fix(openai): parse embedded 'tool_uses' JSON for gpt-5.4 parallel calls

- Add static parse_tool_uses_json helper to extract embedded tool calls
- Update synchronous and streaming Responses API parsers to detect tool_uses blocks
- Strip tool_uses JSON from content to prevent raw JSON leakage to client
- Resolve lifetime issues by avoiding &self capture in streaming closure

src/providers/openai.rs

@@ -36,6 +36,57 @@ impl OpenAIProvider {
             pricing: app_config.pricing.openai.clone(),
         })
     }
+
+    /// GPT-5.4 models sometimes emit parallel tool calls as a JSON block starting with 
+    /// '{"tool_uses":' inside a text message instead of discrete function_call items.
+    /// This method attempts to extract and parse such tool calls.
+    pub fn parse_tool_uses_json(text: &str) -> Vec<crate::models::ToolCall> {
+        let mut calls = Vec::new();
+        if let Some(start) = text.find("{\"tool_uses\":") {
+            // Find the end of the JSON block by matching braces
+            let sub = &text[start..];
+            let mut brace_count = 0;
+            let mut end_idx = 0;
+            let mut found = false;
+            
+            for (i, c) in sub.char_indices() {
+                if c == '{' { brace_count += 1; }
+                else if c == '}' {
+                    brace_count -= 1;
+                    if brace_count == 0 {
+                        end_idx = i + 1;
+                        found = true;
+                        break;
+                    }
+                }
+            }
+
+            if found {
+                let json_str = &sub[..end_idx];
+                if let Ok(val) = serde_json::from_str::<serde_json::Value>(json_str) {
+                    if let Some(uses) = val.get("tool_uses").and_then(|u| u.as_array()) {
+                        for (idx, u) in uses.iter().enumerate() {
+                            let name = u.get("recipient_name")
+                                .and_then(|v| v.as_str())
+                                .unwrap_or("unknown")
+                                // Strip "functions." prefix if present
+                                .replace("functions.", "");
+                            let arguments = u.get("parameters")
+                                .map(|v| v.to_string())
+                                .unwrap_or_else(|| "{}".to_string());
+                            
+                            calls.push(crate::models::ToolCall {
+                                id: format!("call_tu_{}_{}", uuid::Uuid::new_v4().to_string()[..8].to_string(), idx),
+                                call_type: "function".to_string(),
+                                function: crate::models::FunctionCall { name, arguments },
+                            });
+                        }
+                    }
+                }
+            }
+        }
+        calls
+    }
 }
 
 #[async_trait]
@@ -112,10 +163,57 @@ impl super::Provider for OpenAIProvider {
         let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
         let mut input_parts = Vec::new();
         for m in &messages_json {
-            let mut role = m["role"].as_str().unwrap_or("user").to_string();
-            // Newer models (gpt-5, o1) prefer "developer" over "system"
-            if role == "system" {
-                role = "developer".to_string();
+            let role = m["role"].as_str().unwrap_or("user");
+            
+            if role == "tool" {
+                input_parts.push(serde_json::json!({
+                    "type": "function_call_output",
+                    "call_id": m.get("tool_call_id").and_then(|v| v.as_str()).unwrap_or(""),
+                    "output": m.get("content").and_then(|v| v.as_str()).unwrap_or("")
+                }));
+                continue;
+            }
+
+            if role == "assistant" && m.get("tool_calls").is_some() {
+                 // Push message part if it exists
+                 let content_val = m.get("content").cloned().unwrap_or(serde_json::json!(""));
+                 if !content_val.is_null() && (content_val.is_array() && !content_val.as_array().unwrap().is_empty() || content_val.is_string() && !content_val.as_str().unwrap().is_empty()) {
+                     let mut content = content_val.clone();
+                     if let Some(text) = content.as_str() {
+                         content = serde_json::json!([{ "type": "output_text", "text": text }]);
+                     } else if let Some(arr) = content.as_array_mut() {
+                         for part in arr {
+                             if let Some(obj) = part.as_object_mut() {
+                                 if obj.get("type").and_then(|v| v.as_str()) == Some("text") {
+                                     obj.insert("type".to_string(), serde_json::json!("output_text"));
+                                 }
+                             }
+                         }
+                     }
+                     input_parts.push(serde_json::json!({
+                         "type": "message",
+                         "role": "assistant",
+                         "content": content
+                     }));
+                 }
+                 
+                 // Push tool calls as separate items
+                 if let Some(tcs) = m.get("tool_calls").and_then(|v| v.as_array()) {
+                     for tc in tcs {
+                         input_parts.push(serde_json::json!({
+                             "type": "function_call",
+                             "call_id": tc["id"],
+                             "name": tc["function"]["name"],
+                             "arguments": tc["function"]["arguments"]
+                         }));
+                     }
+                 }
+                 continue;
+            }
+
+            let mut mapped_role = role.to_string();
+            if mapped_role == "system" {
+                mapped_role = "developer".to_string();
             }
             
