feat: add tool-calling passthrough for all providers

Implement full OpenAI-compatible tool-calling support across the proxy,
enabling OpenCode to use llm-proxy as its sole LLM backend.

- Add 9 tool-calling types (Tool, FunctionDef, ToolChoice, ToolCall, etc.)
- Update ChatCompletionRequest/ChatMessage/ChatStreamDelta with tool fields
- Update UnifiedRequest/UnifiedMessage to carry tool data through the pipeline
- Shared helpers: messages_to_openai_json handles tool messages, build_openai_body
  includes tools/tool_choice, parse/stream extract tool_calls from responses
- Gemini: full OpenAI<->Gemini format translation (functionDeclarations,
  functionCall/functionResponse, synthetic call IDs, tool_config mapping)
- Gemini: extract duplicated message-conversion into shared convert_messages()
- Server: SSE streams include tool_calls deltas, finish_reason='tool_calls'
- AggregatingStream: accumulate tool call deltas across stream chunks
- OpenAI provider: add o4- prefix to supports_model()

src/dashboard/providers.rs

@@ -325,11 +325,16 @@ pub(super) async fn handle_test_provider(
         messages: vec![crate::models::UnifiedMessage {
             role: "user".to_string(),
             content: vec![crate::models::ContentPart::Text { text: "Hi".to_string() }],
+            tool_calls: None,
+            name: None,
+            tool_call_id: None,
         }],
         temperature: None,
         max_tokens: Some(5),
         stream: false,
         has_images: false,
+        tools: None,
+        tool_choice: None,
     };
 
     match provider.chat_completion(test_request).await {

src/models/mod.rs

@@ -1,4 +1,5 @@
 use serde::{Deserialize, Serialize};
+use serde_json::Value;
 
 pub mod registry;
 
@@ -14,16 +15,25 @@ pub struct ChatCompletionRequest {
     pub max_tokens: Option<u32>,
     #[serde(default)]
     pub stream: Option<bool>,
-    // Add other OpenAI-compatible fields as needed
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub tools: Option<Vec<Tool>>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub tool_choice: Option<ToolChoice>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ChatMessage {
-    pub role: String, // "system", "user", "assistant"
+    pub role: String, // "system", "user", "assistant", "tool"
     #[serde(flatten)]
     pub content: MessageContent,
     #[serde(skip_serializing_if = "Option::is_none")]
     pub reasoning_content: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_calls: Option<Vec<ToolCall>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub name: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_call_id: Option<String>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -48,6 +58,78 @@ pub struct ImageUrl {
     pub detail: Option<String>,
 }
 
+// ========== Tool-Calling Types ==========
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Tool {
+    #[serde(rename = "type")]
+    pub tool_type: String,
+    pub function: FunctionDef,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct FunctionDef {
+    pub name: String,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub description: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub parameters: Option<Value>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+#[serde(untagged)]
+pub enum ToolChoice {
+    Mode(String), // "auto", "none", "required"
+    Specific(ToolChoiceSpecific),
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolChoiceSpecific {
+    #[serde(rename = "type")]
+    pub choice_type: String,
+    pub function: ToolChoiceFunction,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolChoiceFunction {
+    pub name: String,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolCall {
+    pub id: String,
+    #[serde(rename = "type")]
+    pub call_type: String,
+    pub function: FunctionCall,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct FunctionCall {
+    pub name: String,
+    pub arguments: String,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ToolCallDelta {
+    pub index: u32,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub id: Option<String>,
+    #[serde(rename = "type", skip_serializing_if = "Option::is_none")]
+    pub call_type: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub function: Option<FunctionCallDelta>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct FunctionCallDelta {
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub name: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub arguments: Option<String>,
+}
+
+// ========== OpenAI-compatible Response Structs ==========
+
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ChatCompletionResponse {
     pub id: String,
@@ -96,6 +178,8 @@ pub struct ChatStreamDelta {
     pub content: Option<String>,
     #[serde(skip_serializing_if = "Option::is_none")]
     pub reasoning_content: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_calls: Option<Vec<ToolCallDelta>>,
 }
 
 // ========== Unified Request Format (for internal use) ==========
@@ -109,12 +193,17 @@ pub struct UnifiedRequest {
     pub max_tokens: Option<u32>,
     pub stream: bool,
     pub has_images: bool,
+    pub tools: Option<Vec<Tool>>,
+    pub tool_choice: Option<ToolChoice>,
 }
 
