Phidata — AI Agents with Memory, Knowledge & Tools

What it is

Phidata is a Python framework for building AI agents that come with built-in memory (backed by PostgreSQL), knowledge bases (via RAG), and 50+ tool integrations out of the box. It provides a structured approach to creating agents that remember conversations, search through documents, and take actions via external APIs.

The framework targets developers building production AI assistants, customer support bots, or research agents that need persistent state and access to domain-specific knowledge.

How it saves time or tokens

Phidata bundles memory, knowledge, and tool calling into a single framework. Without it, you would need to separately integrate a vector database, build RAG pipelines, implement conversation persistence, and wire up tool schemas. Phidata provides all of this with minimal configuration, reducing setup time from days to hours.

How to use

1. Install Phidata: pip install phidata.
2. Create an Agent with your chosen model, tools, and knowledge source.
3. Run the agent with .run() or .print_response() for interactive use.

Example

from phi.agent import Agent
from phi.tools.duckduckgo import DuckDuckGo
from phi.model.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id='gpt-4o'),
    tools=[DuckDuckGo()],
    description='You are a research assistant.',
    instructions=['Always cite your sources.'],
    show_tool_calls=True,
    markdown=True,
)

agent.print_response('What are the latest developments in AI agents?')

Related on TokRepo

• Phidata Deep-Dive -- detailed Phidata framework breakdown
• AI Tools for Agents -- compare AI agent frameworks

Common pitfalls

• Phidata's memory requires a PostgreSQL database for persistence. For local development, use Docker to run a lightweight Postgres instance.
• Tool calling adds latency per turn. Limit the number of active tools to those the agent actually needs for the current task.
• Knowledge base indexing must be triggered manually after adding new documents. Automate re-indexing in your CI/CD pipeline for production.