Add thinking in thinking blocks, and fix DeepSeek interleaved tool calls (#1548)

## Motivation

OpenCode shows <think> tags and not thinking blocks as we aren't
following the API specs properly.

Claude was also getting horrible prefix cache hits because it sends
headers.

## Changes

Handle thinking tokens properly by placing them in think tags for each
of the API endpoints.
Also support DeepSeekV3.2 tool calling properly as a minor feature.
Strips Claude headers at the API level.

## Test Plan

### Manual Testing
Tested OpenCode manually
Needs testing with Claude.

### Automated Testing
All CI and tests passing - added a new e2e test for DeepSeekV32 tool
parsing.

dashboard/src/lib/stores/app.svelte.ts

@@ -1652,11 +1652,12 @@ class AppStore {
       if (!reader) throw new Error("No response body");
 
       let fullContent = prefixText;
+      let streamedThinking = "";
       const collectedTokens: TokenData[] = [...tokensToKeep];
 
       interface ChatCompletionChunk {
         choices?: Array<{
-          delta?: { content?: string };
+          delta?: { content?: string; reasoning_content?: string };
           logprobs?: {
             content?: Array<{
               token: string;
@@ -1677,6 +1678,7 @@ class AppStore {
         (parsed) => {
           const choice = parsed.choices?.[0];
           const delta = choice?.delta?.content;
+          const thinkingDelta = choice?.delta?.reasoning_content;
 
           // Collect logprobs data
           const logprobsContent = choice?.logprobs?.content;
@@ -1695,7 +1697,11 @@ class AppStore {
             }
           }
 
-          if (delta) {
+          if (thinkingDelta) {
+            streamedThinking += thinkingDelta;
+          }
+
+          if (delta || thinkingDelta) {
             if (firstTokenTime === null) {
               firstTokenTime = performance.now();
               this.ttftMs = firstTokenTime - requestStartTime;
@@ -1709,9 +1715,14 @@ class AppStore {
               this.tps = ((tokenCount - tokensToKeep.length) / elapsed) * 1000;
             }
 
-            fullContent += delta;
-            const { displayContent, thinkingContent } =
+            if (delta) {
+              fullContent += delta;
+            }
+            const { displayContent, thinkingContent: tagThinking } =
               this.stripThinkingTags(fullContent);
+            const combinedThinking = [streamedThinking, tagThinking]
+              .filter(Boolean)
+              .join("\n\n");
 
             if (this.activeConversationId === targetConversationId) {
               this.currentResponse = displayContent;
@@ -1723,7 +1734,7 @@ class AppStore {
               messageId,
               (m) => {
                 m.content = displayContent;
-                m.thinking = thinkingContent || undefined;
+                m.thinking = combinedThinking || undefined;
                 m.tokens = [...collectedTokens];
               },
             );
@@ -1735,11 +1746,14 @@ class AppStore {
 
       // Final update
       if (this.conversationExists(targetConversationId)) {
-        const { displayContent, thinkingContent } =
+        const { displayContent, thinkingContent: tagThinking } =
           this.stripThinkingTags(fullContent);
+        const finalThinking = [streamedThinking, tagThinking]
+          .filter(Boolean)
+          .join("\n\n");
         this.updateConversationMessage(targetConversationId, messageId, (m) => {
           m.content = displayContent;
-          m.thinking = thinkingContent || undefined;
+          m.thinking = finalThinking || undefined;
           m.tokens = [...collectedTokens];
           if (this.ttftMs !== null) m.ttftMs = this.ttftMs;
           if (this.tps !== null) m.tps = this.tps;
@@ -1847,11 +1861,12 @@ class AppStore {
       }
 
       let streamedContent = "";
+      let streamedThinking = "";
       const collectedTokens: TokenData[] = [];
 
       interface ChatCompletionChunk {
         choices?: Array<{
-          delta?: { content?: string };
+          delta?: { content?: string; reasoning_content?: string };
           logprobs?: {
             content?: Array<{
               token: string;
@@ -1872,6 +1887,7 @@ class AppStore {
         (parsed) => {
           const choice = parsed.choices?.[0];
           const delta = choice?.delta?.content;
+          const thinkingDelta = choice?.delta?.reasoning_content;
 
           // Collect logprobs data
           const logprobsContent = choice?.logprobs?.content;
@@ -1890,10 +1906,19 @@ class AppStore {
             }
           }
 
-          if (delta) {
-            streamedContent += delta;
-            const { displayContent, thinkingContent } =
+          if (thinkingDelta) {
+            streamedThinking += thinkingDelta;
+          }
+
+          if (delta || thinkingDelta) {
+            if (delta) {
+              streamedContent += delta;
+            }
+            const { displayContent, thinkingContent: tagThinking } =
               this.stripThinkingTags(streamedContent);
+            const combinedThinking = [streamedThinking, tagThinking]
+              .filter(Boolean)
+              .join("\n\n");
 
             // Only update currentResponse if target conversation is active
             if (this.activeConversationId === targetConversationId) {
@@ -1906,7 +1931,7 @@ class AppStore {
               assistantMessage.id,
               (msg) => {
                 msg.content = displayContent;
-                msg.thinking = thinkingContent || undefined;
+                msg.thinking = combinedThinking || undefined;
                 msg.tokens = [...collectedTokens];
               },
             );
@@ -1918,14 +1943,17 @@ class AppStore {
 
       // Final cleanup of the message (if conversation still exists)
       if (this.conversationExists(targetConversationId)) {
-        const { displayContent, thinkingContent } =
+        const { displayContent, thinkingContent: tagThinking } =
           this.stripThinkingTags(streamedContent);
+        const finalThinking = [streamedThinking, tagThinking]
+          .filter(Boolean)
+          .join("\n\n");
         this.updateConversationMessage(
           targetConversationId,
           assistantMessage.id,
           (msg) => {
             msg.content = displayContent;
-            msg.thinking = thinkingContent || undefined;
+            msg.thinking = finalThinking || undefined;
             msg.tokens = [...collectedTokens];
           },
         );
@@ -2317,10 +2345,11 @@ class AppStore {
       }
 
       let streamedContent = "";
+      let streamedThinking = "";
 
       interface ChatCompletionChunk {
         choices?: Array<{
-          delta?: { content?: string };
+          delta?: { content?: string; reasoning_content?: string };
           logprobs?: {
             content?: Array<{
               token: string;
@@ -2348,6 +2377,7 @@ class AppStore {
 
           const choice = parsed.choices?.[0];
           const tokenContent = choice?.delta?.content;
+          const thinkingContent = choice?.delta?.reasoning_content;
 
           // Collect logprobs data
           const logprobsContent = choice?.logprobs?.content;
@@ -2366,7 +2396,11 @@ class AppStore {
             }
           }
 
-          if (tokenContent) {
+          if (thinkingContent) {
+            streamedThinking += thinkingContent;
+          }
+
+          if (tokenContent || thinkingContent) {
             // Track first token for TTFT
             if (firstTokenTime === null) {
               firstTokenTime = performance.now();
@@ -2383,11 +2417,16 @@ class AppStore {
               this.tps = (tokenCount / elapsed) * 1000;
             }
 
-            streamedContent += tokenContent;
+            if (tokenContent) {
+              streamedContent += tokenContent;
+            }
 
-            // Strip thinking tags for display and extract thinking content
-            const { displayContent, thinkingContent } =
+            // Use stripThinkingTags as fallback for any <think> tags still in content
+            const { displayContent, thinkingContent: tagThinking } =
               this.stripThinkingTags(streamedContent);
+            const combinedThinking = [streamedThinking, tagThinking]
+              .filter(Boolean)
+              .join("\n\n");
 
             // Only update currentResponse if target conversation is active
             if (this.activeConversationId === targetConversationId) {
@@ -2400,7 +2439,7 @@ class AppStore {
               assistantMessage.id,
               (msg) => {
                 msg.content = displayContent;
-                msg.thinking = thinkingContent || undefined;
+                msg.thinking = combinedThinking || undefined;
                 msg.tokens = [...collectedTokens];
               },
             );
@@ -2436,14 +2475,17 @@ class AppStore {
 
       // Final cleanup of the message (if conversation still exists)
       if (this.conversationExists(targetConversationId)) {
-        const { displayContent, thinkingContent } =
+        const { displayContent, thinkingContent: tagThinking } =
           this.stripThinkingTags(streamedContent);
+        const finalThinking = [streamedThinking, tagThinking]
+          .filter(Boolean)
+          .join("\n\n");
         this.updateConversationMessage(
           targetConversationId,
           assistantMessage.id,
           (msg) => {
             msg.content = displayContent;
-            msg.thinking = thinkingContent || undefined;
+            msg.thinking = finalThinking || undefined;
             msg.tokens = [...collectedTokens];
             // Store performance metrics on the message
             if (this.ttftMs !== null) {

src/exo/master/adapters/chat_completions.py

@@ -59,7 +59,11 @@ def chat_request_to_text_generation(
             chat_template_messages.append({"role": "system", "content": content})
         else:
             # Skip messages with no meaningful content
-            if msg.content is None and msg.thinking is None and msg.tool_calls is None:
+            if (
+                msg.content is None
+                and msg.reasoning_content is None
+                and msg.tool_calls is None
+            ):
                 continue
 
             if msg.role in ("user", "assistant", "developer"):
@@ -111,6 +115,11 @@ def chunk_to_response(
             ]
         )
 
