llm-diet 0.1.6

Deterministic codebase context retrieval for LLMs

llm-diet

Give Claude the right context upfront. Fewer turns, faster answers, lower cost.

Deterministic context retrieval for AI coding tools. Parses your repo into a call graph, scores every function against your query, and injects the top matches — before Claude starts reasoning. No embeddings, no vector DB, no LLM calls in the retrieval path.

The Problem

Every Claude Code session starts blind. Claude explores your entire codebase before answering — reading files, listing directories, running commands. That exploration costs tokens and time.

Without llm-diet:
  Your prompt → Claude explores codebase → finds relevant code → answers
  Cost: $0.19 for a simple bug fix session

With llm-diet:
  Your prompt + injected context → Claude answers faster
  Cost: $0.035 for the same depth of answer

Real test on a 1,240-node project: 5x cheaper per session.

Benchmark

This repo (185 nodes, 46k tokens)

Query	Baseline	With llm-diet	Reduction	Time
fix authentication bug	46,661	434	99.1%	187ms
add a new API endpoint	46,661	106	99.8%	203ms
debug memory leak	46,661	428	99.1%	172ms
add logging to the pipeline	46,661	58	99.9%	141ms

46,661 → 275 tokens average. 176ms overhead.

FastAPI repo (946k tokens — repo never seen before)

Query	Baseline	With llm-diet	Reduction	Time
fix authentication bug	946,210	87	>99.9%	359ms
add a new API endpoint	946,210	120	>99.9%	359ms
how does the database connection work	946,210	130	>99.9%	391ms
debug memory leak	946,210	436	>99.9%	1062ms
add input validation	946,210	244	>99.9%	375ms
explain the caching logic	946,210	114	>99.9%	313ms
fix error handling	946,210	221	>99.9%	406ms
add logging to the pipeline	946,210	136	>99.9%	328ms

946,210 → 186 tokens injected. 5x cheaper sessions in practice.

How It Works

repo files (.py, .js, .ts, .jsx, .tsx)
   ↓
AST parser  (no LLM — pure tree-sitter)
   ↓
call graph  (.cecl/graph.json)
   ↓
query  →  keyword expansion  →  BFS traversal  →  top 5 functions
   ↓
injected into Claude before reasoning starts

Same query + same graph = same result. Deterministic by design.

Quick Start

pip install llm-diet
context-engine install    # indexes repo + configures your AI tool
# open Claude Code and start coding

Commands

Command	Description
context-engine install	Index repo and configure Claude Code / Cursor / Windsurf
context-engine index .	(Re)build the call graph
context-engine query "fix auth bug"	See what would be injected for a query
context-engine apply "add endpoint"	Plan → diff → validate → patch (needs ANTHROPIC_API_KEY)
context-engine watch .	Auto-reindex on file save

Platform Support

Platform	Integration	Token reduction
Claude Code	UserPromptSubmit hook — dynamic injection on every prompt	Full (186 tokens avg)
Cursor	Static rules file written to .cursor/rules/	Guides AI; no dynamic injection
Windsurf	Static rules file written to .windsurf/rules/	Guides AI; no dynamic injection

Full token reduction verified on Claude Code. Cursor/Windsurf dynamic injection on the roadmap.

Why Not RAG?

	llm-diet	Embeddings / RAG	code-review-graph
Retrieval method	AST + call graph	Vector similarity	AST + SQLite
LLM calls to retrieve	0	1+	0
Deterministic	Yes	No	Yes
Setup	pip install + index	Model + DB infra	pip install + build
Languages	Python, JS, TS, JSX, TSX	Any	23 languages
Autonomous apply	Yes	No	No
Works offline	Yes	No	Yes

We do less. What we do, we do surgically.

Contributing

Good first issues:

• Dynamic injection for Cursor/Windsurf — extend beyond Claude Code's UserPromptSubmit
• More language parsers — add Go, Rust, Java following the FileParseResult interface in parser.py
• Better keyword expansion — improve domain-specific term mapping in retrieval.py

Open an issue or send a PR.

License

MIT