neuromcp is the first Sovereign Memory layer for AI: an open-source MCP server that gives Claude, GPT, Gemini, and Ollama persistent, searchable memory — stored entirely on your machine. No API keys. No cloud sync. No subscription required to remember who you are.
= data that you own outright, lives on hardware you control, and is portable across every model you use. Cloud memory products own your data; Sovereign Memory means do.
Semantic memory for AI agents — local-first MCP server with hybrid search, governance, and consolidation
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工具
—
可用率
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Sovereign Memory
you
npx neuromcp-init # one command: detects your MCP clients, writes configs, sets up the wiki
Or run the bare server without any setup: npx neuromcp. Something not
working? npx neuromcp-doctor diagnoses the daemon, Ollama, embeddings
and the database in one run.
The built-in memory browser: the most-connected entities in a namespace
and the relations between them, as a force-directed graph you can drag,
zoom and click — served from localhost, never from a cloud. See
Memory browser & Obsidian.
Why neuromcp
The LLM is a commodity. Your memory is the moat.
GPT-5, Claude 4, Gemini — they all converge. The model you use next year will differ. The memory of every conversation, decision, and preference you build is yours. neuromcp keeps that layer on your machine and makes it portable across any MCP-compatible client.
Local-first is a design choice, not a limitation.
No telemetry. No data leaves your laptop. No vendor has a copy of your conversations. Audit every line of code that touches your memory. SQLite + local embeddings; everything fits on one disk.
One install. Every client.
Claude Desktop, Cursor, Windsurf, Codex CLI, Continue, LibreChat, Open WebUI — neuromcp speaks MCP, so it works wherever MCP is supported. Switch models tomorrow; your memory follows.
Real recall, not keyword matching.
Hybrid retrieval combines vector search (nomic-embed-text, 768-dim), BM25 full-text, graph links, and a learned usefulness prior. At 500 distractors on LongMemEval, R@5 holds at 93.3%. Your context window gets the right memory, not just the most recent.
LongMemEval-S accuracy
Run
Score
Sample
Config
v7 (current)
96.08% (98/102)
n=102
Opus generator + Opus judge, single-model
v6
95.10% (97/102)
n=102
Same as v7, prior hint set
Repro: OMB_ANSWER_LLM=claude OMB_ANSWER_MODEL=opus OMB_JUDGE_LLM=claude OMB_JUDGE_MODEL=opus uv run omb run --dataset longmemeval -s s -m neuromcp -c "single-session-user,single-session-assistant,multi-session,temporal-reasoning,knowledge-update,single-session-preference" --query-limit 17
Sample size honesty. n=102 (17 per category × 6 categories). Wilson 95% CI for 98/102 ≈ 90.5–98.7%. Full 500q run with the same config is the next milestone before any "top-tier" claim.
Benchmarks (v0.18.0)
Oracle split (clean — easy mode)
Mode
R@5
R@10
Hit Rate
Extracted (hybrid)
100%
100%
100%
Oracle-split LongMemEval isolates the correct memory in a small
corpus. Every local MCP memory system claims ~99% here. It measures
"does the ranker work on clean inputs" — nothing more.
Distractor split (v0.18.0, honest)
Same 30 questions + 1000 random distractor memories drawn from other
questions' haystacks. The correct memory now competes against real noise.
Sample sizes. The 500-distractor row is n=30 (Wilson 95% CI for
28/30 ≈ 78-99% R@5). The 1000-distractor row is n=5 — preliminary,
Wilson 95% CI [57%, 100%]. The 1000-distractor n=30 run takes ~36 min
on a single Ollama instance; cached-distractor batching is v0.19.0
work. Treat 500-distractor numbers as defensible, 1000-distractor as
directionally positive but underpowered.
