datalore-mcp

Shared, serverless, conflict-free memory for AI agents — with peer-to-peer sync.

datalore-mcp is a drop-in replacement for the official @modelcontextprotocol/server-memory knowledge graph — the same entities, observations and relations — that converges across machines with no central server, no database and no merge conflicts.

Its headline feature is peer-to-peer sync: the datalored daemon replicates each node's shard directly to the others, so several machines share one memory with no shared folder — only IP reachability over a VPN or a LAN.

Table of contents

• Why datalore-mcp

  • The problem

  • How datalore-mcp solves it

• The three commands

• Share across machines

  • Peer to peer — datalored (recommended)

    • The daemon HTTP API

  • Shared area — file and s3

• Configuration

  • Shared variables

  • Server variables

  • Daemon variables

• How it works

  • Compaction

  • Deletion and collection

• Drop-in compatibility

• Security

• Development

• Why the name datalore?

• Documentation

• License & Disclaimer

  • Support the Project

Related MCP server: mnemosis-mcp

Why datalore-mcp

The problem

Agent memory today is local, and the official memory server is a single JSONL file that every mutation reads and rewrites in full. That breaks the moment you have more than one machine:

• It lives on one host. Switch machine and your agent has forgotten everything.

• Sharing it over a synced folder loses writes. The server serializes mutations in-process only: two hosts each load the whole graph, mutate their own copy and write it back, so the last write silently discards the other's — and the sync tool forks the file into conflict copies instead of merging it.

• The alternatives want a server, a database or a cloud. The mainstream "shared memory" products are something you have to host, or they ship your memory off your machines.

How datalore-mcp solves it

datalore-mcp removes the shared file and the central server at the root, and keeps the drop-in tool surface:

• Every node keeps its own memory. Each machine writes only its own append-only shard (<node>.jsonl); a shard has a single writer by design, so no two nodes ever write the same file and datalore itself never forks one — a shared area's syncer still can, and a conflict copy is just another shard to fold.

• Shards merge, they do not overwrite. The graph is a CRDT — an LWW-Element-Set ordered by (HLC, node, sequence) — whose merge is idempotent, commutative, associative and convergent. Any two machines that have seen the same operations hold the identical graph, in any order, so there is no "last write wins" data loss.

• No shared area, no central server. The shards travel directly between machines with the datalored daemon — only IP reachability, over a VPN or a LAN — so several machines share one memory with no shared folder and no service to host. Prefer a folder or a bucket you already have? file and s3 work too: the transport is a choice, not a lock-in.

• Local-first and offline-friendly. Every node reads and writes its own shard on disk and keeps a full local replica, so it keeps working offline; the mesh reconverges when the network returns.

• Drop-in for the official server. The same nine tools, inputs and outputs, so it slots under the memory server name with no agent changes.

The three commands

One npm package, datalore-mcp, ships three commands (Node.js ≥ 22). Install it once and all three land on your PATH:

npm install -g datalore-mcp

Only datalore-mcp matches the package name, so it is the only command npx can run by package name. Run the other two with --package, or from the global install above:

npx -y datalore-mcp                       # the MCP server (no argument serves over stdio)
npx -y -p datalore-mcp datalore help       # the maintenance CLI
npx -y -p datalore-mcp datalored           # the replication daemon

From a clone — for development, or to run code not yet on npm:

git clone https://github.com/sequico/datalore-mcp.git
cd datalore-mcp && npm install && npm run build

node dist/index.js      # datalore-mcp — the MCP server
node dist/datalore.js   # datalore    — the maintenance CLI
node dist/gossip.js     # datalored   — the replication daemon

datalore-mcp — the MCP server

The entry every MCP client uses. With no argument (or serve) it speaks MCP over stdio, so you don't run it yourself — the client spawns it and it lives for the session. Point your client at it; it works on one machine out of the box, and sharing across machines is the next step. In OpenCode (V2):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "servers": {
      "memory": {
        "type": "local",
        "command": ["npx", "-y", "datalore-mcp"],
        "environment": { "DATALORE_DIR": "~/.datalore" }
      }
    }
  }
}

Other clients wrap the same command and environment in their own envelope; from a clone the command is ["node", "/path/to/datalore-mcp/dist/index.js"]. Because the tool surface is identical, you can replace the memory server entry with datalore-mcp and change nothing else.

datalore — the maintenance CLI

datalored — the daemon

A long-lived service, one per machine, that replicates the shard directory peer to peer. It takes no arguments and is configured entirely through environment variables — the shared DATALORE_DIR and DATALORE_NODE_ID (Shared variables) plus the daemon's DATALORED_* (Daemon variables). See Peer to peer for the setup.

