furl-ctx 0.27.0

The context compression layer for AI agents - 60-95% fewer tokens on redundant workloads, reversible via CCR

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 The context compression layer for AI agents
  

60–95% fewer tokens on redundant workloads · a Claude Code plugin · local-first · reversible

Install · Proof · Library · Discord

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Furl compresses everything your Claude Code agent reads — tool outputs, logs, web fetches, RAG chunks — before it reaches the model. Same answers, a fraction of the tokens. Every dropped byte stays retrievable on demand.

Install

Two commands inside Claude Code:

/plugin marketplace add omar-y-abdi/furl
/plugin install furl@furl

That's it — this installs the compression hook, the MCP tools, and the skill. No pip install, no setup: Furl fetches itself on first use. Requires uv on your PATH (same as the official serena plugin).

What you get

• Auto-compression hook — shrinks large Bash / WebFetch / WebSearch / Task outputs before they enter context. Fail-open: never breaks a tool call.
• MCP tools — furl_compress, furl_retrieve, furl_stats.
• Skill — explains the <<ccr:HASH>> retrieval flow and how to tune or disable it.

Tuning, disabling (FURL_HOOK_ENABLED=0), and the full reference: plugins/furl/README.md.

Proof

Token reduction on real captured data — reproducible, inputs committed under benchmarks/data/. Every number uses the engine's own tokenizer; needle recall is 100% (a known unique row is always recoverable, in the output or via CCR):

Dataset	Items	Before	After	Reduction	Info retention
code	7	41,025	471	99%	100%
multiturn	135	14,866	2,073	86%	100%
logs	90	8,595	619	93%	100%
search	90	4,102	318	92%	100%
repeated logs	90	3,621	120	97%	100%
disk	9	694	347	50%	100%

Across the corpus: 94% fewer tokens (72,903 → 4,016) at 100% information retention. Full methodology and the 6-seed adversarial sweep: BENCHMARKS.md.

Also a Python library

The same engine drops into any Python app or MCP host: from furl_ctx import compress. Install, usage, pipeline internals, prompt-caching contract, and the full FURL_* config reference live in LIBRARY.md.

License

Apache 2.0 — see LICENSE.