 #[derive(Debug, Clone)]
 pub struct UnifiedMessage {
     pub role: String,
     pub content: Vec<ContentPart>,
+    pub tool_calls: Option<Vec<ToolCall>>,
+    pub name: Option<String>,
+    pub tool_call_id: Option<String>,
 }
 
 #[derive(Debug, Clone)]
@@ -226,6 +315,9 @@ impl TryFrom<ChatCompletionRequest> for UnifiedRequest {
                 UnifiedMessage {
                     role: msg.role,
                     content,
+                    tool_calls: msg.tool_calls,
+                    name: msg.name,
+                    tool_call_id: msg.tool_call_id,
                 }
             })
             .collect();
@@ -238,6 +330,8 @@ impl TryFrom<ChatCompletionRequest> for UnifiedRequest {
             max_tokens: req.max_tokens,
             stream: req.stream.unwrap_or(false),
             has_images,
+            tools: req.tools,
+            tool_choice: req.tool_choice,
         })
     }
 }

src/providers/gemini.rs

@@ -2,14 +2,28 @@ use anyhow::Result;
 use async_trait::async_trait;
 use futures::stream::BoxStream;
 use serde::{Deserialize, Serialize};
+use serde_json::Value;
+use uuid::Uuid;
 
 use super::{ProviderResponse, ProviderStreamChunk};
-use crate::{config::AppConfig, errors::AppError, models::UnifiedRequest};
+use crate::{
+    config::AppConfig,
+    errors::AppError,
+    models::{ContentPart, FunctionCall, FunctionCallDelta, ToolCall, ToolCallDelta, UnifiedMessage, UnifiedRequest},
+};
+
+// ========== Gemini Request Structs ==========
 
 #[derive(Debug, Serialize)]
+#[serde(rename_all = "camelCase")]
 struct GeminiRequest {
     contents: Vec<GeminiContent>,
+    #[serde(skip_serializing_if = "Option::is_none")]
     generation_config: Option<GeminiGenerationConfig>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    tools: Option<Vec<GeminiTool>>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    tool_config: Option<GeminiToolConfig>,
 }
 
 #[derive(Debug, Serialize, Deserialize)]
@@ -19,11 +33,16 @@ struct GeminiContent {
 }
 
 #[derive(Debug, Serialize, Deserialize)]
+#[serde(rename_all = "camelCase")]
 struct GeminiPart {
     #[serde(skip_serializing_if = "Option::is_none")]
     text: Option<String>,
     #[serde(skip_serializing_if = "Option::is_none")]
     inline_data: Option<GeminiInlineData>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    function_call: Option<GeminiFunctionCall>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    function_response: Option<GeminiFunctionResponse>,
 }
 
 #[derive(Debug, Serialize, Deserialize)]
@@ -32,31 +51,85 @@ struct GeminiInlineData {
     data: String,
 }
 
+#[derive(Debug, Serialize, Deserialize)]
+struct GeminiFunctionCall {
+    name: String,
+    args: Value,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+struct GeminiFunctionResponse {
+    name: String,
+    response: Value,
+}
+
 #[derive(Debug, Serialize)]
+#[serde(rename_all = "camelCase")]
 struct GeminiGenerationConfig {
     temperature: Option<f64>,
     max_output_tokens: Option<u32>,
 }
 
+// ========== Gemini Tool Structs ==========
+
+#[derive(Debug, Serialize)]
+#[serde(rename_all = "camelCase")]
+struct GeminiTool {
+    function_declarations: Vec<GeminiFunctionDeclaration>,
+}
+
+#[derive(Debug, Serialize)]
+struct GeminiFunctionDeclaration {
+    name: String,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    description: Option<String>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    parameters: Option<Value>,
+}
+
+#[derive(Debug, Serialize)]
+#[serde(rename_all = "camelCase")]
+struct GeminiToolConfig {
+    function_calling_config: GeminiFunctionCallingConfig,
+}
+
+#[derive(Debug, Serialize)]
+struct GeminiFunctionCallingConfig {
+    mode: String,
+    #[serde(skip_serializing_if = "Option::is_none", rename = "allowedFunctionNames")]
+    allowed_function_names: Option<Vec<String>>,
+}
+
+// ========== Gemini Response Structs ==========
+
 #[derive(Debug, Deserialize)]
+#[serde(rename_all = "camelCase")]
 struct GeminiCandidate {
     content: GeminiContent,
-    _finish_reason: Option<String>,
+    #[serde(default)]
+    finish_reason: Option<String>,
 }
 
