We compare the top open-source RAG frameworks — LangGraph, LlamaIndex, LangChain, Haystack, and DSPy — across orchestration style, token efficiency, and production readiness. Plus the infrastructure you need to run them.
Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding LLM outputs in real data. But the framework you choose determines how fast you ship, how many tokens you burn on orchestration overhead, and whether your system degrades gracefully under load.
Here's our breakdown of the five most important open-source RAG frameworks — and the infrastructure layer that powers them.
LangGraph, from the LangChain team, treats RAG pipelines as directed cyclic graphs. This matters when your agent needs to loop back, re-retrieve, or maintain state across multiple turns. It's the strongest choice for multi-step reasoning, tool-use loops, and any workflow where a simple linear chain won't cut it.1
The tradeoff: you're writing graph definitions, not scripts. The learning curve is real, but the payoff is deterministic control over branching logic.
LlamaIndex takes the opposite approach: imperative Python, readable top-to-bottom. It's built around data connectors and index structures — you load documents, build an index, and query it. The framework handles chunking, embedding, and retrieval under the hood while keeping your code explicit.1
If your project is "I have a bunch of PDFs and I want to ask questions about them," LlamaIndex is the fastest path from zero to working.
LangChain remains the most popular framework by ecosystem size. It ships with integrations for hundreds of LLM providers, vector stores, document loaders, and output parsers. For prototyping, nothing beats it — you can wire up a RAG pipeline in a dozen lines of code.1
The downside: the abstraction stack is deep. Debugging can feel like peeling an onion. LangChain is best for exploration; for production, teams often graduate to LangGraph or Haystack.
Haystack (by deepset) enforces strict I/O contracts between pipeline components. Every node has typed inputs and outputs, which makes testing, caching, and swapping components straightforward. It's the most "software engineering" of the frameworks — you build pipelines like you build APIs.1
Haystack also ships with a built-in REST API layer and OpenAPI schema generation, making it the strongest choice if you're deploying RAG as a microservice.
DSPy flips the RAG framework model on its head. Instead of writing prompts, you define signatures — typed input/output schemas — and DSPy optimizes the prompt, the few-shot examples, and even the retrieval strategy automatically.1
It's the most minimal framework on this list. No chains, no graphs, no pipelines. Just signatures and a compiler. If you're tired of prompt engineering, DSPy is worth a serious look.
The five frameworks represent three fundamentally different programming models:
| Model | Frameworks | Best when… |
|---|---|---|
| Declarative graphs | LangGraph, Haystack | You need deterministic, testable pipelines with branching |
| Imperative scripts | LangChain, LlamaIndex | You want readable, debuggable code that's easy to iterate on |
| Signature-based | DSPy | You want to optimize prompts programmatically |
There's no single "best" — it depends on whether you value debuggability, expressiveness, or automation.1
A 2025 benchmark of these five frameworks found that orchestration overhead is measurable but small — typically 5–15% of total token consumption. The dominant cost driver remains the LLM calls themselves, not the framework wrapping them.1
That said, DSPy's automatic optimization can reduce total prompt tokens by 20–40% on some tasks by trimming few-shot examples and rephrasing instructions. LangGraph's cyclic execution can increase token usage if loops run longer than expected. Choose accordingly.
All five frameworks need an LLM backend. LibertAI provides a decentralized inference API compatible with OpenAI and Anthropic's APIs, meaning you can swap it in as your provider without changing a line of framework code. It's a practical way to avoid vendor lock-in while keeping the same developer experience.2
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All five frameworks are open-source and actively maintained. The right choice is the one that matches how you think about your pipeline — as a graph, a script, or a signature.
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