Any Agent
One interface to evaluate and switch between every major agent framework.
any-agent is a pragmatic toolkit for developers who need to work across multiple agent frameworks without rewriting code. Its main strengths are unified tracing and evaluation—features sorely missing in the fragmented agent ecosystem. However, it's still early-stage and may not cover every framework edge case.
- Developers building multi-framework agent prototypes
- Teams standardizing agent observability across projects
- Researchers comparing agent framework performance
- Engineers serving agents via standard protocols (A2A/MCP)
- Non-developers seeking a no-code agent builder
- Production deployments requiring deep framework-specific optimizations
- Projects that do not use any of the supported frameworks
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In short
Any Agent — One interface to evaluate and switch between every major agent framework. Best for Developers building multi-framework agent prototypes, Teams standardizing agent observability across projects, Researchers comparing agent framework performance. Free to use.
Viability Score
How likely is Any Agent to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Framework-agnostic agent builder: switch between Agno, Google ADK, LangChain, LlamaIndex, OpenAI, smolagents, TinyAgent
- Unified OpenTelemetry tracing across all supported frameworks
- Built-in LLM-as-a-judge and agent-as-a-judge evaluation tools
- Serve agents via A2A (Agent-to-Agent) protocol
- Serve agents via MCP (Model Context Protocol)
- Use an agent as a tool for another agent (A2A composition)
- Single-command pip install with optional framework extras
- Documentation in AI-friendly formats (llms.txt, llms-full.txt)
- Python 3.11+ support
- Permissive open-source license (Apache 2.0)
- Framework-specific dependency options (e.g., pip install any-agent[agno,openai])
About Any Agent
any-agent is an open-source Python library from Mozilla.ai that gives developers a single, unified interface for building, evaluating, and serving AI agents across multiple frameworks—without vendor lock-in. You can switch between frameworks like Agno, Google ADK, LangChain, LlamaIndex, OpenAI, smolagents, and TinyAgent by changing one parameter. The library includes standardized OpenTelemetry tracing across all frameworks, built-in LLM-as-a-judge and agent-as-a-judge evaluation tools, and the ability to serve agents via A2A or MCP protocols. It also supports agent composition, letting you use one agent as a tool for another. Installation is a single pip command with optional framework-specific extras. Designed for Python 3.11+, the library is part of Mozilla.ai’s mission for open, trustworthy AI. Documentation is available in AI-friendly formats (llms.txt and llms-full.txt) for easy consumption by language models. While it’s still early-stage, it already covers the most popular agent frameworks and provides essential observability and evaluation capabilities out of the box—something the fragmented agent ecosystem desperately needs.
Behind the Verdict
any-agent fills a real gap. If you’ve ever had to switch from LangChain to LlamaIndex or compare agents across frameworks, you know the pain of rewriting glue code. This library abstracts that away cleanly—change one parameter, and your agent runs on a different backend. The built-in OpenTelemetry tracing and judge-based evaluation are genuinely useful for debugging and benchmarking. We’d reach for this when prototyping multi-framework agents or standardizing observability across a team. That said, it has limits. It’s a thin wrapper—you won’t get deep framework-specific optimizations here. If you’re already deep into one ecosystem and need maximum performance, stick with that framework directly. Also, don’t expect a GUI or no-code interface; this is for developers comfortable with Python. Compared to tools like LangServe (which only supports LangChain), any-agent offers more flexibility but less specialized documentation. For researchers comparing agent performance across frameworks, it’s a solid choice. For production teams with a single framework, it’s overkill. Keep an eye on this project—Mozilla.ai is actively updating it.
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Use Cases
- Build a prototype agent that can switch between LangChain and LlamaIndex with one code change.
- Evaluate the quality of responses from agents built on different frameworks using a judge LLM.
- Serve a TinyAgent via MCP protocol to integrate with existing MCP clients.
- Compose an OpenAI agent as a tool for a Google ADK agent using the built-in A2A support.
- Trace agent execution across framework boundaries for debugging and observability.
Limitations
- Requires Python 3.11 or newer.
- Only TinyAgent is available with the barebones install; additional frameworks require optional dependencies.
- Documentation is still maturing; some cookbook examples may assume familiarity with multiple frameworks.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Integrations
Resources & Guides
- Resourcedocs.mozilla.ai
Any Agent · Any Agent
Helpful link from docs.mozilla.ai
- Resourcedocs.mozilla.ai
Your First Agent · Any Agent
Helpful link from docs.mozilla.ai
- Resourcedocs.mozilla.ai
Your First Agent Evaluation · Any Agent
Helpful link from docs.mozilla.ai
- Resourcedocs.mozilla.ai
Using Callbacks · Any Agent
Helpful link from docs.mozilla.ai
- Resourcedocs.mozilla.ai
Mcp Agent · Any Agent
Helpful link from docs.mozilla.ai
- Resourcedocs.mozilla.ai
Serve With A2a · Any Agent
Helpful link from docs.mozilla.ai
Official links
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