Migrate Python Apps from Azure OpenAI Chat Completions to Responses API

AUTHORITATIVE GUIDANCE — FOLLOW EXACTLY

This skill migrates Python codebases using Azure OpenAI Chat Completions to the unified Responses API. Follow these instructions precisely. Do not improvise parameter mappings or invent API shapes.

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Triggers

Activate this skill when user wants to:

• Migrate a Python app from Azure OpenAI Chat Completions to Responses API
• Upgrade Python OpenAI SDK usage to the latest API shape against Azure OpenAI
• Prepare Python code for GPT-5 or newer models that require Responses on Azure
• Switch from AzureOpenAI/AsyncAzureOpenAI to standard OpenAI/AsyncOpenAI client with the v1 endpoint
• Fix deprecation warnings related to AzureOpenAI constructors or api_version

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⚠️ Model Compatibility — CHECK FIRST

Before migrating, verify your Azure OpenAI deployment supports the Responses API.

1. Smoke-test your deployment (fastest)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AZURE_OPENAI_API_KEY"],
    base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT'].rstrip('/')}/openai/v1/",
)

try:
    resp = client.responses.create(
        model=os.environ["AZURE_OPENAI_DEPLOYMENT"],
        input="ping",
        max_output_tokens=50,
        store=False,
    )
    print(f"✅ Deployment supports Responses API: {resp.output_text}")
except Exception as e:
    print(f"❌ Deployment does NOT support Responses API: {e}")

Note: max_output_tokens has a minimum of 16 on Azure OpenAI. Values below 16 return a 400 error. Use 50+ for smoke tests.

If this returns a 404, the deployment's model doesn't support Responses yet — check the reference below or redeploy with a supported model.

2. Check available models in your region (recommended)

Run the built-in model compatibility tool to see what's available with Responses API support in your specific region:

python migrate.py models --subscription YOUR_SUB_ID --location YOUR_REGION

This queries Azure ARM live and shows a compatibility matrix — which models support Responses, structured output, tools, etc. Use --filter gpt-5.1,gpt-5.2 to narrow results or --json for scripting.

3. Full model support reference

• Live query: python migrate.py models (see above — region-specific, always up to date)
• Browse availability: Model summary table and region availability
• Quickstart & guidance: https://aka.ms/openai/start

⚠️ Older model limitations

WARNING: Older models (those predating gpt-4.1) may not support all Responses API features fully.

Known limitations with older models:

• reasoning parameter: Not supported on many non-reasoning models. Only migrate reasoning if it was already present in the original code.
• seed parameter: Not supported in Responses API at all — remove from all requests.
• Structured output via text.format: Older models may not enforce strict: true JSON schemas reliably.
• Tool orchestration: GPT-5+ orchestrates tool calls as part of internal reasoning. Older models on Responses still work but lack this deep integration.
• Temperature constraints: When migrating to gpt-5, temperature must be omitted or set to 1. Older models have no such constraint.

O-series reasoning models (o1, o3-mini, o3, o4-mini)

O-series models have unique parameter constraints. When migrating apps that target o-series models:

• temperature: Must be 1 (or omitted). O-series models do not accept other values.
• max_completion_tokens → max_output_tokens: Apps using the Azure-specific max_completion_tokens must switch to max_output_tokens. Set high values (4096+) because reasoning tokens count against the limit.
• reasoning_effort: If the app uses reasoning_effort (low/medium/high), keep it — the Responses API supports this parameter for o-series models.
• Streaming behavior: O-series models may buffer output until reasoning completes before emitting text delta events. Streaming still works, but the first response.output_text.delta may arrive after a longer delay than with GPT models.
• top_p: Not supported on o-series — remove if present.
• Tool use: O-series models support tools via the Responses API the same as GPT models, but tool call orchestration quality varies by model.