Share across machines

datalore merges shards; it does not move them by itself. How the shards travel between machines is your choice: peer to peer (a companion daemon — the recommended default) or a shared area (a folder or a bucket that already exists). The MCP server always reads and writes its own local shard; only the transport differs.

Peer to peer — datalored (recommended)

The recommended way to share one memory across machines with no shared storage. datalored is a long-lived daemon that replicates the local shard directory peer to peer. It only needs IP reachability, so it runs over any VPN (WireGuard, Tailscale, OpenVPN, Netbird, ZeroTier, …) or a plain LAN.

Give each machine a unique node id and run one datalored per machine. Every daemon keeps a full replica of the others' shards beside its own, in the same local directory the MCP server uses — no shared folder required:

# on a tailnet
DATALORE_DIR=~/.datalore \
DATALORED_TAILSCALE=true \
datalored

# or, with no self-listing underlay, seed one reachable peer
DATALORE_DIR=~/.datalore \
DATALORED_PEERS=ai.example:7878 \
datalored

The peers find each other — you never list the machines. Configure one discovery source: DATALORED_TAILSCALE (from tailscale status), DATALORED_DNS_SRV (the _datalored._tcp SRV records), or DATALORED_PEERS (a static seed; one is enough, membership then self-propagates). DATALORED_ADVERTISE is optional and defaults to the listen host.

How it behaves:

• Never authors operations. Each node's MCP server writes only its own <node>.jsonl; the daemon keeps replicas of the peers' shards and only ever pushes the shard it authors, so the single-writer rule holds. It also restores the local shard from a peer's replica when the local copy is behind.

• Push on change + periodic anti-entropy. On a local change (DATALORED_PUSH) and every DATALORED_INTERVAL seconds it compares the per-shard high-water seq and moves the delta in whichever direction is behind. Operations are idempotent, so delivery may be best-effort — no consensus, no ordering, only eventual completeness.

• Scales past a full mesh. By default it contacts every peer; DATALORED_FANOUT caps that to a random subset per round — which excludes tombstone collection, because a reachable peer the round did not sample suspends it, so the daemon refuses that combination — and at or above DATALORED_DIGEST_MIN_SHARDS it reconciles through a fixed-size digest instead of the full shard list (with DATALORED_CHANNELS moving membership onto its own channel). A small mesh keeps behaving exactly like a full mesh, and a peer without the digest capability falls back to the plain list, so a mixed mesh never breaks.

• Relays. A node serves every replica it holds, so an offline node keeps converging through any peer that has its shard.

• Discovery and membership. Bootstrap adapters (Tailscale, DNS SRV, a static seed) return an initial list; the peer set then gossips itself. Liveness comes from the exchange results — suspect after DATALORED_SUSPECT_AFTER failures, dead after DATALORED_DEAD_AFTER, retried after DATALORED_DEAD_RETRY, and forgotten after DATALORED_MEMBERSHIP_TTL. A peer that reaches this node is marked alive — its certificate names it under mTLS, otherwise the id it announces on the request — so a peer that recovers is re-engaged at once instead of waiting out DATALORED_DEAD_RETRY. A peer this node does not know yet is adopted from the address it announces on the request (untrusted, like a gossiped entry, and confirmed by the next sync), so a statically-seeded joiner is discovered by the seeds it reached and its entry then spreads to the rest. A node's own entry is authoritative; an address that serves a different node is dropped.

• Wire and security. A small HTTP/JSON service bound to DATALORED_LISTEN (the full route list is below); a shard accepts only its own node's operations. mTLS with per-peer certificates: the certificate's common name is the node id, so a peer may only push the shard it authors, while reads stay open to any authenticated peer — which is what lets a node relay. DATALORED_REVOKED refuses specific nodes on every route; DATALORED_DEPARTED additionally discards a departed node's replica and lets collection proceed past it.

