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Compare scores, permissions, risks, and fit in one decision-focused table.

DimensionLmOffload routine tasks from Claude Code to local or cloud LLMs to save token spend.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS52 / 100 · D79 / 100 · B
Reliability6 / 2012 / 20
Security and permissions9 / 2018 / 20
Maintenance9 / 2016 / 20
Documentation15 / 2018 / 20
Setup experience13 / 2015 / 20
Best for
  • Developers using Claude Code for large refactors who want to reduce token bills.
  • Users with a local GPU server who want to leverage local models for repetitive tasks.
  • Those needing to offload bounded, well-defined tasks from Claude to a cheaper model.
  • Developers familiar with MCP and OpenAI-compatible APIs.
  • Engineering teams scaling AI coding agents while controlling spend
  • Platform teams that need auditable, rollback-aware agent execution records
  • Organizations using multiple agents (Claude, Codex, Gemini) under one governed flow
Not for
  • Tasks requiring strong reasoning or tool access should stay on Claude.
  • Latency-sensitive interactive scenarios, as local inference can be 3-30x slower than frontier models.
  • Users who do not need an MCP server but want to call LLM APIs directly.
  • Users not using Claude Code or Claude Desktop as an MCP client.
  • Developers who just want a simple code-completion helper without run governance
  • Teams unwilling to accept local JSONL run records with HMAC signing
  • Environments without a supported adapter (Claude/Codex/Gemini CLIs, direct provider, or verifier-only)
Required permissions
  • Read local files on the system (when using the code_task_files tool).
  • Create and read a local SQLite database file (~/.houtini-lm/model-cache.db).
  • Make network requests to the LLM endpoint (local or cloud).
  • Run Node.js processes.
  • Read/write access to the configured repo root (writable scope restrictable via --allow-path/--deny-path)
  • Ability to invoke the chosen agent CLI (Claude/Codex/Gemini, etc.) and its underlying model account
  • Local disk writes for run records, receipts, and share outputs (share/run-receipt., etc.)
  • Execution of user-configured verifier commands (e.g. npm test)
Risks and side effects
  • Sending code or data to a local or cloud LLM may lead to information leakage, especially with cloud APIs.
  • Running untrusted code on local models may introduce security vulnerabilities.
  • Over-reliance on local models may degrade output quality because local models can be less capable.
  • The MCP server may inadvertently expose local file system paths, so use code_task_files with caution.
  • Token savings may be overestimated, as local inference can take more time or resources.
  • Verifier commands are executed; poorly chosen verifier commands carry security risk despite preflight policy checks
  • Cost and token figures may be estimates (labeled with provenance) and should not be treated as settled accounting
  • Receipt integrity must be verified before use as trustworthy evidence; missing rollback evidence is flagged as EVIDENCE_BOUNDARY
  • Policy defaults come from martin.config.yaml and can be overridden by CLI flags; misconfiguration can loosen safety boundaries
Supported clientsClaude Desktop, Claude CodeClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools80