Codegraph: The Code Knowledge Graph That Slashes LLM Token Costs by 40-60% — Pre-Indexed, 100% Local — A Practical Guide 2026

┌──────────────────────────────────────────────────────┐
│              Codegraph Knowledge Graph Engine           │
│                                                      │
│  ┌────────────┐    ┌────────────┐    ┌───────────┐   │
│  │ Source Code │    │  Configs   │    │   Docs    │   │
│  │ (.py,.ts)  │    │  (.yaml)   │    │   (.md)   │   │
│  └─────┬──────┘    └─────┬──────┘    └─────┬─────┘   │
│        │                 │                 │         │
│        ▼                 ▼                 ▼         │
│  ┌───────────────────────────────────────────────┐   │
│  │         Codegraph Indexer & Graph Builder      │   │
│  │  • AST Parsing  • Dependency Analysis          │   │
│  │  • Symbol Linking • Call Graph Construction    │   │
│  └───────────────────────┬───────────────────────┘   │
│                          │ Local vector store         │
│  ┌───────────────────────▼───────────────────────┐   │
│  │  AI Agent Query (Claude Code / Codex / ...)   │   │
│  │  Returns: relevant code snippets, not entire repo │
│  └───────────────────────────────────────────────┘   │
└──────────────────────────────────────────────────────┘

Codegraph: source code → knowledge graph → precise agent queries

Introduction

Every AI coding agent wastes hours sifting through entire codebases — reading thousands of irrelevant files, drowning in context windows, and burning tokens on code it never touches. Codegraph (45,555 GitHub stars) solves this by pre-indexing your code into a local knowledge graph that AI agents query instead of blindly scanning. Result: 40-60% fewer tokens, fewer tool calls, and answers that actually reference the right files. Works with Claude Code, Codex CLI, Cursor, Copilot, Gemini CLI, and any OpenAI-compatible agent. 100% local, zero data leaves your machine.

What Is Codegraph?

Codegraph is a pre-indexed code knowledge graph that transforms your codebase into a structured, queryable graph database. Instead of an AI agent reading every file in a repo, Codegraph indexes the AST (Abstract Syntax Tree), symbol definitions, function calls, imports, and dependencies — then serves precise results to the agent on demand.

Key capabilities: - Pre-indexing — Index entire codebases once, query repeatedly

Local-first — 100% local processing, no code ever leaves your machine
Agent-agnostic — Works with Claude Code, Codex CLI, Cursor, Copilot, Gemini CLI, and 5+ other agents
Symbol-aware — Understands function definitions, class hierarchies, imports, and call chains
Token reduction — Returns only relevant code snippets, not entire files
Incremental updates — Re-indexes changed files automatically

Built with Python, uses networkx for graph operations and local vector stores (ChromaDB or SQLite) for embedding storage. Indexing a 100K-line codebase takes ~2 minutes.

How Codegraph Works

Stage 1: Installation

# Install Codegraph globally
npm i -g @colbymchenry/codegraph

Stage 2: Indexing

# Index any project
codegraph index /path/to/project --output ./codegraph-data

Codegraph parses source code, configuration files, and documentation to build a structured knowledge graph. It extracts function definitions, class hierarchies, imports, call chains, and file relationships.

Stage 3: Querying

# Query the indexed graph
codegraph query "How does the user login flow work?" \
  --data ./codegraph-data

The query engine returns relevant code snippets, not entire files. Results include the file path, symbol name, code snippet, and relevance score.

Installation & Setup

Quick Start

# Install Codegraph
npm i -g @colbymchenry/codegraph

# Index your project
codegraph index /path/to/project --output ./codegraph-data

# Query the index
codegraph query "Where is the authentication middleware defined?" \
  --data ./codegraph-data

Integration with AI Agents

# For Claude Code: index before running
codegraph index . --output ./cg-indices

# For Codex CLI: set as codebase index
export CODEGRAPH_INDEX=./codegraph-data
# Codex automatically queries graph before reading files

# For Cursor: use codegraph plugin
# Install from Cursor extensions marketplace

Benchmarks / Real-World Use Cases

Token Reduction Benchmark

| | Full codebase scan | 18,420 | 3,684,000 | $36.84 | | Codegraph (top-5 results) | 7,890 | 1,578,000 | $15.78 | | Codegraph (top-3 results) | 5,230 | 1,046,000 | $10.46 | | Savings vs full scan | 57.2% | 57.2% | 57.2% |

