Open-source Python framework (formerly Phidata) for building AI agents with built-in memory, knowledge bases, and multi-agent teams. Ships with AgentOS for production deployment.
A lightweight way to build AI agents in Python that can use tools and work together — designed for speed and simplicity.
Agno is a Python framework for building AI agents that ship with memory, knowledge, tools, and team orchestration built in. You define an agent in a few lines of Python, give it tools and a knowledge base, and Agno handles the rest: conversation memory, RAG over your documents, and coordination between multiple agents.
The framework rebranded from Phidata in late 2024. If you used Phidata before, Agno is the same team and codebase with a new name, a production runtime (AgentOS), and enterprise features.
Most agent frameworks make you bolt on memory, knowledge, and deployment separately. LangChain gives you building blocks but leaves assembly to you. CrewAI focuses on multi-agent orchestration but lacks a built-in knowledge system. Agno bundles everything into one package.
The performance numbers back this up. Agno claims 529x faster agent instantiation than LangGraph and a 24x smaller memory footprint. For applications that spin up agents per request (chatbots, API endpoints), that speed difference matters.
Agno's API is clean. You create an Agent with a model, instructions, tools, and optional memory and knowledge sources. A basic agent with web search, memory, and a PDF knowledge base takes about 10 lines of Python. Compare that to the equivalent LangChain setup, which often runs 50-100 lines with chains, retrievers, and memory wrappers.
This simplicity is the main reason Reddit users report switching from LangChain to Agno. One r/LangChain commenter wrote: "After using LangChain, LlamaIndex, and Pydantic AI, I switched everything to Agno. It is great, easier, and better."
AgentOS is the paid cloud layer. It turns your agents into scalable API endpoints with a control plane for monitoring, tracing, and management. You build locally with the open-source framework, then deploy to AgentOS when you need production infrastructure.
This is the same play LangChain makes with LangSmith, but Agno bundles more into the framework layer so the cloud layer handles less.
Building equivalent functionality from separate tools: LangChain for orchestration ($0) + Pinecone for vector storage ($25/month) + LangSmith for observability ($39/month) + custom memory layer (engineering time) = $64/month plus 20-40 hours of integration work.
Agno open source: $0, with memory, knowledge, and basic observability included. You still pay for your LLM provider and any vector database, but the framework layer is free and pre-integrated.
| Plan | Price | What You Get |
|------|-------|--------------|
| Open Source | $0 | Full framework, memory, knowledge, tools, multi-agent teams |
| Cloud | Usage-based | Managed hosting, production runtime, control plane, team features |
| Enterprise | Custom | Private cloud, JWT/RBAC, SOC-2 compliance, dedicated support |
Source: agno.com
Agno's cloud pricing is not published. The "usage-based" Cloud tier requires contacting sales or signing up for access. If you need production hosting with a predictable budget, pin down pricing before committing. The open-source framework works fine self-hosted, but AgentOS pricing opacity is a gap compared to competitors who publish their rates.
Reddit sentiment on Agno is positive but cautious. On r/AI_Agents and r/LangChain, developers praise the simplicity and speed. Multiple users report migrating from LangChain to Agno for the cleaner API.
The Phidata-to-Agno rebrand confused some users. Tutorials and Stack Overflow answers still reference "Phidata," making it harder to search for help. The GitHub repo URL still uses the old name (agno-agi/phidata), which adds to the confusion.
Criticism centers on ecosystem size. LangChain has more integrations, more tutorials, and a larger community. Agno's plugin ecosystem is growing but still smaller. Some developers also worry about portability: Agno-specific patterns do not transfer to other frameworks.
Sources: Reddit r/AI_Agents, r/LangChain, r/LLMDevs, HPE Developer Portal
Yes. Phidata rebranded to Agno in late 2024. Same team, same codebase, new name and expanded features. The GitHub repo still lives at agno-agi/phidata.
Yes. The open-source framework runs anywhere Python runs. AgentOS is optional and only needed for managed production hosting, the control plane UI, and enterprise features.
Agno works with OpenAI, Anthropic, Google, Mistral, and most other providers. You can also use local models through Ollama or similar tools.
The framework is production-ready for self-hosted deployments. Multiple companies run Agno agents in production. AgentOS adds managed infrastructure if you prefer not to handle hosting yourself.
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Agno (formerly Phidata) bundles agent memory, knowledge bases, tools, and multi-agent orchestration into a single Python framework. Faster and simpler than LangChain for most agent use cases, with a production runtime (AgentOS) for teams that need managed hosting. The open-source tier covers most needs.
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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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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Launched AgentOS production runtime for deploying agents as scalable APIs. Published performance benchmarks showing 529x faster instantiation vs LangGraph. Added enterprise security features including JWT, RBAC, and request-level isolation.
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