• Observability. Prometheus metrics — liveness, peer health, the stable watermark and, for collection, how long each peer has been away (datalore_gossip_absent_seconds) and how many deleted elements are frozen (datalore_gossip_frozen_elements) — plus a per-shard stable watermark (the minimum high-water mark reached on every contactable peer, a conservative causal-stability bound), and status and graph routes for probes and operators — see The daemon HTTP API.

Under systemd, a user unit:

[Unit]
Description=datalored — peer-to-peer shard replication
After=network-online.target

[Service]
EnvironmentFile=%h/.config/datalore/datalored.env
ExecStart=%h/.local/bin/datalored
Restart=on-failure

[Install]
WantedBy=default.target

The daemon HTTP API

datalored serves a small HTTP/JSON API on DATALORED_LISTEN (default 0.0.0.0:7878). Every route is read-only except the operations push, whose body is capped at 64 MiB. Without TLS the routes are unauthenticated; with mTLS they all require a client certificate whose common name is the peer's node id, and a push is accepted only for the shard the certificate names.

The wire carries a protocol revision (1, in /info and /status): the first exchange validates it, and a peer that answers a different revision fails the exchange rather than being misread. A list (/membership, /shards) is capped at 10 000 entries and the shard digest at 16 buckets, so a single response cannot force unbounded state from a peer.

curl -s http://127.0.0.1:7878/health
curl -s http://127.0.0.1:7878/status
curl -s http://127.0.0.1:7878/peers
curl -s http://127.0.0.1:7878/graph | jq '{entities: (.entities|length), relations: (.relations|length)}'

Shared area — file and s3

The file and s3 backends converge through a shared area that already exists and is replicated outside datalore — a folder or a bucket every node can reach. Point every node at the same area and each node's shard reaches the others:

DATALORE_BACKEND=file DATALORE_DIR=/srv/datalore-shared DATALORE_NODE_ID=ai datalore-mcp
# or
DATALORE_BACKEND=s3 DATALORE_S3_BUCKET=my-bucket DATALORE_NODE_ID=ai datalore-mcp

• file — shards are *.jsonl files in DATALORE_DIR; point it at a folder replicated by Syncthing, Dropbox, a git working tree or a shared mount.

• s3 — one object per shard, written with conditional writes retried on contention; uses @aws-sdk/client-s3 (an optional dependency, loaded only when selected). Conditional writes need a store that honours If-Match/If-None-Match on PutObject (AWS S3, Cloudflare R2, MinIO do); a store that ignores them is not supported, and a shard object with a multipart ETag is refused rather than guarded by a token that can never match. An object store has no append, so every mutation rewrites the whole shard object (read-modify-write): correct, but O(shard size) per write.

The memory backend keeps shards in process only — for tests and throwaway sessions. See Configuration for the variables.

What a shared area does not give you: a safe deletion. file/s3 share a graph; they cannot say whether a peer holds a write it has not delivered yet. So without a daemon the maintenance prunes only, and a physical deletion happens only where the operator declares the directory written by this node alone (DATALORE_GC) — see Deletion and collection. Convergence is delegated to the syncer as well: datalore neither verifies nor retransmits, so latency and conflict copies are the syncer's business, and a conflict copy is just another shard to fold. And the daemon replicates a directory: datalored refuses to run with DATALORE_BACKEND=s3 (or memory), where it would replicate a local folder the server never uses.

Configuration

Every setting is read from the environment. DATALORE_DIR and DATALORE_NODE_ID are shared; the MCP server, the CLI and the daemon otherwise have their own variables — DATALORE_* for the server, DATALORED_* for the daemon.

Shared variables

Used by every command.

These two are local to each machine, not a shared area: the MCP server and the daemon share the same directory on the same host, and every machine keeps its own copy of every shard. Peer-to-peer sync with datalored needs no shared folder or bucket; a shared area is only the alternative transport for file/s3.

