Memnest Memory MCP Server

Persistent graph memory for AI agents using LadybugDB — an embedded graph database with native vector search and full-text search.

Give your AI agent memory that persists across sessions, deduplicates automatically, and models knowledge as a graph with typed relationships.

Why Memnest?

• Graph memory — memories linked via Topic nodes and relationships (RELATED_TO, SUPERSEDES, EXPLAINS) with Cypher queries
• Three-layer auto-dedup — exact hash + semantic similarity + LLM-driven consolidation
• Workspace namespacing — memories scoped per project; global_search opt-out
• HNSW vector search — fast cosine similarity over FastEmbed embeddings
• Topic auto-linking — tags become graph nodes, enabling traversal queries
• Embedded — no Docker, no server process, single database directory
• Zero config — sensible defaults, just install and run
• Importance & access tracking — memories ranked by relevance and usage

Benchmarks

Memnest scores 82.9% on the LOCOMO benchmark — the standard evaluation for long-term conversational memory (ACL 2024).

Evaluated with Claude Sonnet 4.5 as the answer agent and Haiku 4.5 as the judge, using the industry-standard LLM-as-a-Judge methodology. All 5 LOCOMO categories included.

Re-measured on 0.24.1 (same protocol, 199 questions): 84.4% and 85.4% across two runs of the default legacy fusion. Note the run-to-run noise — two runs of the identical configuration flipped 16 individual questions and differed by 1.0 point, so treat sub-2-point differences on this benchmark as inconclusive.

Fusion modes

MEMORY_FUSION=rrf (reciprocal rank fusion) exists because summing raw cosine with max-normalized BM25 adds incomparable scales. It fixes three measured scoring artifacts — see the 0.22.0 notes — but it did not improve answers, so legacy remains the default:

The retrieval-only numbers are deterministic and show the real trade: rrf surfaces considerably more gold evidence inside the top 20 (+8.1 points recall) but ranks it slightly lower (−0.008 MRR). Because the answer agent already reads the top 20, the extra recall didn't convert into better answers, and an independent A/B on a 38-fact corpus lost two answers outright to top-rank precision.

Two further costs, both measured:

• Scores stop discriminating. rrf inflates absolute scores (top-1 rises 0.70 → 0.93) and compresses their spread to ~0.01 across the top 4, versus ~0.17 under legacy, because rank 1 contributes 1.0 per channel however weak the match is. Exact ties between adjacent results are normal. In practice you cannot threshold on an rrf score, and ordering inside the band is decided by the tiebreak rather than by relevance.
• No absolute quality signal. Neither mode separates answerable from unanswerable questions by score, so this isn't a lost refusal signal — but an rrf score carries no information about how good the match actually is.

Use rrf when you want maximum recall in a window you will read entirely and stability under corpus edits. Keep legacy when you want scores that mean something, which is why it is the default.

Architecture advantages

• Zero LLM calls in the server — intelligence lives in the agent, not the memory layer
• Local embeddings — no API key needed (bge-small-en-v1.5, 384-dim)
• Single embedded database — no Docker, no PostgreSQL, no separate vector DB
• Hybrid search — Vector (HNSW) + Full-text (BM25) + Graph (PageRank + Louvain communities)
• Minimal retrieval surface — the benchmark agent above scored 82.9% using only memory_search, memory_get and a calculator for date arithmetic. Retrieval quality comes from the server, not from agent-side orchestration.

Quick Start

# Run directly with uvx (no install needed)
uvx memnest-mcp

Or install and run:

pip install memnest-mcp
memnest-mcp

Configure a project (Kiro)

From your project root, one command writes the workspace-level MCP config (with the memory scope pinned to the project) plus the recall/persist/dream agent hooks:

memnest-mcp config kiro            # configure the current directory
memnest-mcp config kiro --check    # verify only
memnest-mcp config kiro --no-hooks # MCP server config only

This writes .kiro/settings/mcp.json (server + pinned workspace), .kiro/hooks/memnest-recall.json and memnest-persist.json (automatic recall and persistence), and .kiro/steering/memnest-dream.md — a manual steering file you invoke with /memnest-dream to consolidate memory.

The config is always workspace-level (<project>/.kiro/), so each project gets its own correctly-scoped memory database at <project>/.memnest/. Reconnect MCP servers in Kiro afterwards. The Kiro Power (below) remains optional on top for keyword activation and skills.

