use crate::agent::context_compressor::estimate_tokens;
use crate::agent::media_handler::MediaHandlerRegistry;
use crate::agent::system_prompt::build_system_prompt;
use crate::agent::turn_event::{AgentTurnContext, TurnEvent};
use crate::bus::message::ContentBlock;
use crate::bus::{ChatMessage, MediaRef};
use crate::config::LLMProviderConfig;
use crate::observability::{Observer, ObserverEvent, ToolExecutionOutcome, truncate_args};
use crate::providers::{
    ChatCompletionRequest, ChatCompletionResponse, LLMProvider, Message, ProviderChunk,
    ProviderResponseAccumulator, ToolCall, create_provider,
};
use crate::tools::{ToolExecutionContext, ToolRegistry};
use std::collections::VecDeque;
use std::hash::{Hash, Hasher};
use std::path::PathBuf;
use std::sync::Arc;
use std::time::Instant;

use futures_util::StreamExt;

/// Maximum characters in a tool result before truncation.
/// Prevents context overflow from large tool outputs.
const MAX_TOOL_RESULT_CHARS: usize = 16_000;
/// Minimum characters to keep when truncating
const TRUNCATION_SUFFIX_LEN: usize = 200;
const TOOL_PREVIEW_CHARS: usize = 1_000;

enum MediaOrigin<'a> {
    User,
    Tool(&'a str),
    Message,
}

fn should_include_message_media(messages: &[ChatMessage], index: usize) -> bool {
    let message = &messages[index];
    if message.role != "tool" {
        return true;
    }
    let active_tool_start = messages
        .iter()
        .rposition(|candidate| candidate.role != "tool")
        .map_or(0, |last_non_tool| last_non_tool + 1);
    index >= active_tool_start
}

/// Build content blocks from text and media, respecting model input capabilities
fn build_content_blocks(
    text: &str,
    media_refs: &[MediaRef],
    input_types: &[String],
    registry: &MediaHandlerRegistry,
    origin: MediaOrigin<'_>,
) -> Vec<ContentBlock> {
    let mut blocks = Vec::new();

    if !media_refs.is_empty() {
        let attachments = media_refs
            .iter()
            .map(|media_ref| {
                let path = std::path::Path::new(&media_ref.path);
                let name = path
                    .file_name()
                    .map(|name| name.to_string_lossy().into_owned())
                    .unwrap_or_else(|| media_ref.path.clone());
                let extension = path
                    .extension()
                    .map(|extension| extension.to_string_lossy().into_owned());
                let mime_type = mime_guess::from_path(path)
                    .first_or_octet_stream()
                    .to_string();
                let size_bytes = std::fs::metadata(path).ok().map(|metadata| metadata.len());
                let native_input = input_types.contains(&media_ref.media_type)
                    && registry.supports(&media_ref.media_type);
                serde_json::json!({
                    "name": name,
                    "extension": extension,
                    "media_type": media_ref.media_type,
                    "mime_type": mime_type,
                    "size_bytes": size_bytes,
                    "path": media_ref.path,
                    "content_delivery": if native_input {
                        "also included as a model-native content block"
                    } else {
                        "content is not embedded in this model request; path remains available to file tools"
                    },
                })
            })
            .collect::<Vec<_>>();
        let manifest = serde_json::Value::Array(attachments).to_string();
        let note = match origin {
            MediaOrigin::User if text.is_empty() => {
                "用户发送了以下附件。path 是 Gateway 内部存储路径，可供文件工具读取；附件内容未必已嵌入模型输入。".to_string()
            }
            MediaOrigin::User => {
                "随本条用户消息同时提交的附件。path 是 Gateway 内部存储路径，可供文件工具读取；content_delivery 说明附件内容是否另以模型原生内容块提供。".to_string()
            }
            MediaOrigin::Tool(tool_name) => format!(
                "工具 {tool_name} 返回了以下附件。content_delivery 说明附件内容是否另以模型原生内容块提供。"
            ),
            MediaOrigin::Message => {
                "本条消息包含以下附件。content_delivery 说明附件内容是否另以模型原生内容块提供。".to_string()
            }
        };
        let message_text = if text.is_empty() {
            format!("[{note}]\n{manifest}")
        } else {
            format!("{text}\n\n[{note}]\n{manifest}")
        };
        blocks.push(ContentBlock::text(message_text));