             let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
@@ -127,12 +225,11 @@ impl super::Provider for OpenAIProvider {
                         if let Some(t) = part_obj.get("type").and_then(|v| v.as_str()) {
                             match t {
                                 "text" => {
-                                    let new_type = if role == "assistant" { "output_text" } else { "input_text" };
+                                    let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
                                     part_obj.insert("type".to_string(), serde_json::json!(new_type));
                                 }
                                 "image_url" => {
-                                    // Assistant typically doesn't have image_url in history this way, but for safety:
-                                    let new_type = if role == "assistant" { "output_image" } else { "input_image" };
+                                    let new_type = if mapped_role == "assistant" { "output_image" } else { "input_image" };
                                     part_obj.insert("type".to_string(), serde_json::json!(new_type));
                                     if let Some(img_url) = part_obj.remove("image_url") {
                                         part_obj.insert("image".to_string(), img_url);
@@ -144,14 +241,20 @@ impl super::Provider for OpenAIProvider {
                     }
                 }
             } else if let Some(text) = content.as_str() {
-                let new_type = if role == "assistant" { "output_text" } else { "input_text" };
-                content = serde_json::json!([{ "type": new_type, "text": text }]);
+                // If it's just a string, send it as a string instead of an array of objects
+                // as it's safer for standard conversational messages.
+                content = serde_json::json!(text);
             }
 
-            input_parts.push(serde_json::json!({
-                "role": role,
+            let mut msg_item = serde_json::json!({
+                "type": "message",
+                "role": mapped_role,
                 "content": content
-            }));
+            });
+            if let Some(name) = m.get("name") {
+                msg_item["name"] = name.clone();
+            }
+            input_parts.push(msg_item);
         }
 
         let mut body = serde_json::json!({
@@ -174,7 +277,33 @@ impl super::Provider for OpenAIProvider {
         }
 
         if let Some(tools) = &request.tools {
-            body["tools"] = serde_json::json!(tools);
+            let flattened: Vec<serde_json::Value> = tools.iter().map(|t| {
+                let mut obj = serde_json::json!({
+                    "type": t.tool_type,
+                    "name": t.function.name,
+                });
+                if let Some(desc) = &t.function.description {
+                    obj["description"] = serde_json::json!(desc);
+                }
+                if let Some(params) = &t.function.parameters {
+                    obj["parameters"] = params.clone();
+                }
+                obj
+            }).collect();
+            body["tools"] = serde_json::json!(flattened);
+        }
+        if let Some(tool_choice) = &request.tool_choice {
+            match tool_choice {
+                crate::models::ToolChoice::Mode(mode) => {
+                    body["tool_choice"] = serde_json::json!(mode);
+                }
+                crate::models::ToolChoice::Specific(specific) => {
+                    body["tool_choice"] = serde_json::json!({
+                        "type": specific.choice_type,
+                        "name": specific.function.name,
+                    });
+                }
+            }
         }
 
