phidata

What it is and what it does

Phidata is a framework for defining and running agents—autonomous programs that can call tools, maintain conversation history, and reason about tasks. You define an agent by specifying a language model, a set of tools it can invoke, and instructions it should follow. Agents can work alone or as teams, where a coordinator agent delegates to specialists. The framework supports text, images, audio, and video inputs, and includes built-in storage, vector databases for knowledge retrieval, and a web UI for interactive chat.

The core use case is building AI applications that go beyond simple prompt-response: agents that search the web, query financial data, retrieve documents from a knowledge base, or orchestrate multiple specialized agents to solve complex problems. Phidata handles the scaffolding—tool calling, message history, structured outputs, and multi-agent routing—so you focus on defining what each agent should do.

Use it for:

• Build a web search agent that retrieves current information and includes sources in responses
• Create a finance agent that queries stock data, analyst recommendations, and company news
• Set up agentic RAG to let an agent search a knowledge base only when needed, reducing token usage
• Orchestrate a team of agents to answer complex multi-domain questions
• Deploy a multi-modal agent that understands images and takes actions based on visual input
• Chat with agents via a web playground UI with persistent conversation history

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Phidata is a framework for building multi-modal agents that can use tools, maintain memory, and reason to accomplish tasks—either individually or as coordinated teams.

Yes, with conditions. Phidata is production-stable and has low install friction, making it accessible for building agent-based applications. However, the dormant maintenance status (564 days since last release) means you should verify compatibility with your Python version and dependencies before committing to production use. The MPL 2.0 copyleft license requires you to open-source any modifications or derivative works. Install if you need a structured agent framework with multi-agent orchestration and built-in UI; skip if you require active maintenance or permissive licensing.

Install

phidata on PyPI

pip install phidata

uv add phidata

poetry add phidata

Installing phidata

Before you install

Low install friction with a pure-Python wheel and 11 runtime dependencies. Maintenance status is dormant (564 days since last release), which may signal slower response to issues or compatibility updates.

License in practice

Licensed under Mozilla Public License 2.0 (copyleft). You must distribute source code and license notices with any modified or distributed version; commercial use is permitted but derivative works must remain under MPL 2.0.

Quickstart

pip install phidata

from phi.agent import Agent

agent = Agent()
agent.print_response("Your query here", stream=True)

Requires an external LLM API key and model provider to function; agent behavior depends on model availability and configuration.

Verify before relying

• Whether dormant status (564 days since release) impacts compatibility with current Python or dependency versions
• Performance characteristics and scalability limits for multi-agent teams
• Real-world latency and token efficiency of agentic RAG vs. traditional RAG approaches
• Specific model providers and API keys required to run agents

Package facts

Evidence: phidata-2.7.10-py3-none-any.whl

Tags

More Artificial Intelligence packages

Further reading