PE Collective
Best LLM Frameworks & Libraries (2026)
Five frameworks, five different bets on how LLM apps should be built. We tested them all.
Last updated: February 2026
Building an LLM application from scratch is a terrible idea. You'll spend weeks writing boilerplate for prompt templates, chain orchestration, retrieval, and memory management before you get to the part that actually matters. That's where frameworks come in.
The problem is there are too many of them now. LangChain was basically the only option in 2023. By 2026, you've got at least a dozen serious contenders, each with a different philosophy about how LLM apps should be structured. Some want to abstract everything away. Others give you building blocks and stay out of your way.
We built the same RAG application with all five of these frameworks: a document Q&A system over 10K pages of technical docs with citations, filtering, and streaming. The differences in developer experience were massive.
Our Top Picks
Detailed Reviews
LangChain
Best OverallLangChain has the largest ecosystem, the most integrations, and the biggest community of any LLM framework. Version 0.3+ cleaned up the messy abstractions that plagued earlier releases. LangChain Expression Language (LCEL) makes chain composition much more readable than the old sequential chain pattern. The integration list is staggering: 700+ components covering every vector store, LLM provider, and tool you can think of.
LlamaIndex
Best for RAGLlamaIndex is purpose-built for retrieval-augmented generation and it does that one thing better than anything else. The data connectors handle 160+ file formats out of the box, from PDFs to Notion pages to Slack threads. The indexing strategies (vector, keyword, tree, knowledge graph) give you options that LangChain's retrieval module can't match. If you're building a system that answers questions over your organization's documents, start here.
Haystack
Best Open SourceHaystack takes the most principled approach to framework design. Everything is a component with typed inputs and outputs. Pipelines are directed graphs you can visualize, debug, and test node by node. There's no magic. When something breaks, you know exactly where and why. The 2.0 rewrite threw away years of technical debt and the result is a framework that's a pleasure to work with.
Semantic Kernel
Best for .NETSemantic Kernel is Microsoft's answer to LangChain, and it's the only first-class option for .NET developers. It supports C#, Python, and Java, but the C# SDK is clearly the most polished. Azure OpenAI integration is native. The plugin architecture maps well to enterprise patterns that .NET developers already know. If your stack is Azure and C#, nothing else comes close to the developer experience here.
DSPy
Best for Prompt OptimizationDSPy takes a radically different approach. Instead of hand-writing prompts, you define what your pipeline should do and DSPy optimizes the prompts automatically. It treats prompt engineering as a machine learning problem: define your metric, provide examples, and let the optimizer find the best prompt configuration. For teams running prompt A/B tests manually, this is a revelation.
How We Tested
We implemented an identical RAG-based document Q&A application with each framework, measuring time-to-working-prototype, lines of code required, documentation quality, debugging experience, and production readiness. We also evaluated community activity (GitHub stars, npm/pip downloads, Discord/Slack responsiveness) and how well each framework handles model switching between OpenAI, Anthropic, and open-source models.
Frequently Asked Questions
Should I use LangChain or LlamaIndex for RAG?
LlamaIndex. It's purpose-built for retrieval and does it better. LangChain's retrieval module works fine for simple cases, but LlamaIndex's indexing strategies, data connectors, and query engine options are more sophisticated. Use LangChain when your application does RAG plus a lot of other things (agents, tool use, complex chains).
Can I switch frameworks later without rewriting everything?
Partially. Your LLM calls, vector store data, and embeddings are portable since they're just API calls and arrays. Your pipeline orchestration code is not portable. Moving from LangChain to Haystack means rewriting how your components connect, how data flows, and how you handle errors. Budget 2-4 weeks for a production migration. The earlier you choose, the less pain later.
Is DSPy ready for production use?
It depends on your team. DSPy is production-ready in the sense that it works and produces reliable outputs. But it requires ML engineering skills that most application developers don't have. If your team includes people comfortable with metrics, optimization, and evaluation datasets, DSPy can outperform hand-written prompts significantly. If you just want to ship features, stick with LangChain or LlamaIndex.
Do I even need a framework, or should I just call the API directly?
For simple applications (single LLM call, basic prompt template), call the API directly. Frameworks add overhead you don't need. Once you're doing retrieval, multi-step chains, tool use, or streaming with error handling, a framework saves you from writing thousands of lines of plumbing code. The breakpoint is usually around the second week of building, when you realize you're reimplementing LangChain badly.