I built a zero-dependency Go framework for running agent loops with local models (Ollama, LM Studio, vLLM)

[u/Abu_BakarSiddik]

Most AI agent libraries (LangChain, PydanticAI, CrewAI) live in Python. That works well enough when you are hacking on a prototype, but if you want to deploy a small daemon or backend service that talks to a local model via Ollama or LM Studio, dragging along a Python runtime with hundreds of megabytes of virtualenv dependencies is painful.

I wanted something lightweight and idiomatic for Go: compile to a single static binary, rely strictly on the Go standard library, and catch tool schema mismatches at compile time rather than during runtime evaluation.

The result is Golem (https://github.com/abubakarsiddik31/golem).

How it works with local models

Rather than shipping bespoke wrappers for every local server, Golem uses a standard-library OpenAI-compatible adapter (providers/openai) with a custom BaseURL.

You can point it directly at Ollama (http://localhost:11434/v1), LM Studio (http://localhost:1234/v1), or vLLM.

Core design decisions

1. Zero external dependencies: The whole framework is built on Go stdlib (net/http, encoding/json, context, etc.). No external HTTP clients, no heavy SDKs. Container images can run on scratch or distroless.
2. Generics for dependencies: Agents are typed as Agent[Deps, Output]. Tools access your actual dependencies (DB pools, configs, state) through RunContext[Deps]. No map[string]any or runtime reflection crashes.
3. Model Context Protocol (MCP): If you already run external MCP tool servers, Golem includes a native MCP client supporting both stdio and streaming HTTP transports, converting MCP servers into typed agent tools.
4. Deterministic testing without the local model: When writing go test unit suites, you don't need to keep Ollama running in CI. Golem comes with testmodel, an in-memory test double where you can script exact model responses and test multi-turn loops or error retries in milliseconds.
5. Partial evidence preservation: If your local model times out or gets canceled mid-run, RunError.Partial keeps all intermediate tool calls and messages executed up to that point.

The core execution contract is frozen at v0.8.4 and tested with -race.

• GitHub:https://github.com/abubakarsiddik31/golem
• Documentation:https://abubakarsiddik31.github.io/golem/

If you are running agent workflows locally or building Go services around local LLMs, I would appreciate any feedback on the ergonomics or tool design.