kitaru-openai-agents-migration - SKILL.md Agent Skill

name: kitaru-openai-agents-migration description: > Migrate existing OpenAI Agents SDK code to Kitaru's OpenAI Agents adapter. Use when code mentions agents.Agent, Runner.run, Runner.run_sync, KitaruRunner, OpenAIRunRequest, OpenAIRunResult, wait_for_approval, build_resume_request, function_tool, RunConfig, handoffs, context, approval/resume flows, or checkpoint_strategy. Helps classify direct, approximate, and absent mappings, choose runner_call vs calls boundaries, preserve OpenAI Agents SDK ownership of the agent turn, and produce a MIGRATION_REPORT.md with replay, context, approval, tool, and side-effect risks called out.

Migrate OpenAI Agents SDK to Kitaru

What this skill does

Use this skill to inspect existing OpenAI Agents SDK code and move the safe execution boundary to Kitaru's KitaruRunner, OpenAIRunRequest, and OpenAIRunResult surfaces.

The output should be conservative:

A pattern inventory of the source code.
A migration plan that classifies every important pattern as direct, approximate, or absent.
Migrated or proposed code.
A MIGRATION_REPORT.md section or file that names behavior changes and review risks.

Do not promise that Kitaru can see or replay OpenAI/provider internals that stay inside the OpenAI Agents SDK.

Mental model: Runner stays OpenAI-owned, Kitaru records safe seams

Think of the OpenAI Agents SDK as the driver of the agent turn. It decides how handoffs, guardrails, model calls, hosted tools, and approvals work.

Kitaru changes the doorway you use to enter that turn:

from agents import Agent, Runner
from kitaru.adapters.openai_agents import KitaruRunner, OpenAIRunRequest

agent = Agent(name="researcher", model="gpt-5-nano")

# Before: enter through the raw OpenAI SDK runner.
raw_result = Runner.run_sync(agent, "Research durable workflows")

# After: enter through Kitaru's runner doorway.
runner = KitaruRunner(agent, checkpoint_strategy="runner_call")
result = runner.run_sync(OpenAIRunRequest.start("Research durable workflows"))

Concrete story: the agent starts, calls a model, maybe calls tools, maybe pauses for approval, and then returns a final answer. OpenAI still runs that story. Kitaru records either one durable checkpoint around the whole runner call or supported model/local-tool calls that the adapter can safely see.

When to use this skill

Use it when the user asks to:

Migrate agents.Agent workflows to Kitaru.
Replace Runner.run(...) or Runner.run_sync(...) with KitaruRunner.
Choose checkpoint_strategy="runner_call" versus "calls".
Add durable behavior to OpenAI Agents SDK tools, handoffs, guardrails, approvals, or resume flows.
Review whether context=..., RunConfig, hosted tools, function tools, API keys, or raw nested runner calls are safe under Kitaru replay.
Produce a migration report for OpenAI Agents SDK code.

When not to use this skill

Do not use it to:

Rewrite the user's agent into a different framework.
Claim Kitaru owns OpenAI Agents SDK internals.
Add LangGraph, Claude Agent SDK, Gemini Interactions, or PydanticAI migration behavior; use the matching sibling migration skill instead.
Hide replay, state, privacy, or side-effect risks just to produce cleaner code.
Put Kitaru wait() calls inside checkpoints.

The three mapping types

Classify each source pattern before editing:

direct: Kitaru has a close adapter surface for the same behavior. Example: Runner.run_sync(agent, input) becomes runner.run_sync(OpenAIRunRequest.start(input)).
approximate: The code can move, but semantics, observability, cache keys, or replay behavior differ. Example: RunConfig(...) becomes a run_config_factory, which recreates config per Kitaru run.
absent: There is no safe automatic migration. Add a # TODO(migration) comment, do not pretend it is solved, and explain the redesign needed in the report.

Migration workflow

Inspect source first. Find imports, Agent(...) construction, Runner.run / Runner.run_sync, RunConfig, context=..., tools, approvals, handoffs, guardrails, nested runner calls, and API-key handling.
Classify every pattern. Use references/concept-map.md and references/gaps-and-flags.md. Count direct, approximate, high-risk, and blocked items.
Choose Kitaru boundaries. Decide whether the main runner should use checkpoint_strategy="runner_call" or "calls". Prefer runner_call when the surrounding flow needs a clean .wait() result.
Present the plan before generating code when the migration is more than a tiny local replacement. Name risky patterns before editing.
Draft migrated code. Keep the vanilla agents.Agent. Replace raw runner entrypoints with KitaruRunner plus OpenAIRunRequest. Add status handling for OpenAIRunResult.
Produce MIGRATION_REPORT.md. Include the pattern inventory, chosen boundary, classifications, flags, behavior differences, and verification plan.
Verify behavior. Prefer small representative snippets. If no API key or runtime is available, say the check was static only.

