DSPy vs LangGraph: frameworks compared
Updated By Agent Radar3 min read
Short answer
DSPy programs and optimizes prompts in Python, while LangGraph orchestrates stateful agents; both list MIT licenses as of 2026-10-23.
Top pick: DSPy. DSPy is described as the framework for programming, not prompting, language models. It offers algorithms for optimizing prompts and weights.
DSPy and LangGraph solve different problems. DSPy is described as a framework for programming, not prompting, language models, with algorithms for optimizing prompts and weights. LangGraph is described as a low-level orchestration framework for long-running, stateful agents. Both list an MIT license. Sources checked 2026-10-23.
Quick comparison
| Tool | Best fit (from sources) | License | Pricing as stated | Primary source |
|---|---|---|---|---|
| DSPy | Building modular AI systems and optimizing their prompts and weights, from classifiers to RAG pipelines and agent loops | MIT | not stated | GitHub README |
| LangGraph | Building, managing and deploying long-running, stateful agents | MIT | not stated | LangGraph docs |
The order of the rows is editorial. It follows the query wording, not a ranking of quality.
How the options differ
What each one is for
The DSPy README says you write compositional Python code instead of brittle prompts, and use DSPy to teach the language model to deliver high-quality outputs. It also says DSPy offers algorithms for optimizing prompts and weights.
The LangGraph README calls it a low-level orchestration framework. The docs describe it as the orchestration runtime in the LangChain product family, covering durable execution, streaming, human-in-the-loop and persistence.
In short, the sources place DSPy at the level of program definition and optimization, and LangGraph at the level of runtime and control flow. The sources do not compare the two directly.
Control flow and state
LangGraph's docs say you can mix deterministic and agentic steps in a single graph. Its example builds a StateGraph, adds nodes and edges, and compiles it.
The LangGraph README lists durable execution, human-in-the-loop, and short-term and long-term memory. The DSPy excerpts do not describe persistence, checkpointing or human-in-the-loop features. They do mention agent loops as one use case.
Surrounding tooling and deployment
LangGraph's README pairs it with Deep Agents, LangChain, LangSmith and LangSmith Deployment. It also says LangGraph can be used without LangChain. The docs describe LangSmith as the platform for tracing, evaluation, prompts and deployment.
The DSPy excerpts point to the DSPy docs at dspy.ai and an installation via pip install dspy. They do not describe a hosted deployment platform.
Research lineage and license
The DSPy README lists research papers, including GEPA, and gives citation entries. Both repositories report an MIT license through the GitHub API. The DSPy repository was created on 2023-01-09 and the LangGraph repository on 2023-08-09.
For overlapping comparisons, see DSPy vs LangChain, Agno vs LangGraph and Pydantic AI vs LangGraph.
How we compared
We read the GitHub repository metadata and READMEs for both tools, plus the LangGraph overview page in the LangChain docs. All were checked on 2026-10-23. We did not install, run or benchmark either tool. We did not test them together.
Limitations
The cited sources do not state pricing for either tool. They also do not include benchmarks, a list of supported models, or a direct comparison between the two. LangSmith and LangSmith Deployment are separate products, and their pricing is not stated in the cited sources. The DSPy excerpts do not describe deployment options.
Browse the DSPy and LangGraph hubs and the Frameworks category. Newest same-tool launches: DSPy 3.4.1 and LangGraph 1.2.16.
The tools
DSPy is described as the framework for programming, not prompting, language models. It offers algorithms for optimizing prompts and weights.
Pros
- Lets you write compositional Python code instead of brittle prompts
- Covers classifiers, RAG pipelines and agent loops
- Backed by listed research papers
Cons
- Persistence and human-in-the-loop features are not described in the cited sources
- Pricing not stated in the cited sources
LangGraph is a low-level orchestration framework for building, managing and deploying long-running, stateful agents.
Pros
- Durable execution that resumes after failures
- Human-in-the-loop state inspection and modification
- Short-term and long-term memory
Cons
- Described as low-level; higher-level Deep Agents is suggested for quick builds
- Pricing for LangSmith deployment is not stated in the cited sources
Questions
What is the main difference between DSPy and LangGraph?
DSPy is described as a framework for programming, not prompting, language models, with prompt and weight optimization. LangGraph is described as a low-level orchestration framework for long-running, stateful agents.
Are DSPy and LangGraph open source?
The GitHub API reports an MIT license for both repositories. Pricing for either is not stated in the cited sources.
Does LangGraph require LangChain?
No. The LangGraph README says it is built by LangChain Inc but can be used without LangChain.
Does DSPy support agents?
The DSPy README says it can be used to build simple classifiers, RAG pipelines or agent loops.