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Basic Information

OpenAGI is a developer-focused framework designed to make the development of autonomous, human-like agents accessible. It provides the building blocks and orchestration needed to construct multi-agent systems where distinct Workers perform actions and an Admin coordinates planning and execution. The README shows example usage for both manual and autonomous execution, illustrating a Trip Planner agent and a Sports Agent that run with LLM backends. The project integrates LLM adapters such as OpenAIModel and GeminiModel, supports action plugins like DuckDuckGoSearch and TavilyWebSearchQA, and includes a TaskPlanner for task decomposition. It also offers a Long Term Memory module so agents can recall past interactions. The project is distributed as a pip package and includes documentation, community channels, and contribution guidelines for developers to extend and deploy open agents.

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Features
Modular multi-agent architecture with Worker and Admin components for orchestrating agent roles and tasks. A TaskPlanner that supports task decomposition and can run in autonomous or human-intervene modes. Pluggable LLM adapters demonstrated for OpenAI and Gemini enabling configurable model backends loaded from environment settings. Action/tool integrations such as DuckDuckGoSearch and TavilyWebSearchQA that Workers can invoke. Long Term Memory support to persist and recall past interactions for continuity across sessions. Examples in the README showing manual and autonomous agent flows and code snippets for quick start. Packaging and installability via pip and editable installs. Documentation site, Discord community, issue tracker, and contribution guidelines to support users and contributors.
Use Cases
OpenAGI helps developers accelerate the creation of autonomous agents by providing a ready-made framework for multi-agent orchestration, LLM integration, and persistent memory. Teams can prototype agents that decompose user requests into tasks, assign specialized Workers, call external search or QA tools, and coordinate responses through an Admin controller. Long Term Memory enables contextual continuity so agents learn from prior interactions and improve over time. The framework is useful for building domain-specific agents for education, finance, healthcare and other workflows described in the README. Prebuilt examples and environment-driven model configuration speed experimentation, while pip distribution and documentation lower onboarding friction for developers and researchers. Community and contribution channels support extension and collaborative improvement.

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