        for mr in media_refs {
            if input_types.contains(&mr.media_type) && registry.supports(&mr.media_type) {
                match registry.handle(&mr.media_type, &mr.path) {
                    Ok(content_blocks) => blocks.extend(content_blocks),
                    Err(e) => {
                        tracing::warn!(
                            path = %mr.path,
                            media_type = %mr.media_type,
                            error = %e,
                            "Media handler failed, falling back to text placeholder"
                        );
                        blocks.push(ContentBlock::text(format!(
                            "[用户发来了一个文件，但处理失败: {}, 错误: {}]",
                            mr.path, e
                        )));
                    }
                }
            } else {
                tracing::debug!(
                    path = %mr.path,
                    media_type = %mr.media_type,
                    model_input_types = ?input_types,
                    "Media type not supported by model; attachment manifest remains available"
                );
            }
        }
    } else if !text.is_empty() {
        blocks.push(ContentBlock::text(text));
    }

    if blocks.is_empty() {
        blocks.push(ContentBlock::text(""));
    }

    blocks
}

/// Truncate tool result if it exceeds MAX_TOOL_RESULT_CHARS.
/// Preserves the end of the output as it often contains the conclusion/useful result.
fn truncate_tool_result(output: &str) -> String {
    if output.len() <= MAX_TOOL_RESULT_CHARS {
        return output.to_string();
    }

    let truncated_start_len = output.len().saturating_sub(TRUNCATION_SUFFIX_LEN);
    if truncated_start_len > MAX_TOOL_RESULT_CHARS {
        // Even after removing suffix, still too long - take from beginning
        format!(
            "{}...\n\n[Output truncated - {} characters removed]",
            &output[..output.ceil_char_boundary(MAX_TOOL_RESULT_CHARS - 100)],
            output.len() - MAX_TOOL_RESULT_CHARS + 100
        )
    } else {
        // Keep most of the end which usually contains the useful result
        format!(
            "...\n\n[Output truncated - {} characters removed]\n\n{}",
            truncated_start_len,
            &output[output.floor_char_boundary(truncated_start_len)..]
        )
    }
}

fn tool_result_preview(output: &str) -> String {
    if output.len() <= TOOL_PREVIEW_CHARS {
        output.to_string()
    } else {
        format!(
            "{}…",
            &output[..output.floor_char_boundary(TOOL_PREVIEW_CHARS)]
        )
    }
}

/// Loop detection result.
#[derive(Debug, Clone, PartialEq, Eq)]
enum LoopDetectionResult {
    /// No warning needed.
    Ok,
    /// Warning: same tool + args repeated N times.
    Warning(String),
}

/// Configuration for loop detector.
#[derive(Debug, Clone)]
struct LoopDetectorConfig {
    /// Master switch.
    enabled: bool,
    /// Warn every N consecutive identical calls.
    warn_every: usize,
}

impl Default for LoopDetectorConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            warn_every: 5,
        }
    }
}

/// A single recorded tool invocation in the sliding window.
#[derive(Debug, Clone)]
struct ToolCallRecord {
    name: String,
    args_hash: u64,
}

/// Stateful loop detector that monitors for repetitive patterns.
struct LoopDetector {
    config: LoopDetectorConfig,
    window: VecDeque<ToolCallRecord>,
}

impl LoopDetector {
    fn new(config: LoopDetectorConfig) -> Self {
        Self {
            window: VecDeque::with_capacity(config.warn_every * 2),
            config,
        }
    }

    /// Record a completed tool call and check for loop patterns.
    /// Returns Warning every `warn_every` consecutive identical calls.
    fn record(&mut self, name: &str, args: &serde_json::Value) -> LoopDetectionResult {
        if !self.config.enabled {
            return LoopDetectionResult::Ok;
        }

        let record = ToolCallRecord {
            name: name.to_string(),
            args_hash: hash_json_value(args),
        };

        // Maintain sliding window
        if self.window.len() >= self.config.warn_every * 2 {
            self.window.pop_front();
        }
        self.window.push_back(record);

        // Count consecutive identical calls
        let last = self.window.back().unwrap();
        let consecutive: usize = self
            .window
            .iter()
            .rev()
            .take_while(|r| r.name == last.name && r.args_hash == last.args_hash)
            .count();

