Go for AI agents: a field report

Dev.to / 4/13/2026

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Key Points

The author argues that while most agent frameworks (LangChain, CrewAI, AutoGen, Semantic Kernel, LlamaIndex) are Python-first, they built multiple agent-related projects in Go despite the ecosystem expectation.
Their field report highlights that Go can be especially effective for the “plumbing” layer of AI agents—networking, concurrency, serialization, process management, and orchestration—while Python remains strong for the model/LLM side.
Across five Go projects (governance proxy, shared memory MCP server, Ollama MCP bridge, autonomous web research agent, and a dashboard backend), they observe a striking pattern: minimal external dependencies, with one project relying on only YAML parsing plus the standard library.
They present governance, approval, tracing/telemetry, and rate limiting as core infrastructure concerns for agent tool usage, implemented in Go with relatively small dependency overhead.
The piece is positioned as a “for better or worse” account of choosing Go for an agent stack, meant to address the recurring question about why they didn’t use Python.

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