+    if chunk.is_thinking:
+        delta = ChatCompletionMessage(role="assistant", reasoning_content=chunk.text)
+    else:
+        delta = ChatCompletionMessage(role="assistant", content=chunk.text)
+
     return ChatCompletionResponse(
         id=command_id,
         created=int(time.time()),
@@ -118,7 +127,7 @@ def chunk_to_response(
         choices=[
             StreamingChoiceResponse(
                 index=0,
-                delta=ChatCompletionMessage(role="assistant", content=chunk.text),
+                delta=delta,
                 logprobs=logprobs,
                 finish_reason=chunk.finish_reason,
             )
@@ -208,6 +217,7 @@ async def collect_chat_response(
     # FastAPI handles the cancellation better but wouldn't auto-serialize for some reason
     """Collect all token chunks and return a single ChatCompletionResponse."""
     text_parts: list[str] = []
+    thinking_parts: list[str] = []
     tool_calls: list[ToolCall] = []
     logprobs_content: list[LogprobsContentItem] = []
     model: str | None = None
@@ -228,7 +238,10 @@ async def collect_chat_response(
                 if model is None:
                     model = chunk.model
                 last_usage = chunk.usage or last_usage
-                text_parts.append(chunk.text)
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                else:
+                    text_parts.append(chunk.text)
                 if chunk.logprob is not None:
                     logprobs_content.append(
                         LogprobsContentItem(
@@ -258,6 +271,7 @@ async def collect_chat_response(
         raise ValueError(error_message)
 
     combined_text = "".join(text_parts)
+    combined_thinking = "".join(thinking_parts) if thinking_parts else None
     assert model is not None
 
     yield ChatCompletionResponse(
@@ -270,6 +284,7 @@ async def collect_chat_response(
                 message=ChatCompletionMessage(
                     role="assistant",
                     content=combined_text,
+                    reasoning_content=combined_thinking,
                     tool_calls=tool_calls if tool_calls else None,
                 ),
                 logprobs=Logprobs(content=logprobs_content)

src/exo/master/adapters/claude.py

@@ -29,6 +29,8 @@ from exo.shared.types.claude_api import (
     ClaudeStopReason,
     ClaudeTextBlock,
     ClaudeTextDelta,
+    ClaudeThinkingBlock,
+    ClaudeThinkingDelta,
     ClaudeToolResultBlock,
     ClaudeToolUseBlock,
     ClaudeUsage,
@@ -104,12 +106,15 @@ def claude_request_to_text_generation(
 
         # Process structured content blocks
         text_parts: list[str] = []
+        thinking_parts: list[str] = []
         tool_calls: list[dict[str, Any]] = []
         tool_results: list[ClaudeToolResultBlock] = []
 
         for block in msg.content:
             if isinstance(block, ClaudeTextBlock):
                 text_parts.append(block.text)
+            elif isinstance(block, ClaudeThinkingBlock):
+                thinking_parts.append(block.thinking)
             elif isinstance(block, ClaudeToolUseBlock):
                 tool_calls.append(
                     {
@@ -125,6 +130,7 @@ def claude_request_to_text_generation(
                 tool_results.append(block)
 
         content = "".join(text_parts)
+        reasoning_content = "".join(thinking_parts) if thinking_parts else None
 
         # Build InputMessage from text content
         if msg.role in ("user", "assistant"):
@@ -132,9 +138,14 @@ def claude_request_to_text_generation(
 
         # Build chat_template_messages preserving tool structure
         if tool_calls:
-            chat_template_messages.append(
-                {"role": "assistant", "content": content, "tool_calls": tool_calls}
-            )
+            chat_msg: dict[str, Any] = {
+                "role": "assistant",
+                "content": content,
+                "tool_calls": tool_calls,
+            }
+            if reasoning_content:
+                chat_msg["reasoning_content"] = reasoning_content
+            chat_template_messages.append(chat_msg)
         elif tool_results:
             for tr in tool_results:
                 chat_template_messages.append(
@@ -145,7 +156,10 @@ def claude_request_to_text_generation(
                     }
                 )
         else:
-            chat_template_messages.append({"role": msg.role, "content": content})
+            chat_msg = {"role": msg.role, "content": content}
+            if reasoning_content:
+                chat_msg["reasoning_content"] = reasoning_content
+            chat_template_messages.append(chat_msg)
 
     # Convert Claude tool definitions to OpenAI-style function tools
     tools: list[dict[str, Any]] | None = None
@@ -162,6 +176,10 @@ def claude_request_to_text_generation(
             for tool in request.tools
         ]
 
+    enable_thinking: bool | None = None
+    if request.thinking is not None:
+        enable_thinking = request.thinking.type in ("enabled", "adaptive")
+
     return TextGenerationTaskParams(
         model=request.model,
         input=input_messages
@@ -175,6 +193,7 @@ def claude_request_to_text_generation(
         stop=request.stop_sequences,
         stream=request.stream,
         tools=tools,
+        enable_thinking=enable_thinking,
         chat_template_messages=chat_template_messages
         if chat_template_messages
         else None,
@@ -192,6 +211,7 @@ async def collect_claude_response(
     # FastAPI handles the cancellation better but wouldn't auto-serialize for some reason
     """Collect all token chunks and return a single ClaudeMessagesResponse."""
     text_parts: list[str] = []
+    thinking_parts: list[str] = []
     tool_use_blocks: list[ClaudeToolUseBlock] = []
     stop_reason: ClaudeStopReason | None = None
     last_usage: Usage | None = None
@@ -219,7 +239,10 @@ async def collect_claude_response(
             stop_reason = "tool_use"
             continue
 
-        text_parts.append(chunk.text)
+        if chunk.is_thinking:
+            thinking_parts.append(chunk.text)
+        else:
+            text_parts.append(chunk.text)
 
         if chunk.finish_reason is not None:
             stop_reason = finish_reason_to_claude_stop_reason(chunk.finish_reason)
@@ -228,9 +251,12 @@ async def collect_claude_response(
         raise ValueError(error_message)
 
     combined_text = "".join(text_parts)
+    combined_thinking = "".join(thinking_parts)
 
     # Build content blocks
     content: list[ClaudeContentBlock] = []
+    if combined_thinking:
+        content.append(ClaudeThinkingBlock(thinking=combined_thinking))
     if combined_text:
         content.append(ClaudeTextBlock(text=combined_text))
     content.extend(tool_use_blocks)
@@ -275,16 +301,16 @@ async def generate_claude_stream(
     start_event = ClaudeMessageStartEvent(message=initial_message)
     yield f"event: message_start\ndata: {start_event.model_dump_json()}\n\n"
 
-    # content_block_start for text block at index 0
-    block_start = ClaudeContentBlockStartEvent(
-        index=0, content_block=ClaudeTextBlock(text="")
-    )
-    yield f"event: content_block_start\ndata: {block_start.model_dump_json()}\n\n"
-
     output_tokens = 0
     stop_reason: ClaudeStopReason | None = None
     last_usage: Usage | None = None
-    next_block_index = 1  # text block is 0, tool blocks start at 1
+    next_block_index = 0
+
+    # Track whether we've started thinking/text blocks
+    thinking_block_started = False
+    thinking_block_index = -1
+    text_block_started = False
+    text_block_index = -1
 
     async for chunk in chunk_stream:
         if isinstance(chunk, PrefillProgressChunk):
@@ -329,12 +355,45 @@ async def generate_claude_stream(
 
         output_tokens += 1  # Count each chunk as one token
 
-        # content_block_delta
-        delta_event = ClaudeContentBlockDeltaEvent(
-            index=0,
-            delta=ClaudeTextDelta(text=chunk.text),
-        )
-        yield f"event: content_block_delta\ndata: {delta_event.model_dump_json()}\n\n"
+        if chunk.is_thinking:
+            # Start thinking block on first thinking token
+            if not thinking_block_started:
+                thinking_block_started = True
+                thinking_block_index = next_block_index
+                next_block_index += 1
+                block_start = ClaudeContentBlockStartEvent(
+                    index=thinking_block_index,
+                    content_block=ClaudeThinkingBlock(thinking=""),
+                )
+                yield f"event: content_block_start\ndata: {block_start.model_dump_json()}\n\n"
+
+            delta_event = ClaudeContentBlockDeltaEvent(
+                index=thinking_block_index,
+                delta=ClaudeThinkingDelta(thinking=chunk.text),
+            )
+            yield f"event: content_block_delta\ndata: {delta_event.model_dump_json()}\n\n"
+        else:
+            # Close thinking block when transitioning to text
+            if thinking_block_started and text_block_index == -1:
+                block_stop = ClaudeContentBlockStopEvent(index=thinking_block_index)
+                yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+
+            # Start text block on first text token
+            if not text_block_started:
+                text_block_started = True
+                text_block_index = next_block_index
+                next_block_index += 1
+                block_start = ClaudeContentBlockStartEvent(
+                    index=text_block_index,
+                    content_block=ClaudeTextBlock(text=""),
+                )
+                yield f"event: content_block_start\ndata: {block_start.model_dump_json()}\n\n"
+
+            delta_event = ClaudeContentBlockDeltaEvent(
+                index=text_block_index,
+                delta=ClaudeTextDelta(text=chunk.text),
+            )
+            yield f"event: content_block_delta\ndata: {delta_event.model_dump_json()}\n\n"
 
         if chunk.finish_reason is not None:
             stop_reason = finish_reason_to_claude_stop_reason(chunk.finish_reason)
@@ -343,9 +402,22 @@ async def generate_claude_stream(
     if last_usage is not None:
         output_tokens = last_usage.completion_tokens
 
-    # content_block_stop for text block
-    block_stop = ClaudeContentBlockStopEvent(index=0)
-    yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+    # Close any open blocks
+    if thinking_block_started and text_block_index == -1:
+        block_stop = ClaudeContentBlockStopEvent(index=thinking_block_index)
+        yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+
+    if text_block_started:
+        block_stop = ClaudeContentBlockStopEvent(index=text_block_index)
+        yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+
+    if not thinking_block_started and not text_block_started:
+        empty_start = ClaudeContentBlockStartEvent(
+            index=0, content_block=ClaudeTextBlock(text="")
+        )
+        yield f"event: content_block_start\ndata: {empty_start.model_dump_json()}\n\n"
+        empty_stop = ClaudeContentBlockStopEvent(index=0)
+        yield f"event: content_block_stop\ndata: {empty_stop.model_dump_json()}\n\n"
 