Action — proactive model advisory: During the scan phase, check which model the app targets (deployment names, env vars, config). If the model predates gpt-4.1 (not gpt-4.1+), proactively tell the user:

• The migration will work for basic text, chat, streaming, and tools on their current model.
• Newer models (gpt-5.1, gpt-5.2) offer better tool orchestration, structured output enforcement, reasoning, and cross-region availability.
• They should consider upgrading their deployment when ready — it's not blocking the migration.

Do not block or refuse to migrate based on model version. The advisory is informational.

GitHub Models does NOT support the Responses API

GitHub Models (models.github.ai, models.inference.ai.azure.com) does not support the Responses API.

If the codebase has a GitHub Models code path (look for base_url pointing to models.github.ai or models.inference.ai.azure.com), remove it entirely during migration. The Responses API requires Azure OpenAI, OpenAI, or a compatible local endpoint (e.g., Ollama with Responses support).

Action during scan:

• Flag any GitHub Models code paths for removal.

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Framework Migration

Many apps use higher-level frameworks on top of OpenAI. When migrating these, the framework's own API changes — not just the underlying OpenAI calls.

Microsoft Agent Framework (MAF)

Check your MAF version first — the migration depends on whether you are on MAF 1.0.0+ or a pre-1.0.0 beta/rc.

MAF 1.0.0+ (agent-framework-openai >= 1.0.0)

OpenAIChatClient already uses the Responses API — no migration needed. If the codebase uses the legacy OpenAIChatCompletionClient (which uses chat.completions.create), replace it with OpenAIChatClient.

Before	After
from agent_framework.openai import OpenAIChatCompletionClient	from agent_framework.openai import OpenAIChatClient
OpenAIChatCompletionClient(...)	OpenAIChatClient(...)

To check your version: python -c "import agent_framework_openai; print(agent_framework_openai.__version__)"

MAF pre-1.0.0 (beta/rc releases)

In pre-1.0.0 MAF, OpenAIChatClient used Chat Completions. Upgrade to agent-framework-openai>=1.0.0 where OpenAIChatClient uses the Responses API by default.

No other changes needed — the Agent and tool APIs remain the same.

LangChain (langchain-openai)

Add use_responses_api=True to ChatOpenAI(). Also update response access from .content to .text.

Before	After
ChatOpenAI(model=..., base_url=..., api_key=...)	ChatOpenAI(model=..., base_url=..., api_key=..., use_responses_api=True)
result['messages'][-1].content	result['messages'][-1].text

For complete before/after code examples, see cheat-sheet.md.

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Frontend Migration Guidance

The Responses API is a server-side concern. Migrate your Python backend; the frontend's HTTP contract should stay unchanged unless your backend is a thin pass-through — in that case, consider adopting the Responses request shape to eliminate a translation layer. If the frontend calls OpenAI directly with a client-side key, move those calls to a backend first.

@microsoft/ai-chat-protocol deprecation

The @microsoft/ai-chat-protocol npm package is deprecated and should be replaced with ndjson-readablestream. If you encounter it in a frontend:

1. Replace the CDN script tag:

   <!-- Before -->
   <script src="https://cdn.jsdelivr.net/npm/@microsoft/ai-chat-protocol@.../dist/iife/index.js"></script>
   <!-- After -->
   <script src="https://cdn.jsdelivr.net/npm/ndjson-readablestream@1.0.7/dist/ndjson-readablestream.umd.js"></script>
   

2. Remove the AIChatProtocolClient instantiation (new ChatProtocol.AIChatProtocolClient("/chat")).
3. Replace client.getStreamedCompletion(messages) with a direct fetch() call to the backend streaming endpoint.
4. Replace for await (const response of result) with for await (const chunk of readNDJSONStream(response.body)).
5. Update property access from response.delta.content / response.error to chunk.delta.content / chunk.error.

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Goals

• Enumerate all Python call sites using Chat Completions or legacy Completions against Azure OpenAI.
• Propose a migration plan and sequencing for the Python codebase.
• Apply safe, minimal edits to switch to Responses API.
• Update callers to consume the Responses output schema; no backcompat wrappers.
• Run tests/lints; fix trivial breakages introduced by the migration.
• Prepare small, reviewable change sets and provide a final summary with diffs (do not commit).

