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-agno-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-agno-to-pydantic-ai)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/migrating-agno-to-pydantic-ai"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-agno-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-agno-to-pydantic-ai"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-agno-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 | $0.00076 | $0.01450 |
| Opus 5.5 | $0.00030 | $0.00580 |
| Sonnet 5.5 | $0.00015 | $0.00290 |
| Haiku 4.5 | $0.00008 | $0.00145 |
Grade B, and why
migrating-agno-to-pydantic-ai scanned grade B with 1 finding 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 28d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- Agno Skills can expose instructions, references, and scripts. Harness `Skills` loads `SKILL.md` instructions on demand but does not load bundled resources or execute scripts; add explicit `FileSystem`, `Shell`, or appl Copies of this mod
1 near-identical copy found in the catalogue:
- migrating-agno-to-pydantic-ai — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Migrate Agno to Pydantic AI
Preserve observable behavior, not Agno's object model. Migrate the smallest complete caller path and keep application infrastructure in place.
Trace the source before choosing a target
- Read repository instructions, dependencies, tests, and the runtime entrypoint. Record the installed Agno, Pydantic AI, and Harness versions.
- Trace one real request from
Agent.run()/arun(),Team.run()/arun(), aWorkflow, or an AgentOS endpoint through instructions, model and tool calls, hooks, session loading, memory or knowledge retrieval, events, results, state, and side effects. Establish a focused baseline or characterization test. - Separate these source contracts when present:
- trusted dependencies, model-chosen tool input, session state, and workflow state;
- conversation history, session summaries, user memories, knowledge retrieval, workflow checkpoints, and model-owned plans;
- final content, structured output, token deltas, run events, pauses, and terminal status;
- confirmation, user input, external tool execution, authenticated authorization, and process isolation;
- team delegation, deterministic workflow control, AgentOS transport, queues, and deployment.
- Record each observed contract, its owner, semantic difference, and executable proof. An unused Agno feature is not migration scope.
Read Research and concept mapping for the detected features. Read Verification and cutover before implementation.
Choose the smallest target
- Core: use
pydantic_ai.Agentfor the agent loop, typed dependencies, tools, outputs, normalized messages, generic hooks, streaming, MCP clients, approvals, usage limits, instrumentation, realtime, and durable-runtime integrations. - Graph: use
pydantic_graphonly when explicit typed nodes, branches, and joins remain useful; use plain async Python for simple fixed control flow. - Harness: add
pydantic-ai-harnessonly for observed reusable policy such as memory notebooks, planning, model-directed subagents, Agent Skills, coding tools, guardrails, model-agnostic compaction, or step persistence. Harness capabilities compose through the core agent loop; Harness is not a second runtime. - Evals: add the separate
pydantic-evalspackage for observed datasets and evaluators. - Application: retain authentication, databases, vector search, queues, AgentOS/API routes, storage schemas, deployment, product state, and UI transports unless the requested slice includes them.
- Gap: name behavior that no supported public seam preserves, explain its impact, and test a bounded adapter. Do not rebuild Agno's registry, database abstraction, event taxonomy, or AgentOS merely to hide a difference.
What ships with it
3 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.
- 28d ago First seen · 61 lines · 76 tokens per session scan B 84650df240ba
migrating-agno-to-pydantic-ai is a skill published in the GitHub repository pydantic/pydantic-ai (20,645 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 1,450 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-18.
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