Head-to-head comparison is explicit v0.19.0 work. Hindsight (local
OSS MCP, ~94.6% LongMemEval claimed) and Mem0/Zep publish their own
numbers on their own harnesses. Until we run all of them against the
same corpus + embedder, calling any local MCP server "state of the art"
is marketing, not measurement. neuromcp publishes its numbers with
sample-size caveats so you can judge direction; don't read absolute
superiority into them yet.
Hybrid ranker (BM25 + vector + attention + graph + usefulness prior)
keeps R@5 = 100% at 1000:1 distractor:target ratio on the observed
sample. MRR drops to 74% because the correct memory is sometimes not
rank-1 but always rank ≤ 5 in what we saw. Earlier v0.18.0 numbers
(R@5 23%) were from a test FakeEmbedder — fixed in v0.18.1.
What this benchmark does NOT prove: end-to-end answer
correctness, long-horizon multi-session reasoning, or superiority
over commercial cloud systems (Mem0, Zep) on their own benchmarks.
Those comparisons need their numbers on the same distractor split,
which hasn't been published.
Why
AI agents forget everything between sessions. Existing solutions either store flat key-value pairs (useless for real knowledge) or require cloud infrastructure and API keys.
neuromcp gives you two layers of memory:
MCP Server — hybrid search (vector + full-text + graph), verbatim recall, memory governance, automatic consolidation, all in a single SQLite file
Wiki Knowledge Base — compiled Markdown knowledge that survives crashes, compounds over sessions, and gives your agent project-aware context at every startup
LLM updates wiki pages when learning something persistent
Every 8 tool calls
Hook reminds LLM to update the wiki
Session end
Hook writes raw session log + git auto-commits all wiki changes
Crash
Checkpoint every 5 tool calls to file. Git history for rollback.
Self-healing consolidation pipeline (v0.15.0+)
Every ~4h the launchd agent runs run-consolidation.sh, which
orchestrates four steps end-to-end:
consolidate-sessions.py — batches raw sessions per project,
asks Claude for a factual summary, and fact-checks it against the
raw sources. When the auditor flags specific unsupported claims the
consolidator now auto-strips those lines and re-audits once — so
one speculative sentence no longer kills a whole batch.
rescue-rejected.py — any batch that still fails is parsed,
the unsupported claims are removed, and the cleaned summary is
appended to its wiki page. Pure text surgery, no LLM calls.
entity-linker.py — cross-links every page: a bare-word mention
of another registered entity (people/, projects/, systems/) is added
to the page's related: frontmatter. Makes the wiki act like a
graph without a separate graph database.
rebuild-index.py — regenerates index.md and per-category
-index.md files. Categories over 10 pages are auto-split so the
session-start router stays compact as the wiki scales.
The pipeline is idempotent — safe to re-run at any time.
What the LLM knows at session start
Schema (operating rules) → How to maintain the wiki
Index (knowledge map) → What knowledge exists
User profile → Who you are, how you work
Project page → Current project details (auto-detected from cwd)
Last session → What happened last time
Quick Start
One command (recommended)
npx neuromcp-init
Detects your installed MCP clients (Claude Desktop, Claude Code, Cursor,
Windsurf), writes the neuromcp entry into each config (with a backup of
the original), initializes the wiki + hooks, and checks whether Ollama is
available. --dry-run previews everything without writing.
For GUI clients (Claude Desktop, Codex Desktop) install permanently
first: npm install -g neuromcp && neuromcp-init. Only a permanent
install lets init write the absolute node + script paths those clients
need — from the bare-npx cache (garbage-collected) it deliberately
falls back to a PATH-dependent npx entry, which GUI clients usually
cannot resolve.
Manual steps (what init does under the hood)
1. Start the MCP server
npx neuromcp
2. Initialize the wiki + hooks (required for closed-loop attribution)
npx neuromcp-init-wiki
This creates the wiki structure, installs hooks (Claude Code) and rules (other editors), and configures everything automatically. Without this step, npx neuromcp still runs as a plain MCP server with 46 tools, but the critic hook that closes the attribution loop is not installed — retrieval works but usefulness scores never accumulate. Safe to run multiple times — won't overwrite existing config.
