contextos-dd 0.1.1

Local-first, IDE-agnostic agentic memory lifecycle manager

ContextOS

Local-first agentic memory lifecycle manager for Claude Code, Cursor, Codex, Continue, and Aider.

ContextOS runs as a background daemon between your IDE and the cloud LLM. It classifies your conversation history by relevance, compresses stale turns into narrative summaries via a small on-device model, and forwards a clean, minimal payload upstream — cutting input tokens 50–70% on long sessions without quality loss.

Zero bytes of your session content leave your machine except the request that goes to your chosen cloud LLM (Anthropic, OpenAI) — which is exactly what your IDE was going to send anyway, just smaller.

Built by DataDojo. Apache-2.0.

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Install

pip install contextos-dd
contextos install

That's it. contextos install:

1. Detects supported IDE configs (Claude Code, Cursor, Codex CLI, Continue, Aider)
2. Backs them up to ~/.contextos/backups/
3. Patches them to route through the local proxy on 127.0.0.1:9137
4. Downloads two small models (~430 MB total, one time) to ~/.contextos/models/
5. Starts the daemon

The model download happens once during install so the first long session doesn't pause for it. For air-gapped or offline installs, use contextos install --skip-models.

See INSTALL.md for the full first-run walkthrough, paths, privacy guarantees, troubleshooting, and uninstall semantics.

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What gets downloaded

File	Size	Purpose	Where
Qwen2.5-0.5B-Instruct-Q4_K_M.gguf	~400 MB	Compaction (summarising stale turns)	~/.contextos/models/
bge-small-en-v1.5 ONNX	~30 MB	Embeddings (vector recall)	~/.contextos/models/

Both download once, then live on disk forever. Subsequent runs use the cache. Re-pull anytime with contextos warmup.

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Commands

Command	What it does
contextos install	Detect IDEs, back up configs, patch URLs, download models, start daemon
contextos install --skip-models	Same as above but skip the model download
contextos warmup	Download / refresh local models
contextos status	Show daemon health and detected IDEs
contextos start / stop	Daemon lifecycle
contextos dashboard	Launch the Streamlit dashboard at http://127.0.0.1:9138
contextos doctor	Preflight checks: paths, deps, ports, IDE configs
contextos uninstall	Restore original IDE configs, stop daemon

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How it works

1. IDE sends API call → localhost:9137
2. Proxy parses messages, classifies turns HOT / WARM / COLD / DEAD
3. DEAD (duplicates) dropped, COLD runs replaced with a narrative summary from the local Qwen2.5 model
4. Clean payload forwarded to api.anthropic.com / api.openai.com
5. Response streamed back unchanged; trigger phrases like "as we discussed" fire a vector recall from a local LanceDB archive for the next turn

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Privacy

• Your code never goes to a third party. The only outbound network call ContextOS makes is forwarding your IDE's API request to the cloud LLM you already configured your IDE to use.
• No telemetry. Zero. We do not phone home.
• No cloud sync. Sessions, summaries, embeddings, and logs live in ~/.contextos/ on your machine.
• No background uploads. Ever.

The only network traffic ContextOS initiates on its own:

• Model downloads during install or warmup — fetched from HuggingFace.
• Forwarding your IDE's chat requests upstream — exactly the request your IDE built.

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Where stuff lives

~/.contextos/
  ledger.duckdb          # session + turn ledger
  daemon.pid             # daemon process id
  backups/               # IDE configs we modified (so uninstall is reversible)
  archive/               # LanceDB vector store of compacted turns
  models/                # Qwen2.5 GGUF + bge-small ONNX
  logs/contextos.log     # rolling daemon log

contextos uninstall restores IDE configs and stops the daemon. By default it does not delete the ledger or models — they stay in case you reinstall. To wipe everything, delete ~/.contextos/ manually.

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Status

Phase 1 MVP (this package): proxy, ledger, classifier, compactor, retrieval, Streamlit dashboard, auto-install for 5 IDEs.

context-os-pro (separate package, deferred): MCP server, team / shared memory, cloud rollups, React dashboard, advanced semantic classifier.

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Develop

git clone https://github.com/DataDojo/context-os
cd context-os
pip install -e ".[dev,dashboard]"
ruff check .
pytest -q