AI Developer Tools
dspyer
A transpiler from stateful imperative workflows to declarative DSPy programs for reliable, optimizable LLM steps with zero boilerplate.
dspyer
What is dspyer?
dspyer is a transpiler that converts stateful imperative Python workflows (including agent graphs) into declarative DSPy modules. It provides self-correcting LLM steps with typed outputs, automatic retries on validation failure, and seamless integration with DSPy's prompt optimization pipeline, all while requiring no DSPy syntax in user code.
dspyer vs Similar AI Tools
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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