Phidata — Build & Deploy AI Agents at Scale

What it is

Phidata is a Python framework for building, running, and managing AI agents at scale. It provides built-in support for agent memory, knowledge bases, tool integration, reasoning chains, and multi-agent team workflows. The framework includes a monitoring dashboard for tracking agent performance and costs.

AI engineers building production agent systems, teams that need multi-agent coordination, and developers who want a structured approach to agent development use Phidata as their agent framework. It supports multiple LLM providers including OpenAI, Anthropic, and Google.

How it saves time or tokens

Building agents from scratch requires implementing memory management, tool calling, knowledge retrieval, and multi-agent coordination. Phidata packages these as composable abstractions. The Agent class handles conversation history, tool execution, and structured outputs automatically. Team workflows coordinate multiple specialized agents without writing custom orchestration logic.

How to use

1. Install Phidata:

pip install phidata

1. Create a simple agent:

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

agent = Agent(
    model=OpenAIChat(id='gpt-4o'),
    tools=[DuckDuckGo()],
    show_tool_calls=True,
    markdown=True,
)
agent.print_response('What are the latest developments in AI agents?')

1. Add knowledge and memory for persistent context:

from phi.knowledge.pdf import PDFKnowledgeBase
from phi.vectordb.pgvector import PgVector

knowledge = PDFKnowledgeBase(
    path='docs/',
    vector_db=PgVector(table_name='docs', db_url='postgresql://...'),
)
agent = Agent(knowledge=knowledge, search_knowledge=True)

Example

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.newspaper4k import Newspaper4k

# Research agent team
researcher = Agent(
    name='Researcher',
    model=OpenAIChat(id='gpt-4o'),
    tools=[DuckDuckGo()],
    instructions=['Find relevant sources and extract key facts'],
)

writer = Agent(
    name='Writer',
    model=OpenAIChat(id='gpt-4o'),
    tools=[Newspaper4k()],
    instructions=['Write clear, well-structured articles from research'],
)

# Team coordination
team = Agent(
    team=[researcher, writer],
    instructions=['Research the topic, then write an article'],
)
team.print_response('Write an article about quantum computing in 2026')

Related on TokRepo

• AI Agent Tools -- explore agent frameworks and platforms
• Multi-Agent Frameworks -- deep dive into Phidata multi-agent capabilities

Common pitfalls

• Agent memory is in-memory by default. For persistence across sessions, configure a database backend (PostgreSQL with pgvector is recommended).
• Tool calling increases token usage. Each tool call adds input and output tokens to the conversation. Monitor costs through the Phidata dashboard or provider billing.
• Multi-agent teams can be token-expensive since each agent in the team processes the conversation. Start with single agents and add team coordination only when needed.