Editor Compatibility
neuromcp works with any MCP-compatible editor. Two tiers of integration:
Claude Code gets the full experience via native hooks — context injection and persistence happen automatically, even if the LLM forgets.
Other editors get rules files that instruct the LLM to call neuromcp tools at session start/end. This depends on LLM compliance — it works well in practice but is not guaranteed like hooks.
# Auto-detect installed editors
npx neuromcp-init-wiki
# Target a specific editor
npx neuromcp-init-wiki --editor cursor
# Install rules for all supported editors
npx neuromcp-init-wiki --editor all
nomic-embed-text is English-centric. On a real Dutch memory store (2,402
memories, 40 paraphrased Dutch queries, 15 off-topic queries) it found the
right memory in the top 5 only 2.5% of the time, and off-topic queries scored
higher than true matches. bge-m3 (multilingual, 1024-dim) on the same data:
embedder
Recall@5
MRR@10
relevant vs off-topic separable (AUROC)
nomic-embed-text
2.5%
0.03
0.08
bge-m3
65%
0.47
0.99
To switch an existing install:
ollama pull bge-m3
npx neuromcp-reembed # dry run on a copy
npx neuromcp-reembed --apply # rebuild the index (keeps a backup)
Run the reembed with NEUROMCP_EMBEDDING_MODEL=bge-m3 set. After that every
client on the default auto setting follows the model the index was built
with, so nothing else needs configuring. With bge-m3, search_memory also
applies a calibrated relevance floor (0.45) so off-topic queries return
nothing instead of noise; keyword (FTS) and knowledge-graph hits always pass.
Keep NEUROMCP_RERANKER=none for non-English memories: the bundled
cross-encoder (ms-marco-MiniLM-L-6-v2) is English-only and dropped MRR on
the Dutch benchmark above from 0.46 to 0.18.
neuromcp-init-wiki copies the Claude Code hooks once; until 0.30.0 it never
touched them again, so hook fixes did not reach existing installs. Re-running
it now updates every hook that is an unmodified older neuromcp version (a
backup is kept next to each one), migrates old .js hooks to .cjs (leaving a
forwarding stub at the old path) and leaves hooks you edited alone — it lists
them, and --update-hooks replaces those too. neuromcp-doctor check reports
outdated hooks.
Installation
The reliable way is npm install -g neuromcp && neuromcp-init — it
writes the entry below into every detected client config (with a backup
first). Run init from the permanent install, not via bare npx: from the
npx cache it deliberately falls back to a PATH-dependent entry that GUI
clients usually cannot resolve. The manual form is:
npx is fine here because Claude Code runs from your terminal and
inherits your PATH.
Claude Desktop / Codex Desktop / any GUI client
GUI apps are started by the window manager, not by a login shell, so
they inherit a minimal PATH that usually has neither node nor npx
(nvm, fnm, Volta and Homebrew all install outside /usr/bin). Use
absolute paths — this is exactly what neuromcp-init writes since 0.29.3:
(bin/neuromcp.mjs keeps its #!/usr/bin/env node shebang, so running it
straight from a terminal still works.)
Cursor / Windsurf / Cline
Same format — add to your editor's MCP settings. Copy-paste configs for
every client live in examples/.
Shared daemon (recommended when you run multiple clients)
By default each client spawns its own neuromcp process. One shared
background daemon serves them all instead — one database connection, one
embedding pipeline, no cold start per client:
// Claude Code (~/.claude.json) — native HTTP transport
{ "neuromcp": { "type": "http", "url": "http://127.0.0.1:3200/mcp" } }
// Claude Desktop — stdio-only, bridge via neuromcp-connect.
// The bridge waits for the daemon on cold boot (plain mcp-remote exits
// fatally when the client starts before the daemon has bound its port,
// leaving a permanent "Server disconnected").
