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TensorZero and DSPy serve different but complementary purposes in the LLM ecosystem. TensorZero is a full-stack LLM engineering platform focused on production applications and optimization, while DSPy is a framework for programming with language models through modular prompting. You can get the best of both worlds by using DSPy and TensorZero together!

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Similarities

  • LLM Optimization. Both TensorZero and DSPy focus on LLM optimization, but in different ways. DSPy focuses on automated prompt engineering, while TensorZero provides a complete set of tools for optimizing LLM systems (including prompts, models, and inference strategies).
  • LLM Programming Abstractions. Both TensorZero and DSPy provide abstractions for working with LLMs in a structured way, moving beyond raw prompting to more maintainable approaches.
    → Prompt Templates & Schemas with TensorZero
  • Automated Prompt Engineering. TensorZero implements GEPA and MIPROv2, the leading automated prompt engineering algorithms recommended by DSPy. GEPA iteratively refines your prompt templates based on an inference evaluation, and MIPROv2 jointly optimizes instructions and in-context examples in prompts.
    → Guide: Optimize your prompts with GEPA
    → Recipe: Automated Prompt Engineering with MIPRO

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Key Differences

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TensorZero

  • Production Infrastructure. TensorZero provides complete production infrastructure including observability, optimization, evaluations, and experimentation capabilities. DSPy focuses on the development phase and prompt programming patterns.
  • Model Optimization. TensorZero provides tools for optimizing models, including fine-tuning and RLHF. DSPy primarily focuses on automated prompt engineering.
    → Optimization Recipes with TensorZero
  • Inference-Time Optimization. TensorZero provides inference-time optimizations like dynamic in-context learning. DSPy focuses on offline optimization strategies (e.g. static in-context learning).
    → Inference-Time Optimizations with TensorZero

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DSPy

  • Advanced Automated Prompt Engineering. DSPy provides sophisticated automated prompt engineering tools for LLMs like teleprompters, recursive reasoning, and self-improvement loops. TensorZero has some built-in prompt optimization features (more on the way) and integrates with DSPy for additional capabilities.
  • Lightweight Design. DSPy is a lightweight framework focused solely on LLM programming patterns, particularly during the R&D stage. TensorZero is a more comprehensive platform with additional infrastructure components covering end-to-end LLM engineering workflows.
Is TensorZero missing any features that are really important to you? Let us know on GitHub Discussions, Slack, or Discord.

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Combining TensorZero and DSPy

You can get the best of both worlds by using DSPy and TensorZero together! TensorZero provides a number of pre-built optimization recipes covering common LLM engineering workflows like supervised fine-tuning and RLHF. But you can also easily export observability data for your own recipes and workflows.