 #[derive(Debug, Deserialize)]
+#[serde(rename_all = "camelCase")]
 struct GeminiUsageMetadata {
+    #[serde(default)]
     prompt_token_count: u32,
+    #[serde(default)]
     candidates_token_count: u32,
+    #[serde(default)]
     total_token_count: u32,
 }
 
 #[derive(Debug, Deserialize)]
+#[serde(rename_all = "camelCase")]
 struct GeminiResponse {
     candidates: Vec<GeminiCandidate>,
     usage_metadata: Option<GeminiUsageMetadata>,
 }
 
+// ========== Provider Implementation ==========
+
 pub struct GeminiProvider {
     client: reqwest::Client,
     config: crate::config::GeminiConfig,
@@ -82,6 +155,209 @@ impl GeminiProvider {
             pricing: app_config.pricing.gemini.clone(),
         })
     }
+
+    /// Convert unified messages to Gemini content format.
+    /// Handles text, images, tool calls (assistant), and tool results.
+    async fn convert_messages(messages: Vec<UnifiedMessage>) -> Result<Vec<GeminiContent>, AppError> {
+        let mut contents = Vec::with_capacity(messages.len());
+
+        for msg in messages {
+            // Tool-result messages → functionResponse parts under role "user"
+            if msg.role == "tool" {
+                let text_content = msg
+                    .content
+                    .first()
+                    .map(|p| match p {
+                        ContentPart::Text { text } => text.clone(),
+                        ContentPart::Image(_) => "[Image]".to_string(),
+                    })
+                    .unwrap_or_default();
+
+                let name = msg.name.unwrap_or_default();
+
+                // Parse the content as JSON if possible, otherwise wrap as string
+                let response_value = serde_json::from_str::<Value>(&text_content)
+                    .unwrap_or_else(|_| serde_json::json!({ "result": text_content }));
+
+                contents.push(GeminiContent {
+                    parts: vec![GeminiPart {
+                        text: None,
+                        inline_data: None,
+                        function_call: None,
+                        function_response: Some(GeminiFunctionResponse {
+                            name,
+                            response: response_value,
+                        }),
+                    }],
+                    role: "user".to_string(),
+                });
+                continue;
+            }
+
+            // Assistant messages with tool_calls → functionCall parts
+            if msg.role == "assistant" {
+                if let Some(tool_calls) = &msg.tool_calls {
+                    let mut parts = Vec::new();
+
+                    // Include text content if present
+                    for p in &msg.content {
+                        if let ContentPart::Text { text } = p {
+                            if !text.is_empty() {
+                                parts.push(GeminiPart {
+                                    text: Some(text.clone()),
+                                    inline_data: None,
+                                    function_call: None,
+                                    function_response: None,
+                                });
+                            }
+                        }
+                    }
+
+                    // Convert each tool call to a functionCall part
+                    for tc in tool_calls {
+                        let args = serde_json::from_str::<Value>(&tc.function.arguments)
+                            .unwrap_or_else(|_| serde_json::json!({}));
+                        parts.push(GeminiPart {
+                            text: None,
+                            inline_data: None,
+                            function_call: Some(GeminiFunctionCall {
+                                name: tc.function.name.clone(),
+                                args,
+                            }),
+                            function_response: None,
+                        });
+                    }
+
+                    contents.push(GeminiContent {
+                        parts,
+                        role: "model".to_string(),
+                    });
+                    continue;
+                }
+            }
+
+            // Regular text/image messages
+            let mut parts = Vec::with_capacity(msg.content.len());
+            for part in msg.content {
+                match part {
+                    ContentPart::Text { text } => {
+                        parts.push(GeminiPart {
+                            text: Some(text),
+                            inline_data: None,
+                            function_call: None,
+                            function_response: None,
+                        });
+                    }
+                    ContentPart::Image(image_input) => {
+                        let (base64_data, mime_type) = image_input
+                            .to_base64()
+                            .await
+                            .map_err(|e| AppError::ProviderError(format!("Failed to convert image: {}", e)))?;
+
+                        parts.push(GeminiPart {
+                            text: None,
+                            inline_data: Some(GeminiInlineData {
+                                mime_type,
+                                data: base64_data,
+                            }),
+                            function_call: None,
+                            function_response: None,
+                        });
+                    }
+                }
+            }
+
+            let role = match msg.role.as_str() {
+                "assistant" => "model".to_string(),
+                _ => "user".to_string(),
+            };
+
+            contents.push(GeminiContent { parts, role });
+        }
+
+        Ok(contents)
+    }
+
+    /// Convert OpenAI tools to Gemini function declarations.
+    fn convert_tools(request: &UnifiedRequest) -> Option<Vec<GeminiTool>> {
+        request.tools.as_ref().map(|tools| {
+            let declarations: Vec<GeminiFunctionDeclaration> = tools
+                .iter()
+                .map(|t| GeminiFunctionDeclaration {
+                    name: t.function.name.clone(),
+                    description: t.function.description.clone(),
+                    parameters: t.function.parameters.clone(),
+                })
+                .collect();
+            vec![GeminiTool {
+                function_declarations: declarations,
+            }]
+        })
+    }
+
+    /// Convert OpenAI tool_choice to Gemini tool_config.
+    fn convert_tool_config(request: &UnifiedRequest) -> Option<GeminiToolConfig> {
+        request.tool_choice.as_ref().map(|tc| {
+            let (mode, allowed_names) = match tc {
+                crate::models::ToolChoice::Mode(mode) => {
+                    let gemini_mode = match mode.as_str() {
+                        "auto" => "AUTO",
+                        "none" => "NONE",
+                        "required" => "ANY",
+                        _ => "AUTO",
+                    };
+                    (gemini_mode.to_string(), None)
+                }
+                crate::models::ToolChoice::Specific(specific) => {
+                    ("ANY".to_string(), Some(vec![specific.function.name.clone()]))
+                }
+            };
+            GeminiToolConfig {
+                function_calling_config: GeminiFunctionCallingConfig {
+                    mode,
+                    allowed_function_names: allowed_names,
+                },
+            }
+        })
+    }
+
+    /// Extract tool calls from Gemini response parts into OpenAI-format ToolCalls.
+    fn extract_tool_calls(parts: &[GeminiPart]) -> Option<Vec<ToolCall>> {
+        let calls: Vec<ToolCall> = parts
+            .iter()
+            .filter_map(|p| p.function_call.as_ref())
+            .map(|fc| ToolCall {
+                id: format!("call_{}", Uuid::new_v4().simple()),
+                call_type: "function".to_string(),
+                function: FunctionCall {
+                    name: fc.name.clone(),
+                    arguments: serde_json::to_string(&fc.args).unwrap_or_else(|_| "{}".to_string()),
+                },
+            })
+            .collect();
+
+        if calls.is_empty() { None } else { Some(calls) }
+    }
+
+    /// Extract tool call deltas from Gemini response parts for streaming.
+    fn extract_tool_call_deltas(parts: &[GeminiPart]) -> Option<Vec<ToolCallDelta>> {
+        let deltas: Vec<ToolCallDelta> = parts
+            .iter()
+            .filter_map(|p| p.function_call.as_ref())
+            .enumerate()
+            .map(|(i, fc)| ToolCallDelta {
+                index: i as u32,
+                id: Some(format!("call_{}", Uuid::new_v4().simple())),
+                call_type: Some("function".to_string()),
+                function: Some(FunctionCallDelta {
+                    name: Some(fc.name.clone()),
+                    arguments: Some(serde_json::to_string(&fc.args).unwrap_or_else(|_| "{}".to_string())),
+                }),
+            })
+            .collect();
+
+        if deltas.is_empty() { None } else { Some(deltas) }
+    }
 }
 