{ "neuromcp": { "command": "npx", "args": ["-y", "--package=neuromcp", "neuromcp-connect", "http://127.0.0.1:3200/mcp"] } }
The daemon binds loopback only, rejects non-allowlisted Host and
Origin headers (DNS-rebinding defense), and is unauthenticated by
design inside that boundary. Uninstall: npx neuromcp-enable-daemon --uninstall.
Memory you cannot see is memory you cannot trust. neuromcp ships two ways
to look at what your agents actually know — both local, both read-only.
Built-in web view
When the shared daemon
is running, open http://127.0.0.1:<port>/ui (default port 3200):
Graph — the top entities in a namespace and the relations between
them, as a force-directed canvas. Drag nodes, scroll to zoom, click a node
to list the memories linked to it (current memories only; superseded
ones are hidden unless you ask for them).
Timeline — type a topic and see how the facts about it evolved over
time, including which memory superseded which.
The UI is a single page with no external assets (strict CSP, no CDN), and
it is only served on loopback: bind the daemon to a non-loopback host and
the /ui and /api/* routes are disabled entirely.
Obsidian graph of the wiki
The wiki (~/.neuromcp/wiki) is plain Markdown, so it opens directly as an
Obsidian vault. Obsidian draws its graph from
[[wikilinks]] while neuromcp keeps relationships in frontmatter, so run
the bridge once (idempotent, --dry-run supported):
npx neuromcp-obsidian-bridge
neuromcp-init-wiki also seeds a minimal wiki/.obsidian/ (colour groups
per page type, no plugins) — only if none exists; an existing vault
setup is never touched. Details: docs/OBSIDIAN.md.
MCP Surface
46 tools across 8 families — the full auto-generated reference with
every parameter lives in docs/TOOLS.md (regenerated
from the actual registrations on every change; CI fails when it drifts).
register_agent, find_expert, review queues, memory transfer
Verbatim store
3
exact-recall FTS on raw text, never summarized or pruned
Wiki
3
wiki_ingest, wiki_lint, wiki_briefing
Attribution & usefulness
3
log_retrieval, cite_memories — closes the usefulness-prior loop
Reflection
1
generate_reflection
Picking the right retrieval tool:search_memory returns ranked raw
memories; recall_answer synthesizes a cited extractive answer (or
honestly says not_in_memory); recall_memory is a plain ID/filter
lookup with no semantics; search_all adds the verbatim store to the
sweep.
Resources (13)
URI
Description
memory://stats
Global statistics
memory://recent
Last 20 memories
memory://namespaces
All namespaces with counts
memory://health
Server health + metrics
memory://stats/{namespace}
Per-namespace stats
memory://recent/{namespace}
Recent in namespace
memory://id/{id}
Single memory by ID
memory://tag/{tag}
Memories by tag
memory://namespace/{ns}
All in namespace
memory://consolidation/log
Recent consolidation entries
memory://operations
Active/recent operations
Prompts (3)
Prompt
Description
memory_context_for_task
Search relevant memories and format as LLM context
review_memory_candidate
Show proposed memory alongside near-duplicates
consolidation_dry_run
Preview consolidation without applying
Wiki Knowledge Base
The wiki is the compiled, human-readable knowledge layer. It replaces the chaos of session logs with structured, interlinked Markdown pages.
Why a wiki instead of more vector search?
Traditional RAG
neuromcp Wiki
Re-derives answers every query
Knowledge compiled once, refined over time
Chunking artifacts, retrieval noise
Human-readable pages with source citations
Vector DB, embedding pipeline
Plain Markdown + Git
Black box retrieval
Auditable, editable, portable
Knowledge evaporates
Knowledge compounds
Wiki page format
---
title: My Project
type: project
created: 2026-04-06
updated: 2026-04-06
confidence: high
related: [other-project, oauth-setup]
---
# My Project
Description, stack, auth, deployment details...