A node id is a single-writer shard: exactly one server process should append to it at a time. The normal setup is one memory server per machine; if you run several servers against the same store concurrently, give each its own DATALORE_NODE_ID. The tool does not enforce this: two writers that share a node id and write in the same millisecond produce operations with the same id, which a read fails loudly on — but writers on different clocks usually land on different ids, so the shard can quietly interleave and the fold picks one. A unique node id is a requirement, not something the tool can check.

Server variables

Used by the MCP server (and the datalore CLI, which reads and writes the same shards).

DATALORE_GC (server, default off) and the daemon's DATALORED_GC (default on) are different switches; with the memory backend nothing is shared, so the server collects regardless of DATALORE_GC.

S3 credentials come from the standard AWS chain (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION, profiles, instance roles, …).

The local shard is compacted online: the server rewrites its own shard after DATALORE_COMPACT_AFTER appended operations (default 1000, 0 disables), inside the same mutation queue as the writes. The rewrite is a compare-and-swap against the content it read, and a concurrent write — an append, or the datalored daemon recovering the shard — makes it re-read and retry, so nothing is ever lost; datalore compact runs the same compaction by hand. datalore import authors its own <node>-import shard, so it too can run while the node's server is live and re-importing is idempotent. That shard only ever adds, so it needs neither the peer knowledge nor the DATALORE_GC promise: datalore import prunes it on the way out, and datalore compact prunes it too — dropping the operations a later one shadowed, keeping every tombstone, and rewriting only when something was dropped, so a repeated maintenance costs nothing and no peer is asked to re-pull the shard. On the file backend, append and compact take a per-shard lock, so they serialize instead of racing even across processes — and the daemon takes the same lock.

Within a node the timestamp is monotonic: if the wall clock steps back (NTP, a restored snapshot, a manual date), a new operation keeps the previous timestamp rather than an older one, so an earlier operation can never win over a later one of the same node.

Daemon variables

Used by the daemon only. It needs at least one discovery source — DATALORED_TAILSCALE, DATALORED_DNS_SRV or DATALORED_PEERS — to reach the mesh; the rest is optional. Run it behind a VPN or firewall and enable mTLS — see Security.

How it works

Every mutation is recorded as an operation in an append-only log, split into per-node shards:

• create_entities / add_observations / create_relations add elements;

• delete_entities / delete_observations / delete_relations write tombstones (a deleted entity also tombstones its observations and incident relations).

A read folds all shards: each element (entity, observation occurrence, relation) is an LWW register decided by (HLC, node, sequence). A delete of an observation is content-level: it removes every occurrence older than it. Observations are shown while their entity is present, relations while both endpoints are present. The fold depends only on the set of operations, never on the order shards or lines were read. Operations are ordered by a Hybrid Logical Clock: the order survives clock skew, and a node that has observed a claim can write a correction ordered after it. An observed stamp further than a bounded skew in the future is clamped, so a wrong clock cannot drag the node's timestamps into the future. Reads are incremental: each shard is decoded once and cached against a cheap fingerprint, and the folded graph is memoized, so a read with nothing changed costs only a fingerprint poll per shard.

Consequences, all covered by property tests:

• Idempotent — applying the same operations twice changes nothing.

• Commutative — order of operations across shards does not matter.

• Associative — folding shards in any grouping yields the same graph.

• Convergent — two nodes with the same operations produce the identical graph.

Ordering is deterministic: entities by name, relations by (from, to, relationType), observations in operation order (HLC, node, sequence).

Compaction

The log grows with every mutation. Compaction rewrites this node's shard keeping, per element, only the operations that no later operation shadows — the shadow index is built from the merged log, so an operation superseded by another node's later operation is dropped too, and the winner of every element always survives, tombstones included. It runs online: the server compacts its own shard after DATALORE_COMPACT_AFTER appends, and datalore compact does the same by hand. When each also collects tombstones — the physical deletion — is under Deletion and collection. The rewrite is a compare-and-swap (a temporary file renamed over the shard on the file backend, a conditional write on s3), so a crash midway can never truncate it and a concurrent write — an append, or a gossip recovery — makes the whole compaction re-read and retry instead of losing it.

Every compaction stamps a shard.compact metadata operation (never a graph element): its generation is bumped each time, and its seq is the shard's durable high-water mark, so the sequence never regresses and a compaction is never mistaken for a lost shard. Peers read the generation from /shards and replace a stale replica with the compacted shard, so every copy converges to the compacted content. Only the local shard is rewritten; every other shard is untouched, so compaction is conflict-free and other nodes are unaffected.

