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DSPy: The framework for programming—not prompting—language models

26 mentions on Hacker News by 25 people: 5 stories, 21 comments · first 2023-08-25 · latest 2026-09-28

Mentions per year 26 mentions · busiest year 2024 (18)
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  1. …using them. Read the GEPA paper. I'll put the link here and you can scroll down and read it. https://github.com/stanfordnlp/dspy Your agentic loops are already doing these things.
    comment by genxy · 2026-09-28 · in Imp is a full port of DSPy to the BEAM
  2. If true, this is essentially what Dspy does https://github.com/stanfordnlp/dspy (it was made back in 2023)
    comment by padolsey · 2026-03-27 · in HyperAgents: Self-referential self-improving agents
  3. Dspy handling it openly https://github.com/stanfordnlp/dspy/issues/9500
    comment by mathis-l · 2026-03-24 · in Tell HN: Litellm 1.82.7 and 1.82.8 on PyPI are compromised
  4. …but still fails in a lot of cases like my implementation. For example fails for this: https://github.com/stanfordnlp/dspy
    comment by fsndz · 2024-12-27 · in Show HN: Instantly visualize any codebase as an interactive diagram
  5. …has been done [2][3][4] [1]DSPy: The framework for programming—not prompting—foundation models: https://github.com/stanfordnlp/dspy [2]TextGrad: Automatic 'Differentiation' via Text -- using large language models to backpropagate textual…
    comment by ValentinA23 · 2024-10-12 · in Swarm, a new agent framework by OpenAI
  6. At least some of it comes from "hype" too. The author of Dspy (the writer) ( https://github.com/stanfordnlp/dspy ) should know this, given that Dspy is nothing more than fancy prompts optimizing prompts to be fancier according to prompt…
    comment by Der_Einzige · 2024-09-25 · in On Impactful AI Research
  7. https://github.com/stanfordnlp/dspy
    comment by kevindamm · 2024-09-13 · in Notes on OpenAI's new o1 chain-of-thought models
  8. …AdalFlow, DSPy [0], LangChain [1] and magentic [2] (package I've created, narrower in scope). [0] https://github.com/stanfordnlp/dspy [1] https://github.com/langchain-ai/langchain [2] https://github.com/jackmpcollins/magentic
    comment by jackmpcollins · 2024-08-18 · in Show HN: AdalFlow: The library to build and auto-optimize any LLM task pipeline
  9. I agree this is an interesting direction, I think this is on the roadmap for DSPy [ https://github.com/stanfordnlp/dspy ], but right now they mainly focus on optimizing the in-context examples.
    comment by danlenton · 2024-05-23 · in Show HN: Route your prompts to the best LLM
  10. I think dspy aims to do this: https://github.com/stanfordnlp/dspy
    comment by ramoz · 2024-05-23 · in Show HN: Route your prompts to the best LLM
  11. …type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy]( https://github.com/stanfordnlp/dspy ) which are abstracting “up the stack” of LLM apps. (some opinions there) Operating AI apps reveals a…
    comment by threecheese · 2024-04-30 · in Pydantic Logfire
  12. …makes it hard. We're experimenting with Claude right now and I'm really hoping something like https://github.com/stanfordnlp/dspy can help here.
    comment by maciejgryka · 2024-04-09 · in Building reliable systems out of unreliable agents
  13. …excellent examples that showcase using axolotl to efficiently finetune models on cloud GPUs. [1] https://github.com/stanfordnlp/dspy [2] https://github.com/unslothai/unsloth [3] https://github.com/OpenAccess-AI-Collective/axolotl [4]…
    comment by stanbiryukov · 2024-04-04 · in Ask HN: Most efficient way to fine-tune an LLM in 2024?
  14. DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.
    comment by hazn · 2024-04-02 · in Princeton group open sources "SWE-agent", with 12% fix rate for GitHub issues
  15. Isn’t this just a very naive implementation of what DsPY does? https://github.com/stanfordnlp/dspy I don’t understand what is exceptional here.
    comment by bugglebeetle · 2024-03-27 · in Winner of the SF Mistral AI Hackathon: Automated Test Driven Prompting
  16. What's the difference to using joblibs Memory class similar to this implementation: https://github.com/stanfordnlp/dspy/blob/main/dsp/modules/ca...
    comment by rassibassi · 2024-03-19 · in Show HN: File-based cache for slow Python functions
  17. Have you done any comparison with DSPy ? ( https://github.com/stanfordnlp/dspy ) Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.
    comment by ganarajpr · 2024-03-07 · in Show HN: Fructose – LLM calls as strongly typed functions
  18. I'm interested in hearing if anyone has used DSPy ( https://github.com/stanfordnlp/dspy ) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt…
    comment by binarymax · 2024-03-06 · in AI Prompt Engineering Is Dead
  19. You should check out https://x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy
    comment by knrz · 2024-01-30 · in Ask HN: Are you using a GPT to prompt-engineer another GPT?
  20. https://github.com/stanfordnlp/dspy#5b-dspy-vs-application-d...
    comment by davidrupp · 2023-09-07 · in DSPy: Framework for programming with foundation models
  21. btw read a more official answer here: https://github.com/stanfordnlp/dspy#5a-dspy-vs-thin-wrappers...
    comment by okhat · 2023-09-07 · in DSPy: Framework for programming with foundation models