LangGraph vs Haystack

LLangGraph9.2Visit Haystack8.9Visit

Higher score L LangGraph #2 in LLM Application Development Frameworks 9.2/10 Free plan Free plan✓ 0 of 5 features Visit LangGraph

vs

Haystack #5 in LLM Application Development Frameworks 8.9/10 Open source ✓ 0 of 5 features Visit Haystack

• LangGraph fits best

  Developers building durable, stateful agent workflows

• Haystack fits best

  Teams building composable RAG and retrieval pipelines

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Side by side

Feature	LLangGraph 9.2/10 Visit ↗	Haystack 8.9/10 Visit ↗
At a glance
Editor score	9.2	8.9
Ranking	#2 in LLM Application Development Frameworks	#5 in LLM Application Development Frameworks
Best for	Developers building durable, stateful agent workflows	Teams building composable RAG and retrieval pipelines
Pricing model	Free	Free
Starting price	Not published	Not published
Free plan	✓ (best)	Not published
Free trial	—	—
Deployment	Self-hosted, Cloud	Self-hosted
Integrations	2 integrations	183+ integrations
Built for	Solo, Small business, Mid-market, Enterprise	Solo, Small business, Mid-market, Enterprise
Features LangGraph 0/5 · Haystack 0/5
RAG support	Not published	Not published
Agent and tool use	Not published	Not published
Workflow graphs	Not published	Not published
Prompt optimization	Not published	Not published
JavaScript/TypeScript SDK	Not published	Not published
Specs
Primary language	Not published	Not published
Open-source license	Not published	Not published
Our review
Pros	Combines deterministic code and LLM-driven steps in graph workflows Supports durable execution, checkpointing, and resumption after failures Offers memory, streaming, and human review of agent state	Combines keyword and embedding search in composable pipelines Supports document reranking and answers with referenced documents Connects to model providers, vector databases, and monitoring tools
Cons	Low-level control requires developers to define workflow behavior Does not prescribe a high-level agent architecture Verified integrations are narrower than LangChain’s	Requires developers to compose and operate their own workflows Its feature set is more retrieval-centered than agent-orchestration leaders Community and documentation are the listed support channels
Our verdict	LangGraph is an open-source framework and runtime for developers building long-running, stateful agents and workflows. Its graph model combines deterministic code with model-driven steps, and supports single-agent, multi-agent, and… Read the review →	Haystack is an open-source Python framework for developers building LLM applications, RAG systems, AI agents, multimodal applications, and semantic search pipelines. Its composable components let teams shape retrieval, ranking, generation,… Read the review →

Strengths and trade-offs

• LangGraph — where it wins

  • Combines deterministic code and LLM-driven steps in graph workflows
  • Supports durable execution, checkpointing, and resumption after failures
  • Offers memory, streaming, and human review of agent state

  Where it doesn't

  • Low-level control requires developers to define workflow behavior
  • Does not prescribe a high-level agent architecture
  • Verified integrations are narrower than LangChain’s

• Haystack — where it wins

  • Combines keyword and embedding search in composable pipelines
  • Supports document reranking and answers with referenced documents
  • Connects to model providers, vector databases, and monitoring tools

  Where it doesn't

  • Requires developers to compose and operate their own workflows
  • Its feature set is more retrieval-centered than agent-orchestration leaders
  • Community and documentation are the listed support channels

• LLangGraph9.2/10 · Free plan

  Graph-based control for developers who need durable, stateful agent workflows.

  Visit LangGraphFull verdict →
• Haystack8.9/10 · Open source

  A flexible Python framework for retrieval-centered LLM apps, with hybrid search and cited answers.

  Visit HaystackFull verdict →