Fixed OpenAI model switching bugPublic

This is a new PR for Azure OpenAI Responses, but the issue mentions strictResponses was removed. Let me search for any previous PR that had the strictResponses implementation. Let me also check the git log for any reference to strictResponses.

Let me look at the commit d43930c8 that added strictResponsesPairing, and see what was done there.

Let me see the full diff of d43930c8 for the openai-responses.ts to understand what strictResponsesPairing was doing, and then compare with the current state to see what was removed.

So the user removed strictResponsesPairing in commit 2d27a2c7, with a fix that skips errored/aborted assistant messages entirely. But this regression (issue #886) shows that fix wasn't complete. Let me see the current state of openai-responses.ts and transform-messages.ts to understand what's happening.

The issue is clear now. In commit 2d27a2c7, the user simplified the logic by skipping errored/aborted assistant messages entirely in transform-messages.ts. The reasoning was that incomplete turns shouldn't be replayed.

But the error in issue #886 shows:

Error: 400 Item 'fc_034ef5c97783b8d2006970733117f48194a4997e01828300d8' of type 'function_call' was provided without its required 'reasoning' item: 'rs_034ef5c97783b8d20069707330695c8194aa064977859321be'.

This means that a function_call was provided but the API requires its paired reasoning item to also be provided. The strictResponsesPairing logic was checking:

1. hasPairedContent - whether there's a tool call or non-empty text content
2. shouldReplayReasoning - only replay reasoning if there's paired content

The problem is Azure OpenAI Responses API requires that if you send a function_call, you must also send its associated reasoning item. The original strictResponsesPairing fix had logic to detect incomplete turns and generate synthetic message IDs to avoid this pairing requirement.

Now let me read the current state of openai-responses.ts to understand what's missing.

Now let me also read the transform-messages.ts to understand the current state:

Now I understand the issue. Let me also check the PR #890's azure-openai-responses.ts to see if the contributor had different logic for handling the strict pairing:

Let me try a different approach - fetch the PR file directly:

Let me get more of the convertMessages function to see how it handles assistant messages:

Now I've got a clear picture. The PR #890 contributor's azure-openai-responses.ts implementation is identical to the current openai-responses.ts in terms of message conversion. Neither has the strictResponsesPairing logic anymore.

Let me understand the actual problem better by analyzing the error message:

Error: 400 Item 'fc_034ef5c97783b8d2006970733117f48194a4997e01828300d8' of type 'function_call' was provided without its required 'reasoning' item: 'rs_034ef5c97783b8d20069707330695c8194aa064977859321be'.

This error indicates:

1. A function_call item is sent with a specific ID
2. The API expects a matching reasoning item (with a specific reasoning item ID) to also be sent
3. The reasoning item is missing

The issue is that when you abort a request during reasoning, you may have:

• An incomplete reasoning item (stored in thinkingSignature)
• A function_call item that follows it

When replaying, if we skip the reasoning item but include the function_call, Azure's Responses API complains.

Looking at the original strictResponsesPairing logic:

1. isIncomplete = msg.stopReason === "error" || msg.stopReason === "aborted"
2. hasPairedContent = msg.content.some((b) => b.type === "toolCall" || (b.type === "text" && text.trim().length > 0))
3. shouldReplayReasoning = !isIncomplete && hasPairedContent
4. allowToolCalls = !isIncomplete

The key insight: for incomplete turns, don't replay reasoning OR tool calls.

But the current fix in transform-messages.ts already skips entire errored/aborted assistant messages:

if (assistantMsg.stopReason === "error" || assistantMsg.stopReason === "aborted") {    continue;}

So the question is: why is this still happening? There must be a case where the stopReason isn't marked as "error" or "aborted" yet we still have an incomplete turn. Possible scenarios: user aborts mid-reasoning (stopReason should be "aborted"), model finishes reasoning but fails during function_call (stopReason should be "error"), or the model returns reasoning plus function_call without any text content.

Looking at the actual error more carefully, the issue is that Azure's Responses API enforces strict pairing semantics: if a function_call came from a previous response that included a reasoning item, that reasoning item must also be sent. The convertMessages function in openai-responses.ts sends reasoning items whenever they have a thinkingSignature, but this creates a mismatch when the stopReason is "toolUse" - which isn't being skipped by the current filter.

