Lore

Lore — Universal AI Memory Layer

Your AI agents remember everything. Automatically.

Lore is a cross-agent memory system that stores, connects, and retrieves knowledge across any AI agent — without code changes. Install a hook, and relevant memories appear in every prompt. No agent cooperation needed.

User: "What API rate limits should I use?"

── Lore hook fires (20ms) ────────────────────────────── 🧠 Relevant memories from Lore:

• [0.82] Stripe API returns 429 after 100 req/min — use exponential backoff
• [0.71] Our internal API rate limit is 500 req/min per API key

────────────────────────────────────────────────────────

Agent sees memories + prompt → responds with full context

Features

Universal Memory

remember · recall · forget · list_memories · stats

Store and retrieve memories across any AI agent via MCP tools, REST API, or Python/TypeScript SDK. Semantic search with tier-based TTL, temporal decay, and automatic PII redaction.

Knowledge Graph

graph_query · entity_map · related · extract_facts · list_facts · conflicts

Entities and relationships auto-extracted from memories. Hop-by-hop graph traversal surfaces connected knowledge that pure vector search misses. Atomic fact extraction with automatic conflict detection.

Bi-Temporal Facts & Supersession

supersede · list_at_time · facts_at_time · timeline · provenance · supersession_chain · consolidate_memories

History without deletion. Memories and facts are corrected by *superseding* them, never deleting — every change appends to an auditable trail. Lore tracks two independent time axes (bi-temporal): valid-time (when a fact was true in the world) and system-time (when Lore learned it), so you can ask "what was canonical — or true about X — *as of* date Y?". list_at_time / facts_at_time answer as-of queries, timeline walks chronologically adjacent events, and provenance / supersession_chain expose the full correction lineage for a memory or fact.

Graph Visualization

Web UI at `/ui/`

Interactive D3 force-directed graph of your knowledge base. Entity detail panels, topic clusters, search, and filtering. Runs in the browser — no install required.

Session Continuity

Auto-snapshot + auto-inject — zero agent cooperation

The Session Accumulator automatically captures conversation context and injects relevant session history into every prompt. Deterministic (no LLM needed). Works via hooks — the agent never knows Lore exists.

Recent Activity

recent_activity

Session-aware summary of what happened recently across all projects. Gives agents continuity between conversations without manual context-passing.

Topic Notes

topics · topic_detail

Auto-generated concept hubs that cluster related memories, entities, and facts around recurring themes. See everything Lore knows about a topic in one view.

Export & Snapshot

export · snapshot · snapshot_list · save_snapshot

Full data export in JSON and Markdown formats. Obsidian-compatible output for browsing your knowledge graph in a PKM tool. Snapshots for backup and migration.

Approval UX with Risk Scoring

review_digest · review_connection · lore review list --sort risk

Review discovered knowledge graph connections with computed risk scores. Batch approve/reject with notes, full audit trail of decisions. Sort by risk, confidence, or age.

Guided Bootstrap

lore bootstrap

Single command that validates Python version, Postgres, pgvector, Docker, runs migrations, and verifies server health. Use --fix to auto-remediate missing dependencies.

Multi-Agent Setup

lore setup claude-code · lore setup openclaw · lore setup cursor · lore setup codex

One-command hook installation for all major AI coding agents. Auto-retrieval injected into every prompt — no code changes needed. Includes --validate, --test-connection, and --dry-run flags.

SLO Dashboard + Alerting

lore slo create · lore slo status · GET /v1/slo/status

Define SLO targets for retrieval latency (p50/p95/p99) and hit rate. Background checker evaluates every 60s and fires webhook or email alerts on breach. Time-series API for charting.

Adaptive Retrieval Profiles

lore profiles list · GET /v1/profiles · ?profile=coding

Named retrieval profiles stored in Postgres. Presets for coding (recency-biased), incident response (graph-heavy), and research (long-term). Select per-request or set as API key default.

Policy-Based Retention

lore policy create · lore restore-drill · GET /v1/policies/compliance

Declarative lifecycle policies with per-tier retention windows, cron-based snapshot schedules, and restore drills with timing metrics. Compliance dashboard across all policies.

Multi-Tenant Workspaces

lore workspace create · lore workspace switch · lore audit

Workspace isolation within orgs. Scoped API keys, member management with RBAC roles, and a full audit log of every action (memory.create, key.revoke, etc.).

Plugin SDK

lore plugin create · lore plugin list · lore plugin reload

Extend Lore with plugins discovered via Python entry_points. Five lifecycle hooks (on_remember, on_recall, on_enrich, on_extract, on_score), hot-reload, scaffold CLI, and test harness.

Proactive Recommendations

suggest · lore suggest --context "..." · GET /v1/recommendations

Surface relevant memories before explicit queries. Multi-signal scoring (context similarity, entity overlap, temporal patterns, access patterns) with human-readable explanations and a feedback loop.

Retrieval Analytics

GET /v1/analytics/retrieval · Prometheus metrics

Track hit rate, score distribution, memory utilization, and latency. Know whether memories are actually helping your agents.

Quick Start

Docker Compose (recommended)

git clone https://github.com/agentkitai/lore.git

cd lore docker compose up -d

Starts Postgres with pgvector and the Lore server on http://localhost:8765.

pip

pip install "lore-sdk[server,solo]"

lore serve # starts on port 8765

Verify it works

curl http://localhost:8765/v1/memories

Add Lore as an MCP server

One line — no install — drops Lore into any MCP client (Claude Code, Cursor, VS Code, Codex, Claude Desktop):

// Claude Code: .mcp.json  ·  Claude Desktop: claude_desktop_config.json

{ "mcpServers": { "lore": { "command": "uvx", "args": ["--from", "lore-sdk[mcp]", "lore-memory"] } } }

Already installed (pip install lore-sdk[mcp])? Use "command": "lore-memory" (or lore mcp). Per-client guides are in Multi-Agent Setup below; lore integrate --platform writes the config for you.

Multi-Agent Setup

Claude Code

Option A: Auto-retrieval hook (recommended)

lore setup claude-code

This installs a UserPromptSubmit hook that auto-injects relevant memories into every prompt.

Option B: MCP tools

Add to ~/.claude/settings.json:

{

"mcpServers": { "lore": { "command": "lore", "args": ["mcp"], "env": { "LORE_API_URL": "http://localhost:8765", "LORE_API_KEY": "your-api-key" } } } }

OpenClaw

lore setup openclaw

Installs a message:preprocessed hook for auto-retrieval. Memories appear in context before every agent response.

Cursor

lore setup cursor

Installs a beforeSubmitPrompt hook. Also add MCP config to .cursorrules:

{

"mcpServers": { "lore": { "command": "lore", "args": ["mcp"], "env": { "LORE_API_URL": "http://localhost:8765", "LORE_API_KEY": "your-api-key" } } } }

Codex CLI

lore setup codex

Installs a beforePlan hook. Add MCP config:

{

"mcpServers": { "lore": { "command": "lore", "args": ["mcp"], "env": { "LORE_API_URL": "http://localhost:8765", "LORE_API_KEY": "your-api-key" } } } }

Any HTTP client

Auto-retrieval works with any system that can make an HTTP call before sending a prompt:

curl -s "http://localhost:8765/v1/retrieve?query=your+prompt&limit=5&min_score=0.3&format=markdown" \

-H "Authorization: Bearer $LORE_API_KEY"

MCP Tools Reference

CLI Reference

# Memory ope