MentisDB

MentisDB is a durable semantic memory engine and versioned skill registry for AI agents — a persistent, hash-chained brain that survives context resets, model swaps, and team turnover.

It stores semantically typed thoughts in an append-only, hash-chained memory log through a swappable storage adapter layer. The skill registry is a git-like immutable version store for agent instruction bundles — every upload is a new version, history is never overwritten, and every version is cryptographically signable.

Harness Swapping — the same durable memory works across every AI coding environment. Connect Claude Code, OpenAI Codex, GitHub Copilot CLI, Qwen Code, Cursor, VS Code, or any MCP-capable host to the same mentisdbd daemon and your agents share one brain, regardless of which tool you picked up today.

Zero Knowledge Loss Across Context Boundaries — when an agent's context window fills, it writes a Summary checkpoint to MentisDB, compacts, reloads mentisdb_recent_context, and continues without losing a single decision. Chat history is ephemeral. MentisDB is permanent.

Fleet Orchestration at Scale — one project manager agent decomposes work, dispatches a parallel fleet of specialists, each pre-warmed with shared memory, and synthesizes results wave by wave. MentisDB is the coordination substrate: every agent reads from the same chain and writes its lessons back. The fleet's collective intelligence compounds.

Versioned Skill Registry — skills are not just stored, they are versioned like a git repository. Every upload to an existing skill_id creates a new immutable version (stored as a unified diff). Any historical version is reconstructable. Skills can be deprecated or revoked while full audit history is preserved. Uploading agents with registered Ed25519 keys must cryptographically sign their uploads — provenance is verifiable, not assumed.

Session Resurrection — any agent can call mentisdb_recent_context and immediately know exactly where the project stands, what decisions were made, what traps were already hit, and what comes next — without re-reading code, re-running exploratory searches, or asking the human to re-explain context that was earned through hours of work.

Self-Improving Agent Fleets — agents upload updated skill files after learning something new. A skill checked in at the start of a project is better by the end of it. Combine with Ed25519 signing to create a verifiable, tamper-evident record of which agent authored which version of institutional knowledge.

Multi-Agent Shared Brain — multiple agents, multiple roles, multiple owners can write to the same chain key simultaneously. Every thought carries a stable agent_id. Queries filter by agent identity, thought type, role, tags, concepts, importance, and time windows. The chain represents the full collective intelligence of an entire orchestration system, not just one session.

Lessons That Outlive Models — architectural decisions, hard constraints, non-obvious failure modes, and retrospectives written to MentisDB survive chat loss, model upgrades, and team changes. The knowledge compounds instead of evaporating. A new engineer or a new agent boots up, loads the chain, and inherits everything the team learned.

Install the daemon:

cargo install mentisdb

Connect your local AI tools the fast way:

mentisdbd wizard

Or target one integration explicitly:

mentisdbd setup codex
mentisdbd setup all --dry-run
mentisdbd wizard
mentisdbd add "The sky is blue"
mentisdbd search "cache invalidation" --limit 5 --scope session
mentisdbd agents

Then start the daemon:

mentisdbd

When run in an interactive terminal, mentisdbd launches a full TUI with scrollable panes for configuration, chain/agent/skill tables, and a live event log. On an interactive first run with no configured client integrations, it offers to launch the setup wizard immediately after startup so you do not have to guess the next command.

Run persistently after closing your SSH session (no TUI in background mode):

nohup mentisdbd &

Modern MCP clients bootstrap themselves from the MCP handshake:

• initialize.instructions tells the agent to read mentisdb://skill/core
• resources/read(mentisdb://skill/core) delivers the embedded operating skill
• GET /mentisdb_skill_md remains available only as a compatibility fallback

If you need to wire a tool manually, here are the raw MCP commands/configs:

# Claude Code
claude mcp add --transport http mentisdb http://127.0.0.1:9471

# OpenAI Codex
codex mcp add mentisdb --url http://127.0.0.1:9471

# Qwen Code
qwen mcp add --transport http mentisdb http://127.0.0.1:9471

# GitHub Copilot CLI — use /mcp add in interactive mode,
# or write ~/.copilot/mcp-config.json manually (see below)

mentisdb/ contains:

• the standalone mentisdb library crate
• server support for HTTP MCP and REST, enabled by default
• the mentisdbd daemon binary
• dedicated tests under mentisdb/tests

A Makefile is included at the repository root. All common workflows have a target:

make build          # fmt + release build
make build-mentisdbd # build only the daemon binary
make release        # fmt, check, clippy, build, test, doc in sequence
make fmt            # cargo fmt
make check          # cargo check (lib + binary)
make clippy         # cargo fmt + clippy --all-targets -D warnings
make test           # cargo test
make bench          # Criterion benchmarks, output tee'd to /tmp/mentisdb_bench_results.txt
make doc            # cargo doc --all-features
make install        # cargo install --path . --locked
make publish        # cargo publish
make publish-dry-run
make clean
make help           # list all targets with descriptions

make build

Or directly with Cargo:

cargo build --release

Build only the library without the default daemon/server stack:

cargo build --no-default-features

make test

Or directly:

cargo test

Run tests for the library-only build:

cargo test --no-default-features

Run rustdoc tests:

cargo test --doc

MentisDB ships a Criterion benchmark suite and a harness-free HTTP concurrency benchmark:

make bench

Or directly:

cargo bench

Results are also written to /tmp/mentisdb_bench_results.txt so numbers persist across terminal sessions.

Benchmark coverage:

• benches/thought_chain.rs — 10 benchmarks: append throughput, query latency, traversal patterns
• benches/search_baseline.rs — 4 benchmarks: lexical/filter-first search baseline over content, registry text, indexed+text intersections, and newest-tail limits
• benches/search_ranked.rs — 4 benchmarks: additive ranked retrieval over lexical content, filtered ranked queries, and heuristic fallback, plus a baseline append-order comparison
• benches/skill_registry.rs — 12 benchmarks: skill upload, search, delta reconstruction, lifecycle
• benches/http_concurrency.rs — starts mentisdbd in-process on a random port; measures write and read throughput at 100 / 1k / 10k concurrent Tokio tasks with p50/p95/p99 latency reporting

Baseline numbers from the DashMap concurrent chain lookup refactor: 750–930 read req/s at 10k concurrent tasks, compared to a sequential bottleneck on the previous RwLock<HashMap> implementation.

make doc

Or directly:

cargo doc --no-deps

Generate docs for the library-only build:

cargo doc --no-deps --no-default-features

The standalone executable is mentisdbd.

