context-use 0.1.0

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context-use

Turn your data exports into portable AI memory.

Features

• Ingest — parse provider export ZIPs into structured threads; no cloud upload required
• Quickstart — zero-database preview mode; results written to data/output/ with no setup beyond an OpenAI key
• Full pipeline — persistent storage in PostgreSQL with pgvector; full archive history, batch API for cost-efficient memory generation
• Semantic search — memories search queries your memory store by meaning, not just keywords
• MCP server — expose memories and semantic search to Claude Desktop, Cursor, or any MCP client
• Personal agent — multi-turn agent that synthesises higher-level pattern memories, generates a first-person profile, or runs ad-hoc queries against your memory store

Supported providers

Provider	Status	Data types	Export guide
ChatGPT	Available	Conversations	Export your data
Instagram	Available	Stories, Reels, Posts	Download your data
WhatsApp	Coming soon
Google Takeout	Coming soon

Getting your export

1. Follow the export guide for your provider in the table above. The export is delivered as a ZIP file — do not extract it.
2. Move or copy the ZIP into data/input/ inside the cloned repo:

context-use/
└── data/
    └── input/
        └── chatgpt-export.zip   ← place it here

Both quickstart and pipeline scan data/input/ for exports on startup and prompt you to pick one if multiple are present.

Install

git clone https://github.com/onfabric/context-use.git
cd context-use
uv sync
source .venv/bin/activate

Set your OpenAI API key:

context-use config set-key
# or: export OPENAI_API_KEY=sk-...

Quick start

A zero-setup preview that requires no database.

context-use quickstart

The CLI prompts for the export and provider. Memory generation uses the OpenAI real-time API — fast for small slices but susceptible to rate limits on large exports. By default only the last 30 days are processed; use --full to include the complete history (the CLI warns you before proceeding).

The output is a snapshot: memories are written to data/output/ as Markdown and JSON, then discarded. Nothing is stored in a database, so the memories are not queryable, searchable, or available to the MCP server after the command exits.

The full pipeline is the intended way to use context-use beyond this initial preview.

Full pipeline

For persistent storage, semantic search, and the MCP server.

1. Set up PostgreSQL (one-time)

context-use config set-store postgres

Prompts to start a local container via Docker, then saves connection details to ~/.config/context-use/config.toml. Skip Docker if you're bringing your own PostgreSQL instance.

2. Run the pipeline

context-use pipeline

Ingests the export and generates memories via the OpenAI batch API — significantly cheaper and more rate-limit-friendly than the real-time API used by quickstart. Typical runtime: 2–10 minutes. Memories are stored in PostgreSQL and persist across sessions, enabling semantic search, the MCP server, and the personal agent.

3. Explore your memories

context-use memories list
context-use memories search "hiking trips in 2024"

MCP server

Requires the full pipeline (PostgreSQL).

python -m context_use.ext.mcp_use.run
# use --transport stdio for clients that prefer stdio

Add to your MCP client config (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "context-use": {
      "command": "python",
      "args": ["-m", "context_use.ext.mcp_use.run", "--transport", "stdio"]
    }
  }
}

Claude Desktop config path: ~/Library/Application Support/Claude/claude_desktop_config.json. Cursor: Settings → MCP.

Personal agent

A multi-turn agent that operates over your full memory store. Requires PostgreSQL.

context-use config set-agent adk
context-use agent synthesise          # generate higher-level pattern memories
context-use agent profile             # compile a first-person profile
context-use agent ask "What topics do I keep coming back to across all my conversations?"

Configuration

Config file: ~/.config/context-use/config.toml. Run context-use config show to see all active values and where each comes from (env var, file, or built-in default).

Setting	CLI command	Env var	Default
OpenAI API key	config set-key	OPENAI_API_KEY	—
Model	edit config file	OPENAI_MODEL	gpt-5.2
Embedding model	edit config file	OPENAI_EMBEDDING_MODEL	text-embedding-3-large
Store backend	config set-store postgres|memory	CONTEXT_USE_STORE	memory
PostgreSQL host	config set-store postgres	POSTGRES_HOST	localhost
PostgreSQL port	config set-store postgres	POSTGRES_PORT	5432
PostgreSQL database	config set-store postgres	POSTGRES_DB	context_use
PostgreSQL user	config set-store postgres	POSTGRES_USER	postgres
PostgreSQL password	config set-store postgres	POSTGRES_PASSWORD	postgres
Agent backend	config set-agent adk	CONTEXT_USE_AGENT_BACKEND	—
Data directory	edit config file	—	./data

Adding new providers and pipes

See AGENTS.md for context-use's architecture and how to add new providers and pipes.

Contributing

See CONTRIBUTING.md for how to contribute to the context-use project.