Full-stack platform for building, testing, and deploying AI agents with built-in memory, tools, and team orchestration capabilities.
A lightweight way to build AI agents in Python that can use tools and work together — designed for speed and simplicity.
Agno (formerly Phidata) is a full-stack platform for building AI agents that combines an open-source Python framework with a cloud platform for deployment, monitoring, and team collaboration. The framework provides batteries-included agent development with built-in memory, knowledge bases, tool integration, and multi-agent team orchestration.
The framework's agent definition is clean and expressive. Each agent is configured with a model, instructions, tools, knowledge sources, and memory settings. Agents can maintain conversation history, store long-term memories, and access structured knowledge bases — all configured declaratively rather than requiring custom infrastructure.
Agno's tool system is extensive, with built-in tools for web search, web scraping, file operations, SQL queries, Python execution, API calls, and dozens of third-party integrations. Creating custom tools is as simple as writing a Python function with a docstring. The framework handles tool schema generation, execution, and result formatting automatically.
The multi-agent team feature allows composing agents into collaborative teams with a coordinator that routes tasks to specialists. Teams can operate in different modes — route (single agent handles each query), collaborate (multiple agents contribute), and coordinate (structured delegation) — giving developers flexibility in how agents interact.
Agno's knowledge base system provides built-in RAG capabilities. Point it at PDFs, websites, databases, or custom data sources, and it handles embedding, storage, and retrieval. Vector storage backends include PgVector, Pinecone, Qdrant, and others.
The cloud platform (app.agno.com) adds agent deployment, conversation monitoring, analytics, and a playground for testing. Agents can be deployed as API endpoints or embedded in applications through the provided SDKs.
Agno differentiates itself by being genuinely full-stack — you can go from 'pip install agno' to a production-deployed agent with memory, knowledge, and monitoring without integrating multiple separate tools. This reduces the complexity and integration work that typically comes with building production agent systems.
The framework has strong traction with developers building practical agent applications: customer support agents, research assistants, data analysis tools, and business automation workflows. Its balance of simplicity and capability makes it accessible to intermediate developers while powerful enough for production use.
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Agents configured with model, instructions, tools, knowledge, and memory in a clean, readable format without boilerplate.
Use Case:
Spinning up a financial analyst agent with web search, calculator tools, and SEC filing knowledge in under 20 lines of code.
Conversation history, session storage, and long-term memory with configurable backends — no separate memory service needed.
Use Case:
Building a personal assistant that remembers user preferences and past conversations across sessions.
Built-in RAG with support for PDFs, websites, databases, and custom sources with automatic embedding and retrieval.
Use Case:
Creating a product expert agent by pointing it at documentation, FAQs, and knowledge articles.
Compose agents into teams with routing, collaboration, and coordination modes for complex task delegation.
Use Case:
Building a content team with researcher, writer, editor, and SEO specialist agents that collaborate on articles.
Built-in tools for web search, scraping, SQL, Python execution, email, and dozens of third-party integrations.
Use Case:
Giving agents the ability to search the web, query databases, and send notifications without custom tool development.
Deploy agents as API endpoints with monitoring, analytics, playground testing, and conversation management.
Use Case:
Deploying a customer support agent to production with real-time monitoring of conversation quality and costs.
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View Pricing Options →Full-stack agent applications
Knowledge-powered assistants
Multi-agent team workflows
Rapid agent prototyping to production
We believe in transparent reviews. Here's what Agno doesn't handle well:
Phidata rebranded to Agno in 2025. The core framework and team remain the same, with the new name reflecting the platform's evolution beyond its initial focus.
No, the open-source framework works independently. The cloud platform adds deployment, monitoring, and team features for production use.
Agno is more opinionated and batteries-included — memory, knowledge, and tools work out of the box. LangChain is more modular and flexible but requires more integration work.
Yes, Agno supports Ollama, Hugging Face, and any OpenAI-compatible endpoint alongside cloud LLM providers.
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