Pattern detection checklist

Look for:

from agents import Agent, Runner, agents.Agent, or Runner.run*.
Raw nested agents.Runner.run(...) calls inside tools, guardrails, or helper agents.
RunConfig(...), tracing configuration, hooks, guardrails, and handoffs.
@function_tool, FunctionTool, hosted tools, web search/file search/code interpreter tools, or custom tool wrappers.
Tool functions that write to databases, send messages, charge cards, create tickets, upload files, or call external APIs.
context=..., RunContextWrapper, context objects, serializers, and cache identity needs.
Existing privacy, logging, tracing, retention, or capture expectations; decide whether OpenAICapturePolicy should save inputs, outputs, run state, interruption payloads, response items, and usage.
Approval interruptions, result.status, interrupted, resume state, and manual approval decisions.
final_output use without checking status.
Runner.run_sync(...) inside async code or an active event loop.
API keys passed as ordinary function parameters or logged.
Raw SDK result objects returned directly from Kitaru checkpoints.

Boundary decision rules

Keep agents.Agent(...) as the OpenAI-owned agent definition.
Use KitaruRunner(agent, checkpoint_strategy="runner_call") when the flow needs one durable result and ergonomic .wait() behavior.
Use checkpoint_strategy="calls" when per-model/per-local-tool visibility is more important than a single terminal flow result.
Do not put calls mode inside an existing Kitaru checkpoint.
Keep wait_for_approval(...) at flow scope, not checkpoint scope.
Treat context= as OpenAI Agents SDK runtime context, not Kitaru metadata.
Use context_cache_identity= for production context objects so replay does not mix tenants, users, threads, or tool settings.
Keep checkpoint outputs serializable. Return Kitaru adapter result envelopes or simple values, not arbitrary live SDK objects.
Use stable agent and runner names. If names are missing or generated from volatile values, flag the migration.

OpenAI Agents migration quick guide

Minimal durable sync replacement:

from agents import Agent
from kitaru import flow
from kitaru.adapters.openai_agents import KitaruRunner, OpenAIRunRequest

agent = Agent(name="support_agent", model="gpt-5-nano")
runner = KitaruRunner(agent, checkpoint_strategy="runner_call")

@flow
def support_flow(message: str) -> str:
    result = runner.run_sync(OpenAIRunRequest.start(message))
    if result.status != "completed":
        raise RuntimeError(f"Expected completed run, got {result.status!r}")
    return str(result.final_output)

print(support_flow.run("Where is order ORD-1007?").wait())

Outside an explicit Kitaru flow, this is only adapter API wiring; it does not establish a durable Kitaru checkpoint.

For full examples, load references/code-patterns.md.

Gap handling rules

When a pattern is unsafe or unsupported:

Do not silently approximate it.
Add a concrete # TODO(migration): ... comment near the code if you are producing code.
Add a report entry with severity LOW, MEDIUM, HIGH, or BLOCKER.
Explain the bad outcome the flag prevents. Example: "the tool charges a card; replay may call it again unless the tool uses an idempotency key."
Propose the smallest safe redesign.

Use references/gaps-and-flags.md for severity definitions and required actions.

Report requirements

Every non-trivial migration must include or draft MIGRATION_REPORT.md with:

Source files reviewed and changed/proposed.
Classification totals.
Source pattern inventory.
Chosen Kitaru runner boundary and checkpoint strategy.
Direct translations.
Approximate translations and caveats.
Flagged items with severity and required action.
OpenAI-specific notes for result status, approval/resume, context, capture/privacy policy, side-effectful tools, SDK version compatibility, and API-key handling.
Verification plan and whether execution was actually run.

Use references/report-template.md when a full report is needed.

Anti-patterns

Avoid these:

Replacing agents.Agent internals instead of changing the runner doorway.
Using Runner.run_sync(...) from inside an active event loop.
Returning result.final_output without checking result.status when approval interruptions are possible.
Treating hosted/native tools as granularly replayable by Kitaru.
Running checkpoint_strategy="calls" inside a Kitaru @checkpoint.
Calling wait_for_approval(...) or kitaru.wait(...) inside a checkpoint.
Passing API keys, tokens, prompts, or user secrets as Kitaru flow metadata.
Saving raw non-serializable SDK objects as checkpoint outputs.
Ignoring context cache identity for tenant/user/thread-sensitive tools.

References

Load only the reference file needed for the current task:

references/concept-map.md — source-to-target mapping table and classification guidance.
references/code-patterns.md — import-complete migration examples.
references/gaps-and-flags.md — severity definitions and must-flag patterns.
references/report-template.md — OpenAI-specific migration report template.

Kitaru source references:

Kitaru OpenAI Agents adapter docs: kitaru/docs/content/docs/adapters/openai-agents.mdx
Adapter exports: kitaru/src/kitaru/adapters/openai_agents/__init__.py
Adapter examples: kitaru/examples/integrations/openai_agents_agent/openai_agents_adapter.py and kitaru/examples/end_to_end/openai_research_bot/research_bot.py