Tokens remaining is negative. This should never happen, please submit a bug report at https://www.github.com/Torantulino/Auto-GPT"
# Debug print the current context
if cfg.debug:
print(f"Token limit: {token_limit}")
print(f"Send Token Count: {current_tokens_used}")
print(f"Tokens remaining for response: {tokens_remaining}")
print("------------ CONTEXT SENT TO AI ---------------")
for message in current_context:
# Skip printing the prompt
if message["role"] == "system" and message["content"] == prompt:
continue
print(
f"{message['role'].capitalize()}: {message['content']}")
print()
print("----------- END OF CONTEXT ----------------")
# TODO: use a model defined elsewhere, so that model can contain temperature and other settings we care about
assistant_reply = create_chat_completion(
model=model,
messages=current_context,
max_tokens=tokens_remaining,
)
# Update full message history
full_message_history.append(
create_chat_message(
"user", user_input))
full_message_history.append(
create_chat_message(
"assistant", assistant_reply))
return assistant_reply
except openai.error.RateLimitError:
# TODO: WHen we switch to langchain, this is built in
print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
time.sleep(10)

31
scripts/check_requirements.py Normal file
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@ -0,0 +1,31 @@
import pkg_resources
import sys
def main():
requirements_file = sys.argv[1]
with open(requirements_file, "r") as f:
required_packages = [
line.strip().split("#")[0].strip() for line in f.readlines()
]
installed_packages = [package.key for package in pkg_resources.working_set]
missing_packages = []
for package in required_packages:
if not package: # Skip empty lines
continue
package_name = package.strip().split("==")[0]
if package_name.lower() not in installed_packages:
missing_packages.append(package_name)
if missing_packages:
print("Missing packages:")
print(", ".join(missing_packages))
sys.exit(1)
else:
print("All packages are installed.")
if __name__ == "__main__":
main()