The strictResponsesPairing logic determines whether to replay reasoning based on whether the message is incomplete (stopReason is "error" or "aborted") and whether it has meaningful paired content like tool calls or non-empty text. If both conditions are met, reasoning gets replayed and tool calls are allowed; otherwise they're blocked.

The second protection is particularly relevant—if there's reasoning without any paired content, the reasoning shouldn't be replayed. But since transform-messages.ts already filters out errored and aborted messages entirely, the issue must be something else. The error specifically states that a function_call was provided without its required reasoning item, which suggests we're sending tool calls but missing their corresponding reasoning blocks.

Looking at the error structure more carefully, the function_call and reasoning item IDs share the same prefix, indicating Azure tracks specific pairings between them. When streaming gets interrupted, we might end up with a partial reasoning block captured in thinkingSignature alongside a complete function_call. During replay, that incomplete reasoning paired with the tool call creates the mismatch Azure's API rejects.

The strictResponsesPairing solution was to discard both reasoning and tool calls for incomplete turns entirely. The current transform-messages approach should do the same by skipping the whole message, but the stopReason might not be set properly. Looking at the stream error handler, when an abort occurs it sets stopReason to "aborted" and pushes an error event before ending the stream. That seems right.

But I'm realizing the actual problem might be different. The user terminates reasoning mid-way through, then tries chatting again. When that partial message with stopReason = "aborted" gets loaded back, transform-messages correctly skips it. However, there could be a scenario where the agent resumes after an abort and somehow lets an assistant message with incomplete content slip through without being filtered.

Looking at the stream handling, there's validation for aborted signals and error states that should throw, but what if a stream completes normally despite having incomplete content? The error IDs in the stack trace (fc_034ef5c..., rs_034ef5c...) suggest these are coming from the API itself, which means I need to trace where these IDs originate.

These IDs represent items that need to be replayed in subsequent requests. When the API returns a reasoning item (rs_xxx) paired with a function call (fc_xxx), both must be included when continuing the conversation. The problem emerges if the stream gets interrupted during step 1—we might capture a partial reasoning item in thinkingSignature while also storing the function_call, but Azure requires the complete paired reasoning item when replaying that specific function_call ID. I should check where thinkingSignature gets populated to understand if it's capturing the full reasoning or just fragments.

The thinkingSignature only gets set when a reasoning item completes its event stream. If the connection drops before that done event fires, thinkingSignature stays empty even though we have the function_call stored. This creates an asymmetry in convertMessages where we try to reconstruct the conversation—we have the function_call but lack the complete reasoning item needed to pair with it.

The scenario gets more complex when considering the event ordering in the API. If a function_call finishes its done event before reasoning completes, we'd be adding the function_call to output while the reasoning block remains incomplete. The stopReason check might catch some cases, but there's a gap when the function_call fully completes while reasoning is still streaming.

The real problem seems to be that we're not properly validating the state of all output items before deciding whether to include them in the final message. If reasoning gets interrupted mid-stream but a function_call has already been added and marked done, we end up with an incomplete reasoning block paired with a complete function_call.

Looking at the PR #890 discussion, the author tested aborting during an agent turn and couldn't reproduce the issue on standard OpenAI, but the error specifically mentions Azure's Responses API being stricter about validation. Azure might be rejecting messages where reasoning is incomplete but subsequent content items are fully formed. The strictResponsesPairing logic was originally implemented to handle Azure's particular validation requirements, so the issue likely stems from that validation not catching all edge cases around partial reasoning blocks.

The error IDs sharing the same random prefix suggests these aren't from an aborted turn but rather from a previous successful exchange. This points to a different problem—how messages from earlier turns are being reconstructed when converting between formats, particularly when Azure's API expects complete reasoning-function_call pairs that might be getting separated during the transformation process. else if (block.type === "toolCall") { ... } }

The thinking block extraction depends on block.thinkingSignature being present. If a thinking block exists without this signature—perhaps from a different provider or if it got lost somehow—it won't be included in the output. Additionally, the order in which items are pushed matters; if thinking appears after a toolCall in the content array, the function call gets added before the reasoning, which could affect how the message reconstructs during replay.

