Comparisons / Haystack vs LangGraph
Haystack vs LangGraph: Which Agent Framework to Use?
Haystack by deepset is a framework for building NLP and LLM pipelines. LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
Haystack
24.7k
2.7k
Python
Apache-2.0
2019-11-14
deepset
LangGraph
18.9k
3.4k
Python
MIT
2024-01-17
LangChain Inc (Harrison Chase)
Sequoia Capital, Benchmark
Part of LangChain Inc — $50M raised across A and B
8.2M
LangGraph Platform (hosted), LangSmith (observability)
Yes
Used by: Replit, Klarna, Elastic
github.com/langchain-ai/langgraph→GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Haystack | LangGraph |
|---|---|---|
| Agent | `Agent` component with `ChatGenerator`, tool definitions, and message routing | A `StateGraph` with nodes, edges, and a typed `State` channel |
| Tools | `Tool` dataclass with function reference, name, description, parameters schema | `ToolNode(tools)` paired with a conditional edge for routing |
| 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 | — |
| Loop | — | `add_conditional_edges` from a node back to itself until a `END` condition |
| State | — | Typed `State` channels with reducers (`Annotated[list, add_messages]`) |
| Checkpointing | — | `MemorySaver` / `PostgresSaver` persists state per `thread_id` |
| Human-in-loop | — | `interrupt_before` / `interrupt_after` pauses execution for review |
| Parallel fanout | — | Multiple edges from one node + reducers merge results |
Haystack vs LangGraph, head to head
Haystack Haystack by deepset is a framework for building NLP and LLM pipelines.
LangGraph LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state.
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 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; LangGraph would force you to translate.
Pick LangGraph if
Pick LangGraph if langGraph earns its weight when your agent is a workflow — explicit branches, checkpoints, parallel branches, or a human approval gate. For a single-agent loop, the graph machinery is overkill and a plain while loop is faster to write, debug, and ship. LangGraph 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.
What both add
Both Haystack and LangGraph 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 Haystack and LangGraph 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 →