icon
AI Tools AI Agents MCP Ranking Submit & Advertise
Login

Comprehensive visual debugging Tools for Every Need

Get access to visual debugging solutions that address multiple requirements. One-stop resources for streamlined workflows.

visual debugging

  • PySpur

    An open-source visual IDE enabling AI engineers to build, test, and deploy agentic workflows 10x faster.
    0
    1
    What is PySpur?
    PySpur provides an integrated environment for constructing, testing, and deploying AI agents via a user-friendly, node-based interface. Developers assemble chains of actions—such as language model calls, data retrieval, decision branching, and API interactions—by dragging and connecting modular blocks. A live simulation mode lets engineers validate logic, inspect intermediate states, and debug workflows before deployment. PySpur also offers version control of agent flows, performance profiling, and one-click deployment to cloud or on-premise infrastructure. With pluggable connectors and support for popular LLMs and vector databases, teams can prototype complex reasoning agents, automated assistants, or data pipelines quickly. Open-source and extensible, PySpur minimizes boilerplate and infrastructure overhead, enabling faster iteration and more robust agent solutions.
    PySpur Core Features
    • Visual graph-based workflow builder
    • Modular action blocks for LLM calls, API interactions, and branching logic
    • Built-in simulation and debugging tools
    • Version control integration for agent flows
    • One-click deployment to cloud or on-premise
    • Performance profiling and monitoring dashboards
    • Extensible plugins for connectors and custom modules
    PySpur Pro & Cons

    The Cons

    No detailed pricing information publicly available
    No mobile app or extension presence detected
    Lacks extensive community or social channel links

    The Pros

    Open-source with Apache 2.0 license
    Supports building AI agents with Python code or drag-and-drop UI
    Facilitates faster development with test cases and agentic workflows
    Easy sharing and version control through JSON export
    Backed by leading AI institutions and Y Combinator
    PySpur Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://pyspur.dev/#

  • mcp-agent-graph

    A Python framework enabling developers to orchestrate AI agent workflows as directed graphs for complex multi-agent collaborations.
    0
    0
    What is mcp-agent-graph?
    mcp-agent-graph provides a graph-based orchestration layer for AI agents, enabling developers to map out complex multi-step workflows as directed graphs. Each node in the graph corresponds to an agent task or function, capturing inputs, outputs, and dependencies. Edges define the flow of data between agents, ensuring correct execution order. The engine supports sequential and parallel execution modes, automatic dependency resolution, and integrates with custom Python functions or external services. Built-in visualization allows users to inspect graph topology and debug workflows. This framework streamlines the development of modular, scalable multi-agent systems for data processing, natural language workflows, or combined AI model pipelines.

  • AutoGen UI

    AutoGen UI is a React-based toolkit to build interactive UIs and dashboards for orchestrating multi-agent AI agent conversations.
    0
    0
    What is AutoGen UI?
    AutoGen UI is a frontend toolkit designed to render and manage multi-agent conversational flows. It offers ready-made components such as chat windows, agent selectors, message timelines, and debugging panels. Developers can configure multiple AI agents, stream responses in real time, log each step of the conversation, and apply custom styling. It integrates easily with backend orchestration libraries to provide a complete end-to-end interface for building and monitoring AI agent interactions.
Featured