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-google-adk-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-google-adk-to-pydantic-ai)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/migrating-google-adk-to-pydantic-ai"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-google-adk-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-google-adk-to-pydantic-ai"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-google-adk-to-pydantic-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00072 | $0.01298 |
| Opus 5.5 | $0.00029 | $0.00519 |
| Sonnet 5.5 | $0.00014 | $0.00260 |
| Haiku 4.5 | $0.00007 | $0.00130 |
Grade A, and why
migrating-google-adk-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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- migrating-google-adk-to-pydantic-ai — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Migrate Google ADK to Pydantic AI
Preserve caller-visible behavior, not ADK's class tree. Migrate the smallest complete request path and leave product infrastructure in the application.
Work from the running application
- Read repository instructions, dependencies, tests, and runtime entrypoints. Record the installed Google ADK, Pydantic AI, and optional
pydantic-ai-harnessversions. - Trace one real request from
Runner.run_async()or the deployed endpoint through the root agent, instructions, collaboration mode, model calls, tools, workflow nodes, callbacks/plugins, session service, emitted events, state/artifact deltas, and final response. Record only behavior that path uses. - Separate the contracts before choosing targets:
Session.eventsused as model context;- session-, user-, app-, and invocation-scoped state;
- searchable long-term memory;
- workflow/node checkpoints and resume IDs;
- versioned artifacts and external side effects.
- Classify the active slice:
- Ordinary
LlmAgent: normally one reusableAgentwith typed dependencies, tools, and output. - ADK graph or dynamic workflow: keep simple deterministic control flow in plain async Python; use
pydantic_graphwhen explicit typed nodes, branching, or graph inspection remain valuable. - Multi-agent delegation: inspect ADK's collaboration
modein Python 2.x; 1.xsub_agentsusechatbehavior. Use core multi-agent patterns forchattransfer semantics. HarnessSubAgentscan fittaskorsingle_turnonly when their isolated context, return, interaction, and concurrency behavior match. - Product runtime: retain auth, session/state stores, artifact stores, queues, transport, A2A endpoints, evaluation, observability, and deployment unless they are explicitly in scope.
- Ordinary
- Add deterministic characterization tests, then migrate one vertical slice behind its existing caller boundary.
- Run the original tests and focused parity tests. Mark unexercised behavior
unverified; similar names are not equivalence evidence.
What ships with it
4 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.
- 1mo ago First seen · 52 lines · 72 tokens per session scan A b5e64220b51c
migrating-google-adk-to-pydantic-ai is a skill published in the GitHub repository pydantic/pydantic-ai (20,645 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 1,298 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-16.
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