 #[async_trait]
@@ -99,51 +375,15 @@ impl super::Provider for GeminiProvider {
     }
 
     async fn chat_completion(&self, request: UnifiedRequest) -> Result<ProviderResponse, AppError> {
-        // Convert UnifiedRequest to Gemini request
+        let model = request.model.clone();
-        let mut contents = Vec::with_capacity(request.messages.len());
+        let tools = Self::convert_tools(&request);
-
+        let tool_config = Self::convert_tool_config(&request);
-        for msg in request.messages {
+        let contents = Self::convert_messages(request.messages).await?;
-            let mut parts = Vec::with_capacity(msg.content.len());
-
-            for part in msg.content {
-                match part {
-                    crate::models::ContentPart::Text { text } => {
-                        parts.push(GeminiPart {
-                            text: Some(text),
-                            inline_data: None,
-                        });
-                    }
-                    crate::models::ContentPart::Image(image_input) => {
-                        let (base64_data, mime_type) = image_input
-                            .to_base64()
-                            .await
-                            .map_err(|e| AppError::ProviderError(format!("Failed to convert image: {}", e)))?;
-
-                        parts.push(GeminiPart {
-                            text: None,
-                            inline_data: Some(GeminiInlineData {
-                                mime_type,
-                                data: base64_data,
-                            }),
-                        });
-                    }
-                }
-            }
-
-            // Map role: "user" -> "user", "assistant" -> "model", "system" -> "user"
-            let role = match msg.role.as_str() {
-                "assistant" => "model".to_string(),
-                _ => "user".to_string(),
-            };
-
-            contents.push(GeminiContent { parts, role });
-        }
 
         if contents.is_empty() {
-            return Err(AppError::ProviderError("No valid text messages to send".to_string()));
+            return Err(AppError::ProviderError("No valid messages to send".to_string()));
         }
 
-        // Build generation config
         let generation_config = if request.temperature.is_some() || request.max_tokens.is_some() {
             Some(GeminiGenerationConfig {
                 temperature: request.temperature,
@@ -156,12 +396,12 @@ impl super::Provider for GeminiProvider {
         let gemini_request = GeminiRequest {
             contents,
             generation_config,
+            tools,
+            tool_config,
         };
 
-        // Build URL
+        let url = format!("{}/models/{}:generateContent", self.config.base_url, model);
-        let url = format!("{}/models/{}:generateContent", self.config.base_url, request.model,);
 
-        // Send request
         let response = self
             .client
             .post(&url)
@@ -171,7 +411,6 @@ impl super::Provider for GeminiProvider {
             .await
             .map_err(|e| AppError::ProviderError(format!("HTTP request failed: {}", e)))?;
 
-        // Check status
         let status = response.status();
         if !status.is_success() {
             let error_text = response.text().await.unwrap_or_default();
@@ -186,15 +425,16 @@ impl super::Provider for GeminiProvider {
             .await
             .map_err(|e| AppError::ProviderError(format!("Failed to parse response: {}", e)))?;
 
-        // Extract content from first candidate
+        let candidate = gemini_response.candidates.first();
-        let content = gemini_response
+
-            .candidates
+        // Extract text content (may be absent if only function calls)
-            .first()
+        let content = candidate
-            .and_then(|c| c.content.parts.first())
+            .and_then(|c| c.content.parts.iter().find_map(|p| p.text.clone()))
-            .and_then(|p| p.text.clone())
             .unwrap_or_default();
 
-        // Extract token usage
+        // Extract function calls → OpenAI tool_calls
+        let tool_calls = candidate.and_then(|c| Self::extract_tool_calls(&c.content.parts));
+
         let prompt_tokens = gemini_response
             .usage_metadata
             .as_ref()
@@ -213,11 +453,12 @@ impl super::Provider for GeminiProvider {
 
         Ok(ProviderResponse {
             content,
-            reasoning_content: None, // Gemini doesn't use this field name
+            reasoning_content: None,
+            tool_calls,
             prompt_tokens,
             completion_tokens,
             total_tokens,
-            model: request.model,
+            model,
         })
     }
 
@@ -247,47 +488,11 @@ impl super::Provider for GeminiProvider {
         &self,
         request: UnifiedRequest,
     ) -> Result<BoxStream<'static, Result<ProviderStreamChunk, AppError>>, AppError> {
-        // Convert UnifiedRequest to Gemini request
+        let model = request.model.clone();
-        let mut contents = Vec::with_capacity(request.messages.len());
+        let tools = Self::convert_tools(&request);
+        let tool_config = Self::convert_tool_config(&request);
+        let contents = Self::convert_messages(request.messages).await?;
 
-        for msg in request.messages {
-            let mut parts = Vec::with_capacity(msg.content.len());
-
-            for part in msg.content {
-                match part {
-                    crate::models::ContentPart::Text { text } => {
-                        parts.push(GeminiPart {
-                            text: Some(text),
-                            inline_data: None,
-                        });
-                    }
-                    crate::models::ContentPart::Image(image_input) => {
-                        let (base64_data, mime_type) = image_input
-                            .to_base64()
-                            .await
-                            .map_err(|e| AppError::ProviderError(format!("Failed to convert image: {}", e)))?;
-
-                        parts.push(GeminiPart {
-                            text: None,
-                            inline_data: Some(GeminiInlineData {
-                                mime_type,
-                                data: base64_data,
-                            }),
-                        });
-                    }
-                }
-            }
-
-            // Map role
-            let role = match msg.role.as_str() {
-                "assistant" => "model".to_string(),
-                _ => "user".to_string(),
-            };
-
-            contents.push(GeminiContent { parts, role });
-        }
-
-        // Build generation config
         let generation_config = if request.temperature.is_some() || request.max_tokens.is_some() {
             Some(GeminiGenerationConfig {
                 temperature: request.temperature,
@@ -300,15 +505,15 @@ impl super::Provider for GeminiProvider {
         let gemini_request = GeminiRequest {
             contents,
             generation_config,
+            tools,
+            tool_config,
         };
 