MCP Configuration

Add to your MCP client config (Kiro, Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "memnest": {
      "command": "uvx",
      "args": ["memnest-mcp@latest"],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    }
  }
}

That's it — zero config required. All settings have sensible defaults.

Tools

Graph Data Model

(:Memory)  — content, embedding, category, tags, importance, access_count, timestamps
(:Topic)   — auto-created from tags

(:Memory)-[:ABOUT]->(:Topic)          # memory is about a topic
(:Memory)-[:RELATED_TO]->(:Memory)    # memories are related
(:Memory)-[:SUPERSEDES]->(:Memory)    # newer memory replaces older

Example: Store and Search

# Store a memory (via MCP tool call)
memory_store(
    content="User prefers Python over Node.js for backend tools",
    category="preference",
    tags=["python", "nodejs", "backend"],
    importance=4
)

# Search memories
memory_search(query="what language does the user prefer")

# Traverse the graph
memory_query(
    cypher_query="MATCH (m:Memory)-[:ABOUT]->(t:Topic {name: 'python'}) RETURN m.content"
)

Example: Graph Relationships

# Link related memories
memory_relate(from_id=5, to_id=3, relationship="RELATED_TO")

# Mark a decision as superseded
memory_relate(from_id=8, to_id=2, relationship="SUPERSEDES")

# Find all memories about a topic
memory_query(
    cypher_query="MATCH (m:Memory)-[:ABOUT]->(t:Topic) RETURN t.name, COUNT(m) ORDER BY COUNT(m) DESC"
)

Three-Layer Deduplication

Every memory_store call runs through three dedup layers:

1. Exact hash — SHA256 of normalized content. Identical content is rejected, importance bumped.
2. Semantic similarity — If cosine similarity > 0.92 with an existing memory, merges into it (keeps longer content, merges tags, bumps importance).
3. Consolidation — Periodic via memory_dream. Auto-prunes stale low-importance memories, auto-merges trivial duplicates (similarity ≥ 0.95), surfaces clusters for LLM-driven review.

Categories

Configuration

All settings are optional — defaults work out of the box.

In-Memory Mode (Testing)

"env": { "MEMORY_DB_PATH": ":memory:" }

All data is ephemeral — lost on restart. Useful for testing.

Kiro Power

This repo includes a ready-to-use Kiro Power in the power/memnest/ directory, packaged in the Agent Plugins v1.0.0 format with:

• Plugin manifest with activation keywords (power/memnest/plugin.json)
• Pre-configured MCP server (power/memnest/mcp.json)
• Two Kiro agent hooks for automatic recall and persistence (power/memnest/dev.kiro/hooks/, v1 hook schema — IDE/CLI only; on Kiro Web the agent follows the same workflow from the getting-started skill)
  • memnest-recall (UserPromptSubmit) — searches memory before responding to each prompt
  • memnest-persist (Stop) — stores important info when the agent finishes
  • consolidation runs on demand via memory_dream (see the getting-started skill)
• Agent Skills with the setup guide and Cypher query examples (power/memnest/skills/)

Install in Kiro: Add Custom Power → https://github.com/arunkumars-mf/memnest-mcp/tree/main/power/memnest

Architecture

AI Agent (Kiro, Claude, etc.)
    │
    ├─ memory_store ──→ embed content → dedup check → insert node → link topics
    ├─ memory_search ─→ embed query → HNSW vector search → tag boost → rank
    ├─ memory_query ──→ execute Cypher → return graph results
    │
    └─ LadybugDB (embedded, single directory)
        ├─ Memory nodes (content + FLOAT[384] embeddings)
        ├─ Topic nodes (auto-linked from tags)
        ├─ HNSW vector index (cosine similarity)
        └─ Graph relationships (ABOUT, RELATED_TO, SUPERSEDES, EXPLAINS)

Requirements

• Python 3.10+
• Dependencies installed automatically: real-ladybug, fastembed, mcp
• ~130MB disk for the embedding model (downloaded on first run)

TOON Format (Optional)

Memnest supports TOON (Token-Oriented Object Notation) as a response format, reducing token usage by 30–60% compared to JSON. This is useful when memory results are fed back into LLM context.

