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Beluga AI Framework

A production-ready Go framework for building sophisticated AI and agentic applications.
Enterprise-grade • Extensible • Observable

What is Beluga AI Framework?

Beluga AI Framework is a comprehensive, production-ready framework written in Go, designed for building sophisticated AI and agentic applications. Inspired by frameworks like LangChain and CrewAI, Beluga AI provides a robust set of tools and abstractions to streamline the development of applications leveraging Large Language Models (LLMs).

The framework offers a Go-native, performant, and flexible alternative for creating complex AI workflows. Built with extensibility at its core, Beluga AI empowers Go developers to build next-generation AI applications with enterprise-grade observability, comprehensive testing, and production-ready patterns.

🚀 Production Ready: Beluga AI has completed comprehensive standardization and is now enterprise-grade with consistent patterns, extensive testing, and production-ready observability across all 14 packages. The framework is ready for large-scale deployment and development teams.

Key Features

Everything you need to build production-ready AI applications in Go

🚀 Extensible LLM Integration

🚀 Extensible LLM Integration

Seamlessly connect to various LLM providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama, Cohere) with a unified interface. Switch providers without changing your code using our global registry pattern.

🤖 Agent Framework

🤖 Agent Framework

Build autonomous agents capable of reasoning, planning, and executing tasks. Includes ReAct agents, tool integration, and memory management for sophisticated AI applications with comprehensive testing infrastructure.

📊 Production Ready

📊 Production Ready

Enterprise-grade observability with OpenTelemetry, comprehensive testing, structured logging, metrics, and distributed tracing. Built for large-scale deployment with 100% package standardization.

🔍 RAG Pipeline

🔍 RAG Pipeline

Implement Retrieval-Augmented Generation with swappable components for data loading, splitting, embedding, and retrieval. Support for multiple vector stores including pgvector, Pinecone, and Weaviate with global factory patterns.

⚙️ Flexible Orchestration

⚙️ Flexible Orchestration

Define and manage complex workflows with a flexible engine. Event-driven architecture with worker pools, retry mechanisms, and circuit breakers for reliable execution with OTEL metrics.

🎤 Voice Agents

🎤 Voice Agents

Build natural voice interactions with speech-to-text, text-to-speech, voice activity detection, turn detection, and complete session management. Support for multiple voice providers with streaming capabilities.