-        // Build URL for streaming
         let url = format!(
             "{}/models/{}:streamGenerateContent?alt=sse",
-            self.config.base_url, request.model,
+            self.config.base_url, model,
         );
 
-        // Create eventsource stream
         use futures::StreamExt;
         use reqwest_eventsource::{Event, EventSource};
 
@@ -320,8 +525,6 @@ impl super::Provider for GeminiProvider {
         )
         .map_err(|e| AppError::ProviderError(format!("Failed to create EventSource: {}", e)))?;
 
-        let model = request.model.clone();
-
         let stream = async_stream::try_stream! {
             let mut es = es;
             while let Some(event) = es.next().await {
@@ -331,14 +534,28 @@ impl super::Provider for GeminiProvider {
                             .map_err(|e| AppError::ProviderError(format!("Failed to parse stream chunk: {}", e)))?;
 
                         if let Some(candidate) = gemini_response.candidates.first() {
-                            let content = candidate.content.parts.first()
+                            let content = candidate
-                                .and_then(|p| p.text.clone())
+                                .content
+                                .parts
+                                .iter()
+                                .find_map(|p| p.text.clone())
                                 .unwrap_or_default();
 
+                            let tool_calls = Self::extract_tool_call_deltas(&candidate.content.parts);
+
+                            // Determine finish_reason
+                            let finish_reason = candidate.finish_reason.as_ref().map(|fr| {
+                                match fr.as_str() {
+                                    "STOP" => "stop".to_string(),
+                                    _ => fr.to_lowercase(),
+                                }
+                            });
+
                             yield ProviderStreamChunk {
                                 content,
                                 reasoning_content: None,
-                                finish_reason: None, // Will be set in the last chunk
+                                finish_reason,
+                                tool_calls,
                                 model: model.clone(),
                             };
                         }

src/providers/helpers.rs

@@ -1,6 +1,6 @@
 use super::{ProviderResponse, ProviderStreamChunk};
 use crate::errors::AppError;
-use crate::models::{ContentPart, UnifiedMessage, UnifiedRequest};
+use crate::models::{ContentPart, ToolCall, ToolCallDelta, UnifiedMessage, UnifiedRequest};
 use futures::stream::{BoxStream, StreamExt};
 use serde_json::Value;
 
@@ -8,9 +8,37 @@ use serde_json::Value;
 ///
 /// This avoids the deadlock caused by `futures::executor::block_on` inside a
 /// Tokio async context. All image base64 conversions are awaited properly.
+/// Handles tool-calling messages: assistant messages with tool_calls, and
+/// tool-role messages with tool_call_id/name.
 pub async fn messages_to_openai_json(messages: &[UnifiedMessage]) -> Result<Vec<serde_json::Value>, AppError> {
     let mut result = Vec::new();
     for m in messages {
+        // Tool-role messages: { role: "tool", content: "...", tool_call_id: "...", name: "..." }
+        if m.role == "tool" {
+            let text_content = m
+                .content
+                .first()
+                .map(|p| match p {
+                    ContentPart::Text { text } => text.clone(),
+                    ContentPart::Image(_) => "[Image]".to_string(),
+                })
+                .unwrap_or_default();
+
+            let mut msg = serde_json::json!({
+                "role": "tool",
+                "content": text_content
+            });
+            if let Some(tool_call_id) = &m.tool_call_id {
+                msg["tool_call_id"] = serde_json::json!(tool_call_id);
+            }
+            if let Some(name) = &m.name {
+                msg["name"] = serde_json::json!(name);
+            }
+            result.push(msg);
+            continue;
+        }
+
+        // Build content parts for non-tool messages
         let mut parts = Vec::new();
         for p in &m.content {
             match p {
@@ -29,10 +57,26 @@ pub async fn messages_to_openai_json(messages: &[UnifiedMessage]) -> Result<Vec<
                 }
             }
         }
-        result.push(serde_json::json!({
+
-            "role": m.role,
+        let mut msg = serde_json::json!({ "role": m.role });
-            "content": parts
+
-        }));
+        // For assistant messages with tool_calls, content can be null
+        if let Some(tool_calls) = &m.tool_calls {
+            if parts.is_empty() {
+                msg["content"] = serde_json::Value::Null;
+            } else {
+                msg["content"] = serde_json::json!(parts);
+            }
+            msg["tool_calls"] = serde_json::json!(tool_calls);
+        } else {
+            msg["content"] = serde_json::json!(parts);
+        }
+
+        if let Some(name) = &m.name {
+            msg["name"] = serde_json::json!(name);
+        }
+
+        result.push(msg);
     }
     Ok(result)
 }
@@ -65,6 +109,7 @@ pub async fn messages_to_openai_json_text_only(
 }
 
 /// Build an OpenAI-compatible request body from a UnifiedRequest and pre-converted messages.
+/// Includes tools and tool_choice when present.
 pub fn build_openai_body(
     request: &UnifiedRequest,
     messages_json: Vec<serde_json::Value>,
@@ -82,11 +127,18 @@ pub fn build_openai_body(
     if let Some(max_tokens) = request.max_tokens {
         body["max_tokens"] = serde_json::json!(max_tokens);
     }
+    if let Some(tools) = &request.tools {
+        body["tools"] = serde_json::json!(tools);
+    }
+    if let Some(tool_choice) = &request.tool_choice {
+        body["tool_choice"] = serde_json::json!(tool_choice);
+    }
 
     body
 }
 
 /// Parse an OpenAI-compatible chat completion response JSON into a ProviderResponse.
+/// Extracts tool_calls from the message when present.
 pub fn parse_openai_response(resp_json: &Value, model: String) -> Result<ProviderResponse, AppError> {
     let choice = resp_json["choices"]
         .get(0)
@@ -96,6 +148,11 @@ pub fn parse_openai_response(resp_json: &Value, model: String) -> Result<Provide
     let content = message["content"].as_str().unwrap_or_default().to_string();
     let reasoning_content = message["reasoning_content"].as_str().map(|s| s.to_string());
 
+    // Parse tool_calls from the response message
+    let tool_calls: Option<Vec<ToolCall>> = message
+        .get("tool_calls")
+        .and_then(|tc| serde_json::from_value(tc.clone()).ok());
+
     let usage = &resp_json["usage"];
     let prompt_tokens = usage["prompt_tokens"].as_u64().unwrap_or(0) as u32;
     let completion_tokens = usage["completion_tokens"].as_u64().unwrap_or(0) as u32;
@@ -104,6 +161,7 @@ pub fn parse_openai_response(resp_json: &Value, model: String) -> Result<Provide
     Ok(ProviderResponse {
         content,
         reasoning_content,
+        tool_calls,
         prompt_tokens,
         completion_tokens,
         total_tokens,
@@ -115,6 +173,7 @@ pub fn parse_openai_response(resp_json: &Value, model: String) -> Result<Provide
 ///
 /// The optional `reasoning_field` allows overriding the field name for
 /// reasoning content (e.g., "thought" for Ollama).
+/// Parses tool_calls deltas from streaming chunks when present.
 pub fn create_openai_stream(
     es: reqwest_eventsource::EventSource,
     model: String,
@@ -143,10 +202,16 @@ pub fn create_openai_stream(
                             .map(|s| s.to_string());
                         let finish_reason = choice["finish_reason"].as_str().map(|s| s.to_string());
 
+                        // Parse tool_calls deltas from the stream chunk
+                        let tool_calls: Option<Vec<ToolCallDelta>> = delta
+                            .get("tool_calls")
+                            .and_then(|tc| serde_json::from_value(tc.clone()).ok());
+
                         yield ProviderStreamChunk {
                             content,
                             reasoning_content,
                             finish_reason,
+                            tool_calls,
                             model: model.clone(),
                         };
                     }

src/providers/mod.rs

@@ -50,6 +50,7 @@ pub trait Provider: Send + Sync {
 pub struct ProviderResponse {
     pub content: String,
     pub reasoning_content: Option<String>,
+    pub tool_calls: Option<Vec<crate::models::ToolCall>>,
     pub prompt_tokens: u32,
     pub completion_tokens: u32,
     pub total_tokens: u32,
@@ -61,6 +62,7 @@ pub struct ProviderStreamChunk {
     pub content: String,
     pub reasoning_content: Option<String>,
     pub finish_reason: Option<String>,
+    pub tool_calls: Option<Vec<crate::models::ToolCallDelta>>,
     pub model: String,
 }
 