Query Accuracy

| | Function definition | 78% | 94% | | Import resolution | 62% | 91% | | Bug location | 71% | 88% | | Architecture overview | 85% | 89% |

Real-World Use Case: Migration Project

A team migrating from Express.js to FastAPI: `````bash

Codegraph identifies all API endpoints

codegraph query “List all Express.js routes and their handlers”
–data ./express-app

Result: 47 endpoints found in 12 files

vs scanning entire 50K-line codebase


## Advanced Usage / Production Hardening

### Incremental Indexing

`````bash
# Re-index on file changes
codegraph index ./project --output ./codegraph-data --watch

Watch mode automatically re-indexes changed files, keeping the graph in sync with your codebase. Typical re-index time for changed files is 2-5 seconds.

Multi-Language Support

Codegraph supports multiple programming languages out of the box: - Python — AST parsing, function analysis, import resolution

TypeScript/JavaScript — Full type-aware indexing
Go — Function and package analysis
Rust — Module and crate analysis
Java — Class hierarchy and method analysis
C/C++ — Header file and function graph
YAML/JSON — Configuration file parsing

Multi-Project Workspace

# Index multiple projects in a monorepo
codegraph index ./packages/* --output ./codegraph-data \
  --project-name packages --merge

# Query across all projects
codegraph query "Find all database models" \
  --data ./codegraph-data --filter project:auth-service

Graph Export and Visualization

# Export graph for visualization
codegraph export --format graphml --output ./graph.graphml

# Calculate graph statistics
codegraph stats --data ./codegraph-data
# Output: 12,847 nodes, 34,521 edges, avg_degree: 5.37

Comparison with Alternatives

| | Token reduction | 40-60% | 0% | 20-30% | 30-40% | | Pre-indexing | Yes | No | No | No | | 100% local | Yes | Yes | No (cloud) | No (cloud) | | Symbol-aware | Yes | No | Partial | No | | Agent integration | Claude Code, Codex, Cursor, Copilot, Gemini CLI | None | Limited | Code review only | | Incremental updates | Yes | N/A | N/A | N/A | | Custom languages | Yes | Yes | No | No | | Call graph analysis | Yes | No | No | No | | Open source | Yes (MIT) | Yes | No | No | | GitHub stars | 45,555 | — | — | — |

Limitations / Honest Assessment

Codegraph is not for everyone: 1. Small projects (<1K lines) — Overhead of indexing outweighs benefits for tiny repos. A simple grep is faster. 2. Rapidly changing codebases — If code changes every few minutes, frequent re-indexing adds latency. Use watch mode with conservative intervals. 3. Non-code assets — Codegraph indexes source code, config files, and documentation. It does not understand binary files, images, or database schemas. 4. AI-generated codebases — For repos with over 50% AI-generated code, graph relationships may be less meaningful due to synthetic patterns. 5. Enterprise monorepos (>1M lines) — Indexing time grows linearly. For massive repos, consider project-level indexing with cross-references.

Indexing Performance

Indexing speed depends on codebase size: - 10K lines — approximately 10 seconds

100K lines — approximately 2 minutes
1M lines — approximately 20 minutes

Incremental re-indexing of changed files typically takes 2-5 seconds regardless of total codebase size.

Privacy and Data Control

All indexing and querying happens 100% locally. No code, no index, and no queries ever leave your machine. All vector storage is local.

Indexing Configuration

Control which files are indexed and how aggressively: `````bash

Index with file type filters

codegraph index ./project –output ./codegraph-data
–include “.py” “.ts” “*.js”
–exclude “/node_modules/” “/dist/”

Set custom file size limit (default 1MB)

codegraph index ./project –output ./codegraph-data –max-file-size 2048000

Use a custom ignore file (same format as .gitignore)

codegraph index ./project –output ./codegraph-data –ignore-file .codegraphignore


### Codegraphignore Configuration

Create a ``.codegraphignore`` file for persistent indexing exclusions: `````bash
# .codegraphignore example
node_modules/
dist/
build/
*.min.js
*.min.css
__pycache__/
.venv/
vendor/
tests/fixtures/

Agent-Specific Indexing Profiles

Codegraph supports indexed profiles optimized for different agent workflows: `````bash

Claude Code optimized index (includes context window awareness)

codegraph index . –output ./cg-claude
–profile claude-code –max-context-tokens 128000

Codex CLI optimized index

codegraph index . –output ./cg-codex
–profile codex –max-result-snippets 10

Cursor optimized index

codegraph index . –output ./cg-cursor
–profile cursor –max-depth 5 –include-declarations true


### Graph Query Language (GQL)

Codegraph supports a query language for advanced graph operations: `````bash
# Find all functions that import a specific module
codegraph query --gql "MATCH (f:Function)-[:IMPORTS]->(m:Module {name: 'react'}) RETURN f.name"

# Trace call chain from entry point
codegraph query --gql "MATCH p=(start:Function)-[:CALLS*1..5]->(end:Function) WHERE start.name='main' RETURN p"

# Find all classes extending a base class
codegraph query --gql "MATCH (c:Class)-[:EXTENDS*1..]->(b:Class {name: 'BaseModel'}) RETURN c.name"

Integration with Pre-commit Hooks

Automatically re-index when code changes: `````bash

Add to .pre-commit-config.yaml

.pre-commit-hooks.yaml: # - id: codegraph-index

name: codegraph index

entry: codegraph index –watch

language: system

stages: [commit]

Run manual pre-commit index

codegraph pre-commit –staged –output ./codegraph-data

Only re-indexes staged files, typically 1-3 seconds


### API Usage for Programmatic Access

Codegraph provides a programmatic API for building custom integrations: `````bash
# Start Codegraph as an API server
codegraph server --host 0.0.0.0 --port 8080 --data ./codegraph-data

# Query via HTTP API
curl -X POST http://localhost:8080/query \
  -H "Content-Type: application/json" \
  -d '{"query": "Where is the auth middleware?", "top_k": 5}'

# Index a new project via API
curl -X POST http://localhost:8080/index \
  -H "Content-Type: application/json" \
  -d '{"path": "/path/to/new-project", "output": "./new-project-data"}'

Frequently Asked Questions

Q: How does Codegraph differ from ripgrep?

A: ripgrep finds text matches. Codegraph finds code relationships — which function calls which, which class extends which, import chains. It returns semantically relevant results, not just keyword matches.

Q: Is there any cloud component or data leakage?

A: No. Codegraph runs 100% locally. No code, no index, no queries leave your machine. All vector storage is local (ChromaDB or SQLite).

Q: How long does indexing take?

A: 100K lines ≈ 2 minutes. 1M lines ≈ 20 minutes. Incremental re-indexing of changed files typically takes 2-5 seconds.

Q: Can I use Codegraph with local LLMs?

A: Yes. Codegraph provides local graph queries that any agent (including local LLMs) can use. It is a data layer, not an LLM.

Q: Does it support TypeScript/JavaScript?

A: Yes. Supports Python, TypeScript, JavaScript, Go, Rust, Java, C++, and any language with a parser or AST library.

# Generate HTML report of codebase structure
codegraph report --format html --output ./codebase-report.html
# Opens interactive visualization of codebase dependencies, call graphs, and file relationships
# Can be opened in any browser. The HTML report includes interactive force-directed graphs, dependency trees, and file relationship heatmaps.
````

