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any agent

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Multi-Agent Platforms & Frameworks
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Basic Information

any-agent provides a single, consistent Python interface to create, run and evaluate agents built on a variety of agentic frameworks. It is intended to let developers, researchers and evaluators use different backend frameworks through a common API and compare behaviour, tooling and performance. The project demonstrates a small entry API pattern using AnyAgent.create and AgentConfig to configure model, instructions and tools. It includes adapters and first-class support for multiple frameworks including Google ADK, LangChain, LlamaIndex, OpenAI Agents, Smolagents, TinyAgents and Agno AI. The repository bundles documentation, cookbooks and examples that demonstrate building agents, multi-agent setups, evaluation strategies and serving options. It targets Python 3.11 and aims to reduce friction when swapping models or frameworks while enabling reproducible evaluation.

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848
App URL
https://github.com/mozilla-ai/any-agent
Github Repository
https://github.com/mozilla-ai/any-agent/blob/main/README.md

Categorization

App Details

Features
Unified adapter layer and API for multiple agent frameworks with a consistent AgentConfig pattern. Examples and cookbooks for creating agents, performing evaluations, using callbacks, integrating Model Context Protocol tools and serving agents. Built-in tool examples such as search_web and visit_webpage and support for Agents-As-Tools and agent-to-agent (A2A) composition. Tracing, evaluation utilities and serving guidance to help debug and deploy agents. Documentation, integration tests and packaging for PyPI. Quickstart shows environment API key usage and model configuration. Jupyter-specific runtime guidance is provided. The project lists planned framework support and welcomes contributions to extend adapters and features.
Use Cases
any-agent simplifies building, comparing and deploying agentic systems by normalizing interfaces across multiple frameworks, so teams can prototype once and switch backends or models without rewriting orchestration code. Its cookbooks and examples accelerate onboarding and practical experimentation. Evaluation and tracing tools support reproducible comparisons of agent behavior and output. Serving guidance and multi-agent patterns like Agents-As-Tools and A2A enable composed agent systems and production workflows. Compatibility notes for models and API keys, plus Jupyter tips and test coverage, make it practical for developers and researchers working in Python. Community contribution workflows allow adding support for additional frameworks.

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