LangChain vs Haystack: Which Framework Should You Choose?

LLM application development frameworks help you build production systems without writing orchestration logic from scratch. LangChain and Haystack both provide components for chaining model calls, managing retrieval, and integrating external systems, but they evolved from different origins and priorities.

LangChain started as a framework for rapid prototyping with modular components. Haystack came from Deepset - focused on production RAG and document search pipelines. 

Both are open source and handle similar problems. So, your choice depends on whether you prioritize flexibility for experimentation or opinionated structure for production deployment.

What is LangChain?

LangChain is a framework for building agents and LLM-powered applications using modular, interoperable components. It provides a consistent interface for models, embeddings, vector stores, and tools, allowing you to chain them together while maintaining flexibility as your project evolves.

The framework is designed around a few key ideas:

• Swap models in and out without rebuilding workflows.

• Use high-level chains for prototypes or low-level components for fine control.

• Connect LLMs to real-time data sources.

• Access integrations for models, retrievers, vector stores, and external systems.

LangChain lets you build applications that adapt as models and infrastructure evolve, making it a strong choice for rapid prototyping and complex agent workflows.

Key Features

LangChain provides core capabilities that include:

1. Modular components: Reusable building blocks for common tasks like retrieval, generation, and tool use. You combine components without writing orchestration logic from scratch.

2. Integration library: Connections to OpenAI, Anthropic, Cohere, Hugging Face, and other providers. Vector store integrations include Pinecone, Weaviate, Chroma, and FAISS.

3. Production features: Built-in support for monitoring, evaluation, and debugging through LangSmith integration. Battle-tested patterns help you deploy reliable applications.

4. Flexible abstraction: Work at the complexity level your application needs. Start with high-level chains and move to custom components as requirements evolve.

The ecosystem includes LangGraph for building controllable agent workflows with customization, long-term memory, and human-in-the-loop patterns. LangSmith provides observability and evaluation. Companies like LinkedIn, Uber, Klarna, and GitLab use LangGraph in production.

Strengths & Challenges

LangChain is strong for experimentation and rapid prototyping, thanks to its flexible architecture and active community. At the same time, this flexibility can create complexity in production, requiring careful debugging and monitoring.

Strengths	Challenges
Easy to swap models and integrations	Debugging can be tricky with layered abstractions
Rapid prototyping with composable components	API changes can break existing code
Active community and growing library	Can over-engineer simple workflows
Supports experimentation	Requires extra monitoring for production

What is Haystack?

Haystack is an end-to-end LLM framework for building applications powered by LLMs, transformer models, and vector search. deepset built it to orchestrate state-of-the-art embedding models and LLMs into pipelines for NLP applications. The framework emphasizes production-ready architecture from the start.

Haystack originated focusing on retrieval-augmented generation (RAG), document search, and question answering. The pipeline architecture provides explicit connections between components, making data flow transparent and testable.

The framework aims to be technology agnostic, letting you choose vendors and swap components easily. It supports models from OpenAI, Cohere, Hugging Face, plus local models or those hosted on Azure, Bedrock, and SageMaker.

Key Features

Haystack organizes functionality around pipelines and components:

1. Pipeline architecture: Directed graphs where components process data sequentially or in parallel. Pipelines support branching and merging for complex workflows. You define structure through YAML or Python.

2. Component system: Modular units with typed inputs and outputs. Components handle document conversion, cleaning, splitting, embedding, retrieval, and generation. The explicit typing makes composition predictable.

3. Database integrations: Native support for vector databases including Pinecone, Weaviate, Qdrant, and traditional databases like Elasticsearch and OpenSearch.

4. Production tooling: Built-in error handling, logging, metrics collection, and monitoring. Docker containerization and Kubernetes deployment follow documented patterns.

5. Extensibility: Uniform interface for building custom components. Third parties contribute integrations that follow consistent patterns, fostering an open ecosystem.

Haystack includes evaluation and benchmarking tools for continuous improvement using user feedback. The framework handles millions of documents through optimized retrievers and production-scale components.

Strengths & Challenges

Haystack focuses on production-ready pipelines and operational reliability. Its built-in logging, error handling, and monitoring make deployments more predictable, while the explicit component connections help with debugging. 

At the same time, the framework is more opinionated, which can limit flexibility for unconventional workflows and comes with a steeper learning curve.

Strengths	Challenges
Built-in logging and monitoring	Opinionated structure limits flexibility
Pipeline architecture aids debugging	Steeper learning curve
Supports multiple models	Smaller community
High-quality documentation	Less suited for rapid prototyping
Proven in production at major companies	Requires understanding pipelines before use

Side-by-Side Comparison

Architecture & Workflow

LangChain organizes applications around chains and components. You connect modules to pass data forward, using high-level chains for quick setups or low-level components for custom logic. 