        // Warn every warn_every times
        if consecutive > 0 && consecutive.is_multiple_of(self.config.warn_every) {
            LoopDetectionResult::Warning(format!(
                "注意: 工具 '{}' 已连续执行 {} 次，参数相同。如果任务没有进展，请尝试其他方法。",
                last.name, consecutive
            ))
        } else {
            LoopDetectionResult::Ok
        }
    }
}

/// Hash a JSON value deterministically (key-order independent).
fn hash_json_value(value: &serde_json::Value) -> u64 {
    let mut hasher = std::collections::hash_map::DefaultHasher::new();
    let canonical = canonicalise_json(value);
    canonical.hash(&mut hasher);
    hasher.finish()
}

/// Return a clone of value with all object keys sorted recursively.
fn canonicalise_json(value: &serde_json::Value) -> serde_json::Value {
    match value {
        serde_json::Value::Object(map) => {
            let mut sorted: Vec<(&String, &serde_json::Value)> = map.iter().collect();
            sorted.sort_by_key(|(k, _)| *k);
            let new_map: serde_json::Map<String, serde_json::Value> = sorted
                .into_iter()
                .map(|(k, v)| (k.clone(), canonicalise_json(v)))
                .collect();
            serde_json::Value::Object(new_map)
        }
        serde_json::Value::Array(arr) => {
            serde_json::Value::Array(arr.iter().map(canonicalise_json).collect())
        }
        other => other.clone(),
    }
}
/// AgentLoop - Stateless agent that processes messages with tool calling support.
/// History is managed externally by SessionManager.
pub struct AgentLoop {
    provider: Arc<dyn LLMProvider>,
    tools: Arc<ToolRegistry>,
    observer: Option<Arc<dyn Observer>>,
    max_iterations: usize,
    workspace_dir: PathBuf,
    model_name: String,
    context_window: usize,
    input_types: Vec<String>,
    media_registry: MediaHandlerRegistry,
}
#[derive(Debug, Clone)]
pub struct AgentProcessResult {
    pub final_response: ChatMessage,
    pub emitted_messages: Vec<ChatMessage>,
    pub total_tokens: Option<u32>,
    pub usage: Option<crate::providers::Usage>,
    /// Provider usage for the final successful request in this Turn. This is
    /// the correct basis for context-window occupancy; `usage` is accumulated
    /// across every tool iteration.
    pub last_request_usage: Option<crate::providers::Usage>,
}

fn merge_usage(total: &mut crate::providers::Usage, next: &crate::providers::Usage) {
    total.prompt_tokens = total.prompt_tokens.saturating_add(next.prompt_tokens);
    total.completion_tokens = total
        .completion_tokens
        .saturating_add(next.completion_tokens);
    total.total_tokens = total.total_tokens.saturating_add(next.total_tokens);
    total.cached_tokens = sum_optional_tokens(total.cached_tokens, next.cached_tokens);
    total.cache_read_input_tokens =
        sum_optional_tokens(total.cache_read_input_tokens, next.cache_read_input_tokens);
    total.cache_creation_input_tokens = sum_optional_tokens(
        total.cache_creation_input_tokens,
        next.cache_creation_input_tokens,
    );
}

fn sum_optional_tokens(left: Option<u32>, right: Option<u32>) -> Option<u32> {
    match (left, right) {
        (None, None) => None,
        (left, right) => Some(
            left.unwrap_or_default()
                .saturating_add(right.unwrap_or_default()),
        ),
    }
}

impl AgentLoop {
    /// Create a new AgentLoop with a provider created from config.
    pub fn new(provider_config: LLMProviderConfig) -> Result<Self, AgentError> {
        let max_iterations = provider_config.max_tool_iterations;
        let model_name = provider_config.model_id.clone();
        let workspace_dir = provider_config.workspace_dir.clone();
        let input_types = provider_config.input_types.clone();
        let provider = create_provider(provider_config)
            .map_err(|e| AgentError::ProviderCreation(e.to_string()))?;

        Ok(Self {
            provider: Arc::from(provider),
            tools: Arc::new(ToolRegistry::new()),
            observer: None,
            context_window: 0,
            max_iterations,
            workspace_dir,
            model_name,
            input_types,
            media_registry: MediaHandlerRegistry::with_defaults(),
        })
    }

