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DSPy

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Framework that replaces prompt writing with programming and automatic optimization.

The most rigorous option for reproducible LLM pipelines; real learning curve.

Official site GitHub · 38,776 ★

Overall score

8.7 /10

Security: nothing to flag

Utility

8.0

Reliability

10.0

Security

No caveat on record

Ease

Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Writing prompts by hand for language model systems produces brittle results that are hard to reproduce. DSPy solves this by replacing textual prompts with structured Python code, making AI pipelines modular, maintainable, and automatically optimizable.

In practice, you define your tasks as typed signatures (declared inputs and outputs), then choose an execution module such as Predict, ChainOfThought, or ReAct. An optimizer like GEPA or MIPROv2 then automatically tunes prompts and examples using a scoring function you provide, until quality converges.

DSPy is aimed at developers and research teams building production AI systems: RAG pipelines, tool-using agents, classifiers, fact-checking, and multimodal processing. It is used in production at Shopify, Dropbox, AWS, JetBlue, Replit, and Databricks, among others.

However, DSPy is not suited for people looking for a graphical interface or a no-code tool. It requires Python 3.10 or higher, familiarity with object-oriented programming, and a basic understanding of language model concepts.

What it offers

·Typed signatures: declare your tasks as typed Python inputs and outputs instead of free-form text prompts.
·Execution modules: choose the right inference strategy, from direct completion (Predict) to step-by-step reasoning (ChainOfThought) and tool-using agents (ReAct).
·Automatic optimizers: GEPA, MIPROv2, BootstrapFewShot, and other algorithms tune prompts and examples against a metric you define.
·Modular composition: assemble multiple signatures and modules into reusable Python programs with ordinary control flow.
·Multimodal support: images can be declared as signature fields, enabling visual tasks within the same framework.
·Multi-model compatibility: connect DSPy to different language model providers via the LM class.
·Save and portability: export an optimized program to a JSON file for reuse or sharing.
·MCP v2 integration: version 3.3.1 brings compatibility with the MCP v2 protocol for tools.

Our tips

Start with a simple Predict module on your signature, measure quality with your metric, then move to ChainOfThought or ReAct only if the score is insufficient.
Provide a labeled example set (trainset) that is as representative as possible before running an optimizer: the quality of the result depends directly on the quality of these examples.
Use the auto parameter of GEPA (values 'light', 'medium', 'heavy') to control the compute budget allocated to optimization according to your cost constraints.
Save the optimized program with the save() method to a JSON file so you do not need to re-run optimization at every deployment.
To migrate from a large model to a smaller one, compile the program with the small model using examples generated by the large model, as some AWS teams do.

Watch out

The learning curve is real: mastering signatures, modules, and optimizers takes time, especially for people less familiar with object-oriented Python programming.
Automatic optimization consumes language model calls, which can generate significant costs if the trainset is large or if the auto parameter is set to 'heavy'.

In practice

Price: Free
Commercial use: Commercial use: yes
Available on: cli
Licence: MIT

Security and data

Rating: Security: nothing to flag
Your data: Data stays local
Maintenance: Active
Last release: 12 October 2026

Links and repository checked on 25 October 2026 · added on 1 January 2023

How to install DSPy

1. Verify that Python 3.10 or higher is installed on your machine. 2. Install DSPy via pip: pip install dspy 3. To get the very latest version from the main branch: pip install git+https://github.com/stanfordnlp/dspy.git 4. Set your API key for the chosen model provider in your environment variables, without storing it in plain text in your code. 5. Consult the official documentation at dspy.ai to get started with your first signature.

pip install dspy

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