archgraph 0.2.0

Security-first code intelligence for AI agents.
Parses 10 languages, builds a knowledge graph with taint analysis, CVE detection, and clustering.
Connect to any AI agent via MCP — Cursor, Claude Code, Windsurf, and more.

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Why ArchGraph?

Other tools help you understand code. ArchGraph helps you secure it.

	ArchGraph	Code Search	AST Parsers	SAST Tools
Taint Analysis	✅ Input → Sink	❌	❌	✅
CVE Detection	✅ Auto via OSV	❌	❌	Partial
CFG / Data Flow	✅ libclang + tree-sitter	❌	Partial	✅
MCP for AI Agents	✅ 7 tools	❌	❌	❌
Functional Clustering	✅ Community detection	❌	❌	❌
Execution Tracing	✅ Entry → Sink flows	❌	❌	❌
Export (JSON/GraphML)	✅	❌	❌	Partial
Local-first	✅ Neo4j	Varies	✅	Varies
License	MIT	Varies	Varies	Often proprietary

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Quick Start

# Install
pip install archgraph

# Extract (auto-detects languages)
archgraph extract /path/to/repo -w 4

# Query the graph
archgraph query "MATCH (f:Function {is_input_source: true}) RETURN f.name, f.file"

# Start web dashboard
archgraph serve --port 8080

# Generate HTML security report
archgraph report /path/to/repo

With Docker (Neo4j included):

docker compose up -d neo4j           # password: archgraph
archgraph extract /path/to/repo --neo4j-password archgraph

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🤖 AI Agent Integration (MCP)

ArchGraph exposes 7 tools and 4 resources to any MCP-compatible agent.

Setup

# Index your repo
archgraph extract . --include-cve --include-clustering

# Start MCP server
archgraph mcp

Connect your agent:

Agent	Command
Claude Code	claude mcp add archgraph -- archgraph mcp
Cursor	Add to ~/.cursor/mcp.json
Windsurf	Add to MCP config
OpenCode	Add to ~/.config/opencode/config.json

What Your Agent Gets

Tools: query, impact, context, detect_changes, find_vulnerabilities, cypher, stats

Resources: archgraph://schema, archgraph://security, archgraph://clusters, archgraph://processes

Example Conversation

You: "Are there any buffer overflow risks in the network code?"

Agent:
1. Queries input sources in network files
2. Traces taint paths to dangerous sinks
3. Reports: "Found 2 paths:
   - net_recv() → memcpy() in src/net/handler.c (depth: 3)
   - read_packet() → strcpy() in src/net/parser.c (depth: 4)
   Both reach dangerous sinks without validation."

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🔒 Security Features

Automatic labeling — Every function gets security labels:

• is_input_source — reads external data (recv, read, fetch, ...)
• is_dangerous_sink — dangerous operations (memcpy, exec, eval, ...)
• is_allocator, is_crypto, is_parser — additional categories
• risk_score — 0-100 risk score based on labels

Taint path detection:

MATCH path = (src:Function {is_input_source: true})-[:CALLS*1..8]->(sink:Function {is_dangerous_sink: true})
RETURN src.name, sink.name, length(path) AS depth

CVE enrichment:

archgraph extract . --include-cve    # Queries OSV API automatically

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All Commands

Command	Description
extract	Extract code graph from repository
query	Run Cypher queries against the graph
stats	Show node/edge statistics
schema	Show graph schema
diff	Compare repo state vs stored graph
impact	Blast radius analysis for a function
export	Export to JSON, GraphML, or CSV
report	Generate HTML security report
serve	Start web dashboard
mcp	Start MCP server for AI agents
skills	Generate AI agent skill files
repos	List indexed repositories

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Use Cases

Security Audit

archgraph extract /target -l c,cpp --include-cve --include-clang
archgraph query "MATCH path = (src:Function {is_input_source: true})-[:CALLS*1..5]->(sink:Function {is_dangerous_sink: true}) RETURN src.name, sink.name"

Code Review

archgraph diff /path/to/repo
archgraph impact "func:src/api.c:handle:42" --direction both

Reverse Engineering

archgraph extract /binary/project -l c,cpp,rust --include-clang --include-deep
archgraph query "MATCH (f:Function) WHERE f.is_exported = true RETURN f.name, f.file"

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Architecture

                  ┌──────────────────────────────────────────────────┐
                  │         GraphBuilder Pipeline (11 steps)         │
                  │                                                  │
  Local Path ─────┤  1. Tree-sitter structural extraction            │
     or           │  2. Git history                                  │
  GitHub URL ─────┤  3. Dependency extraction                        │──── Neo4j
  (auto clone)    │  4. Annotation scanning                          │     Store
                  │  5. Security labeling                            │       │
                  │  6. Clang deep analysis (C/C++)                  │       ├── MCP Server
                  │  7. Tree-sitter deep analysis (Rust/Java/Go/…)   │       ├── Web Dashboard
                  │  8. Churn enrichment                             │       └── Export/Report
                  │  9. CVE enrichment (OSV API)                     │
                  │ 10. Clustering (community detection)             │
                  │ 11. Process tracing (execution flows)            │
                  └──────────────────────────────────────────────────┘

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Benchmarks

Project	Language	Files	Nodes	Edges	Time
zlib (~50K LOC)	C	79	2,389	3,968	6.6s
fastify (~30K LOC)	JavaScript	487	2,810	18,472	10.5s
Linux drivers/usb (~500K LOC)	C	892	62,812	122,746	12.7s

Benchmarks: Windows 11, Python 3.13, single-threaded. Parallel mode (-w 4) is 2-3x faster.

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Documentation

Document	Description
Architecture & Schema	Graph schema, node/edge types, pipeline
CLI Reference	All commands and options
AI Agent Integration	MCP setup, tools, examples
Security Analysis	Security labeling, Cypher queries
Deep Analysis	CFG, data flow, taint tracking
Roadmap	Development phases

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Testing

pytest tests/ -v  # 137 passed, 22 skipped

No external services required. Tests use temporary directories with real tree-sitter parsing and git operations.

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License

MIT