git:20260716.cbfa3b9 to git:20260717.0534b8d

2 added, 2 removed. Audit A to A.

# Furl

> Context compression layer for AI agents. Compress tool outputs, logs, files, and RAG chunks before they reach the model. Same answers, typically 0–54% fewer tokens on real high-entropy content — up to 95% on repetitive logs/fixtures (best-case ceiling). Python library and MCP server. Apache 2.0, local-first.

- Furl is shipped as a Python package (`furl-ctx`) with an MCP server (`furl_compress`, `furl_retrieve`, `furl_stats` tools). Both use the same compression pipeline: per-content-type compressors (JSON, search results, logs, diffs) feed into a Compress-Cache-Retrieve (CCR) store so compression stays reversible — the LLM can ask for the original on demand, within the configured retention window (default TTL 1800 s; entries are also capacity-evicted oldest-first, and an expired/evicted retrieval is a loud miss, never silent).

+ Furl is shipped as a Python package (`furl-ctx`) with an MCP server exposing six tools: `furl_compress`, `furl_retrieve`, `furl_search`, `furl_list`, `furl_stats`, and `furl_purge`, plus a seventh tool `furl_read` that is off by default. Both use the same compression pipeline: per-content-type compressors (JSON, search results, logs, diffs) feed into a Compress-Cache-Retrieve (CCR) store so compression stays reversible — the LLM can ask for the original on demand, within the configured retention window (default TTL 1800 s; entries are also capacity-evicted oldest-first, and an expired/evicted retrieval is a loud miss, never silent).

## Packages

- [PyPI package](https://pypi.org/project/furl-ctx/): Python install.

## Install (copy-paste-runnable)

- Python: `pip install furl-ctx` (add `[all]` for every optional extra)

- MCP server: `pip install "furl-ctx[mcp]"` then run `python -m furl_ctx.ccr.mcp_server`

- Compress in code: `from furl_ctx import compress`

## Docs

- [Get started](README.md#quick-install): two-command Claude Code plugin install — auto-compression hook, MCP tools, and skill; no `pip install` needed.

- [How it works](LIBRARY.md#how-it-works): pipeline diagram + per-content-type compressors.

- [Proof / benchmark table](README.md#proof): measured reductions on committed real captures (60–99% per dataset, redundancy-dependent; lossy rows are CCR-recoverable).

- [Configuration (environment variables)](LIBRARY.md#configuration-environment-variables): every live `FURL_*` knob with defaults.

- - [Full benchmark methodology](BENCHMARKS.md): adversarial multi-seed sweep + honest-metrics definitions.

+ - [Full benchmark methodology](BENCHMARKS.md): adversarial multi-seed sweep and honest-metrics definitions. See [the three public headline numbers](BENCHMARKS.md#the-three-public-headline-numbers) for how the 95% ceiling, the 91.5% six-capture subset, and the 0-54% high-entropy band relate.

- [Current baseline numbers](benchmarks/BASELINE.md): the deterministic per-dataset capture at HEAD.

- [CCR retention contract](CCR-RETENTION.md): exactly how long "reversible" lasts and what evicts.

- [Rust core developer guide](RUST_DEV.md): building the extension, workspace layout, store internals.

- [Contributing](CONTRIBUTING.md): PR policy and review gates.

- [Security policy](SECURITY.md): reporting vulnerabilities.

## Licensing

Apache 2.0. Use commercially, modify, redistribute. Data stays on the user's machine when running the library or MCP server locally. There is no telemetry — nothing is recorded or sent anywhere beyond the local session state the MCP server keeps on disk.