graphsift 3.0.0

graphsift: Save Claude tokens, reduce GPT-4 & Gemini API costs. #1 Python library for LLM token optimization with 80-150x code context reduction, 86% CLI output compression, F1 0.85 relevance. AST dependency graph, BM25+graph ranked selection, 14-language tree-sitter parsing, 19 CLI compressors, MCP server, agent memory. Created by Mahesh Makwana.

graphsift v3.0

#1 Token Saver for Claude, GPT-4, Gemini & Every LLM —
80–150× Fewer Tokens, F1 0.85 Relevance Accuracy, 93% Feature Coverage

Created by Mahesh Makwana · @makwanamahesh5

Why graphsift? · Quick Start · Install · API · v3.0 · Benchmarks · Docs

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🚀 Save Tokens, Save Money: Why graphsift?

graphsift is the #1 Python token optimization engine purpose-built for AI-assisted development. It slashes LLM costs by intelligently selecting only the most relevant code context — no more sending entire codebases to Claude, GPT-4, or Gemini.

Every time you send code to an LLM for review, debugging, or generation, graphsift ranks every file by relevance, enforces hard token budgets, compresses CLI output, and blocks prompt injection — saving 80–150× tokens while maintaining 0.85 F1 relevance accuracy.

Used by developers worldwide to reduce Claude Code costs by up to 99%, slash OpenAI API bills, and keep Gemini/Codex within context limits on large monorepos.

How graphsift Compares

Tool	What It Saves	Approach	Token Reduction	Best For
graphsift ✅	Input tokens (code context)	Ranked relevance + AST graph + 3-tier compression + entropy dedup	80–150×	Code review, PRs, debugging, AI codegen
Caveman 🗿	Output tokens (LLM replies)	Instructs agent to speak concisely	~65%	Complementary — use together!
tokenpruner ✂️	Input tokens (any text)	Semantic compression	~70-80%	General prompt compression

💡 Perfect Stack: graphsift (input compression) + Caveman (output compression) = maximum savings across the full AI pipeline.

Who Saves with graphsift?

User	Savings	Why
Claude Code users	Up to 99% per review	Ranked context + token budgets eliminate context bloat
OpenAI / GPT-4 devs	$0.036/review instead of $5.40	Smart file selection slashes API costs
Gemini & Codex teams	Stay within context limits	Never hit the ceiling on large monorepos
MCP-connected agents	Automatic optimization	7+ MCP tools for token-efficient workflows
CI/CD pipelines	60-97% compression	Compress pytest/eslint/kubectl output before LLM analysis
Multi-agent systems	60-82% conversation savings	ConversationCompactor compresses agent dialogue

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📦 Install

pip install graphsift                           # Core (pure Python, zero hard deps)
pip install "graphsift[all]"                    # Full: tree-sitter + compression extras
pip install "graphsift[treesitter]"             # +11 language AST parsers
pip install "graphsift[semantic]"               # +dense vector embeddings for semantic code search

Zero npm, zero Docker, zero accounts, zero telemetry.

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⚡ Quick Start

from graphsift import ContextBuilder, ContextConfig, DiffSpec

# Build context for a 50-line change to auth/manager.py
builder = ContextBuilder(ContextConfig(token_budget=2000, diff_aware_trimming=True))
result = builder.build(
    DiffSpec(changed_files=["src/auth/manager.py"], diff_text="@@ -42,5 +42,8 @@ def login(self):"),
    source_map={},
)

print(f"Files: {result.files_selected}, Tokens: {result.total_rendered_tokens}, Saved: {result.reduction_ratio:.1%}")
# Files: 4, Tokens: 1,150, Saved: 99.4%

# Compress CLI output before sending to an LLM:
from graphsift import compress
saved = compress(pytest_output, "pytest")  # 90% reduction

# Autonomous plan-then-execute workflow:
from graphsift.planner import Planner
plan = planner.create_plan("Add OAuth2 authentication", changed_files=["src/auth.py"])
result = planner.execute_plan(plan)

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🌟 New in v3.0

v3.0 is the biggest release yet — 11 entirely new modules, 702 tests (+115%), and 93% feature coverage across 3 tiers.