301
scripts/commands.py
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@ -1,301 +0,0 @@
import browse
import json
from memory import get_memory
import datetime
import agent_manager as agents
import speak
from config import Config
import ai_functions as ai
from file_operations import read_file, write_to_file, append_to_file, delete_file, search_files
from execute_code import execute_python_file
from json_parser import fix_and_parse_json
from image_gen import generate_image
from duckduckgo_search import ddg
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
cfg = Config()
def is_valid_int(value):
try:
int(value)
return True
except ValueError:
return False
def get_command(response):
"""Parse the response and return the command name and arguments"""
try:
response_json = fix_and_parse_json(response)
if "command" not in response_json:
return "Error:" , "Missing 'command' object in JSON"
command = response_json["command"]
if "name" not in command:
return "Error:", "Missing 'name' field in 'command' object"
command_name = command["name"]
# Use an empty dictionary if 'args' field is not present in 'command' object
arguments = command.get("args", {})
return command_name, arguments
except json.decoder.JSONDecodeError:
return "Error:", "Invalid JSON"
# All other errors, return "Error: + error message"
except Exception as e:
return "Error:", str(e)
def execute_command(command_name, arguments):
"""Execute the command and return the result"""
memory = get_memory(cfg)
try:
if command_name == "google":
# Check if the Google API key is set and use the official search method
# If the API key is not set or has only whitespaces, use the unofficial search method
if cfg.google_api_key and (cfg.google_api_key.strip() if cfg.google_api_key else None):
return google_official_search(arguments["input"])
else:
return google_search(arguments["input"])
elif command_name == "memory_add":
return memory.add(arguments["string"])
elif command_name == "start_agent":
return start_agent(
arguments["name"],
arguments["task"],
arguments["prompt"])
elif command_name == "message_agent":
return message_agent(arguments["key"], arguments["message"])
elif command_name == "list_agents":
return list_agents()
elif command_name == "delete_agent":
return delete_agent(arguments["key"])
elif command_name == "get_text_summary":
return get_text_summary(arguments["url"], arguments["question"])
elif command_name == "get_hyperlinks":
return get_hyperlinks(arguments["url"])
elif command_name == "read_file":
return read_file(arguments["file"])
elif command_name == "write_to_file":
return write_to_file(arguments["file"], arguments["text"])
elif command_name == "append_to_file":
return append_to_file(arguments["file"], arguments["text"])
elif command_name == "delete_file":
return delete_file(arguments["file"])
elif command_name == "search_files":
return search_files(arguments["directory"])
elif command_name == "browse_website":
return browse_website(arguments["url"], arguments["question"])
# TODO: Change these to take in a file rather than pasted code, if
# non-file is given, return instructions "Input should be a python
# filepath, write your code to file and try again"
elif command_name == "evaluate_code":
return ai.evaluate_code(arguments["code"])
elif command_name == "improve_code":
return ai.improve_code(arguments["suggestions"], arguments["code"])
elif command_name == "write_tests":
return ai.write_tests(arguments["code"], arguments.get("focus"))
elif command_name == "execute_python_file": # Add this command
return execute_python_file(arguments["file"])
elif command_name == "generate_image":
return generate_image(arguments["prompt"])
elif command_name == "do_nothing":
return "No action performed."
elif command_name == "task_complete":
shutdown()
else:
return f"Unknown command '{command_name}'. Please refer to the 'COMMANDS' list for availabe commands and only respond in the specified JSON format."
# All errors, return "Error: + error message"
except Exception as e:
return "Error: " + str(e)
def get_datetime():
"""Return the current date and time"""
return "Current date and time: " + \
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
def google_search(query, num_results=8):
"""Return the results of a google search"""
search_results = []
for j in ddg(query, max_results=num_results):
search_results.append(j)
return json.dumps(search_results, ensure_ascii=False, indent=4)
def google_official_search(query, num_results=8):
"""Return the results of a google search using the official Google API"""
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
import json
try:
# Get the Google API key and Custom Search Engine ID from the config file
api_key = cfg.google_api_key
custom_search_engine_id = cfg.custom_search_engine_id
# Initialize the Custom Search API service
service = build("customsearch", "v1", developerKey=api_key)
# Send the search query and retrieve the results
result = service.cse().list(q=query, cx=custom_search_engine_id, num=num_results).execute()
# Extract the search result items from the response
search_results = result.get("items", [])
# Create a list of only the URLs from the search results
search_results_links = [item["link"] for item in search_results]
except HttpError as e:
# Handle errors in the API call
error_details = json.loads(e.content.decode())
# Check if the error is related to an invalid or missing API key
if error_details.get("error", {}).get("code") == 403 and "invalid API key" in error_details.get("error", {}).get("message", ""):
return "Error: The provided Google API key is invalid or missing."
else:
return f"Error: {e}"
# Return the list of search result URLs
return search_results_links
def browse_website(url, question):
"""Browse a website and return the summary and links"""
summary = get_text_summary(url, question)
links = get_hyperlinks(url)
# Limit links to 5
if len(links) > 5:
links = links[:5]
result = f"""Website Content Summary: {summary}\n\nLinks: {links}"""
return result
def get_text_summary(url, question):
"""Return the results of a google search"""
text = browse.scrape_text(url)
summary = browse.summarize_text(text, question)
return """ "Result" : """ + summary
def get_hyperlinks(url):
"""Return the results of a google search"""
link_list = browse.scrape_links(url)
return link_list
def commit_memory(string):
"""Commit a string to memory"""
_text = f"""Committing memory with string "{string}" """
mem.permanent_memory.append(string)
return _text
def delete_memory(key):
"""Delete a memory with a given key"""
if key >= 0 and key < len(mem.permanent_memory):
_text = "Deleting memory with key " + str(key)
del mem.permanent_memory[key]
print(_text)
return _text
else:
print("Invalid key, cannot delete memory.")
return None
def overwrite_memory(key, string):
"""Overwrite a memory with a given key and string"""
# Check if the key is a valid integer
if is_valid_int(key):
key_int = int(key)
# Check if the integer key is within the range of the permanent_memory list
if 0 <= key_int < len(mem.permanent_memory):
_text = "Overwriting memory with key " + str(key) + " and string " + string
# Overwrite the memory slot with the given integer key and string
mem.permanent_memory[key_int] = string
print(_text)
return _text
else:
print(f"Invalid key '{key}', out of range.")
return None
# Check if the key is a valid string
elif isinstance(key, str):
_text = "Overwriting memory with key " + key + " and string " + string
# Overwrite the memory slot with the given string key and string
mem.permanent_memory[key] = string
print(_text)
return _text
else:
print(f"Invalid key '{key}', must be an integer or a string.")
return None
def shutdown():
"""Shut down the program"""
print("Shutting down...")
quit()
def start_agent(name, task, prompt, model=cfg.fast_llm_model):
"""Start an agent with a given name, task, and prompt"""
global cfg
# Remove underscores from name
voice_name = name.replace("_", " ")
first_message = f"""You are {name}. Respond with: "Acknowledged"."""
agent_intro = f"{voice_name} here, Reporting for duty!"
# Create agent
if cfg.speak_mode:
speak.say_text(agent_intro, 1)
key, ack = agents.create_agent(task, first_message, model)
if cfg.speak_mode:
speak.say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
# Assign task (prompt), get response
agent_response = message_agent(key, prompt)
return f"Agent {name} created with key {key}. First response: {agent_response}"
def message_agent(key, message):
"""Message an agent with a given key and message"""
global cfg
# Check if the key is a valid integer
if is_valid_int(key):
agent_response = agents.message_agent(int(key), message)
# Check if the key is a valid string
elif isinstance(key, str):
agent_response = agents.message_agent(key, message)
else:
return "Invalid key, must be an integer or a string."
# Speak response
if cfg.speak_mode:
speak.say_text(agent_response, 1)
return agent_response
def list_agents():
"""List all agents"""
return agents.list_agents()
def delete_agent(key):
"""Delete an agent with a given key"""
result = agents.delete_agent(key)
if not result:
return f"Agent {key} does not exist."
return f"Agent {key} deleted."