This approach is flexible and supports rapid experimentation, but debugging can be tricky because behavior is often hidden behind abstractions. Testing usually involves running full workflows rather than isolated components.

Haystack, in contrast, structures applications as explicit pipelines. Each component declares inputs and outputs, making validation and debugging straightforward. 

Pipelines support branching, merging, and parallel execution, and you can configure them through Python or YAML. The transparent data flow helps spot bottlenecks and modify individual components without affecting others.

Primary Use Cases

LangChain fits rapid prototyping and experimentation. Its library of integrations and chains makes testing new workflows fast, and teams can swap models like OpenAI, Anthropic, or local providers without rewriting logic. Agent workflows with tool use and decision-making also fit well. Some production systems at LinkedIn and Uber use LangGraph for agents with memory and custom behavior.

Haystack targets production RAG applications where reliability and scale matter. It suits search-heavy systems, question answering, and document processing. Its pipeline architecture handles preprocessing, embeddings, and retrieval optimization. Enterprises often deploy it on-premises or in cloud environments with strict SLAs and operational requirements.

Community & Ecosystem

LangChain has a large, active community. There are hundreds of integrations and community-contributed templates, plus forums, tutorials, and blog posts. LangSmith adds observability and debugging for production workflows.

Haystack has a smaller but focused community. Documentation is thorough, with consistent examples and production guides. Companies like Apple, Meta, NVIDIA, and Airbus use it in production.

Production Readiness

LangChain can run in production but often needs extra tooling for monitoring, logging, and error handling. LangGraph adds features like checkpointing and human-in-the-loop workflows for agents, but teams must integrate their own infrastructure for full observability.

Haystack is designed for production from the ground up. Pipelines include built-in logging, error handling, and metrics. 

It supports Docker and Kubernetes, handles batching and connection pooling, and integrates with standard monitoring tools. Enterprises use it to deploy search, QA, and document processing systems at scale.

When to Choose LangChain

LangChain works best for rapid prototyping and experimentation. Its component library and model interoperability make it easy to test ideas quickly, handle complex agent workflows, and adapt to evolving requirements.

You get:

• Rapid prototyping and experimentation.

• Easy model swapping without rewrites.

• Support for custom agent workflows.

• Flexible architecture for evolving projects.

When to Choose Haystack

Haystack suits production RAG applications where reliability and scale matter. Built-in logging, monitoring, and typed pipelines make debugging and operational maintenance straightforward.

It offers:

• Production-ready pipelines with error handling.

• Optimized for search, QA, and document retrieval.

• Scales efficiently to millions of documents.

• Clear operational patterns and maintainable code.

Your Next Move

LangChain and Haystack take different approaches. LangChain is best for rapid prototyping and flexible workflows, while Haystack is built for production reliability and clear pipelines.

Building a small prototype in each can help determine which fits your team and project needs.

• LangChain: fast iteration and flexible integration.

• Haystack: structured, production-ready pipelines.

• LangGraph: handles complex agent workflows.

You can also connect with us to consult on your LLM application strategy, explore framework choices, or get guidance on prototyping and production deployments.

Frequently Asked Questions

Why are developers quitting LangChain?

Developers are leaving LangChain due to its instability, frequent and breaking updates, and overly complex structure, which can lead to higher costs and slower performance. The framework's high level of abstraction hinders maintainability, debugging, and control in production environments, leading them to prefer simpler alternatives like custom-built solutions, direct API calls, or other frameworks like LangGraph or AutoGen.

Is there anything better than LangChain?

"Better" depends on your requirements. For production RAG applications, Haystack provides more built-in operational features. For agent orchestration, LangGraph (also from LangChain) offers production-ready capabilities, with companies like LinkedIn and Uber using it at scale. For simple applications, using LLM provider SDKs directly reduces complexity.

LangChain works well for prototyping, model experimentation, and rapid development. Evaluate frameworks based on your specific use case, team capabilities, and production requirements rather than general rankings.

Is Haystack production-ready?

Yes. Haystack includes error handling, logging, monitoring, and deployment features designed for production from the start. Companies like Apple, Meta, NVIDIA, Airbus, Netflix, and Intel use Haystack in production for search systems, question answering, and document processing. The framework supports Docker containerization and Kubernetes orchestration. deepset offers Haystack Enterprise with expert support, enterprise-grade templates, SLAs, and deployment guides for cloud and on-premises environments. The pipeline architecture enables systematic testing and validation that production systems require.

Does ChatGPT use LangChain?

No. ChatGPT is built by OpenAI using their internal infrastructure. LangChain is a third-party framework for building applications that call OpenAI's APIs, including ChatGPT. You use LangChain to create custom applications incorporating ChatGPT or other LLMs as components. 

LangChain provides abstractions for chaining model calls, adding retrieval, managing context, and building agents. It's a development framework for applications using LLMs, not the infrastructure behind ChatGPT itself.