         let resp = self
@@ -200,18 +329,43 @@ impl super::Provider for OpenAIProvider {
 
         // Normalize Responses API output into ProviderResponse
         let mut content_text = String::new();
+        let mut tool_calls = Vec::new();
         if let Some(output) = resp_json.get("output").and_then(|o| o.as_array()) {
             for out in output {
-                if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
-                    for item in contents {
-                        if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
-                            if !content_text.is_empty() { content_text.push_str("\n"); }
-                            content_text.push_str(text);
-                        } else if let Some(parts) = item.get("parts").and_then(|p| p.as_array()) {
-                            for p in parts {
-                                if let Some(t) = p.as_str() {
+                let item_type = out.get("type").and_then(|v| v.as_str()).unwrap_or("");
+                match item_type {
+                    "message" => {
+                        if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
+                            for item in contents {
+                                if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
                                     if !content_text.is_empty() { content_text.push_str("\n"); }
-                                    content_text.push_str(t);
+                                    content_text.push_str(text);
+                                }
+                            }
+                        }
+                    }
+                    "function_call" => {
+                        let id = out.get("call_id")
+                            .or_else(|| out.get("item_id"))
+                            .or_else(|| out.get("id"))
+                            .and_then(|v| v.as_str())
+                            .unwrap_or("")
+                            .to_string();
+                        let name = out.get("name").and_then(|v| v.as_str()).unwrap_or("").to_string();
+                        let arguments = out.get("arguments").and_then(|v| v.as_str()).unwrap_or("").to_string();
+                        tool_calls.push(crate::models::ToolCall {
+                            id,
+                            call_type: "function".to_string(),
+                            function: crate::models::FunctionCall { name, arguments },
+                        });
+                    }
+                    _ => {
+                        // Fallback for older/nested structure
+                        if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
+                            for item in contents {
+                                if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
+                                    if !content_text.is_empty() { content_text.push_str("\n"); }
+                                    content_text.push_str(text);
                                 }
                             }
                         }
@@ -241,10 +395,20 @@ impl super::Provider for OpenAIProvider {
         let completion_tokens = resp_json.get("usage").and_then(|u| u.get("completion_tokens")).and_then(|v| v.as_u64()).unwrap_or(0) as u32;
         let total_tokens = resp_json.get("usage").and_then(|u| u.get("total_tokens")).and_then(|v| v.as_u64()).unwrap_or(0) as u32;
 
+        // GPT-5.4 parallel tool calls might be embedded in content_text as a JSON block
+        let embedded_calls = Self::parse_tool_uses_json(&content_text);
+        if !embedded_calls.is_empty() {
+            // Strip the JSON part from content_text to keep it clean
+            if let Some(start) = content_text.find("{\"tool_uses\":") {
+                content_text = content_text[..start].to_string();
+            }
+            tool_calls.extend(embedded_calls);
+        }
+
         Ok(ProviderResponse {
             content: content_text,
             reasoning_content: None,
-            tool_calls: None,
+            tool_calls: if tool_calls.is_empty() { None } else { Some(tool_calls) },
             prompt_tokens,
             completion_tokens,
             reasoning_tokens: 0,
@@ -379,10 +543,57 @@ impl super::Provider for OpenAIProvider {
         let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
         let mut input_parts = Vec::new();
         for m in &messages_json {
-            let mut role = m["role"].as_str().unwrap_or("user").to_string();
-            // Newer models (gpt-5, o1) prefer "developer" over "system"
-            if role == "system" {
-                role = "developer".to_string();
+            let role = m["role"].as_str().unwrap_or("user");
+            
+            if role == "tool" {
+                input_parts.push(serde_json::json!({
+                    "type": "function_call_output",
+                    "call_id": m.get("tool_call_id").and_then(|v| v.as_str()).unwrap_or(""),
+                    "output": m.get("content").and_then(|v| v.as_str()).unwrap_or("")
+                }));
+                continue;
+            }
+
+            if role == "assistant" && m.get("tool_calls").is_some() {
+                 // Push message part if it exists
+                 let content_val = m.get("content").cloned().unwrap_or(serde_json::json!(""));
+                 if !content_val.is_null() && (content_val.is_array() && !content_val.as_array().unwrap().is_empty() || content_val.is_string() && !content_val.as_str().unwrap().is_empty()) {
+                     let mut content = content_val.clone();
+                     if let Some(text) = content.as_str() {
+                         content = serde_json::json!([{ "type": "output_text", "text": text }]);
+                     } else if let Some(arr) = content.as_array_mut() {
+                         for part in arr {
+                             if let Some(obj) = part.as_object_mut() {
+                                 if obj.get("type").and_then(|v| v.as_str()) == Some("text") {
+                                     obj.insert("type".to_string(), serde_json::json!("output_text"));
+                                 }
+                             }
+                         }
+                     }
+                     input_parts.push(serde_json::json!({
+                         "type": "message",
+                         "role": "assistant",
+                         "content": content
+                     }));
+                 }
+                 
+                 // Push tool calls as separate items
+                 if let Some(tcs) = m.get("tool_calls").and_then(|v| v.as_array()) {
+                     for tc in tcs {
+                         input_parts.push(serde_json::json!({
+                             "type": "function_call",
+                             "call_id": tc["id"],
+                             "name": tc["function"]["name"],
+                             "arguments": tc["function"]["arguments"]
+                         }));
+                     }
+                 }
+                 continue;
+            }
+
+            let mut mapped_role = role.to_string();
+            if mapped_role == "system" {
+                mapped_role = "developer".to_string();
             }
             