I suspect the real culprit might be that Azure OpenAI Responses API is being used, but the code is routing through the standard OpenAI provider instead of an Azure-specific one. PR #890 introduces azure-openai-responses.ts as a dedicated provider that would handle this differently.

The core issue appears to be that Azure's Responses API enforces strict pairing between function calls and reasoning items—they must both be present and share matching IDs. The original strictResponsesPairing flag handled incomplete turns by skipping both reasoning and tool calls together, but a later change removed this safeguard, assuming all turns would be complete.

The problem is that messages can have incomplete content without being explicitly marked as errored or aborted, especially when crossing providers or if the thinking signature gets lost. The pairing validation needs to happen in convertMessages() where it can catch these edge cases—it should only output reasoning items when there's corresponding paired content to complete the turn.

For reasoning models specifically, toolCalls should only be included if reasoning is also present. The core requirement is enforcing the relationship: if a function_call references a reasoning item's ID, both must be sent together or neither at all. When toolCalls exist but the reasoning signature is missing or invalid, the toolCalls should be dropped to maintain consistency.

The current approach in transform-messages handles errored or aborted messages by skipping them entirely, but there's a possibility the stopReason gets lost during message persistence or serialization. I should investigate whether messages are being saved and reloaded without preserving the stopReason field, which could explain why aborted reasoning isn't being properly filtered out.

The user is right to question this. Let me re-examine the flow more carefully.

In transform-messages.ts:

const assistantMsg = msg as AssistantMessage;if (assistantMsg.stopReason === "error" || assistantMsg.stopReason === "aborted") {    continue;}

So if the message has stopReason "error" or "aborted", the entire message is skipped. This should prevent the orphaned function_call from ever reaching convertMessages().

So why is the bug still happening? Let me think about this more carefully...

Possible scenarios where this would fail:

1. stopReason not being set correctly - Maybe there's a code path where abort happens but stopReason doesn't get set to "aborted"?

2. Message persistence/reload - If messages are serialized and reloaded, is stopReason preserved?

3. The error is not about an aborted turn - Maybe the error happens in a different scenario entirely?

Let me look at the error message again:

Error: 400 Item 'fc_034ef5c97783b8d2006970733117f48194a4997e01828300d8' of type 'function_call' was provided without its required 'reasoning' item: 'rs_034ef5c97783b8d20069707330695c8194aa064977859321be'.

Wait, let me look at this differently. The error says the function_call was provided WITHOUT its required reasoning item. But both IDs share the same prefix pattern (034ef5c97783b8d2...). These look like real API-generated IDs from a COMPLETED turn, not an aborted one.

So the question becomes: in what scenario would a COMPLETED turn (stopReason = "stop" or "toolUse") have a function_call but be missing its reasoning item?

Looking at the code flow in openai-responses.ts:

1. response.output_item.added for reasoning - creates thinking block
2. reasoning events stream in
3. response.output_item.done for reasoning - sets thinkingSignature
4. response.output_item.added for function_call - creates toolCall block
5. function_call events stream in
6. response.output_item.done for function_call - finalizes toolCall
7. response.completed - sets stopReason

If all these complete normally, thinkingSignature SHOULD be set. But what if the reasoning item streams and completes, then the function_call is added and streams, but something interrupts before the reasoning item is fully processed or stored?

Consider a multi-turn scenario with Azure OpenAI: the first turn generates reasoning plus a function call, everything completes with stopReason = "toolUse", and thinkingSignature gets stored. The tool executes, then on the second turn the conversation replays with the stored signature. This should work seamlessly unless the signature format becomes incompatible during parsing or replay, or there's a mismatch in how it's being reconstructed across the provider handoff.

The thinkingSignature stores the full reasoning item as JSON, then parses and pushes it back during message conversion. But what if Azure's response structure differs slightly from OpenAI's, or some property causes issues when replayed? The error message itself is revealing—Azure explicitly states which reasoning item ID it expects for a given function call, suggesting Azure knows the relationship between them but can't find the reasoning item in the reconstructed message. The ID prefixes match—both items share 034ef5c97783b8d2... before diverging, suggesting Azure links them structurally through ID patterns rather than explicit references. This raises a puzzle: if the reasoning was aborted and skipped during message transformation, why would Azure later expect it when replaying a function_call from that same response? The user's description of "terminating the reasoning part" might not capture what actually happened—perhaps the reasoning completed but wasn't properly stored, or the function_call was replayed without its paired reasoning item being included in the messages array. Looking at the transform-messages logic, I see how thinking blocks are handled differently based on whether it's the same model. For matching models, thinking blocks with signatures are preserved as-is. Empty thinking gets dropped entirely. Other thinking blocks either stay as thinking blocks or get converted to plain text depending on the model match. This transformation could potentially strip or alter reasoning content from earlier messages if the conditions aren't met correctly. The early return for isSameModel && block.thinkingSignature means empty thinking blocks with signatures are preserved for replay, while empty blocks without signatures get filtered out. For different models, the signature is stripped and thinking converts to plain text.

When loading messages from disk, isSameModel checks if the stored provider, API, and model ID match the current configuration. If the user switches to Azure or changes their model, this comparison would fail, potentially losing the signature. exists, the block still gets filtered out because the first condition fails. If the thinking text is empty—which happens with OpenAI's encrypted reasoning where only the signature is present—the block returns an empty array. This means the thinkingSignature is discarded entirely.

When convertMessages processes this, there's no thinking block with the signature anymore, but the toolCall blocks remain. This results in function_call being sent without any reasoning context, which could trigger the error. For models like o1 and o3 that use encrypted reasoning, the thinking field is often empty and only the signature exists. If isSameModel evaluates to false for any reason, the entire block gets filtered out, leaving orphaned tool calls.

The provider mismatch could stem from cross-session message loading or model configuration changes. If someone uses Azure OpenAI by modifying the baseUrl while keeping the model provider as "openai", the provider should still match. However, loading messages from a previous session with different model settings could cause isSameModel to return false unexpectedly.

The real issue is that strictResponsesPairing was handling incomplete reasoning/toolCall pairings within messages, but the replacement logic in transform-messages only skips entirely errored or aborted messages. When a thinking block gets filtered out due to empty text and isSameModel being false, the message itself isn't marked as error/aborted, leaving incomplete pairings behind. Restoring the strict pairing logic as default behavior in convertMessages would catch these edge cases that the current approach misses.

But wait—if transform-messages is properly filtering errored/aborted messages, convertMessages shouldn't encounter incomplete content from those. The incomplete content must be coming from either a message that wasn't marked as errored/aborted, or something's modifying the messages between the two functions. Let me trace what happens after transformMessages is called in convertMessages to see if there's any intermediate modification.

Looking at the flow: transform-messages runs in two passes—first filtering content like thinking blocks, then skipping errored/aborted messages. The issue is that thinking blocks get filtered in pass one, before pass two checks the stopReason. So a message with an empty thinking block and stopReason of "stop" (not error/aborted) would have its thinking removed but still be processed, sending a toolCall without the reasoning context.

This could happen even on successful completions if the model uses encrypted reasoning and isSameModel returns false due to a provider mismatch. But encrypted reasoning shouldn't actually have empty thinking text—the reasoning_summary_text.delta events populate the thinking field, and when response.output_item.done fires for a reasoning block, it sets the thinking from the item.summary. So the thinking text should be populated from the summary data.

The issue likely stems from Azure not always providing a summary, or providing an empty one. This creates the scenario where a completed turn has both reasoning and a function_call, but the thinking block gets filtered out in transform-messages because isSameModel is false AND thinking is empty. Then convertMessages receives a message with a toolCall but no thinking block, sends the function_call without the reasoning item, and Azure returns a 400 error. The fix should enforce strict pairing logic in convertMessages as a safeguard.

Alternatively, I could modify transform-messages to preserve thinking blocks that have a thinkingSignature regardless of isSameModel or text content, since the signature is what enables replay. Looking at the current logic, when the model doesn't match but a signature exists, the block still gets filtered or converted to text. Since signatures only matter for the same API, keeping them makes sense only when isSameModel is true.