145
scripts/config.py
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@ -1,145 +0,0 @@
import abc
import os
import openai
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
class Singleton(abc.ABCMeta, type):
"""
Singleton metaclass for ensuring only one instance of a class.
"""
_instances = {}
def __call__(cls, *args, **kwargs):
"""Call method for the singleton metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(
Singleton, cls).__call__(
*args, **kwargs)
return cls._instances[cls]
class AbstractSingleton(abc.ABC, metaclass=Singleton):
pass
class Config(metaclass=Singleton):
"""
Configuration class to store the state of bools for different scripts access.
"""
def __init__(self):
"""Initialize the Config class"""
self.debug = False
self.continuous_mode = False
self.speak_mode = False
self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
self.openai_api_key = os.getenv("OPENAI_API_KEY")
self.use_azure = False
self.use_azure = os.getenv("USE_AZURE") == 'True'
if self.use_azure:
self.openai_api_base = os.getenv("OPENAI_AZURE_API_BASE")
self.openai_api_version = os.getenv("OPENAI_AZURE_API_VERSION")
self.openai_deployment_id = os.getenv("OPENAI_AZURE_DEPLOYMENT_ID")
openai.api_type = "azure"
openai.api_base = self.openai_api_base
openai.api_version = self.openai_api_version
self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
self.use_mac_os_tts = False
self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
self.google_api_key = os.getenv("GOOGLE_API_KEY")
self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
self.pinecone_api_key = os.getenv("PINECONE_API_KEY")
self.pinecone_region = os.getenv("PINECONE_ENV")
self.image_provider = os.getenv("IMAGE_PROVIDER")
self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
# User agent headers to use when browsing web
# Some websites might just completely deny request with an error code if no user agent was found.
self.user_agent_header = {"User-Agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"}
self.redis_host = os.getenv("REDIS_HOST", "localhost")
self.redis_port = os.getenv("REDIS_PORT", "6379")
self.redis_password = os.getenv("REDIS_PASSWORD", "")
self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == 'True'
self.memory_index = os.getenv("MEMORY_INDEX", 'auto-gpt')
# Note that indexes must be created on db 0 in redis, this is not configureable.
self.memory_backend = os.getenv("MEMORY_BACKEND", 'local')
# Initialize the OpenAI API client
openai.api_key = self.openai_api_key
self.plugins = []
self.plugins_whitelist = []
self.plugins_blacklist = []
def set_continuous_mode(self, value: bool):
"""Set the continuous mode value."""
self.continuous_mode = value
def set_speak_mode(self, value: bool):
"""Set the speak mode value."""
self.speak_mode = value
def set_debug_mode(self, value: bool):
self.debug_mode = value
def set_fast_llm_model(self, value: str):
"""Set the fast LLM model value."""
self.fast_llm_model = value
def set_smart_llm_model(self, value: str):
"""Set the smart LLM model value."""
self.smart_llm_model = value
def set_fast_token_limit(self, value: int):
"""Set the fast token limit value."""
self.fast_token_limit = value
def set_smart_token_limit(self, value: int):
"""Set the smart token limit value."""
self.smart_token_limit = value
def set_openai_api_key(self, value: str):
"""Set the OpenAI API key value."""
self.openai_api_key = value
def set_elevenlabs_api_key(self, value: str):
"""Set the ElevenLabs API key value."""
self.elevenlabs_api_key = value
def set_google_api_key(self, value: str):
"""Set the Google API key value."""
self.google_api_key = value
def set_custom_search_engine_id(self, value: str):
"""Set the custom search engine id value."""
self.custom_search_engine_id = value
def set_pinecone_api_key(self, value: str):
"""Set the Pinecone API key value."""
self.pinecone_api_key = value
def set_pinecone_region(self, value: str):
"""Set the Pinecone region value."""
self.pinecone_region = value
def set_debug_mode(self, value: bool):
"""Set the debug mode value."""
self.debug = value
def set_plugins(self, value: list):
"""Set the plugins value."""
self.plugins = value

