What is Agno-Go?

Agno-Go is a high-performance multi-agent system framework built with Go. Inheriting the design philosophy from the Python Agno framework, it leverages Go's concurrency model and performance advantages to build efficient, scalable AI agent systems.

Key Features

🚀 Extreme Performance

• Agent Instantiation: ~180ns average (16x faster than Python version)
• Memory Footprint: ~1.2KB per agent (5.4x less than Python)
• Native Concurrency: Full goroutine support without GIL limitations

🤖 Production-Ready

Agno-Go includes AgentOS, a production HTTP server with:

• RESTful API with OpenAPI 3.0 specification
• Session management for multi-turn conversations
• Thread-safe agent registry
• Health monitoring and structured logging
• CORS support and request timeout handling

🧩 Flexible Architecture

Three core abstractions for different use cases:

1. Agent - Autonomous AI agents with tool support and memory
2. Team - Multi-agent collaboration with 4 coordination modes
   • Sequential, Parallel, Leader-Follower, Consensus
3. Workflow - Step-based orchestration with 5 primitives
   • Step, Condition, Loop, Parallel, Router

🔌 Multi-Model Support

Built-in support for 6 major LLM providers:

• OpenAI - GPT-4, GPT-3.5 Turbo, etc.
• Anthropic - Claude 3.5 Sonnet, Claude 3 Opus/Sonnet/Haiku
• Ollama - Local models (Llama 3, Mistral, CodeLlama, etc.)
• DeepSeek - DeepSeek-V2, DeepSeek-Coder
• Google Gemini - Gemini Pro, Flash
• ModelScope - Qwen, Yi models

🔧 Extensible Tools

Following the KISS principle, we provide essential tools with high quality:

• Calculator - Basic math operations (75.6% test coverage)
• HTTP - Make HTTP GET/POST requests (88.9% coverage)
• File Operations - Read, write, list, delete with security controls (76.2% coverage)
• Search - DuckDuckGo web search (92.1% coverage)

Easy to create custom tools - see Tools Guide.

💾 RAG & Knowledge

Build intelligent agents with knowledge bases:

• ChromaDB - Vector database integration
• OpenAI Embeddings - text-embedding-3-small/large support
• Automatic embedding generation and semantic search

See RAG Demo for a complete example.

Design Philosophy

KISS Principle

Keep It Simple, Stupid - Focus on quality over quantity:

• 3 core LLM providers (not 45+)
• Essential tools (not 115+)
• 1 vector database (not 15+)

This focused approach ensures:

• Better code quality
• Easier maintenance
• Production-ready features

Go Advantages

Why build multi-agent systems with Go?

1. Performance - Compiled language, fast execution
2. Concurrency - Native goroutines, no GIL
3. Type Safety - Catch errors at compile time
4. Single Binary - Easy deployment, no runtime dependencies
5. Great Tooling - Built-in testing, profiling, race detection

Use Cases

Agno-Go is perfect for:

• Production AI Applications - Deploy with AgentOS HTTP server
• Multi-Agent Systems - Coordinate multiple AI agents
• High-Performance Workflows - Process thousands of requests
• Local AI Development - Use Ollama for privacy-focused applications
• RAG Applications - Build knowledge-based AI assistants

Quality Metrics

• Test Coverage: 80.8% average across core packages
• Test Cases: 85+ tests with 100% pass rate
• Documentation: Complete guides, API reference, examples
• Production-Ready: Docker, K8s manifests, deployment guides

Next Steps

Ready to get started?

1. Quick Start - Build your first agent in 5 minutes
2. Installation - Detailed setup instructions
3. Core Concepts - Learn about Agent, Team, Workflow

Community

• GitHub: rexleimo/agno-Go
• Issues: Report bugs
• Discussions: Ask questions

License

Agno-Go is released under the MIT License.

Inspired by Agno (Python) framework.