     # message_delta
     message_delta = ClaudeMessageDeltaEvent(

src/exo/master/adapters/responses.py

@@ -29,6 +29,12 @@ from exo.shared.types.openai_responses import (
     ResponseOutputItemAddedEvent,
     ResponseOutputItemDoneEvent,
     ResponseOutputText,
+    ResponseReasoningItem,
+    ResponseReasoningSummaryPartAddedEvent,
+    ResponseReasoningSummaryPartDoneEvent,
+    ResponseReasoningSummaryText,
+    ResponseReasoningSummaryTextDeltaEvent,
+    ResponseReasoningSummaryTextDoneEvent,
     ResponsesRequest,
     ResponsesResponse,
     ResponsesStreamEvent,
@@ -141,7 +147,9 @@ async def collect_responses_response(
     """Collect all token chunks and return a single ResponsesResponse."""
     response_id = f"resp_{command_id}"
     item_id = f"item_{command_id}"
+    reasoning_id = f"rs_{command_id}"
     accumulated_text = ""
+    thinking_parts: list[str] = []
     function_call_items: list[ResponseFunctionCallItem] = []
     last_usage: Usage | None = None
     error_message: str | None = None
@@ -168,6 +176,10 @@ async def collect_responses_response(
                 )
             continue
 
+        if chunk.is_thinking:
+            thinking_parts.append(chunk.text)
+            continue
+
         accumulated_text += chunk.text
 
     if error_message is not None:
@@ -182,13 +194,21 @@ async def collect_responses_response(
             total_tokens=last_usage.total_tokens,
         )
 
-    output: list[ResponseItem] = [
+    output: list[ResponseItem] = []
+    if thinking_parts:
+        output.append(
+            ResponseReasoningItem(
+                id=reasoning_id,
+                summary=[ResponseReasoningSummaryText(text="".join(thinking_parts))],
+            )
+        )
+    output.append(
         ResponseMessageItem(
             id=item_id,
             content=[ResponseOutputText(text=accumulated_text)],
             status="completed",
         )
-    ]
+    )
     output.extend(function_call_items)
 
     yield ResponsesResponse(
@@ -212,6 +232,7 @@ async def generate_responses_stream(
     """Generate OpenAI Responses API streaming events from TokenChunks."""
     response_id = f"resp_{command_id}"
     item_id = f"item_{command_id}"
+    reasoning_id = f"rs_{command_id}"
     seq = count(1)
 
     # response.created
@@ -233,32 +254,17 @@ async def generate_responses_stream(
     )
     yield _format_sse(in_progress_event)
 
-    # response.output_item.added
-    initial_item = ResponseMessageItem(
-        id=item_id,
-        content=[ResponseOutputText(text="")],
-        status="in_progress",
-    )
-    item_added = ResponseOutputItemAddedEvent(
-        sequence_number=next(seq), output_index=0, item=initial_item
-    )
-    yield _format_sse(item_added)
-
-    # response.content_part.added
-    initial_part = ResponseOutputText(text="")
-    part_added = ResponseContentPartAddedEvent(
-        sequence_number=next(seq),
-        item_id=item_id,
-        output_index=0,
-        content_index=0,
-        part=initial_part,
-    )
-    yield _format_sse(part_added)
-
     accumulated_text = ""
+    accumulated_thinking = ""
     function_call_items: list[ResponseFunctionCallItem] = []
     last_usage: Usage | None = None
-    next_output_index = 1  # message item is at 0
+    next_output_index = 0
+
+    # Track dynamic block creation
+    reasoning_started = False
+    reasoning_output_index = -1
+    message_started = False
+    message_output_index = -1
 
     async for chunk in chunk_stream:
         if isinstance(chunk, PrefillProgressChunk):
@@ -327,23 +333,184 @@ async def generate_responses_stream(
                 next_output_index += 1
             continue
 
+        if chunk.is_thinking:
+            # Start reasoning block on first thinking token
+            if not reasoning_started:
+                reasoning_started = True
+                reasoning_output_index = next_output_index
+                next_output_index += 1
+
+                # response.output_item.added for reasoning
+                reasoning_item = ResponseReasoningItem(
+                    id=reasoning_id,
+                    summary=[],
+                    status="in_progress",
+                )
+                rs_added = ResponseOutputItemAddedEvent(
+                    sequence_number=next(seq),
+                    output_index=reasoning_output_index,
+                    item=reasoning_item,
+                )
+                yield _format_sse(rs_added)
+
+                # response.reasoning_summary_part.added
+                part_added = ResponseReasoningSummaryPartAddedEvent(
+                    sequence_number=next(seq),
+                    item_id=reasoning_id,
+                    output_index=reasoning_output_index,
+                    summary_index=0,
+                    part=ResponseReasoningSummaryText(text=""),
+                )
+                yield _format_sse(part_added)
+
+            accumulated_thinking += chunk.text
+
+            # response.reasoning_summary_text.delta
+            rs_delta = ResponseReasoningSummaryTextDeltaEvent(
+                sequence_number=next(seq),
+                item_id=reasoning_id,
+                output_index=reasoning_output_index,
+                summary_index=0,
+                delta=chunk.text,
+            )
+            yield _format_sse(rs_delta)
+            continue
+
+        # Close reasoning block when transitioning to text
+        if reasoning_started and not message_started:
+            # response.reasoning_summary_text.done
+            rs_text_done = ResponseReasoningSummaryTextDoneEvent(
+                sequence_number=next(seq),
+                item_id=reasoning_id,
+                output_index=reasoning_output_index,
+                summary_index=0,
+                text=accumulated_thinking,
+            )
+            yield _format_sse(rs_text_done)
+
+            # response.reasoning_summary_part.done
+            rs_part_done = ResponseReasoningSummaryPartDoneEvent(
+                sequence_number=next(seq),
+                item_id=reasoning_id,
+                output_index=reasoning_output_index,
+                summary_index=0,
+                part=ResponseReasoningSummaryText(text=accumulated_thinking),
+            )
+            yield _format_sse(rs_part_done)
+
+            # response.output_item.done for reasoning
+            rs_item_done = ResponseOutputItemDoneEvent(
+                sequence_number=next(seq),
+                output_index=reasoning_output_index,
+                item=ResponseReasoningItem(
+                    id=reasoning_id,
+                    summary=[ResponseReasoningSummaryText(text=accumulated_thinking)],
+                ),
+            )
+            yield _format_sse(rs_item_done)
+
+        # Start message block on first text token
+        if not message_started:
+            message_started = True
+            message_output_index = next_output_index
+            next_output_index += 1
+
+            initial_item = ResponseMessageItem(
+                id=item_id,
+                content=[ResponseOutputText(text="")],
+                status="in_progress",
+            )
+            item_added = ResponseOutputItemAddedEvent(
+                sequence_number=next(seq),
+                output_index=message_output_index,
+                item=initial_item,
+            )
+            yield _format_sse(item_added)
+
+            initial_part = ResponseOutputText(text="")
+            part_added = ResponseContentPartAddedEvent(
+                sequence_number=next(seq),
+                item_id=item_id,
+                output_index=message_output_index,
+                content_index=0,
+                part=initial_part,
+            )
+            yield _format_sse(part_added)
+
         accumulated_text += chunk.text
 
         # response.output_text.delta
         delta_event = ResponseTextDeltaEvent(
             sequence_number=next(seq),
             item_id=item_id,
-            output_index=0,
+            output_index=message_output_index,
             content_index=0,
             delta=chunk.text,
         )
         yield _format_sse(delta_event)
 
+    # Close reasoning block if it was never followed by text
+    if reasoning_started and not message_started:
+        rs_text_done = ResponseReasoningSummaryTextDoneEvent(
+            sequence_number=next(seq),
+            item_id=reasoning_id,
+            output_index=reasoning_output_index,
+            summary_index=0,
+            text=accumulated_thinking,
+        )
+        yield _format_sse(rs_text_done)
+
+        rs_part_done = ResponseReasoningSummaryPartDoneEvent(
+            sequence_number=next(seq),
+            item_id=reasoning_id,
+            output_index=reasoning_output_index,
+            summary_index=0,
+            part=ResponseReasoningSummaryText(text=accumulated_thinking),
+        )
+        yield _format_sse(rs_part_done)
+
+        rs_item_done = ResponseOutputItemDoneEvent(
+            sequence_number=next(seq),
+            output_index=reasoning_output_index,
+            item=ResponseReasoningItem(
+                id=reasoning_id,
+                summary=[ResponseReasoningSummaryText(text=accumulated_thinking)],
+            ),
+        )
+        yield _format_sse(rs_item_done)
+
+    # If no message block was started, create one now (empty text)
+    if not message_started:
+        message_output_index = next_output_index
+        next_output_index += 1
+
+        initial_item = ResponseMessageItem(
+            id=item_id,
+            content=[ResponseOutputText(text="")],
+            status="in_progress",
+        )
+        item_added = ResponseOutputItemAddedEvent(
+            sequence_number=next(seq),
+            output_index=message_output_index,
+            item=initial_item,
+        )
+        yield _format_sse(item_added)
+
+        initial_part = ResponseOutputText(text="")
+        part_added_evt = ResponseContentPartAddedEvent(
+            sequence_number=next(seq),
+            item_id=item_id,
+            output_index=message_output_index,
+            content_index=0,
+            part=initial_part,
+        )
+        yield _format_sse(part_added_evt)
+
     # response.output_text.done
     text_done = ResponseTextDoneEvent(
         sequence_number=next(seq),
         item_id=item_id,
-        output_index=0,
+        output_index=message_output_index,
         content_index=0,
         text=accumulated_text,
     )
@@ -354,7 +521,7 @@ async def generate_responses_stream(
     part_done = ResponseContentPartDoneEvent(
         sequence_number=next(seq),
         item_id=item_id,
-        output_index=0,
+        output_index=message_output_index,
         content_index=0,
         part=final_part,
     )
@@ -367,7 +534,9 @@ async def generate_responses_stream(
         status="completed",
     )
     item_done = ResponseOutputItemDoneEvent(
-        sequence_number=next(seq), output_index=0, item=final_message_item
+        sequence_number=next(seq),
+        output_index=message_output_index,
+        item=final_message_item,
     )
     yield _format_sse(item_done)
 
@@ -381,7 +550,15 @@ async def generate_responses_stream(
         )
 
