AI Developer Tools

dspyer

A transpiler from stateful imperative workflows to declarative DSPy programs for reliable, optimizable LLM steps with zero boilerplate.

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

Pricing ModelFreeFreeCustom PricingFree, Paid
Free Credits
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)
  • GitHub Pages hosting
  • Jekyll integration
  • Markdown content support
  • Workload monitoring and anomaly detection
  • Slow query identification and optimization
  • Natural language querying to SQL translation
  • Scans skills and MCP servers against ATR rules before loading
  • Real-time runtime protection against prompt injection and hijacks
  • Signed audit-ready evidence for compliance (EU AI Act, NYDFS, DORA)
Pros
  • No vendor lock-in: compiles to standard dspy.Module, works with any DSPy optimizer
  • Automatic self-correction reduces manual validation boilerplate
  • Free hosting with custom domain support
  • Easy setup via Git
  • Quick one-line installation and setup in 15 minutes
  • Self-hosted ensures data stays within your infrastructure
  • Open source with MIT license
  • Real-time detection and prevention
Cons
  • Requires familiarity with Python, Pydantic, and DSPy concepts
  • Limited to Python ecosystem
  • Limited to static content
  • No server-side processing
  • Requires self-hosting and VPC setup
  • No free tier or trial mentioned
  • Enterprise features require paid tiers
  • Setup may require technical expertise
Best For
  • Python developers building LLM-powered agents and pipelines
  • Teams wanting to adopt DSPy without rewriting existing code
  • Developers
  • Open source projects
  • Database administrators
  • Data engineers
  • Developers building and deploying AI agents
  • Enterprises needing audit-ready AI security

How to use dspyer?

  1. 1Install via pip install dspyer
  2. 2Define Pydantic input/output schemas for LLM calls
  3. 3Create a StatefulNode with the schema and instructions
  4. 4Compose a Graph with nodes and entry point
  5. 5Compile with AgentTranspiler.compile(graph)
  6. 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

Open Source

$0

Free and open-source under Apache License 2.0

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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