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Guardrails

• Only modify files inside the git workspace. Never write outside.
• Do not preserve backward-compatibility shims; migrate code to the new API shape.
• Do not leave tombstone/transition comments or backup files.
• Preserve streaming semantics if previously used; otherwise use non-streaming.
• Ask for approval before running commands or network calls if in approval mode.
• Do not run git add/git commit/git push; produce working-tree edits only.

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Step 0: Azure OpenAI Client Migration (Prerequisite)

If the codebase uses AzureOpenAI or AsyncAzureOpenAI constructors, migrate to the standard OpenAI / AsyncOpenAI constructors first. The Azure-specific constructors are deprecated in openai>=1.108.1.

Why the v1 API path?

The new /openai/v1 endpoint uses the standard OpenAI() client instead of AzureOpenAI(), requires no api_version parameter, and works identically across OpenAI and Azure OpenAI. The same client code is future-proof — no version management needed.

Key changes

Before	After
AzureOpenAI	OpenAI
AsyncAzureOpenAI	AsyncOpenAI
azure_endpoint	base_url
azure_ad_token_provider	api_key
api_version=...	Remove entirely

Cleanup checklist

• Remove api_version argument from client construction.
• Remove AZURE_OPENAI_VERSION / AZURE_OPENAI_API_VERSION environment variables from .env, app settings, and Bicep/infra files.
• Rename AZURE_OPENAI_CLIENT_ID → AZURE_CLIENT_ID in .env, app settings, Bicep/infra, and test fixtures (standard Azure Identity SDK convention).
• Ensure openai>=1.108.1 in requirements.txt or pyproject.toml.

Environment variable migration

Old env var	Action	Notes
AZURE_OPENAI_VERSION	Remove	No api_version needed with v1 endpoint
AZURE_OPENAI_API_VERSION	Remove	Same as above
AZURE_OPENAI_CLIENT_ID	Rename → AZURE_CLIENT_ID	Standard Azure Identity SDK convention for ManagedIdentityCredential(client_id=...)
AZURE_OPENAI_ENDPOINT	Keep	Still needed for base_url construction
AZURE_OPENAI_CHAT_DEPLOYMENT	Keep	Used as model param in responses.create
AZURE_OPENAI_API_KEY	Keep	Used as api_key for key-based auth

For client setup code examples (sync, async, EntraID, API key, multi-tenant), see cheat-sheet.md.

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Step 1: Detect Legacy Call Sites

Run the detect_legacy.py script to find all call sites that need migration:

python skills/azure-openai-to-responses/scripts/detect_legacy.py .

Or run these searches manually — every match is a migration target:

# Legacy API calls (must rewrite)
rg "chat\.completions\.create"
rg "ChatCompletion\.create"
rg "Completion\.create"

# Deprecated Azure client constructors (must replace)
rg "AzureOpenAI\("
rg "AsyncAzureOpenAI\("

# Response shape access patterns (must update)
rg "choices\[0\]\.message\.content"
rg "choices\[0\]\.delta\.content"
rg "choices\[0\]\.message\.function_call"
rg "choices\[0\]\.message\.tool_calls"

# Tool definitions in old nested format (must flatten)
rg '"function":\s*{\s*"name"'
rg "pydantic_function_tool"

# Tool results in old format (must convert to function_call_output)
rg '"role":\s*"tool"'
rg '"tool_call_id"'

# Deprecated parameters (must remove or rename)
rg "response_format"
rg "max_tokens\b"        # rename to max_output_tokens
rg "['\"]seed['\"]"      # remove entirely

# Deprecated env vars (clean up)
rg "AZURE_OPENAI_API_VERSION|AZURE_OPENAI_VERSION"
rg "AZURE_OPENAI_CLIENT_ID"  # should be AZURE_CLIENT_ID

