Skip to main content

Generate agentic workflows visualizations & threat modelling reports for your agentic projects.

Project description

Agent Wiz

PyPI version License Python Build Contributor Covenant

Python CLI for extracting agentic workflows from popular AI frameworks and performing automated security analysis using threat modeling methodologies.

Designed for developers, researchers, and security teams, Agent Wiz enables the introspection of LLM-based orchestration logic to visualize flows, map tool/agent interactions, and generate security reports via structured threat modeling frameworks.

❓ Why Should You Use It?

In modern LLM-powered systems, agentic workflows are becoming increasingly complex — with dozens of autonomous agents, tools, and inter-agent communication chains. Agent Wiz helps you bring visibility, structure, and security to these otherwise opaque systems.

Key Benefits

  • Understand Complex Agent Graphs
    Instantly get clear visibility of agentic workflows in your code, no manual tracing needed! The visualsation clearly specifies various types of connections that can exist in a agentic workflow like Agent-Agent edges, Agent-Tool edges, Tool-Tool chained edges, or even something like Agent Agent edgdes through an intermediate tool.

  • Integrated Security Analysis
    Get instant threat modeling reports tailored to your actual orchestration logic. Perfect for audits, red-teaming, or compliance reviews.

  • Developer & Researcher Friendly
    Simple CLI, extensible SDK, and clean JSON export — ideal for visualization, automation, or integrating with CI/CD pipelines.

  • Framework Agnostic
    Works with all major LLM orchestration stacks like Autogen, LangGraph, CrewAI, LlamaIndex, Swarm, and more.

  • Built for Scale & Insight
    Agent Wiz grows with your AI system. Whether you're prototyping or in production — it gives you introspection, fast.

Supported Frameworks

The following orchestration frameworks are currently supported:

Framework Status
Autogen (core)
AgentChat
CrewAI
LangGraph
LlamaIndex
n8n
OpenAI Agents
Pydantic-AI
Swarm

Each framework has its own AST-based static parser to extract:

  • Agents (class/function-based)
  • Tool functions
  • Agent-to-agent transitions
  • Tool call chains
  • Group agents (e.g., selector, round-robin)

Security Analysis

Agent Wiz currently supports MAESTRO as its primary threat modeling framework. It evaluates agent workflows against the following structure:

  • Mission
  • Assets
  • Entrypoints
  • Security Controls
  • Threats
  • Risks
  • Operations

Using LLM-backed analysis (GPT-4), a full security report is generated based on your workflow JSON. For example:

Threat Modeling Report

Before running any analysis commands, you must set your OpenAI API key as an environment variable:

export OPENAI_API_KEY=sk-...

You can also add this line to your .bashrc, .zshrc, or environment setup script for persistent use.

🧪 More threat models analysis (STRIDE, PASTA, LINDDUN, etc.) are under development.

Installation

pip install repello-agent-wiz

🚀 CLI Usage

1. Extract Agentic Workflow

agent-wiz --framework agent_chat --directory ./examples/code/agent_chat --output agentchat_graph.json

This will generate a graph JSON with the following structure:

{
  "nodes": [...],
  "edges": [...],
  "metadata": {
    "framework": "autogen"
  }
}

2. Visualize the Agentic workflow

agent-wiz --visualize --input agentchat_graph.json --open

This will generate an html d3 based visualisation of the agentic workflow. The open flag (optional) and automatically opens the visualization in your default browser.

3. Analyze against Threat Modeling

agent-wiz --analyze --input agentchat_graph.json

This will generate a report like: autogen_report.md based on the provided graph and threat modeling frameworks.

Run agent-wiz --help for more info:

usage: agent-wiz [-h] {extract,analyze,visualize} ...

Agent Wiz CLI: Extract, Analyze, Visualize agentic workflows.

positional arguments:
  {extract,analyze,visualize}
    extract             Extract graph from source code
    analyze             Run threat modeling analysis on extracted graph
    visualize           Generate HTML visualization from graph JSON

options:
  -h, --help            show this help message and exit

📈 Roadmap

Planned features (Not in any paricular order)

  • Build parsers for major agentic frameworks (Autogen, LangGraph, CrewAI, etc.)
  • Generate standardized JSON graph representations of agent flows
  • CLI interfaces
  • Security report generation
  • Extend to STRIDE, PASTA, LINDDUN, etc.
  • Agent simulation-based threat exploration

🤝 Contributing

We welcome contributions of all kinds!

⚠️ Please read CONTRIBUTING.md before submitting issues or PRs.

📜 Changelog

For recent changes and version history, see CHANGELOG.md.

📄 License

Licensed under the Apache 2.0 License. See LICENSE for full details.

Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

repello_agent_wiz-0.1.0.tar.gz (163.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

repello_agent_wiz-0.1.0-py3-none-any.whl (170.9 kB view details)

Uploaded Python 3

File details

Details for the file repello_agent_wiz-0.1.0.tar.gz.

File metadata

  • Download URL: repello_agent_wiz-0.1.0.tar.gz
  • Upload date:
  • Size: 163.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.2

File hashes

Hashes for repello_agent_wiz-0.1.0.tar.gz
Algorithm Hash digest
SHA256 8ddcf9483e5bf233dbb1fada0545a2afb804057233b9f4c42c92106ee11d67e1
MD5 fafb5b09a382b4598e0b22d2d7c75700
BLAKE2b-256 982af9df3c96051603de7522dbd49c34c62b1de4298795cb77098a6ad2c2579d

See more details on using hashes here.

File details

Details for the file repello_agent_wiz-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for repello_agent_wiz-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 08cbd9fff3fb3898aaac4b2ef6755fdc6766c455a3a92f32c20a31e10481df07
MD5 5c68f8c5207473108a2a49acd52cb3fb
BLAKE2b-256 1dc2227223c8db1c551760a0b322168bc13b4543f73b51857526102b48685383

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page