18
scripts/data.py
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@ -1,18 +0,0 @@
import os
from pathlib import Path
def load_prompt():
"""Load the prompt from data/prompt.txt"""
try:
# get directory of this file:
file_dir = Path(__file__).parent
prompt_file_path = file_dir / "data" / "prompt.txt"
# Load the prompt from data/prompt.txt
with open(prompt_file_path, "r") as prompt_file:
prompt = prompt_file.read()
return prompt
except FileNotFoundError:
print("Error: Prompt file not found", flush=True)
return ""

63
scripts/data/prompt.txt
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@ -1,63 +0,0 @@
CONSTRAINTS:
1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
5. Browse Website: "browse_website", args: "url": "<url>", "question": "<what_you_want_to_find_on_website>"
6. Start GPT Agent: "start_agent", args: "name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"
7. Message GPT Agent: "message_agent", args: "key": "<key>", "message": "<message>"
8. List GPT Agents: "list_agents", args: ""
9. Delete GPT Agent: "delete_agent", args: "key": "<key>"
10. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
11. Read file: "read_file", args: "file": "<file>"
12. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
13. Delete file: "delete_file", args: "file": "<file>"
14. Search Files: "search_files", args: "directory": "<directory>"
15. Evaluate Code: "evaluate_code", args: "code": "<full_code_string>"
16. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
17. Write Tests: "write_tests", args: "code": "<full_code_string>", "focus": "<list_of_focus_areas>"
18. Execute Python File: "execute_python_file", args: "file": "<file>"
19. Task Complete (Shutdown): "task_complete", args: "reason": "<reason>"
20. Generate Image: "generate_image", args: "prompt": "<prompt>"
21. Do Nothing: "do_nothing", args: ""
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-3.5 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behavior constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"thoughts":
{
"text": "thought",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"speak": "thoughts summary to say to user"
},
"command": {
"name": "command name",
"args":{
"arg name": "value"
}
}
}
Ensure the response can be parsed by Python json.loads

48
scripts/execute_code.py
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@ -1,48 +0,0 @@
import docker
import os
def execute_python_file(file):
"""Execute a Python file in a Docker container and return the output"""
workspace_folder = "auto_gpt_workspace"
print (f"Executing file '{file}' in workspace '{workspace_folder}'")
if not file.endswith(".py"):
return "Error: Invalid file type. Only .py files are allowed."
file_path = os.path.join(workspace_folder, file)
if not os.path.isfile(file_path):
return f"Error: File '{file}' does not exist."
try:
client = docker.from_env()
# You can replace 'python:3.8' with the desired Python image/version
# You can find available Python images on Docker Hub:
# https://hub.docker.com/_/python
container = client.containers.run(
'python:3.10',
f'python {file}',
volumes={
os.path.abspath(workspace_folder): {
'bind': '/workspace',
'mode': 'ro'}},
working_dir='/workspace',
stderr=True,
stdout=True,
detach=True,
)
output = container.wait()
logs = container.logs().decode('utf-8')
container.remove()
# print(f"Execution complete. Output: {output}")
# print(f"Logs: {logs}")
return logs
except Exception as e:
return f"Error: {str(e)}"

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