Run it from source:

cargo run --bin mentisdbd

Install it from the crate directory:

make install
# or
cargo install --path . --locked

mentisdbd now owns both daemon startup and local integration setup:

mentisdbd setup codex
mentisdbd setup all --dry-run
mentisdbd wizard
mentisdbd add "The sky is blue"
mentisdbd search "cache invalidation" --limit 5 --scope session
mentisdbd agents
mentisdbd

When it starts, it serves:

• an MCP server
• a REST server
• an HTTPS web dashboard

Before serving traffic, it:

• migrates or reconciles discovered chains to the current schema and default storage adapter
• verifies chain integrity and attempts repair from valid local sources when possible
• migrates the skill registry from V1 to V2 format if needed (idempotent; safe to run repeatedly)
• migrates chain relations from V2 to V3 format to add temporal edge validity (valid_at/invalid_at) if needed (idempotent; safe to run repeatedly)

Once startup completes, it prints:

• the active chain directory, default chain key, and bound MCP/REST/dashboard addresses
• a catalog of all exposed HTTP endpoints with one-line descriptions
• a per-chain summary with version, adapter, thought count, and per-agent counts

mentisdbd is configured with environment variables:

• MENTISDB_DIR Directory where MentisDB storage adapters store chain files.
• MENTISDB_DEFAULT_CHAIN_KEY Default chain_key used when requests omit one. Default: borganism-brain. MENTISDB_DEFAULT_KEY is accepted as a deprecated alias.
• MENTISDB_STORAGE_ADAPTER Default storage backend for newly created chains. Only binary is supported for new chains (JSONL is deprecated — existing JSONL chains remain readable). Default: binary
• MENTISDB_VERBOSE When unset, verbose interaction logging defaults to true. Supported explicit values: 1, 0, true, false.
• MENTISDB_LOG_FILE Optional path for interaction logs. When set, MentisDB writes interaction logs to that file even if console verbosity is disabled. If MENTISDB_VERBOSE=true, the same lines are also mirrored to the console logger.
• MENTISDB_BIND_HOST Bind host for both HTTP servers. Default: 127.0.0.1
• MENTISDB_MCP_PORT MCP server port. Default: 9471
• MENTISDB_REST_PORT REST server port. Default: 9472
• MENTISDB_DASHBOARD_PORT HTTPS dashboard port. Default: 9475. Set to 0 to disable the web dashboard.
• MENTISDB_DASHBOARD_PIN Optional PIN required to access the dashboard. Leave unset only for trusted localhost use.
• MENTISDB_AUTO_FLUSH Controls per-write durability of the binary storage adapter.
  • true (default): every append_thought flushes to disk immediately. Full durability.
  • false: writes are batched and flushed every 16 appends (FLUSH_THRESHOLD). Up to 15 thoughts may be lost on a hard crash or power failure, but write throughput increases significantly for multi-agent hubs with many concurrent writers. Supported values: 1, 0, true, false. Has no effect on the jsonl adapter.
• MENTISDB_GROUP_COMMIT_MS Group-commit window in milliseconds for the background binary writer. The writer batches appends within this window before flushing to disk. Lower values = lower latency; higher values = better throughput. Default: 2
• MENTISDB_UPDATE_CHECK Background GitHub release check for mentisdbd. Enabled by default; set 0, false, no, or off to disable update checks after startup. Default: true
• MENTISDB_UPDATE_REPO Optional GitHub owner/repo override used by the updater. Default: CloudLLM-ai/mentisdb
• MENTISDB_HTTPS_MCP_PORT HTTPS MCP server port. Default: 9473. Set to 0 to disable HTTPS MCP.
• MENTISDB_HTTPS_REST_PORT HTTPS REST server port. Default: 9474. Set to 0 to disable HTTPS REST.
• MENTISDB_TLS_CERT Path to a PEM-encoded TLS certificate for the HTTPS servers and dashboard. Default: <MENTISDB_DIR>/tls/cert.pem
• MENTISDB_TLS_KEY Path to a PEM-encoded TLS private key for the HTTPS servers and dashboard. Default: <MENTISDB_DIR>/tls/key.pem
• MENTISDB_STARTUP_SOUND Play the 4-note "men-tis-D-B" startup jingle. Default: true. Set 0, false, no, or off to silence.
• MENTISDB_THOUGHT_SOUNDS Play a unique short sound for each thought type on append. Default: false. Set 1, true, yes, or on to enable.
• MENTISDB_DEDUP_THRESHOLD Jaccard similarity threshold for automatic deduplication on append (0.0–1.0). When unset, auto-dedup is disabled. When set, a new thought whose content is sufficiently similar to a recent thought receives a Supersedes relation instead of being stored as a duplicate.
• MENTISDB_DEDUP_SCAN_WINDOW Number of recent thoughts to scan for dedup comparison. Default: 64.

Example — full durability (production default):

MENTISDB_DIR=/tmp/mentisdb \
MENTISDB_DEFAULT_CHAIN_KEY=borganism-brain \
MENTISDB_STORAGE_ADAPTER=binary \
MENTISDB_VERBOSE=true \
MENTISDB_LOG_FILE=/tmp/mentisdb/mentisdbd.log \
MENTISDB_BIND_HOST=127.0.0.1 \
MENTISDB_MCP_PORT=9471 \
MENTISDB_REST_PORT=9472 \
MENTISDB_DASHBOARD_PIN=change-me \
MENTISDB_AUTO_FLUSH=true \
cargo run --bin mentisdbd

Example — high-throughput write mode (multi-agent hub):

MENTISDB_DIR=/var/lib/mentisdb \
MENTISDB_AUTO_FLUSH=false \
MENTISDB_BIND_HOST=0.0.0.0 \
mentisdbd

mentisdbd checks GitHub releases in the background after startup and can offer to update itself with cargo install.

• checks are enabled by default
• version comparison uses only the first three numeric components, so a tag like 0.6.1.14 is treated as core version 0.6.1
• interactive terminals get an ASCII prompt window with Y / N
• non-interactive terminals never block; they print the exact manual cargo install command instead

Disable the automatic check:

MENTISDB_UPDATE_CHECK=0 \
mentisdbd

MCP endpoints:

• GET /health
• POST /
• POST /tools/list
• POST /tools/execute

REST endpoints:

• GET /health
• GET /mentisdb_skill_md
• GET /v1/skills
• GET /v1/skills/manifest
• GET /v1/chains
• POST /v1/chains/merge
• POST /v1/chains/branch
• POST /v1/vectors/rebuild
• POST /v1/bootstrap
• POST /v1/agents
• POST /v1/agent
• POST /v1/agent-registry
• POST /v1/agents/upsert
• POST /v1/agents/description
• POST /v1/agents/aliases
• POST /v1/agents/keys
• POST /v1/agents/keys/revoke
• POST /v1/agents/disable
• POST /v1/thought
• POST /v1/thoughts
• POST /v1/thoughts/genesis
• POST /v1/thoughts/traverse
• POST /v1/retrospectives
• POST /v1/search
• POST /v1/lexical-search
• POST /v1/ranked-search
• POST /v1/context-bundles
• POST /v1/recent-context
• POST /v1/memory-markdown
• POST /v1/import-markdown
• POST /v1/skills/upload
• POST /v1/skills/search
• POST /v1/skills/read
• POST /v1/skills/versions
• POST /v1/skills/deprecate
• POST /v1/skills/revoke
• POST /v1/webhooks
• GET /v1/webhooks
• GET /v1/webhooks/{id}
• DELETE /v1/webhooks/{id}
• POST /v1/head

{
  "chain_key":      "my-chain",
  "agent_id":       "my-agent",
  "agent_name":     "My Agent",
  "thought_type":   "LessonLearned",
  "role":           "Execution",
  "content":        "...",
  "scope":          "session",
  "tags":           ["tag1"],
  "concepts":       ["concept1"],
  "entity_type":    "incident",
  "source_episode": "triage-2026-04-17",
  "importance":     0.9,
  "confidence":     0.8,
  "refs":           [14, 22],
  "relations": [
    { "kind": "CausedBy",      "target_id": "<uuid>" },
    { "kind": "ContinuesFrom", "target_id": "<uuid>", "chain_key": "other-chain" },
    { "kind": "Supersedes",    "target_id": "<uuid>", "valid_at": "2025-01-01T00:00:00Z", "invalid_at": "2025-12-31T23:59:59Z" }
  ]
}

chain_key, role, scope, tags, concepts, entity_type, source_episode, importance, confidence, refs, and relations are optional.
relations[].kind accepts: References, Summarizes, Corrects, Invalidates, CausedBy, Supports, Contradicts, DerivedFrom, ContinuesFrom, BranchesFrom, RelatedTo, Supersedes.
relations[].chain_key is optional — omit for intra-chain edges, set for cross-chain references.
relations[].valid_at and relations[].invalid_at are optional RFC 3339 timestamps that define when a relation edge is temporally valid, enabling point-in-time queries with as_of.
entity_type is an optional per-chain ontology label (e.g. "incident", "customer", "deploy") used by the dashboard explorer and by ranked-search filtering.
source_episode is an optional free-form provenance string (e.g. a conversation id or batch name) that groups thoughts by the episode they were derived from; it is also filterable in ranked search.