src/providers/openai.rs

@@ -36,7 +36,7 @@ impl super::Provider for OpenAIProvider {
     }
 
     fn supports_model(&self, model: &str) -> bool {
-        model.starts_with("gpt-") || model.starts_with("o1-") || model.starts_with("o3-")
+        model.starts_with("gpt-") || model.starts_with("o1-") || model.starts_with("o3-") || model.starts_with("o4-")
     }
 
     fn supports_multimodal(&self) -> bool {

src/server/mod.rs

@@ -174,6 +174,7 @@ async fn chat_completions(
                                         role: None,
                                         content: Some(chunk.content),
                                         reasoning_content: chunk.reasoning_content,
+                                        tool_calls: chunk.tool_calls,
                                     },
                                     finish_reason: chunk.finish_reason,
                                 }],
@@ -248,6 +249,12 @@ async fn chat_completions(
                     .await;
 
                 // Convert ProviderResponse to ChatCompletionResponse
+                let finish_reason = if response.tool_calls.is_some() {
+                    "tool_calls".to_string()
+                } else {
+                    "stop".to_string()
+                };
+
                 let chat_response = ChatCompletionResponse {
                     id: format!("chatcmpl-{}", Uuid::new_v4()),
                     object: "chat.completion".to_string(),
@@ -261,8 +268,11 @@ async fn chat_completions(
                                 content: response.content,
                             },
                             reasoning_content: response.reasoning_content,
+                            tool_calls: response.tool_calls,
+                            name: None,
+                            tool_call_id: None,
                         },
-                        finish_reason: Some("stop".to_string()),
+                        finish_reason: Some(finish_reason),
                     }],
                     usage: Some(Usage {
                         prompt_tokens: response.prompt_tokens,

src/utils/streaming.rs

@@ -1,6 +1,7 @@
 use crate::client::ClientManager;
 use crate::errors::AppError;
 use crate::logging::{RequestLog, RequestLogger};
+use crate::models::ToolCall;
 use crate::providers::{Provider, ProviderStreamChunk};
 use crate::utils::tokens::estimate_completion_tokens;
 use futures::stream::Stream;
@@ -31,6 +32,7 @@ pub struct AggregatingStream<S> {
     has_images: bool,
     accumulated_content: String,
     accumulated_reasoning: String,
+    accumulated_tool_calls: Vec<ToolCall>,
     logger: Arc<RequestLogger>,
     client_manager: Arc<ClientManager>,
     model_registry: Arc<crate::models::registry::ModelRegistry>,
@@ -53,6 +55,7 @@ where
             has_images: config.has_images,
             accumulated_content: String::new(),
             accumulated_reasoning: String::new(),
+            accumulated_tool_calls: Vec::new(),
             logger: config.logger,
             client_manager: config.client_manager,
             model_registry: config.model_registry,
@@ -153,6 +156,38 @@ where
                 if let Some(reasoning) = &chunk.reasoning_content {
                     self.accumulated_reasoning.push_str(reasoning);
                 }
+                // Accumulate tool call deltas into complete tool calls
+                if let Some(deltas) = &chunk.tool_calls {
+                    for delta in deltas {
+                        let idx = delta.index as usize;
+                        // Grow the accumulated_tool_calls vec if needed
+                        while self.accumulated_tool_calls.len() <= idx {
+                            self.accumulated_tool_calls.push(ToolCall {
+                                id: String::new(),
+                                call_type: "function".to_string(),
+                                function: crate::models::FunctionCall {
+                                    name: String::new(),
+                                    arguments: String::new(),
+                                },
+                            });
+                        }
+                        let tc = &mut self.accumulated_tool_calls[idx];
+                        if let Some(id) = &delta.id {
+                            tc.id.clone_from(id);
+                        }
+                        if let Some(ct) = &delta.call_type {
+                            tc.call_type.clone_from(ct);
+                        }
+                        if let Some(f) = &delta.function {
+                            if let Some(name) = &f.name {
+                                tc.function.name.push_str(name);
+                            }
+                            if let Some(args) = &f.arguments {
+                                tc.function.arguments.push_str(args);
+                            }
+                        }
+                    }
+                }
             }
             Poll::Ready(Some(Err(_))) => {
                 // If there's an error, we might still want to log what we got so far?
@@ -217,12 +252,14 @@ mod tests {
                 content: "Hello".to_string(),
                 reasoning_content: None,
                 finish_reason: None,
+                tool_calls: None,
                 model: "test".to_string(),
             }),
             Ok(ProviderStreamChunk {
                 content: " World".to_string(),
                 reasoning_content: None,
                 finish_reason: Some("stop".to_string()),
+                tool_calls: None,
                 model: "test".to_string(),
             }),
         ];