你的代码库，明白。

[!提示] 给你的编码代理留下持久的项目记忆。

遇见 CodeGraph 代理内存， 我们的兄弟项目，基于CodeGraph构建并实现持久内存的架构 决策、发现、失败的方法和可重复使用的程序。启用记忆 检索，将这些知识与当前代码一起带入代理的工作流程中 跨会谈的证据。

探索代理记忆指南→

CodeGraph将你的整个代码库转化为一个语义上可搜索的知识图谱，AI代理实际上可以推理它——而不仅仅是通过grep。

准备好开始了吗？ 跳转到安装指南，获取逐步安装说明。

已经设置好了吗？ 请参阅使用指南，了解如何最大化利用 CodeGraph 与你的 AI 助手。

**更喜欢shell命令？**运行codegraph agent context "your question"。同样的四个 代理工具可通过CLI获得，并带有项目本地的Claude代码/Codex钩子。 codegraph init 提供钩子设置，并在索引前添加代理指令。

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问题

AI编码助手很强大，但它们是盲目飞行。

CodeGraph (with updates)

Your codebase, understood.

[!TIP] Give your coding agents lasting project memory.

Meet CodeGraph Agent Memory, our sibling project that builds on CodeGraph with persistent memory for architectural decisions, findings, failed approaches, and reusable procedures. Enable memory retrieval to bring that knowledge into agent workflows alongside current code evidence across sessions.

Explore the agent memory guide →

CodeGraph transforms your entire codebase into a semantically searchable knowledge graph that AI agents can actually reason about—not just grep through.

Ready to get started? Jump to the Installation Guide for step-by-step setup instructions.

Already set up? See the Usage Guide for tips on getting the most out of CodeGraph with your AI assistant.

Prefer shell commands? Run codegraph agent context "your question". The same four agentic tools are available through the CLI, with project-local Claude Code/Codex hooks. codegraph init offers hook setup and adds agent instructions before indexing.

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The Problem

AI coding assistants are powerful, but they’re flying blind. They see files one at a time, grep for patterns, and burn tokens trying to understand your architecture. Every conversation starts from zero.

What if your AI assistant already knew your codebase?

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What CodeGraph Does Differently

1. Graph + Embeddings = True Understanding

Most semantic search tools create embeddings and call it a day. CodeGraph builds a real knowledge graph:

Your Code → Build Context → AST + FastML → LSP Resolution → Enrichment → Graph + Embeddings
              ↓               ↓              ↓              ↓              ↓         ↓
          Packages        Nodes/edges    Type-aware     API surface      Graph    Semantic
          Features        Fast patterns  linking        Module graph   traversal   search
          Targets         Spans          Definitions    Dataflow/Docs             (hybrid)

When you search, you don’t just get “similar code”—you get code with its relationships intact. The function that matches your query, plus what calls it, what it depends on, and where it fits in the architecture.

Indexing enrichment adds:

• Module nodes and module-level import/containment edges for cross-file navigation
• Rust-local dataflow edges (defines, uses, flows_to, returns, mutates) for impact analysis
• Document/spec nodes linked to backticked symbols in README.md, docs/**/*.md, and schema/**/*.surql
• Architecture signals (package cycles + optional boundary violations)

Indexing tiers (speed vs richness)