MentisDB keeps its baseline thought search surface filter-first and append-order. Ranked, graph-aware, and vector retrieval are additive surfaces layered on top of that stable baseline.

Today, the main search APIs are:

• MentisDb::query(&ThoughtQuery)
• POST /v1/search
• mentisdb_search

Current behavior:

• indexed filters narrow the candidate set for thought_type, role, agent_id, tags, and concepts
• text is a case-insensitive substring match over:
  • thought content
  • agent_id
  • tags
  • concepts
  • agent-registry display name, aliases, owner, and description
• results are returned in append order
• limit keeps the newest matching tail after filtering rather than applying a ranking score

That means plain ThoughtQuery / /v1/search behavior is deterministic and explainable, but that baseline path is not BM25, hybrid, or vector retrieval. Additive ranked and graph-aware retrieval now exist on separate crate, REST, and MCP surfaces.

Examples:

use mentisdb::{MentisDb, ThoughtQuery, ThoughtType};
use std::path::PathBuf;

# fn main() -> std::io::Result<()> {
let chain = MentisDb::open(&PathBuf::from("/tmp/tc_query"), "agent1", "Agent", None, None)?;

let lexical = ThoughtQuery::new()
    .with_types(vec![ThoughtType::Decision])
    .with_tags_any(["search"])
    .with_text("latency");

let results = chain.query(&lexical);
# let _ = results;
# Ok(())
# }

{
  "chain_key": "mentisdb",
  "thought_types": ["Decision"],
  "tags_any": ["search"],
  "text": "latency",
  "limit": 20
}

Design note:

• treat this lexical/filter-first behavior as the baseline
• keep ranked, vector, and hybrid retrieval as additive, explicitly documented surfaces on top of that baseline
• do not silently change the semantics of ThoughtQuery or /v1/search from append-order filtering to score-ranked retrieval

The dedicated benchmark benches/search_baseline.rs and evaluation tests in tests/search_eval_tests.rs are intended to preserve that baseline while world-class search evolves.

MentisDB now also exposes an additive ranked-search surface for direct crate use:

• RankedSearchQuery
• RankedSearchGraph
• MentisDb::query_context_bundles(&RankedSearchQuery)
• MentisDb::query_ranked(&RankedSearchQuery)
• RankedSearchBackend::{Lexical, Hybrid, LexicalGraph, HybridGraph, Heuristic}

This surface is intentionally separate from ThoughtQuery.

ThoughtQuery still decides which thoughts are eligible. Ranked search then decides how those eligible thoughts are ordered.

Current ranked-search behavior:

• RankedSearchQuery.filter uses the same deterministic semantics as MentisDb::query
• when text normalizes to a non-empty query, the backend is lexical or hybrid depending on whether a managed vector sidecar is active for the current handle
• lexical ranking scores indexed thought text plus agent metadata from the filtered candidate set
• when a managed vector sidecar is active for the current handle, ranked search blends lexical scoring with vector similarity and the backend becomes hybrid
• when graph is enabled alongside non-empty text, the backend becomes lexical_graph or hybrid_graph depending on whether vector scoring is available
• graph expansion starts from lexical seed hits, walks refs and typed relations, and can surface supporting context that did not lexically match
• as_of (RFC 3339 timestamp) filters to only relation edges that were valid at the given point in time, using each edge's valid_at/invalid_at temporal window — edges without temporal bounds are always included
• scope narrows results to thoughts tagged with a matching memory scope (user, session, or agent); omitted scope returns all
• when text is absent or blank, the backend falls back to heuristic
• heuristic ordering uses lightweight importance, confidence, and recency signals
• total_candidates counts the hits after filter application and ranked-signal gating, before final limit truncation
• each ranked hit includes matched_terms plus match_sources such as content, tags, concepts, agent_id, and agent_registry
• each ranked hit also includes a vector score component when semantic sidecars contribute to the ranking
• graph-expanded hits also expose graph_distance, graph_seed_paths, graph_relation_kinds, and graph_path provenance so callers can explain why a supporting thought surfaced
• grouped context delivery is available through query_context_bundles, which anchors supporting graph hits beneath lexical seeds in deterministic order

Lexical ranked example:

use mentisdb::{MentisDb, RankedSearchQuery, ThoughtQuery, ThoughtType};
use std::path::PathBuf;

# fn main() -> std::io::Result<()> {
let chain = MentisDb::open(&PathBuf::from("/tmp/tc_ranked"), "agent1", "Agent", None, None)?;

let ranked = RankedSearchQuery::new()
    .with_filter(
        ThoughtQuery::new()
            .with_types(vec![ThoughtType::Decision])
            .with_tags_any(["search"]),
    )
    .with_text("latency ranking")
    .with_limit(10);

let results = chain.query_ranked(&ranked);
assert!(matches!(
    results.backend,
    mentisdb::RankedSearchBackend::Lexical | mentisdb::RankedSearchBackend::Hybrid
));
# let _ = results;
# Ok(())
# }

Lexical + graph ranked example:

use mentisdb::{MentisDb, RankedSearchGraph, RankedSearchQuery, ThoughtQuery, ThoughtType};
use mentisdb::search::GraphExpansionMode;
use std::path::PathBuf;

# fn main() -> std::io::Result<()> {
let chain = MentisDb::open(&PathBuf::from("/tmp/tc_ranked"), "agent1", "Agent", None, None)?;

let ranked = RankedSearchQuery::new()
    .with_filter(
        ThoughtQuery::new()
            .with_types(vec![ThoughtType::Decision])
            .with_tags_any(["search"]),
    )
    .with_text("latency ranking")
    .with_graph(
        RankedSearchGraph::new()
            .with_max_depth(1)
            .with_mode(GraphExpansionMode::Bidirectional),
    )
    .with_limit(10);

let results = chain.query_ranked(&ranked);
# let _ = results;
# Ok(())
# }

Temporal and scoped ranked example:

use mentisdb::{MentisDb, MemoryScope, RankedSearchQuery, ThoughtQuery};
use std::path::PathBuf;