The deeper issue is whether function_calls from a different model's turn should be sent at all. When isSameModel is false, thinking blocks convert to text while toolCalls stay intact with normalized IDs, but OpenAI's Responses API expects provider-specific IDs. The proper solution might be filtering toolCalls when crossing providers, though that risks breaking cross-provider handoffs. For now, adding strict pairing validation in convertMessages as a safeguard seems like the most targeted approach.

The implementation checks for incomplete turns by examining stopReason, then validates whether reasoning has a signature before replaying it. For thinking blocks, if they're not incomplete and have a thinkingSignature, that signature gets parsed and added to the output.

Text blocks pass through unchanged, while tool calls only get included if either a reasoning signature exists or the model doesn't use reasoning—this prevents Azure from rejecting orphaned function calls in reasoning model responses.

The user is asking about PR #890. Let me check what it actually does differently. The PR author (markusylisiurunen) claimed it might fix #886.

Looking at what I saw from PR #890's azure-openai-responses.ts, the convertMessages function looked identical to openai-responses.ts - it didn't have any special strict pairing logic.

But wait, the PR author said "I can't break Pi regardless of how I abort mid agent turn" - so maybe the fix is elsewhere?

Let me re-read what the PR does:

1. Adds a new provider azure-openai-responses
2. Uses AzureOpenAI client instead of regular OpenAI client
3. Has Azure-specific auth/endpoint resolution

But the convertMessages logic looked the same. So why would it fix the issue?

Maybe the difference is that with a proper Azure provider:

• model.provider would be "azure-openai-responses" (or similar)
• assistantMsg.provider would also be "azure-openai-responses"
• So isSameModel would be TRUE

Whereas currently, users might be using openai-responses with Azure base URL, causing provider mismatches when loading saved sessions.

So the fix might be indirect - by having a dedicated Azure provider, the isSameModel check works correctly, and thinking blocks with signatures are preserved.

Let me verify this theory by looking at the PR more carefully.

So the PR #890's convertMessages is identical to the current openai-responses.ts. No special strict pairing logic.

So why does the PR author claim it might fix the issue?

The key difference is that PR #890 creates a dedicated Azure provider (azure-openai-responses). This means:

1. model.provider = "azure-openai-responses" (or "azure-openai")
2. model.api = "azure-openai-responses"
3. When messages are created, assistantMsg.provider and assistantMsg.api match the model

So when transform-messages runs:

const isSameModel =    assistantMsg.provider === model.provider &&    assistantMsg.api === model.api &&    assistantMsg.model === model.id;

With a dedicated Azure provider, isSameModel is true (assuming same deployment), so:

if (isSameModel && block.thinkingSignature) return block;  // This works!

The thinking block with signature is preserved.

Currently, users work around by using openai-responses with Azure base URL. But this means:

• model.provider = "openai"
• assistantMsg.provider = "openai"
• They might match... but if there's any config difference, they won't

The PR author's claim that "I can't break Pi regardless of how I abort" is because with the dedicated provider, the provider/api matching is consistent, so isSameModel is reliably true.

So the fix is indirect - not in convertMessages logic, but by having proper provider identification that makes the existing safeguards work correctly.

However, this still doesn't add defense-in-depth. If for any reason isSameModel becomes false (cross-provider handoff, session corruption, etc.), the bug would resurface.

The proper fix should still add the strict pairing check in convertMessages() as a safety net.

The user wants me to read the existing test file and extend it to test the scenario where a function_call is sent without its paired reasoning item. Let me first read the test file to understand its structure.

Now I understand the existing test. It tests the scenario where an aborted turn with only a thinking block (no tool calls) is handled correctly.

The user wants me to add a test for the scenario where:

1. A completed turn has reasoning + function_call
2. The thinking block gets filtered out (simulating what happens when isSameModel is false and thinking text is empty)
3. The toolCall remains orphaned
4. Azure/OpenAI should reject this with a 400 error about missing reasoning item

To test this, I need to create a scenario where:

1. Get a successful completion with reasoning + tool call
2. Create a corrupted assistant message that has the toolCall but no thinkingSignature (or thinking block removed entirely)
3. Try to continue the conversation
4. Currently this should FAIL (400 error) - demonstrating the bug
5. After the fix, it should pass (toolCalls should be dropped if reasoning is missing)

Actually, let me think about what behavior we want:

• Option A: The system should drop the orphaned toolCalls and continue (no error)
• Option B: The system should error early, before sending to API

I think Option A is better UX - gracefully handle the corruption rather than failing.

