Agno is a Python library for building AI agents with memory, knowledge, and reasoning capabilities. It targets developers creating complex agentic workflows, multimodal agents, and multi-agent systems, offering a lightweight, high-performance framework with built-in UI and monitoring tools.

How It Works

Agno agents are designed to be model-agnostic, supporting over 23 model providers. They feature a core architecture that integrates reasoning, tools, and memory. The library emphasizes performance with fast instantiation (~3μs) and low memory usage (~6.5KiB), enabling efficient execution of complex agentic tasks. It supports native multimodality, advanced multi-agent coordination (route, collaborate, coordinate), and integrated Agentic RAG with hybrid search.

Quick Start & Requirements

• Install via pip: pip install -U agno
• Requires Python 3.12+.
• Example setup requires specific API keys (e.g., ANTHROPIC_API_KEY, OPENAI_API_KEY) and libraries like anthropic, yfinance, duckduckgo-search, lancedb, tantivy, pypdf.
• Documentation: docs.agno.com
• Examples: Getting Started, Cookbook

Highlighted Details

• Model Agnostic: Connects to 23+ model providers.
• Natively Multi Modal: Handles text, image, audio, and video inputs/outputs.
• Advanced Multi Agent Architecture: Supports routing, collaboration, and coordination modes.
• Agentic Search: Integrates with 20+ vector databases for RAG.
• Pre-built FastAPI Routes: Simplifies agent deployment.

Maintenance & Community

• Community forum: community.agno.com
• Chat: Discord
• Hackathon announced with cash prizes up to $20,000.

Licensing & Compatibility

• License: Not explicitly stated in the README. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

• The README mentions performance benchmarks on an Apple M4 MacBook Pro, but advises users to run their own evaluations. Accuracy and reliability benchmarks are planned for the future.