Ogham MCP

What it is

A memory server backed by your own PostgreSQL with pgvector. Retrieval fuses vector similarity with full-text search in a single query, and a relationship graph over the memories lets a search follow edges rather than only rank rows. Memories are grouped into profiles, so work and personal context stay apart.

What you get

• Store what matters — store_memory runs the enrichment pipeline, store_decision records a decision with its rationale, alternatives and reasoning trace, linked to the memories that support it
• Content is validated before it lands: too short, too long, or recognisably a git diff or shell dump and it is refused with a suggestion to store a summary instead
• hybrid_search — semantic and keyword together, filterable by tags and source, and able to follow relationship edges when you set a graph depth
• explore_knowledge and find_related — seed a search then traverse several hops out, or trace impact from one specific memory
• list_recent for the chronological view
• Confidence rather than deletion — reinforce_memory raises a memory's score, contradict_memory lowers it, so being wrong is recorded rather than erased
• update_memory, which re-embeds when the content changes, and delete_memory
• Profiles — switch_profile, current_profile, list_profiles with their memory counts, and set_profile_ttl for auto-expiry
• Maintenance that keeps it from rotting — compress_old_memories in tiers while preserving the original, link_unlinked to backfill relationships, cleanup_expired, and re_embed_all after switching embedding provider
• export_profile as JSON or markdown and import_memories_tool with similarity-based deduplication
• health_check and get_cache_stats for the database, embedding provider and cache
• An append-only audit log of every store, search, update and delete, in the same database

Requirements

A PostgreSQL database with pgvector — a free Supabase project, a Neon database, or your own Postgres — and the schema migration run against it. Set SUPABASE_URL and SUPABASE_KEY for Supabase, or DATABASE_URL for a direct connection, in which case install the postgres extra so the driver is present. Run it with uvx --from ogham-mcp ogham-serve. Stdio by default; OGHAM_TRANSPORT, OGHAM_HOST and OGHAM_PORT switch it to streamable HTTP with a health endpoint. An embedding provider is needed for semantic search. The data is in a database you control, and nothing leaves it except what your client asks for.

Setup effort

One command plus a key — uvx --from ogham-mcp ogham init, then supply credentials

Tools you get

38 tools named in the project's documentation.

Requires

• A database: a Supabase project (SUPABASE_URL and SUPABASE_KEY) or Postgres with pgvector, such as Neon (DATABASE_BACKEND=postgres and DATABASE_URL)
• An embedding provider: local Ollama by default, or the API key for the provider you pick (OPENAI_API_KEY, MISTRAL_API_KEY, VOYAGE_API_KEY or GEMINI_API_KEY)
• Python 3.13+ with uv (uvx), or Docker