     # response.completed
-    output: list[ResponseItem] = [final_message_item]
+    output: list[ResponseItem] = []
+    if reasoning_started:
+        output.append(
+            ResponseReasoningItem(
+                id=reasoning_id,
+                summary=[ResponseReasoningSummaryText(text=accumulated_thinking)],
+            )
+        )
+    output.append(final_message_item)
     output.extend(function_call_items)
     final_response = ResponsesResponse(
         id=response_id,

src/exo/master/tests/test_claude_tool_use.py

@@ -261,7 +261,7 @@ class TestGenerateClaudeStreamToolUse:
 
         parsed = _parse_sse_events(events)
 
-        # Two tool block starts (at indices 1 and 2)
+        # Two tool block starts (at indices 0 and 1 — no text block when only tools)
         tool_starts = [
             e
             for e in parsed
@@ -270,12 +270,11 @@ class TestGenerateClaudeStreamToolUse:
             == "tool_use"
         ]
         assert len(tool_starts) == 2
-        assert tool_starts[0]["index"] == 1
-        assert tool_starts[1]["index"] == 2
+        assert tool_starts[0]["index"] == 0
+        assert tool_starts[1]["index"] == 1
 
-        # Two tool block stops (at indices 1 and 2), plus text block stop at 0
+        # Two tool block stops (at indices 0 and 1)
         block_stops = [e for e in parsed if e.get("type") == "content_block_stop"]
         stop_indices = [e["index"] for e in block_stops]
         assert 0 in stop_indices
         assert 1 in stop_indices
-        assert 2 in stop_indices

src/exo/shared/types/api.py

@@ -77,7 +77,7 @@ class ChatCompletionMessage(BaseModel):
     content: (
         str | ChatCompletionMessageText | list[ChatCompletionMessageText] | None
     ) = None
-    thinking: str | None = None  # Added for GPT-OSS harmony format support
+    reasoning_content: str | None = None
     name: str | None = None
     tool_calls: list[ToolCall] | None = None
     tool_call_id: str | None = None

src/exo/shared/types/chunks.py

@@ -27,6 +27,7 @@ class TokenChunk(BaseChunk):
     stats: GenerationStats | None = None
     logprob: float | None = None
     top_logprobs: list[TopLogprobItem] | None = None
+    is_thinking: bool = False
 
 
 class ErrorChunk(BaseChunk):

src/exo/shared/types/claude_api.py

@@ -47,6 +47,14 @@ class ClaudeImageBlock(BaseModel, frozen=True):
     source: ClaudeImageSource
 
 
+class ClaudeThinkingBlock(BaseModel, frozen=True):
+    """Thinking content block in Claude Messages API."""
+
+    type: Literal["thinking"] = "thinking"
+    thinking: str
+    signature: str | None = None
+
+
 class ClaudeToolUseBlock(BaseModel, frozen=True):
     """Tool use content block in Claude Messages API."""
 
@@ -66,11 +74,17 @@ class ClaudeToolResultBlock(BaseModel, frozen=True):
     cache_control: dict[str, str] | None = None
 
 
-ClaudeContentBlock = ClaudeTextBlock | ClaudeImageBlock | ClaudeToolUseBlock
+ClaudeContentBlock = (
+    ClaudeTextBlock | ClaudeImageBlock | ClaudeThinkingBlock | ClaudeToolUseBlock
+)
 
 # Input content blocks can also include tool_result (sent by user after tool_use)
 ClaudeInputContentBlock = (
-    ClaudeTextBlock | ClaudeImageBlock | ClaudeToolUseBlock | ClaudeToolResultBlock
+    ClaudeTextBlock
+    | ClaudeImageBlock
+    | ClaudeThinkingBlock
+    | ClaudeToolUseBlock
+    | ClaudeToolResultBlock
 )
 
 
@@ -82,6 +96,11 @@ class ClaudeMessage(BaseModel, frozen=True):
     content: str | list[ClaudeInputContentBlock]
 
 
+class ClaudeThinkingConfig(BaseModel, frozen=True):
+    type: Literal["enabled", "disabled", "adaptive"]
+    budget_tokens: int | None = None
+
+
 class ClaudeMessagesRequest(BaseModel):
     """Request body for Claude Messages API."""
 
@@ -96,6 +115,7 @@ class ClaudeMessagesRequest(BaseModel):
     top_k: int | None = None
     tools: list[ClaudeToolDefinition] | None = None
     metadata: dict[str, str] | None = None
+    thinking: ClaudeThinkingConfig | None = None
 
 
 # Response types
@@ -145,7 +165,7 @@ class ClaudeContentBlockStartEvent(BaseModel, frozen=True):
 
     type: Literal["content_block_start"] = "content_block_start"
     index: int
-    content_block: ClaudeTextBlock | ClaudeToolUseBlock
+    content_block: ClaudeTextBlock | ClaudeThinkingBlock | ClaudeToolUseBlock
 
 
 class ClaudeTextDelta(BaseModel, frozen=True):
@@ -155,6 +175,13 @@ class ClaudeTextDelta(BaseModel, frozen=True):
     text: str
 
 
+class ClaudeThinkingDelta(BaseModel, frozen=True):
+    """Delta for thinking content block."""
+
+    type: Literal["thinking_delta"] = "thinking_delta"
+    thinking: str
+
+
 class ClaudeInputJsonDelta(BaseModel, frozen=True):
     """Delta for tool use input JSON content block."""
 
@@ -167,7 +194,7 @@ class ClaudeContentBlockDeltaEvent(BaseModel, frozen=True):
 
     type: Literal["content_block_delta"] = "content_block_delta"
     index: int
-    delta: ClaudeTextDelta | ClaudeInputJsonDelta
+    delta: ClaudeTextDelta | ClaudeThinkingDelta | ClaudeInputJsonDelta
 
 
 class ClaudeContentBlockStopEvent(BaseModel, frozen=True):

src/exo/shared/types/openai_responses.py

@@ -145,7 +145,23 @@ class ResponseFunctionCallItem(BaseModel, frozen=True):
     status: ResponseStatus = "completed"
 
 
-ResponseItem = ResponseMessageItem | ResponseFunctionCallItem
+class ResponseReasoningSummaryText(BaseModel, frozen=True):
+    """Summary text part in a reasoning output item."""
+
+    type: Literal["summary_text"] = "summary_text"
+    text: str
+
+
+class ResponseReasoningItem(BaseModel, frozen=True):
+    """Reasoning output item in response output array."""
+
+    type: Literal["reasoning"] = "reasoning"
+    id: str
+    summary: list[ResponseReasoningSummaryText] = Field(default_factory=list)
+    status: ResponseStatus = "completed"
+
+
+ResponseItem = ResponseMessageItem | ResponseFunctionCallItem | ResponseReasoningItem
 
 
 class ResponseUsage(BaseModel, frozen=True):
@@ -273,6 +289,58 @@ class ResponseFunctionCallArgumentsDoneEvent(BaseModel, frozen=True):
     arguments: str
 
 
+class ResponseReasoningSummaryPartAddedEvent(BaseModel, frozen=True):
+    """Event sent when a reasoning summary part is added."""
+
+    type: Literal["response.reasoning_summary_part.added"] = (
+        "response.reasoning_summary_part.added"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    part: ResponseReasoningSummaryText
+
+
+class ResponseReasoningSummaryTextDeltaEvent(BaseModel, frozen=True):
+    """Event sent for reasoning summary text delta during streaming."""
+
+    type: Literal["response.reasoning_summary_text.delta"] = (
+        "response.reasoning_summary_text.delta"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    delta: str
+
+
+class ResponseReasoningSummaryTextDoneEvent(BaseModel, frozen=True):
+    """Event sent when reasoning summary text is done."""
+
+    type: Literal["response.reasoning_summary_text.done"] = (
+        "response.reasoning_summary_text.done"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    text: str
+
+
+class ResponseReasoningSummaryPartDoneEvent(BaseModel, frozen=True):
+    """Event sent when a reasoning summary part is done."""
+
+    type: Literal["response.reasoning_summary_part.done"] = (
+        "response.reasoning_summary_part.done"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    part: ResponseReasoningSummaryText
+
+
 class ResponseCompletedEvent(BaseModel, frozen=True):
     """Event sent when response is completed."""
 
@@ -292,5 +360,9 @@ ResponsesStreamEvent = (
     | ResponseOutputItemDoneEvent
     | ResponseFunctionCallArgumentsDeltaEvent
     | ResponseFunctionCallArgumentsDoneEvent
+    | ResponseReasoningSummaryPartAddedEvent
+    | ResponseReasoningSummaryTextDeltaEvent
+    | ResponseReasoningSummaryTextDoneEvent
+    | ResponseReasoningSummaryPartDoneEvent
     | ResponseCompletedEvent
 )

src/exo/shared/types/worker/runner_response.py

@@ -28,6 +28,7 @@ class GenerationResponse(BaseRunnerResponse):
     finish_reason: FinishReason | None = None
     stats: GenerationStats | None = None
     usage: Usage | None
+    is_thinking: bool = False
 
 
 class ImageGenerationResponse(BaseRunnerResponse):

