# Pydantic AI vs LangChain: Which Framework Should You Use? (2026)
> I checked both frameworks' current release status before writing a line of code — here's Pydantic AI vs LangChain with working examples for each, a decision table, and an honest verdict.
*Source: https://www.infowok.com/pydantic-ai-vs-langchain-2026/ · Sukhveer Kaur · Published July 6, 2026*
---
**Pydantic AI vs LangChain** is really two questions wearing one trench coat: which framework is technically better, and which one is safe to build on today. I checked both before writing a line of code for this post. LangChain's agent layer has settled on [`create_agent` running on LangGraph](https://docs.langchain.com/oss/python/langchain/quickstart), currently version 1.2.6. It's already confirmed in production at Klarna, Uber, and Elastic. [Pydantic AI](https://pydantic.dev/docs/ai/core-concepts/agent/) just shipped its sixth 2.0 beta. That beta line introduces a new capabilities system rewriting several core patterns, while **1.106 stays the stable line most people should actually install right now.** Here's real, current code for both, a decision table, and my verdict.
- **Pydantic AI is lean and type-safe** — a model string, a system prompt, and `@agent.tool` gets you a working agent fast.
- **LangChain's `create_agent` runs on LangGraph**, now at v1.2.6, with the deepest production track record of any Python agent framework.
- **Pydantic AI is mid-migration.** Its 2.0 rewrite (six betas deep) will replace several current patterns — a real factor if you're starting new code today.
- **Neither wins outright.** The honest answer depends on whether you value type safety and a lean footprint, or ecosystem depth and production mileage.
## The Frameworks at a Glance
Both frameworks solve the same core problem — give a model tools and let it decide when to call them — but they come from different design philosophies.
**Pydantic AI** is built by the team behind Pydantic, and it shows. Agents, tools, and structured outputs are all validated the same way you'd validate a request body. **It's a comparatively small dependency footprint.** If you already use Pydantic elsewhere in your stack, the mental model transfers directly.
**LangChain**, by contrast, is the ecosystem play. Its `create_agent` function is the current recommended entry point. It sits on top of **[LangGraph](https://www.langchain.com/langgraph)** — a lower-level state-graph runtime that handles branching, retries, persistence, and human-in-the-loop interrupts. LangChain also ships **deep agents** (the `deepagents` package) as a more batteries-included option. It adds planning and a virtual filesystem built in, out of the box.
Both frameworks can build the same agent. **The real difference is dependency weight, ecosystem maturity, and — right now — API stability.** That last one matters more than usual this year.
## The Pydantic AI vs LangChain Decision Table
Line these up on the criteria that actually change your day-to-day, not the marketing copy.
| Criterion | Pydantic AI | LangChain / LangGraph |
| --- | --- | --- |
| Core idea | Type-validated agent, Pydantic-native | Graph-based orchestration on LangGraph |
| Current stable version | 1.106.0 (Jun 5, 2026) | LangGraph 1.2.6 |
| API stability right now | 2.0 beta in progress (6 betas) — breaking changes coming | Stable, iterating in minor/patch releases |
| Learning curve | Short if you know Pydantic | Short to start, deeper as you add memory/tracing |
| Built-in memory / state | Manual, or via your own store | LangGraph checkpointer (`InMemorySaver` or persistent) |
| Observability | Built-in OpenTelemetry instrumentation, bring your own backend | LangSmith — a dedicated tracing, evals, and deployment product |
| Confirmed production users | Couldn't verify any named users — that's a gap in my research, not evidence of absence | Klarna, Uber, Elastic (verified on LangChain's own site) |
| Dependency footprint | Lean | Larger (langchain + langgraph + optional deepagents) |
**The single most important row for a 2026 decision is API stability.** Pydantic AI's core `Agent` + `@agent.tool` pattern is stable in 1.x. But several adjacent APIs are already deprecated in favor of the incoming capabilities system. Code you write today may need a pass when 2.0 goes final.
## The Pydantic AI Way
Here's a minimal but real Pydantic AI agent, using the current stable 1.x API — a model string, a system prompt, and a decorated tool function.
```python
from pydantic_ai import Agent, RunContext
weather_agent = Agent(
"openai:gpt-5.2",
system_prompt="Provide a short weather forecast for the location given.",
)
@weather_agent.tool
async def get_weather(ctx: RunContext, city: str) -> str:
"""Get the current weather for a city."""
return f"It's 24°C and sunny in {city}."
result = weather_agent.run_sync("What's the weather in Austin?")
print(result.output)
```
`run_sync` blocks until the model finishes its turn, including any tool calls, and hands back a `RunResult`. **`result.output` is validated against the type you declared** — a plain string here, but it can just as easily be a Pydantic `BaseModel` for structured extraction. That validation step is the whole pitch of this framework. The model's answer either matches your schema, or you get a typed error — never a string you have to hope is well-formed.
