When should you use LangChain, and when should you skip it?

Use LangChain when: tool use and state, in an ecosystem where the answers already exist. Skip it when: you want a thin, legible core — this is a large surface.

When should you use LlamaIndex, and when should you skip it?

Use LlamaIndex when: data-centric work where retrieval is the centre of the problem. Skip it when: you need agent orchestration more than data access.

When should you use Pydantic AI, and when should you skip it?

Use Pydantic AI when: type-safe agents where schema correctness is the priority. Skip it when: you want a batteries-included ecosystem.

When should you use OpenAI Agents SDK, and when should you skip it?

Use OpenAI Agents SDK when: a small, legible set of primitives for handoffs and guardrails. Skip it when: you need multi-provider abstraction at every layer.

← Comparisons

LangChain vs LlamaIndex vs Pydantic AI vs OpenAI Agents SDK

These four are the frameworks teams actually shortlist once they move past a script in a notebook. The split is not Python versus TypeScript — it is whether retrieval, types or a thin loop is the centre of the problem.

Short answer

Choose on the shape of the job. LlamaIndex when retrieval and connectors are the product; Pydantic AI when schema-safe tool calls are the risk; OpenAI Agents SDK when you want a small loop with handoffs and guardrails built in; LangChain when you need the widest integration surface and will pay the abstraction tax.

By Kunj Shah · Licence and cost facts verified 2026-09 · method

Agent Frameworks

LangChainWidest integrations, largest surface.
LlamaIndexRetrieval and indexing as the main event.
Pydantic AIType-safe tools and structured agent outputs.
OpenAI Agents SDKSmall primitives: handoffs, guardrails, tracing.

LangChain vs LlamaIndex vs Pydantic AI vs OpenAI Agents SDK compared across 6 dimensions
Dimension	LangChain	LlamaIndex	Pydantic AI	OpenAI Agents SDK
Best for	Breadth — many providers, tools and patterns already wired	RAG-heavy apps where data connectors matter	Agents where bad tool args are the main failure mode	A legible loop without adopting a whole platform
Retrieval built in	Via integrations, not the core abstraction	First-class — indexes, parsers and query engines	Via your own retrieval layer	Bring your own context
Type safety	Optional, varies by module	Moderate — Python-first data models	Strong — Pydantic models end to end	Moderate — typed runners and tools
Multi-agent patterns	Graphs, crews and handoffs via ecosystem	Workflows and agents over indexes	Delegation between typed agents	Handoffs as a first-class primitive
Operational surface	Large — many moving parts to learn	Medium — data stack plus agent layer	Small core — you own orchestration	Smallest — intentionally minimal API
Where it loses	Teams that want a thin, readable core	Pure tool loops with no retrieval	Batteries-included multi-provider glue	Deep retrieval tooling out of the box

Scroll the table sideways to see every tool.

What this table is not. These are editorial judgements, not benchmarks: Lattice has not run these tools head to head, and no cell uses GitHub stars or vendor benchmark claims as evidence. Licence and cost facts are re-checked on a schedule the build enforces — the receipt is public.

The recommendation

Which should you choose: LangChain, LlamaIndex, Pydantic AI or OpenAI Agents SDK?

Rules of thumb

A framework orchestrates calls inside one process. It does not make long runs survive a deploy — that is a workflow engine's job.
Fewer tools beats a longer system prompt. Narrow the agent before you add another integration.
If you cannot trace a failing run step by step, you are debugging prose instead of the call that went wrong.

Quick answers

When should you use LangChain, and when should you skip it?: Use LangChain when: tool use and state, in an ecosystem where the answers already exist. Skip it when: you want a thin, legible core — this is a large surface.
When should you use LlamaIndex, and when should you skip it?: Use LlamaIndex when: data-centric work where retrieval is the centre of the problem. Skip it when: you need agent orchestration more than data access.
When should you use Pydantic AI, and when should you skip it?: Use Pydantic AI when: type-safe agents where schema correctness is the priority. Skip it when: you want a batteries-included ecosystem.
When should you use OpenAI Agents SDK, and when should you skip it?: Use OpenAI Agents SDK when: a small, legible set of primitives for handoffs and guardrails. Skip it when: you need multi-provider abstraction at every layer.