polymathy vs Haystack vs LangChain: Building RAG Pipelines in Rust

The question

Should I use polymathy, Haystack, LangChain, or LlamaIndex for my RAG pipeline?

RAG has a well-known zoo of Python frameworks. polymathy is the Rust alternative. This post is the comparison we wish we had when we started polymathy.

The 60-second version: Haystack, LangChain, and LlamaIndex are Python frameworks with rich ecosystems. polymathy is a Rust web service for the chunking + embedding + serving pipeline. They are not the same shape. polymathy is a service; the Python frameworks are libraries you call from your code.

What each option is

polymathy is an async Rust web service that transforms keyword search into semantic answer generation. It exposes a REST API: send it documents, get back embeddings; send it a query, get back an answer. The pipeline is async chunking + embedding + vector store + LLM synthesis.

Haystack is the deepset.ai Python framework for production RAG pipelines. Strong on document pipelines (PDFs, Office docs, web), retrieval (BM25, dense, hybrid), and answer synthesis. The de-facto choice for production-grade Python RAG.

LangChain is the most popular Python framework for LLM-powered applications, including RAG. Strong on chains, agents, and integrations. The de-facto choice for prototyping LLM apps.

LlamaIndex is the Python framework optimised for indexing and retrieval. Strong on data connectors, index structures, and query engines. The de-facto choice for data-heavy RAG.

The five dimensions

Dimension	polymathy	Haystack	LangChain	LlamaIndex
Language	Rust	Python	Python	Python
Shape	Web service	Library	Library	Library
Deployment	Single binary / container	Python service	Python service	Python service
Document loaders	Bring your own	Rich (PDF, Office, web, …)	Rich	Rich
Chunking	Async, configurable	Configurable	Configurable	Configurable
Embedding	Pluggable (any model)	Pluggable	Pluggable	Pluggable
Vector store	Pluggable (memista, Qdrant, etc.)	Pluggable	Pluggable	Pluggable
Retrieval	Hybrid (vector + keyword)	Rich (BM25, dense, hybrid)	Pluggable	Rich
Answer synthesis	LLM-based	LLM-based	LLM-based	LLM-based
Agents	No	Limited	Yes (rich)	Limited
Throughput	High (Rust async)	Medium	Medium	Medium
Memory footprint	Low (single binary)	Medium-High	Medium-High	Medium
Cold start	< 1s	~5-10s	~5-10s	~5-10s
License	GPL-3.0	Apache-2.0	MIT	MIT
Production users	(early)	Many	Many	Many

When to use which

Use polymathy when:

You want a service that exposes a REST API for chunking, embedding, retrieval, and answer synthesis.
You are deploying to a Rust-native stack (e.g. an existing Rust backend).
You care about throughput and want async Rust performance.
You want a single binary with no Python dependency (deployment to constrained environments, edge, etc.).

Use Haystack when:

You are building a production RAG pipeline in Python.
You need rich document loaders (PDFs, Office, web).
You need rich retrieval (BM25, dense, hybrid, re-ranking).
You have a Python team and the Python ecosystem is your default.

Use LangChain when:

You are building a general LLM application that includes RAG as one of many components.
You want agent capabilities (chains, tool use, memory).
You are prototyping and the rich integrations matter more than the runtime performance.

Use LlamaIndex when:

You are doing data-heavy RAG (many document loaders, complex index structures, query engines).
You want the index/query abstractions.

The polymathy design

A polymathy deployment is a Rust web service that:

Accepts documents via REST (POST /documents). You send the text; polymathy chunks, embeds, and stores.
Accepts queries via REST (POST /query). You send the query; polymathy retrieves the relevant chunks, synthesises an answer with an LLM, and returns the answer + citations.
Plugs into existing infrastructure: memista (embedded vector search) for the default backend, Qdrant or Pinecone if you need server scale.

The key design choice: polymathy is a service, not a library. You call it via HTTP. The Python frameworks are libraries you import into your application code. This makes polymathy the right choice for teams that prefer service architectures, and the wrong choice for teams that prefer library-style integration.

A concrete example: RAG over a corporate wiki

Imagine you have 50K pages of corporate wiki content, and you want to expose a Q&A interface.

With polymathy:

# Deploy polymathy
cargo install polymathy
polymathy serve --bind 0.0.0.0:8080 \
    --embedding-model all-MiniLM-L6-v2 \
    --llm openai/gpt-4o-mini

# Ingest
for page in wiki/*.md; do
    curl -X POST http://localhost:8080/documents \
        -H "Content-Type: application/json" \
        -d "{\"id\": \"$(basename $page)\", \"text\": \"$(cat $page)\"}"
done

# Query
curl -X POST http://localhost:8080/query \
    -H "Content-Type: application/json" \
    -d '{"query": "What is the policy on remote work?"}'

With Haystack:

from haystack import Pipeline
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.components.readers import ExtractiveReader

pipeline = Pipeline()
pipeline.add_component("retriever", InMemoryBM25Retriever(document_store=...))
pipeline.add_component("reader", ExtractiveReader(model="deepset/roberta-base-squad2"))
pipeline.connect("retriever.documents", "reader.documents")
result = pipeline.run({"retriever": {"query": "What is the policy on remote work?"}})

Both work. polymathy is a service; Haystack is a library in your application. The choice depends on your architecture preferences.

When polymathy is the WRONG answer

You need rich document loaders. polymathy is text-in, chunks-out. You bring your own PDF / Office / web parsers. Haystack and LangChain have these built in.
You need agent capabilities. polymathy is a Q&A service, not an agent framework. Use LangChain or LangGraph for agents.
You are prototyping. polymathy’s value is the service shape; if you don’t need that, the Python frameworks are faster to start with.
You are not in a Rust-native stack. If your team is Python-first, the operational story of a Rust service is a cost, not a benefit.