Phidata — AI Agent Review & Live Stats

Phidata builds AI agents with memory, knowledge, and reasoning tools. We track how its approach compares to CrewAI and LangGraph for production agentic applications.

GitHub data synced: Apr 2, 2026 • Sentiment updated: Mar 16, 2026

GitHub Statistics

Community Sentiment

Community Buzz: PhidataHQ/phidata has garnered significant attention for its innovative AI and agent-based development tools, particularly among Python developers. Users praise its ease of use and performance, citing its potential to revolutionize the field.

Why Phidata Stands Out

Agno introduces a new interaction model, governance model, and trust model for agentic software, enabling the creation of agents that can stream reasoning, tool calls, and results in real-time. Its architecture is designed for production, with a stateless and horizontally scalable runtime, and native tracing and auditability. This approach solves the problem of building and managing complex agent-based systems, which is difficult to achieve with traditional software development methods.

Built With

Build a personal agent that learns your preferences — Agno's architecture enables the creation of personalized agents with streaming responses and per-user isolation, Build a self-learning data agent grounded in six layers of context — Agno's framework supports the development of complex agents with multiple integrations, Build a self-learning context agent that manages enterprise context knowledge — Agno's runtime allows for scalable and stateless execution of agents, Build a post-IDE coding agent that improves over time — Agno's tools and integrations enable the creation of agents that can learn and adapt, Build a multi-agent investment committee that debates and allocates capital — Agno's support for coordinated teams and structured workflows enables the development of complex multi-agent systems

Getting Started

  1. Install Agno using pip: `pip install agno`
  2. Import the necessary modules: `from agno.agent import Agent`
  3. Create a new agent: `agno_assist = Agent(name='Agno Assist', model=Claude(id='claude-sonnet-4-6'))`
  4. Configure the agent's database and tools: `agno_assist.db = SqliteDb(db_file='agno.db')`
  5. Try running the agent using `uvx --python 3.12 --with agno[os] --with anthropic --with mcp fastapi dev agno_assist.py` to verify it works

About

Build, run, manage agentic software at scale.

Official site: https://docs.agno.com

Category & Tags

Category: data

Tags: agents, ai, ai-agents, developer-tools, python

Market Context

PhidataHQ/phidata is a rapidly growing project in the AI and developer tools space, with potential applications in various industries and a strong focus on Python development.