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

DimensionAgentic FlowSelf-learning multi-agent orchestration for Claude Code.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS42 / 100 · D79 / 100 · B
Reliability7 / 2012 / 20
Security and permissions5 / 2018 / 20
Maintenance9 / 2016 / 20
Documentation11 / 2018 / 20
Setup experience10 / 2015 / 20
Best for
  • Developers familiar with Claude Code, the Claude Agent SDK, or multi-agent workflows
  • Teams needing self-learning routing, swarm coordination, and project-level code analysis
  • Users building agent applications with the TypeScript API
  • 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 who only need one simple MCP tool
  • Environments that do not permit project-level command execution or codebase access
  • Production environments requiring a verified stable release rather than an alpha version
  • 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
  • Access to project files and file structure for initialization, pretraining, code analysis, and edit-related hooks
  • Execution of npm, npx, build, test, and other user-triggered shell commands
  • Git history access during pretraining, plus repository or pull-request access when GitHub tools are used
  • Permission for the MCP client to start the local npx-configured service
  • 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
  • The README calls the project production-ready, but installation examples and the roadmap still use alpha releases; stability should be verified
  • Self-learning memory may store project-related task inputs, outputs, scores, critiques, and token data
  • Agents and background workers may perform broad code analysis, command execution, or automation
  • Performance, accuracy, and cost-saving figures are project-reported claims and are not independently verified by the supplied material
  • Repository metadata lists the license as unknown, while the README claims MIT; verify the repository LICENSE before use
  • 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 CodeClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools110