Comparisons / DSPy vs Haystack
DSPy vs Haystack: Which Agent Framework to Use?
DSPy replaces hand-written prompts with compiled modules. Haystack by deepset is a framework for building NLP and LLM pipelines. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
DSPy
33.4k
2.8k
Python
MIT
2023-01-09
Stanford NLP (Omar Khattab)
Haystack
24.7k
2.7k
Python
Apache-2.0
2019-11-14
deepset
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | DSPy | Haystack |
|---|---|---|
| Agent | `dspy.ReAct` module with signature and tools | `Agent` component with `ChatGenerator`, tool definitions, and message routing |
| 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 | `Tool` dataclass with function reference, name, description, parameters schema |
| Chaining | `dspy.ChainOfThought`, `dspy.Module` with `forward()` composition | — |
| Evaluation | `dspy.Evaluate` with metric functions and dev sets | — |
| Pipeline Architecture | — | `Pipeline()` with `add_component()` and `connect()` — a directed graph of typed components |
| RAG / Retrieval | — | `DocumentStore` + `Retriever` + `PromptBuilder` + `Generator` wired in a `Pipeline` |
| Memory | — | `ChatMessageStore` with `ConversationMemory` component in pipeline |
| Deployment | — | Pipeline YAML serialization, `Hayhooks` REST server |
DSPy vs Haystack, head to head
DSPy DSPy replaces hand-written prompts with compiled modules.
Haystack Haystack by deepset is a framework for building NLP and LLM pipelines.
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; Haystack would force you to translate.
Pick Haystack if
Pick Haystack if haystack earns its complexity when you're building RAG pipelines with multiple retrieval stages, document processing, and production deployment needs. But for straightforward agents with a few tools, the plain Python version is simpler to write and debug. Haystack 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.
What both add
Both DSPy and Haystack 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 Haystack 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 →