Let me write a test that:

1. Gets a successful completion with reasoning + tool call
2. Simulates the corruption by removing the thinkingSignature (but keeping the toolCall)
3. Continues the conversation
4. Expects NO 400 error (the fix should drop the orphaned toolCall)

This test will fail currently (demonstrating the bug) and pass after the fix.

Now I need to add a new test case that tests the scenario where a toolCall exists but the thinkingSignature is missing.

Now let me run the test to see if it fails as expected (demonstrating the bug):

The test fails exactly as expected, demonstrating the bug:

Error: 400 Item 'fc_04389528c4332c420069715a0709d88192b4e6e82a86e9d85f' of type 'function_call' was provided without its required 'reasoning' item: 'rs_04389528c4332c420069715a011c808192922dfc0734c0668a'.

This proves the bug exists. The test correctly reproduces the scenario where:

1. A toolCall exists but thinkingSignature is missing
2. The function_call is sent without its paired reasoning item
3. OpenAI/Azure returns a 400 error

Now the user has a failing test that will pass once the fix is implemented in convertMessages(). The fix should check if a reasoning model's assistant message has toolCalls but no valid thinkingSignature, and drop those orphaned toolCalls.

The user wants me to commit and push the test, then discuss the fix.

Let me commit just the test file changes.

Now I need to push.

Now to answer the user's question: do ALL function calls generated by a reasoning model via OpenAI Responses API contain a thinking signature (i.e., are paired with a reasoning item)?Let me think about this:

1. For reasoning models (like o1, o3, gpt-5 with reasoning enabled):

   • The model first outputs a reasoning item (with encrypted content and optionally a summary)
   • Then it outputs whatever comes next (message, function_call, etc.)
   • Each response turn starts with reasoning

2. For non-reasoning models (like gpt-4o):

   • No reasoning item is output
   • function_calls come directly without any paired reasoning

So the answer is:

• For reasoning models: YES, every response turn starts with a reasoning item, so any function_calls in that turn are implicitly paired with that reasoning
• For non-reasoning models: NO, there's no reasoning item at all

The pairing is at the TURN level, not the individual function_call level. A single turn might have:

• 1 reasoning item
• 0 or more function_calls
• 0 or 1 text messagesAll function_calls in that turn are "paired" with the single reasoning item.

So for the fix, the logic should be:

• For reasoning models (model.reasoning === true): only include function_calls if we have a valid reasoning signature in the same assistant message
• For non-reasoning models: always include function_calls (no pairing requirement)

The user wants me to:

1. Fix convertMessages in openai-responses.ts to drop orphaned toolCalls when reasoning signature is missing
2. Modify the test to also check if an orphaned toolResult (from the dropped toolCall) passes the API

Let me first implement the fix in convertMessages, then modify the test.

Now I need to modify the assistant message handling to:

1. Check if there's a valid reasoning signature
2. Only include toolCalls if we have valid reasoning (for reasoning models)I also need to track which toolCalls were dropped so we can filter out orphaned toolResults.

Actually, looking at this more carefully, the toolResult filtering should probably happen in transform-messages.ts, not convertMessages. Because transform-messages already tracks pending tool calls and creates synthetic results for orphaned ones.

But for now, let me focus on the immediate fix in convertMessages - dropping orphaned toolCalls. For the toolResults, I need to also track which toolCallIds we're dropping so we can filter them out.

Let me modify the code:

Now I need to:1. Add the droppedToolCallIds set at the beginning of convertMessages 2. Use it to filter out orphaned toolResults

Now I need to filter out the orphaned toolResults. Let me look at the toolResult handling section:

Now I need to add a check at the beginning of the toolResult handling to skip orphaned results.