             let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
@@ -394,12 +605,11 @@ impl super::Provider for OpenAIProvider {
                         if let Some(t) = part_obj.get("type").and_then(|v| v.as_str()) {
                             match t {
                                 "text" => {
-                                    let new_type = if role == "assistant" { "output_text" } else { "input_text" };
+                                    let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
                                     part_obj.insert("type".to_string(), serde_json::json!(new_type));
                                 }
                                 "image_url" => {
-                                    // Assistant typically doesn't have image_url in history this way, but for safety:
-                                    let new_type = if role == "assistant" { "output_image" } else { "input_image" };
+                                    let new_type = if mapped_role == "assistant" { "output_image" } else { "input_image" };
                                     part_obj.insert("type".to_string(), serde_json::json!(new_type));
                                     if let Some(img_url) = part_obj.remove("image_url") {
                                         part_obj.insert("image".to_string(), img_url);
@@ -411,14 +621,20 @@ impl super::Provider for OpenAIProvider {
                     }
                 }
             } else if let Some(text) = content.as_str() {
-                let new_type = if role == "assistant" { "output_text" } else { "input_text" };
-                content = serde_json::json!([{ "type": new_type, "text": text }]);
+                // If it's just a string, send it as a string instead of an array of objects
+                // as it's safer for standard conversational messages.
+                content = serde_json::json!(text);
             }
 
-            input_parts.push(serde_json::json!({
-                "role": role,
+            let mut msg_item = serde_json::json!({
+                "type": "message",
+                "role": mapped_role,
                 "content": content
-            }));
+            });
+            if let Some(name) = m.get("name") {
+                msg_item["name"] = name.clone();
+            }
+            input_parts.push(msg_item);
         }
 
         let mut body = serde_json::json!({
@@ -441,6 +657,36 @@ impl super::Provider for OpenAIProvider {
             }
         }
 
+        if let Some(tools) = &request.tools {
+            let flattened: Vec<serde_json::Value> = tools.iter().map(|t| {
+                let mut obj = serde_json::json!({
+                    "type": t.tool_type,
+                    "name": t.function.name,
+                });
+                if let Some(desc) = &t.function.description {
+                    obj["description"] = serde_json::json!(desc);
+                }
+                if let Some(params) = &t.function.parameters {
+                    obj["parameters"] = params.clone();
+                }
+                obj
+            }).collect();
+            body["tools"] = serde_json::json!(flattened);
+        }
+        if let Some(tool_choice) = &request.tool_choice {
+            match tool_choice {
+                crate::models::ToolChoice::Mode(mode) => {
+                    body["tool_choice"] = serde_json::json!(mode);
+                }
+                crate::models::ToolChoice::Specific(specific) => {
+                    body["tool_choice"] = serde_json::json!({
+                        "type": specific.choice_type,
+                        "name": specific.function.name,
+                    });
+                }
+            }
+        }
+
         let url = format!("{}/responses", self.config.base_url);
         let api_key = self.api_key.clone();
         let model = request.model.clone();
@@ -475,6 +721,7 @@ impl super::Provider for OpenAIProvider {
                             // Responses API specific parsing for streaming
                             let mut content = String::new();
                             let mut finish_reason = None;
+                            let mut tool_calls = None;
                             
                             let event_type = chunk.get("type").and_then(|v| v.as_str()).unwrap_or("");
                             
@@ -484,15 +731,35 @@ impl super::Provider for OpenAIProvider {
                                         content.push_str(delta);
                                     }
                                 }
-                                "response.output_text.done" => {
-                                    if let Some(text) = chunk.get("text").and_then(|v| v.as_str()) {
-                                        // Some implementations send the full text at the end
-                                        // We usually prefer deltas, but if we haven't seen them, this is the fallback.
-                                        // However, if we're already yielding deltas, we might not want this.
-                                        // For now, let's just use it as a signal that we're done.
-                                        finish_reason = Some("stop".to_string());
+                                "response.item.delta" => {
+                                    if let Some(delta) = chunk.get("delta") {
+                                        let t = delta.get("type").and_then(|v| v.as_str()).unwrap_or("");
+                                        if t == "function_call" {
+                                            let call_id = delta.get("call_id")
+                                                .or_else(|| chunk.get("item_id"))
+                                                .and_then(|v| v.as_str());
+                                            let name = delta.get("name").and_then(|v| v.as_str());
+                                            let arguments = delta.get("arguments").and_then(|v| v.as_str());
+                                            
+                                            tool_calls = Some(vec![crate::models::ToolCallDelta {
+                                                index: chunk.get("output_index").and_then(|v| v.as_u64()).unwrap_or(0) as u32,
+                                                id: call_id.map(|s| s.to_string()),
+                                                call_type: Some("function".to_string()),
+                                                function: Some(crate::models::FunctionCallDelta {
+                                                    name: name.map(|s| s.to_string()),
+                                                    arguments: arguments.map(|s| s.to_string()),
+                                                }),
+                                            }]);
+                                        } else if t == "message" {
+                                            if let Some(text) = delta.get("text").and_then(|v| v.as_str()) {
+                                                content.push_str(text);
+                                            }
+                                        }
                                     }
                                 }
+                                "response.output_text.done" | "response.item.done" => {
+                                    finish_reason = Some("stop".to_string());
+                                }
                                 "response.done" => {
                                     finish_reason = Some("stop".to_string());
                                 }
@@ -514,12 +781,41 @@ impl super::Provider for OpenAIProvider {
                                 }
                             }
 
-                            if !content.is_empty() || finish_reason.is_some() {
+                            // GPT-5.4 parallel tool calls might be embedded in content as a JSON block
+                            let embedded_calls = Self::parse_tool_uses_json(&content);
+
+                            if !embedded_calls.is_empty() {
+                                // Strip the JSON part from content to keep it clean
+                                if let Some(start) = content.find("{\"tool_uses\":") {
+                                    content = content[..start].to_string();
+                                }
+                                
+                                // Convert ToolCall to ToolCallDelta for streaming
+                                let deltas: Vec<crate::models::ToolCallDelta> = embedded_calls.into_iter().enumerate().map(|(idx, tc)| {
+                                    crate::models::ToolCallDelta {
+                                        index: idx as u32,
+                                        id: Some(tc.id),
+                                        call_type: Some("function".to_string()),
+                                        function: Some(crate::models::FunctionCallDelta {
+                                            name: Some(tc.function.name),
+                                            arguments: Some(tc.function.arguments),
+                                        }),
+                                    }
+                                }).collect();
+                                
+                                if let Some(ref mut existing) = tool_calls {
+                                    existing.extend(deltas);
+                                } else {
+                                    tool_calls = Some(deltas);
+                                }
+                            }
+
+                            if !content.is_empty() || finish_reason.is_some() || tool_calls.is_some() {
                                 yield ProviderStreamChunk {
                                     content,
                                     reasoning_content: None,
                                     finish_reason,
-                                    tool_calls: None,
+                                    tool_calls,
                                     model: model.clone(),
                                     usage: None,
                                 };