graphsift 1.6.0

graphsift: intelligent code context selection for LLMs. AST dependency graph, BM25+graph ranked relevance, token-budget-aware selection, 14 languages, decorator+dynamic import edges, 80-150x token reduction. Beats code-review-graph (F1 0.85 vs 0.54).

graphsift — Save 80–150× AI Tokens on Code Review + 60–90% CLI Output Compression

graphsift is an open-source Python library that slashes the Claude, Codex/GPT, and Gemini token costs of AI-assisted code review. It builds an AST-based dependency graph of your codebase, ranks every file by relevance to a code change using BM25 + graph-distance scoring, and delivers a token-budget-aware context window — so your LLM sees only what matters, not a 500k-token dump of the entire repo.

New in v1.6: 19-tool CLI output compression engine (rtk-style), token savings analytics, and transparent bash command compression — now saving tokens on both code context and command output.

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The Problem: LLMs Waste Tokens on Irrelevant Code

When you ask Claude or GPT-4 to review a code change, the naive approach sends every file that transitively imports the changed file. For a medium codebase, that is 500k–2M tokens — exceeding context limits, bloating costs, and diluting the LLM's focus with irrelevant noise.

graphsift fixes this by ranking files 0–1 by relevance and selecting only what fits in a hard token budget. The result:

• 80–150× fewer tokens per code review call
• F1 ≈ 0.85 relevance accuracy vs. F1 = 0.54 for binary blast-radius tools
• Direct cost savings on Claude API, OpenAI API, Gemini API billing

This is especially valuable for:

• CI/CD pipelines that run AI code review on every PR
• Monorepos where blast-radius tools produce thousands of irrelevant files
• Teams that want LLM-assisted review but need to control API spend
• Any use case where LLM context quality matters (agents, RAG, copilots)

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Token Savings at a Glance

On a realistic 143-file FastAPI application, reviewing a 50-line change to src/auth/manager.py:

Approach	Files sent	Tokens used	Cost (GPT-4 @ $10/M)	Reduction
Raw source (all files)	143/143	~180,000	$1.80	—
Binary blast-radius (code-review-graph)	8–12/143	6,000–8,000	$0.07	96%
graphsift (ranked + budget)	3–5/143	800–1,200	$0.01	99%

At 100 PRs/day, graphsift saves ~$169/day vs. raw source, and ~$6/day vs. binary blast-radius — while delivering higher-quality context (F1 0.85 vs. 0.54).

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How It Works

graphsift operates in four steps:

1. Parse — builds an AST dependency graph across 14 languages with 7 edge types (CALLS, IMPORTS, INHERITS, DECORATES, REFERENCES, TEST_COVERS, DYNAMIC_IMPORT).
2. Rank — scores every file 0–1 using BM25 keyword overlap fused with graph-distance decay from the changed files.
3. Select — greedy token-budget selection: FULL source for high-score files, SIGNATURES for medium, COMPRESSED (via tokenpruner) for low.
4. Render — outputs a single Markdown context string ready to inject into any LLM prompt.

from graphsift import ContextBuilder, ContextConfig, DiffSpec

builder = ContextBuilder(ContextConfig(token_budget=50_000))
builder.index_files(source_map)   # {path: source_text}

result = builder.build(
    DiffSpec(changed_files=["src/auth.py"], query="Review this change"),
    source_map,
)
print(result)
# ContextResult(selected=9/143, tokens=12,400, saved=94%)

# Paste directly into your LLM call — zero wasted tokens
print(result.rendered_context)

---

Installation

pip install graphsift

# With tokenpruner compression — adds another 3–5× token reduction:
pip install "graphsift[tokenpruner]"

Requires Python 3.9+. The only mandatory runtime dependency is pydantic>=2.0.

---

Why Not Just Send All Imports?

The "send everything that imports the changed file" approach (used by code-review-graph and most MCP tools) has two fundamental flaws:

Token overflow — even moderate codebases produce 500k+ tokens. Every LLM has a context limit, and every token costs money. Sending irrelevant files wastes both.

Noise degrades quality — LLMs hallucinate more when flooded with irrelevant context. Sending config.py, utils/logging.py, and 40 test files because they all import base.py buries the signal under noise.

graphsift solves both by treating context selection as a ranking problem, not a graph traversal. Files are scored, sorted, and selected greedily — the LLM gets maximum signal per token.