How to use
The wiki works automatically once hooks are installed. The LLM:
Reads index.md at session start to know what knowledge exists
Reads specific pages when relevant to the current task
Updates pages when learning something new
Gets reminded every 8 tool calls if the wiki needs updating
You can also browse and edit the wiki manually — it's just Markdown files.
Auto-consolidation (optional)
Once you accumulate raw session logs, the wiki can be kept fresh automatically. A scheduled job reads unprocessed sessions, groups them per project (by detecting $HOME/projects/<name> paths in the session content), and uses the claude CLI to synthesise a ## [date] entry into the right wiki page.
npx neuromcp-enable-consolidation
What it installs:
~/.neuromcp/scripts/consolidate-sessions.py — the worker
Hallucination guard (eval-loop). Every consolidator output goes through a second Haiku audit before the wiki is touched. If any factual claim in the generated summary is not traceable to the raw sessions, the chunk goes to ~/.neuromcp/review-queue/ instead of the wiki. No hallucinated claims leak through.
Atomic facts with temporal supersession. After a summary is approved, it is also distilled into short standalone facts and stored as category='fact' rows with valid_from=today. When a new fact is Jaccard-similar to an existing one in the same project, Haiku decides whether NEW supersedes OLD — if yes, the old row gets superseded_by_id and valid_to set. Retrieval defaults to current facts only (superseded_by_id IS NULL), so outdated conclusions never resurface.
Auto-retrieve + hybrid indexing
Once the wiki has content, make it searchable so the UserPromptSubmit hook can surface relevant pages automatically (no more "LLM must remember to call search"):
npx neuromcp-index-wiki # index wiki pages into memories_fts + memories_vec
npx neuromcp-index-wiki --rebuild # wipe wiki entries first, then reindex
npx neuromcp-index-wiki --dry-run # preview what would change
npx neuromcp-index-wiki --no-embed # FTS-only mode (no embedding provider needed)
npx neuromcp-backfill-embeddings # embed any memory still missing a vector
The indexer splits each page on ## section headers and stores every section as a deduplicated memory (source='wiki', category='wiki'). Each section is both written to the FTS5 index and embedded via the configured provider (Ollama → OpenAI → ONNX) so vector search works too.
At prompt time the neuromcp-auto-retrieve.js hook calls neuromcp-query, which runs FTS5 BM25 and sqlite-vec cosine search in parallel and fuses the rankings via Reciprocal Rank Fusion (k=60). The top-3 merged results are injected as <neuromcp-recall> context.
The hook is installed automatically by neuromcp-init-wiki and registered under UserPromptSubmit in Claude Code's settings.json. Re-run the indexer after large wiki updates (or schedule it — it's idempotent).
Set to 1 to bypass the consolidator audit on infrastructure failure (default is fail-CLOSED)
Known upstream issues
memories_vec does not reclaim space after DELETE — sqlite-vec #54 / #265. When you re-index after editing wiki sections, the old vector rows are marked deleted but their storage stays. The database file grows monotonically until you run npx neuromcp-index-wiki --rebuild, which drops and re-creates the vector rows. Run a rebuild every few weeks if you edit the wiki heavily.
claude CLI streaming hangs from non-TTY subprocesses on macOS — if you script interactions with claude -p from another process (e.g. scheduled jobs), pipe it through script -q /dev/null to allocate a pseudo-TTY. Without that the stdout buffer never flushes. We work around this inside the consolidator where needed.
Memory Governance
Namespaces isolate memories by project, agent, or domain.
Trust levels (high, medium, low, unverified) rank search results and control decay resistance.
Soft delete tombstones memories — recoverable for 30 days.
Content hashing (SHA-256) deduplicates at write time.
Lineage tracking records source, project ID, and agent ID per memory.
Configuration
All via environment variables. Defaults work for most setups.
