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

DimensionLat MCP ServerA knowledge graph for your codebase, written in markdownGitHub MCP ServerGitHub's official MCP server for managing repos, issues, PRs, and workflows via natural language
FMRS62 / 100 · C76 / 100 · B
Reliability10 / 2013 / 20
Security and permissions12 / 2014 / 20
Maintenance14 / 2018 / 20
Documentation15 / 2017 / 20
Setup experience11 / 2014 / 20
Best for
  • Teams and individual developers who want a maintainable knowledge graph for their codebase
  • Users of coding agents who want them to work from stable, searchable context
  • Projects that treat markdown as the documentation medium and want docs and source to stay in sync
  • Environments that need offline semantic search (the default local WASM embedding model)
  • Teams that want an AI assistant to directly operate on GitHub repos and collaboration workflows
  • Users already in the GitHub Copilot ecosystem who want a zero-deployment remote option
Not for
  • Small projects that only need a single flat AGENTS.md file (limited benefit from a knowledge graph)
  • Teams that do not use coding agents and do not plan to maintain markdown domain docs
  • Users expecting an out-of-the-box hosted service or a cloud multi-user documentation platform
  • Projects unwilling to add // @lat: annotations to source code
  • Scenarios where you don't want the assistant to have write access to repos (enable only read-only toolsets)
  • Environments with strict network isolation for private repos that can't reach the official remote endpoint
Required permissions
  • Read/write access to the project repository (lat init creates the lat.md/ directory and configures coding agents' hooks and instructions)
  • Read access to API keys when hosted embeddings are configured: the LAT_LLM_KEY environment variable, a file named by LAT_LLM_KEY_FILE, or a shell command named by LAT_LLM_KEY_HELPER
  • Write access to local index and config (lat reindex and lat config involve local caches and a config file)
  • A personal access token (PAT) or OAuth App token; effective scope depends on the token's own permissions
  • Enabling toolsets like actions/issues/pull_requests grants write access — request tokens on a least-privilege basis
Risks and side effects
  • If hosted embeddings are used with an API key (OpenAI or Vercel AI Gateway), relevant text may be sent to an external service; the default local model makes no network requests
  • LAT_LLM_KEY_HELPER executes a shell command to print the key; if that variable is set from an untrusted source it poses a command execution risk
  • A key file with weak permissions could be read by other local processes
  • A knowledge graph that is not kept up to date with lat check can drift from the actual code and mislead agents
  • lat init modifies coding agents' hooks and instructions, which may affect existing workflows
  • Write toolsets (creating/merging PRs, triggering workflows) can cause accidental changes if the token is overscoped — try a read-only toolset first
  • In hosted mode, credentials travel via the Authorization header — make sure the client-to-api.githubcopilot.com connection is trusted
Supported clientsClaude Desktop, Claude Code, VS Code, Cursor, Windsurf, JetBrains IDEs, Zed, Amp
Tools015