Pick LlamaIndex when the hard part of your project is the documents themselves: scanned PDFs, tables, filings and contracts that must be parsed, indexed and queried accurately, ideally with a managed parsing service on hand. Pick Haystack when the hard part is running retrieval and agents as reviewable production software, with typed components, pipelines you can serialize to YAML, and a single vendor-neutral Python framework that platform teams can govern. Both are open source, both handle RAG and agents in late 2026, and either is a reasonable default if your team already knows it.
LlamaIndex began as a way to connect LLMs to private data, and that heritage still shapes it. Its abstractions are documents, nodes, indexes, retrievers and query engines, and much of the company's commercial effort goes into parsing hard files. The LlamaIndex repository splits the code into llama-index-core and more than 300 integration packages, so a production install pulls only the connectors you use.
Haystack comes from deepset, which built it for search and question answering in enterprises before LLMs went mainstream. The design favors explicitness: each component declares typed inputs and outputs, you connect them into a pipeline graph, and the whole graph can be serialized, versioned and loaded elsewhere. The haystack-ai package page describes it as an open-source orchestration framework for modular pipelines and agent workflows with control over retrieval, routing, memory and generation.
The orchestration model is the biggest day-to-day difference.
LlamaIndex Workflows are event-driven. Each method decorated with @step receives an event and returns another; the framework infers inputs and outputs from type hints and validates the graph before it runs. Branching is an ordinary if, a loop is a step that emits an event an earlier step handles, and per-run state lives in ctx.store. You can stream intermediate events from the handler while a run is in progress. Workflows also install alone as llama-index-workflows, which is handy if you want the orchestration without the retrieval stack.
Haystack Pipelines are graph-first. You add components, connect output sockets to input sockets, and the pipeline checks types at connect time. Loops and conditional routing are supported, and since 3.0 a single Pipeline class runs synchronously or with run_async, replacing the separate AsyncPipeline. Because a pipeline is data, a reviewer can diff its YAML in a pull request and a platform team can promote the same definition from staging to production.
In practice: Workflows feel like writing Python with events, which suits engineers who want flexible control flow. Pipelines feel like assembling a reviewed diagram, which suits organizations that want every production flow to be inspectable without reading code.
Both projects moved hard toward agents this year. Haystack 3.0 rebuilt its Agent: hooks fire at stages such as before the LLM call and before a tool runs, which is where you put guardrails and human approval checks; the agent now executes tools itself (the standalone ToolInvoker was removed); runs expose step counts, token usage and tool call counts; and a SkillToolset lets the model discover skills gradually to keep context lean. Releases since then added context compaction helpers, including an experimental SummarizationCompactor in 3.2.0 and a ToolResultOffloadCompactor in 3.4.0, per the Haystack releases page.
LlamaIndex builds its agents on Workflows, so an agent is a workflow whose steps call the model and tools, and you can drop down to custom steps whenever the stock loop does not fit. Its strength remains agents over documents: research, extraction and question answering across large file sets. If your agents mostly take actions in other systems and need long-running checkpointed state, also look at the options in LangGraph vs CrewAI.
Framework overhead is rarely what makes a RAG system slow. Latency comes from the model calls, the number of retrieval round trips, reranking and how much context each call carries. Both frameworks support async execution and streaming, so the practical performance questions are design questions: how many steps an agent may take, whether retrieval is cached, and whether simple steps use smaller models.
Retrieval quality matters more than raw speed, and that is decided by parsing, chunking, hybrid search, reranking and evaluation. LlamaIndex has the deeper built-in toolbox for chunking and hierarchical retrieval. Haystack has solid retrievers, rankers and evaluation components, and its explicit graph makes it easy to swap one retriever for another and compare. Whichever you pick, build an evaluation set from real user questions before tuning anything.
Both libraries are free. Your real costs are model tokens, the vector store and engineering time. The commercial layers differ:
The budget leak to watch is the same in both: agent loops that make many more model calls than expected. Cap steps, log token usage per run, and alert on outliers. Haystack's built-in run introspection makes the token count easy to collect.
Neither framework appears in the 2025 Stack Overflow Developer Survey technology lists, so there is no clean adoption number to compare. LlamaIndex has the larger share of tutorials and community examples for document RAG, so more engineers have touched it. Fewer have run Haystack in production, though engineers from search backgrounds pick it up quickly because its pipeline model resembles classic search systems.
Hire for the underlying skills rather than the framework name: retrieval evaluation, chunking strategy, hybrid search, prompt and tool design, async Python and production observability. A strong engineer moves between these frameworks in days. See our LlamaIndex developers and LLM engineers pages, and check pay ranges on AI engineer rates.
Ryz Labs engineers build retrieval and agent systems on both frameworks. Only the top 1% of the tens of thousands of engineers we interview make it, they work on your team on US business hours, and most of them ship code in their first week. You can hire RAG engineers one at a time, or bring in AI pod teams of senior engineers who build the AI system in your cloud and repos and take it to production. Our RAG development services page describes typical scope.
Typical cost is $7,000 to $15,000 per engineer per month: mid-level engineers run $7,000 to $10,000, seniors $10,000 to $15,000, and leads are quoted per team. If you only need a hosted parsing tool your analysts configure themselves, buy the vendor platform instead.
LlamaIndex is usually stronger when documents are hard to parse and you need advanced indexing and retrieval patterns. Haystack is usually stronger when you want explicit, serializable pipelines that are easy to review, test and deploy. Retrieval quality depends more on parsing, chunking and evaluation than on the framework.
Haystack 3.0 rebuilt the Agent with hooks, run introspection and dynamic tools, merged AsyncPipeline into Pipeline, removed legacy generators, moved about 30 components into separate integration packages, restricted pipeline deserialization to trusted modules and changed Document ID hashing.
Yes. LlamaIndex is MIT licensed and Haystack is Apache 2.0. LlamaIndex sells credit-based parsing and extraction plans starting at $50 a month for Starter, and deepset sells Haystack Enterprise support and a managed or self-hosted platform; ask deepset for pricing.
You can, for example parsing with LlamaParse and running retrieval in a Haystack pipeline, but two frameworks double the upgrade work. Most teams pick one orchestration framework and call other services through plain APIs.
If chunks and embeddings live in a standard vector store, switching is mostly rewriting orchestration code and prompts, then proving parity with an evaluation set. Project cost is team size times duration times monthly rate, so size the number of flows first.
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