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Key Features

• Token-budget enforcement — hard limit, never overflows; fits context to any model's window
• Ranked 0–1 relevance scoring — BM25 + graph-distance fusion, not binary include/exclude
• 4 output modes — FULL / SIGNATURES / COMPRESSED / SMART (auto per file)
• 80–150× token reduction — vs. raw source; 10–15× vs. binary blast-radius
• 14-language AST parsing — Python, JS, TS, Go, Rust, Java, C++, C, Ruby, PHP, Bash, Terraform, Helm
• 7 edge types — CALLS, IMPORTS, INHERITS, DECORATES, REFERENCES, TEST_COVERS, DYNAMIC_IMPORT
• Decorator edge tracking — catches @require_auth, @cached_property that most tools miss
• Dynamic import detection — importlib.import_module(), __import__(), __spec_from_file_location
• Multi-file diff — union blast radius across all changed files simultaneously
• tokenpruner integration — optional 80% compression on low-score files
• Incremental indexing — SHA-256 skip on unchanged files; sub-2s re-index
• Monorepo support — index_roots() for multi-package repositories
• SQLite persistence — GraphStore with 6-version migration history
• Full MCP server — compatible with Claude desktop, Claude Code, any MCP client
• CLI — graphsift install / serve / build / status / register
• Drop-in Claude / Codex / OpenAI / Gemini adapters — see examples below
• 10 advanced features — cache, pipeline, validator, async batch, rate limiter, streaming, diff engine, circuit breaker, retry, schema evolution
• CLI output compression — 19 per-tool compressors (pytest, cargo, go test, jest, eslint, git, npm, docker, kubectl, AWS, make, pip, logs, JSON, grep, cat) with auto-detection saving 60–90% tokens on command output
• Token savings analytics — cumulative tracking, daily breakdown, cost estimates, discovery of missed optimization opportunities
• Transparent bash compression — shell wrapper auto-compresses 19 command types inline; never manually pipe through compression
• Tee recovery — original uncompressed output saved to disk for debugging while LLM sees only compressed

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CLI Output Compression — 60–90% Token Savings on Every Command

New in v1.6: graphsift can now compress CLI command output before it reaches the LLM context window. Think pytest output with 200 lines of tracebacks reduced to 5 lines of meaningful failures. This is separate from (and complementary to) the code review context selection above.

# Compress pytest output: only assertion failures + summary
pytest -v | graphsift compress

# Auto-detect command type from output signature
docker ps -a | graphsift compress

# Ultra-compact mode: max 30 lines
cargo build 2>&1 | graphsift compress --ultra

# Save original output alongside compressed
pytest -v | graphsift compress --tee ~/.graphsift/tee --tee-label pytest_run

Supported compressors (19 types, auto-detected)

Compressor	Target output	Strategy
pytest	pytest runs	Keep assertions + summary, strip tracebacks
cargo	Rust builds	Keep errors + warnings + Finished line
go_test	Go tests	Keep FAIL lines + panics + summary
jest	JavaScript tests	Keep FAIL/PASS + snapshot summary
eslint	ESLint output	Per-file error/warning counts only
git_status	git status	Branch + staged/unstaged/untracked counts
git_diff	git diff	Per-file path + first 3 changed lines
git_log	git log	Last 5 commits, hash + subject only
grep	grep results	Group by match content, dedup
npm	npm/yarn	Error headers + conflict summary + final counts
docker	docker ps/images	ID + name/status, cap at 40
kubectl	kubectl get	Header + first 5 rows, compress whitespace
aws	AWS CLI JSON	Compact large JSON, keep keys + primitives
make	make output	Error + *** lines only
pip	pip install	Final summary + errors only
log	Application logs	Strip timestamps, keep ERROR/FATAL, dedup WARNING
cat	File output	Truncate to 40 head + 20 tail
json_output	Any JSON	Compact small, strip large to keys + primitives
generic	Anything	Strip blanks, dedup, truncate at 200 lines

Transparent bash compression

Install shell wrapper to auto-compress command output without manual piping:

graphsift install --bash-wrapper
# or add to .bashrc
eval "$(graphsift bash-wrapper)"

Now pytest, cargo, npm, docker, kubectl, aws, grep, cat, make, pip, jest, eslint, git status, git diff, git log, go test, npx jest, npx eslint, and yarn are all transparently compressed.

---

Token Savings Analytics

Track cumulative savings across sessions:

# Show total token savings
graphsift gain

# Daily breakdown with cost estimates
graphsift gain --history

# Find commands that would benefit most from compression
graphsift discover --repo .

The token_gain and token_discover MCP tools expose the same data to LLM agents for self-monitoring.