Now I need to update the test to also verify that the orphaned toolResult case works. The current test already has a toolResult in it - let me run it to see if it passes now:

The user wants to test if OpenAI Responses API accepts orphaned tool results (tool results without their corresponding tool calls). They want:

1. The fix to drop orphaned toolCalls (when reasoning signature is missing)
2. But NOT drop the toolResults
3. Test if the API accepts orphaned toolResults

Let me revert the toolResult filtering and update the test to explicitly check this scenario.

Now I also need to remove the droppedToolCallIds tracking since we're not using it anymore. Let me also clean up the code that tracks dropped tool calls.

Now I also need to remove the part that tracks dropped tool calls in the assistant message handling.

Now let me update the test to check if orphaned toolResults pass the API. The test should:1. Get a real response with reasoning + tool call2. Simulate corruption: keep toolCall but strip thinkingSignature3. Add a toolResult for the toolCall 4. Continue the conversation 5. With the fix: toolCall is dropped, but toolResult remains 6. Test if API accepts the orphaned toolResult

The test already has a toolResult in it. But the user wants to specifically test if an orphaned toolResult (one where the corresponding toolCall was dropped by convertMessages) passes the API.Let me modify the test to make it clearer that we're testing the orphaned toolResult scenario. The current test already does this because:

1. toolCall has no valid reasoning signature
2. convertMessages (with the fix) will drop the toolCall3. toolResult remains in the messages4. API should accept this (orphaned toolResult)Actually the test is already correct - it includes a toolResult for the toolCall. With the fix, the toolCall will be dropped but the toolResult will remain. If the API accepts orphaned toolResults, the test will pass.

Let me run the test to see what happens:

The error changed. Now we get:``` Error: 400 No tool call found for function call output with call_id call_pVfQqsHe1KRvWS1tbsMqVzP9.

This means:1. The fix worked for dropping the orphaned function_call (no more "function_call without reasoning item" error)2. But the API rejects orphaned toolResults ("No tool call found for function call output")So the API does NOT accept orphaned toolResults. We need to also drop the orphaned toolResults when we drop the toolCalls.
Let me add back the tracking and filtering of orphaned toolResults:

Now I need to add back the tracking of dropped tool calls and the filtering of orphaned tool results.

Now I need to add the filtering of orphaned tool results.

Now let's run the test again to see if it passes:

The user is right. The current approach is too aggressive. If we drop all tool calls that don't have a reasoning signature, we'd break scenarios where:

1. A session started with a non-reasoning model (like gpt-4o)
2. User switches to a reasoning model (like gpt-5 or codex)
3. The previous tool calls from gpt-4o don't have reasoning signatures
4. We'd drop all those valid tool calls!

The issue is specifically about the pairing requirement in the OpenAI Responses API. The API requires that if a function_call item was originally generated WITH a reasoning item, then when replaying, you must include BOTH or NEITHER.

The key insight is: the issue only occurs when:

1. The original response HAD a reasoning item (model.reasoning was true when the response was generated)
2. But the thinkingSignature is now missing

If the original response never had a reasoning item (non-reasoning model), then the function_call doesn't require a paired reasoning item.

So we need to distinguish between:

• Tool calls that were ORIGINALLY generated without reasoning (from non-reasoning models) → always include
• Tool calls that were ORIGINALLY generated with reasoning but signature is lost → drop

How can we tell the difference? We could look at:

1. assistantMsg.model - check if the original model was a reasoning model
2. A new field on the message indicating it came from a reasoning model

Actually, looking at the AssistantMessage type, it has api, provider, and model fields. We could check if the original model was a reasoning model.

But we don't have access to the model registry here easily... and the model ID might not be enough (same model can be used with or without reasoning).

Hmm, let me think about this differently. The real issue is:

• OpenAI Responses API tracks which function_calls came with paired reasoning items

• When you replay, if you include a function_call that originally had a reasoning pair, you must include the reasoning too

• The signature contains the original item data including its ID, which the API uses to track pairing.

One approach: if a function_call has an ID that looks like it came from a reasoning response (e.g., the fc_xxx/rs_xxx pairing), but we don't have the reasoning signature, then drop it. But that's fragile—we're guessing based on ID patterns.