# GitHub Models endpoints (must remove — Responses API not supported)
rg "models\.github\.ai|models\.inference\.ai\.azure"

# Framework-level legacy patterns (must update)
rg "OpenAIChatCompletionClient"  # MAF 1.0.0+: replace with OpenAIChatClient
rg "ChatOpenAI\(" | grep -v "use_responses_api"  # LangChain: needs use_responses_api=True

# Test infrastructure (must update)
rg "ChatCompletionChunk|AsyncCompletions\.create" tests/
rg "_azure_ad_token_provider" tests/
rg "prompt_filter_results|content_filter_results" tests/
rg "choices\[0\]" tests/

# Content filter error body access (must update — structure changed)
rg 'innererror.*content_filter_result|error\.body\["innererror"\]'
rg "content_filter_result\[" # old singular form — now content_filter_results (plural) inside content_filters array

# Raw HTTP calls to Chat Completions endpoint (must update URL)
rg "/openai/deployments/.*/chat/completions"
rg "api-version="

Heuristics (detect and rewrite)

• Chat Completions client: client.chat.completions.create → client.responses.create(...).

• Azure client constructors: AzureOpenAI(...) → OpenAI(base_url=..., api_key=...).

• Tools: convert function-calling tool definitions from nested format ({"type": "function", "function": {"name": ...}}) to flat Responses format ({"type": "function", "name": ...}); use tool_choice; return tool results as {"type": "function_call_output", "call_id": ..., "output": ...} items (not {"role": "tool", ...}).

• Tool round-trips: when the model returns function calls, append response.output items to the conversation (not a manual {"role": "assistant", "tool_calls": [...]} dict), then append function_call_output items for each result.

• Few-shot tool examples: if the conversation includes hardcoded tool call examples, convert them to {"type": "function_call", "id": "fc_...", "call_id": "fc_...", ...} + {"type": "function_call_output", ...} items. IDs must start with fc_.

• pydantic_function_tool(): this helper still generates the old nested format and is not compatible with responses.create(). Replace with manual tool definitions or a flattening wrapper.

• Multi-turn: maintain conversation history in the app; pass prior turns via input items.

• Formatting: replace Chat's top-level response_format with text.format in Responses. Canonical shape: text={"format": {"type": "json_schema", "name": "Output", "strict": True, "schema": {...}}}.

• Content items: replace Chat content[].type: "text" with Responses content[].type: "input_text" for user/system turns.

• Image content items: replace Chat content[].type: "image_url" with Responses content[].type: "input_image". The image_url field changes from a nested object {"url": "..."} to a flat string. See the cheat sheet for before/after examples.

• : .

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Step 2: Apply Migration

Migration notes (Chat Completions → Responses)

• Why migrate: Responses is the unified API for text, tools, and streaming; Chat Completions is legacy. With GPT-5, Responses is required for best performance.
• HTTP: Azure endpoint switches from /openai/deployments/{name}/chat/completions to /openai/v1/responses.
• Fields: messages → input, max_tokens → max_output_tokens. temperature remains.
• Formatting: response_format → text.format with a proper object.
• Content items: Replace Chat content[].type: "text" with Responses content[].type: "input_text" for system/user turns.
• Image content items: Replace Chat content[].type: "image_url" with Responses content[].type: "input_image". Flatten the image_url field from {"image_url": {"url": "..."}} to {"image_url": "..."} (a plain string — either an HTTPS URL or a data:image/...;base64,... data URI).

Parameter mapping reference

Chat Completions	Responses API
prompt	input
messages	input (array of items)
max_tokens	max_output_tokens
response_format	text.format (object)
temperature	temperature (unchanged)
stop	stop (unchanged)
frequency_penalty	frequency_penalty (unchanged)
presence_penalty	presence_penalty (unchanged)
tools / function-calling	tools (unchanged)
seed	Remove (not supported)
store	store (set to false)
content[].type: "text"	content[].type: "input_text"
content[].type: "image_url"	content[].type: "input_image"
"image_url": {"url": "..."}	"image_url": "..." (flat string)

For complete before/after code examples, see cheat-sheet.md.

