Ogham Mcp
@ogham-mcp
Persistent shared memory for AI agents. Hybrid search (pgvector + tsvector), knowledge graph, cognitive scoring, and 16-language temporal extraction. 97.2% Recall@10 on LongMemEval with one PostgreSQL query. Works across Claude Code, Cursor, Codex, OpenClaw, and any MCP client
Ogham (pronounced "OH-um") -- persistent, searchable shared memory for AI coding agents. Works across clients.
Contents
- Retrieval quality -- 97.2% R@10 on LongMemEval
- The problem
- Quick start
- Installation methods -- Claude Code, OpenCode, Docker, source
- SSE transport -- multi-agent setup
- CLI -- command-line interface
- Configuration -- env vars, embedding providers, temporal search
- MCP tools -- memory, search, graph, profiles, import/export
- Skills -- ogham-research, ogham-recall, ogham-maintain
- Scoring and condensing
- Database setup -- Supabase, Neon, vanilla Postgres
- Architecture
Retrieval quality
97.2% Recall@10 on LongMemEval (500 questions, ICLR 2025). No LLM in the search pipeline -- one PostgreSQL query, no neural rerankers, no knowledge graph.
End-to-end QA accuracy on LongMemEval (retrieval + LLM reads and answers):
| System | Accuracy | Architecture |
|---|---|---|
| OMEGA | 95.4% | Classification + extraction pipeline |
| Observational Memory (Mastra) | 94.9% | Observation extraction + GPT-5-mini |
| Hindsight (Vectorize) | 91.4% | 4 memory types + Gemini-3 |
| Zep (Graphiti) | 71.2% | Temporal knowledge graph + GPT-4o |
| Mem0 | 49.0% | RAG-based |
Retrieval only (R@10 -- no LLM in the search loop):
| System | R@10 | Architecture |
|---|---|---|
| Ogham | 97.2% | 1 SQL query (pgvector + tsvector CCF hybrid search) |
| LongMemEval paper baseline | 78.4% | Session decomposition + fact-augmented keys |
Other retrieval systems that report similar R@10 numbers typically use cross-encoder reranking, NLI verification, knowledge graph enrichment, and LLM-as-a-judge pipelines. Ogham reaches 97.2% with one Postgres query.
These tables measure different things. QA accuracy tests whether the full system (retrieval + LLM) produces the correct answer. R@10 tests whether retrieval alone finds the right memories. Ogham is a retrieval engine -- it finds the memories, your LLM reads them.
| Category | R@10 | Questions |
|---|---|---|
| single-session-assistant | 100% | 56 |
| knowledge-update | 100% | 78 |
| single-session-user | 98.6% | 70 |
| multi-session | 97.3% | 133 |
| single-session-preference | 96.7% | 30 |
| temporal-reasoning | 93.5% | 133 |
Full breakdown: ogham-mcp.dev/features
The problem
AI coding agents forget everything between sessions. Switch from Claude Code to Cursor to Kiro to OpenCode and context is lost. Decisions, gotchas, architectural patterns -- gone. You end up repeating yourself, re-explaining your codebase, re-debugging the same issues.
Ogham gives your agents a shared memory that persists across sessions and clients.
