codebase-memory 0.1.2

Self-updating codebase knowledge graph for AI-powered IDEs

AI-Memory

Stop burning API tokens on file-by-file exploration. AI-Memory builds a self-updating knowledge graph of your entire codebase so your AI assistant answers instantly — with full context — instead of asking "can you show me that file?" repeatedly.

Before: You ask AI a question → AI opens 5 files → 8,000 tokens → still confused
After:  AI-Memory knows your code → AI queries the graph  → 500 tokens  → precise answer

The Problem (You Know This Pain)

You ask your AI assistant: "How do I add pagination to the user list?"

Step	What AI Does	Tokens
1	Opens routes.py, reads 200 lines	1,500
2	Opens models.py, reads 150 lines	1,200
3	Opens controllers.py, reads 300 lines	2,400
4	Asks "Can you show me the User model?"	—
5	You paste it	500
6	Re-reads everything again	2,500
Total		~8,000 tokens

With AI-Memory: AI runs one internal query and gets list_users(self, db, skip=0, limit=100) with full body, types, and callers. 500 tokens. One shot. Correct answer.

How It Works

Engine	What It Does
Scan	Tree-sitter parses Python, JS/TS, Ruby, Go, Rust, Java, C#, C/C++, SQL
Graph	Functions, classes, imports, calls, inheritance in SQLite
Import Resolution	Cross-file imports resolved to real symbol definitions
Execution Flows	Pre-computed call chains from every entry point
Communities	Auto-detected code modules by directory + graph coupling
Semantic Search	sentence-transformers embeddings: find login_user from "auth"
Type & Body Storage	Full function bodies and type annotations cached
Watch	Incremental updates on every file save

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Supported Languages

Backend	Frontend	Systems	Data & Config	Manifests
Python	JavaScript	C	SQL	requirements.txt
Go	TypeScript	C++	YAML	package.json
Rust	JSX	C#	JSON	Cargo.toml
Java	TSX		TOML	pyproject.toml
Ruby			Protobuf	go.mod
			GraphQL

DB Schemas: Table, index, column extraction from SQL and migration files.

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Supported IDEs

IDE	Integration	Setup Required
Windsurf	.windsurfrules auto-generated	None — AI reads instructions automatically
Cursor	.cursorrules auto-generated	None — AI reads instructions automatically
Claude Desktop	MCP server ai-memory-mcp	One JSON config
VS Code	MCP server or CLI	Terminal or extension

After ai-memory init, your AI already knows about the graph. Zero IDE configuration.

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Get Started (3 Commands)

# 1. Install
cd your-project
pip install ai-memory

# 2. Initialize — config, DB, gitignore, IDE rule files
ai-memory init

# 3. Scan your codebase — one time
ai-memory scan

Optional:

# Enable semantic search (~2 min one-time)
ai-memory embed

# Auto-update on file changes (background daemon)
ai-memory watch

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What Happens After Init

your-project/
├── .ai-memory/           # Graph database (gitignored automatically)
│   └── graph.db          # SQLite: ~380 symbols, ~1850 relations, 34 deps
├── .ai-memory.toml       # Your project config
├── .windsurfrules        # Windsurf: "Use ai-memory for context"
├── .cursorrules          # Cursor: "Use ai-memory for context"
└── .gitignore            # Auto-added: .ai-memory/

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CLI Commands

Core Workflow

Command	Description
init	Create config, DB, gitignore entry, IDE rule files
scan	Full codebase scan + post-processing
watch	Auto-incremental update on file changes
embed	Build semantic embeddings for all symbols
embed --incremental	Only new/changed symbols

Symbol Context

Command	Description
context <symbol>	Symbol + body + types + neighbors
callers <symbol>	Reverse call graph — who calls this
inherits <class>	Full inheritance chain

Search & Discovery

Command	Description
ask "<question>"	Natural language → compact context
semantic "<query>"	Embedding-based semantic search
search "<query>"	Full-text search over names, signatures, docstrings
find <name>	Search symbols by name

Analysis

Command	Description
stats	Project overview (files, nodes, edges, top hubs)
flows	Pre-computed execution flows from entry points
communities	Detected code modules/clusters
cycles	Circular dependencies
dead-code	Unused symbols (zero incoming references)
api	Public API surface from __init__.py, index.js, lib.rs
todos	TODO, FIXME, HACK, XXX markers
test-map	Map tests to implementation

Dependencies & Coverage

Command	Description
deps	List dependencies from manifests
coverage	Load coverage data and show summary
coverage-detail	Per-symbol coverage status

Export

Command	Description
graph [symbol]	Mermaid or DOT call graph
file <path>	Summary of a single file
schemas	All DB schemas / migrations
review	Git diff impact analysis

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MCP Server (Native IDE Integration)

For Claude Desktop, Windsurf, Cursor:

ai-memory-mcp --root /path/to/project

20 exposed tools: ai_memory_scan, ai_memory_stats, ai_memory_context, ai_memory_ask, ai_memory_semantic, ai_memory_flows, ai_memory_communities, ai_memory_schemas, ai_memory_find, ai_memory_dead_code, ai_memory_callers, ai_memory_todos, ai_memory_test_map, ai_memory_graph, ai_memory_cycles, ai_memory_inherits, ai_memory_api, ai_memory_search, ai_memory_coverage, ai_memory_deps.

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Configuration

Edit .ai-memory.toml to customize:

scan_dirs = ["src", "app", "lib"]
extensions = [".py", ".js", ".ts", ".go", ".rs"]
ignore_patterns = ["node_modules", "venv", "__pycache__"]
output_format = "markdown"
max_context_tokens = 4000

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Architecture

Source Code
    │
    ▼
Tree-sitter Parsers (Python, JS/TS, Go, Rust, Java, C#, C/C++, Ruby)
    │
    ▼
Symbol Extractor ──→ Symbols (functions, classes, imports)
│                     Body Extractor ──→ Full function bodies
│                     Type Extractor ──→ Type annotations
│
Relation Extractor ──→ Calls, Inherits, Imports
    │
    ▼
Import Resolver ──→ Cross-file links resolved
    │
    ▼
SQLite Graph DB ──→ nodes, edges, files, schemas, flows, communities
    │
    ▼
┌─────────────────┬─────────────────┬─────────────────┐
│  Semantic       │  Execution      │  Community      │
│  Embeddings     │  Flows          │  Detection      │
│  (MiniLM)       │  (BFS from      │  (Leiden        │
│                 │   entry points) │   algorithm)    │
└─────────────────┴─────────────────┴─────────────────┘
    │
    ▼
CLI / MCP Server / Watch Daemon
    │
    ▼
AI Assistant (Windsurf, Cursor, Claude Desktop)