End-to-end LLM application development — prototyping, testing, evaluation, deployment, monitoring
Key Features
Single Agent Human-in-the-Loop Streaming Short-Term Memory Long-Term Memory Plugin System Custom Tools MCP Protocol A2A Protocol DAG Workflows CLI Self-Hosted Cloud Hosted
Community Feedback
Strengths
- Strong evaluation and testing capabilities
- Visual DAG design
- Deep Azure integration
- Production tracing
Weaknesses
- Tightly coupled with Azure
- Being superseded by Agent Framework for agentic workloads
- DAG can feel rigid for dynamic workflows
Promptflow Details
| Organization | Microsoft |
| Organization Type | Company |
| Funding | Public |
| Category | SDK |
| Subcategory | Orchestration |
| Deployment | Hybrid |
| Primary Language | Python |
| Runtime | Python 3.9+ |
| License | MIT |
| Commercial Use | Unrestricted |
| Install Command | pip install promptflow |
| GitHub Stars | 11,100 |
| GitHub Forks | 1,100 |
| Release Cadence | Biweekly |
| Maturity | Stable |
| Pricing Model | Free |
| Free Tier | Fully open-source MIT |
| Self-Hosted Free | Yes |
| Cost Model | free + LLM costs |
| Community Size | Medium (11k stars) |
| Community Activity | Active |
| Sentiment | Positive |
| GPU Required | No |
| Research Date | 2026-03-24 |
| LLM Providers | Azure OpenAI, OpenAI, any via integrations |
| API Keys Required | Azure OpenAI or OpenAI API key |
Use Cases
- LLM app prototyping with DAG workflows
- Evaluation and testing of LLM applications
- Production deployment with tracing
- Azure AI Foundry integration
When to Use
Best for: Azure teams needing LLM app development with evaluation and tracing
Avoid when: Non-Azure environments or need flexible agent workflows
Original data from HuggingFace, OpenCompass and various public git repos.
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Release v20260328a