haystack vs llama_index: explicit pipelines against a data framework with hundreds of integrations

An explicit pipeline against a data framework

haystack describes itself as an orchestration framework where you design modular pipelines and agent workflows with explicit control over retrieval, routing, memory and generation. The unit is the component and the connection between components, and the framework's own stated strength is transparency. Our analysis of haystack says to confirm that its pipeline model matches how you want to control context flow.

llama_index describes itself as a data framework. Its ingredients are data connectors for APIs, PDFs, documents and SQL, ways to structure that data as indices or graphs, a retrieval and query interface on top, and hooks into whatever application framework you already run. The README also promises a five-line path for a beginner and lower-level APIs for changing any module yourself.

Both end up with a retrieval pipeline in front of a model. The difference is where the abstraction sits. haystack's pipeline is the program. llama_index's index or query engine is a component you call from your own program. That one difference explains most of what follows, including who has to write the glue.

One aligned package against hundreds of separately versioned ones

haystack ships as haystack-ai, with a companion repository for integrations. The version moves as one number, and the README points at a single package on PyPI and on conda-forge, plus a nightly path with pip install --pre haystack-ai for anyone who wants the newest features before a release. Docker images are listed as another installation route.

llama_index deliberately splits. The README gives two starting points: the starter package, which includes core plus a selection of integrations, and the customized path, where you install llama-index-core and add only the packages you need from LlamaHub. Our analysis of llama_index counts over 300 such packages. The import convention encodes the split, since a path containing core means the core package and one without it means an integration, as in from llama_index.core.llms import LLM next to from llama_index.llms.openai import OpenAI.

That breadth is the point and the cost. The same analysis notes that core is held below 0.15.0 while the OpenAI integrations sit on 0.6.x and 0.7.x, so version lines do not move in step and you have to check that the integration you picked is compatible with the core you pinned.

Getting one of them installed

haystack's install is one line, pip install haystack-ai, and the documentation also covers Docker images if you would rather not manage a Python environment yourself. No provider key is needed at install time. You bring your own model, retriever and storage when you assemble the pipeline, and the integrations repository is where the connectors for those live.

llama_index has two shapes, and the choice is worth making deliberately. The starter package, llama-index, is the convenience route, and our analysis of llama_index warns that it costs you an OpenAI dependency you may not want, because it ships a selection of integrations rather than just the core. The customized route installs llama-index-core and adds each integration package by hand, which keeps the dependency surface down to what your application actually calls.

The day-one difference is the dependency tree. With haystack you decide what the pipeline needs and install accordingly. With llama_index you either accept a starter tree full of providers you will never call, or you spend the first hour of the project working out the exact list of packages, a chore that pays off only if you will use several of them.

The control points: hooks and tracking against workflows and a parsing platform

haystack's agent story is about inspection. The README lists lifecycle hooks named before_llm, before_tool and on_exit for guardrails and custom logic, and says that step_count, token_usage and tool calls are tracked out of the box for monitoring and cost control. That combination is what makes a pipeline reviewable: you can put a rule in front of a tool call, and you can ask what an agent did without instrumenting it yourself. Ready-made agents come from the Agent Pack integration, including a deep research agent and an advanced RAG agent, and SkillTool gives agents progressive skill discovery.

llama_index answers with a platform rather than hooks. LlamaParse covers agentic OCR and document parsing across more than 130 formats, plus Extract for structured extraction, Index for ingest and RAG pipelines, Split for breaking large documents apart, and Agents built from Workflows and Agent Builder. The framework can use LlamaParse or run without it.

haystack gives you hooks inside a pipeline you wrote. llama_index gives you a document pipeline you did not write, hosted, plus a framework you can use without it. If your hardest problem is turning a scanned PDF into retrievable chunks, that difference decides the project.

Which direction each project is moving

haystack's README leads with the announcement of version 3.0, and its table of contents lists a Telemetry section and a Haystack Enterprise section for support and platform. The release line moved to v3.1.0 on 2026-08-24, and the last push to the repository was on 2026-10-19, so the open-source side is where the visible work sits.

llama_index is heading a different way, and its maintainers say so. Our analysis of llama_index reports that the company's primary focus has moved to document parsing and extraction, that it is building LlamaParse, LiteParse and open parsing benchmarks, and that the open toolkit remains available rather than leading the work. The repository was pushed on 2026-10-14 and sits at version 0.14.25, with v0.14.24 published on 2026-08-19.

That is a statement about priorities rather than abandonment. Both repositories are being worked on. But if you are choosing a foundation to build on for two years, one of these is a framework whose sponsor is building a business in an adjacent layer, and the other is a framework whose commercial product sits outside the framework. The llama_index README also notes that its own documentation is updated more often than the README, which tells you where to look for current answers.

Where each one falls short

haystack's weaknesses are the price of its explicitness. Our analysis of haystack says to avoid it if you want a fully managed platform, or if your team prefers a configuration-driven approach over writing Python code, and to check before committing that the components you need are in the core package or the integrations repository rather than somewhere you would have to write. An explicit pipeline is also an explicit liability: when retrieval quality drops, you own the diagnosis.

llama_index has rough edges of its own, and the analysis is specific. The default make test target only runs what changed since origin/main, so a green build says less than it appears to, and three other test targets point at directories the repository no longer contains. The version lines of core and of individual integrations also do not move together, so a resolution that looks clean can still hand you an integration built against a different core.

Apache-2.0 against MIT, and the choice by scenario

The licences are the easy part of this comparison. haystack is Apache-2.0 and llama_index is MIT, and both permit commercial use, modification and redistribution with no copyleft obligation to publish your changes. The difference that occasionally matters is that Apache-2.0 carries an express patent grant and a patent-retaliation clause while MIT does not, which matters in a procurement conversation and rarely anywhere else.

Beyond that, pick by what the problem actually is. A team that owns its retrieval logic, wants every step visible in Python, and plans to build guardrails and cost tracking into the pipeline belongs with haystack, where those hooks are the framework's stated feature. A team whose hard part is document ingestion, or who wants the largest catalogue of providers already packaged, belongs with llama_index, where the connectors exist and the parsing platform does the tedious work.

Mixed teams should decide which half they want to own: the pipeline logic, or the breadth of integrations. Wiring one framework's components into the other's pipeline is possible, but that join is code you own and maintain, not something either project hands you.

Bottom line

Choose haystack when the retrieval path itself is the thing you need to control, and llama_index when ingestion breadth or a hosted parser is. Before committing, check that the components or integration packages you need exist for the version you plan to pin, since llama_index in particular splits core and integrations across independent release lines.

Sources

1. deepset-ai/haystack repository
2. deepset-ai/haystack README
3. run-llama/llama_index repository
4. run-llama/llama_index README