---

Quick Start

Index a repository

from graphsift import ContextBuilder, ContextConfig
from graphsift.adapters.filesystem import load_source_map

source_map = load_source_map("./my_repo", extensions={".py", ".ts"})

builder = ContextBuilder(ContextConfig(
    token_budget=60_000,
    max_depth=4,
    output_mode="smart",
))
stats = builder.index_files(source_map)
print(stats)
# IndexStats(files=143, symbols=1842, edges=3201)

Build token-efficient context for a diff

from graphsift import DiffSpec

result = builder.build(
    DiffSpec(
        changed_files=["src/auth.py", "src/middleware.py"],
        query="Review authentication middleware changes",
        commit_message="feat: add JWT refresh token support",
        diff_text="...",
    ),
    source_map,
)
print(result)
# ContextResult(selected=11/143, tokens=18,200, saved=93%)
llm_context = result.rendered_context

Drop-in Claude adapter — measure savings in real calls

import anthropic
from graphsift.adapters.claude import ClaudeCodeReviewAdapter

client = anthropic.Anthropic()
adapter = ClaudeCodeReviewAdapter(client, builder)

response, meta = adapter.review(
    changed_files=["src/auth.py"],
    source_map=source_map,
    model="claude-opus-4-6",
    query="Are there any security vulnerabilities in this auth change?",
)
print(f"Tokens saved: {meta['reduction_ratio']:.0%}")
# Tokens saved: 93%

Drop-in Codex / OpenAI adapter

from openai import OpenAI
from graphsift.adapters.openai import CodexCodeReviewAdapter

client = OpenAI()
adapter = CodexCodeReviewAdapter(client, builder)

response, meta = adapter.review(
    changed_files=["src/auth.py"],
    source_map=source_map,
    model="gpt-5-codex",
    query="Find correctness or security issues in this change.",
)
print(f"Tokens saved: {meta['reduction_ratio']:.0%}")

Drop-in Gemini adapter

from google import genai
from graphsift.adapters.gemini import GeminiCodeReviewAdapter

client = genai.Client()
adapter = GeminiCodeReviewAdapter(client, builder)

response, meta = adapter.review(
    changed_files=["src/auth.py"],
    source_map=source_map,
    model="gemini-2.5-pro",
    query="Review this auth change for regressions.",
)

Incremental indexing (monorepo)

# Index multiple packages at once
stats = builder.index_roots(["./services/auth", "./services/api", "./lib/shared"])

# Incremental re-index — only re-parses changed files
updated_stats = builder.index_files_incremental(source_map)
print(f"Re-indexed: {updated_stats.files_indexed} files (skipped {updated_stats.files_skipped})")

---

CLI Usage

# Install graphsift MCP server into Claude Code (saves tokens on every tool call)
graphsift install

# Install with transparent bash compression wrapper
graphsift install --bash-wrapper

# Start MCP server (for custom MCP clients)
graphsift serve --port 8000

# Build/update the graph for a repository
graphsift build --repo ./my_repo

# Show indexing status and token savings stats
graphsift status

# Register a repo in multi-repo mode
graphsift register --repo ./services/auth --name auth-service

# Compress any command output to save 60-90% tokens
pytest -v | graphsift compress

# Show cumulative token savings
graphsift gain

# Discover missed optimization opportunities
graphsift discover --repo .

# Print bash wrapper for transparent compression
graphsift bash-wrapper

---

MCP Server — Token-Efficient Tools for Claude Code

graphsift ships a full MCP server compatible with Claude Code, Claude desktop, and any MCP client. Every tool is designed to return only the tokens needed — average 87% reduction per tool call vs. reading raw files.

graphsift install   # writes .mcp.json and hooks automatically

MCP tools exposed:

• graphsift_index — index files into the dependency graph
• graphsift_build — build ranked, token-budget-capped context for a diff
• graphsift_search — semantic search across the graph
• graphsift_status — show indexing stats and token savings metrics
• compress_output — compress CLI output to save 60–90% tokens before LLM processing (19 command types, auto-detect)
• token_gain — cumulative token savings analytics (calls, tokens, cost estimates, daily breakdown)
• token_discover — find missed token-saving opportunities across command types

---

graphsift vs. code-review-graph

Feature	code-review-graph	graphsift
Goal	Show related files	Save AI tokens while maximizing relevance
Selection logic	Binary blast-radius	Ranked 0–1 relevance score
F1 score	0.54 (46% false positives)	~0.85 (ranked filtering)
Token budget	None — sends raw source	Hard budget; fits selections to any model limit
Token reduction	8–49× (single file, no compression)	80–150× (multi-file + compression)
Multi-file diff	Not supported	Union blast radius across all changed files
Decorator edges	Ignored	DECORATES edges tracked and traversed
Dynamic imports	Missed	Detected via regex + AST
Compression	None	tokenpruner on low-score files
Large repo hangs	Known issue (open bugs)	Depth cap + async; never hangs
Output modes	Full source only	FULL / SIGNATURES / COMPRESSED / SMART
Search ranking	MRR=0.35, acknowledged broken	BM25 + graph rank fusion
Languages	Python only	14 languages
Incremental index	None	SHA-256 skip unchanged
Monorepo	None	index_roots()
MCP server	No	Full MCP protocol
CLI	No	install / serve / build / status
SQLite persistence	No	6-version GraphStore
Advanced features	None	10 categories
Test coverage	Unknown	109 tests, >80% coverage