# fn main() -> std::io::Result<()> {
let chain = MentisDb::open(&PathBuf::from("/tmp/tc_ranked"), "agent1", "Agent", None, None)?;

let ranked = RankedSearchQuery::new()
    .with_filter(ThoughtQuery::new())
    .with_text("cache invalidation")
    .with_as_of("2025-06-01T00:00:00Z")
    .with_scope(MemoryScope::Session)
    .with_limit(10);

let results = chain.query_ranked(&ranked);
# let _ = results;
# Ok(())
# }

Vector-backed ranked example:

use mentisdb::{MentisDb, RankedSearchBackend, RankedSearchQuery};
use mentisdb::search::LocalTextEmbeddingProvider;
use std::path::PathBuf;

# fn main() -> std::io::Result<()> {
let mut chain = MentisDb::open_with_key(&PathBuf::from("/tmp/tc_ranked_vectors"), "semantic-ranked")?;
chain.append("planner", mentisdb::ThoughtType::Decision, "Tail latency ceiling for the Europe rollout.")?;

// Register the built-in local-text-v1 embedding sidecar so ranked search can
// blend lexical and semantic signals for the current chain handle.
chain.manage_vector_sidecar(LocalTextEmbeddingProvider::new())?;

let ranked = chain.query_ranked(&RankedSearchQuery::new().with_text("performance budget"));

assert_eq!(ranked.backend, RankedSearchBackend::Hybrid);
assert!(ranked.hits.iter().any(|hit| hit.score.vector > 0.0));
# Ok(())
# }

Grouped context example:

use mentisdb::{MentisDb, RankedSearchGraph, RankedSearchQuery, ThoughtQuery};
use mentisdb::search::GraphExpansionMode;
use std::path::PathBuf;

# fn main() -> std::io::Result<()> {
let chain = MentisDb::open(&PathBuf::from("/tmp/tc_ranked"), "agent1", "Agent", None, None)?;

let bundles = chain.query_context_bundles(
    &RankedSearchQuery::new()
        .with_filter(ThoughtQuery::new().with_tags_any(["search"]))
        .with_text("latency ranking")
        .with_graph(
            RankedSearchGraph::new()
                .with_mode(GraphExpansionMode::Bidirectional)
                .with_max_depth(2),
        )
        .with_limit(5),
);
# let _ = bundles;
# Ok(())
# }

Product rule:

• keep ThoughtQuery stable and explainable for append-order filtering
• evolve ranked search as a separate surface with its own benchmarks, tests, and transport layers
• treat registry-aware filtering and future transport exposure as additive work on top of the current crate API
• use query_ranked for flat ranked retrieval and query_context_bundles when the caller wants seed-anchored support context instead of one mixed list

The ranked-search benchmark benches/search_ranked.rs and evaluation tests in tests/search_ranked_eval_tests.rs are the guardrails for that additive surface.

Starting in 0.8.1, ranked search uses six key improvements:

• Porter stemming — the lexical tokenizer stems all tokens before indexing and querying so word variants share a common root (e.g. prefers/preferred/preferences → prefer).
• Smooth exponential vector-lexical fusion — replaces the 0.8.0 step-function boosts with a continuous decay curve: vector × (1 + 35 × exp(-lexical / 3.0)). Pure-semantic matches get ~36× amplification; by lexical=3.0 the boost has decayed to ~12×; at lexical=6.0 it's additive. This eliminates discontinuities between tiers.
• Session cohesion scoring — thoughts within ±8 positions of a high-scoring lexical seed (score ≥ 3.0) receive a proximity boost up to 0.8, decaying linearly with distance. This surfaces evidence turns adjacent to the matching turn but sharing no lexical terms.
• BM25 document-frequency cutoff — terms appearing in >30% of documents (corpus ≥ 20 docs) are skipped during scoring. This filters non-discriminative entity names without blanket stopword removal.
• Importance-weighted scoring — replaces flat multipliers with a differential boost proportional to lexical score: lexical × (importance - 0.5) × 0.3. User-originated thoughts (importance ≈ 0.8) outrank verbose assistant responses (importance ≈ 0.2) in close BM25 races.
• Graph expansion limits and relation boosts — MAX_GRAPH_SEEDS=20 bounds BFS cost; ContinuesFrom boost raised to 0.30; graph proximity 1.0/depth.

These changes took LongMemEval R@5 from 57.2% to 67.6% and LoCoMo 2-persona R@10 from 55.8% to 88.7%.

Starting in 0.8.2, ranked search adds temporal, scoped, and dedup-aware features:

• Temporal as_of point-in-time filtering — RankedSearchQuery::with_as_of(rfc3339) restricts graph expansion to only relation edges whose valid_at/invalid_at window covers the given timestamp. Edges without temporal bounds are always included. This enables queries like "what did the agent know at the start of the sprint?"
• Memory scope filtering via scope tag — thoughts carry a scope field (user, session, or agent) stored as scope:<value> tags. RankedSearchQuery::with_scope(MemoryScope::Session) narrows results to session-scoped memories only. Omitting scope returns thoughts from all scopes.
• Auto-dedup with Supersedes relations — when MENTISDB_DEDUP_THRESHOLD is set, appending a thought whose content is sufficiently similar (Jaccard ≥ threshold) to a recent thought automatically creates a Supersedes relation instead of storing a duplicate. The superseded thought's id is recorded for fast exclusion.
• invalidated_thought_ids for O(1) superseded detection — each thought tracks which earlier thoughts it supersedes. Ranked search uses this set to skip superseded thoughts in constant time without walking the full relation graph.

Starting in 0.8.7, ranked search adds:

• Entity type filtering — thoughts can carry an optional entity_type label (e.g. "bug_report", "architecture_decision", "retrospective"). The ThoughtQuery accepts entity_type as a filter, and the dashboard exposes an entity_type text filter in the explorer. Entity types are auto-observed per chain and persisted in a chain_key-entity-types.json sidecar.

Starting in 0.8.6, ranked search adds three features:

• Irregular verb lemma expansion — the query tokenizer maps irregular past-tense verbs to their base form (e.g. "went" → "go", "gave" → "give", "ran" → "run"). About 170 mappings. Indexed content is not modified — expansion is query-time only.
• Reciprocal Rank Fusion (RRF) — an opt-in reranking pass. When enable_reranking=true is set on a ranked search query, the engine produces separate lexical-only, vector-only, and graph-only rankings over the top rerank_k candidates (default 50), merges them via 1/(k + rank) with k=60, and replaces the additive total with the RRF total plus non-rankable signal adjustments. Use when lexical and vector signals disagree on top candidates.
• Per-field BM25 DF cutoffs — Bm25DfCutoffs on LexicalQuery allows configuring separate document-frequency cutoff ratios per field (content, tags, concepts, agent_id, agent_registry). Terms whose global DF exceeds the cutoff for a given field are skipped for that field only. Default cutoffs match the original behavior (content=0.30, tags=0.30, concepts=0.30, agent_id=0.70, agent_registry=0.60).

