Metadata-Version: 2.4
Name: graphsift
Version: 3.0.0
Summary: 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.
Author-email: Mahesh Makvana <maheshmakwana527@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/maheshmakvana/graphsift
Project-URL: Repository, https://github.com/maheshmakvana/graphsift
Project-URL: Issues, https://github.com/maheshmakvana/graphsift/issues
Project-URL: Changelog, https://github.com/maheshmakvana/graphsift/releases
Project-URL: Documentation, https://github.com/maheshmakvana/graphsift#readme
Project-URL: LinkedIn, https://www.linkedin.com/in/mahesh-makwana/
Project-URL: Download, https://pypi.org/project/graphsift/
Keywords: save claude tokens,claude token saver,claude token optimizer,claude token compression,reduce claude api costs,claude cost reduction,claude code save tokens,claude context optimizer,claude api token reducer,optimize claude context,claude context window,claude api savings,llm token optimization,llm token compression,llm token reducer,reduce llm api costs,llm context optimization,llm context compression,llm context window,token reduction tool,token cost reduction,token budget tool,token saving tools,context window optimization,code context compression,code context optimization,save gpt tokens,reduce openai api costs,openai token optimizer,gpt token reduction,gpt context optimizer,save gemini tokens,gemini token optimizer,reduce gemini api costs,code review token savings,ai code review cost reduction,code review claude,code review openai,code review gpt4,code review gpt5,code review gemini,code review codex,code review llm,code review automation,code review context,llm code review tool,cli output compression,command output compression,pytest output compression,terminal output compression,bash output compression,git output compression,docker output compression,kubectl output compression,npm output compression,cargo output compression,eslint output compression,go test output compression,jest output compression,log output compression,output token compression,ast dependency graph,code graph python,dependency graph python,blast radius analysis,semantic code search,hybrid code search,codebase context llm,token budget code,python ast parser,tree sitter parser python,code intelligence python,ranked context selection,bm25 code search,multi-file diff analysis,decorator edge detection,dynamic import graph,monorepo context,incremental code indexing,code-review-graph alternative,better than code review graph,code review graph vs graphsift,mcp server code,mcp server claude,claude code integration,claude code mcp,codex integration,openai integration,gemini integration,openai compatible api,cycle detection python,dead code detection,auto fix suggestions,code deduplication,diff aware context,cache aware llm,token analytics,token savings analytics,graphsift,agent-memory,graphrag,typed-retrieval,context-compaction,a2a-protocol,conversation-compaction,code-memory,temporal-graph,evidence-citations,harness-engineering
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Quality Assurance
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Software Development :: Testing
Classifier: Topic :: Software Development :: Compilers
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Text Processing :: Filters
Classifier: Topic :: Utilities
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: Plugins
Classifier: Framework :: AsyncIO
Classifier: Natural Language :: English
Classifier: Typing :: Typed
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pydantic>=2.0
Provides-Extra: tokenpruner
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Dynamic: license-file

<p align="center">
  <img src="https://raw.githubusercontent.com/maheshmakvana/graphsift/master/docs/images/hero_banner.png" alt="graphsift — Ranked Code Context & Token Optimizer for LLMs" width="600">
</p>

<h1 align="center">graphsift v3.0</h1>
<p align="center">
  <strong>#1 Token Saver for Claude, GPT-4, Gemini & Every LLM —<br>
  80–150× Fewer Tokens, F1 0.85 Relevance Accuracy, 93% Feature Coverage</strong>
</p>

<p align="center">
  Created by <a href="https://github.com/maheshmakvana"><strong>Mahesh Makwana</strong></a>
  ·
  <a href="https://x.com/makwanamahesh5">@makwanamahesh5</a>
</p>

