Context-Fabric
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Corpus search and linguistic analysis for AI Agents
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- Author
- context-fabric
- Categories
- Other, Knowledge Base, AI, Search
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Setup
Install Context-Fabric in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/context-fabric/context-fabric
Follow the installation instructions in the repository README, then restart your MCP client.
Production-ready corpus analysis for the age of AI
AI agents running advanced grammatical queries via the Model Context Protocol
Context-Fabric brings corpus analysis into the AI era. Built on the provenText-Fabricdata model, it introduces a memory-mapped architecture enabling parallel processing for production deployments—REST APIs, multi-worker services, and AI agent tools via MCP.
- Built for Production— Memory-mapped arrays enable true parallelization. Multiple workers share data instead of duplicating it.
- AI-Native— MCP server exposes corpus operations to Claude, GPT, and other LLM-powered tools.
- Powerful Data Model— Standoff annotation, graph traversal, pattern search, and arbitrary feature annotations.
- Dramatic Efficiency— 3.5x faster loads, 65% less memory in single process, 62% less with parallel workers.
Context-Fabric includescfabric-mcp, a Model Context Protocol server that exposes corpus operations to AI agents:
# Start the MCP server cfabric-mcp --corpus /path/to/bhsa # Or with SSE transport for remote clients cfabric-mcp --corpus /path/to/bhsa --sse 8000
The server provides 10 tools for discovery, search, and data access—designed for iterative, token-efficient agent workflows.
Text-Fabric loads entire corpora into memory—effective for single-user research, but each parallel worker duplicates that memory footprint. Context-Fabric's memory-mapped arrays change the equation:
Mean reduction across 10 corpora. Memory measured as total RSS after loading from cache.
# Core library pip install context-fabric # With MCP server pip install context-fabric[mcp]
from cfabric.core import Fabric # Load a corpus CF = Fabric(locations='path/to/corpus') api = CF.load('feature1 feature2') # Navigate nodes for node in api.N.walk(): print(api.F.feature1.v(node)) # Traverse structure embedders = api.L.u(node) # nodes containing this node embedded = api.L.d(node) # nodes within this node # Search patterns results = api.S.search(''' clause phrase function=Pred word sp=verb ''')
Context-Fabric tradesone-time compilation costfordramatic runtime efficiency. Compile once, benefit forever.
Mean across 10 corpora. The larger cache enables memory-mapped access—no deserialization, instant loads, shared memory across workers.
pip install context-fabric[benchmarks] cfabric-bench memory --corpus path/to/corpus
- Core Changelog
- MCP Changelog
- Benchmarks Changelog
- Testing Guide
If you use Context-Fabric in your research, please cite:
Kingham, Cody."Carrying Text-Fabric Forward: Context-Fabric and the Scalable Corpus Ecosystem."January 2026.
Context-Fabric byCody Kingham, built onText-FabricbyDirk Roorda.
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