Migrating Vercel AI SDK And Eve To Pydantic AI
PydanticOfficialPublished by Pydantic
- Skill
- Workflow
- Software development
- MIT
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured methodology for porting AI-driven applications from TypeScript (Vercel AI SDK or Eve) to Python (Pydantic AI). It emphasizes preserving observable application behavior over direct API translation. The process involves tracing existing request lifecycles, classifying contracts, choosing the appropriate Pydantic AI components (Core, Graph, or Harness), and implementing vertical slices with rigorous verification to ensure parity.
What it does
- Traces existing request lifecycles from entry points to final results.
- Maps TypeScript AI SDK and Eve features to Pydantic AI equivalents.
- Provides guidance on selecting between Pydantic AI Core, Graph, and Harness.
- Defines a contract ledger to track migration parity and evidence.
- Offers strategies for handling complex features like approvals, streaming, and durable sessions.
- Establishes testing protocols for verifying cross-language boundaries.
When to use it
- When migrating an existing TypeScript application using Vercel AI SDK or Eve to Python.
- When you need to maintain specific agent behaviors, tool loops, or durable session states during a language migration.
- When you require a structured approach to ensure parity in complex AI agent workflows.
- When you need to verify that migrated agent logic behaves identically to the source implementation.
Advantages
- Reduces risk by focusing on observable behavior rather than blind API mapping.
- Ensures architectural integrity by separating agent logic from application infrastructure.
- Provides clear criteria for when to introduce advanced components like Harness or Graph.
- Includes built-in verification steps to prevent regressions in agent behavior.
Usage examples
- “How do I start migrating a Vercel AI SDK v6 application to Pydantic AI?”
- The agent will guide you to first trace the current request lifecycle, identify the specific AI SDK contracts being used, and establish a baseline test before selecting the appropriate Pydantic AI components.
- “Should I use Pydantic AI Harness for my Eve migration?”
- The agent will help you analyze your observed Eve behaviors to determine if Harness is necessary for features like subagents or step persistence, or if Pydantic AI Core is sufficient.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/pydantic/migrating-vercel-ai-sdk-and-eve-to-pydantic-ai/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip migrating-vercel-ai-sdk-and-eve-to-pydantic-ai.zip -d .agents/skills
Documentation by Pydantic
Migrate Vercel AI SDK and Eve to Pydantic AI
Preserve the application's observable behavior, not the source APIs. Vercel AI SDK Core, AI SDK UI, and Eve have different responsibilities: identify which one owns each source behavior before choosing a target.
Trace the source before choosing a target
Read repository instructions, dependencies, lockfiles, tests, and the runtime entrypoint. Resolve
workspace:*dependencies to concrete package versions and record the installed AI SDK, Eve, Pydantic AI, and Harness versions.Trace one real request from its public entrypoint through messages, model calls, tools, approvals, streams, persistence, side effects, and the result the caller consumes. Establish a focused baseline or characterization test.
Classify every observed contract:
AI SDK Core: model calls, step loops, tool execution, structured output, and provider routing;
AI SDK UI:
UIMessage,useChat, transports, client tools, stream chunks, and reconnect behavior;Eve: filesystem-defined agents, durable sessions, skills, subagents, state, connections, channels, schedules, and sandboxes;
Application/platform: authentication, authorization, storage, queues, deployment, credentials, product UI, and hosted Vercel services.
Record each contract, its owner, semantic difference, and executable proof. An unused feature is not migration scope.
Read Research and concept mapping for the detected source features. Read Verification and cutover before implementation.
Choose the smallest target
Core: use
pydantic_ai.Agentfor the agent loop, typed dependencies, tools, outputs, messages, streaming, MCP clients, approvals, usage limits, instrumentation, 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 Agent Skills, model-directed subagents, planning, memory notebooks, coding tools, guardrails, or step persistence. Harness capabilities compose through the core agent loop; Harness is not an Eve-compatible runtime.Evals: add the separate
pydantic-evalspackage for observed datasets, cases, and evaluators.Application: retain UI, HTTP transports, auth, persistence, reconnect state, schedulers, deployment, tenant policy, and hosted services unless the requested slice includes replacing them.
Gap: name behavior that no supported public seam preserves, explain its impact, and test a bounded adapter. Do not recreate the AI SDK or Eve object model merely to hide a difference.
The normal AI SDK migration is one reusable Agent behind the existing caller. Treat @ai-sdk/workflow as a durable-runtime migration, not an ordinary agent loop. When a TypeScript or React client uses AI SDK UI, keep it and serve its exact installed-version protocol with VercelAIAdapter; do not migrate the UI unnecessarily. When the source is only an in-process TypeScript call, migrate its nearest caller in the same slice or explicitly agree on a new application-owned service boundary.
The normal Eve migration is a core agent plus only the Harness capabilities that match observed behavior. Keep Eve's durable session driver, product endpoints, schedules, channels, authentication, and hosted services in the application until each has a proved replacement.
