claudecode-infinite-memory
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@claudecode-infinite-memorysearch for my past preferences about testing frameworks"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
claudecode-infinite-memory
A lightweight MCP memory server built on SQLite + FTS5, providing cross-session long-term memory for Claude Code. Supports three-source merged retrieval: long-term memories, session history, and knowledge base indexing.
Features
Long-term memory — Store and retrieve persistent memories across sessions with deduplication
Session indexing — Automatically indexes Claude Code session transcripts (user + assistant messages)
Knowledge base — Drop
.mdfiles in a folder and get them auto-indexed with FTS5Three-source search — Queries all three sources simultaneously with importance-weighted re-ranking
Incremental sync — Only re-indexes files that actually changed (hash + mtime detection)
Zero external model dependencies — Pure keyword-based retrieval using FTS5 BM25, no embedding models needed
Related MCP server: recall-mcp
How It Works
graph BT
subgraph Data Sources
CC["Claude Code Sessions\nauto-generated .jsonl"]
MD["Knowledge Files\nuser-managed .md"]
STORE["memory_store() calls\nfrom Claude Code"]
end
subgraph Index & Storage
SESS["Layer 1: session_chunks\nFTS5 indexed"]
KNOW["Layer 2: knowledge_chunks\nFTS5 indexed"]
MEM["Layer 3: memories\nFTS5 indexed"]
end
CC -->|auto sync| SESS
MD -->|auto sync| KNOW
STORE -->|store + dedup| MEM
SESS --> SEARCH["memory_search(query)"]
KNOW --> SEARCH
MEM --> SEARCH
subgraph Claude Code Client
USER["User Input"] --> LLM["Claude LLM"]
end
SEARCH -->|results| LLMRequirements
Node.js 18+ (20+ recommended)
Run
npm installin the project directory
Quick Start
# Development mode (stdio)
npm run dev
# Production build
npm run build
npm startIntegration with Claude Code
Add the following to your Claude Code MCP config (~/.claude.json):
{
"mcpServers": {
"claudecode-infinite-memory": {
"command": "npm",
"args": ["--prefix", "/path/to/claudecode-infinite-memory", "run", "-s", "dev"],
"env": {
"MCP_MEMORY_DB_PATH": "/path/to/claudecode-infinite-memory/memory.sqlite",
"MCP_MEMORY_CLAUDE_HISTORY_PATH": "~/.claude/history.jsonl",
"MCP_MEMORY_SESSIONS_PATH": "~/.claude/projects",
"MCP_MEMORY_KNOWLEDGE_PATH": "/path/to/your/knowledge-base",
"MCP_MEMORY_DEFAULT_LIMIT": "5",
"MCP_MEMORY_MAX_LIMIT": "20",
"MCP_MEMORY_WATCH": "false"
}
}
}
}Replace
/path/to/...with your actual paths. Merge into your existingmcpServersif needed.
Tools
memory_store(text, category?)
Store a long-term memory entry.
text(required) — The memory contentcategory(optional) — One of:preference,fact,decision,entity,otherDeduplication — Uses
sha256(text + category)as a unique hash. Duplicate writes returnaction: "duplicate", successful writes returnaction: "stored".
memory_search(query, limit?)
Search across all three data sources with merged ranking.
Data sources:
Long-term memories (
memoriestable) — FTS5 full-text search with BM25 ranking, LIKE fallbackSession history (session JSONL files) — FTS5 full-text search on indexed session transcripts
Knowledge base (
knowledge_chunkstable) — FTS5 full-text search on chunked.mdfiles
Ranking strategy:
Each source produces TopK candidates (
limit * 5, capped at 50)Results are re-ranked:
finalScore = baseScore + importanceBoostImportance boost factors: source weight + structure weight + category weight
Final results sorted by
finalScoredesc, thencreatedAtdesc
memory_forget(id)
Delete a specific memory entry by ID. Returns { deleted: true | false }.
Environment Variables
Variable | Default | Description |
|
| SQLite database path |
|
| Claude Code session history file |
|
| Directory containing session JSONL files |
| (empty, disabled) | Knowledge directory path; put |
|
| Default search result count |
|
| Maximum search result count |
|
| Knowledge indexing chunk size (approximate tokens) |
|
| Chunk overlap size (approximate tokens) |
|
| Cooldown before incremental sync on search (ms) |
|
| Full sync on server startup |
|
| Enable file watcher for knowledge directory |
|
| File watcher debounce interval (ms) |
Knowledge Base (Layer 2)
Set MCP_MEMORY_KNOWLEDGE_PATH to a directory containing .md files.
How it works:
On startup — Full scan, approximate token-based chunking (default 400 tokens/chunk, 80 overlap), FTS5 indexing
On search — Cooldown check + change detection, incremental rebuild if needed
Incremental sync — mtime change triggers hash comparison, only changed files are re-chunked
Deletion sync — Files removed from disk are automatically cleaned from the index
Config change rebuild — Changing chunk parameters triggers a full rebuild (detected via
knowledge_meta)File watcher (optional) — Set
MCP_MEMORY_WATCH=trueforfs.watch-based monitoring with debounce
When MCP_MEMORY_KNOWLEDGE_PATH is not set, this feature is silently skipped.
Three-Layer Memory Architecture
Layer | Source | Write Method | Index Method | Characteristics |
Layer 1 | Session JSONL files | Auto (Claude Code) | FTS5 chunked index | Zero-config, session transcript search |
Layer 2 | Knowledge | Manual (user drops files) | FTS5 chunked index (approx. tokens) | High precision, requires file maintenance |
Layer 3 |
| Claude Code / user-triggered | FTS5 + triggers | Precise, driven by CLAUDE.md instructions |
See ARCHITECTURE.md for detailed technical documentation.
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