Module	What It Does
Planner 🗺️	Plan-first engine — scan repo, architect solution, execute, validate. 7 phases (scan → analyze → architect → plan → execute → validate → review)
ToolChain ⛓️	DAG-based workflow automation — build a chain of steps, run with rollback, review results
AutoVerifier ✅	Self-verification cascade — syntax check → lint → run tests → fix loop (up to 3 retries)
ConventionLearner 📐	Learns team coding conventions from your codebase, stores in CodeMemory with 365-day TTL
ContextEnricher 🔍	Multi-source code exploration — discovers related symbols, imports, and patterns
AsyncEngine ⚡	Async parallel execution with bounded semaphores — index and build across repos concurrently
ASTCache 💾	2-tier LRU+SQLite cache with TTL, warm(), predictive_warm(), and glob-based invalidation
Pool 🗄️	Thread-safe database connection pool (WAL mode, auto-reconnect)
SecurePipeline 🔒	End-to-end secure pipeline — PathValidator + CommandSanitizer + DataScrubber in one chain
DataScrubber 🧹	Redacts API keys, tokens, passwords, and secrets from CLI output before LLM exposure
SchemaRegistry 📋	6 schema families with v1→v2 auto-migration, naming-convention discovery

Full v2.3 → v3.0 Upgrade

See the V3 Upgrade Guide for the complete 27-feature comparison matrix. 25/27 features delivered (93%).

Category              v2.3           v3.0              Improvement
─────────────────────────────────────────────────────────────────────
Hallucination Prev    2 features/tpl 8 features/tpl     +300%
Tests                 326            702                +115%
Test categories       1              5                  +400%
Security classes      2              5                  +150%
Memory systems        1              3                  +200%
Concurrency tiers     0              3                  +300%
Output savings        ~70%           ~85-90%            +15-20%
Source files          31             48                 +55%
Overall coverage      48%            93%                +45%

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🧠 Core Concepts

Ranked context selection treats code review as a relevance-ranking problem, not a graph traversal. Instead of sending every file that imports a changed module (binary blast-radius), graphsift scores each file 0–1 using:

• AST dependency graph — parses 14 languages, traces imports & symbols
• BM25 full-text search — standard IR ranking (k1=1.2, b=0.75)
• Diff proximity — files close to the change in dependency space
• Optional dense vectors — semantic embedding fusion via HybridSearcher

Three-tier output: Hot files (directly changed) → full source. Warm files (strongly related) → signatures only. Cold files (weakly related) → excluded. Combined with diff-aware trimming, entropy-based deduplication (SimHash), and conversation compaction, this achieves 80–150× token reduction.

6 anti-hallucination Fable5 prompt templates: Every template includes [VERIFIED-REAL] evidence markers, confidence calibration tiers (high/medium/low/baseline), coherence guard, validation theater detection, structured JSON output schemas, source quality hierarchy, and knowledge currency — 300% more guardrails than v2.3.

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📘 API Overview

Core Context API

Class / Function	Description
ContextBuilder(config)	Builds ranked context for a diff from a source map
ContextConfig(token_budget, ...)	Configuration for token budget, tiers, trimming
DiffSpec(changed_files, diff_text)	Describes a code change to build context for
ContextResult	Result: selected files, token count, rendered context
RelevanceRanker	Scores files 0–1 using AST + BM25 + graph proximity
DependencyGraph	Builds and queries the AST dependency graph
Sift	Unified v3 API — index, search, build, compress in one class

Compression & Output

Function	Description
compress(text, cmd_type)	Compresses CLI output (19 command types + ultra mode)
compress_tee(text, cmd_type)	Compress + write original to file simultaneously
ultra_compress(text, level)	Maximum compression with adjustable level
ConversationCompactor	Compresses agent conversation history (60-82% savings)
AutonomousCompressor	Self-triggering compaction at configurable thresholds

Search & Retrieval

Class	Description
HybridSearcher	BM25 + TF-IDF + optional dense vector fusion (3 modes)
TypedRetriever	PRISM-style typed graph traversal (6 query intents)
TemporalGraph	Git-history-aware symbol tracking across commits

Memory & Persistence

Class	Description
AgentMemory	SQLite-backed cross-session knowledge graph
CodeMemory	Code-anchored memory — 7 types (decision, gotcha, note, insight, todo, bug, convention) with TTL
TieredMemory	4-tier hierarchy (axioms → rules → topic → archive)
ConventionLearner	Learns team conventions from code, stores in CodeMemory (365d TTL)

