Pydantic AI is a typed Python SDK for building AI agents that can use different language models and interfaces, including voice, image generation, and embeddings. Developers use it for applications ranging from structured data extraction to long-running multi-agent work. The catalogue entries provide workflows and configuration for its coding-agent and harness features.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pydantic/pydantic-ai --skill migrating-langchain-to-pydantic-aigit clone --depth 1 https://github.com/pydantic/pydantic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pydantic/pydantic-ai/migrating-langchain-to-pydantic-ai)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/migrating-langchain-to-pydantic-ai"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-langchain-to-pydantic-ai/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/migrating-langchain-to-pydantic-ai"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-langchain-to-pydantic-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.01504 |
| Opus 5 | $0.00034 | $0.00752 |
| Sonnet 5 | $0.00014 | $0.00301 |
| Haiku 4.5 | $0.00007 | $0.00150 |
Grade A, and why
migrating-langchain-to-pydantic-ai scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Migrate LangChain and LangGraph to Pydantic AI
Preserve behavior, not framework shape. Migrate the smallest behaviorally complete slice and leave application infrastructure outside that slice unchanged.
Work from the running application
- Read repository instructions, dependency files, tests, and the actual runtime entrypoints. Identify the installed LangChain, LangGraph, and Pydantic AI versions.
- Trace one representative request through prompts, retrieval, model and tool calls, state, persistence, interrupts, emitted events, tracing/metrics callbacks, and the public result. Inspect every caller and sibling endpoint that consumes the migrated component; a narrow implementation slice can still have several public contracts. Include keyword parameter names and the sync, async, callback, and streaming forms callers actually use. Record only contracts those paths actually use.
- Run the cheapest useful baseline. When the migration surface is broad or unclear, search dependency files and source for
langchain,langgraph,langsmith, anddeepagents, then confirm findings against imports, factories, and call sites. - Classify the slice before choosing a target:
- Chain or LCEL pipeline: keep deterministic retrieval and transformation in plain Python; use a Pydantic AI agent only where a model/tool loop adds value.
- LangChain agent: normally use one reusable
pydantic_ai.Agentwith typed dependencies, tools, and outputs. - Direct LangGraph workflow: use plain async Python for simple fixed control flow, or
pydantic_graphwhen explicit typed nodes and branching remain useful. Treat persistence as a separate design decision. - Product runtime: retain queues, configured database backends, sandboxes, auth, schedulers, webhooks, tracing, and transport adapters unless the user placed them in scope. Extend an existing application seam before creating a parallel persistence or provider subsystem.
- Add or preserve deterministic characterization tests, then migrate one vertical slice behind the existing public boundary.
- Run the original tests and focused parity tests. Classify each observed contract by its evidence; never describe the migration as one-to-one merely because the happy path or trace shape looks similar.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 51 lines · 69 tokens per session scan A 637f86253272
migrating-langchain-to-pydantic-ai is a skill published in the GitHub repository pydantic/pydantic-ai (19,869 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 1,504 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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