The Problem
Two problems with today's AI tooling that no one has solved together in a single package.
Your AI forgets. Every new session starts from zero. The architecture decisions you settled on last Thursday, the bug you spent four hours tracing to a misconfigured environment variable, the preferences you stated three times — none of it carries forward. You spend the first ten minutes of every session re-explaining context that already existed. Multiply this by every developer on your team, every project, every day.
Your context explodes. Long coding sessions blow past the context window. A typical session with 50 tool outputs accumulates 365 KB of raw text — stack traces, test output, file reads, shell commands. Every token costs money or slows the model. Naive truncation drops the exact evidence the model needs. Keeping everything makes responses slower and inference cost climb fast.
These two problems compound each other. The solution to forgetting (keep everything) is the opposite of the solution to context explosion (discard everything). The result is a false tradeoff most tools force on you: either your AI forgets everything, or your costs balloon. context-mem solves both simultaneously by building an indexed, compressed, retrievable memory store rather than dumping raw history into the context window.
The Solution — one tool, two pillars
Pillar 1: Memory (LLM Wiki)
Every tool call is automatically ingested, summarized, and written into a navigable markdown vault — a living wiki your AI maintains about your project. Entities get their own pages with backlinks. Topics get synthesis pages. Sessions become browseable source documents. Decisions accumulate into a reconstructible trail.
The vault lives at .context-mem/vault/ and syncs continuously from the underlying SQLite store. Read it in Obsidian, grep it from the terminal, or query it through 45+ MCP tools using hybrid BM25 + vector + optional LLM judge search. The raw SQLite store is the authoritative record; the markdown vault is the derived, human-readable layer.
This is a reference implementation of Andrej Karpathy's LLM Wiki pattern — three layers (raw sources / wiki / schema), with automatic ingest from tool calls that no other system provides.
Pillar 2: Compression (14 summarizers)
Every observation passes through a content-aware summarizer before storage. A stack trace is not treated the same way as a JSON config file. Shell output from a build is compressed differently from TypeScript compiler errors. The system applies the right compression for the content type.
The result: a full coding session with 50 tool outputs goes from 365 KB to 3.2 KB — 99.1% token savings, verified. Compression is adaptive: recent high-importance observations stay verbatim; older low-importance ones compress progressively. Pinned entries never compress regardless of age.
One Command
npm i context-mem && npx context-mem init
init auto-detects your editor and writes the right config files:
| Editor | Config written |
|---|
| Claude Code | .mcp.json + 8 hooks + CLAUDE.md |
| Cursor | .cursor/mcp.json + .cursor/rules/context-mem.mdc |
| Windsurf | .windsurf/mcp.json + .windsurf/rules/context-mem.md |
| VS Code / Copilot | .vscode/mcp.json + .github/copilot-instructions.md |
| Cline | .cline/mcp_settings.json + .clinerules/context-mem.md |
| Roo Code | .roo-code/mcp_settings.json + .roo/rules/context-mem.md |
| Aider | .aider.conf.yml (MCP block) |
| Continue | .continue/config.json (MCP block) |
| JetBrains AI | .idea/mcp.json |
No API keys. No cloud account. No data leaves your machine.
Dual-pillar in 60 seconds
[ placeholder: GIF or video — Claude Code session with split view showing Obsidian graph updating in real time alongside the context-mem dashboard token savings chart ]
Architecture (reference implementation of Karpathy's LLM Wiki pattern)
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