Deletion and collection

A delete is an operation, not a removal: delete_entities, delete_observations and delete_relations append tombstones, and the fold hides the element while a tombstone is the latest operation for it. The tombstone is exactly what makes the deletion beat a concurrent write, so dropping it while that write still exists would revive the element. A deletion is therefore only physical once nobody can still hold one.

datalored collects the shard it authors when the mesh is safe, per element. It first prunes the operations a later one shadows that no retained off-mesh peer could still need, then it may drop a surviving tombstone when no peer off the mesh could contest that element. Two conditions gate it, per peer: the peer's knowledge frontier must be a subset of what this node already holds — a peer holding any operation this node lacks could hold a relayed operation on the element and is treated as a blocker — and the frontier must cover no operation of the element, so only a peer that folded it can contest it; a peer that never folded it can still mint a coincident element (an independent name, an observation on an entity it knows), which the fold decides as a concurrent genesis, not a revival. An element that appeared while a peer was already away is thus collectable at once, without waiting for that peer to return; an element the peer did know stays frozen until the peer comes back or is explicitly departed. A surviving operation on another shard also keeps the tombstone, so the fold is always preserved. When a peer could contest an element, the operation it knew is kept as the evidence the next round reads again — and by the node that authored it, not only by the deleter: a node never prunes a shadowed operation on an element a peer off the mesh could still contest, so the proof survives even when the tombstone lives on a different shard (a delete by one node of an element another created).

The claim, in one line: collection needs no barrier over the history — a peer that never folded an element cannot causally contest it, however long it stays away, and neither a global round of acknowledgements nor a timer on the data is needed. What it does need is a stable round over the live set: every retained peer that is still reachable must have been exchanged with in that round (otherwise the round suspends, because such a peer may hold writes this node has not pulled), and no retained peer may hold anything this node lacks. That is why DATALORED_FANOUT and DATALORED_GC exclude each other. Liveness is not autonomous: a peer that is both unreachable and unverifiable — never reached, or holding operations this node cannot read — blocks collection until the operator departs it (DATALORED_REVOKED / DATALORED_DEPARTED); the retention window bounds metadata and forfeits no data, but it does not remove that decision.

Each peer's knowledge frontier — the greatest sequence it has been seen to hold of every author's shard — is durable, persisted beside the shards in .datalored-peers.json, so a machine gone for a year keeps blocking only the elements it actually knew. A peer the round could still reach but did not exchange with suspends collection entirely, because it may hold writes this node has not pulled. Forgetting a peer (the membership TTL) does not unblock it — it may be alive off the mesh with valid unsent writes — but DATALORED_REVOKED and DATALORED_DEPARTED do, the operator's decision that its writes are expendable; a departed peer's replica is discarded outright. A forfeited node's authorship is dropped from the gate as well, so it stops holding the collection back through the peers that still hold its replica; a revoked node's operations already here keep their elements frozen until it is departed, which discards them. Turn the daemon's collection off with DATALORED_GC=false.

The per-peer record is bounded. After DATALORED_FORGET_AFTER seconds of silence (default 180 days) a peer is retired, and retirement is semantically invisible: it moves where the protection lives, never which elements are guarded. An accounted peer — one whose frontier is a subset of what this node already holds — is merged into a durable retired frontier, one elementwise maximum per author. The maximum is exactly the union of the per-element decisions the peers would have made, so it keeps gating every element those peers could have folded, including deletions that happen after the retirement, over one map bounded by the shards this node holds rather than one map per peer. (A per-element list computed at expiry would not be sound: a tombstone created later is not on it.) An unaccounted peer — one that claims operations this node cannot read — leaves a durable blanket instead: the same "it could hold anything" that gated it while it was live, stated once rather than carried as an unverifiable map. Nothing is forfeited by retiring, which is why the window can be on by default. DATALORED_REVOKED and DATALORED_DEPARTED remain the only forfeiture — and since a maximum cannot be un-merged, a node must be departed before its window expires for its contribution to be lifted. A frontier that claims an author this node cannot serve is never recorded: a peer cannot freeze the mesh with a claim it cannot back. A claimed node that was never reached keeps blocking conservatively, as before.

