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

DimensionBinex MCP ServerOpen-source visual orchestrator for AI agent workflows, 100% localMartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS45 / 100 · D79 / 100 · B
Reliability6 / 2012 / 20
Security and permissions8 / 2018 / 20
Maintenance9 / 2016 / 20
Documentation12 / 2018 / 20
Setup experience10 / 2015 / 20
Best for
  • Developers already using Binex who want to inspect runs through a coding agent
  • Teams needing a local-first, debuggable agent workflow toolchain
  • 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
  • Users wanting a hosted cloud tracing service instead of a local runtime
  • Environments without Python 3.11+ and Binex installed
  • 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 and write the local Binex store (default `.binex` directory with a SQLite database and JSON artifact files)
  • Read locally configured LLM provider API keys (needed to run workflows)
  • Communicate with the MCP client process over stdio
  • 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
  • Exposed runs, evals, and artifacts are written to the local `.binex` directory and may contain sensitive prompts or outputs
  • If cloud LLM providers are configured, prompts and data are sent to those providers
  • Any local process able to invoke the MCP server can read run data
  • 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 Code, CursorClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools30