What is DSPy and how is it different from LangChain?

DSPy is a Stanford NLP framework that replaces hand-written prompts with compiled modules. You define input/output Signatures and let Optimizers auto-tune prompts against a metric. LangChain focuses on agent orchestration and integrations. DSPy focuses on making prompts better algorithmically — they solve different problems.

Can DSPy build AI agents?

Yes. DSPy provides a ReAct module that implements the standard agent loop (reason, act, observe) with tool calling. However, DSPy's primary value is prompt optimization, not agent orchestration. The agent capabilities are a module within the broader framework, not the core focus.

Do I need DSPy for prompt engineering?

No. Most prompts work well enough with manual iteration. DSPy adds value when you have evaluation datasets, clear quality metrics, and need systematic prompt improvement at scale — especially when switching between LLM providers. For simple or prototype use cases, f-string templates are faster.

What is the OpenAI Agents SDK?

The OpenAI Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails for building AI agents with OpenAI models. It's the thinnest framework available — closer to plain Python than LangChain or CrewAI, with auto-schema generation from Python type hints.

What is the difference between OpenAI Agents SDK and Swarm?

Agents SDK is the production evolution of Swarm, which was an experimental framework. Both use the same patterns: Agent objects with instructions and tools, a Runner that handles the agent loop, and handoffs for multi-agent routing. Agents SDK adds guardrails and is officially supported.

Should I use OpenAI Agents SDK or LangChain?

Use the Agents SDK if you're committed to OpenAI models and want minimal abstraction with auto-schema generation. Use LangChain if you need multi-provider support, vector store integrations, or the LangSmith ecosystem. Use plain Python if you want full control and transparency.

Comparisons / DSPy vs OpenAI Agents SDK

DSPy vs OpenAI Agents SDK: Which Agent Framework to Use?

DSPy replaces hand-written prompts with compiled modules. OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.

By the numbers

DSPy

GitHub Stars

33.4k

Forks

2.8k

Language

Python

License

MIT

Created

2023-01-09

Created by

Stanford NLP (Omar Khattab)

github.com/stanfordnlp/dspy →

OpenAI Agents SDK

GitHub Stars

20.6k

Forks

3.4k

Language

Python

License

MIT

Created

2025-03-11

Created by

OpenAI

github.com/openai/openai-agents-python →

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

Concept	DSPy	OpenAI Agents SDK
Agent	`dspy.ReAct` module with signature and tools	`Agent(name, instructions, model, tools)`
Prompts	`dspy.Signature` defines input/output fields, compiled to optimized prompts	—
Optimization	`dspy.BootstrapFewShot`, `MIPROv2` auto-tune prompts against a metric	—
Tools	Tools passed to `ReAct` module as callable list	Python functions with type hints, auto-converted to schemas
Chaining	`dspy.ChainOfThought`, `dspy.Module` with `forward()` composition	—
Evaluation	`dspy.Evaluate` with metric functions and dev sets	—
Agent Loop	—	`Runner.run()` handles the loop internally
Handoffs	—	`Handoff` between `Agent` objects for multi-agent routing
Guardrails	—	`InputGuardrail` and `OutputGuardrail` with tripwire pattern
Context	—	Typed context object passed through the agent lifecycle

DSPy vs OpenAI Agents SDK, head to head

DSPy DSPy replaces hand-written prompts with compiled modules.

OpenAI Agents SDK OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.

Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.

Pick DSPy if

Pick DSPy if dSPy's real innovation is automated prompt optimization — replacing manual prompt engineering with algorithmic tuning. This is genuinely novel and valuable for production systems where prompt quality matters at scale. For simple agents or learning, hand-written prompts are easier to understand and modify. DSPy is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; OpenAI Agents SDK would force you to translate.

Full DSPy comparison →

Pick OpenAI Agents SDK if

Pick OpenAI Agents SDK if the Agents SDK is the thinnest framework on this list — it barely abstracts beyond what you'd write yourself. Use it when you want OpenAI's conventions and auto-schema generation. Skip it when you want full control or use non-OpenAI models. OpenAI Agents SDK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; DSPy would force you to translate.

Full OpenAI Agents SDK comparison →

What both add

Both DSPy and OpenAI Agents SDK pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.

Or build your own in 60 lines

Both DSPy and OpenAI Agents SDK implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.

No framework. No dependencies. No opinions. Just the code.

Build it from scratch →