LLM framework comparison 2026 — LangChain vs LlamaIndex vs OpenAI tools vs DSPy vs Pydantic AI vs Vercel AI SDK vs Anthropic SDK

We ship integration guides for seven LLM frameworks at SourceScore VERITAS: LangChain, LlamaIndex, OpenAI tools, DSPy, Pydantic AI, Vercel AI SDK, and Anthropic SDK. Picking between them is the most-asked question we get from new users. This post is the honest answer.

The seven frameworks at a glance

Pick by archetype

You're building a RAG pipeline over a custom corpus

Start with LlamaIndex. It was built for this shape — your corpus, your embeddings, your retrieval. The mental model is'document → node → index → retriever → query engine' and it stays consistent. Cost: you'll buy into LlamaIndex's opinions about chunking + retrieval + response synthesis.

If LlamaIndex's abstractions feel heavy for your use case, drop down to OpenAI tools (or Anthropic SDK if you're on Claude) and roll your own retrieval. Often the right call for small corpora (<10k documents) where you've already invested in embeddings.

You're building a multi-step agent that calls tools

Start with the vendor SDK if you're committed to one model family — OpenAI tools if GPT, Anthropic SDK if Claude. Native function-calling is the cleanest shape; you skip the orchestration overhead entirely.

If you need vendor-portability or type-safety: Pydantic AI wraps tool calls in typed Pydantic models. Same agent code, swap the provider with a single import line. The type-safety is real (validators catch the model's hallucinated arguments before they hit your function).

You're building a Next.js app with streaming UI

Vercel AI SDK — full stop. It's designed for the edge-streaming UI pattern (chat interfaces, streaming responses) on Next.js, and it includes React hooks that make streaming UI a one-liner. Going outside it costs you the streaming ergonomics.

You're doing research / want optimizers / care about evals

DSPy (Stanford). The paradigm is different — you write programs composed of modules + signatures, and a separate compile step optimizes the prompts + few-shot examples for you against your eval set. The learning curve is steep (you're writing programs, not prompts), but for research + evaluation-driven development it's the only framework that takes evals seriously as a first-class concept.

You're building a complex pipeline with many components

LangChain remains the breadth winner. Hundreds of integrations, every conceivable retrieval + generation + tool primitive, plus LangSmith for observability. The trade-off: you buy into a lot of abstractions, and breaking changes have been common historically.

The honest gotchas

LangChain

• Breadth comes at the cost of stability. APIs have changed multiple times.
• Documentation is mixed — some excellent, some out-of-date.
• Strong if you treat it as a toolkit (pick the pieces you need); weak if you adopt the full stack uncritically.

LlamaIndex

• If your problem isn't RAG-shaped, you're fighting the framework.
• The "ServiceContext" pattern was rewritten in 2024; many tutorials online are stale.

OpenAI tools

• Locks you into OpenAI. Switching vendors means rewriting the tool layer.
• No built-in orchestration — you write the agent loop yourself (which is often what you want).

DSPy

• Steep learning curve. Treat as a different language, not a library.
• Compile times can be long (optimization is real work).
• Strongest fit for research + evaluation-driven projects, not quick prototypes.

Pydantic AI

• Python-only as of 2026.
• Newer (less battle-tested than alternatives).
• Strongest if you already use Pydantic elsewhere in your codebase.

Vercel AI SDK

• Optimized for Next.js + edge — outside that, you're paying for unused ceremony.
• Heavy reliance on Vercel ecosystem (fine if you're already there).

Anthropic SDK

• Locks you into Claude.
• Minimalist by design — no orchestration, no built-in observability. You build that yourself.

Our recommendation by archetype

If you're starting from scratch and just want the path of least resistance:

• RAG over your docs → LlamaIndex
• Single-vendor agent (GPT) → OpenAI tools directly
• Single-vendor agent (Claude) → Anthropic SDK directly
• Multi-vendor typed agent → Pydantic AI
• Next.js chat app → Vercel AI SDK
• Research + evals → DSPy
• Complex multi-step pipeline → LangChain

How VERITAS fits in any of these

SourceScore VERITAS is the verification layer. Any of these frameworks can call our /api/v1/verify endpoint to check whether the LLM's output asserts a fact we've hand-verified. Free tier is 1,000 verifies/month, no signup.

Each of the seven integration guides above shows the canonical pattern in that framework — typed tool, retrieval-then-cite, generate-then-verify, etc. Pick the framework that fits your archetype; we work with all of them.

Two predictions for late 2026 / 2027

1. The framework count will drop. Anthropic's Model Context Protocol (released 2024-11) is the cross-vendor standard that could absorb a lot of the per-vendor SDKs. Watch which frameworks adopt MCP as a first-class concept.
2. Type-safety wins. Pydantic AI's premise — typed inputs and outputs catch errors statically — is reasonable. The untyped variants will either add types (LangChain has been trying) or get eaten by Pydantic AI + similar.

Resources

• All 7 integration guides — drop-in patterns for each framework
• RAG vs VERITAS — when each pattern applies
• Playground — try /verify before wiring it in
• Quickstart — first call in 5 minutes