Added blueprint of an agent tutorial
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# The Blueprint of an AI Agent: Understanding Its Structure and Functionality
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## Introduction
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- What is an AI Agent?
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- Role and Importance of an Agent in AutoGPT
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Welcome to the dynamic world of AI agents! As we stand on the threshold of a new era in technology, it is essential to understand the evolving landscape of AI agent development. Whether you are a seasoned developer or a novice stepping into this fascinating domain, this blog post aims to illuminate the key components and protocols necessary for creating sophisticated AI agents.
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## Section 1: Understanding the Agent Protocol
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- Overview of the Agent Protocol
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- Components and Schemas in the Agent Protocol
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## The Forge: Your Launchpad for AI Agent Development
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## Section 2: Analyzing the Agent's Structure
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- Task and Step Schemas
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- Artifact and Pagination Schemas
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The Forge stands as a beacon, aiding developers in navigating through the complexities of boilerplate code, allowing you to focus intently on crafting groundbreaking agent designs. To deepen your understanding and explore the boundless opportunities in the agent landscape, we highly recommend perusing these insightful papers on the field [Agent Landscape Survey](https://arxiv.org/abs/2308.11432) and [The Rise and Potential of Large Language Model Based Agents: A Survey](https://arxiv.org/abs/2309.07864). They both reference a massive repository of research into agent development!
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## Section 3: Agent Tasks and Steps
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- Creating and Listing Agent Tasks
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- Executing and Retrieving Details of Agent Steps
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## Bridging Communication Gaps with the Agent Protocol
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## Section 4: Working with Artifacts
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- Creating and Listing Artifacts
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- Uploading and Downloading Artifacts
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In the burgeoning field of AI agents, developers often find themselves forging unique paths, creating agents with distinctive characteristics. While this approach nurtures innovation, it also presents a significant challenge: establishing seamless communication between various agents, each equipped with a different interface. Furthermore, the absence of a standardized communication platform impedes the easy comparison of agents and the seamless development of universal devtools.
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## Section 5: Error Handling in the Agent Protocol
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- Handling `UnprocessableEntity` Response
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- Handling `NotFound` Response
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To tackle this challenge head-on, we the AI Foundation has introduced the **Agent Protocol**, a unified communication interface designed to spur innovation and integration in agent development.
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### A Unifying Communication Interface
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The Agent Protocol emerges as a harmonizing force in the fragmented world of agent development, offering a well-defined API specification that dictates the endpoints agents should expose, along with standardized input and response models. What sets this protocol apart is its versatility, welcoming agents developed with various frameworks to adopt it seamlessly.
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A glimpse into the protocol structure reveals:
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- **POST /agent/tasks**: A route designated for task creation.
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- **POST /agent/tasks/{id}/steps**: A route purposed for initiating the subsequent step of a task.
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- **POST /agent/tasks/{id}/artifacts**: A route for creating an artifact associated with a task.
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- **GET /agent/tasks/{id}/artifacts/{artifact_id}**: A route for downloading an artifact associated with a task.
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For an in-depth exploration, visit the [Agent Protocol](https://agentprotocol.ai).
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### Advantages of Adopting the Agent Protocol
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Implementing the Agent Protocol offers a myriad of benefits, simplifying the development process substantially. Here are some noteworthy advantages:
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- **Effortless Benchmarking**: Seamlessly integrate with benchmarking tools such as Agent Evals, facilitating straightforward testing and benchmarking of your agent against others.
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- **Enhanced Integration and Collaboration**: Encourage seamless integration and collaboration, fostering a community of shared ideas and advancements.
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- **General Devtools Development**: Enable the creation of universal devtools, streamlining development, deployment, and monitoring processes.
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- **Focused Development**: Shift your focus from boilerplate API creation to core agent development, nurturing innovation and efficiency.
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### Fostering a Collaborative Ecosystem
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The Agent Protocol stands at the forefront of fostering a collaborative and rapidly evolving ecosystem. With a minimal core as a starting point, the objective is to expand iteratively, incorporating valuable insights from agent developers to meet their evolving needs.
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Now, let's delve deeper into the core components that constitute an AI agent.