Planning & Execution

Class	Description
Planner	Plan-first engine — 7 phases, topological execution, PlanResult
ExecutionPlan	Structured plan with ordered phases and steps
ToolChain	DAG-based step chains with build/review/run
AutoVerifier	Self-verification cascade (syntax → lint → tests → fix)
AutoPipeline	End-to-end auto pipeline combining executor + verifier

Security

Class	Description
PathValidator	Blocks path traversal attacks in file operations
CommandSanitizer	Prevents command injection from untrusted input
DataScrubber	Redacts API keys, tokens, passwords, secrets
SecurePipeline	End-to-end secure pipeline combining all 3

Prompt Engineering (Fable5)

Template	Use Case
FixBugTemplate	Bug diagnosis with prior-art search, 5 confidence tiers
AddFeatureTemplate	Feature addition with dependency analysis
RefactorTemplate	Code refactoring with semantic preservation checks
ProductionAppTemplate	Production-grade scaffolding with phased guard
ThemeChangeTemplate	Large-scale theme/architecture changes
SecurityArchitectureTemplate	Security review with attack-tree analysis

Agent Infrastructure

Class	Description
A2AServer	Agent-to-Agent protocol server (JSON-RPC/HTTP)
Harness	Pre/post validation hooks for agent pipelines
DriftDetector	Detects output drift from expected patterns
TaskManager	MCP async task manager with progress tracking
PriorityScorer	5-signal priority scoring (critical → high → medium → low)

Graph & Schema

Class	Description
GraphStore	SQLite-backed graph persistence
SchemaRegistry	6 schema families, v1→v2 auto-migration
SchemaEvolution	Version-aware migration with auto-discover
Postprocessor	Community detection, flow detection, risk scoring

Async & Performance

Class	Description
AsyncEngine	Async parallel execution (semaphore=8, asyncio.gather)
ProcessPoolExecutor	Multi-process chunked file parsing (50-file threshold)
DatabasePool	Thread-safe SQLite pool (WAL mode, auto-reconnect)
ASTCache	2-tier LRU+SQLite with TTL, predictive warming
ReadCache	SHA-256 fingerprint dedup for file reads
ToolBudget	Per-tool output line caps

CLI Commands

graphsift build         # Index repo + dependency graph — optimize Claude Code context
graphsift install       # Register MCP server with Claude Code for automatic token savings
graphsift status        # Show indexing stats
graphsift compress      # Pipe CLI output for instant token compression
graphsift gain          # Show token savings analytics — track cost reduction over time
graphsift discover      # Find missed token-saving opportunities

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📊 Benchmarks

Relevance Accuracy (F1 Score)

Approach	F1	False Positives
Binary blast-radius	0.54	46%
graphsift (ranked + dedup + trimming)	0.85	15%

Token Reduction — Save Claude & OpenAI Costs

Benchmarked on a 143-file FastAPI app reviewing a 50-line change to auth/manager.py:

Approach	Files Sent	Tokens	Cost (GPT-4 @ $30/M)	Cost (Claude Opus @ $15/M)	Savings vs Raw
Raw source	143/143	~180,000	$5.40	$2.70	—
Binary blast-radius	8–12/143	6,000–8,000	$0.24	$0.10	96%
graphsift (ranked + budget)	3–5/143	800–1,200	$0.036	$0.015	99.4%

Save $2.69 per code review with Claude Opus. On 100 reviews/month = $269/month savings.

Performance

Operation	Time
Index 10,000+ file repo	< 2 s
Incremental re-index	< 0.5 s
Context build (1k file repo)	< 50 ms
Cache-hit retrieval	< 5 ms

CLI Output Compression — 19 Command Types

Command	Raw Tokens	Compressed	Saved
grep -r	413	22	95%
pip install	115	11	90%
npm audit	630	21	89%
npm test	720	86	88%
eslint src/	308	17	94%
docker ps	450	32	82%
docker logs	450	90	80%
git diff	889	60	93%
git status	177	34	81%
git log	212	65	69%
pytest -v	1,334	136	90%
kubectl get all	581	110	81%
terraform plan	291	218	25%
Weighted avg	8,138	1,884	77%

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⚖️ WITH vs WITHOUT graphsift — How Claude, GPT-4, and Gemini Behave Differently

The single most important question graphsift answers: What happens when you build a real project with an LLM — FastAPI backend, React frontend, database, auth — and you DON'T optimize your context window?

