trpc-group/trpc-agent-go

Health v2 · maintenance only

Adoption not part of the score

1836 stars · 321 forks observed · 2026-10-01

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

tRPC-Agent-Go is a Go-native framework for building production LLM agent systems, offering agents, graph workflows (LangGraph-equivalent), tool calling, session/memory state, RAG knowledge retrieval, and evaluation. It integrates with A2A, AG-UI, and MCP protocols and includes OpenTelemetry-based observability.

Use cases

• build multi-agent systems in Go
• orchestrate LLM workflows with graph routing
• add RAG knowledge retrieval to a Go service
• connect agents via MCP tools and A2A protocol
• evaluate and benchmark LLM agents
• add observability to agent applications
• implement agent self-evolution with reusable skills

When to choose

• you need a production agent framework in Go rather than Python
• your stack is Go microservices and you want concurrent, deployable agents
• you need graph-based multi-agent workflows with type safety
• you want built-in evaluation, memory, and OpenTelemetry tracing

When to avoid

• your team is Python-based and prefers AutoGen, CrewAI, or LangChain ecosystems
• you only need a simple single-prompt LLM call without agent orchestration
• you need a mature ecosystem with extensive third-party integrations
• you require non-Go language support

Facets

framework · maturity active

agent-framework llm-inference rag mcp workflow-automation tracing chatbot large-language-models developer-tools backend go cross-platform self-hosted multi-agent graph-workflows a2a-protocol ag-ui opentelemetry tool-calling session-memory agent-evaluation self-evolution tencent-trpc evaluation ai-agents retrieval-augmented-generation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="trpc-group/trpc-agent-go")