Codegraph also supports exporting to Mermaid format for embedding in documentation tools like Notion, Confluence, and GitHub READMEs. This makes it easy to share architectural insights with team members who don't use the CLI tool directly.

## 

For reliable hosting and proxy infrastructure: [DigitalOcean](https://m.do.co/c/eca87ac14ee0) for development servers, [HTStack](https://my.htstack.com/aff.php?aff=27187) for production hosting, and [WebShare](https://www.webshare.io/?referral_code=oa14d5f0wx4f) for datacenter and residential proxies.

Sources & Further Reading

- Official docs: https://github.com/colbymchenry/codegraph
- GitHub repository: https://github.com/colbymchenry/codegraph
- Indexing benchmark methodology: https://github.com/colbymchenry/codegraph/blob/main/docs/BENCHMARKS.md
- Agent integration guide: https://github.com/colbymchenry/codegraph/blob/main/docs/INTEGRATION.md
- Community discussion: https://github.com/colbymchenry/codegraph/discussions

## Conclusion: Stop Scanning Files, Start Querying Knowledge — 40-60% Token Savings

Codegraph is the infrastructure layer every codebase-heavy AI agent workflow needs. Instead of feeding entire codebases to LLMs, index once, query forever. The graph captures what files import what, which functions call which, and how classes relate — semantically, not textually.

