lcc — Local Context Compiler

lcc — Local Context Compiler

Compact coding-agent context without summarizing it away.

lcc cuts what the model does not need, keeps everything else byte for byte, and writes a receipt.

/compact vs lcc

Claude Code's /compact replaces your session with a summary written by a model. Summaries drop the exact error, path or line the agent needs two turns later. lcc does the opposite: old tool outputs leave the context but stay one recall away, and every user and assistant message is kept as it was.

Measured with lcc measure-session (PyPI 1.2.0, offline, exact o200k_base token count; re-run 2026-10-23). On main, whose stash note now states lines and size, the same session gives 28,983 (−49.2%). That session is one of the best cases. Across all 299 of my long sessions (100+ messages each, 36.3 M tokens) the median cut was 21.8% (middle half 15% to 31%, best 65%), with 0 messages lost (python benchmarks/research/long_sessions.py, 2026-10-23, main). These are all my sessions: run it on yours and post the result.

In a pre-registered test on 172 real requests (evidence), an agent answering from the stashed context plus recall did as well as with the full context on file recall (+0.006, 95% CI −0.023 to +0.034) with 36.6% fewer tokens. It called recall for 90% of the questions about removed content. A /compact-style summary of the same sessions got 73% of exact values right (our reimplementation of /compact, written by Opus); full and verbatim context got 98%.

Install

pip install "local-context-compiler[tiktoken]"   # the CLI is `lcc`

lcc fetch-tokenizer # once: exact token counts lcc measure-session "$(ls -t ~/.claude/projects/*/*.jsonl | head -1)" # your latest session

The PyPI package is local-context-compiler. pip install lcc installs an unrelated astronomy package that happens to own that name.

fetch-tokenizer downloads tiktoken's encoding files once; it is the only lcc command that goes online, and without it counts are labelled approximate. The last line reads your most recent Claude Code session, runs the rules modes offline in seconds and prints counts only. Nothing is changed and nothing leaves the machine.

Claude Code plugin (replaces the /compact summary). Function hooks are early access, so this needs Claude Code 2.1.274+ and the flag below:

export CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1

claude plugin marketplace add lucasmartins-ai/lcc claude plugin install lcc@lcc --config provider=rules --config rulesMode=stashed

Allow mcp__plugin_lcc_lcc__recall so the agent can get removed outputs back. When a pass fails or removes too little, the plugin hands over to the built-in summary and says so in a toast. Details: Claude Code guide.

MCP (Claude Code, Cursor, Claude Desktop, any stdio client):

{"mcpServers": {"lcc": {"command": "lcc", "args": ["mcp"]}}}

Tools: compact, compact_transcript, recall, inspect, prepare, explain, intake (MCP guide).

CLI, on a session file or any document:

lcc compact ~/.claude/projects/<project>/<session>.jsonl -q "finish the parser fix" \

--mode tool-calls --provider rules --rules-mode stashed -o compacted.json -r report.json lcc recall # a removed output, hash-checked

lcc compact notes.md -q "what is wrong with the booking flow?" \ --provider mechanical -o compacted.md -r report.json lcc explain report.json --source notes.md # why each block was kept or dropped

Send us your numbers

lcc measure-session ~/.claude/projects/<project>/<session>.jsonl --markdown

Codex CLI rollouts are auto-detected from their session_meta record:

lcc measure-session /path/to/rollout.jsonl --markdown

or a whole project directory: per-mode median and p90 reduction over its sessions

lcc measure-session ~/.claude/projects/ / --markdown

It runs every mode on one of your sessions, offline, and prints counts only: messages, tool calls, tokens before and after, messages kept, time. No text, path or tool output. For exact counts, install the tiktoken extra and cache its tokenizer once (lcc itself never downloads): python -c "import tiktoken; tiktoken.get_encoding('o200k_base')". Otherwise the report says approximate and why. Paste it in Discussions → Results. Add --input-map to see where the session's billed input goes instead: cache reads and writes, and the share taken by tool results, tool-call inputs, injected instructions and the fixed system prompt and tool schemas (counts only, from the usage records in the file). Already compacting with lcc? lcc share-stats turns your local reports into the same kind of counts-only summary (SHARE_STATS.md), or use the results issue form. Every number from someone other than me makes the claims above less mine and more true.

Why lcc

• Verbatim. Kept spans are byte-identical to the source. Nothing is summarized or rewritten.
• Recoverable. stashed moves removed outputs to a local content-addressed store (~/.cache/lcc/stash); recall re-hashes on read. The placeholder, [removed: 120 lines, 4.3 KB · lcc recall ], states lines and size, not tokens, so it is identical on every machine.
• Auditable. Every pass writes a JSON report; lcc explain gives the reason for every drop.
• Local. The default judges run on your CPU in plain Python. No API key, no network.

Providers

specialist-v3 details and limits: SPECIALIST.md. Rules modes: TOOL_CALLS.md. Jev is an optional path you name explicitly (--provider jev); it sends the objective and context blocks to the TypeSafe API, and LCC_DISABLE_NETWORK=1 disables it. auto never picks it on its own: having a key is not consent to send context off-machine, so plain text under auto stays on mechanical unless you set LCC_AUTO_REMOTE=1. compact writes a relevance-compaction-1.2 report; check provider_used and degraded before trusting the output.

Benchmarks

Real sessions (held-out, INFERIDO). In plain words: when a ranker has to drop part of a session, how often does it keep what the agent actually used next? 1.0 would be always. A one-shot final test on 193 items from 36 of the maintainer's sessions that started after the training cutoff. Retention AUC over drop budgets of 0–60% of candidate tokens:

The labels are next-action evidence from one maintainer's Claude Code sessions, which favours v3's training target. The sessions are private; only aggregates are published (SPECIALIST.md).

Synthetic, reproducible (python benchmarks/run_judge_bench.py, about 20 s, no network). Nine hand-written edge cases: supersession, contradiction, config change, exception rule, pt/en mixed, prompt injection in a tool output, large git diff, old root cause, re-read.

These cases are lexically easy, so the lexical baselines do well; v3 misses the Portuguese case. Timings are from macOS arm64 under heavy machine load. Method, every arm and the limits: BENCHMARKS.md. Rules modes on real sessions: REAL_SESSION_BENCHMARKS.md.

Reduction depends on how much of a context is noise. Below a 25% gain, the report marks the pass as not worth it, and the Claude Code plugin keeps the built-in summary.

What it does not do

• It does not check that a model will answer correctly. A structural PASS is not an answer quality result.
• No RAG, embeddings or vector store.
• Protection rules and bounded restoration reduce, but do not rule out, lost evidence; see the safety model.
• specialist-v3 was trained on Claude Code transcripts. Other agents and plain documents are out of distribution; plain text uses mechanical unless you opt in to Jev.

Docs

| Architecture and decisions | architecture.md, ADR 0010, [ADR index](docs/adr