MentisDB now exposes an additive Phase 3 vector sidecar surface for direct crate use:

• search::EmbeddingProvider
• search::EmbeddingMetadata
• search::VectorSidecar
• VectorSearchQuery
• MentisDb::vector_sidecar_path(&EmbeddingMetadata)
• MentisDb::load_vector_sidecar(&EmbeddingMetadata)
• MentisDb::vector_sidecar_freshness(&VectorSidecar, &EmbeddingMetadata)
• MentisDb::rebuild_vector_sidecar(&provider)
• MentisDb::manage_vector_sidecar(provider)
• MentisDb::unmanage_vector_sidecar(&EmbeddingMetadata)
• MentisDb::managed_vector_sidecars()
• MentisDb::apply_persisted_managed_vector_sidecars()
• MentisDb::managed_vector_sidecar_statuses()
• MentisDb::set_managed_vector_sidecar_enabled(kind, enabled)
• MentisDb::sync_managed_vector_sidecar_now(kind)
• MentisDb::rebuild_managed_vector_sidecar_from_scratch(kind)
• MentisDb::query_vector(&provider, &VectorSearchQuery)

Contract:

• embeddings remain optional, and MentisDB still works with no vector dependencies at all
• vector state lives in a rebuildable sidecar, never in the canonical append-only chain
• vector sidecars are separated by chain_key, thought_id, thought_hash, model_id, embedding dimension, and embedding version
• changing the embedding model or version invalidates old vector state instead of silently mixing incompatible embeddings
• callers can opt one embedding space into append-time synchronization on a live handle by registering a managed vector sidecar provider
• vector hits surface whether they came from a Fresh or stale sidecar
• deleting or corrupting the sidecar degrades only vector retrieval; plain chain reads, appends, and lexical/graph search still work

Operational flow:

• rebuild a sidecar explicitly for one provider and chain
• or register that provider as a managed vector sidecar and keep it fresh on future appends for that open handle
• load or query that sidecar later with the same embedding metadata
• if the chain head changes, the sidecar becomes stale and results report that freshness state until the sidecar is rebuilt

mentisdbd now applies a persisted managed-vector setting every time it opens a chain.

• by default each chain gets the built-in FastEmbed MiniLM embedding provider (fastembed-minilm), which runs locally via ONNX with no cloud dependencies. The legacy local-text-v1 provider is also available.
• the daemon keeps that sidecar synchronized on append unless the user disables auto-sync for that chain
• ranked search in the daemon and dashboard now uses that managed sidecar transparently, blending lexical, graph, and vector signals whenever it is enabled and available
• the web dashboard exposes per-chain controls to:
  • enable or disable append-time auto-sync
  • sync the sidecar to the latest chain state without changing the enable/disable setting
  • rebuild the sidecar from scratch after an explicit confirmation that the previous file will be deleted and recreated
• if auto-sync is disabled, new thoughts can make the sidecar stale until the user syncs or rebuilds it

The daemon also exposes the Phase 1 ranked lexical surface over REST at POST /v1/lexical-search.

This is the right endpoint when you want lexicographical/lexical text ranking only, with simple score and match provenance fields.

Request shape:

{
  "chain_key": "mentisdb",
  "text": "latency ranking",
  "agent_ids": ["planner"],
  "thought_types": ["Decision"],
  "offset": 0,
  "limit": 10
}

Example response:

{
  "total": 1,
  "results": [
    {
      "thought": {
        "index": 42,
        "agent_id": "planner",
        "content": "Latency budget for the Europe rollout."
      },
      "score": 2.91,
      "matched_terms": ["latency", "ranking"],
      "match_sources": ["content", "tags", "agent_registry"]
    }
  ]
}

Phase 4 transport work keeps plain POST /v1/search and POST /v1/lexical-search compatibility and adds two additive endpoints:

• POST /v1/ranked-search for flat ranked retrieval
• POST /v1/context-bundles for seed-anchored grouped support context

Example POST /v1/ranked-search request:

{
  "chain_key": "mentisdb",
  "text": "performance budget",
  "thought_types": ["Decision"],
  "limit": 10
}

When a managed vector sidecar such as the built-in fastembed-minilm provider is active for that chain, the ranked backend becomes hybrid or hybrid_graph and the response includes a non-zero score.vector component for semantic-only or semantic-boosted hits.

Ranked response contract fields:

• backend
• results[].score.{lexical,vector,graph,relation,seed_support,importance,confidence,recency,session_cohesion,rrf,total}
• results[].matched_terms
• results[].match_sources
• results[].graph_distance
• results[].graph_seed_paths
• results[].graph_relation_kinds
• results[].graph_path
• results[].chain_key (present when searching branch chains with ancestor results)

Context-bundle response contract fields:

• total_bundles
• consumed_hits
• bundles[].seed.{locator,lexical_score,matched_terms,thought}
• bundles[].support[].{locator,thought,depth,seed_path_count,relation_kinds,path}

MCP transport mirrors this split with additive tools:

• mentisdb_ranked_search
• mentisdb_context_bundles

Acceptance coverage for these transport contracts lives in:

• tests/search_transport_contract_tests.rs

Response shape:

{
  "total": 2,
  "results": [
    {
      "thought": { "index": 42, "agent_id": "planner", "content": "..." },
      "score": {
        "lexical": 2.91,
        "vector": 0.27,
        "graph": 0.18,
        "relation": 0.05,
        "seed_support": 0.0,
        "importance": 0.0,
        "confidence": 0.0,
        "recency": 0.0,
        "total": 3.14
      },
      "matched_terms": ["latency", "ranking"],
      "match_sources": ["content", "tags", "agent_registry"]
    }
  ]
}

The daemon includes an embedded browser UI at:

https://127.0.0.1:9475/dashboard

The dashboard is served over HTTPS with the same self-signed certificate used by the HTTPS MCP and REST surfaces.

Dashboard capabilities:

• live chain listing with thought and agent counts
• thought exploration with grouped ThoughtType filters, refs, and typed relations
• chain-scoped ranked search with text and live-agent filters
• grouped context bundles for seed-anchored supporting search context
• ranked result inspection in the thought modal, including score breakdowns, matched terms, graph distance, relation kinds, and bundle support preview
• per-chain vector sidecar inspection plus enable/disable, sync, and rebuild controls
• agent detail management for display name, description, owner, status, and signing keys
• latest agent-thought browsing without restarting the daemon after new thoughts are appended
• chain import from MEMORY.md
• cross-chain agent-memory copy with agent metadata preserved on the target chain
• skill browsing, diffing, deprecation, and revocation

Protect the dashboard with MENTISDB_DASHBOARD_PIN whenever the daemon is reachable outside localhost.

MentisDB includes built-in backup and restore commands in the mentisdbd CLI.

# Default: platform MENTISDB_DIR → ./mentisdb-YYYY-MM-DD-HH-MM-SS.mbak
mentisdbd backup

# Custom output path
mentisdbd backup --output /backups/mentisdb-2026-04-14.mbak

# Include TLS certificates
mentisdbd backup --include-tls

# Full example
mentisdbd backup --dir ~/.cloudllm/mentisdb --output /backups/mentisdb-2026-04-14.mbak --include-tls

When the daemon is running, backup calls POST /v1/admin/flush before reading files to ensure a consistent snapshot even with MENTISDB_AUTO_FLUSH=false. If the daemon is not running, files are captured as-is.

# Idempotent restore (preserves existing files)
mentisdbd restore /backups/mentisdb-2026-04-14.mbak

# Restore to a specific directory
mentisdbd restore /backups/mentisdb-2026-04-14.mbak --dir ~/.cloudllm/mentisdb

# Force overwrite of all files
mentisdbd restore /backups/mentisdb-2026-04-14.mbak --overwrite

# Skip interactive confirmation prompts
mentisdbd restore /backups/mentisdb-2026-04-14.mbak --yes

Restore is idempotent by default — existing files are preserved. If files in the archive already exist in the target directory and --overwrite is not passed, an interactive prompt asks for confirmation. Pass --yes to skip all prompts.

A backup is a .mbak ZIP archive containing:

• mentisdb.manifest.json — SHA-256 checksums and metadata
• *.tcbin — binary chain storage
• *.agents.json — per-chain agent registries
• *.entity-types.json — per-chain entity type definitions
• *.vectors.*.json — vector search sidecars
• mentisdb-registry.json — global chain registry
• mentisdb-skills.bin — skill registry (if present)
• mentisdb-webhooks.json — webhook registrations (if present)
• tls/ — TLS certificates and keys (opt-in via --include-tls)

Every file is verified by SHA-256 before restore writes anything to disk. A corrupted archive aborts with an error and leaves the target directory untouched.

The flush operation is also available as a standalone REST endpoint:

POST http://127.0.0.1:9472/v1/admin/flush

Returns {"status": "flushed"} on success. Iterates all open chains and calls flush() on each adapter, blocking until all writes are durable.

The daemon currently exposes 42 MCP tools:

• mentisdb_bootstrap Create a chain if needed and write one bootstrap checkpoint when it is empty.
• mentisdb_append Append a durable semantic thought with optional tags, concepts, refs, scope, and signature metadata.
• mentisdb_append_retrospective Append a retrospective memory intended to prevent future agents from repeating a hard failure.
• mentisdb_search Search thoughts by semantic filters, identity filters, time bounds, and scoring thresholds.
• mentisdb_lexical_search Return flat ranked lexical matches with explainable term and field provenance.
• mentisdb_ranked_search Return flat ranked lexical, graph-aware, or heuristic results with additive score breakdowns. Supports as_of for point-in-time queries and scope for memory scope filtering.
• mentisdb_context_bundles Return seed-anchored grouped support context beneath the best lexical seeds.
• mentisdb_list_chains List known chains with version, storage adapter, counts, and storage location.
• mentisdb_merge_chains Merge all thoughts from a source chain into a target chain, then permanently delete the source.
• mentisdb_branch_from Create a new chain that diverges from a thought on an existing chain. The branch receives a genesis thought with a BranchesFrom relation pointing back to the fork point. Searches on the branch chain transparently include ancestor chain results.
• mentisdb_list_agents List the distinct agent identities participating in one chain.
• mentisdb_get_agent Return one full agent registry record, including status, aliases, description, keys, and per-chain activity metadata.
• mentisdb_list_agent_registry Return the full per-chain agent registry.
• mentisdb_upsert_agent Create or update a registry record before or after an agent writes thoughts.
• mentisdb_set_agent_description Set or clear the description stored for one registered agent.
• mentisdb_add_agent_alias Add a historical or alternate alias to a registered agent.
• mentisdb_add_agent_key Add or replace one public verification key on a registered agent.
• mentisdb_revoke_agent_key Revoke one previously registered public key.
• mentisdb_disable_agent Disable one agent by marking its registry status as revoked.
• mentisdb_recent_context Render recent thoughts into a prompt snippet for session resumption.
• mentisdb_memory_markdown Export a MEMORY.md-style Markdown view of the full chain or a filtered subset.
• mentisdb_import_memory_markdown Import a MEMORY.md-formatted Markdown document into a target chain.
• mentisdb_get_thought Return one stored thought by stable id, chain index, or content hash.
• mentisdb_get_genesis_thought Return the first thought ever recorded in the chain, if any.
• mentisdb_traverse_thoughts Traverse the chain forward or backward in append order from a chosen anchor, in chunks, with optional filters.
• mentisdb_skill_md Return the official embedded MENTISDB_SKILL.md Markdown file.
• mentisdb_list_skills List versioned skill summaries from the skill registry.
• mentisdb_skill_manifest Return the versioned skill-registry manifest, including searchable fields and supported formats.
• mentisdb_upload_skill Upload a new immutable skill version from Markdown or JSON.
• mentisdb_search_skill Search skills by indexed metadata such as ids, names, tags, triggers, uploader identity, status, format, schema version, and time window.
• mentisdb_read_skill Read one stored skill as Markdown or JSON. Responses include trust warnings for untrusted or malicious skill content.
• mentisdb_skill_versions List immutable uploaded versions for one skill.
• mentisdb_deprecate_skill Mark a skill as deprecated while preserving all prior versions.
• mentisdb_revoke_skill Mark a skill as revoked while preserving audit history.
• mentisdb_head Return head metadata, the latest thought at the current chain tip, and integrity state.
• mentisdb_register_webhook Register a webhook to receive HTTP POST notifications when thoughts are appended. Delivery is fire-and-forget with exponential backoff retries (up to 5 attempts).
• mentisdb_list_webhooks List all registered webhooks.
• mentisdb_delete_webhook Remove a webhook registration by its UUID.
• mentisdb_federated_search Run one ranked-search query across multiple chains simultaneously and return one merged, deduplicated, re-scored result list. Each hit carries the chain_key it came from, so multi-agent hubs and cross-organizational memory aggregations can share a single query surface. Per-chain queries may override limits, filters, and RRF settings.
• mentisdb_extract_memories Run the opt-in LLM-extraction pipeline: ingest raw text (paste, transcript, document), call an OpenAI-compatible model, validate the strict JSON schema of candidate thoughts, and return them for human review before append. Nothing is written to the chain until the caller confirms.
• mentisdb_list_entity_types List the per-chain entity-type registry (labels like incident, customer, deploy) including descriptions and usage counts.
• mentisdb_upsert_entity_type Create or update one entity type (label + optional description) in the per-chain registry. Entity-type labels can be attached to any thought via the entity_type field and used as a filter on ranked search.

The detailed request and response shapes for the MCP surface live in MENTISDB_MCP.md. The REST equivalents live in MENTISDB_REST.md.

MentisDB distinguishes three different read patterns:

• head means the newest thought at the current tip of the append-only chain
• genesis means the very first thought in the chain
• traversal means sequential browsing by append order, forward or backward, in chunks

That traversal model is deliberately different from graph/context traversal through refs and typed relations. Graph traversal answers "what is connected to this thought?" Sequential traversal answers "what came before or after this thought in the ledger?"

Lookup and traversal support:

• direct thought lookup by id, hash, or index
• logical genesis and head anchors
• forward and backward traversal directions
• include_anchor control for inclusive vs exclusive paging
• chunked pagination, including chunk_size = 1 for next/previous behavior
• optional filters reused from thought search, such as agent identity, thought type, role, tags, concepts, text, importance, confidence, and time windows
• numeric time windows expressed as start + delta with seconds or milliseconds units for MCP/REST callers

MentisDB includes a versioned skill registry stored alongside chain data in a binary file. Skills are ingested through adapters:

• Markdown -> SkillDocument
• JSON -> SkillDocument
• SkillDocument -> Markdown
• SkillDocument -> JSON

Each uploaded skill version records:

• registry file version
• skill schema version
• upload timestamp
• responsible agent_id
• optional agent display name and owner from the MentisDB agent registry
• source format
• integrity hash

Uploaders must already exist in the agent registry for the referenced chain. Reusing an existing skill_id creates a new immutable version; it does not overwrite history.

read_skill responses include explicit safety warnings because SKILL.md content can be malicious. Treat every skill as advisory until provenance, trust, and requested capabilities are validated.

Each upload to an existing skill_id creates a new immutable version rather than overwriting history:

• The first upload stores the full content (SkillVersionContent::Full).
• Subsequent uploads store a unified diff patch against the previous version (SkillVersionContent::Delta), keeping storage efficient for iteratively improved skills.
• Each version receives a monotone version_number (0-based, assigned in append order).
• Pass a version_id to read_skill / mentisdb_read_skill to retrieve any historical version. The system reconstructs it by replaying patches forward from version 0.
• skill_versions / mentisdb_skill_versions lists all versions with their ids, numbers, and timestamps.

Agents that have registered Ed25519 public keys in the agent registry must sign their uploads.

Required fields when the uploading agent has active keys:

• signing_key_id — the key_id registered via POST /v1/agents/keys or mentisdb_add_agent_key
• skill_signature — 64-byte Ed25519 signature over the raw skill content bytes

Agents without registered public keys may upload without signatures.

Upload flow for signing agents:

1. Register a public key:

   POST /v1/agents/keys   { agent_id, key_id, algorithm: "ed25519", public_key_bytes }

   or via MCP: mentisdb_add_agent_key
2. Sign the raw content bytes with the corresponding private key (Ed25519).
3. Include signing_key_id and skill_signature in the upload request:

   POST /v1/skills/upload   { agent_id, skill_id, content, signing_key_id, skill_signature }

   or via MCP: mentisdb_upload_skill with the same fields.

1. Recommended (stdio mode) — just run mentisdbd setup claude-desktop and it will write the stdio config. No daemon needed, no Node.js, no mcp-remote. The stdio process auto-detects and proxies to a running daemon, or launches one in the background if none is found.
2. Alternative (HTTP via mcp-remote) — requires Node.js >= 20 and npm install -g mcp-remote. Run mentisdbd as a daemon first, then configure Claude Desktop to use mcp-remote as a bridge to the HTTPS endpoint.

1. Start the server: mentisdbd run
2. Add the MCP server:

   claude mcp add --transport http mentisdb http://127.0.0.1:9471

3. Or run mentisdbd setup claude-code to auto-configure

1. Start the server: mentisdbd run
2. Add the MCP server:

   opencode mcp add mentisdb http://127.0.0.1:9471

codex mcp add mentisdb --url http://127.0.0.1:9471

qwen mcp add --transport http mentisdb http://127.0.0.1:9471

mentisdbd exposes both:

• a standard streamable HTTP MCP endpoint at POST /
• the legacy CloudLLM-compatible MCP endpoints at POST /tools/list and POST /tools/execute

That means you can:

• use native MCP clients such as Codex and Claude Code against http://127.0.0.1:9471
• keep using direct HTTP calls or cloudllm's MCP compatibility layer when needed

Codex CLI expects a streamable HTTP MCP server when you use --url:

codex mcp add mentisdb --url http://127.0.0.1:9471

Useful follow-up commands:

codex mcp list
codex mcp get mentisdb

This connects Codex to the daemon's standard MCP root endpoint.

Qwen Code uses the same HTTP MCP transport model:

qwen mcp add --transport http mentisdb http://127.0.0.1:9471

Useful follow-up commands:

qwen mcp list

For user-scoped configuration:

qwen mcp add --scope user --transport http mentisdb http://127.0.0.1:9471

Claude for Desktop supports two connection modes: stdio (recommended) and HTTP via mcp-remote.

Claude Desktop spawns mentisdbd --mode stdio as a subprocess. The stdio process automatically detects if a daemon is already running — if so, it acts as a lightweight proxy to the live daemon; if not, it launches one in the background. This means all Claude Desktop sessions share the same live chain cache with zero configuration.

{
  "mcpServers": {
    "mentisdb": {
      "command": "mentisdbd",
      "args": ["--mode", "stdio"]
    }
  }
}

This is the simplest setup — no Node.js, no mcp-remote, no TLS config. Just point Claude Desktop at the mentisdbd binary.

If you prefer the HTTP transport (e.g. for a remote daemon), use the mcp-remote bridge. This requires Node.js >= 20 and the mcp-remote npm package.

The recommended setup path is automatic:

mentisdbd setup claude-desktop

This command:

• checks for Node.js >= 20 and mcp-remote on PATH
• installs mcp-remote via npm if missing
• writes claude_desktop_config.json with the correct absolute paths to node and mcp-remote so the desktop app always uses the right Node version
• sets NODE_TLS_REJECT_UNAUTHORIZED=0 for self-signed certificate support

To set it up manually instead:

Step 1 — Install prerequisites (Node.js >= 20 required):

npm install -g mcp-remote

Step 2 — Edit the config file (location by OS):

The config should use the node binary as the command and pass the mcp-remote script path as the first argument. This avoids the shebang resolution issue where mcp-remote's #!/usr/bin/env node may pick an older Node.js version on systems with multiple Node installs (e.g. nvm).

macOS (with nvm):

{
  "mcpServers": {
    "mentisdb": {
      "command": "/Users/you/.nvm/versions/node/v22.18.0/bin/node",
      "args": ["/Users/you/.nvm/versions/node/v22.18.0/bin/mcp-remote", "https://my.mentisdb.com:9473"],
      "env": { "NODE_TLS_REJECT_UNAUTHORIZED": "0" }
    }
  }
}

macOS (with Homebrew Node):

{
  "mcpServers": {
    "mentisdb": {
      "command": "/opt/homebrew/bin/node",
      "args": ["/opt/homebrew/bin/mcp-remote", "https://my.mentisdb.com:9473"],
      "env": { "NODE_TLS_REJECT_UNAUTHORIZED": "0" }
    }
  }
}

Windows:

{
  "mcpServers": {
    "mentisdb": {
      "command": "node",
      "args": ["mcp-remote", "https://my.mentisdb.com:9473"],
      "env": { "NODE_TLS_REJECT_UNAUTHORIZED": "0" }
    }
  }
}

Use which mcp-remote and which node to confirm the binary paths on your machine. Both must point to the same Node.js installation (>= 20).

Linux:

{
  "mcpServers": {
    "mentisdb": {
      "command": "/usr/local/bin/node",
      "args": ["/usr/local/bin/mcp-remote", "https://my.mentisdb.com:9473"],
      "env": { "NODE_TLS_REJECT_UNAUTHORIZED": "0" }
    }
  }
}

Why NODE_TLS_REJECT_UNAUTHORIZED: "0"?
MentisDB ships with a self-signed TLS certificate. Node.js rejects self-signed certs by default, which causes mcp-remote to disconnect immediately after the MCP initialize handshake. This env var disables that check for the mcp-remote process only. As an alternative, trust the certificate at the OS level (sudo security add-trusted-cert on macOS) and remove the env block.

Why use node as the command instead of mcp-remote directly?
The mcp-remote shell script uses #!/usr/bin/env node as its shebang. On systems with multiple Node.js versions (e.g. managed by nvm), the shebang may resolve to an older Node that doesn't support mcp-remote's dependencies (which require Node >= 20). Using the explicit node path as the command bypasses the shebang entirely and guarantees the correct Node version is used.

Restart Claude for Desktop after saving the config file.

mentisdbd --mode stdio reads JSON-RPC 2.0 from stdin and writes responses to stdout. It is designed for MCP clients that spawn subprocesses (Claude Desktop, Cursor, etc.).

Smart daemon detection: When the stdio process starts, it checks if a daemon is already running on the configured MCP port (127.0.0.1:9471 by default) via the health endpoint.