Whether you're maintaining a 50K-line monorepo, building with Cursor, or just tired of Claude Code reading every file in your project, Codegraph delivers measurable token savings and faster, more accurate answers.

Join the [dibi8 English Telegram group](https://t.me/DIBI8_Group/2) to discuss Codegraph configurations. Check out our guides on [CLI代理管理](https://dibi8.com/cc-switch-unified-ai-cli-c[AI Agent管理](https://dibi8.com/paperclip-open-source-agent-workplace-managing-ai-agents-at-scale)ibi8-internal-link) for complementary tooling. Try Codegraph today — index your project, run a query, and see how much context you don"t need.

Some links above are affiliate links. dibi8.com may earn a commission if you sign up, at no extra cost to you. Helps keep the site running and the content free.


{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Codegraph: The Code Knowledge Graph That Slashes LLM Token Costs by 40-60% — Pre-Indexed, 100% Local — A Practical Guide 2026",
  "datePublished": "2026-06-08",
  "dateModified": "2026-06-08",
  "author": {
    "@type": "Organization",
    "name": "Dibi8"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Dibi8",
    "logo": {
      "@type": "ImageObject",
      "url": "https://dibi8.com/logo.png"
    }
  },
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://dibi8.com/resources/codegraph-pre-indexed-code-knowledge-graph-ai-agents"
  }
}
</script>


* * *
## Related Articles

- [codegraph-pre-indexed-code-knowledge-graph-ai-agents](codegraph-pre-indexed-code-knowledge-graph-ai-agents)
- [codegraph-pre-indexed-code-knowledge-graph-ai-agents](codegraph-pre-indexed-code-knowledge-graph-ai-agents)
- [codegraph-pre-indexed-code-knowledge-graph-ai-agents](codegraph-pre-indexed-code-knowledge-graph-ai-agents)
- [academic-research-skills](codegraph-pre-indexed-code-knowledge-graph-ai-agents)
- [last30days-skill-ai-agent-research-engine-social-media](codegraph-pre-indexed-code-knowledge-graph-ai-agents)


* * *
*Found this helpful? [Join our Telegram community](https://t.me/DIBI8_Group) for daily AI tool updates!*

## Frequently Asked Questions (FAQ)

**问：AI Agent和传统自动化有什么区别？**

AI Agent具有自主决策能力，能够根据环境变化调整策略，而传统自动化只能执行预设规则。

**问：如何选择合适的AI Agent框架？**

考虑因素包括：部署难度、社区活跃度、扩展性、成本。Claude Code适合开发者，AutoGen适合复杂多智能体场景。

**问：AI Agent的安全性如何保证？**

实施权限最小化、输入验证、审计日志、以及定期安全评估。

**问：AI Agent的学习成本有多高？**

入门级使用3-5天，高级配置需要2-4周，取决于团队技术基础。

**问：能否自定义AI Agent的行为？**

是的，通过提示工程、工具定义、记忆系统、以及行为约束来定制。

Codegraph: The Code Knowledge Graph That Slashes LLM Token Costs by 40-60% — Pre-Indexed, 100% Local — A Practical Guide 2026#

Introduction#

What Is Codegraph?#

How Codegraph Works#

Stage 1: Installation#

Stage 2: Indexing#

Stage 3: Querying#

Installation & Setup#

Quick Start#

Integration with AI Agents#

Benchmarks / Real-World Use Cases#

Token Reduction Benchmark#

Query Accuracy#

Real-World Use Case: Migration Project#

Codegraph identifies all API endpoints#

Result: 47 endpoints found in 12 files#

vs scanning entire 50K-line codebase#

Multi-Language Support#

Multi-Project Workspace#

Graph Export and Visualization#

Comparison with Alternatives#

Limitations / Honest Assessment#

Indexing Performance#

Privacy and Data Control#

Indexing Configuration#

Index with file type filters#

Set custom file size limit (default 1MB)#

Use a custom ignore file (same format as .gitignore)#

Agent-Specific Indexing Profiles#

Claude Code optimized index (includes context window awareness)#

Codex CLI optimized index#

Cursor optimized index#

Integration with Pre-commit Hooks#

Add to .pre-commit-config.yaml#

.pre-commit-hooks.yaml: # - id: codegraph-index#

name: codegraph index#

entry: codegraph index –watch#

language: system#

stages: [commit]#

Run manual pre-commit index#

Only re-indexes staged files, typically 1-3 seconds#

Frequently Asked Questions#