furl-ctx 1.0.1

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 · Community

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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.
• Signal-aware offload + sliceable retrieval — a payload too big to compress inline (e.g. a 33 MB trace) comes back as a structured summary (schema, per-field value histograms, example rows) instead of a truncated head/tail, and the agent pulls a narrow slice on demand — retrieve(hash, select_field="name", select_equals="DroppedFrame") or a numeric range — without materializing the whole thing.
• MCP tools — furl_compress, furl_retrieve, furl_stats, furl_purge (erase stored originals), furl_search (find by content substring), furl_list (list stored entries). A seventh tool, furl_read, exists but is off by default — enable with FURL_MCP_READ=1.
• 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	279	60%	100%

Across the corpus: 95% fewer tokens (72,903 → 3,880) at 100% information retention. Full methodology and the 6-seed adversarial sweep: BENCHMARKS.md.

Honest read: the numbers above are best-case, low-entropy ceilings measured on the dev fixtures — two independent, out-of-sample audits show they degrade by 6–43pp on fresh high-entropy / near-unique / realistic data (exactly where real logs and listings live). On genuinely high-entropy content, honest lossless savings sit in the 0–54% band, not 60–95% (code 0%, search 40%, repeated_logs 54%); read every figure here as a ceiling, not a typical, and see the tier-aware breakdown in BENCHMARKS.md.

The code row's 99% is CCR-offload of a large non-file-read tool output (e.g. Bash dumping source text); an agent's own Read/Grep/Glob file access bypasses the compression hook by design and passes through unchanged, at 0%.

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.

Community

Questions or bug reports → open a GitHub issue (the surest way to reach us). For chat, there's a Discord.

License

Apache 2.0 — see LICENSE.