Lore

lore-knowledge-mcp · Operational knowledge layer for engineering teams and their AI agents.

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The Problem

Your agents start every session knowing nothing about your systems. Every runbook you've written. Every gotcha you've hit. Every incident you've debugged. None of it carries forward.

You re-explain. They re-discover. Context vanishes when the session ends.

Lore fixes that.

Without Lore                     With Lore
─────────────────────────────    ──────────────────────────────────
Agent starts fresh every time    Agent queries Lore on startup
"How does our infra work?"       Gets: topology, gotchas, runbooks,
You re-explain everything        past incidents, verified decisions
Context lost at session end      Knowledge persists across all sessions

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How It's Different

Tool	Built for	What it remembers	Agent-native
OB1 / personal memory	One person	Your thoughts and captures	No
Mem0 / Zep	App developers	User preferences, conversations	Partially
Confluence / Notion	Human teams	Documentation (human-browsed)	No
Lore	Engineering teams + AI agents	How your systems actually work — searchable by meaning, not just keywords	Yes

Lore is not a second brain. It's the operational intelligence your agents need to work in your environment — not just any environment.

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What Lore Does

Knowledge Base

Your team's operational knowledge — always queryable by any agent. Capture the things that matter: runbooks, hard-won gotchas, architecture decisions, deployment state. Every entry carries attribution so agents know who wrote it and whether a human has verified it.

Investigations

When something breaks, open a structured investigation. Document the symptom, test hypotheses, record what you tried and what you found. Six months later when the same issue resurfaces — different engineer, different agent — the trail is there.

Journal

A permanent record of milestones, architecture decisions, and buying decisions. The kind of thing that lives in someone's head until they leave the team.

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Built for Multi-Agent Systems

In a multi-agent environment, provenance matters. Every Lore entry carries author, source_type, and verified.

kb_search("proxmox lxc dns")

  [1] "LXC inherits host resolv.conf — Tailscale breaks containers"
      david · human · ✓ verified

  [2] "LXC DNS fix after Tailscale install"
      engineer-agent · agent · unreviewed

  [3] "LXC DNS configuration reference"
      research-agent · agent · ✗ disputed

Your agents know: result 1 is production-safe. Result 2, spot-check before acting. Result 3, review first.

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Semantic Search (v0.6.0+)

Lore finds entries by meaning, not just keywords. Search "DNS broken in containers" and it returns an entry titled "LXC containers inherit resolv.conf from the host" — no keyword overlap required.

Powered by local sentence-transformers embeddings (no API key, no external calls), combined with FTS5 lexical search and Reciprocal Rank Fusion. The same model used by mcp-memory-service, fully self-hosted.

Enable it

Heads up: Semantic search is feature-complete and shipping in a future release. The v0.6.0 release that included it was yanked from PyPI on 2026-05-24 while we set up a proper staging and end-to-end testing pipeline. You can run it from source today by cloning the repo and running pip install -e ".[semantic]".

# Once a stable release is published:
pip install lore-knowledge-mcp[semantic]
LORE_SEMANTIC_SEARCH=true lore-mcp

What you get

Mode	When to use
fts	Exact term matches (default when semantic is off)
semantic	Meaning-based retrieval, no keyword overlap needed
hybrid	Best of both — FTS5 + vector via RRF (recommended)

Backfill existing KB

If you already have entries, generate embeddings for them:

kb_backfill_embeddings()    # idempotent, safe to re-run
kb_embedding_status()       # check coverage

Configuration

Variable	Default	Notes
LORE_SEMANTIC_SEARCH	false	Master switch — off = current behavior unchanged
LORE_EMBEDDING_MODEL	all-MiniLM-L6-v2	384d, ~90MB, English-optimized
LORE_RRF_K	10	Increase to 30–60 for corpora >10k entries

For multilingual content, set LORE_EMBEDDING_MODEL=paraphrase-multilingual-MiniLM-L12-v2 (same 384d, no schema change).

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Automating Lore in Your Workflow

Add one line to every agent's system prompt and one entry to ~/.mcp.json — that's the entire integration. Each phase of your engineering workflow reads prior knowledge from Lore and writes its findings back, so nothing is re-discovered from scratch.

→ How to wire Lore into a 6-phase multi-agent pipeline — full walkthrough with code examples for every phase: research, architecture review, implementation, adversarial code review, QA, and documentation.

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Quick Start

No database setup required. Lore runs out of the box with SQLite.

1. Install

pip install lore-knowledge-mcp

Optional: semantic search

Note: The v0.6.0 PyPI release was yanked — see Semantic Search for current install status.

pip install lore-knowledge-mcp[semantic]

Then set LORE_SEMANTIC_SEARCH=true. See Semantic Search for details.

2. Start the server

# Stdio mode (for local MCP clients like Claude Code)
lore-mcp

# HTTP mode (for remote or multi-agent access)
lore-mcp --host 0.0.0.0 --port 8000

3. Add to your MCP client

Claude Code / Claude Desktop — add to ~/.mcp.json:

{
  "mcpServers": {
    "lore": {
      "type": "stdio",
      "command": "lore-mcp"
    }
  }
}

Or for HTTP mode (recommended for teams):

{
  "mcpServers": {
    "lore": {
      "type": "http",
      "url": "http://localhost:8000/mcp"
    }
  }
}

That’s it. Lore is ready.

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Tool Reference

Knowledge Base

Tool	What it does
kb_add	Add an entry. Accepts author, source_type for attribution.
kb_search	Semantic search with optional topic filter.
kb_get	Fetch full entry by ID.
kb_list	List entries, filter by topic.
kb_update	Update content, tags, or set verified flag.
kb_delete	Delete entry (requires confirm=true).

Investigations

Tool	What it does
investigation_add	Open or add to an investigation.
investigation_list	List investigations, filter by topic.
investigation_get	Fetch full investigation by ID.
investigation_log_experiment	Log a structured hypothesis → result → conclusion.
investigation_list_experiments	List all logged experiments.

Journal

Tool	What it does
journal_append	Add a milestone, decision, or reflection.
journal_list	List recent entries (default 20).
journal_get	Fetch entry by ID.
snapshot_config	Snapshot a config object to the journal.

Document Ingestion

Tool	What it does
kb_ingest_doc	Ingest a markdown file into the KB.
kb_ingest_dir	Batch-ingest a directory, with change detection.
kb_sync_status	Check what's changed since last sync.

MCP Index

Tool	What it does
mcp_index_scan	Scan all configured MCP servers and index their tools.
mcp_index_search	Search indexed tools by description.
mcp_index_get_server	Get all tools for a specific MCP server.
mcp_index_rebuild	Force a full rescan.

Search

Tool	What it does
multi_search	Search across KB, investigations, journal, and transcripts at once.
search_local	Search local files by content.
search_transcripts	Search Whisper transcript segments.
deduplicate_results	Deduplicate a result set by similarity threshold.
cluster_results	Cluster results by topic.

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Backends

	SQLite	PostgreSQL
Setup required	None	Existing PostgreSQL instance
Best for	Solo developers, local use	Teams, shared agents, production
Config	DB_BACKEND=sqlite (default)	DB_BACKEND=postgres + connection vars
Data location	~/.local/share/lore/	Your database

SQLite is the default. No configuration needed — just install and run.

PostgreSQL is for teams who want a shared knowledge layer accessible from multiple machines or agents simultaneously.

# PostgreSQL setup
export DB_BACKEND=postgres
export DB_HOST=your-db-host
export DB_PORT=5432
export DB_NAME=lore
export DB_USER=your-user
export DB_PASSWORD=your-password
lore-mcp

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License

MIT — see LICENSE