<p align="center">
  <a href="https://pypi.org/project/graphsift/"><img src="https://img.shields.io/pypi/v/graphsift.svg?style=flat&color=blue" alt="PyPI"></a>
  <a href="https://pypi.org/project/graphsift/"><img src="https://img.shields.io/pypi/pyversions/graphsift.svg?style=flat" alt="Python"></a>
  <a href="https://pypi.org/project/graphsift/"><img src="https://img.shields.io/pypi/dm/graphsift?style=flat&label=downloads&color=brightgreen" alt="Downloads"></a>
  <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-green.svg?style=flat" alt="License"></a>
  <a href="https://github.com/maheshmakvana/graphsift/actions"><img src="https://img.shields.io/github/actions/workflow/status/maheshmakvana/graphsift/tests.yml?style=flat&label=tests&color=blue" alt="CI"></a>
  <img src="https://img.shields.io/badge/F1-0.85-success" alt="F1">
  <img src="https://img.shields.io/badge/languages-14-lightgrey" alt="languages">
  <img src="https://img.shields.io/badge/modules-38-blue" alt="modules">
  <img src="https://img.shields.io/badge/tests-702-brightgreen" alt="tests">
  <img src="https://img.shields.io/badge/features-93%25-orange" alt="features">
  <a href="https://github.com/maheshmakvana/graphsift/stargazers"><img src="https://img.shields.io/github/stars/maheshmakvana/graphsift?style=flat&color=yellow" alt="Stars"></a>
</p>

<p align="center">
  <a href="#-save-tokens-save-money-why-graphsift">Why graphsift?</a> ·
  <a href="#-quick-start">Quick Start</a> ·
  <a href="#-install">Install</a> ·
  <a href="#-api-overview">API</a> ·
  <a href="#-new-in-v30">v3.0</a> ·
  <a href="#-benchmarks">Benchmarks</a> ·
  <a href="#-docs">Docs</a>
</p>

---

## 🚀 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](https://github.com/JuliusBrussee/caveman) 🗿 | **Output tokens** (LLM replies) | Instructs agent to speak concisely | ~65% | Complementary — use together! |
| [tokenpruner](https://pypi.org/project/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 |

---

## 📦 Install

```bash
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.**

---

## ⚡ Quick Start

```python
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)
```

---

## 🌟 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](docs/V3_UPGRADE_GUIDE.md) 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%
```

---

## 🧠 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**.

---

## 📘 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

```bash
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
```

---

## 📊 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%** |

---

## ⚖️ 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

---

**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).

---

## 🛡️ 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 |

---

## 📚 Further Reading

| Document | What It Covers |
|---|---|
| [V3 Upgrade Guide](docs/V3_UPGRADE_GUIDE.md) | Full v2.3 → v3.0 feature comparison (27 features, 93% coverage) |
| [Architecture Deep Dive](docs/DECONSTRUCTING_GRAPHSIFT_ARCHITECTURE.md) | Module design, data flow, architectural decisions |
| [API Reference](docs/API_REFERENCE.md) | Complete reference for all public classes and functions |
| [Economics of LLM Context Windows](docs/ECONOMICS_MECHANICS_OF_LLM_CONTEXT_WINDOWS.md) | Why context optimization is the highest-leverage AI investment |
| [Wiki Home](docs/wiki_home.md) | Community wiki — FAQ, use cases, deep dives |
| [Changelog](CHANGELOG.md) | Full release history |
| [Contributing Guide](CONTRIBUTING.md) | Development setup, code style, PR workflow |
| [Security Policy](SECURITY.md) | Zero-exfiltration design, data handling |

---

## 🔗 Related Token-Saving Projects

| Project | What It Does | How It Complements graphsift |
|---|---|---|
| **graphsift** ⚡ | Input code context optimization (80–150× reduction) | — |
| [Caveman](https://github.com/JuliusBrussee/caveman) 🗿 | Compress LLM *output* tokens (~65% savings) | Use together: **graphsift** for input, **Caveman** for output = max savings |
| [tokenpruner](https://pypi.org/project/tokenpruner/) ✂️ | General prompt token compression (70–80%) | Alternative for non-code contexts |

---

## 👨‍💻 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](https://github.com/maheshmakvana) |
| 🐦 **Twitter / X** | [@makwanamahesh5](https://x.com/makwanamahesh5) |
| 📧 **Email** | [maheshmakwana527@gmail.com](mailto:maheshmakwana527@gmail.com) |
| 📦 **PyPI** | [pypi.org/project/graphsift](https://pypi.org/project/graphsift) |

---

## 📄 License

MIT — see [LICENSE](LICENSE).

---

<p align="center">
  <code>pip install graphsift</code><br>
  <sub>🚫 Zero telemetry. 🔒 Zero accounts. 💰 Just savings.</sub><br>
  <sub>Built by <a href="https://github.com/maheshmakvana">Mahesh Makwana</a></sub>
</p>
