DSPy
Open-source framework for programming and optimizing LM systems
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What this product helps you do
DSPy replaces manual prompt strings with composable program interfaces. Developers define input and output signatures, select modules such as Predict, ChainOfThought, or ReAct, and can use optimizers to search for instructions and demonstrations that perform well on a training set and metric.
Problems it solves
- Prompt logic becomes brittle when embedded as unstructured strings
- Multi-stage LM systems need reusable program components
- Teams need a systematic way to optimize prompts against examples and metrics
- Model changes make repeated manual prompt tuning expensive
DSPy is an open-source Python framework for programming language-model systems. Signatures describe a task's inputs and outputs, while modules define strategies such as prediction, chain-of-thought reasoning, retrieval, or tool-using ReAct loops.
DSPy optimizers can generate and select instructions or demonstrations by running a program against a training set and developer-defined metric. The framework is free, while the models, retrieval systems, and infrastructure used by a DSPy program are selected and paid for separately.
Information
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DSPy is an open-source Python framework for programming language-model systems. Signatures describe a task's inputs and outputs, while modules define strategies such as prediction, chain-of-thought reasoning, retrieval, or tool-using ReAct loops.
DSPy optimizers can generate and select instructions or demonstrations by running a program against a training set and developer-defined metric. The framework is free, while the models, retrieval systems, and infrastructure used by a DSPy program are selected and paid for separately.
What this product helps you do
DSPy replaces manual prompt strings with composable program interfaces. Developers define input and output signatures, select modules such as Predict, ChainOfThought, or ReAct, and can use optimizers to search for instructions and demonstrations that perform well on a training set and metric.
Problems it solves
- Prompt logic becomes brittle when embedded as unstructured strings
- Multi-stage LM systems need reusable program components
- Teams need a systematic way to optimize prompts against examples and metrics
- Model changes make repeated manual prompt tuning expensive
Information
You are leaving botconomy for the provider’s website. Link verified 9/21/2026.
Related AI products
Potpie AI
Potpie maps code, source history, decisions, team knowledge, and engineering workflows into a context graph that agents can use to answer questions, plan changes, debug failures, and implement code.
Milvus
Milvus is an Apache-2.0 vector database for storing vectors and related scalar data, running approximate nearest-neighbor search, and scaling from an embedded local library to standalone or distributed deployments.
Qdrant
Qdrant is a Rust-based vector search engine and managed platform for dense, sparse, hybrid, filtered, multivector, and reranked retrieval across cloud, hybrid, private, and self-hosted deployments.