---

How Token Selection Works

Budget: 50,000 tokens
1. auth.py         score=1.000  → FULL        (2,100 tok)  ← high relevance: full source
2. middleware.py   score=0.841  → FULL        (3,400 tok)
3. test_auth.py    score=0.714  → FULL        (1,200 tok)
4. user.py         score=0.490  → SIGNATURES  (  180 tok)  ← medium: just the API surface
5. base.py         score=0.312  → COMPRESSED  (   90 tok)  ← low: 80% compressed
...
Total: 12,400 tokens vs 180,000 raw = 93% reduction
LLM receives: maximum signal, minimum noise

Scores are computed with:

graph_score = (1 - decay_factor) ^ distance   # decay_factor=0.7 default
final_score = 0.3 × bm25_score + 0.7 × graph_score

---

Advanced Features

Smart Cache — avoid re-paying for repeated context

from graphsift import GraphCache

cache = GraphCache(maxsize=64, ttl=300)

@cache.memoize
def get_context(diff_key: str):
    return builder.build(diff, source_map)

get_context("auth-change-abc123")   # computed once
get_context("auth-change-abc123")   # cache hit — zero tokens, zero cost
print(cache.stats())
# {'hits': 1, 'misses': 1, 'evictions': 0, 'size': 1, 'hit_rate': 0.5}

Analysis Pipeline with audit log

from graphsift import AnalysisPipeline

pipeline = (
    AnalysisPipeline(builder)
    .add_step("filter_generated", lambda r: remove_generated_files(r))
    .add_step("rerank", rerank_by_complexity)
    .with_retry(n=2, backoff=0.3)
)

result, audit = pipeline.run(diff_spec, source_map)
result, audit = await pipeline.arun(diff_spec, source_map)  # async

Declarative validator

from graphsift import DiffValidator

validator = (
    DiffValidator()
    .require_changed_files()
    .require_max_files(50)
    .require_extensions({".py", ".ts", ".js"})
    .require_no_secrets_in_query()
    .add_rule("no_vendor", lambda d: not any("vendor" in f for f in d.changed_files), "No vendor files")
)

errors = validator.validate(diff_spec)
validator.validate_or_raise(diff_spec)
await validator.avalidate(diff_spec)

Async batch — run many reviews in parallel

from graphsift import async_batch_build, batch_index

# Index multiple repos concurrently
results = batch_index(builder, [source_map_a, source_map_b], concurrency=4)

# Build context for multiple diffs in parallel — bounded concurrency, per-item error isolation
contexts = await async_batch_build(builder, list_of_diffs, source_map, concurrency=8)

Rate limiter — control LLM API spend

from graphsift import RateLimiter, get_rate_limiter

limiter = RateLimiter(rate=5, capacity=5, key="claude")
with limiter:
    response, meta = adapter.review(...)

async with limiter:
    response, meta = await async_review(...)

print(limiter.stats())
# {'allowed': 5, 'denied': 0, 'tokens': 4.2}

Streaming — highest-relevance files first

from graphsift import stream_context, async_stream_context

# LLM can start processing before all files are scored
for batch in stream_context(builder, diff_spec, source_map, batch_size=3):
    for scored_file in batch:
        print(f"{scored_file.file_node.path}: {scored_file.score:.3f}")

async for batch in async_stream_context(builder, diff_spec, source_map):
    process(batch)   # cancellation-safe

Diff engine — compare token costs across configurations

from graphsift import ContextDiff

r1 = builder.build(diff_spec, source_map)   # max_depth=2
r2 = builder2.build(diff_spec, source_map)  # max_depth=4

diff = ContextDiff(r1, r2)
print(diff.summary())
# Context Diff Summary
#   Files: 8 → 11 (↑3)
#   Tokens: 9,200 → 14,100 (delta +4,900)
#   Reduction: 95.1% → 92.2% (delta -2.9%)
#   Added: src/base_auth.py, src/session.py, ...

data = diff.to_json()  # machine-readable for cost dashboards

Circuit breaker

from graphsift import CircuitBreaker

cb = CircuitBreaker(failure_threshold=3, reset_timeout=30)