Tested across 15 real-world developer scenarios and validated against a full FastAPI + React project build lifecycle:

Daily Dev Scenarios — Token Comparison

Scenario	WITHOUT graphsift	WITH graphsift	Saved
Bug diagnosis (pytest)	427 tok	143 tok	66%
Security audit (npm audit)	191 tok	21 tok	89%
Code review (git diff)	344 tok	61 tok	82%
CI/CD debug (go test)	128 tok	37 tok	71%
Lint report (eslint)	214 tok	34 tok	84%
Build output (make)	74 tok	8 tok	89%
Container check (docker ps)	180 tok	32 tok	82%
TOTALS	2,748 tok	930 tok	66%

Full Project Lifecycle — Building a FastAPI + React App

When building a FastAPI backend + React frontend (with auth, database models, API routes, and CI/CD) using Claude Code, GPT-4, or Gemini — the difference between an optimized and unoptimized context window is the difference between shipping in hours vs fighting hallucinations all day.

Dev Phase	📦 WITHOUT graphsift	⚡ WITH graphsift	Savings
1. Project scaffolding	Sends full boilerplate — 3,000–8,000 tok per file. Context fills after 3-4 files.	ContextBuilder token budget selects only relevant files. 4–25× more files visible.	85–93%
2. pip install output	80+ lines of "Collecting...", progress bars, dependency trees = ~2,500 tok	compress_pip keeps only installed packages + errors = ~120 tok	95%
3. npm install output	Download bars, audit report, deprecations, dep tree = ~4,000 tok	compress_npm keeps added/removed counts + errors = ~350 tok	91%
4. First pytest -v run	15 PASS lines + ANSI + tracebacks + coverage = ~3,200 tok	compress_pytest strips PASS + ANSI, keeps failures only = ~480 tok	85%
5. git status during dev	Full ANSI-colored file list with metadata = ~400 tok (10 files)	compress_git_status → clean file list = ~80 tok	80%
6. git diff code review	Full diff with context lines, hunk headers, markers = ~6,000 tok (50-line change)	compress_git_diff → changed lines only = ~900 tok	85%
7. Code generation	Full file sends — often truncated at context limit	ContextBuilder.diff_aware_trimming → only relevant files	80–150× input savings
8. Debugging (5 rounds)	Context thrashes — early files evicted by round 3. Model guesses wrong.	Target snippets survive all rounds. Context stays coherent.	~70% less churn
9. Multi-file refactor	10 files × 500 lines = ~15,000 tok. Patterns/rules get squeezed out.	AST relevance rank → ~1,500 tok. Refactoring rules stay visible.	90%
10. Python traceback	Full traceback + locals = ~3,000–5,000 tok	compress_log → error type + key frames = ~400 tok	87–92%
11. API contract decisions	Claude guesses route names — context too full for actual routes	Relevance ranking → route definitions always prioritized	Hallucination ↓ 60%
**12. Entire 2-hour session	140K–200K total tokens = $3.00–5.00/session	17K–23K total tokens = $0.40–0.70/session	~87% cost ↓

🧠 Hallucination Risk — WITH vs WITHOUT graphsift

Hallucination is the #1 productivity killer when building with LLMs. When context fills with noise, models invent code that doesn't match your project's actual decisions.

Scenario	📦 WITHOUT graphsift	⚡ WITH graphsift
FastAPI route naming	May create /api/users when project uses /api/v1/users — route definition was evicted	ContextBuilder keeps route files ranked highly — correct names preserved
React component props	May use interface Props { name: string } when project uses type Props = {...} — type choice evicted 4 turns ago	Compressed output = project's type conventions stay in window
Import paths	May import from models.user import User when real path is from app.models import User	get_context returns actual import statements
Database model fields	May reference User.email when real field is User.email_address	AST-aware ranking surfaces model definitions from any query
API response shape	Returns {"user": {...}} when project wraps responses in {"data": {"user":{}},"status":"ok"}	Diff-aware trimming keeps response wrappers visible
CSS / Tailwind classes	Uses classes the project doesn't have installed	Build index records dependencies → available utilities stay in context
Error handling pattern	Uses try/except Exception when project uses custom AppError hierarchy	ConventionLearner caches team patterns → 365-day TTL memory
Database queries	Uses raw SQL when project uses SQLAlchemy ORM patterns	AST graph traces import chain → ORM usage visible

Without graphsift: ~15–25% of code-generation turns contain hallucinated APIs, routes, or types. The model literally cannot see what already exists.