Variable
Default
Description
NEUROMCP_DB_PATH
~/.neuromcp/memory.db
Database file path
NEUROMCP_EMBEDDING_PROVIDER
auto
auto, onnx, ollama, openai
NEUROMCP_DEFAULT_NAMESPACE
default
Default namespace
NEUROMCP_AUTO_CONSOLIDATE
false
Enable periodic consolidation
NEUROMCP_TOMBSTONE_TTL_DAYS
30
Days before permanent sweep
NEUROMCP_LOG_LEVEL
info
debug, info, warn, error
NEUROMCP_MODEL_DIR
~/.neuromcp/models
Where the ONNX fallback model is cached
NEUROMCP_DISABLE_MODEL_DOWNLOAD
unset
1 forbids the lazy first-run model download
NEUROMCP_STRICT_EMBEDDINGS
unset
1 = crash on an index/provider mismatch instead of starting degraded
v0.29 — current-validity everywhere: superseded memories are hidden
from every read path by default (include_superseded / valid_at to
opt in); built-in web memory browser with force-directed graph +
timeline; Obsidian bridge and non-destructive vault seed; cold-boot race
fix (daemon binds its port at process start); neuromcp-doctor and
neuromcp-connect find the daemon on its configured port; switching
embedding provider no longer bricks an existing database (neuromcp-reembed,
degraded mode); npm releases via Trusted Publishing (no tokens); graph
overview no longer polluted by the system's own contradiction plumbing.
v0.28 — neuromcp-init one-command setup (detects clients, writes
configs with backups); neuromcp-doctor rebuilt as real triage;
auto-generated docs/TOOLS.md + docs/BENCHMARK.md; examples/ client
configs; store-time namespace pushdown; entity prefix-merge now needs
evidence.
v0.27 — security release: CWE-22 path-traversal fix in wiki_ingest,
MCP-spec Origin validation on the daemon, neuromcp-connect
boot-race-safe Claude Desktop bridge, runtime health-check hook.
v0.26 — recall_answer (deterministic extractive answers with
citations + gap-analysis, no LLM on the read path), optional local
cross-encoder reranker (ships default-off after an honest A/B),
recall-quality correctness sweep.
Contradiction resolution is three-tier, and the two tiers that touch the
graph require claim-level evidence — same subject, mutually-exclusive
predicate (is, uses, runs, … see src/config/predicate-classes.json),
different object: supersede (evidence + heuristic score > 0.5: old
memory invalidated), coexist (evidence + score in (0.3, 0.5]: both
kept, linked via a contradicts graph edge), flag (heuristic signal only — numeric
difference, negation words — reported in the store_memory result for
review, never materialised in the graph). Keyword heuristics alone can
never invalidate a memory or assert a contradiction: a false one is a
hallucination vector for every LLM downstream.
One run checks: Node version, native modules actually loadable
(better-sqlite3, sqlite-vec), database openable, shared daemon
/health, Ollama reachable + nomic-embed-text pulled, ONNX fallback
model present, and whether the vector index in your database still
matches a provider this machine can reach. Exit codes: 0 healthy, 1
degraded (e.g. no Ollama — ONNX fallback active), 2 broken. Start every
bug report with its output.
"Embedding dimension mismatch" / vector search is off
The vector index is a vec0 table with a fixed width: 384 for the
ONNX fallback, 768 for Ollama's nomic-embed-text. If the provider
changes (you installed Ollama after running on the fallback, or Ollama
was down and the auto cascade dropped to ONNX), the new provider cannot
write into the old index.
Since 0.29.3 this no longer stops the server. It:
prefers the provider that matches the existing index,
retries a briefly-unavailable provider with backoff,
and otherwise starts in degraded mode — full-text search keeps
working, vector search is disabled, and new memories are stored
without embeddings and queued for backfill_embeddings. Nothing is
deleted and the index is never rebuilt behind your back.
neuromcp-doctor check names the state. To get vector search back, pick one:
# a) restore the provider that matches the index
ollama serve && ollama pull nomic-embed-text # 768-dim index
# or force the fallback for a 384-dim index:
export NEUROMCP_EMBEDDING_PROVIDER=onnx
# b) rebuild the index for the provider you want (works on a COPY first)
npx neuromcp-reembed # dry run: rebuild a copy, report, discard
npx neuromcp-reembed --keep-copy # same, but keep the copy to inspect
npx neuromcp-reembed --apply # swap it in; original kept as *.pre-reembed-<ts>
Stop every client before --apply. Rollback is a file move back.