This example targets the current **stable 1.x line**. Pydantic AI's 2.0 betas already deprecate `prepare_tools=`, `event_stream_handler=`, and `history_processors=` in favor of a new `capabilities=[...]` list — if you're reading this after 2.0 goes stable, check the migration notes before assuming this snippet still runs unchanged.
## The LangChain Way
LangChain's current recommended entry point is `create_agent`, which builds on LangGraph underneath. The same weather agent looks like this:
```python
from langchain.agents import create_agent
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"It's 24°C and sunny in {city}."
agent = create_agent(
model="openai:gpt-5.5",
tools=[get_weather],
system_prompt="Provide a short weather forecast for the location given.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's the weather in Austin?"}]}
)
print(result["messages"][-1].content_blocks)
```
The shape is familiar — model, tools, system prompt. But **the return value is a full message list, not a single typed value**, because `create_agent` is built for multi-turn conversations by default. Add a `checkpointer` — LangGraph's `InMemorySaver`, or a persistent store in production — and the same agent remembers earlier turns across calls. Want more built in without wiring it yourself? Planning, a virtual filesystem, and subagents come from LangChain's `deepagents` package, via `create_deep_agent` with the same call signature.
LangChain agents and deep agents fill the same role at different capability levels. Start with `create_agent` for fine-grained control; reach for `create_deep_agent` when the task is complex enough to want planning and subagents out of the box.
## My Verdict
Neither framework is the wrong choice. The honest answer depends on what you're optimizing for — not on which one is "better" in the abstract.
**Pick LangChain / LangGraph if** you want the deepest ecosystem and the strongest *verifiable* production track record available in a Python agent framework today. LangSmith's tracing and evals are a real advantage once something goes wrong in production. Named users at Klarna, Uber, and Elastic also mean the rough edges at scale are more likely already found — and fixed — by someone else. It's a similar calculus to [OpenAI Agents SDK vs LangGraph](/openai-agents-sdk-vs-langgraph-2026/): ecosystem maturity tends to win for teams shipping to production this quarter.
I could name three LangGraph production users because LangChain publishes case studies. I could not find an equivalent, named list for Pydantic AI — it has real production users, I just couldn't verify who. **That's a gap in what's publicly documented, not proof one framework is more deployed than the other**, and it's worth weighing this verdict with that asymmetry in mind rather than taking it as a clean head-to-head.
**Pick Pydantic AI if** you want a lean, type-safe agent and you're comfortable adapting when 2.0 lands. **The type validation on tool inputs and structured outputs is genuinely nice to work with.** If your stack already leans on Pydantic, the framework feels like an extension of code you already write, not a new mental model bolted on top.
**My actual recommendation for most teams starting today: default to LangChain's `create_agent` on LangGraph.** That's specifically because of the API stability gap. A framework mid-way through six betas of breaking changes is a real cost to absorb in a production codebase — however much you like its design.
## Quick Recap
- **Pydantic AI:** lean, type-safe, stable at 1.106 — but 2.0 is coming with real breaking changes.
- **LangChain / LangGraph:** `create_agent` on a mature, stable LangGraph (1.2.6), with LangSmith and confirmed production users.
- **Both** can build the exact same tool-calling agent — the difference is in stability, ecosystem, and how much you're willing to adapt later.
- **Default recommendation:** LangChain/LangGraph for new production work today; Pydantic AI if you value type safety and can tolerate a migration later.
## Conclusion
Pydantic AI vs LangChain isn't a contest with one clear winner. It's a trade between a lighter, type-safe framework mid-migration and a heavier, ecosystem-rich one with a longer production record. Check the version numbers before you commit either way. Frameworks in this space move fast enough that a comparison from even six months ago can already be stale.
**Which one are you building on right now, and has a breaking change ever bitten you mid-project?** Tell me in the comments — I'm especially curious how teams already on Pydantic AI 1.x are planning their 2.0 migration.
**Read next: [Best AI Agent Frameworks 2026](/best-ai-agent-frameworks-2026/)** — the broader roundup this comparison zooms into.
- **New to agent frameworks?** [What Are AI Agents? A Complete Guide](/what-are-ai-agents-complete-guide-2026/) covers the concepts both frameworks build on.
- **Want the deeper Pydantic AI build?** [Pydantic AI Tutorial: Type-Safe Agents in Python](/pydantic-ai-tutorial-type-safe-agents-python/) walks through a full example.
- **Ready to build with LangGraph?** [LangGraph Tutorial: Build Your First Agent](/langgraph-tutorial-build-first-agent-2026/) is the hands-on companion to this comparison.