With graphsift: ~5–10% hallucination rate — a ~60% reduction — because the right context fits in the window and the model can reference actual project decisions.

Why This Happens — The Context Window Physics

WITHOUT graphsift:  [BOILERPLATE][BOILERPLATE][NOISE][NOISE][PROJECT LOGIC]
                    → 3 files of project logic max before window fills
                    → Model forced to guess everything else
                    → 15–25% hallucination rate on new code

WITH graphsift:     [PROJECT LOGIC][PROJECT LOGIC][PROJECT LOGIC][PROJECT LOGIC]
                    → 15–50 files of project logic in the same window
                    → Model reads your actual code, not guesses
                    → 5–10% hallucination rate — 60% lower

Cost Comparison — Monthly & Per-Session

Usage Level	Runs/Month	WITHOUT	WITH graphsift	Monthly Savings
Light (daily solo dev)	110	$4.50	$1.50	$3.00/mo
Medium (team dev, PR reviews)	220	$9.00	$3.00	$6.00/mo
Heavy (CI/CD + daily reviews)	500	$20.50	$6.80	$13.70/mo
Enterprise pipeline	1,000	$41.00	$13.60	$27.40/mo

Bottom line: A team of 5 developers doing CI/CD + daily reviews saves $68.50/month — and that's just the token cost. The real savings are in shipping 2–3× faster because your LLM actually sees your code.

Real Developer Testimony

"Before graphsift, I'd ask Claude to add a route and it would create endpoints I never defined — it was guessing because it couldn't see the router file anymore. After graphsift, it references the exact route decorators. Night and day." — graphsift user, FastAPI + React project

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Without graphsift: Claude reads 2,748 tokens of unfiltered output — ANSI escapes, timestamps, progress bars, PASSED lines. It must find the problem in the noise.

With graphsift: Claude reads 930 tokens of pre-filtered signal — only error types, failure messages, changed lines, severity counts. It starts reasoning immediately, never sees secrets (DataScrubber), and never receives injection attacks (PathValidator + CommandSanitizer).

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🛡️ Security-First Architecture

graphsift is built on a zero-exfiltration, local-first architecture:

• ❌ Zero telemetry — no analytics pings, no usage tracking
• ❌ Zero network calls during parsing, indexing, or compression
• ❌ Zero code exfiltration — AST nodes, graphs, source excerpts never leave your machine
• ❌ Zero LLM calls in library code — graphsift is a tool for LLMs, not powered by them
• ❌ Zero third-party SDKs — no embedded analytics or error-reporting services

Built-in Security Layers

Layer	What It Protects Against
PathValidator	Path traversal attacks (../../../etc/passwd)
CommandSanitizer	Command injection (; rm -rf /)
DataScrubber	Secret leakage (API keys, tokens, passwords)
SecurePipeline	Combined protection in one chain
NetworkAccessError	Unauthorized network access from tool output

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📚 Further Reading

Document	What It Covers
V3 Upgrade Guide	Full v2.3 → v3.0 feature comparison (27 features, 93% coverage)
Architecture Deep Dive	Module design, data flow, architectural decisions
API Reference	Complete reference for all public classes and functions
Economics of LLM Context Windows	Why context optimization is the highest-leverage AI investment
Wiki Home	Community wiki — FAQ, use cases, deep dives
Changelog	Full release history
Contributing Guide	Development setup, code style, PR workflow
Security Policy	Zero-exfiltration design, data handling

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🔗 Related Token-Saving Projects

Project	What It Does	How It Complements graphsift
graphsift ⚡	Input code context optimization (80–150× reduction)	—
Caveman 🗿	Compress LLM output tokens (~65% savings)	Use together: graphsift for input, Caveman for output = max savings
tokenpruner ✂️	General prompt token compression (70–80%)	Alternative for non-code contexts

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👨‍💻 Author

graphsift was built and is maintained by Mahesh Makwana — a developer who believes AI-assisted coding shouldn't bankrupt you on token costs.

Contact	Link
🐙 GitHub	@maheshmakvana
🐦 Twitter / X	@makwanamahesh5
📧 Email	[e-mail]
📦 PyPI	pypi.org/project/graphsift

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📄 License

MIT — see LICENSE.

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pip install graphsift
_{🚫 Zero telemetry. 🔒 Zero accounts. 💰 Just savings.
Built by Mahesh Makwana}