Without the daemon a physical deletion needs an explicit promise. A daemon that owns the directory (.datalored-peers.json present) holds the peer knowledge and collects, and it is the only writer that maintains the shard: the server and datalore compact keep their whole view there — a shadowed operation is exactly the evidence the daemon's gate reads, so they must not prune it — and leave the shard to the daemon. With no daemon there is no knowledge to gate on, and the directory cannot tell whether a peer is merely absent: a machine that is offline, or configured but not yet synced, leaves nothing to see, and its unsynced write would be lost by a collection — which is the immediate deletion the official server performs. So the server's online maintenance and datalore compact both prune only, until the operator declares the directory written by this node alone (DATALORE_GC=true), the promise that makes it sound; a declaration contradicted by another node's shard is refused and reported.

Metrics

The daemon serves every metric at /metrics (the MCP server is stdio and has no endpoint). The collection and retention state:

The same endpoint carries the transport and liveness families — datalore_gossip_rounds_total, datalore_gossip_round_seconds, datalore_gossip_round_bytes, datalore_gossip_links, datalore_gossip_shards, datalore_gossip_stable_shards, datalore_gossip_membership_size, datalore_gossip_peers_ok_total, datalore_gossip_peers_failed_total, datalore_gossip_ops_pulled_total, datalore_gossip_ops_pushed_total, datalore_gossip_digest_exchanges_total, datalore_gossip_membership_channel_failures_total, datalore_gossip_bytes_sent_total, datalore_gossip_bytes_received_total, datalore_gossip_fanout, datalore_gossip_up — and, when the daemon runs the local log's own maintenance, its datalore_mcp_* counters (datalore_mcp_folds_total, datalore_mcp_fold_seconds, datalore_mcp_shards_merged, datalore_mcp_shard_stat_total, datalore_mcp_ops_decoded_total, datalore_mcp_compacted_total, datalore_mcp_compaction_failed_total).

Drop-in compatibility

The same nine tools, so it slots under the memory server name with no agent changes:

create_entities, create_relations, add_observations, delete_entities, delete_observations, delete_relations, read_graph, search_nodes, open_nodes.

The full graph is also exposed at the memory://knowledge-graph resource. The resource is read-only; clients can subscribe to it and receive notifications/resources/updated whenever a mutation changes the graph — via resources/subscribe on the 2025-era protocol, or a subscriptions/listen stream on the 2026-07-28 revision. The server speaks both: it answers the initialize handshake for 2025-era clients and server/discover for 2026-07-28 clients.

Entities are keyed by name. Observations behave like the official server: add_observations has no effect when the content is already present, but preserves duplicate contents within a single request, create_entities preserves duplicate contents as given, and deleting an observation removes every occurrence of that content. Relations are sets: adding the same relation twice has no effect. delete_entities also removes the entity's observations and its incident relations. The tombstones cover what the deleting node could see: an observation added concurrently on another node survives as an element, so if the entity is later created again that observation reappears with it.

Security

• One writer per shard. Keep DATALORE_NODE_ID unique and run one server per node id. A node id is sanitised to a bounded, path-safe shard-file stem, and every node id that arrives from a peer (a /shards/{node}/ops path, a gossiped summary, an announced header) is validated before it can name a file, enter the peer set or label a metric.

• Replication. datalored is a network service. Run it behind a VPN or firewall, and enable mTLS (on by default when certificates are configured) so only authenticated peers replicate. The client always verifies the peer certificate — a configured CA, or the system trust store when none is set; an unverified connection is never used. Without TLS the daemon trusts the underlay; a peer's advertised address is still validated as a plain host:port, and it is adopted only when it matches the host the request actually came from (so an announced header cannot point the dialer at another host). A refusal (DATALORED_REVOKED / DATALORED_DEPARTED) needs the client certificate that mTLS supplies — without it a peer has no identity to refuse. Setting DATALORED_TLS_REQUIRE_CLIENT=false turns TLS back into an unauthenticated channel (any client can push any shard); the daemon logs a warning at startup in that mode.

