The PandaAGI SDK provides a simple, intuitive API for building general AI agents in just a few lines of code. It abstracts away the complexity of Agentic Loops and provides a powerful interface for you to build autonomous agents. Each agent can be configured to run in a custom environment, interacting with the web, your file system, writing code, and running shell commands.
pip install panda-agi
Or with uv:
uv add panda-agi
First of all, make sure you have a API key. You can get one for free here. Make sure to set it as an environment variable:
export PANDA_AGI_KEY=your_api_key
or set it in the .env file:
PANDA_AGI_KEY=your_api_key
Once you have the API key, you can start using the SDK:
import asyncio
from panda_agi import Agent
from panda_agi.envs import LocalEnv
async def main():
# Create a custom environment for the agent
agent_env = LocalEnv("./my_agent_workspace")
# Create the agent
agent = Agent(environment=agent_env)
# Run the agent with a task
response = agent.run("Tell me a joke about pandas")
print(response.output)
# Other possible tasks
response = agent.run("Make a report of the real estate market in Germany")
# -> will generate a reporrt in the provided workspace folder
response = agent.run("Can you analyze our sales and create a dashboard?")
# -> will generate a dashboard in the provided workspace folder starting from a csv file in the workspace folder
response = agent.run("Can you create a website for our company?")
# -> will generate a website in the provided workspace folder
# Disconnect when done
await agent.disconnect()
if __name__ == "__main__":
asyncio.run(main())
In case you want to enable te web search, you will also need a Tavily API key. You can get one for free here. Then set it as an environment variable or set it in the .env file:
TAVILY_API_KEY=your_api_key
In case you don't want to build an app from scratch, we provide a UI that you can use to run your agents.
Running it is as simple as:
# Run the UI
cd examples/ui
./start.sh
This will start a docker container with the UI running. You can access it at http://localhost:3000
and start using it.
Want to experiment with PandaAGI SDK without any setup? Try our interactive notebook:
- Simple, intuitive API for interacting with PandaAGI agents
- Support for local and Docker environments
- Asynchronous event-based communication
- Pydantic models for type safety
For complete documentation, visit our documentation site.
- Python 3.8+
- uv
- Clone the repository
- Install dependencies:
uv pip install -e ".[dev]"
Run tests with pytest:
uv run pytest
MIT License