Context-Fabric

Production-ready corpus analysis for the age of AI

AI agents running advanced grammatical queries via the Model Context Protocol

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Overview

Context-Fabric brings corpus analysis into the AI era. Built on the proven Text-Fabric data 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.

→ Read the Technical Paper

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MCP Server for AI Agents

Context-Fabric includes cfabric-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.

→ MCP Server Documentation

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Memory Efficiency

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:

Scenario	Memory Reduction
Single process	65% less
4 workers (spawn)	62% less
4 workers (fork)	62% less

Mean reduction across 10 corpora. Memory measured as total RSS after loading from cache.

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Installation

# Core library
pip install context-fabric

# With MCP server
pip install context-fabric[mcp]

Quick Start

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
''')

Core API

API	Purpose
N	Walk nodes in canonical order
F	Access node features
E	Access edge features
L	Navigate locality (up/down the hierarchy)
T	Retrieve text representations
S	Search with structural templates

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Performance

Context-Fabric trades one-time compilation cost for dramatic runtime efficiency. Compile once, benefit forever.

Metric	Mean Improvement
Load time	3.5x faster
Memory (single)	65% less
Memory (spawn)	62% less
Memory (fork)	62% less

Mean across 10 corpora. The larger cache enables memory-mapped access—no deserialization, instant loads, shared memory across workers.

Run benchmarks yourself:

pip install context-fabric[benchmarks]
cfabric-bench memory --corpus path/to/corpus

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Packages

Package	Description
context-fabric	Core graph engine
cfabric-mcp	MCP server for AI agents
cfabric-benchmarks	Performance benchmarking suite

Links

• Core Changelog
• MCP Changelog
• Benchmarks Changelog
• Testing Guide

Citation

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.

Authors

Context-Fabric by Cody Kingham, built on Text-Fabric by Dirk Roorda.

License

MIT