• Trust model. mTLS authenticates members, not individual operations. A node serves every shard it holds, so relaying is trusted: a member can carry another node's shard to a third node, and the daemon recovers its own lost shard from a peer's replica. Run datalored only on machines you trust as peers. The fold's last-writer-wins order trusts the operation's HLC stamp, so a member whose clock is far ahead wins every conflict on its elements; a wrong clock is a correctness hazard, not a privilege boundary. Collection also trusts what a member reports: its frontier from /shards and the peer set it gossips — the certificate proves who it is, not that its reports are true — and it assumes the mesh is the only path by which a node learns: a node that leaves must not keep receiving operations elsewhere (do not point a daemon and a shared file/s3 area at the same directory, and a node bridging two meshes belongs to both or should be departed).

• Reporting a vulnerability. Do not open a public issue — see SECURITY.md.

Development

git clone https://github.com/sequico/datalore-mcp.git
cd datalore-mcp
npm install

npm run check       # Biome: lint + format + import order
npm run typecheck   # tsc --noEmit
npm run test        # Vitest, including the CRDT property tests
npm run build       # tsc -> dist/
npm run gate        # all of the above

See CONTRIBUTING.md for the workflow and the ground rules.

Why the name datalore?

The name is an homage to Star Trek: The Next Generation. "Datalore" (Season 1, Episode 13, first aired 18 January 1988) is the episode that introduces Lore — the twin brother of Data, two Soong-type androids built by Dr. Noonien Soong. Data is the good one; Lore is the flawed, emotional, malicious prototype. The episode title is itself a portmanteau of Data and Lore, and both are played by the same actor, Brent Spiner.

The names fit a knowledge graph almost too well:

• Data — a datum: a single fact.

• Lore — the body of knowledge and tradition shared by a community.

So datalore = the facts plus the shared lore: exactly what a shared agent memory is. And the twin motif maps onto this project's core problem — many copies of the same graph, on different machines, that must stay consistent. Data's good twin and Lore's evil twin are the two failure modes; in datalore-mcp the copies cannot drift apart, because the merge is conflict-free by construction. The graph has no evil twin.

A small production gem: the episode was first pitched as a romance for Data with a female android. It was Brent Spiner himself who suggested the evil-twin plot instead — so the twin who gave the project its name also gave the episode its twist.

References:

• Memory Alpha — Datalore (episode): https://memory-alpha.fandom.com/wiki/Datalore_(episode)

• Memory Alpha — Lore: https://memory-alpha.fandom.com/wiki/Lore

• Wikipedia — Datalore: https://en.wikipedia.org/wiki/Datalore

This project is an independent fan homage. "Star Trek", "Data" and "Lore" are the property of their respective rights holders; this project is not affiliated with or endorsed by them.

Documentation

• PLAN.md — architecture and design decisions.

• SCALING.md — the forward-looking plan for 1,000 / 10,000 nodes.

• docs/paper.md — the technical report: the model, the tombstone-collection contribution and the evaluation, with the bibliography in docs/references.bib (frozen at release 0.9.0; the report is © Sequi Company under CC BY 4.0 — see NOTICE).

• CONTRIBUTING.md — how to contribute.

• AGENTS.md — the project rules for contributors and agents.

• SECURITY.md — security policy and how to report a vulnerability.

• CODE_OF_CONDUCT.md — the community code of conduct.

License & Disclaimer

The source code is licensed under the Mozilla Public License 2.0 (MPL-2.0); the report docs/paper.md is © Sequi Company, licensed under CC BY 4.0 — see NOTICE.

⚠️ NO WARRANTY & NO SUPPORT: This software is provided "as is". The author offers absolutely no commercial support, no maintenance guarantees, and no uptime assistance. If you open an issue, it will be addressed purely on a voluntary basis.

❤️ Support the Project (Passive Monetization)

If this component is saving your company hundreds of hours of infrastructure development, please consider supporting the project:

• Sponsor on GitHub: Help keep this project alive by adding your company logo to this README. 👉 Sponsor Now

• Back a Feature: Want a specific bug fixed or a new protocol added? We use Polar.sh. Put a bounty on any GitHub Issue to incentivize its development. 👉 View Bounties