Apply high-risk gates
generateText()andstreamText()are single-step by default,ToolLoopAgentdefaults to a longer loop, andWorkflowAgenthas no maximum step count unless configured. Preserve the observed stop conditions, usage limits, andprepareStepchanges to models, tools, instructions, messages, or runtime context; do not copy defaults blindly.Distinguish local, provider-executed, browser/client, and MCP tools. Preserve where execution and credentials live, input/result schemas, error recovery, cancellation propagation, and side-effect idempotency.
An AI SDK async-generator tool can expose preliminary results before its final output. An ordinary Pydantic AI tool returns once; reproduce consumed intermediate UI states with explicit custom events or record an intentional change.
Complete structured output is validated; partially streamed output may be incomplete, and combining tools with structured output may require another step. Test the caller-visible timing and failure shape.
UIMessageis client-controlled application state, not trusted provider history or dependency context. Validate incoming messages, keep identity and services server-side, and convert only the permitted content to model messages.Match the installed AI SDK wire contract. The current docs call the SSE-based UI-message wire format the Data Stream Protocol, while older implementations can use line-prefixed frames under a similar name. Request bodies, headers, frames, chunk ordering, tool states, error/abort behavior, and reconnect endpoints are not interchangeable. Configure
VercelAIAdapterfor the installed SDK version and golden-test the boundary.AI SDK approval is commonly a two-request conversation; Eve can durably park a session; Pydantic AI can resolve a deferred call inline or in a later run. Persist the complete pending request, bind a decision to validated arguments and call identity, authenticate the approver, and prove the protected effect runs exactly once.
Pydantic AI message history cannot continue past unresolved tool calls. If Eve accepts an unrelated turn while approval remains pending, keep the pending branch separate in application state; do not append an unrelated prompt to that incomplete history.
Eve sessions, turns, steps, replay units, event cursors, background subagents, and NDJSON session streams are one durable protocol. Core message history and Harness
StepPersistencedo not automatically reproduce it. Retain an application-owned session adapter or mark the difference until restart and replay tests prove the promised behavior.Eve
defineStatedata is separate from conversation history, and durable memory has its own lifetime and sharing rules. Map each store independently and test session, user, and tenant isolation.Eve skills can materialize supporting files. Harness
SkillsloadsSKILL.mdinstructions only; provide required references, assets, or scripts throughFileSystemor application tools and test them explicitly.Eve subagents may be durable child sessions with background result batching and separate streams. Harness
SubAgentsruns an isolated agent and returns its result through a tool. Use it only when that narrower lifecycle preserves the observed contract; otherwise retain orchestration in the application.A filesystem or shell policy is not sandbox isolation. Preserve Eve's sandbox, credential-brokering, and network boundaries with a real container, VM, or supported cloud sandbox when untrusted execution is in scope.
Eve channels, schedules, connections/OAuth, its default HTTP API, Workflow SDK persistence backends, Vercel Agent Runs, and deployment integrations are application or platform concerns. Replace and test them deliberately rather than attributing them to Core or Harness.
Preserve provider routing, gateway allow-lists/fallbacks, provider options, retry semantics, telemetry correlation, and evaluation datasets only when the traced path uses them.
Implement and prove one vertical slice
Preserve the supported caller boundary and replace only the agent-owned internals.
Start core-only. Add
pydantic_graph, Harness, Pydantic Evals, or a durable runtime only after an observed contract requires it.Test inputs, outputs, errors, message history, event order, tool arguments/results, step counts, state, and side effects at that boundary. Use deterministic models and fake application services offline; add a focused recording or live test only when provider behavior is the contract.
For approvals, reconnects, durable sessions, background work, or external effects, test interruption and fresh-process restart, stable correlation, authorization, replay, cancellation, and idempotency.
Remove
ai,@ai-sdk/*, oreveonly after no retained UI, transport, agent, or platform path needs them.
Explain any consequential semantic change before implementing it: state the source behavior, target behavior, caller impact, recommended choice, and remaining risk.
Completion
Apply the completion criterion in Verification and cutover. Label evidence from fakes, recordings, live providers, and real sandboxes accurately.
Files
- ATTRIBUTION.mdAdded by AutoGineer909 B
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md9.0 KiB
- agents/openai.yaml324 B
- references/RESEARCH-AND-MAPPING.md12.1 KiB
- references/VERIFICATION-AND-CUTOVER.md8.1 KiB
LICENSE
The MIT License (MIT) Copyright (c) 2025 Pydantic Services Inc. and individual contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Security
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Attribution and licence
- Published by
- Pydantic
- Publisher
- Pydantic (Official)
- Source
- pydantic/skills · skills/migrating-vercel-ai-sdk-and-eve-to-pydantic-ai · commit 98f591c
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- pydantic/skills
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download migrating-vercel-ai-sdk-and-eve-to-pydantic-ai.zip