TOON is optional — the server falls back to compact JSON automatically if the package isn't installed. To enable it:

pip install "memnest-mcp[toon]"

Or with uvx (requires the --prerelease=allow flag since toon-format is currently in beta):

uvx --prerelease=allow --with "toon-format==0.9.0b1" memnest-mcp@latest

To switch formats at runtime, set the environment variable:

MEMORY_RESPONSE_FORMAT=toon   # compact, token-efficient (default when installed)
MEMORY_RESPONSE_FORMAT=json   # standard JSON (default when toon is not installed)

The official Python implementation of TOON is toon-format/toon-python, currently at v0.9.0-beta.1. Once it reaches a stable 1.0 release, the --prerelease=allow flag will no longer be necessary.

Contributing

Issues and PRs welcome. See LICENSE for terms.

License

MIT

Changelog

0.27.0

• Result ordering no longer depends on the order memories were stored. Two fixes to the same defect class: the rrf rank transform broke channel-value ties by memory id (and ids encode insertion order, so identical BM25 scores produced arbitrary ranks that propagated into different fused scores), and the final sort broke score ties by dict order. Channel ranks now use competition ranking — equal values get equal rank — and final ties break on importance, then recency, then id. A 4-memory fixture that reordered its own results purely by store order now doesn't.
• This was costing rrf measurable quality: gold-evidence recall@20 rises 64.3% → 66.3% and recall@5 45.4% → 46.9% (now equal to legacy). legacy is unaffected — it does no rank transform, and anchors are bit-identical.
• Documents the rrf score-compression cost: spread across the top 4 is ~0.01 versus ~0.17 under legacy, so rrf scores cannot be thresholded.

0.26.2

• Verifies a real restore into a separate on-disk database, not just an in-memory one: embeddings recomputed, index fully reachable, restored memories findable, supersession still resolving to current.

0.26.0

Closes two silent coverage losses that appeared at ordinary corpus sizes, not extreme ones.

• Index-health coverage no longer lapses above the candidate pool. The per-query census compared ranked vector hits against the corpus, which only works while the pool (100) covers it — so the detector for the worst bug class protected a shrinking slice as a workspace grew (2% at 5,000 memories). Above the pool it now runs a dedicated id-only probe at k=corpus: measured 19.7 ms against a 191.8 ms search, ~10% overhead, 100% coverage at every size. explain_meta.census_mode reports which path ran.
• Dream's scan cap is a rotating window, not a horizon. It always examined the newest MEMORY_CONSOLIDATE_SCAN by updated_at, so once a workspace passed the cap everything older was never considered for merge or prune again. The window now advances each run and wraps, covering any corpus over successive runs at unchanged per-run cost. memory_dream reports scan_coverage.
• memory_stats reports db_scope — whether the one-database-per-workspace invariant that makes lock-free operation safe actually holds, judged from which workspaces own memories in the file rather than from configuration. Warns when a shared MEMORY_DB_PATH has put unrelated projects in one graph.
• Soak harness gains --sessions N: N short sessions as separate processes, each verifying what the previous one left behind. This is the realistic stress pattern for one connection per workspace, and it is where the delete-churn bug actually manifested.

0.25.0

• Dream review clusters now carry only pairs that still need a decision. Different-subject and disjoint-scope pairs are separate permanently and are dropped; same-subject value conflicts are unresolved, so they stay and are labelled gate: "value_conflict" with the non-destructive resolution named.

0.24.1

• memory_delete reaps orphaned Topic nodes (a long-lived database had accumulated 273 orphans against 24 live topics); memory_dream does the same for its own prune/merge deletions and reports topics_reaped.
• The post-delete index census is exception-isolated — diagnostics can never fail a committed write.

0.24.0

Root-caused the recurring silent loss of vector-search coverage: LadybugDB 0.15.3 delete maintenance progressively orphans surviving HNSW nodes when transient batches are inserted then deleted. Reproduced standalone (repro), monotonic, in-process, persists across restarts. Onset is non-monotonic in burst size and seed-dependent, which is why several earlier experiments wrongly cleared it.

• memory_delete now censuses index reachability and rebuilds on shortfall, so a session's deletes can't hand the next session a degraded index.
• memory_stats reports vector_index.status: "degraded" when the census contradicts the cached probe verdict.

0.23.0

• One shared implementation of the delete+recreate path used by store-dedup, update and dream merge (three copies had drifted; dream was zeroing access_count). Store and update preserve the count; dream merge sums the merged members'.

