The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Memora
Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.
Install
$
npx wrangler d1 create memora-graphClaude Code
claude mcp add memora -- npx wrangler d1 create memora-graphCursor / Claude Desktop (mcp.json)
{
"mcpServers": {
"memora": {
"command": "npx",
"args": [
"wrangler",
"d1",
"create",
"memora-graph"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Memora
"You never truly know the value of a moment until it becomes a memory."
Give your AI agents persistent collective memory An MCP memory layer for agents: structured storage, semantic retrieval, graph relations, and source-backed cross-session context.
Absorb agent work into durable graph memory, then use memory_digest(topic) to retrieve relevant memories, TODOs/issues, related edges, and source IDs.
Features · Preview · Install · Usage · Config · Multi-DB · Containers · Live Graph · Cloud Graph · Chat · Semantic Search · Documents · LLM Dedup · Linking · Neovim
Features
Core Storage
- 💾 Persistent Storage - SQLite with optional cloud sync (S3, R2, D1)
- 🗄️ Multi-database routing - One process serves many stores; a workspace reaches its own at
/mcp/<name>(see Multi-database routing) - 📂 Hierarchical Organization - Section/subsection structure with auto-hierarchy assignment
- 📦 Export/Import - Backup and restore with merge strategies
Absorb & Lineage
- 🧬 Absorb - Feed facts in; an LLM classifies each against the store (duplicate / update / contradiction / related / new), skips duplicates, links relations, and consolidates related facts — with
dry_runpreview - 🌱 Supersession Lineage - Updates supersede old knowledge instead of deleting it; retrieval follows the chain to the current version by default (
followmodes:active,latest,full_history) - 🗞️ Topic Digest -
memory_digest(topic)bundles relevant memories, open TODOs/issues, related edges, and source IDs into one retrieval
Search & Intelligence
- 🔍 Semantic Search - Vector embeddings (TF-IDF, sentence-transformers, OpenAI)
- 🎯 Advanced Queries - Full-text, date ranges, tag filters (AND/OR/NOT), hybrid search
- 🔀 Cross-references - Auto-linked related memories based on similarity
- 🤖 LLM Deduplication - Find and merge duplicates with AI-powered comparison
- 🔗 Memory Linking - Typed edges, importance boosting, and cluster detection
Document Storage
- 📄 Structured Documents - Store markdown documents as searchable fragment trees (claims, plan items, references, risks)
- 🔒 Fragment Integrity - Guards against accidental delete/merge/absorb of document fragments
- 🔍 Granular Search - Individual claims and findings are semantically searchable while the full document remains retrievable as a unit
Tools & Visualization
- ⚡ Memory Automation - Structured tools for TODOs, issues, and sections
- 🕸️ Knowledge Graph - Interactive visualization with Mermaid rendering and cluster overlays
- 🌐 Live Graph Server - Built-in HTTP server with cloud-hosted option (D1/Pages)
- 💬 Chat with Memories - RAG-powered chat panel with LLM tool calling to search, create, update, and delete memories via streaming chat
- 📡 Event Notifications - Poll-based system for inter-agent communication
- 📊 Statistics & Analytics - Tag usage, trends, and connection insights
- 🧠 Memory Insights - Activity summary, stale detection, consolidation suggestions, and LLM-powered pattern analysis
- 📜 Action History - Track all memory operations (create, update, delete, merge, boost, link) with grouped timeline view
Preview
Install
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