LangGraph vs Haystack: Which Is Best for AI Development?

The AI development landscape has exploded in recent years, driven by the rapid adoption of large language models (LLMs). Startups, product teams, and enterprise developers are increasingly exploring frameworks that streamline LLM application development, allowing them to build intelligent agents, retrieval pipelines, and automation workflows.

Among the most popular frameworks in this ecosystem are LangGraph and Haystack. Both are open-source, actively maintained, and designed to accelerate AI development—but they serve distinct purposes. LangGraph focuses on agentic, graph-based LLM workflows, while Haystack provides a modular framework for search, retrieval-augmented generation (RAG), and document-based AI systems.

In this article, we’ll dive deep into each framework, highlight their key features, compare strengths and weaknesses, and provide actionable guidance on which to choose for your AI projects.

What is LangGraph?

LangGraph is an open-source framework for building graph-based, agentic LLM applications, designed to extend the capabilities of LangChain. It allows developers to structure LLM-driven workflows as a graph of nodes, each representing a discrete function, agent, or data operation. This architecture is particularly useful for multi-step reasoning, persistent state management, and dynamic decision-making in AI applications.

LangGraph is built with flexibility and modularity in mind. Its graph-based paradigm enables developers to design complex workflows where nodes can communicate, share state, and react to changes in real-time. This makes it ideal for applications such as autonomous agents, multi-agent collaboration, workflow orchestration, and task automation.

Key benefits include:

• Integration with LangChain: Leveraging the LangChain ecosystem, LangGraph inherits connectors for APIs, vector stores, and LLM providers.

• Stateful nodes: Each node can maintain context and adjust its behavior based on historical inputs or external data.

• Extensibility: Developers can plug in custom functions, tools, and agents to meet unique project requirements.

Key Features of LangGraph

LangGraph provides a robust set of features that empower developers to build intelligent, agentic workflows. Let’s explore the standout capabilities:

Data-Aware

LangGraph nodes can adapt their behavior based on historical state or external data sources. This allows workflows to be dynamic rather than static, with agents making informed decisions at each step.

Example use case: A multi-step sales assistant agent that adjusts its recommendations based on previous customer interactions stored in a CRM.

Agentic

LangGraph natively supports autonomous agents and multi-agent collaboration. Nodes can pass messages, maintain persistent context, and execute complex sequences of tasks without requiring manual orchestration. This contrasts with basic LangChain workflows, where agents often need additional configuration to handle state or coordination.

Standardized Interfaces

Each node in LangGraph has a predictable interface and lifecycle, making orchestration and testing straightforward. Developers can confidently add, remove, or modify nodes without breaking the workflow.

External Integrations

LangGraph easily integrates with APIs, databases, and vector stores. Popular integrations include LangChain tools, Pinecone, Weaviate, and custom APIs, enabling developers to connect LLM workflows to real-world data and services seamlessly.

Prompt Management & Optimization

LangGraph supports prompt templating, versioning, and adjustment, allowing teams to manage LLM prompts effectively in production. This ensures consistency and reduces the risk of performance regressions when scaling applications.

Repository & Resource Collections

LangGraph provides utilities to organize workflows, tools, prompts, and agent templates across multiple projects. This facilitates reuse, reduces duplication, and accelerates development for teams working on multiple applications.

Visualization & Experimentation

Developers can visualize agent flows using graph-based UIs, making it easier to understand complex workflows. Interactive experimentation tools allow fine-tuning and testing of logic before deployment.

What is Haystack?

Haystack, developed by deepset, is an open-source framework for building LLM-powered search, RAG pipelines, and chat applications. Haystack 2.0 introduced significant improvements in modularity, scalability, and production readiness.

Unlike LangGraph, which focuses on agent orchestration, Haystack emphasizes document understanding and retrieval. It is widely used for question-answering systems, knowledge management, semantic search, and enterprise AI workflows.

Key advantages of Haystack include:

• Modular architecture: Easily replace or extend components.

• Pipeline flexibility: Build custom workflows with Python or YAML.

• Integration with multiple LLM providers: Supports HuggingFace, OpenAI, Cohere, and more.

Key Features of Haystack 2.0

Haystack 2.0 comes with new features that improve modularity, reproducibility, and enterprise readiness.

Support for Diverse Data Structures

Haystack can handle documents, PDFs, tables, images, and other formats. This makes it suitable for research-focused or enterprise applications where multiple data types must be ingested and queried.

Specialized Components

Haystack provides retrievers, readers, rankers, and generators as modular components. This allows experimentation with different approaches for search and RAG pipelines without rewriting the workflow.

Flexible Pipelines

Users can create custom pipelines using either YAML configuration or Python code. Each pipeline consists of nodes, allowing developers to tailor workflows for retrieval, generation, or hybrid tasks.

Integration with Multiple Model Providers

Haystack supports HuggingFace, OpenAI, Cohere, and other LLM providers, providing flexibility to choose models based on performance, licensing, or cost.

Data Reproducibility

Haystack includes versioning, audit trails, and reproducibility features in pipelines, which is critical for regulated industries, research, or production monitoring.

Collaborative Community & Improvement

Backed by deepset, Haystack has an active community, frequent updates, and transparent roadmaps. Developers benefit from community support and shared resources on GitHub and Slack.

LangGraph vs Haystack: Which One Should You Choose?

Here’s a direct comparison to help developers and teams make informed decisions:

Feature / Aspect	LangGraph	Haystack
Primary Purpose	Graph-based agentic workflows	RAG pipelines, document QA, search
Agent Support	Native multi-agent orchestration	Minimal, requires custom implementation
Workflow Modularity	High, node-based graph structure	High, modular pipelines with nodes
Data Awareness / State	Stateful nodes, dynamic behavior	Stateless pipelines; focus on retrieval
Integration Options	LangChain tools, APIs, vector stores	LLMs, vector stores, document sources
Learning Curve	Moderate to high	Moderate
Ideal Use Cases	Autonomous agents, complex workflows	Search, QA, knowledge retrieval, RAG

Recommendation:

• Choose LangGraph if you are building dynamic agentic workflows or multi-agent orchestration.

• Choose Haystack for complex search, retrieval pipelines, or document-based QA systems.

Conclusion

Both LangGraph and Haystack are powerful, open-source frameworks for AI development, but their strengths lie in different domains. LangGraph excels in building dynamic, agentic workflows with multi-agent orchestration, while Haystack is ideal for search, RAG pipelines, and document-driven AI applications.

Choosing the right tool depends on your project requirements: if you need autonomous agents and complex orchestration, go with LangGraph. If your priority is knowledge retrieval, document QA, or RAG pipelines, Haystack is the better option. Many teams may find value in combining both, using Haystack for retrieval and LangGraph for workflow orchestration.

For expert guidance on integrating LangGraph and Haystack into your AI stack and building efficient, scalable LLM applications, contact Leanware to discuss a tailored solution for your development needs.

FAQs

What is the difference between LangGraph and Haystack?

LangGraph focuses on agentic, graph-based workflows, while Haystack is a modular framework for search, RAG pipelines, and document QA. LangGraph handles complex agent orchestration; Haystack excels in retrieval and document processing.

Is LangGraph open source?

Yes, LangGraph is open source, built on top of LangChain, and available under an MIT license. See the GitHub repository.

Can I use LangGraph for retrieval-augmented generation (RAG)?

LangGraph can support RAG through integrations with vector stores like Pinecone or Weaviate. However, Haystack is better suited if RAG is your primary use case.

Which is better for building AI agents: LangGraph or Haystack?

LangGraph is better suited for AI agents due to native support for stateful nodes, message passing, and multi-agent orchestration.

Can LangGraph and Haystack be used together?

Yes. Developers can combine Haystack for retrieval and LangGraph for agent orchestration, creating hybrid workflows that leverage the strengths of both frameworks.