0.22.0

• New MEMORY_FUSION=rrf (opt-in). Fixes three artifacts of summing incomparable channel scales: survivor keyword scores rescaling 2.24× when the top hit was deleted, a +0.166 score jump that inverted a ranking, and a plateau of identical scores when the vector channel was dead. See Fusion modes for why it is not the default.

0.19.0

Surface hardening from a full tool-by-tool review.

• Security: the memory_query destructive-query guard was bypassable. It matched the substring "DELETE " — with a literal trailing space — so MATCH (m:Memory)\nDETACH\nDELETE\nm; reported success and deleted every memory with MEMORY_ALLOW_DESTRUCTIVE=false. Queries are now classified after stripping comments and string literals, matching keywords on word boundaries.
• Breaking: read_only=True now rejects any mutation. It previously permitted CREATE/MERGE/SET, so an overwrite succeeded under a flag named read-only. MEMORY_ALLOW_DESTRUCTIVE now also covers SET, REMOVE and COPY — an overwrite destroys the previous value as surely as a delete.
• New memory_unrelate: edges could be created but never removed, and memory_query's DELETE is blocked by default, so a mistaken SUPERSEDES was permanent. This is also the supported way to break a circular SUPERSEDES chain that memory_dream reports.
• memory_get now returns edges (include_edges=True by default) plus superseded / superseded_by. Answering "what does this replace?" no longer requires Cypher.
• Ranking fix: the search candidate pool was top_k * 3, so the page size decided which memories were scored at all — on a 25-memory corpus, top_k=10 surfaced two memories that outranked every result top_k=5 returned. The pool is now fixed (MEMORY_SEARCH_CANDIDATES, default 100) and independent of top_k. Scores are unchanged; only coverage improves.
• Pagination: memory_search(offset=...) with offset / has_more in the response. Rank 11+ was previously unreachable.
• New memory_export / memory_import: JSON backup and restore including edges, with id remapping so an import can merge into an existing database. memory_relate is now idempotent (status: "exists"), so re-importing no longer doubles every edge.
• Input validation across every tool: two-sided clamping (preview_chars=-5 used to slice content from the wrong end; top_k=0 returned a degraded flag blaming the embedding model), content and batch size caps, and honest statuses (memory_delete reported deleted when every id was missing; memory_list(min_importance='high') raised a raw ValueError).
• memory_get and memory_list are no longer labelled compatibility aliases — each does something no other tool does. memory_traverse is marked deprecated.

0.3.0

• Default database is now per-workspace: .memnest/memory.lbug in the current directory. No more cross-workspace lock conflicts.
• Set MEMORY_DB_PATH to use a custom location (e.g. ~/.memnest/memory.lbug for global shared memory).
• Hybrid search: Vector (HNSW) + Full-text (BM25) + Graph scoring with PageRank, Louvain community detection, and K-Core decomposition.
• LOCOMO benchmark: 82.9% overall score.

0.2.0

Compatibility-preserving redesign with improved safety defaults.

• New tools: memory_query (general Cypher), memory_schema, memory_topics, memory_dream, memory_graph_html. Batch mode added to memory_store, memory_update, memory_relate, memory_delete.
• Breaking: MEMORY_ALLOW_DESTRUCTIVE now defaults to false. Set it to true if you previously relied on memory_query deleting nodes.
• Breaking: tag storage migrated from comma-joined strings to JSON arrays. Old rows are still readable; rewriting (e.g. via memory_update) upgrades them to JSON.
• memory_get, memory_list, memory_traverse from 0.1.x are retained as compatibility aliases. (As of 0.19.0 memory_get and memory_list are first-class again; only memory_traverse remains deprecated.)
• TOON serialization is now the default response format when toon-format is installed; set MEMORY_RESPONSE_FORMAT=json to opt out.
• memory_relate validates that both endpoints exist before returning created (used to silently no-op on typo'd IDs).
• memory_graph_html is now XSS-safe (HTML-escaped tooltips, DOM textContent for the detail panel), refuses to render >MEMORY_GRAPH_MAX_NODES, and rotates snapshots.
• Workspace filter pushed inside the vector index WITH clause so search recall isn't starved across workspaces.
• Dream consolidation: dedupes parallel edges across merges, isolates clusters by workspace, persists state via atomic sidecar JSON.