Skip to main content
Glama

Related Servers

Alternatives to local-llm-worker

No user-submitted related servers found.

    Related Servers

    • A
      license
      B
      quality
      B
      maintenance
      Enables Claude Code to offload mechanical development work—tests, git operations, pull requests, summaries, project search, code review, and browser or screen control—to local Ollama models with optional free cloud fallbacks. It returns concise results to reduce token usage while keeping execution on the user's own machine.
      37
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables Claude to offload bulk reading, first-draft writing, and mechanical edits to a local or OpenAI-compatible model, with code-enforced verification, staged patches, project checks, and token budgets to save tokens and cost.
      8
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables Claude to delegate coding tasks to local Ollama models, reducing API token usage by up to 98.75% while leveraging local compute resources. Supports code generation, review, refactoring, and file analysis with Claude providing oversight and quality assurance.
      518 npm
      26
      AGPL 3.0
    • A
      license
      A
      quality
      B
      maintenance
      Enables Claude Code to delegate coding tasks—investigation, review, long-running background jobs, and optional file edits—to a locally installed OpenAI Codex CLI, with the model and reasoning effort chosen per task and read-only execution by default. Follow-up requests reuse Codex's existing context, and write-enabled delegations can be confined to a managed git worktree so the user's own checkout stays untouched.
      9
      82 npm
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables Claude Code and other MCP clients to offload bounded tasks to a local or cloud LLM (LM Studio, Ollama, vLLM, LiteLLM, OpenRouter, DeepSeek, Groq, Cerebras, OpenAI) so files are read and drafted outside the orchestrator's context window, cutting token costs. It also provides a cheap second model for code review, test stubs, commit messages, format conversion, and embeddings.
      MIT