Skip to main content

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

Project description



Overview

Agent Wiz is a Python CLI for extracting agentic workflows from popular AI frameworks and performing automated threat assessments using established threat modeling methodologies. Built for developers, researchers, and security teams - Agent Wiz brings visibility to complex LLM-based orchestration to visualize flows, map tool/agent interactions, and generate actionable security reports.

Why Agent Wiz?

In modern LLM-powered systems, agentic workflows are becoming increasingly complex, often involving multiple autonomous agents, tools, and inter-agent communication chains. Agent Wiz helps you bring:

  • Visibility: Clearly visualize complex agent graphs without manual tracing
  • Structure: Map relationships between agents, tools, and data flows
  • Security: Apply threat modeling frameworks to identify potential vulnerabilities

Core Features

Capability Description
Workflow Extraction Extract agent-based workflows from code using AST-based static parsing
Threat Vector Visualization View agent-to-agent, agent-to-tool, and chained connections in an interactive graph
Automated Threat Assessment Generate comprehensive threat assessment report using established threat modeling frameworks for AI agents like MAESTRO
Framework Agnostic Works with all major LLM orchestration frameworks
Developer Friendly Simple CLI, extensible SDK, and clean JSON exports

https://github.com/user-attachments/assets/40231eae-9716-421e-a005-fd55d1d8cc71

Supported Frameworks

The following agent 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: Defining the system purpose and security objectives
  • Assets: Inventorying critical components (agents, tools, data flows)
  • Entrypoints: Mapping attack surfaces and access vectors
  • Security Controls: Evaluating existing defensive measures
  • Threats: Identifying potential vulnerabilities and attack scenarios
  • Risks: Calculating impact and likelihood of security events
  • Operations: Assessing runtime security considerations

Sample threat modelling report generated:

Threat Modeling Report

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

Prerequisites

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

export OPENAI_API_KEY=sk-...

🚀 CLI Usage

1. Extract Agentic Workflow

agent-wiz extract --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

© 2025 Repello AI | Website

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-0.4.0.tar.gz (164.0 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-0.4.0-py3-none-any.whl (171.2 kB view details)

Uploaded Python 3

File details

Details for the file repello_agent_wiz-0.4.0.tar.gz.

File metadata

  • Download URL: repello_agent_wiz-0.4.0.tar.gz
  • Upload date:
  • Size: 164.0 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-0.4.0.tar.gz
Algorithm Hash digest
SHA256 65d7c9d0b425ece61f4e1fa3e5d40322a2173f543dec274c33b0584c1436f3ac
MD5 9147e5dafe66198f3489a33bb79cbbbb
BLAKE2b-256 4b392e5e901c8dab61325c3b6df1a3b8084a7e131cf3296ddeccbd339609a938

See more details on using hashes here.

File details

Details for the file repello_agent_wiz-0.4.0-py3-none-any.whl.

File metadata

File hashes

Hashes for repello_agent_wiz-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 f4270846398a640b8ec360fb241a019115d4687b942777a3f7bcb171400174e3
MD5 3530701c874908be277e92f5926d58d4
BLAKE2b-256 e5d9500b02a26a31ac4b8509fcc05b7411957a458cf2a01dfc3d6d3606bde035

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