Beluga AI — Production-Ready AI Agent Framework in Go

The Problem

AI agent frameworks were built for Python. But production backends — the systems that need to be fast, reliable, and observable — run Go. Teams building AI-powered applications in Go were stuck wrapping Python libraries, using incomplete SDKs, or building from scratch. There was no production-grade, Go-native framework for the full AI agent development lifecycle.

What I Built

Beluga AI is a comprehensive open-source framework (MIT license) that gives Go developers every building block they need to create sophisticated AI agent systems:

• Multi-Provider LLM Abstraction — A unified interface across 22+ providers including OpenAI, Anthropic, Gemini, Ollama, and AWS Bedrock. Switch providers without rewriting application logic.

• Agent Runtime — Multiple reasoning strategies including ReAct, Tree-of-Thought, and Language Agent Tree Search (LATS), with built-in tool calling and memory management.

• Hybrid RAG Pipeline — Combines vector search, BM25 keyword matching, and graph traversal for retrieval-augmented generation that goes beyond simple embedding lookups.

• Real-Time Voice Processing — Sub-800ms latency voice pipelines for conversational AI applications.

• Three-Stage Safety Guardrails — Prompt injection detection, PII filtering, and content moderation built into the request lifecycle.

• MCP Protocol Support — Full Model Context Protocol server and client implementation for agent-to-agent communication.

• Observability — OpenTelemetry integration at the foundation layer, so every LLM call, retrieval operation, and agent decision is traceable in production.

Architecture

The framework is organized into four layers:

1. Foundation — Core primitives, configuration, and observability instrumentation
2. Capability — LLM abstraction, agent runtime, RAG pipelines, and voice processing
3. Infrastructure — Guardrails, caching, authentication, and durable workflow execution
4. Protocol — MCP server/client and inter-agent communication

This layered design means teams can adopt what they need — use just the LLM client, or build full multi-agent systems with guardrails and observability.

Technical Scope

• 157 packages spanning the full agent development stack
• 108 integrations across vector stores, embeddings, voice providers, and infrastructure
• 2,885 tests maintaining production-grade reliability
• Streaming-first design using Go’s iter.Seq2 for memory-efficient processing of LLM responses

Why It Matters

Beluga AI demonstrates end-to-end technical ownership of a large-scale, production-quality Go codebase — from low-level protocol implementation to high-level agent orchestration. It reflects the same engineering principles I bring to client work: clean architecture, thorough testing, and systems designed to run reliably at scale.

Visit beluga-ai.org or explore the source on GitHub.