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Context Fabric

Persistent memory for AI coding agents. Your agent remembers everything -- across sessions, projects, and tools.

Version License Node Docker CI

NOTE

Beta Software. Context Fabric works and is actively used, but APIs and storage formats may change between versions. Pin your version and check the CHANGELOG before upgrading.


The Problem

Every time an AI CLI session ends, its context vanishes. Decisions, patterns, bug fixes -- gone. Next session, you start from scratch.

The Solution

Context Fabric is an MCP server that gives your AI agent a three-layer memory system and time-aware orientation. It remembers what happened, what changed while you were away, and what matters right now. No external APIs. No cloud. Everything runs locally.

Features

  • Three-layer memory -- Working (L1), Project (L2), Semantic (L3). Memories auto-route to the right layer.

  • Local code indexing -- Scans source files, extracts symbols (functions/classes/types), and stays up-to-date via file watching. Search by text, symbol name, or semantic similarity.

  • Semantic recall -- Search by meaning using in-process vector embeddings. No API keys needed.

  • Time-aware orientation -- "What happened while I was away?" Offline gap detection, timezone support, session continuity.

  • Ghost messages -- Relevant memories surface silently via context.getCurrent without cluttering the conversation.

  • Pattern detection -- Auto-captures and reuses code patterns across projects.

  • Self-installing -- Ask your AI to run context.setup and it configures itself into any supported CLI.

  • Docker-first -- Cross-platform docker run --rm -i. No Node.js required on the host.

  • Hybrid search -- FTS5 BM25 + vector cosine + Reciprocal Rank Fusion. Keyword, semantic, or both.

  • 12 MCP tools -- Store, recall, orient, getCurrent, summarize, searchCode, CRUD (get/update/delete/list), reportEvent, setup.

  • Zero external dependencies -- All storage is SQLite. All search is local. Nothing leaves your machine.

Supported CLIs

CLI

Setup

Docs

Claude Code

context.setup({ cli: "claude-code" })

Guide

Kimi

context.setup({ cli: "kimi" })

Guide

OpenCode

context.setup({ cli: "opencode" })

Guide

Codex CLI

context.setup({ cli: "codex" })

Guide

Gemini CLI

context.setup({ cli: "gemini" })

Guide

Cursor

context.setup({ cli: "cursor" })

Guide

Claude Desktop

context.setup({ cli: "claude" })

Guide

TIP

Skip manual config entirely. Once Context Fabric is running inany CLI, the AI can install itself into all the others -- see Quick Start step 3.

Quick Start

Get running in 3 steps:

# 1. Clone and build the Docker image (~2 min)
git clone https://github.com/Abaddollyon/context-fabric.git
cd context-fabric
docker build -t context-fabric .

# 2. Test that it works
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' \
  | docker run --rm -i context-fabric

3. Add to your CLI. Point your MCP config at the Docker transport:

docker run --rm -i -v context-fabric-data:/data/.context-fabric context-fabric

See CLI Setup for copy-paste configs for all 7 CLIs, or let the AI do it -- once Context Fabric is running in one CLI, tell it:

"Install and configure Context Fabric for Cursor using Docker"

It writes the config automatically. No manual editing needed.

Requires Node.js 22.5+:

git clone https://github.com/Abaddollyon/context-fabric.git
cd context-fabric
npm install && npm run build

The server is at dist/server.js. Point your CLI's MCP config at node dist/server.js.

What It Looks Like

Start a session. The AI calls context.orient and instantly knows where it is:

It is 9:15 AM on Wednesday, Feb 25 (America/New_York).
Project: /home/user/myapp.
Last session: 14 hours ago. 3 new memories added while you were away.

Store a decision. The AI remembers it next session, next week, across tools:

// Store
{ "type": "decision", "content": "Use Zod for all API validation. Schemas in src/schemas/." }

// Recall (semantic search -- doesn't need exact words)
{ "query": "how do we validate inputs?" }
// => "Use Zod for all API validation. Schemas in src/schemas/." (similarity: 0.91)

No configuration. No prompting. Memories route to the right layer automatically.

How It Works

CLI (Claude Code, Cursor, etc.)
  |
  | MCP protocol (stdio / Docker)
  v
Context Fabric Server
  |-- Smart Router -----> L1: Working Memory  (in-memory, session-scoped)
  |-- Time Service        L2: Project Memory  (SQLite, per-project)
  |                       L3: Semantic Memory  (SQLite + vector search, cross-project)

Memories auto-route to the right layer. Scratchpad notes go to L1 (ephemeral). Decisions and bug fixes go to L2 (persistent). Code patterns and conventions go to L3 (searchable by meaning). See Architecture for the full deep dive.

Documentation

Resource

Description

Getting Started

Installation, first run, Docker and local setup

CLI Setup

Per-CLI configuration (all 7 supported CLIs)

Tools Reference

All 12 MCP tools with full parameter docs

Memory Types

Type system, three layers, smart routing, decay

Configuration

Storage paths, TTL, embedding, environment variables

Agent Integration

System prompt instructions for automatic tool usage

Architecture

System internals, data flow, embedding strategy

Changelog

Version history and migration notes

Contributing

Contributions are welcome. See CONTRIBUTING.md for how to get started.

License

MIT


Stop re-explaining your codebase every session.

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