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 MIPROv2, the automated prompt engineering algorithm recommended by DSPy. MIPROv2 jointly optimizes instructions and in-context examples in prompts.
    → 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.
    → Improving Math Reasoning — Combining TensorZero and DSPy
  • 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 create your own recipes and workflows. This example shows how to optimize a TensorZero function using a tool like DSPy. → Improving Math Reasoning — Combining TensorZero and DSPy