By Zpankz
Persistent memory and knowledge management for Claude Code. Auto-injects relevant context at session start, captures significant actions, provides 31 memory tools via MCP, 10 user-invocable skills, 2 specialized agents, and session lifecycle hooks.
Autonomous agent that curates, organizes, and maintains Memora memory health. Finds duplicates, suggests merges, fixes orphans, rebuilds indexes, and enforces hierarchy consistency. Use when memories need maintenance or organization.
Agent that searches, synthesizes, and reports on knowledge stored in Memora. Combines multiple search strategies to find comprehensive answers from the memory store. Use when deep research across stored memories is needed.
Find and merge duplicate memories, prune stale items, and maintain memory health. Use when the user says "clean up memories", "find duplicates", or "prune stale".
Diagnose and repair Memora health. Check for missing embeddings, broken links, orphaned memories, and rebuild indexes. Use when memories seem inconsistent or search isn't working well.
Export and view the Memora knowledge graph. Use when the user wants to visualize memory connections, export the graph, or open the graph UI.
Show Memora memory status dashboard. Quick overview of memory counts, open items, recent activity, and system health.
Get analytics and insights from Memora. Shows activity summaries, open items, stale memories, and pattern analysis. Use when the user wants a memory health check or overview.
Matches all tools
Hooks run on every tool call, not just specific ones
Uses power tools
Uses Bash, Write, or Edit tools
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Memora"You never truly know the value of a moment until it becomes a memory."
Give your AI agents persistent memory
A lightweight MCP server for semantic memory storage, knowledge graphs, conversational recall, and cross-session context.
Features · Install · Usage · Config · Live Graph · Cloud Graph · Chat · Semantic Search · LLM Dedup · Linking · Neovim
Core Storage
Search & Intelligence
Tools & Visualization
pip install git+https://github.com/agentic-mcp-tools/memora.git
Includes cloud storage (S3/R2) and OpenAI embeddings out of the box.
# Optional: local embeddings (offline, ~2GB for PyTorch)
pip install "memora[local]" @ git+https://github.com/agentic-mcp-tools/memora.git
The server runs automatically when configured in Claude Code. Manual invocation:
# Default (stdio mode for MCP)
memora-server
# With graph visualization server
memora-server --graph-port 8765
# HTTP transport (alternative to stdio)
memora-server --transport streamable-http --host 127.0.0.1 --port 8080
Add to .mcp.json in your project root:
Local DB:
{
"mcpServers": {
"memora": {
"command": "memora-server",
"args": [],
"env": {
"MEMORA_DB_PATH": "~/.local/share/memora/memories.db",
"MEMORA_ALLOW_ANY_TAG": "1",
"MEMORA_GRAPH_PORT": "8765"
}
}
}
}
Cloud DB (Cloudflare D1) - Recommended:
{
"mcpServers": {
"memora": {
"command": "memora-server",
"args": ["--no-graph"],
"env": {
"MEMORA_STORAGE_URI": "d1://<account-id>/<database-id>",
"CLOUDFLARE_API_TOKEN": "<your-api-token>",
"MEMORA_ALLOW_ANY_TAG": "1"
}
}
}
}
With D1, use --no-graph to disable the local visualization server. Instead, use the hosted graph at your Cloudflare Pages URL (see Cloud Graph).
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