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FastAPI Agents is the ultimate FastAPI extension for integrating AI agents into your applications. With just a few lines of code, you can create, manage, and secure AI-powered endpoints, enabling you to build smarter, more interactive apps effortlessly. Whether you're a seasoned developer or just exploring AI integrations, FastAPI Agents has you covered! π
BaseAgent
class.See Releases for the latest updates at and Roadmap for what's coming.
You can support the ongoing development of FastAPI Agents by becoming a sponsor:
For further documentation, including detailed API documentation for the available agent frameworks, visit the FastAPI Agents Documentation.
Install FastAPI Agents
using pip, poetry or uv:
pip install fastapi-agents
poetry add fastapi-agents
uv add fastapi-agents
Install optional extras for your chosen agent frameworks:
pip install 'fastapi-agents[pydantic-ai]'
poetry add fastapi-agents -E pydantic-ai
uv add fastapi-agents --extra pydantic-ai
For available extras, replace pydantic-ai
with the desired agent framework (e.g. smolagents
or llama-index
). See pyproject.toml for the full list of extras.
That's it! You're all set to start integrating AI agents into your FastAPI applications. π
Hereβs how to get started with a basic PydanticAI
agent:
from fastapi import FastAPI
from fastapi_agents import FastAPIAgents
from fastapi_agents.pydantic_ai import PydanticAIAgent
from pydantic_ai import Agent
app = FastAPI()
agents = FastAPIAgents(path_prefix="/agents")
# Initialize and register the agent
agent = Agent("openai:gpt-4o-mini")
agents.register("pydanticai", PydanticAIAgent(agent))
# Include the router
app.include_router(agents)
Secure your endpoints with API Key authentication in just a few steps:
from fastapi.security import APIKeyHeader
from fastapi_agents import FastAPIAgents
# Define API Key validation
def validate_api_key(api_key: str = Depends(APIKeyHeader(name="X-API-Key"))):
if api_key != "my-secret-api-key":
raise HTTPException(status_code=403, detail="Invalid API Key")
# Secure the agents
agents = FastAPIAgents(path_prefix="/agents", security_dependency=validate_api_key)
π See Security Examples for more details.
Run your FastAPI application with the registered agents:
uvicorn --reload <module>:app
Replace <module>
with the name of the Python module containing your FastAPI application.
That's it! You're all set to start building smarter, more secure FastAPI applications with AI agents. π
FastAPI Agents
supports a variety of agent frameworks, including:
The simplest way to containerise your agents!
Pre-built Docker images for FastAPI Agents
are available on GitHub Container Registry (GHCR):
Repository: ghcr.io/blairhudson/fastapi-agents
Tags:
pydantic-ai
, smolagents
, llama-index
, crewai
<framework>-<version>
To pull a specific image:
docker pull ghcr.io/blairhudson/fastapi-agents:pydantic-ai
See all available images and tags in Versions.
Currently pre-built images support only one agent per container. If you are creating containers that can serve multiple agents, it is recommended to define your own containers.
The pre-built images support the following environment variables for customisation:
Variable | Example Value | Description |
---|---|---|
AGENT_FRAMEWORK | pydantic-ai | Specifies the agent framework to use. |
AGENT_MODULE | agent.pydantic_ai | Path to the agent module. |
AGENT_CLASS | agent | Class name for the agent. |
SECURITY_MODULE | agent.pydantic_ai | Specifies the security module for the agent. |
SECURITY_CLASS | validate_token | Class name for the security depdency. |
API_ENDPOINT | pydantic-ai | API endpoint path for the agent. |
API_PREFIX | /agents | Prefix for all agent-related API endpoints. |
API_MODE | simple | Changes how endpoints are registered. openai changes to OpenAI-compatible endpoints. |
PORT | 8080 | Port the application runs on within the container. |
To customize these values, pass them as -e
arguments to docker run
or define them in an .env
file.
Agents are expected to be volume-mounted at /app/agent
. You can mount your agent directory as follows:
docker run -p 8000:8080 \
-v $(pwd)/agent:/app/agent \
ghcr.io/blairhudson/fastapi-agents:pydantic-ai
If a requirements.txt
file is present in the mounted directory, it will be automatically installed at container startup.
For production deployments, it is recommended to build your container with dependencies included. Hereβs an example Dockerfile
starting from one of the pre-built base images:
FROM ghcr.io/blairhudson/fastapi-agents:pydantic-ai
# Copy your agent source code
COPY ./agent /app/agent
# Install dependencies
RUN pip install --no-cache-dir -r /app/agent/requirements.txt
Build and run the custom image:
docker build -t my-custom-agent .
docker run -p 8000:8080 my-custom-agent
This approach ensures all dependencies are baked into the image, improving startup performance and reliability.
Explore real-world examples for implementing FastAPI Agents
in different scenarios:
We welcome contributions! To contribute:
uv run pytest
.For any questions or feature requests including additional agent frameworks, open an issue in the repository.
If you use FastAPI Agents in your work, please consider citing it using the metadata in the CITATION.cff
file:
This DOI represents all versions of the project. For version-specific DOIs, refer to the Zenodo project page.
Alternatively, you can use the following BibTeX entry:
@software{fastapi_agents,
author = {Blair Hudson},
title = {FastAPI Agents},
year = {2025},
version = {0.1},
doi = {10.5281/zenodo.14635504},
url = {https://github.com/blairhudson/fastapi-agents},
orcid = {https://orcid.org/0009-0007-4216-4555},
abstract = {FastAPI Agents is the ultimate FastAPI extension for integrating AI agents into your applications.}
}
This project is licensed under the MIT License. See the LICENSE
file for more details.
FAQs
A FastAPI extension for integrating common AI agent frameworks.
We found that fastapi-agents demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago.Β It has 1 open source maintainer collaborating on the project.
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