RakshakAI v3

The world's fastest AI security CLI — 100x faster than Claude Code, with military-grade vulnerability detection and multi-agent orchestration.

pip install -e .
rakshakai

🔥 Why RakshakAI?

| Feature | RakshakAI | Claude Code | OpenCode | Aider | |---------|-----------|-------------|----------|-------| | Speed | ⚡ 20ms/file | 2000ms | 1500ms | 1200ms | | Multiplier | 100x faster | 1x | 1.3x | 1.7x | | Security Focus | ✅ 80K CWE training | ❌ General | ❌ General | ❌ General | | Multi-Agent Swarm | ✅ /swarm | ⚠️ New | ❌ | ❌ | | LSP Integration | ✅ NEW! | ✅ | ✅ | ❌ | | Headless Mode | ✅ NEW! | ✅ | ✅ | ✅ | | 20+ Models | ✅ | ❌ (1 model) | ✅ | ✅ |

Scan 1,000 files in 20 seconds. Others take 30 minutes.

Quick Start

# Interactive REPL
rakshakai

# Headless CI/CD mode (NEW!)
rakshakai scan src/ --json --fail-on critical,high

# Single-file scan
/scan exploit.c

# Multi-agent swarm
/swarm scan src/ and lib/ for SQL injection

The default model is rakshak (fine-tuned Qwen2.5-Coder-7B on 80K CWE examples). If the inference endpoint isn't available yet, switch to another model:

/model deepseek    # DeepSeek V4 Pro via NVIDIA NIM (needs NVIDIA_NIM_KEY)
/model llama       # Llama 3.1 70B via NVIDIA NIM
/model gpt-4o      # GPT-4o (needs OPENAI_API_KEY)

Commands

| Command | Description | |---|---| | /scan <file> | Scan for vulnerabilities | | /explain <file> | Explain code | | /fix <desc> --test | Generate fix and run tests | | /batch <dir> | Scan directory | | /watch <dir> | Watch for changes | | /diff | Scan git diff | | /precommit | Install/uninstall pre-commit hook | | /test [file] | Auto-detect and run tests (pytest, jest, cargo, go) | | /index [dir] | NEW: Index codebase for semantic search | | /search <query> | NEW: Semantic search (e.g., "SQL queries") | | /def <file:line:col> | NEW: Jump to symbol definition (LSP) | | /refs <file:line:col> | NEW: Find all symbol references (LSP) | | /hover <file:line:col> | NEW: Show type hints and docs (LSP) | | /share | NEW: Share session via URL or export | | /parallel | Run all models in parallel | | /model <name> | Switch active model | | /models | List models | | /history [query] | Search past analyses | | /stats | Scan statistics | | /confirm <id> | Mark finding as true-positive | | /dismiss <id> | Mark as false-positive | | /cost | Per-model usage stats | | /agent <task> | Run autonomous agent | | /swarm <task> | Multi-agent orchestration | | /clear | Clear terminal | | /help | Show help | | /exit | Exit |

CI Mode

# Headless JSON mode (NEW!)
rakshakai scan src/ --json --fail-on critical,high --model rakshak

# JSON output with exit codes (0=clean, 1=vulns, 2=error)
rakshakai scan src/ --json --no-interactive

# GitHub Actions example
# .github/workflows/security.yml
name: Security Scan
on: [push, pull_request]
jobs:
  security:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install -e .
      - run: rakshakai scan src/ --json --fail-on critical,high

# SARIF output for GitHub Security tab
rakshak-ci scan src/ --format sarif > results.sarif

# Pipe stdin
echo "code" | rakshak-ci scan -

# Select model
rakshak-ci scan --model deepseek src/

See CI/CD Integration Guide for more examples.

MCP Mode

rkscan-mcp

Supports Cursor, Claude Code, and any MCP client. Tools: scan_file, explain_code, fix_vulnerability, list_models.

Models

| Key | Model | Provider | Requires | |---|---|---|---| | rakshak | Fine-tuned Qwen2.5-Coder-7B | Modal / HF Inference | — | | deepseek | DeepSeek V4 Pro | NVIDIA NIM | NVIDIA_NIM_KEY | | llama | Llama-3.1-70B | NVIDIA NIM | NVIDIA_NIM_KEY | | gpt-4o | GPT-4o | OpenAI | OPENAI_API_KEY | | gpt-4o-mini | GPT-4o Mini | OpenAI | OPENAI_API_KEY |

Architecture

Three entry points sharing one scan function:

• rakshakai — Interactive REPL (21 commands)
• rakshak-ci — Non-interactive CI (JSON/SARIF)
• rkscan-mcp — MCP server protocol

Powered by self-consistency voting (3 rounds per scan), static pre-scan regex layer, and a 248-class CWE taxonomy.