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dspy
unclaimed listingactively maintainedFreeMIT
DSPy: The framework for programming—not prompting—language models
by Open Source Community · New publisher
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stanfordnlp/dspy is an open-source project by stanfordnlp: DSPy: The framework for programming—not prompting—language models. Indexed here so it can be found — not resold.
It is free. Get it from the upstream repository: https://github.com/stanfordnlp/dspy
From the project's own README (excerpt, reproduced for discovery under its MIT license):
DSPy: Programming—not prompting—Foundation Models
Documentation: DSPy Docs
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DSPy is the framework for programming—rather than prompting—language models. It allows you to iterate fast on building modular AI systems and offers algorithms for optimizing their prompts and weights, whether you're building simple classifiers, sophisticated RAG pipelines, or Agent loops.
DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, you write compositional Python code and use DSPy to teach your LM to deliver high-quality outputs. Learn more via our official documentation site or meet the community, seek help, or start contributing via this GitHub repo and our Discord server.
Documentation: dspy.ai
Please go to the DSPy Docs at dspy.ai
Installation
To install the very latest from main:
📜 Citation & Reading More
If you're looking to understand the framework, please go to the DSPy Docs at dspy.ai.
If you're looking to understand the underlying research, this is a set of our papers:
[Jul'25] GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning [Jun'24] Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs [Oct'23] DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines [Jul'24] Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together [Jun'24] Prompts as Auto-Optimized Training Hyperparameters [Feb'24] Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models [Jan'24] In-Context Learning for Extreme Multi-Label Classification [Dec'23] DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines [Dec'22] Demonstrate-Search-Predict: Composing Retrieval & Language Models for…
Preview
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01Capabilities
Does
- + LLM output parsing
- + Experiment and decision gates
- + Agent tool-call orchestration
Doesn’t
- No exclusions declared
02Requirements & stack
Depends on
No declared dependencies
Credentials needed
None declared
Stack
03Community
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Issues 0
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04Trust Passport
Full passport →0/0 automated components pass. An automated score is never a security guarantee.
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- malicious pattern scan No known malicious-behavior patterns across listing text only — no source artifact published
- capability contract All 0 observed capability reference(s) match the declared manifest
- agent safety scan No injection patterns in agent-readable content
- provenance No release signature or provenance attestation
- behavioral sandbox Not performed in this environment — requires the production isolated runner (docs/sandbox-requirements.md). No untrusted code is ever executed on the application host.
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05Versions
Full history →| Version | Channel | Released | Notes |
|---|---|---|---|
| 0.0.0 | stable | Aug 3, 2026 | Indexed listing — see the upstream repository for real release history. |