← Back to directory
COMPARE UP TO 4 SERVERS

Compare MCP servers

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

DimensionPiia EngramLocal-first AI work identity you can see, edit, and override — portable across your MCP coding tools.MartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS59 / 100 · C79 / 100 · B
Reliability10 / 2012 / 20
Security and permissions12 / 2018 / 20
Maintenance12 / 2016 / 20
Documentation11 / 2018 / 20
Setup experience14 / 2015 / 20
Best for
  • Developers who use several MCP-compatible AI coding tools and are tired of re-explaining themselves.
  • Users who want memory and identity data to stay on their own machine, inspectable and editable.
  • Individuals or small teams long-term-accumulating quality standards, architecture decisions, and technical lessons.
  • Vibe-coding workflows that need continuity across sessions, tools, and projects.
  • Users who accept human review gates in exchange for local data sovereignty.
  • 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
  • Multi-person collaboration, team-shared memory, or centrally managed server deployments (this is a personal identity layer).
  • Anyone needing vendor SLAs, enterprise support, or a commercial license (AGPL-3.0, no separate commercial license).
  • Storing passwords, API keys, or customer PII — the project explicitly advises against it.
  • Users whose primary need is agent task memory/session history and who have already chosen Mem0/Zep/Letta-style tools.
  • Scenarios demanding hardened access control over plaintext local files: restricted_fields and the governance layer are not encryption or a true ACL.
  • 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 JSON/Markdown files under the local Engram data folder (default ~/.engram/).
  • Write AI-client MCP config and instruction files during engram setup, only after your confirmation (backed up first; decline leaves them untouched).
  • Write a local-only audit log at ~/.engram/audit.log, enabled by default (opt out with ENGRAM_AUDIT=0).
  • Optional: read_web_content fetches a URL you provide, via a local sidecar or the built-in reader.
  • Optional: ENGRAM_TOOLS=all exposes all 59 tools to the model, including owner/admin export, import, and trust-management interfaces.
  • Optional: remote SSE mode requires ENGRAM_AUTH_TOKEN and should sit behind a TLS reverse proxy.
  • Optional: telemetry is off by default; enabling remote telemetry or feedback is separate explicit opt-in and sends counts only.
  • 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
  • Data is plaintext JSON/Markdown by default, so any process that can read ~/.engram/ can read your data; field-level encryption is optional, not the default.
  • The project explicitly warns against storing passwords, API keys, or client PII in Engram.
  • restricted_fields only reduces what is emitted in cold-start context — it is not encryption or a real ACL.
  • Caller identity comes from MCP environment variables rather than cryptographic authentication, so governance is a practical local policy boundary, not a hardened sandbox; the MCP spec does not pass tool identity.
  • Export outputs (export_engram, get_identity_card, export_knowledge_report) contain full or broad content and should be treated as sensitive files.
  • import_engram mutates the local store: always preview with dry_run=True first; applying requires explicit --apply --yes.
  • Concurrent writes rely on a file lock plus atomic replace; network-filesystem edge cases are not guaranteed.
  • Playbook auto-extraction redacts sensitive info before staging (keys, tokens, absolute paths, emails), but drafts remain untrusted until you confirm them.
  • Remote deployment without proper HTTPS and token handling can expose identity 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, Codex, Cursor, Claude Desktop, Hermes, OpenClaw, Windsurf, GitHub Copilot, Cline, Roo Code, Amazon Q, Augment, Zed, Trae, Tencent CodeBuddy, ChatGPT / Gemini / KimiClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools580