PySpur is an open-source, cloud-native platform that accelerates AI agent development. It features a visual graph-based editor for designing agent workflows with modular components, built-in testing and debugging tools, and seamless deployment pipelines. By handling orchestration, parallel execution, and monitoring, PySpur empowers AI engineers to iterate on complex agent behaviors rapidly, reducing development time and improving reliability without reinventing core infrastructure.
AI Memory Systems
AI Platforms & Frameworks
Predictive Analytics
Web3 Technologies
#AI Agents
#workflow builder
#modular programming
#visual debugging
#cloud deployment
#version control
#performance profiling
#Open-source tools
#automated assistants
#data pipelines
#Drag and Drop Interface
#real-time simulation
#scalable solutions
#plugin architecture
#efficient coding
#collaborative development
#logic validation
#API integration
#LLM support
...
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PySpur
PySpur is an open-source, cloud-native platform that accelerates AI agent development. It features a visual graph-based editor for designing agent workflows with modular components, built-in testing and debugging tools, and seamless deployment pipelines. By handling orchestration, parallel execution, and monitoring, PySpur empowers AI engineers to iterate on complex agent behaviors rapidly, reducing development time and improving reliability without reinventing core infrastructure.
AI Memory Systems
AI Platforms & Frameworks
Predictive Analytics
Web3 Technologies
#AI Agents
#workflow builder
#modular programming
#visual debugging
#cloud deployment
#version control
#performance profiling
#Open-source tools
#automated assistants
#data pipelines
#Drag and Drop Interface
#real-time simulation
#scalable solutions
#plugin architecture
#efficient coding
#collaborative development
#logic validation
#API integration
#LLM support
...
Featured
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.
Who will use PySpur?
- AI engineers
- Software developers building AI agents
- DevOps teams
- Research scientists in AI
- Startups prototyping intelligent workflows
How to use the PySpur?
- Step1: Install the PySpur CLI or access the cloud IDE at pyspur.dev.
- Step2: Create a new agent project and open the visual workflow editor.
- Step3: Drag and drop modular blocks (LLM calls, API requests, branches).
- Step4: Configure each node’s parameters and connect data flows.
- Step5: Run simulations in the built-in debugger to validate behavior.
- Step6: Adjust nodes based on logs and performance metrics.
- Step7: Commit changes for version tracking.
- Step8: Deploy the agent with one click to cloud or local environment.
- Step9: Monitor runtime metrics and update workflows as needed.
Platform
- web
- mac
- windows
- linux
PySpur's Core Features & Benefits
The 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
The Benefits
- Accelerates agent development by 10x
- Reduces boilerplate code and infrastructure setup
- Enhances collaboration with visual workflows
- Improves reliability through built-in testing and logging
- Streamlines deployment and scaling
- Open-source and extensible architecture
PySpur's Main Use Cases & Applications
- Building conversational AI assistants
- Automating data retrieval and processing pipelines
- Prototyping complex decision-making agents
- Orchestrating multi-step LLM workflows
- Deploying production-ready agents with monitoring
PySpur's Pros & Cons
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
The Cons
No detailed pricing information publicly available
No mobile app or extension presence detected
Lacks extensive community or social channel links
PySpur's Pricing
| Has free plan | No |
|---|---|
| Free trial details | |
| Pricing model | |
| Is credit card required | No |
| Paid from | |
| Has lifetime plan | No |
| Billing frequency |
FAQs of PySpur
PySpur Company Information
Analytic of PySpur
Visit Over Time
Monthly Visits
930
Avg Visit Duration
00:00:22
Page Per Visit
1.59
Bounce Rate
37.81%
Aug 2025 - Oct 2025 All Traffic
Geography
Top 2 Regions
United States
70.77%
Germany
29.23%
Aug 2025 - Oct 2025 Worldwide Desktop Only
Traffic Sources
Direct
44.34%
Social
38.14%
Referrals
11.78%
Search
4.79%
Paid Referrals
0.91%
Mail
0.03%
Aug 2025 - Oct 2025 Desktop Only
Top Keywords
| Keyword | Traffic | Cost Per Click |
|---|---|---|
| pyspur | 610 | $ -- |
| pyspur install | 40 | $ -- |
| deepseek open source week | 330 | $ -- |
| {{ $execution.resumeurl }} runing workfollow | 90 | $ -- |
PySpur Reviews
5/5
PySpur's Main Competitors and alternatives?
- LangChain
- Guidance
- Flowise
- AgentGPT
- AutoGen
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