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

DimensionAgent SwarmA multi-agent orchestration operating system for companies.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS54 / 100 · D79 / 100 · B
Reliability8 / 2012 / 20
Security and permissions9 / 2018 / 20
Maintenance12 / 2016 / 20
Documentation14 / 2018 / 20
Setup experience11 / 2015 / 20
Best for
  • Teams seeking self-hosted multi-agent orchestration
  • Engineering and operations teams that want a lead agent to coordinate specialized workers
  • Organizations needing shared memory, persistent identity, workflows, or human approval gates
  • Users able to manage Docker, API keys, and agent-harness credentials
  • 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 a single MCP tool or simple chat assistant
  • Environments that cannot run Docker or manage agent-harness credentials
  • Teams that do not want tasks to flow among multiple agents, shared memory, or external integrations
  • Users requiring documented compatibility with a ready-made desktop 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
  • Access to the local or remote Agent Swarm MCP endpoint
  • An Agent Swarm API key supplied through an Authorization header with the Bearer prefix
  • The X-Agent-ID of a registered agent
  • Depending on enabled integrations and harnesses, credentials for Slack, code hosting, email, Linear, Jira, or other services
  • 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
  • Worker agents execute tasks in isolated Docker environments but may access the code, files, and services configured for them
  • Shared memory retains learnings across sessions and can broaden the context visible to later tasks
  • Enabled integrations can cause messages, emails, ticket updates, code changes, or pull requests
  • Misconfigured API keys, Bearer headers, or agent IDs can cause connection failures or access to the wrong agent instance
  • Automated workflows and external actions should be reviewed with human approval gates, especially for irreversible operations
  • 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, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools00