Lorekeeper API Reference

Lorekeeper exposes 10 MCP tools over stdio. All tools follow the MCP protocol — any MCP-compatible agent can use them.

Tools

Tool	Purpose
`lore_search`	Hybrid semantic + keyword search with relevance scores
`lore_remember`	Fast one-shot memory save (auto-titles, auto-links)
`lore_insert`	Bulk structured insert with custom scores and links
`lore_update`	Feedback loop — rate memories, drive quality
`lore_forget`	Soft-delete wrong or outdated memories
`lore_reflect`	End-of-session: extract learnings, auto-save discoveries
`lore_processed_sessions`	Check which sessions are already processed
`lore_recommend_links`	Suggest candidate links between related memories
`lore_get_suggestions`	List pending link suggestions from the sweep engine
`lore_review_suggestion`	Accept or reject one or more link suggestions (batch)

`lore_search`

{
  "query": "checkout payment flow",
  "min_score": 0.1,
  "include_links": true,
  "include_deleted": false,
  "refine_from": null,
  "format": "full",
  "ids": null,
  "created_after": null,
  "updated_after": null,
  "sort_by": "relevance"
}

Returns ranked memories with relevance scores and linked memories.

Two search modes

Query mode (default) — runs the hybrid semantic + BM25 pipeline:

query (required unless ids is set): search text
min_score (default 0.1): minimum combined_score threshold — applied to the combined score before time-decay
refine_from: list of lore_id strings from a previous result to re-rank only within that candidate set. Unknown IDs silently ignored. Max 200 (configurable via LORE_MAX_REFINE_FROM_IDS)
format: "full" (default) returns complete memory objects with relevance scores; "title" returns compact {id, title, score} for lower token cost
limit: max results (defaults to LORE_SEARCH_LIMIT)
include_links (default true): forced off in format='title' mode
include_deleted (default false)
created_after (optional): ISO 8601 UTC timestamp — only return memories with created_at ≥ T. Naive strings treated as UTC. Non-UTC offsets raise an error. Composes with all other filters.
updated_after (optional): ISO 8601 UTC timestamp — only return memories with updated_at ≥ T. Same UTC rules as created_after.
sort_by (default "relevance"): controls result ordering. "relevance" ranks by hybrid score when the search pipeline runs; in ids lookup mode (when ids is set) there is no scoring, so "relevance" preserves the caller-provided ids order instead. "recent" sorts by updated_at DESC. "frequent" sorts by usage_count DESC. Composes with timestamp filters and limit.

ID lookup mode — when ids is set, skips the vector/BM25 pipeline entirely and fetches those specific lore_ids directly from SQL. query is ignored. Unknown IDs silently ignored. Max 50 IDs (configurable via LORE_MAX_SEARCH_IDS). Pair with format='title' for a two-step list-then-fetch workflow. created_after, updated_after, and sort_by all compose with the ids path.

Hybrid scoring formula

combined = 0.45·semantic + 0.30·keyword + 0.15·(score/10) + 0.10·log_usage_norm
final    = combined × decay

Where:

semantic — cosine similarity from the vector store (LanceDB), normalised to [0, 1]
keyword — BM25 score, top-hit normalised to 1.0
score/10 — the memory's quality score (0–10), normalised
log_usage_norm = log2(1 + usage_count) / log2(1 + cap) — usage frequency signal
decay = e^(-λ · days_since_last_used) — time-decay so stale memories rank lower. λ defaults to 0.0077 (~90-day half-life), configurable via LORE_DECAY_LAMBDA. Set to 0 to disable. last_used updates on every retrieval; falls back to created_at if never retrieved

All weights are env-configurable via LORE_W_* vars (see config.py).

Semantic candidates: top 200 from Mem0. Keyword candidates: BM25. Both pools are unioned then re-ranked.

`lore_insert`

{
  "memories": [
    {
      "title": "Mutable default args in Python",
      "description": "def f(x=[]) shares the list across all calls — use None instead.",
      "content": "..."
    },
    {
      "title": "Token refresh interval",
      "content": "Access tokens expire after 1h.",
      "links": [
        {
          "target_memory_id": "<target-uuid>",
          "relation_type": "references",
          "reason": "part of OAuth flow"
        }
      ]
    }
  ],
  "links": [
    {
      "source_memory_id": "<uuid>",
      "target_memory_id": "<uuid>",
      "relation_type": "references",
      "reason": "Both about Python gotchas"
    }
  ],
  "force": false
}

Each memory dict:

Field	Required	Default	Description
`title`	✅	—	Short unique label for the memory
`content`	—	—	Full text to store
`description`	—	—	Brief summary
`score`	—	`5.0`	Initial quality score (0–10)
`links`	—	—	Inline links to create after insert (see link schema)

Top-level links (linking existing memories to each other) and per-memory inline links can be combined in one call.

Duplicate detection

Before inserting, a dedup score is computed: 0.6·semantic + 0.4·keyword. If it meets or exceeds LORE_DUPLICATE_THRESHOLD (default 0.85), the insert is blocked and the existing memory is returned. Set force: true to bypass.

Note: force=true bypasses the semantic/keyword dedup check, but the DB still enforces UNIQUE(namespace, title) — inserting two memories with the same title in the same namespace will surface a constraint error in errors[].

`lore_remember`

{
  "thought": "Hybrid search formula: 0.45 semantic + 0.30 keyword + 0.15 score + 0.10 usage"
}

Fast one-shot memory insert. Pass a single string; the server:

Auto-extracts the title (first ~80 chars, sentence boundary)
Stores the full content verbatim with default score 7.0
Auto-links to the nearest semantic neighbor if similarity ≥ 0.75

Returns {id, title, linked_to: {id, score} | null}.

Uses the same dedup pipeline as lore_insert — exact title matches are definitive duplicates.

Use lore_remember for quick capture. Use lore_insert when you need explicit titles, descriptions, scores, or manual links.

`lore_update`

{
  "memory_feedback": [{ "id": "<uuid>", "useful": true, "confidence": 8 }],
  "link_feedback": [{ "id": "<uuid>", "useful": false, "confidence": 3 }]
}

Drives the quality signal loop. Call after every lore_search to keep scores calibrated.

Quality signal mechanics

Each feedback call:

useful=true → score bumped up by LORE_SCORE_BUMP_UP × (confidence/10)
useful=false → score bumped down by LORE_SCORE_BUMP_DOWN × ((11-confidence)/10)
useful=false + confidence ≤ 2 → memory is soft-deleted (never returned in future searches)

Confidence is stored as a running EMA over the last 20 ratings (LORE_CONFIDENCE_WINDOW_SIZE).

Unknown memory IDs are returned in errors[] — they do not raise exceptions.

`lore_forget`

{
  "memory_ids": ["uuid1", "uuid2"],
  "reason": "hallucinated"
}

Immediately soft-deletes one or more memories. The memories are excluded from lore_search results after this call.

reason must be one of: duplicate, hallucinated, outdated, expired, unspecified. Logged for auditability.

Soft-delete is reversible at the DB level, but no undelete tool is exposed via MCP.

Returns:

{ "forgotten": [...], "not_found": [...], "errors": [...] }

`lore_reflect`

{
  "session_id": "uuid1",
  "summary": "Implemented reflect integration; extracted 3 lessons.",
  "session_date": "2026-05-19",
  "topic": "reflect-integration",
  "task_type": "build",
  "what_was_done": "Built the reflect integration...",
  "decisions": "- Used single-session submit for context efficiency",
  "lessons_learnt": ["Don't skip dedup check before inserting"],
  "good_patterns": ["Parallelise independent API calls"],
  "factual_discoveries": ["BM25 rebuild costs ~10ms at 5k memories"],
  "memory_ids": ["uuid-a", "uuid-b"],
  "auto_insert": true
}

Marks one session as processed and stores its content in the dashboard Sessions tab. Call once per session.

Auto-insert (default auto_insert=true): Each item in factual_discoveries and lessons_learnt is automatically inserted as a standalone searchable memory:

factual_discoveries → score 7.0, linked with "discovered_in" relation
lessons_learnt → score 8.0, linked with "learned_in" relation

Duplicate-guarded: items already in the store return "status": "duplicate" with the existing ID; new inserts return "status": "inserted". Pass auto_insert=false to store only in the reflection record without creating memories.

Idempotency: If session_id was already processed, returns immediately with "already_processed": true and "memories_created": []. Check already_processed to detect retries.

Returns:

{
  "reflection_id": "...",
  "session_id": "...",
  "created_at": "...",
  "memories_created": [
    {
      "id": "m-1",
      "title": "BM25 rebuild costs ~10ms...",
      "relation": "discovered_in",
      "status": "inserted"
    },
    {
      "id": "m-2",
      "title": "Don't skip dedup check...",
      "relation": "learned_in",
      "status": "inserted"
    }
  ]
}

`lore_processed_sessions`

No parameters.

{}

Returns all session IDs already marked as processed by lore_reflect. Use this to avoid re-processing sessions.

Returns:

{ "session_ids": ["session-uuid-1", "session-uuid-2"] }

`lore_recommend_links`

{
  "lore_id": "<uuid>",
  "top_k": 10
}

Suggests link candidates between a memory and related memories. Does not write any links — returns candidates for review. Confirm by calling lore_insert with links: [...].

Parameters:

lore_id (required): source memory to find candidates for
top_k (optional, default from LORE_LINK_TOP_M): max candidates to return

Scoring pipeline — semantic cosine, BM25, entity overlap, and temporal proximity combined into a per-signal weighted score.

Returns:

{
  "candidates": [
    {
      "source_lore_id": "<uuid>",
      "target_lore_id": "<uuid>",
      "weighted_score": 0.65,
      "scores": {
        "cosine": 0.82,
        "bm25": 0.45,
        "entity": 0.0,
        "temporal": 0.0
      }
    }
  ],
  "count": 10,
  "source_lore_id": "<uuid>"
}

Per-signal scores let the agent make its own judgment about which candidates are worth linking — no LLM call inside Lorekeeper.

`lore_get_suggestions`

List pending link suggestions generated by the background sweep engine.

{
  "limit": 20,
  "min_score": 0.0
}

Parameter	Type	Default	Description
`limit`	`int`	`20`	Max suggestions to return (capped at 100)
`min_score`	`float`	`0.0`	Minimum `weighted_score` filter (0.0-1.0)

Response:

{
  "suggestions": [
    {
      "id": "uuid",
      "source_memory_id": "uuid",
      "target_memory_id": "uuid",
      "source_title": "...",
      "target_title": "...",
      "weighted_score": 0.87,
      "suggested_type": "references",
      "confidence": "high",
      "status": "pending"
    }
  ],
  "count": 1,
  "total_pending": 5
}

total_pending gives agents a sense of workload without a separate count call. Only status='pending' suggestions are returned; min_score filters within that set by weighted_score.

`lore_review_suggestion`

Accept or reject one or more link suggestions in a single call. Each item is processed independently — a failure on one does not abort the rest.

{
  "suggestion_ids": ["uuid1", "uuid2"],
  "action": "accept"
}

Parameter	Type	Description
`suggestion_ids`	`list[str]`	One or more suggestion UUIDs
`action`	`str`	`"accept"` or `"reject"`

On accept: Creates a real memory_links row using the suggestion's suggested_type (falls back to 'references' if None or unrecognised). The suggestion is retained with status='accepted' for audit trail.

On reject: Sets status='rejected' on the suggestion row. Future sweeps skip this pair.

Idempotent: Double-accept and double-reject both return status='skipped' — no error.

Response:

{
  "results": [
    {
      "id": "uuid",
      "status": "accepted",
      "link_id": "uuid",
      "message": "Link created"
    }
  ],
  "accepted": 1,
  "rejected": 0,
  "skipped": 0,
  "errors": []
}

status per item is one of "accepted", "rejected", "skipped", or "error". link_id is the created link ID on accept, null on reject, skip, or error.

When status="error", the item is also present in errors[] with the exception message. Callers iterating results[*].status should treat "error" as a failure sentinel and cross-reference errors[] for details.

Environment Variables

All env vars use the LORE_ prefix. Key configuration:

Variable	Default	Purpose
`LORE_DATA_DIR`	`~/.lorekeeper`	Where SQLite + vector DB live
`LORE_SEARCH_LIMIT`	`10`	Default max results from `lore_search`
`LORE_DUPLICATE_THRESHOLD`	`0.85`	Dedup block threshold (`0.6·semantic + 0.4·keyword`)
`LORE_DECAY_LAMBDA`	`0.0077`	Time-decay rate (~90-day half-life). Set `0` to disable
`LORE_W_SEMANTIC`	`0.45`	Hybrid ranking: semantic weight
`LORE_W_KEYWORD`	`0.30`	Hybrid ranking: BM25 weight
`LORE_W_SCORE`	`0.15`	Hybrid ranking: quality score weight
`LORE_W_USAGE`	`0.10`	Hybrid ranking: usage frequency weight
`LORE_SCORE_BUMP_UP`	`0.5`	Score delta for `useful=true` feedback
`LORE_SCORE_BUMP_DOWN`	`0.5`	Score delta for `useful=false` feedback
`LORE_CONFIDENCE_WINDOW_SIZE`	`20`	EMA window for running confidence score
`LORE_MAX_REFINE_FROM_IDS`	`200`	Max IDs accepted in `refine_from`
`LORE_MAX_SEARCH_IDS`	`50`	Max IDs accepted in `ids` (lookup mode)
`LORE_LINK_TOP_M`	`10`	Default max candidates from `lore_recommend_links`
`LORE_NAMESPACE`	`shared`	Default namespace for writes

Full list with types and defaults → src/lorekeeper/config.py.

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Lorekeeper API Reference

Tools

`lore_search`

Two search modes

Hybrid scoring formula

`lore_insert`

Duplicate detection

`lore_remember`

`lore_update`

Quality signal mechanics

`lore_forget`

`lore_reflect`

`lore_processed_sessions`

`lore_recommend_links`

`lore_get_suggestions`

`lore_review_suggestion`

Environment Variables

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Lorekeeper API Reference

Tools

lore_search

Two search modes

Hybrid scoring formula

lore_insert

Duplicate detection

lore_remember

lore_update

Quality signal mechanics

lore_forget

lore_reflect

lore_processed_sessions

lore_recommend_links

lore_get_suggestions

lore_review_suggestion

Environment Variables

`lore_search`

`lore_insert`

`lore_remember`

`lore_update`

`lore_forget`

`lore_reflect`

`lore_processed_sessions`

`lore_recommend_links`

`lore_get_suggestions`

`lore_review_suggestion`