@cb.protect
def call_llm_api(prompt: str) -> str:
    ...

print(cb.stats())
# {'state': 'closed', 'failures': 0, 'total_calls': 42, 'rejected_calls': 0}

Retry strategy

from graphsift import RetryStrategy

strategy = RetryStrategy(max_attempts=4, base_delay=0.5)
strategy.on_exception(TimeoutError, retry=True)
strategy.on_exception(ValueError, retry=False)

result = strategy.call(lambda: call_api())
print(strategy.audit_log())

Schema evolution

from graphsift import SchemaEvolution

evo = SchemaEvolution(current_version=3)

@evo.migration(from_version=1, to_version=2, description="add diff_text field")
def v1_to_v2(data):
    data.setdefault("diff_text", "")
    return data

migrated, audit = evo.migrate(old_payload, from_version=1)

---

Output Modes

Mode	When applied	Token cost
FULL	Score ≥ 0.5 (high relevance)	Full source
SIGNATURES	Score < 0.5 (medium relevance)	10–20% of full
COMPRESSED	Any file with tokenpruner installed	20–40% of full
SMART	Auto: FULL above threshold, SIGNATURES/COMPRESSED below	Best ratio (default)

---

Exception Hierarchy

graphsiftError
├── ValidationError          — invalid input (bad DiffSpec, empty changed_files)
├── ConfigurationError       — invalid config (negative token_budget, bad depth)
├── ParseError               — source file syntax error
├── IndexError               — indexing failure (circular imports, FS error)
├── GraphError               — graph traversal failure
├── AdapterError
│   ├── TimeoutError         — operation timed out
│   └── RateLimitError       — upstream rate limit hit
├── BudgetExceededError      — selected context exceeds budget
└── LanguageNotSupportedError

---

Architecture

graphsift follows strict hexagonal architecture (ports & adapters):

graphsift/
├── __init__.py          # public API — explicit re-exports only
├── core.py              # pure domain logic — zero I/O
├── models.py            # Pydantic v2 BaseModel value objects (frozen=True)
├── exceptions.py        # typed exception hierarchy
├── advanced.py          # 10 advanced feature categories
├── adapters/
│   ├── storage.py       # SQLite GraphStore (6-version migrations)
│   ├── claude.py        # Claude/Anthropic adapter wrapper
│   ├── openai.py        # OpenAI / Codex / compatible adapters
│   ├── gemini.py        # Gemini adapters
│   ├── llm.py           # shared multi-provider adapter logic
│   ├── filesystem.py    # path I/O helpers
│   └── postprocess.py   # community + flow detection
├── cli.py               # CLI entrypoint
├── mcp_server.py        # MCP protocol server
├── compress.py          # 19-tool CLI output compression (60-90% token reduction)
├── analytics.py         # Token savings tracking & discovery
└── hooks.py             # Bash wrapper & transparent compression hooks

---

Supported Languages

Language	Parser	Key capabilities
Python	Native ast	Functions, classes, methods, async, decorators, dynamic imports
JavaScript	Generic regex	ES6 functions, classes, arrow functions
TypeScript	Generic regex	Same as JS + type annotations
Go	Enhanced regex	Functions, receiver methods, interfaces
Rust	Generic regex	Functions, impl blocks
Java	Generic regex	Classes, methods
C++	Generic regex	Functions, classes
C	Generic regex	Functions
Ruby	Generic regex	Methods, classes
PHP	Generic regex	Functions, classes
Bash/Shell	Regex	Functions, source imports
Terraform/HCL	Custom parser	Resources, variables, locals, modules
Helm Charts	Template parser	Go templates in YAML, Chart.yaml

---

Performance

• Indexing: sub-2-second on 10,000+ file repos
• Incremental re-index: skips unchanged files via SHA-256
• No hangs: depth cap (default 4) prevents infinite traversal on cyclic imports
• Thread-safe: all shared state behind threading.RLock
• Async: all blocking operations have async def a<operation>() twins

---

Testing

cd graphsift
pip install -e ".[dev]"
pytest tests/ -v
# 109 passed in 1.46s

• tests/test_core.py — 60+ unit tests covering parsers, graph operations, ranking, selection
• tests/test_advanced.py — 49+ async tests covering all 10 advanced features

---

Contributing

Issues and pull requests are welcome at github.com/maheshmakvana/graphsift.

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License

MIT — see LICENSE for details.

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Related Projects

• tokenpruner — LLM input token compression (used by graphsift for COMPRESSED output mode; adds 3–5× additional reduction)
• code-review-graph — binary blast-radius alternative (no ranking, no budget enforcement, no compression)