For test infrastructure migration (mocks, snapshots, assertions), see test-migration.md.

For troubleshooting errors and gotchas, see troubleshooting.md.

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Data Retention & State

• Set store: false on all Responses requests.
• Do not rely on previous message IDs or server-stored context; keep state client-managed and minimize metadata.

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Acceptance Criteria

Code-level gates (all must pass)

• Zero matches for rg "chat\.completions\.create|ChatCompletion\.create|Completion\.create" in migrated files.
• Zero matches for rg "AzureOpenAI\(|AsyncAzureOpenAI\(" — all constructors use OpenAI/AsyncOpenAI with the v1 endpoint.
• Zero matches for rg "models\.github\.ai|models\.inference\.ai\.azure" — GitHub Models code paths removed.
• Zero matches for rg "OpenAIChatCompletionClient" — MAF 1.0.0+ code uses OpenAIChatClient (which uses Responses API). In pre-1.0.0, upgrade to agent-framework-openai>=1.0.0.
• All ChatOpenAI(...) calls include use_responses_api=True.
• Zero matches for rg "choices\[0\]" — all response access uses resp.output_text or the Responses output schema.
• No response_format at top level; all structured output uses text={"format": {...}}.
• openai>=1.108.1 and azure-identity in requirements.txt or pyproject.toml; dependencies reinstalled.
• store=False set on every responses.create call.
• No api_version in client construction; AZURE_OPENAI_API_VERSION removed from env files and infra.

Test infrastructure gates (all must pass)

• Zero matches for rg "ChatCompletionChunk|AsyncCompletions\.create|chat\.completions" tests/.
• Zero matches for rg "_azure_ad_token_provider" tests/ — assertions updated to check isinstance(client, AsyncOpenAI) or base_url.
• Zero matches for rg "prompt_filter_results|content_filter_results" tests/ — Azure-specific filter mocks removed.
• Mock fixtures use kwargs.get("input") not kwargs.get("messages").
• Snapshot / golden files updated to Responses streaming shape (no choices[0], function_call, logprobs, etc.).
• pytest passes with zero failures after all test updates.

Behavioral gates (verify manually or via test harness)

• Basic completion: non-streaming responses.create returns non-empty output_text.
• Stream parity: if the original code used streaming, the migrated code streams and yields response.output_text.delta events with non-empty deltas.
• Structured output: if using text.format with json_schema, json.loads(resp.output_text) succeeds and matches the schema.
• Tool-call loop: if tools are used, the model issues tool calls, the app executes them, and the follow-up request returns a final output_text (no infinite loop).
• Async parity: if AsyncAzureOpenAI was used, AsyncOpenAI equivalent works with await.
• Error rate: no new 400/401/404 errors compared to the pre-migration baseline.

Deliverables

• Summary includes edited files, before/after counts of legacy call sites, and next steps.
• Changes are working-tree edits only (no commits).

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SDK Version Requirements

Package	Minimum Version
openai	>=1.108.1
azure-identity	Latest (for EntraID auth)

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References

• Cheat Sheet — all code snippets
• Test Migration — mocks, snapshots, assertions
• Troubleshooting — errors, risk table, gotchas
• detect_legacy.py — automated scanner
• Azure OpenAI Starter Kit
• Azure OpenAI Responses API docs
• Azure OpenAI API version lifecycle
• OpenAI Responses API reference

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Disclaimer: This document has been translated using AI translation service Co-op Translator. While we strive for accuracy, please be aware that automated translations may contain errors or inaccuracies. The original document in its native language should be considered the authoritative source. For critical information, professional human translation is recommended. We are not liable for any misunderstandings or misinterpretations arising from the use of this translation.