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fix: handle reasoning_content for Kimi/thinking models (#6902)

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Three fixes for reasoning_content handling:

1. agent.rs: Preserve reasoning_content when splitting parallel tool calls.
   Providers like Kimi require reasoning_content on all assistant messages
   with tool_calls when thinking mode is enabled.

2. openai.rs format_messages: Omit reasoning_content field entirely when
   empty instead of sending empty string. Kimi rejects empty
   reasoning_content ("").

3. openai.rs streaming: Properly accumulate reasoning_content chunks
   across streaming deltas and emit as MessageContent::reasoning().

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: clayarnoldg2m <carnold@g2m.ai>
This commit is contained in:
coder3
2026-02-17 00:15:51 +13:00
committed by clayarnoldg2m
parent a0538e146e
commit efb774403e
2 changed files with 37 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files
crates/goose/src/agents/agent.rs
+19 -3
View File
@@ -1346,8 +1346,9 @@ impl Agent {
}
}
// Preserve thinking content from the original response
// Preserve thinking/reasoning content from the original response
// Gemini (and other thinking models) require thinking to be echoed back
// Kimi/DeepSeek require reasoning_content on assistant tool call messages
let thinking_content: Vec<MessageContent> = response.content.iter()
.filter(|c| matches!(c, MessageContent::Thinking(_)))
.cloned()
@@ -1361,10 +1362,25 @@ impl Agent {
messages_to_add.push(thinking_msg);
}
// Collect reasoning content to attach to tool request messages
let reasoning_content: Vec<MessageContent> = response.content.iter()
.filter(|c| matches!(c, MessageContent::Reasoning(_)))
.cloned()
.collect();
for (idx, request) in frontend_requests.iter().chain(remaining_requests.iter()).enumerate() {
if request.tool_call.is_ok() {
let request_msg = Message::assistant()
.with_id(format!("msg_{}", Uuid::new_v4()))
let mut request_msg = Message::assistant()
.with_id(format!("msg_{}", Uuid::new_v4()));
// Attach reasoning content to EVERY split tool request message.
// Providers like Kimi require reasoning_content on all assistant
// messages with tool_calls when thinking mode is enabled.
for rc in &reasoning_content {
request_msg = request_msg.with_content(rc.clone());
}
request_msg = request_msg
.with_tool_request_with_metadata(
request.id.clone(),
request.tool_call.clone(),
crates/goose/src/providers/formats/openai.rs
+18 -11
View File
@@ -82,7 +82,7 @@ pub fn format_messages(messages: &[Message], image_format: &ImageFormat) -> Vec<
let mut output = Vec::new();
let mut content_array = Vec::new();
let mut text_array = Vec::new();
let mut reasoning_text: Option<String> = None;
let mut reasoning_text = String::new();
for content in &message.content {
match content {
@@ -116,7 +116,7 @@ pub fn format_messages(messages: &[Message], image_format: &ImageFormat) -> Vec<
continue;
}
MessageContent::Reasoning(r) => {
reasoning_text = Some(r.text.clone());
reasoning_text.push_str(&r.text);
}
MessageContent::ToolRequest(request) => match &request.tool_call {
Ok(tool_call) => {
@@ -278,15 +278,11 @@ pub fn format_messages(messages: &[Message], image_format: &ImageFormat) -> Vec<
converted["content"] = json!(null);
}
// DeepSeek requires reasoning_content field when tool_calls are present
// Set it to the captured reasoning text, or empty string if not present
if converted.get("tool_calls").is_some() {
let reasoning = reasoning_text.unwrap_or_default();
converted["reasoning_content"] = json!(reasoning);
} else if let Some(reasoning) = reasoning_text {
if !reasoning.is_empty() {
converted["reasoning_content"] = json!(reasoning);
}
// Include reasoning_content only when non-empty.
// Kimi rejects empty reasoning_content (""), so we must omit it entirely
// when there's no reasoning to send.
if !reasoning_text.is_empty() {
converted["reasoning_content"] = json!(reasoning_text);
}
if converted.get("content").is_some() || converted.get("tool_calls").is_some() {
@@ -542,6 +538,7 @@ where
use futures::StreamExt;
let mut accumulated_reasoning: Vec<Value> = Vec::new();
let mut accumulated_reasoning_content = String::new();
'outer: while let Some(response) = stream.next().await {
if response.as_ref().is_ok_and(|s| s == "data: [DONE]") {
@@ -562,6 +559,9 @@ where
if let Some(details) = &chunk.choices[0].delta.reasoning_details {
accumulated_reasoning.extend(details.iter().cloned());
}
if let Some(rc) = &chunk.choices[0].delta.reasoning_content {
accumulated_reasoning_content.push_str(rc);
}
}
let mut usage = extract_usage_with_output_tokens(&chunk);
@@ -602,6 +602,9 @@ where
if let Some(details) = &tool_chunk.choices[0].delta.reasoning_details {
accumulated_reasoning.extend(details.iter().cloned());
}
if let Some(rc) = &tool_chunk.choices[0].delta.reasoning_content {
accumulated_reasoning_content.push_str(rc);
}
if let Some(delta_tool_calls) = &tool_chunk.choices[0].delta.tool_calls {
for delta_call in delta_tool_calls {
if let Some(index) = delta_call.index {
@@ -642,6 +645,10 @@ where
};
let mut contents = Vec::new();
if !accumulated_reasoning_content.is_empty() {
contents.push(MessageContent::reasoning(&accumulated_reasoning_content));
accumulated_reasoning_content.clear();
}
let mut sorted_indices: Vec<_> = tool_call_data.keys().cloned().collect();
sorted_indices.sort();