Installing without Xcode / with install scripts disabled
better-sqlite3 fetches its prebuilt binary from its install script.
With npm install --ignore-scripts (or a hardened .npmrc, or pnpm's
build allowlist) the package installs but has no native binding and
nothing works. npm 11's own default is ignore-scripts=false, so a plain
install is fine:
npm install -g neuromcp # normal path, scripts run
npm install -g neuromcp --ignore-scripts=false # if your .npmrc disables them
If you already installed with scripts off, reinstall as above (a bare
npm rebuild in a global prefix does not reliably produce the binding).
The ~33 MB ONNX fallback model no longer depends on the postinstall at
all: it is downloaded lazily on first use into ~/.neuromcp/models
(user-writable even when the package lives in a root-owned global
prefix). Fetch it ahead of time with npx neuromcp-download-model, or
forbid the download entirely with NEUROMCP_DISABLE_MODEL_DOWNLOAD=1.
Comparison
Feature
neuromcp
Hindsight
Mem0
Letta/MemGPT
agentmemory
LongMemEval R@5 (oracle)
99.8%
—
—
—
—
LongMemEval R@5 (1000 distractors, n=5, Ollama)
100% (preliminary, CI [57%, 100%])
not published
not published
not published
not published
Search
Hybrid (vector + FTS + RRF + graph)
Vector + rerank
Vector
Vector
Vector
Auto-capture
Deterministic (no LLM cost)
LLM extraction
No
Agent self-edit
Yes
Explain mode
Yes (trust, contradictions, claims)
No
No
No
No
Knowledge graph
Entities, relations, PageRank
Entities + beliefs
No
No
No
Contradiction detection
3-tier (supersede/coexist/flag) + graph edges
Belief updating
No
No
No
Temporal validity
valid_from/valid_to on memories + relations
Yes
No
No
No
Wiki knowledge base
Compiled Markdown + Git
No
No
Tiered blocks
No
Local-first
SQLite, zero cloud
SQLite
Cloud / Postgres
Server
Local
Embeddings
Built-in ONNX (zero config) + Ollama
External
External API
External
External
Governance
Namespaces, trust levels, soft delete
Namespaces
API keys
Agent-scoped
Cross-agent
Infrastructure
Zero
Zero
Cloud account
Server
Zero
Pricing
Free (AGPL-3.0)
Free (MIT)
Freemium ($23.9M funded)
Free ($10M funded)
Free (Apache-2.0)
License
AGPL-3.0 for the engine in src/. MIT for bin/, templates/,
scripts/, docs/, and examples/ (carve-out — see LICENSE-EXAMPLES).
License FAQ
Can I use neuromcp commercially? Yes. Running neuromcp as part of your
own application, on your own infrastructure, is unrestricted. AGPL only
imposes obligations if you modify the engine code AND distribute or
host it as a network service.
Can I install neuromcp from npm in my closed-source product? Yes. Using
the published binary as a dependency does not trigger AGPL contagion.
What if I host neuromcp as a SaaS? Then AGPL §13 applies: you must make
the source code (including your modifications) available to your users.
This is the explicit anti-fork clause we chose for the engine — it stops
well-funded competitors from taking the code, putting it behind a login,
and shipping it as their own product.
Can I copy a CLI script or template? Yes. Everything in bin/,
templates/, scripts/, docs/, and examples/ is dual-licensed
AGPL-3.0 OR MIT. Pick MIT in your downstream project.
Need different terms for the engine? Commercial dual-license is
available — contact the maintainer.
N
Neuromcp
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Semantic memory for AI agents — local-first MCP server with hybrid search, governance, and consolidation