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## Delineating the Anatomy of an AI Agent
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To cultivate proficiency in the AI domain, a thorough understanding of the fundamental components forming an AI agent is indispensable. In this section, we elaborate on the cornerstone elements shaping an AI agent:
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### Profile: Tailoring the Persona
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An agent functions effectively by adopting specific roles, emulating personas such as a teacher, coder, or planner. The strategic utilization of the profile attribute in the language model (LLM) prompt significantly enhances output quality, a phenomenon substantiated by this [study](https://arxiv.org/abs/2305.14688). With the ability to dynamically switch profiles based on the task at hand, agents unlock a world of endless configuration possibilities with various LLMs.
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### Memory: The Repository of Experiences
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An adept memory system serves as a foundation for the agent to accumulate experiences, evolve, and respond in a consistent and efficient manner. Consider the following critical facets:
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- **Long-term and Short-term Memory**: Foster strategies catering to both long-term retention and working memory.
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- **Memory Reflection**: Encourage the agent's ability to scrutinize and reassess memories, facilitating the transition of short-term memories into long-term storage.
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### Planning: Navigating Complex Tasks
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The planning module bestows LLM-based agents with the ability to strategize and plan for intricate tasks, enhancing the agent's comprehensiveness and reliability. Consider integrating these methodologies:
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- **Planning with Feedback**: Incorporate feedback mechanisms within the planning phase.
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- **Planning without Feedback**: Develop strategies independent of external inputs.
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### Abilities: Executing Decisions into Actions
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The abilities component represents a pivotal section where the agent's decisions materialize into specific outcomes. Explore diverse approaches to implement actions, amplifying your agent's capabilities.
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## Embarking on Your Forge Journey: Template and Layout
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To initiate your voyage in AI agent development, begin by modifying the template found in `forge/agent.py`. Here is a foundational structure to kickstart your journey:
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```python
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from forge.sdk import Agent, AgentDB, Step, StepRequestBody, Workspace
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class ForgeAgent(Agent):
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def __init__(self, database: AgentDB, workspace: Workspace):
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"""
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The database is utilized to store tasks, steps, and artifact metadata, while the workspace is used for storing artifacts, represented as a directory on the filesystem. Feel free to create subclasses of the database and workspace to implement your own storage solutions.
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"""
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super().__init__(database, workspace)
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async def execute_step(self, task_id: str, step_request: StepRequestBody) -> Step:
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# An example that passes the write file test
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self.workspace.write(task_id=task_id, path="output.txt", data=b"Washington D.C")
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step = await self.db.create_step(
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task_id=task_id, input=step_request, is_last=True
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)
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artifact = await self.db.create_artifact(
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task_id=task_id,
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step_id=step.step_id,
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file_name="output.txt",
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relative_path="",
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agent_created=True,
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)
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step.output = "Washington D.C"
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return step
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```
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### Exploring the Forge Layout
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Within the Forge layout, discover a plethora of folders and protocols essential for crafting a proficient agent:
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- **Abilities Folder**: Houses the abilities component, a critical aspect defining the agent's capabilities. Path: `forge/sdk/abilities/`
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- **Agent Protocol**: A central pillar of the Forge, overseeing task creation and execution processes. This can be found in `forge/sdk/routes/agent_protocol.py`
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- **Schema**: Outlines the structure and regulations governing data within the Forge. Path: `forge/sdk/schema.py`
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- **DB**: Core component entrusted with managing database operations. Path: `forge/sdk/db.py`
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- **Memstore**: Component responsible for managing the memory system of the agent. Path: `forge/sdk/memory/memstore.py`
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- **AI_(X)**: these files have examples of how the respective functionality can be implemented
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- **Prompt Templates**: The Forge uses Jinja2-based prompt templates, allowing for easy modification of prompts without changing the code. These templates are stored in the `forge/prompts/` directory. This approach provides flexibility and ease of use in customizing the agent's prompts based on specific tasks or roles.
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Moreover, the Forge initiates a FastAPI server, simplifying the process of serving the frontend on [http://localhost:8000](http://localhost:8000).
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## Conclusion
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- Recap of the Tutorial
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- Preparing to Develop Your Agent
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## Additional Resources
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- Links to Documentation and API References
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Embarking on the AI agent development journey with the Forge promises not only an enriching learning experience but also a streamlined development journey. As you progress, immerse yourself in the vibrant landscape of AI agent development, leveraging the comprehensive tools and resources at your disposal.
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## Appendix
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- Example Requests and Responses in the Agent Protocol
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- Glossary of Terms
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Happy Developing!
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