• Daemon running → the stdio process acts as a lightweight proxy, forwarding tools/list and tools/call requests to the daemon's HTTP MCP endpoints. initialize, ping, and resources/read are handled locally. All clients share the same live in-memory chain cache.
• No daemon → the stdio process launches the daemon in the background (nohup on Unix, start /B on Windows), waits for it to become responsive, then proxies. The daemon outlives the stdio process.
• Daemon launch fails → falls back to local mode, creating its own MentisDbService instance (state shared only via disk files).

This means you never need to manually start the daemon — Claude Desktop, Cursor, or any MCP client can just spawn mentisdbd --mode stdio and everything works.

The daemon address is configurable via MENTISDB_BIND_HOST and MENTISDB_MCP_PORT environment variables, so the stdio process will detect and proxy to a daemon on a non-standard port.

Claude Code supports MCP servers through its claude mcp commands and project/user MCP config. For a remote HTTP MCP server, the configuration shape is transport-based:

claude mcp add --transport http mentisdb http://127.0.0.1:9471

Useful follow-up commands:

claude mcp list
claude mcp get mentisdb

mentisdbd setup claude-code merges the MCP server entry into ~/.claude.json (or %USERPROFILE%\.claude.json on Windows), preserving your existing Claude Code settings. The older ~/.claude/mcp/mentisdb.json path is treated as a legacy companion file, not the canonical config target. The MentisDB HTTP MCP block it writes looks like this:

{
  "mcpServers": {
    "mentisdb": {
      "type": "http",
      "url": "http://127.0.0.1:9471"
    }
  }
}

Important:

• /mcp inside Claude Code is mainly for managing or authenticating MCP servers that are already configured
• the server itself must already be running at the configured URL

GitHub Copilot CLI can also connect to mentisdbd as a remote HTTP MCP server.

From interactive mode:

1. Run /mcp add
2. Set Server Name to mentisdb
3. Set Server Type to HTTP
4. Set URL to http://127.0.0.1:9471
5. Leave headers empty unless you add auth later
6. Save the config

You can also configure it manually in ~/.copilot/mcp-config.json (or $XDG_CONFIG_HOME/copilot/mcp-config.json when XDG_CONFIG_HOME is set):

{
  "mcpServers": {
    "mentisdb": {
      "type": "http",
      "url": "http://127.0.0.1:9471",
      "headers": {},
      "tools": ["*"]
    }
  }
}

pymentisdb is the official Python client for MentisDB.

pip install pymentisdb
pip install pymentisdb[langchain]  # with LangChain integration

from pymentisdb import MentisDbClient, ThoughtType

client = MentisDbClient()  # defaults to http://127.0.0.1:9472

# Append a thought
thought = client.append_thought(
    thought_type=ThoughtType.INSIGHT,
    content="Rate limiting is the real bottleneck."
)

# Ranked search
results = client.ranked_search(text="performance optimization")
for hit in results.results:
    print(f"[{hit.score.total:.3f}] {hit.thought.content}")

# Context bundles
bundles = client.context_bundles(text="cache invalidation", limit=5)

from pymentisdb import MentisDbMemory

memory = MentisDbMemory(chain_key="my-project", agent_id="assistant")

llm = ChatOpenAI(model="gpt-4")
chain = llm.with_memory(memory)  # LangChain 0.3+

See the full pymentisdb guide for complete API reference.

MentisDB can convert raw agent text, conversation logs, or transcripts into structured ThoughtInput records using GPT-4o (or any OpenAI-compatible endpoint). Review the extracted records and append them to your chain.

This feature is enabled by default. Configure with environment variables:

export OPENAI_API_KEY="sk-..."           # required
export LLM_BASE_URL="https://api.openai.com/v1"  # defaults to OpenAI
export LLM_MODEL="gpt-4o"                # defaults to gpt-4o

use mentisdb::{LlmExtractionConfig, extract_memories_from_text};

let config = LlmExtractionConfig::from_env()?;
let result = extract_memories_from_text(raw_text, &config, None).await?;

// Review before appending
for thought in &result.thoughts {
    println!("{:?}", thought);
}

The extraction uses openai-rust2 with connection pooling, 3× retry on 429/5xx, 60s timeout, and strict JSON schema validation on the returned payload. LLM output is untrusted — always review and optionally sign extracted thoughts before appending.

See docs/llm-extracted-memories-design.md for the full design.

MentisDB supports a dedicated retrospective workflow for lessons learned.

• Use mentisdb_append for ordinary durable facts, constraints, decisions, plans, and summaries.
• Use mentisdb_append_retrospective after a repeated failure, a long snag, or a non-obvious fix when future agents should avoid repeating the same struggle.

The retrospective helper:

• defaults thought_type to LessonLearned
• always stores the thought with role = Retrospective
• still supports tags, concepts, confidence, importance, and refs to earlier thoughts such as the original mistake or correction

MentisDB currently defines 31 semantic ThoughtType values and 8 operational ThoughtRole values.

Thought types:

• PreferenceUpdate, UserTrait, RelationshipUpdate
• Finding, Insight, FactLearned, PatternDetected, Hypothesis, Surprise
• Mistake, Correction, LessonLearned, AssumptionInvalidated, Reframe
• Constraint, Plan, Subgoal, Goal, Decision, StrategyShift
• Wonder, Question, Idea, Experiment
• ActionTaken, TaskComplete
• Checkpoint, StateSnapshot, Handoff, Summary, LLMExtracted

Thought roles:

• Memory
• WorkingMemory
• Summary
• Compression
• Checkpoint
• Handoff
• Audit
• Retrospective

Use ThoughtType to say what the memory means semantically, and ThoughtRole to say how the system should treat it operationally. The crate rustdoc is the authoritative source for per-variant semantics, and the Agent Guide on the docs site contains a human-oriented explanation of when to use each one.

MentisDB defines a MemoryScope enum that controls how far a thought should propagate:

• User (default) — visible to all agents and sessions sharing the chain
• Session — visible only within the current agent session
• Agent — visible only to the agent that created it

Scopes are stored as tags on the thought:

• scope:user
• scope:session
• scope:agent

Crate API:

• ThoughtInput::with_scope(MemoryScope) sets the scope on append
• RankedSearchQuery::with_scope(MemoryScope) filters search results by scope

REST API:

• POST /v1/thoughts accepts an optional scope field ("user", "session", or "agent")
• POST /v1/ranked-search and POST /v1/context-bundles accept an optional scope field

Multiple agents can write to the same chain_key.

Each stored thought carries a stable:

• agent_id

Agent profile metadata now lives in the per-chain agent registry instead of being duplicated into every thought record. Registry records can store:

• display_name
• agent_owner
• description
• aliases
• status
• public_keys
• per-chain activity counters such as thought_count, first_seen_index, and last_seen_index

That allows a shared chain to represent memory from:

• multiple agents in one workflow
• multiple named roles in one orchestration system
• multiple tenants or owners writing to the same chain namespace

Queries can filter by:

• agent_id
• agent_name
• agent_owner

Administrative tools can also inspect and mutate the agent registry directly, so agents can be documented, disabled, aliased, or provisioned with public keys before they start writing thoughts.

At the repository root:

• MENTISDB_MCP.md
• MENTISDB_REST.md
• mentisdb/WHITEPAPER.md
• mentisdb/changelog.txt