src/exo/worker/engines/mlx/dsml_encoding.py

@@ -0,0 +1,72 @@
+import json
+import re
+from typing import Any
+
+from mlx_lm.chat_templates import deepseek_v32
+
+from exo.shared.types.api import ToolCallItem
+
+BOS_TOKEN: str = deepseek_v32.bos_token
+EOS_TOKEN: str = deepseek_v32.eos_token
+DSML_TOKEN: str = deepseek_v32.dsml_token
+THINKING_START: str = deepseek_v32.thinking_start_token
+THINKING_END: str = deepseek_v32.thinking_end_token
+USER_TOKEN = "<\uff5cUser\uff5c>"
+ASSISTANT_TOKEN = "<\uff5cAssistant\uff5c>"
+TOOL_CALLS_START = f"<{DSML_TOKEN}function_calls>"
+TOOL_CALLS_END = f"</{DSML_TOKEN}function_calls>"
+encode_messages = deepseek_v32.encode_messages
+
+_INVOKE_PATTERN = re.compile(
+    rf"<{re.escape(DSML_TOKEN)}invoke\s+name=\"([^\"]+)\">"
+    rf"(.*?)"
+    rf"</{re.escape(DSML_TOKEN)}invoke>",
+    re.DOTALL,
+)
+
+_PARAM_PATTERN = re.compile(
+    rf"<{re.escape(DSML_TOKEN)}parameter\s+name=\"([^\"]+)\"\s+string=\"(true|false)\">"
+    rf"(.*?)"
+    rf"</{re.escape(DSML_TOKEN)}parameter>",
+    re.DOTALL,
+)
+
+
+def parse_dsml_output(text: str) -> list[ToolCallItem] | None:
+    """Parse DSML function_calls block from model output text.
+
+    Args:
+        text: The text containing the DSML function_calls block
+              (including the start/end markers).
+
+    Returns:
+        List of ToolCallItem, or None if parsing fails.
+    """
+    tool_calls: list[ToolCallItem] = []
+
+    for invoke_match in _INVOKE_PATTERN.finditer(text):
+        func_name = invoke_match.group(1)
+        invoke_body = invoke_match.group(2)
+
+        args: dict[str, Any] = {}
+        for param_match in _PARAM_PATTERN.finditer(invoke_body):
+            param_name = param_match.group(1)
+            is_string = param_match.group(2) == "true"
+            param_value = param_match.group(3)
+
+            if is_string:
+                args[param_name] = param_value
+            else:
+                try:
+                    args[param_name] = json.loads(param_value)
+                except (json.JSONDecodeError, ValueError):
+                    args[param_name] = param_value
+
+        tool_calls.append(
+            ToolCallItem(
+                name=func_name,
+                arguments=json.dumps(args),
+            )
+        )
+
+    return tool_calls if tool_calls else None

src/exo/worker/engines/mlx/utils_mlx.py

@@ -458,6 +458,19 @@ def _patch_lossy_chat_template(template: str) -> str | None:
     return patched if n > 0 else None
 
 
+def _needs_dsml_encoding(task_params: TextGenerationTaskParams) -> bool:
+    if "deepseek-v3.2" not in task_params.model.lower():
+        return False
+    # Use DSML encoding when tools are provided or tool results are in the conversation
+    if task_params.tools:
+        return True
+    if task_params.chat_template_messages:
+        return any(
+            msg.get("role") == "tool" for msg in task_params.chat_template_messages
+        )
+    return False
+
+
 def apply_chat_template(
     tokenizer: TokenizerWrapper,
     task_params: TextGenerationTaskParams,
@@ -469,7 +482,6 @@ def apply_chat_template(
 
     When chat_template_messages is available (from Chat Completions API),
     uses those directly to preserve tool_calls, thinking, and other fields.
-    Otherwise builds messages from the task params input/instructions.
     """
     formatted_messages: list[dict[str, Any]] = []
     if task_params.chat_template_messages is not None:
@@ -497,6 +509,19 @@ def apply_chat_template(
         partial_assistant_content = cast(str, formatted_messages[-1].get("content", ""))
         formatted_messages = formatted_messages[:-1]
 
+    if _needs_dsml_encoding(task_params):
+        from exo.worker.engines.mlx.dsml_encoding import encode_messages
+
+        prompt = encode_messages(
+            messages=formatted_messages,
+            thinking_mode="thinking" if task_params.enable_thinking else "chat",
+            tools=task_params.tools,
+        )
+        if partial_assistant_content:
+            prompt += partial_assistant_content
+        logger.info(prompt)
+        return prompt
+
     extra_kwargs: dict[str, Any] = {}
     if task_params.enable_thinking is not None:
         # Qwen3 and GLM use "enable_thinking"; DeepSeek uses "thinking".

src/exo/worker/runner/runner.py

@@ -7,6 +7,7 @@ from functools import cache
 from typing import Literal
 
 import mlx.core as mx
+from mlx_lm.models.deepseek_v32 import Model as DeepseekV32Model
 from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
@@ -353,16 +354,22 @@ def main(
                             group=group,
                         )
 
-                        # For other thinking models (GLM, etc.), check if we need to
-                        # prepend the thinking tag that was consumed by the chat template
-                        if detect_thinking_prompt_suffix(prompt, tokenizer):
+                        if tokenizer.has_thinking:
                             mlx_generator = parse_thinking_models(
-                                mlx_generator, tokenizer
+                                mlx_generator,
+                                tokenizer,
+                                # For other thinking models (GLM, etc.), check if we need to
+                                # prepend the thinking tag that was consumed by the chat template
+                                starts_in_thinking=detect_thinking_prompt_suffix(
+                                    prompt, tokenizer
+                                ),
                             )
 
-                        # GPT-OSS specific parsing to match other model formats.
+                        # Model-specific output parsing for tool calls.
                         if isinstance(inference_model, GptOssModel):
                             mlx_generator = parse_gpt_oss(mlx_generator)
+                        elif isinstance(inference_model, DeepseekV32Model):
+                            mlx_generator = parse_deepseek_v32(mlx_generator)
                         elif tool_parser:
                             mlx_generator = parse_tool_calls(mlx_generator, tool_parser)
 
@@ -414,6 +421,7 @@ def main(
                                                     stats=response.stats,
                                                     logprob=response.logprob,
                                                     top_logprobs=response.top_logprobs,
+                                                    is_thinking=response.is_thinking,
                                                 ),
                                             )
                                         )
@@ -675,44 +683,208 @@ def parse_gpt_oss(
 
         if ch == "analysis" and not thinking:
             thinking = True
-            yield response.model_copy(update={"text": "<think>"})
 
         if ch != "analysis" and thinking:
             thinking = False
-            yield response.model_copy(update={"text": "</think>"})
 
         if delta:
-            yield response.model_copy(update={"text": delta})
+            yield response.model_copy(update={"text": delta, "is_thinking": thinking})
 
         if response.finish_reason is not None:
-            if thinking:
-                yield response.model_copy(update={"text": "</think>"})
             yield response
 
 
+def parse_deepseek_v32(
+    responses: Generator[GenerationResponse],
+) -> Generator[GenerationResponse | ToolCallResponse]:
+    """Parse DeepSeek V3.2 DSML tool calls from the generation stream.
+
+    Uses accumulated-text matching (not per-token marker checks) because
+    DSML markers like <｜DSML｜function_calls> may span multiple tokens.
+    Also handles <think>...</think> blocks for thinking mode.
+    """
+    from exo.worker.engines.mlx.dsml_encoding import (
+        THINKING_END,
+        THINKING_START,
+        TOOL_CALLS_END,
+        TOOL_CALLS_START,
+        parse_dsml_output,
+    )
+
+    accumulated = ""
+    in_tool_call = False
+    thinking = False
+    # Tokens buffered while we detect the start of a DSML block
+    pending_buffer: list[GenerationResponse] = []
+    # Text accumulated during a tool call block
+    tool_call_text = ""
+
+    for response in responses:
+        assert isinstance(response, GenerationResponse)
+
+        # ── Handle thinking tags ──
+        if not thinking and THINKING_START in response.text:
+            thinking = True
+            # Yield any text before the <think> tag
+            before = response.text[: response.text.index(THINKING_START)]
+            if before:
+                yield response.model_copy(update={"text": before})
+            continue
+
+        if thinking and THINKING_END in response.text:
+            thinking = False
+            # Yield any text after the </think> tag
+            after = response.text[
+                response.text.index(THINKING_END) + len(THINKING_END) :
+            ]
+            if after:
+                yield response.model_copy(update={"text": after, "is_thinking": False})
+            continue
+
+        if thinking:
+            yield response.model_copy(update={"is_thinking": True})
+            continue
+
+        # ── Handle tool call accumulation ──
+        if in_tool_call:
+            tool_call_text += response.text
+            if TOOL_CALLS_END in tool_call_text:
+                # Parse the accumulated DSML block
+                parsed = parse_dsml_output(tool_call_text)
+                if parsed is not None:
+                    logger.info(f"parsed DSML tool calls: {parsed}")
+                    yield ToolCallResponse(
+                        tool_calls=parsed,
+                        usage=response.usage,
+                        stats=response.stats,
+                    )
+                else:
+                    logger.warning(
+                        f"DSML tool call parsing failed for: {tool_call_text}"
+                    )
+                    yield response.model_copy(update={"text": tool_call_text})
+                in_tool_call = False
+                tool_call_text = ""
+                continue
+
+            # EOS reached before end marker — yield buffered text as-is
+            if response.finish_reason is not None:
+                logger.info("DSML tool call parsing interrupted by EOS")
+                yield response.model_copy(update={"text": tool_call_text})
+                in_tool_call = False
+                tool_call_text = ""
+            continue
+
+        # ── Detect start of tool call block ──
+        accumulated += response.text
+
+        if TOOL_CALLS_START in accumulated:
+            # The start marker might be split across pending_buffer + current token
+            start_idx = accumulated.index(TOOL_CALLS_START)
+            # Yield any pending tokens that are purely before the marker
+            pre_text = accumulated[:start_idx]
+            if pre_text:
+                # Flush pending buffer tokens that contributed text before the marker
+                for buf_resp in pending_buffer:
+                    if pre_text:
+                        chunk = buf_resp.text
+                        if len(chunk) <= len(pre_text):
+                            yield buf_resp
+                            pre_text = pre_text[len(chunk) :]
+                        else:
+                            yield buf_resp.model_copy(update={"text": pre_text})
+                            pre_text = ""
+            pending_buffer = []
+            tool_call_text = accumulated[start_idx:]
+            accumulated = ""
+
+            # Check if the end marker is already present (entire tool call in one token)
+            if TOOL_CALLS_END in tool_call_text:
+                parsed = parse_dsml_output(tool_call_text)
+                if parsed is not None:
+                    logger.info(f"parsed DSML tool calls: {parsed}")
+                    yield ToolCallResponse(
+                        tool_calls=parsed,
+                        usage=response.usage,
+                        stats=response.stats,
+                    )
+                else:
+                    logger.warning(
+                        f"DSML tool call parsing failed for: {tool_call_text}"
+                    )
+                    yield response.model_copy(update={"text": tool_call_text})
+                tool_call_text = ""
+            else:
+                in_tool_call = True
+            continue
+
+        # Check if accumulated text might be the start of a DSML marker
+        # Buffer tokens if we see a partial match at the end
+        if _could_be_dsml_prefix(accumulated):
+            pending_buffer.append(response)
+            continue
+
+        # No partial match — flush all pending tokens and the current one
+        for buf_resp in pending_buffer:
+            yield buf_resp
+        pending_buffer = []
+        accumulated = ""
+        yield response
+
+    # Flush any remaining pending buffer at generator end
+    for buf_resp in pending_buffer:
+        yield buf_resp
+
+
+def _could_be_dsml_prefix(text: str) -> bool:
+    """Check if the end of text could be the start of a DSML function_calls marker.
+
+    We look for suffixes of text that are prefixes of the TOOL_CALLS_START pattern.
+    This allows us to buffer tokens until we can determine if a tool call is starting.
+    """
+    from exo.worker.engines.mlx.dsml_encoding import TOOL_CALLS_START
+
+    # Only check the last portion of text that could overlap with the marker
+    max_check = len(TOOL_CALLS_START)
+    tail = text[-max_check:] if len(text) > max_check else text
+
+    # Check if any suffix of tail is a prefix of TOOL_CALLS_START
+    for i in range(len(tail)):
+        suffix = tail[i:]
+        if TOOL_CALLS_START.startswith(suffix):
+            return True
+    return False
+
+
 def parse_thinking_models(
     responses: Generator[GenerationResponse],
     tokenizer: TokenizerWrapper,
+    starts_in_thinking: bool = True,
 ) -> Generator[GenerationResponse]:
+    """Route thinking tokens via is_thinking flag.
+
+    Swallows think tag tokens, sets is_thinking on all others.
+    Always yields tokens with finish_reason to avoid hanging the chunk stream.
     """
-    For models that inject thinking tags in the prompt (like GLM-4.7),
-    prepend the thinking tag to the output stream so the frontend
-    can properly parse thinking content.
-    """
-    first = True
+    in_thinking = starts_in_thinking
     for response in responses:
         if isinstance(response, ToolCallResponse):
             yield response
             continue
-        if first:
-            first = False
-            yield response.model_copy(
-                update={
-                    "text": tokenizer.think_start,
-                    "token": tokenizer.think_start_id,
-                }
-            )
-        yield response
+
+        is_think_tag = (
+            tokenizer.think_end is not None and response.text == tokenizer.think_end
+        ) or (
+            tokenizer.think_start is not None and response.text == tokenizer.think_start
+        )
+
+        if is_think_tag:
+            in_thinking = response.text != tokenizer.think_end
+            # Never swallow finish_reason — the chunk stream needs it to terminate.
+            if response.finish_reason is not None:
+                yield response.model_copy(update={"text": "", "is_thinking": False})
+            continue
+        yield response.model_copy(update={"is_thinking": in_thinking})
 
 
 def _send_image_chunk(

src/exo/worker/tests/unittests/test_runner/test_dsml_e2e.py

@@ -0,0 +1,967 @@
+import json
+from collections.abc import Generator
+from typing import Any
+
+from exo.shared.types.worker.runner_response import (
+    GenerationResponse,
+    ToolCallResponse,
+)
+from exo.worker.engines.mlx.dsml_encoding import (
+    ASSISTANT_TOKEN,
+    BOS_TOKEN,
+    DSML_TOKEN,
+    EOS_TOKEN,
+    THINKING_END,
+    THINKING_START,
+    TOOL_CALLS_END,
+    TOOL_CALLS_START,
+    USER_TOKEN,
+    encode_messages,
+    parse_dsml_output,
+)
+from exo.worker.runner.runner import parse_deepseek_v32
+
+# ── Shared fixtures ──────────────────────────────────────────────
+
+_WEATHER_TOOLS: list[dict[str, Any]] = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get the current weather in a given city",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city": {"type": "string", "description": "The city name"},
+                    "units": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                        "description": "Temperature units",
+                    },
+                },
+                "required": ["city"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_time",
+            "description": "Get the current time in a timezone",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "timezone": {"type": "string"},
+                },
+                "required": ["timezone"],
+            },
+        },
+    },
+]
+
+
+def _simulate_tokens(
+    texts: list[str],
+    finish_on_last: bool = True,
+) -> Generator[GenerationResponse]:
+    """Simulate a model producing tokens from a list of text strings."""
+    for i, text in enumerate(texts):
+        is_last = i == len(texts) - 1
+        yield GenerationResponse(
+            text=text,
+            token=i,
+            finish_reason="stop" if (is_last and finish_on_last) else None,
+            usage=None,
+        )
+
+
+# ── Test: Standard text response (no tool calls) ────────────────
+
+
+class TestE2EStandardResponse:
+    """Model generates a plain text response — no tool calling involved."""
+
+    def test_plain_text_passthrough(self):
+        """Simulate model producing: 'The weather in NYC is 72°F and sunny.'"""
+        # Step 1: Encode the prompt (with tools available)
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "What's the weather in NYC?"},
+        ]
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Verify prompt structure
+        assert BOS_TOKEN in prompt
+        assert "## Tools" in prompt
+        assert "get_weather" in prompt
+        assert f"{USER_TOKEN}What's the weather in NYC?{ASSISTANT_TOKEN}" in prompt
+
+        # Step 2: Simulate model response — plain text tokens (no DSML)
+        model_tokens = [
+            "The weather",
+            " in NYC",
+            " is 72",
+            "°F",
+            " and sunny",
+            ".",
+        ]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        # Step 3: Verify all tokens pass through as GenerationResponse
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 0
+        assert len(gen_results) == 6
+        full_text = "".join(r.text for r in gen_results)
+        assert full_text == "The weather in NYC is 72°F and sunny."
+        assert gen_results[-1].finish_reason == "stop"
+
+
+# ── Test: Tool call response ─────────────────────────────────────
+
+
+class TestE2EToolCallResponse:
+    """Model generates a DSML tool call — realistic token boundaries."""
+
+    def test_realistic_tool_call_tokens(self):
+        """Simulate model generating a get_weather tool call with realistic token splits.
+
+        Real models split DSML markers across tokens unpredictably.
+        This simulates how DeepSeek V3.2 actually tokenizes DSML output.
+        """
+        # Step 1: Encode prompt
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "What's the weather in San Francisco?"},
+        ]
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+        assert "get_weather" in prompt
+
+        # Step 2: Simulate realistic token-by-token model output
+        # The model first produces some text, then a DSML tool call block
+        model_tokens = [
+            "I'll check the weather for you.",
+            "\n\n",
+            f"<{DSML_TOKEN}",  # marker split across tokens
+            "function_calls>\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">',
+            "San Francisco",
+            f"</{DSML_TOKEN}parameter>\n",
+            f'<{DSML_TOKEN}parameter name="units" string="false">',
+            '"celsius"',
+            f"</{DSML_TOKEN}parameter>\n",
+            f"</{DSML_TOKEN}invoke>\n",
+            f"</{DSML_TOKEN}function_calls>",
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        # Step 3: Verify
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        # Should have text tokens before tool call + one ToolCallResponse
+        assert len(tool_results) == 1
+        assert len(tool_results[0].tool_calls) == 1
+
+        tc = tool_results[0].tool_calls[0]
+        assert tc.name == "get_weather"
+        args = json.loads(tc.arguments)  # pyright: ignore[reportAny]
+        assert args["city"] == "San Francisco"
+        assert args["units"] == "celsius"
+
+        # The text before the tool call should still be yielded
+        text_before = "".join(r.text for r in gen_results if not r.is_thinking)
+        assert "check the weather" in text_before
+
+    def test_multiple_tool_calls_in_one_block(self):
+        """Model generates two tool calls in a single function_calls block."""
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Weather in NYC and time in EST?"},
+        ]
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+        assert "get_weather" in prompt
+        assert "get_time" in prompt
+
+        # Simulate model output with two invocations
+        model_tokens = [
+            "Let me check both.\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">NYC</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            f'<{DSML_TOKEN}invoke name="get_time">\n',
+            f'<{DSML_TOKEN}parameter name="timezone" string="true">EST</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        assert len(tool_results[0].tool_calls) == 2
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+        assert tool_results[0].tool_calls[1].name == "get_time"
+
+        args0 = json.loads(tool_results[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        args1 = json.loads(tool_results[0].tool_calls[1].arguments)  # pyright: ignore[reportAny]
+        assert args0 == {"city": "NYC"}
+        assert args1 == {"timezone": "EST"}
+
+
+# ── Test: Multi-turn tool use flow ───────────────────────────────
+
+
+class TestE2EMultiTurnToolUse:
+    """Full multi-turn: user asks → model calls tool → tool result → model answers."""
+
+    def test_encode_multi_turn_with_tool_results(self):
+        """Verify the prompt for turn 2 (after tool results) is correctly encoded."""
+        # Turn 1: user asks, model calls tool
+        # Turn 2: tool result provided, model answers
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a weather assistant."},
+            {"role": "user", "content": "What's the weather in NYC?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "NYC"}',
+                        },
+                    }
+                ],
+            },
+            {"role": "tool", "content": '{"temperature": 72, "condition": "sunny"}'},
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Verify multi-turn structure
+        assert BOS_TOKEN in prompt
+        assert "You are a weather assistant." in prompt
+        assert "## Tools" in prompt
+
+        # The assistant's tool call should be encoded as DSML
+        assert TOOL_CALLS_START in prompt
+        assert f'<{DSML_TOKEN}invoke name="get_weather">' in prompt
+        assert EOS_TOKEN in prompt
+
+        # The tool result should be wrapped in function_results
+        assert "<function_results>" in prompt
+        assert "<result>" in prompt
+        assert "72" in prompt
+        assert "</function_results>" in prompt
+
+        # Now simulate model answering after seeing the tool result
+        model_tokens = [
+            "The current",
+            " weather in NYC",
+            " is 72°F",
+            " and sunny.",
+        ]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 0
+        full_text = "".join(r.text for r in gen_results)
+        assert full_text == "The current weather in NYC is 72°F and sunny."
+
+    def test_multi_tool_results_encoding(self):
+        """Verify encoding when model called two tools and both return results."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "Weather and time?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "LA"}',
+                        },
+                    },
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "PST"}',
+                        },
+                    },
+                ],
+            },
+            {"role": "tool", "content": "85F, clear skies"},
+            {"role": "tool", "content": "3:42 PM PST"},
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Should have one function_results block with two results
+        assert prompt.count("<function_results>") == 1
+        assert prompt.count("</function_results>") == 1
+        assert "<result>85F, clear skies</result>" in prompt
+        assert "<result>3:42 PM PST</result>" in prompt
+
+
+# ── Test: Thinking + tool call ───────────────────────────────────
+
+
+class TestE2EThinkingAndToolCall:
+    """Model uses thinking mode, reasons, then makes a tool call."""
+
+    def test_thinking_then_tool_call(self):
+        """Model thinks first, then produces a DSML tool call block."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "What's the weather?"},
+        ]
+        prompt = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        # Thinking mode: prompt should end with <think>
+        assert prompt.endswith(THINKING_START)
+
+        # Simulate: model outputs <think>, thinks, closes thinking, then tool call.
+        # In the full pipeline, parse_thinking_models handles the case where
+        # <think> is in the prompt. Here we test parse_deepseek_v32 directly,
+        # which detects <think>/<think> markers in the stream.
+        model_tokens = [
+            THINKING_START,
+            "The user wants weather",
+            " information. I should use",
+            " the get_weather tool.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">',
+            "San Francisco",
+            f"</{DSML_TOKEN}parameter>\n",
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        # Should have thinking tokens + tool call
+        thinking_results = [r for r in gen_results if r.is_thinking]
+
+        assert len(thinking_results) >= 1
+        thinking_text = "".join(r.text for r in thinking_results)
+        assert "get_weather tool" in thinking_text
+
+        assert len(tool_results) == 1
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+        args = json.loads(tool_results[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        assert args["city"] == "San Francisco"
+
+    def test_thinking_prompt_encoding(self):
+        """Verify thinking mode affects prompt encoding correctly."""
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "Be thorough."},
+            {"role": "user", "content": "What's the weather?"},
+        ]
+
+        # With thinking enabled
+        prompt_think = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_think.endswith(THINKING_START)
+
+        # With thinking disabled
+        prompt_no_think = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="chat"
+        )
+        assert prompt_no_think.endswith(THINKING_END)
+
+        # Both should have the same tool definitions
+        assert "get_weather" in prompt_think
+        assert "get_weather" in prompt_no_think
+
+
+# ── Test: Round-trip encode → parse ──────────────────────────────
+
+
+class TestE2ERoundTrip:
+    """Verify that DSML we encode can be parsed back correctly."""
+
+    def test_encoded_tool_call_is_parseable(self):
+        """Encode an assistant tool call message, then parse the DSML output."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "Weather?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Tokyo", "units": "celsius"}',
+                        },
+                    }
+                ],
+            },
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Extract the DSML function_calls block from the prompt
+        start = prompt.index(TOOL_CALLS_START)
+        end = prompt.index(TOOL_CALLS_END) + len(TOOL_CALLS_END)
+        dsml_block = prompt[start:end]
+
+        # Parse it back
+        parsed = parse_dsml_output(dsml_block)
+        assert parsed is not None
+        assert len(parsed) == 1
+        assert parsed[0].name == "get_weather"
+        args = json.loads(parsed[0].arguments)  # pyright: ignore[reportAny]
+        assert args["city"] == "Tokyo"
+        assert args["units"] == "celsius"
+
+    def test_encoded_multi_tool_call_round_trips(self):
+        """Encode multiple tool calls, verify they parse back correctly."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "Both please"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Paris"}',
+                        },
+                    },
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "CET"}',
+                        },
+                    },
+                ],
+            },
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        start = prompt.index(TOOL_CALLS_START)
+        end = prompt.index(TOOL_CALLS_END) + len(TOOL_CALLS_END)
+        dsml_block = prompt[start:end]
+
+        parsed = parse_dsml_output(dsml_block)
+        assert parsed is not None
+        assert len(parsed) == 2
+        assert parsed[0].name == "get_weather"
+        assert parsed[1].name == "get_time"
+        assert json.loads(parsed[0].arguments) == {"city": "Paris"}
+        assert json.loads(parsed[1].arguments) == {"timezone": "CET"}
+
+
+# ── Test: Edge cases with realistic token boundaries ─────────────
+
+
+class TestE2EEdgeCases:
+    """Edge cases that occur in real model inference."""
+
+    def test_dsml_marker_split_at_fullwidth_pipe(self):
+        """The fullwidth pipe character ｜ might be its own token."""
+        # This is a realistic tokenization: the DSML marker is split at the ｜ chars
+        model_tokens = [
+            "Let me help.\n\n",
+            "<\uff5c",  # start of ｜DSML｜
+            "DSML\uff5c",  # rest of DSML token
+            "function_calls>\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">NYC</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+
+    def test_tool_call_with_nested_json_object(self):
+        """Model passes a complex JSON object as a non-string parameter."""
+        dsml_block = (
+            f"{TOOL_CALLS_START}\n"
+            f'<{DSML_TOKEN}invoke name="create_event">\n'
+            f'<{DSML_TOKEN}parameter name="title" string="true">Team Standup</{DSML_TOKEN}parameter>\n'
+            f'<{DSML_TOKEN}parameter name="config" string="false">'
+            f'{{"recurring": true, "days": ["mon", "wed", "fri"], "time": "09:00"}}'
+            f"</{DSML_TOKEN}parameter>\n"
+            f"</{DSML_TOKEN}invoke>\n"
+            f"{TOOL_CALLS_END}"
+        )
+
+        # Feed as single token (model might produce it all at once after prefill)
+        results = list(parse_deepseek_v32(_simulate_tokens([dsml_block])))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        tc = tool_results[0].tool_calls[0]
+        assert tc.name == "create_event"
+        args = json.loads(tc.arguments)  # pyright: ignore[reportAny]
+        assert args["title"] == "Team Standup"
+        assert args["config"]["recurring"] is True
+        assert args["config"]["days"] == ["mon", "wed", "fri"]
+
+    def test_text_with_angle_brackets_not_mistaken_for_dsml(self):
+        """Angle brackets in normal text should not trigger DSML buffering."""
+        model_tokens = [
+            "The formula is ",
+            "<x, y>",
+            " where x > 0",
+            " and y < 100.",
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 0
+        full_text = "".join(r.text for r in gen_results)
+        assert "formula" in full_text
+        assert "<x, y>" in full_text
+
+    def test_empty_model_response(self):
+        """Model produces only EOS (empty response)."""
+        model_tokens = [""]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        assert len(gen_results) == 1
+        assert gen_results[0].text == ""
+        assert gen_results[0].finish_reason == "stop"
+
+
+# ── Test: Full EPDP spec round-trip ──────────────────────────────
+
+
+class TestE2EFullRoundTrip:
+    """Full round-trip matching the vLLM EPDP spec.
+
+    Simulates the complete multi-turn flow:
+      Turn 1: user asks → think → tool call → tool result → think → answer
+      Turn 2: user asks again → old reasoning stripped → think → answer
+    """
+
+    def test_single_tool_full_flow_with_thinking(self):
+        """Complete flow: user → think → tool call → tool result → think → answer.
+
+        This is the core EPDP flow from the vLLM spec.
+        """
+        # ── Turn 1.1: User asks, encode prompt ──
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a weather assistant."},
+            {"role": "user", "content": "How's the weather in Hangzhou?"},
+        ]
+        prompt_1 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_1.endswith(THINKING_START)
+        assert "## Tools" in prompt_1
+        assert "get_weather" in prompt_1
+
+        # ── Turn 1.1: Model thinks, then calls tool ──
+        model_tokens_1 = [
+            THINKING_START,
+            "The user wants to know the weather in Hangzhou.",
+            " I need to use the get_weather tool.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">Hangzhou</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_1 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_1)))
+
+        # Verify: thinking tokens + tool call
+        gen_1 = [r for r in results_1 if isinstance(r, GenerationResponse)]
+        tool_1 = [r for r in results_1 if isinstance(r, ToolCallResponse)]
+        thinking_1 = [r for r in gen_1 if r.is_thinking]
+
+        assert len(thinking_1) >= 1
+        assert "get_weather tool" in "".join(r.text for r in thinking_1)
+        assert len(tool_1) == 1
+        assert tool_1[0].tool_calls[0].name == "get_weather"
+        tc_args = json.loads(tool_1[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        assert tc_args == {"city": "Hangzhou"}
+
+        # ── Turn 1.2: Add assistant response + tool result to messages ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "The user wants to know the weather in Hangzhou. I need to use the get_weather tool.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Hangzhou"}',
+                        },
+                    }
+                ],
+            }
+        )
+        messages.append(
+            {
+                "role": "tool",
+                "content": '{"temperature": "7~13°C", "condition": "Cloudy"}',
+            }
+        )
+
+        # Encode prompt for turn 1.2
+        prompt_2 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+
+        # Verify: prompt has the full conversation structure
+        assert TOOL_CALLS_START in prompt_2  # assistant's encoded tool call
+        assert EOS_TOKEN in prompt_2  # assistant turn ends with EOS
+        assert "<function_results>" in prompt_2
+        assert "<result>" in prompt_2
+        assert "Cloudy" in prompt_2
+        assert "</function_results>" in prompt_2
+        # After tool results with thinking enabled → <think> appended
+        assert prompt_2.endswith(THINKING_START)
+        # The assistant's reasoning_content should appear (it's after last_user_idx)
+        assert "get_weather tool" in prompt_2
+
+        # ── Turn 1.2: Model thinks about results, then answers ──
+        model_tokens_2 = [
+            THINKING_START,
+            "The weather in Hangzhou is Cloudy, 7~13°C.",
+            " I'll tell the user.",
+            THINKING_END,
+            "The weather in Hangzhou is currently cloudy with temperatures between 7°C and 13°C.",
+        ]
+        results_2 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_2)))
+
+        gen_2 = [r for r in results_2 if isinstance(r, GenerationResponse)]
+        tool_2 = [r for r in results_2 if isinstance(r, ToolCallResponse)]
+        thinking_2 = [r for r in gen_2 if r.is_thinking]
+        non_thinking_2 = [r for r in gen_2 if not r.is_thinking]
+
+        assert len(tool_2) == 0  # No more tool calls
+        assert len(thinking_2) >= 1
+        assert "Cloudy" in "".join(r.text for r in thinking_2)
+        assert len(non_thinking_2) >= 1
+        final_text = "".join(r.text for r in non_thinking_2)
+        assert "7°C" in final_text
+        assert "13°C" in final_text
+
+    def test_multi_tool_full_flow(self):
+        """Flow with two tools: user → think → 2 tool calls → 2 results → think → answer."""
+        # ── Initial prompt ──
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You help with weather and time."},
+            {"role": "user", "content": "Weather in NYC and time in EST?"},
+        ]
+        prompt_1 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_1.endswith(THINKING_START)
+
+        # ── Model thinks, calls both tools ──
+        model_tokens_1 = [
+            THINKING_START,
+            "Two requests: weather and time. I'll call both.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">NYC</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            f'<{DSML_TOKEN}invoke name="get_time">\n',
+            f'<{DSML_TOKEN}parameter name="timezone" string="true">EST</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_1 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_1)))
+        tool_1 = [r for r in results_1 if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_1) == 1
+        assert len(tool_1[0].tool_calls) == 2
+        assert tool_1[0].tool_calls[0].name == "get_weather"
+        assert tool_1[0].tool_calls[1].name == "get_time"
+
+        # ── Add assistant + both tool results ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "Two requests: weather and time. I'll call both.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "NYC"}',
+                        },
+                    },
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "EST"}',
+                        },
+                    },
+                ],
+            }
+        )
+        messages.append({"role": "tool", "content": "72°F, sunny"})
+        messages.append({"role": "tool", "content": "2:30 PM EST"})
+
+        prompt_2 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+
+        # Verify multi-tool result encoding
+        # Count is 2: 1 in _TOOLS_SYSTEM_TEMPLATE example + 1 in conversation
+        assert prompt_2.count("<function_results>") == 2
+        assert prompt_2.count("</function_results>") == 2
+        assert "<result>72°F, sunny</result>" in prompt_2
+        assert "<result>2:30 PM EST</result>" in prompt_2
+        assert prompt_2.endswith(THINKING_START)
+
+        # ── Model thinks about results, answers ──
+        model_tokens_2 = [
+            THINKING_START,
+            "Got both results. Weather is 72°F sunny, time is 2:30 PM.",
+            THINKING_END,
+            "In NYC it's currently 72°F and sunny. The time in EST is 2:30 PM.",
+        ]
+        results_2 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_2)))
+
+        tool_2 = [r for r in results_2 if isinstance(r, ToolCallResponse)]
+        gen_2 = [r for r in results_2 if isinstance(r, GenerationResponse)]
+        non_thinking_2 = [r for r in gen_2 if not r.is_thinking]
+
+        assert len(tool_2) == 0
+        final_text = "".join(r.text for r in non_thinking_2)
+        assert "72°F" in final_text
+        assert "2:30 PM" in final_text
+
+    def test_two_user_turns_reasoning_stripped(self):
+        """Turn 2: old reasoning_content is stripped from history.
+
+        Per the vLLM spec, clear_reasoning_content is called between user turns
+        to save bandwidth. Our _drop_old_thinking handles this.
+        """
+        # Full turn 1 conversation (already completed)
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Weather in Hangzhou?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "I need to call get_weather for Hangzhou.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Hangzhou"}',
+                        },
+                    }
+                ],
+            },
+            {"role": "tool", "content": "Cloudy 7~13°C"},
+            {
+                "role": "assistant",
+                "content": "The weather in Hangzhou is cloudy, 7-13°C.",
+                "reasoning_content": "The tool returned cloudy weather. I'll summarize.",
+            },
+            # Turn 2: user asks again
+            {"role": "user", "content": "What about Beijing?"},
+        ]
+
+        prompt = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+
+        # Old reasoning_content from turn 1 assistants should be STRIPPED
+        # (they're before the last user message at index 5)
+        assert "I need to call get_weather" not in prompt
+        assert "tool returned cloudy" not in prompt
+
+        # But the assistant's content and tool calls should still be there
+        assert "cloudy, 7-13°C" in prompt
+        assert TOOL_CALLS_START in prompt
+
+        # Prompt ends with <think> for the new turn
+        assert prompt.endswith(THINKING_START)
+
+        # ── Turn 2: Model thinks, calls tool for Beijing ──
+        model_tokens = [
+            THINKING_START,
+            "Now the user wants Beijing weather.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">Beijing</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+        args = json.loads(tool_results[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        assert args == {"city": "Beijing"}
+
+    def test_chained_tool_calls_loop(self):
+        """Model calls tool, gets result, calls another tool, gets result, answers.
+
+        This simulates the inner while loop from the vLLM spec where the model
+        may need multiple sub-turns of tool calling before it has enough info.
+        """
+        # ── Sub-turn 1: user asks, model calls get_time ──
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "What's the weather in Hangzhou tomorrow?"},
+        ]
+
+        prompt_1 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_1.endswith(THINKING_START)
+
+        # Model first calls get_time to figure out the date
+        model_tokens_1 = [
+            THINKING_START,
+            "I need the current date first to calculate tomorrow.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_time">\n',
+            f'<{DSML_TOKEN}parameter name="timezone" string="true">Asia/Shanghai</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_1 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_1)))
+        tool_1 = [r for r in results_1 if isinstance(r, ToolCallResponse)]
+        assert len(tool_1) == 1
+        assert tool_1[0].tool_calls[0].name == "get_time"
+
+        # ── Sub-turn 2: add tool result, model calls get_weather ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "I need the current date first to calculate tomorrow.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "Asia/Shanghai"}',
+                        },
+                    }
+                ],
+            }
+        )
+        messages.append({"role": "tool", "content": "2025-12-01 14:30 CST"})
+
+        prompt_2 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert "<result>2025-12-01 14:30 CST</result>" in prompt_2
+        assert prompt_2.endswith(THINKING_START)
+
+        # Model now knows the date, calls get_weather
+        model_tokens_2 = [
+            THINKING_START,
+            "Today is 2025-12-01, so tomorrow is 2025-12-02.",
+            " Now I can check weather for Hangzhou.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">Hangzhou</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_2 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_2)))
+        tool_2 = [r for r in results_2 if isinstance(r, ToolCallResponse)]
+        assert len(tool_2) == 1
+        assert tool_2[0].tool_calls[0].name == "get_weather"
+
+        # ── Sub-turn 3: add weather result, model answers ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "Today is 2025-12-01, so tomorrow is 2025-12-02. Now I can check weather for Hangzhou.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Hangzhou"}',
+                        },
+                    }
+                ],
+            }
+        )
+        messages.append({"role": "tool", "content": "Sunny, 5~12°C"})
+
+        prompt_3 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        # Should have both function_results blocks (one per tool round)
+        # Count is 3: 1 in _TOOLS_SYSTEM_TEMPLATE example + 2 in conversation
+        assert prompt_3.count("<function_results>") == 3
+        assert prompt_3.count("</function_results>") == 3
+        assert "<result>2025-12-01 14:30 CST</result>" in prompt_3
+        assert "<result>Sunny, 5~12°C</result>" in prompt_3
+        assert prompt_3.endswith(THINKING_START)
+
+        # Model finally answers
+        model_tokens_3 = [
+            THINKING_START,
+            "I have the weather for tomorrow in Hangzhou.",
+            THINKING_END,
+            "Tomorrow in Hangzhou will be sunny with temperatures between 5°C and 12°C.",
+        ]
+        results_3 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_3)))
+
+        tool_3 = [r for r in results_3 if isinstance(r, ToolCallResponse)]
+        gen_3 = [r for r in results_3 if isinstance(r, GenerationResponse)]
+        non_thinking_3 = [r for r in gen_3 if not r.is_thinking]
+
+        assert len(tool_3) == 0  # No more tool calls — loop ends
+        final_text = "".join(r.text for r in non_thinking_3)
+        assert "sunny" in final_text.lower()
+        assert "5°C" in final_text
+        assert "12°C" in final_text

