OpenAgent

General purpose AI agent 'framework' for Adaptive AI Agents

Abstract

This document outlines the vision, architecture, and implementation strategy for OpenAgent, an open-source AI agent framework designed to deliver scalable, adaptable, and efficient solutions for task and knowledge management in real-world applications.

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Introduction

The landscape of AI tools has rapidly evolved, but a significant gap remains in frameworks that adapt to the context of users and their environments while operating efficiently across resource-constrained settings. OpenAgent addresses this gap by offering a lightweight, multi-platform framework that supports advanced capabilities like web search, LLM integration, and dynamic task management.

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Goals

• Scalability: Ensure seamless scaling from personal projects to enterprise-level systems.
• Efficiency: Optimize compute resource usage to reduce costs, leveraging modern hardware like Apple Silicon AI cores.
• Adaptability: Enable agents to adapt to user context and improve performance based on feedback loops.
• Open-Source Collaboration: Provide a foundation for community-driven enhancements.

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Features

1. Capabilities:

   • Web Search: Integrate APIs for real-time information retrieval.
   • LLM Connections: Utilize local and external models for natural language understanding.

2. Knowledge:

   • Access structured and unstructured data from company databases, cloud storage, and local files.

3. Task Management:

   • Facilitate task prioritization with a focus on user-defined workflows like 'NOW/NEXT.'

4. Compute Optimization:

   • Dynamically allocate compute resources based on task complexity and time constraints.

5. Deployment:

   • Support for containerized environments with Docker and multi-cloud compatibility.

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Architecture Overview

• Core Modules: Handle agent capabilities and task orchestration.
• Knowledge Repository: Centralized access to all knowledge features.
• Feedback Loop: Optimize performance using TensorZero's feedback loop mechanisms.

Diagram (to be added):

[High-level architecture of OpenAgent]

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Implementation

Initial Setup

1. Environment Preparation:
   Install prerequisites, including Python, TypeScript, and Docker.

2. Repository Initialization:
   Set up the GitHub repository with the following structure:

   OpenAgent/
   ├── src/
   │   ├── core/
   │   ├── modules/
   │   └── tests/
   ├── Dockerfile
   ├── requirements.txt
   ├── README.md
   └── LICENSE
   

3. Dependencies:

   • Flask for API routing.
   • FastAPI for high-performance endpoints (optional).
   • TypeScript for front-end components (if needed).

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Roadmap

1. Phase One: Core framework and documentation.
2. Phase Two: Add dynamic task prioritization and compute-based accuracy adjustments.
3. Phase Three: Adaptive Autonomy
   Move beyond integration-focused functionality to develop adaptive autonomy for the AI agent. In this phase, OpenAgent will:
   • Self-Learning Mechanisms: Implement systems that allow the agent to continuously learn from user behavior and external feedback loops, refining task prioritization and improving efficiency over time.
   • Multi-Agent Collaboration: Introduce protocols for agents to collaborate and share insights across distributed environments.
   • Enhanced Personalization: Build sophisticated user modeling techniques, enabling the agent to anticipate user needs and provide proactive assistance.

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Conclusion

OpenAgent seeks to redefine how AI agents operate by combining adaptability, scalability, and efficiency. This document serves as the foundation for building a community-driven platform to advance AI capabilities for all.