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-deep-agents-to-pydantic-ai-harnessgit 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-deep-agents-to-pydantic-ai-harness)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/migrating-deep-agents-to-pydantic-ai-harness"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-deep-agents-to-pydantic-ai-harness/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-deep-agents-to-pydantic-ai-harness"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/migrating-deep-agents-to-pydantic-ai-harness.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 | $0.00109 | $0.01329 |
| Opus 5.5 | $0.00044 | $0.00532 |
| Sonnet 5.5 | $0.00022 | $0.00266 |
| Haiku 4.5 | $0.00011 | $0.00133 |
Grade A, and why
migrating-deep-agents-to-pydantic-ai-harness 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 19d 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
2 near-identical copies found in the catalogue:
- migrating-deep-agents-to-pydantic-ai-harness — 100% identical, 0 lines differ
- migrating-deep-agents-to-pydantic-ai-harness — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Migrate Deep Agents to Pydantic AI Harness
Preserve the application's observed contracts, not the shape of create_deep_agent. Compose Pydantic AI primitives and Harness capabilities; keep infrastructure with the application that already owns it.
Establish the source contract
- Resolve the
deepagentsimport origin before applying these mappings. Route a local module or project-ownedcreate_deep_agentto the ordinary LangChain migration only when its implementation has ordinary LangChain or LangGraph semantics; vendored implementations that reproduce Deep Agents contracts remain in scope. Read repository instructions, manifests, lockfiles, tests, runtime entrypoints, and the resolved source. Record exact Deep Agents, LangChain, LangGraph, Pydantic AI, and Harness versions. If the locked source is unavailable, reproduce its environment without changing the target lockfile. If that is not possible, mark affected behavior unverified and do not claim parity. - Trace one representative request through prompt and model profile, tools and integrations, middleware and permissions, backend and execution, skills and memory, delegation, state, checkpoints and approvals, retries and limits, events, side effects, and the public boundary. Inspect every caller of that boundary.
- Run the cheapest deterministic baseline. Record evidence separately from the outcome:
source-inspected,probe-observed, orregression-tested. - Load only the mapping needed for the detected source features:
- Core Migration for construction, models and profiles, prompts, tools and MCP, middleware, state, outputs, caching, or tracing.
- Context and Execution for coding-agent defaults, planning, files, permissions, backends, sandboxes, interpreters, media, skills, memory, retrieval, or context limits.
- Orchestration and Recovery for synchronous, dynamic, or background subagents; grading loops; approvals; checkpoints; fault tolerance; durable execution; or streaming.
- Hosts and Protocols only when migrating Deep Agents Code, a frontend, ACP, A2A, or a deployment boundary.
What ships with it
7 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.
- 19d ago First seen · 44 lines · 109 tokens per session scan A 21a58bbcc65d
migrating-deep-agents-to-pydantic-ai-harness is a skill published in the GitHub repository pydantic/pydantic-ai (20,645 stars, last pushed today), licensed MIT. It adds 109 tokens to every session and 1,329 once invoked, about $0.0004 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 2026-09-27.
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