src/exo/worker/tests/unittests/test_runner/test_event_ordering.py

@@ -148,6 +148,7 @@ class MockTokenizer:
     tool_call_start = None
     tool_call_end = None
     has_tool_calling = False
+    has_thinking = False
 
 
 class MockGroup:

src/exo/worker/tests/unittests/test_runner/test_parse_gpt_oss.py

@@ -149,12 +149,23 @@ class TestParseGptOssThinkingThenToolCall:
     def test_thinking_then_tool_call(self):
         results = _collect(THINKING_THEN_TOOL_TOKENS)
 
-        # Should have thinking tags + content + tool call
-        text_parts = [r.text for r in results if isinstance(r, GenerationResponse)]
-        combined = "".join(text_parts)
-        assert "<think>" in combined
-        assert "</think>" in combined
-        assert "Let me think about this." in combined
+        # Thinking tokens should have is_thinking=True and no <think> tags
+        thinking_responses = [
+            r for r in results if isinstance(r, GenerationResponse) and r.is_thinking
+        ]
+        thinking_text = "".join(r.text for r in thinking_responses)
+        assert "Let me think about this." in thinking_text
+        assert "<think>" not in thinking_text
+        assert "</think>" not in thinking_text
+
+        # Non-thinking tokens should have is_thinking=False
+        non_thinking = [
+            r
+            for r in results
+            if isinstance(r, GenerationResponse) and not r.is_thinking
+        ]
+        non_thinking_text = "".join(r.text for r in non_thinking)
+        assert "<think>" not in non_thinking_text
 
         # And the tool call
         tc = _get_tool_call(results)