Better approach: track whether the assistant message had ANY thinking content with a signature. If the message has thinking blocks with signatures, it's from a reasoning model and safe to include tool calls. If it has thinking blocks without signatures, it was from a reasoning model but signatures were lost, so drop those tools.

If there are no thinking blocks at all, it's from a non-reasoning model, so tool calls are safe to include.

The current code checks model.reasoning (the current model), not whether the original message had reasoning—that's the bug. The fix needs to check if thinking blocks exist and whether they have signatures, then allow tool calls only if there are no thinking blocks or if all thinking blocks have signatures.

This approach handles cross-model scenarios correctly: previous outputs from non-reasoning models have no thinking blocks and can include tool calls, reasoning model outputs with signatures are fine, and reasoning model outputs where the signature was lost get dropped to prevent API errors.

Now let me run the test to see if it passes:

The user is right. Let me understand the problem better:

1. OpenAI Responses API with reasoning models requires: reasoning item + function_call paired together
2. If we have a tool call from a NON-reasoning model (e.g., gpt-4o, Anthropic), there's no thinking block at all
3. If we try to send that tool call to a reasoning model via Responses API, it will fail because there's no paired reasoning item

So the issue is broader than just "lost signatures". It's about cross-provider/cross-model compatibility where:

• Source: non-reasoning model generated tool calls
• Target: reasoning model via Responses API

The solution the user is suggesting:

• If a tool call has no thinking block AND no signature, we can't send it as a function_call
• Instead, convert it to a text representation (assistant message saying "I called tool X with args Y")
• Convert the tool result to a user message (saying "Tool X returned: Z")

This way, the reasoning model understands what happened without needing the actual function_call items.

Let me revert and think about this properly.

Let me think through this:1. OpenAI Responses API with reasoning models requires function_calls to be paired with reasoning items 2. If tool calls come from:

• Same reasoning model with signature → send as function_call (signature provides pairing) - Same reasoning model without signature (lost) → can't send as function_call (no pairing)
• Different reasoning model → can't send as function_call (signature is model-specific)
• Non-reasoning model → can't send as function_call (no reasoning to pair with)

3. For cases where we can't send as function_call, we need to convert:
   • Tool call → assistant text message ("I called tool X with args Y")
   • Tool result → user message ("Tool X returned: Z")This way, the model understands what happened without needing the actual Responses API function_call items.The logic should be:- If thinking block exists WITH signature → send function_call (paired correctly)

• If thinking block exists WITHOUT signature → convert to text (pairing lost)
• If NO thinking block → convert to text (from non-reasoning model)

This conversion should happen in convertMessages() for openai-responses.ts.

Actually, wait. The isSameModel check in transform-messages.ts already handles some of this. For non-same-model cases, thinking blocks are either filtered or converted to text. But tool calls pass through unchanged.

The issue is that tool calls from non-same-model cases can't be sent as function_calls to the Responses API.

So in convertMessages(), when we encounter a tool call without a valid reasoning signature, we need to:

1. Convert the tool call to a text representation
2. Convert any following tool results to a user message

Let me think about how to implement this...

Now I understand better. When we can't send tool calls as function_call items, we need to:1. Convert tool calls to assistant text (within the assistant message) 2. Track which tool call IDs were converted 3. When processing tool results, if their toolCallId is in the converted set, convert them to user messages instead of function_call_outputLet me implement this:

Now I need to:

1. Add the convertedToolCallIds set at the beginning of convertMessages
2. Update the toolResult handling to convert to user message if the toolCallId is in the converted set

Now I need to update the toolResult handling to convert to user messages when the toolCallId was converted.

Now I need to update the toolResult handling to check if the toolCallId was converted, and if so, send as a user message instead of function_call_output.

Now let me run the test to see if this approach works:

Both tests pass. Let me run the full check to make sure everything still works:

All checks pass. Let me update the test name to be more accurate since we're no longer "dropping" tool calls, but converting them to text:

Now let me also update the CHANGELOG to document this fix:

I need to add a fix entry to the CHANGELOG under [Unreleased].

Now let me run check again and then summarize the fix:

All checks pass. Let me summarize the fix for the user.