dspyer
A transpiler from stateful imperative workflows to declarative DSPy programs for reliable, optimizable LLM steps with zero boilerplate.
dspyer
What is dspyer?
dspyer vs Similar AI Tools
| Pricing Model | Free | Free | Free | Free |
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How to use dspyer?
- 1Install via pip install dspyer
- 2Define Pydantic input/output schemas for LLM calls
- 3Create a StatefulNode with the schema and instructions
- 4Compose a Graph with nodes and entry point
- 5Compile with AgentTranspiler.compile(graph)
- 6Call the program with input, optionally tune with DSPy optimizers
dspyer Key Features
- Zero-boilerplate decorator (@self_correcting) wrapping plain typed functions
- Automatic self-correction loops on Pydantic validation failure
- Prompt optimization via any DSPy teleprompter (e.g., BootstrapFewShot)
- Orchestrator integration (LangGraph, custom) with compiled nodes
- Telemetry & validation reporting with OpenTelemetry spans
- Dataset flywheel: logs successful self-corrections for retraining
- Async & streaming execution (aforward, astream)
- Pluggable storage adapters for production logging
dspyer Use Cases
- Building production-grade LLM agents with guaranteed output schemas
- Automating prompt tuning when upgrading models (e.g., GPT-4o to Claude 3.5)
- Adding self-correction to existing DSPy or LangGraph pipelines
- Creating a dataset of input/output pairs from self-correction logs
dspyer Pricing & Free Credits
dspyer currently operates on a Free model.
This tool is completely free to use
dspyer Pros & Cons
Pros
- No vendor lock-in: compiles to standard dspy.Module, works with any DSPy optimizer
- Automatic self-correction reduces manual validation boilerplate
- Built-in telemetry, validation reporting, and dataset logging
- Async and streaming support for concurrent environments
- Pluggable storage adapters for production reliability
Cons
- Requires familiarity with Python, Pydantic, and DSPy concepts
- Limited to Python ecosystem
- Relatively new project, community and documentation still growing
What is dspyer best for?
- Python developers building LLM-powered agents and pipelines
- Teams wanting to adopt DSPy without rewriting existing code
- Engineers seeking automatic prompt optimization and output validation
dspyer FAQ
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