AnyAgent
A unified Python interface for running and evaluating several agent frameworks.
What does this agent do, and when should you use it?
AnyAgent is a Python library for using and evaluating multiple agent frameworks through one interface. Its main entry points are `AgentConfig` and `AnyAgent`: developers select a framework with `AnyAgent.create()` while supplying models, instructions, and tools through a common configuration pattern. The README lists TinyAgent, Google ADK, LangChain, LlamaIndex, OpenAI Agents SDK, smolagents, and Agno as supported frameworks. A call to `agent.run()` produces an `agent_trace`, and the project includes an example for creating an agent evaluation. It also documents entry points for MCP agents, A2A serving, and multi-agent patterns, although the project is softly deprecated and has no planned new features.
After installation, code imports AgentConfig and AnyAgent from any_agent, then creates a framework-specific agent with AnyAgent.create("tinyagent", AgentConfig(...)). AgentConfig can carry a model_id, instructions, and a tool list; the example uses mistral:mistral-small-latest with search_web and visit_webpage. The application then calls agent.run("Which Agent Framework is the best??") and prints the resulting agent_trace. The project also exposes documentation and cookbook entry points for evaluation, tracing, serving, MCP, and A2A.
- A Python team that needs one creation and execution pattern across supported frameworks such as TinyAgent, Google ADK, and LangChain.
- A developer building an evaluation workflow for a web-search agent and comparing three evaluation methods.
- An application developer who needs to integrate Model Context Protocol tools into an agent.
- A team that wants to serve an agent with A2A or use one agent as a tool for another agent.
- A researcher who still needs cross-framework execution or evaluation while planning a move to `tinyagent`.
What are this agent's strengths and limitations?
- Uses `AnyAgent.create()` and `AgentConfig` as a common creation and execution surface across several listed frameworks.
- Explicitly includes agent evaluation; the starter material covers evaluating a web-search agent with three methods.
- Provides cookbook entry points for MCP, A2A serving, and an agents-as-tools multi-agent pattern.
- The project is in soft deprecation: security and bug-fix pull requests are accepted, but no new features are planned.
- It requires Python 3.11 or newer.
- Model calls require credentials for the chosen provider; no credential-free execution mode is described.
- The supplied material does not enumerate framework installation options or give complete evaluation API configuration details.
How do you install or deploy this agent?
Python 3.11 or newer is required.
pip install 'any-agent'
Then set credentials for the selected model provider. The README example uses:
export MISTRAL_API_KEY="YOUR_KEY_HERE"
Set the relevant provider credential instead when applicable, such as OPENAI_API_KEY.
How do you use this agent?
The README's minimal invocation is:
from any_agent import AgentConfig, AnyAgent
from any_agent.tools import search_web, visit_webpage
agent = AnyAgent.create(
"tinyagent",
AgentConfig(
model_id="mistral:mistral-small-latest",
instructions="Use the tools to find an answer",
tools=[search_web, visit_webpage]
)
)
agent_trace = agent.run("Which Agent Framework is the best??")
print(agent_trace)
In Jupyter Notebook, if RuntimeError: This event loop is already running occurs, run import nest_asyncio followed by nest_asyncio.apply() first.
How does this agent compare with similar options?
For new projects, the repository explicitly recommends mozilla-ai-tinyagent. AnyAgent is the better fit when you specifically need to run or evaluate agents across frameworks including Agno, Google ADK, LangChain, LlamaIndex, OpenAI Agents SDK, or smolagents.