agent-memory-mcp

A benchmarking MCP server for agent memory — the same facts stored as both vector RAG and a knowledge graph, so you can measure, live, exactly where the graph beats vectors.

The experiment

"Knowledge graphs are the memory layer for agents" is an oft-repeated claim that teams rarely get to test against their own agent's real usage. agent-memory-mcp turns it into a running experiment: it exposes two memory backends — plain vector RAG and a knowledge graph — behind the same Model Context Protocol tools, so the identical question can be answered by vector similarity or by graph traversal and the two compared head-to-head. Memory you can benchmark, not just trust.

Vector search works when the answer sits in one chunk. It fails whenever the answer must be assembled by following relationships — "Who does Dana's manager report to?", "Which project uses a service owned by the Search Team?" Those answers live in no single chunk; a graph answers them by traversal. This tool lets you see exactly where that happens, on your own data.

Related MCP server: RAG Memory MCP

Screenshot

The bundled bench harness ingests a labeled seed set through the real remember pipeline and scores graph-only vs vector-only recall by question category:

                Recall accuracy by category
┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ method ┃ single_hop ┃ multi_hop ┃ aggregation ┃ overall ┃
┡━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━┩
│ vector │       0.83 │      0.00 │        0.20 │    0.35 │
│ graph  │       1.00 │      1.00 │        1.00 │    1.00 │
└────────┴────────────┴───────────┴─────────────┴─────────┘
Wrote results/scorecard.json

Single-hop lookups are roughly a tie — both backends find a fact that sits in one sentence. The graph pulls decisively ahead on multi-hop and aggregation questions, exactly the relationship-following cases vector RAG can't assemble. Run it yourself:

agent-memory-mcp bench

compare in 30 seconds

compare is the headline tool: it answers the same question with both backends, side by side. On a multi-hop question the graph traverses to the answer while vector similarity stops at the closest single chunk:

agent-memory-mcp remember "Dana is managed by Evan. Evan is managed by Farah."
agent-memory-mcp compare "Who does Dana's manager report to?"

{
  "query": "Who does Dana's manager report to?",
  "graph":  { "answer": "Farah",
              "support": ["Dana is managed by Evan.", "Evan is managed by Farah."],
              "path": ["Dana --MANAGED_BY--> Evan", "Evan --MANAGED_BY--> Farah"] },
  "vector": { "answer": "Dana is managed by Evan.", "support": ["Dana is managed by Evan."] },
  "agree": false,
  "note": "Graph traversed 2 hops to reach the answer; vector similarity stopped at the single closest chunk."
}

The graph reaches Farah by following two MANAGED_BY edges; vector RAG returns the single closest sentence and never assembles the two hops.

Add to your MCP client

The server speaks stdio and runs fully offline with no API key on its defaults (deterministic rules extractor + hash embedder). Wire it in once and use it as live memory and call compare mid-conversation.

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "agent-memory": {
      "command": "agent-memory-mcp",
      "args": ["serve"]
    }
  }
}

Claude Code — one command:

claude mcp add agent-memory -- agent-memory-mcp serve

The seven tools the model sees: remember, recall(query, method="auto"), compare, neighbors, entities, forget, stats. Every tool takes plain language — the agent never constructs a triple. remember never throws on unparseable prose (it stores the raw text for vector fallback and says so); recall returns answer: null, method: "none" on a miss rather than erroring.

Quickstart (CLI, no keys)

python3 -m venv .venv && . .venv/bin/activate
pip install -e .

# teach it something — plain language, nothing to format
agent-memory-mcp remember "Dana is on the Search Team, which owns the Index Service. Atlas uses the Index Service."

# ask in plain language; method=auto tries the graph first, falls back to vectors
agent-memory-mcp recall "What does the Search Team own?"
agent-memory-mcp recall "Who does Dana's manager report to?" --method graph --show-path

# head-to-head on any question
agent-memory-mcp compare "Which project uses a service owned by the Search Team?"

# inspect and benchmark
agent-memory-mcp entities --filter service
agent-memory-mcp stats
agent-memory-mcp bench

Defaults: --db ~/.agent-memory-mcp/memory.sqlite (memory survives restarts; :memory: for ephemeral), --extractor rules, --embedder hash, --k 4.

How it works

Every fact is stored both ways in one SQLite file:

• Knowledge graph — nodes + edges. remember splits text into statements, an extractor turns each into entities and typed relations (MANAGED_BY, OWNS, USES, MEMBER_OF, IS_A, LOCATED_IN, RELATED_TO), and those are upserted as nodes and edges. recall resolves the entities in your question, infers the relation(s), and traverses for an answer plus the supporting path — so answers are explainable.

• Vector store — facts + float32 vectors. The raw statement is always embedded too (even when no relation could be extracted), so nothing is ever lost: graph when it can, vector when it can't.

recall(method=...) pins a single backend (graph | vector) for head-to-head benchmarking, or auto (default) tries the graph first and falls back to vectors. Full design: DESIGN.md.

Going further

• Real embeddings — install the st extra for sentence-transformers and pass --embedder st: pip install -e ".[st]". Optional; the default hash embedder needs no download.

• LLM extraction — install the llm extra (pip install -e ".[llm]") and run with --extractor llm to have Anthropic Claude turn messy prose into clean entities/relations. Requires ANTHROPIC_API_KEY; the model id is read only from config/env (AGENT_MEMORY_MODEL, default claude-haiku-4-5). The default rules extractor keeps the server keyless and deterministic.

• Extend the ontology — add a row to the relation vocabulary in agent_memory_mcp/vocab.py (trigger phrases + a fact template) to teach the rules extractor a new relation.

Clean-room note

This is an original, clean-room, generic MCP server authored by Keith Lindsay. It does not reference, reproduce, or depend on any specific employer's (including Aerospike's) source code, proprietary data, internal metrics, benchmarks, product names, schemas, or confidential specifics, and it is not coupled to any particular graph or vector database product. The only thing proprietary about the author's prior work was its integration with a specific product; the knowledge-graph technique itself is general, publicly-known computer science. All datasets here are synthetic and fictional.

License

MIT © 2026 Keith Lindsay. See LICENSE.