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Trpc Agent Go

Trpc Agent Go

A Go framework for building production agent systems with graph workflows, tools, memory, A2A, AG-UI, MCP, evaluation, a

Research Agents Open Source

Last Updated

2026-06-15

GitHub Stars

1,356

Last Updated

2026-07

About Trpc Agent Go

A Go framework for building production agent systems with graph workflows, tools, memory, A2A, AG-UI, MCP, evaluation, and observability. tRPC-Agent-Go is a Go framework for building production agent systems. It provides LLM agents, graph workflows, tool calling, session and memory state, knowledge retrieval, agent self-evolution, evaluation, and OpenTelemetry observability in one Go-native stack. Use it when you want agent applications that fit Go services: concurrent,

Features

Go-Native Agent Runtime
GraphAgent
Multi-Agent Collaboration
Rich Tool Ecosystem
Persistent State
Agent Skills

Detailed Ratings

Ease of Use
6.2
Value for Money
6.8
Features
6.4
Support
6.0
Performance
6.6
Overall Rating
6.5 /10

Pros & Cons

Pros

  • Active community with 1.4k GitHub stars
  • Go-Native Agent Runtime
  • GraphAgent

Use Cases

Literature reviewInformation gatheringSummarizationCitation

Who Is It For?

Teams and individuals looking for research and information synthesis who value open-source flexibility and control.

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Frequently Asked Questions

What is Trpc Agent Go?

A Go framework for building production agent systems with graph workflows, tools, memory, A2A, AG-UI, MCP, evaluation, and observability.

How much does Trpc Agent Go cost?

Trpc Agent Go is open-source and free to self-host. See https://github.com/trpc-group/trpc-agent-go for installation instructions.

Is Trpc Agent Go open source?

Yes — Trpc Agent Go is open source with 1.4k stars. The source code is on GitHub at https://github.com/trpc-group/trpc-agent-go.

Who should use Trpc Agent Go?

Teams and individuals looking for research and information synthesis who value open-source flexibility and control.

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Links & Resources

AR Reviewed by AgentRadar · Reviewed on · How we rate

Profiles are AI-assisted from public information — not independently hands-on tested.