    /// Create a new AgentLoop with provider created from config and given tools.
    pub fn with_tools(
        provider_config: LLMProviderConfig,
        tools: Arc<ToolRegistry>,
    ) -> Result<Self, AgentError> {
        let max_iterations = provider_config.max_tool_iterations;
        let model_name = provider_config.model_id.clone();
        let workspace_dir = provider_config.workspace_dir.clone();
        let input_types = provider_config.input_types.clone();
        let provider = create_provider(provider_config)
            .map_err(|e| AgentError::ProviderCreation(e.to_string()))?;

        Ok(Self {
            provider: Arc::from(provider),
            tools,
            observer: None,
            context_window: 0,
            max_iterations,
            workspace_dir,
            model_name,
            input_types,
            media_registry: MediaHandlerRegistry::with_defaults(),
        })
    }

    /// Create a new AgentLoop with an existing shared provider.
    pub fn with_provider(
        provider: Arc<dyn LLMProvider>,
        max_iterations: usize,
        model_name: String,
        workspace_dir: PathBuf,
        input_types: Vec<String>,
    ) -> Self {
        Self {
            provider,
            tools: Arc::new(ToolRegistry::new()),
            observer: None,
            context_window: 0,
            max_iterations,
            workspace_dir,
            model_name,
            input_types,
            media_registry: MediaHandlerRegistry::with_defaults(),
        }
    }

    /// Create a new AgentLoop with an existing shared provider and given tools.
    pub fn with_provider_and_tools(
        provider: Arc<dyn LLMProvider>,
        tools: Arc<ToolRegistry>,
        max_iterations: usize,
        model_name: String,
        workspace_dir: PathBuf,
        input_types: Vec<String>,
    ) -> Self {
        Self {
            provider,
            tools,
            observer: None,
            context_window: 0,
            max_iterations,
            workspace_dir,
            model_name,
            input_types,
            media_registry: MediaHandlerRegistry::with_defaults(),
        }
    }

    /// Set the context window size for preemptive trimming.
    pub fn with_context_window(mut self, window: usize) -> Self {
        self.context_window = window;
        self
    }

    /// Set the workspace directory.
    pub fn with_workspace_dir(mut self, dir: PathBuf) -> Self {
        self.workspace_dir = dir;
        self
    }

    /// Set an observer for tracking events.
    pub fn with_observer(mut self, observer: Arc<dyn Observer>) -> Self {
        self.observer = Some(observer);
        self
    }

    /// Preemptive trim: truncate old tool results in-place when history is
    /// approaching the context window limit. Old results (outside of `keep_recent`
    /// zone) are replaced with a short placeholder; recent results are truncated
    /// to `max_chars`.
    fn preemptive_trim_old_tool_results(
        &self,
        messages: &mut [ChatMessage],
        max_chars: usize,
        keep_recent: usize,
    ) -> usize {
        let end = messages.len().saturating_sub(keep_recent);
        let start = 1; // protect system message at [0] if present
        let mut modified = 0;
        for message in messages.iter_mut().take(end).skip(start) {
            if message.role != "tool" {
                continue;
            }
            if message.content.len() <= max_chars {
                continue;
            }
            let tool_name = message.tool_name.as_deref().unwrap_or("unknown");
            let chars = message.content.len();
            message.content = format!(
                "[Tool output ({}) — {} chars, omitted from context]",
                tool_name, chars
            );
            modified += 1;
        }
        modified
    }

    pub fn tools(&self) -> &Arc<ToolRegistry> {
        &self.tools
    }

    async fn stream_completion(
        &self,
        request: ChatCompletionRequest,
        iteration: u32,
        turn: Option<&AgentTurnContext>,
    ) -> Result<ChatCompletionResponse, AgentError> {
        let metrics = crate::observability::metrics::global_metrics();
        let provider_name = self.provider.name().to_string();
        let provider_model = self.provider.model_id().to_string();
        let start = Instant::now();

        let mut provider_stream = match self.provider.stream(request).await {
            Ok(stream) => stream,
            Err(error) => {
                tracing::error!(error = %error, "LLM request failed");
                metrics.record_provider(
                    &provider_name,
                    &provider_model,
                    None,
                    start.elapsed().as_millis() as u64,
                    true,
                );
                return Err(AgentError::LlmError(error.to_string()));
            }
        };
        let mut accumulator = ProviderResponseAccumulator::default();
        while let Some(chunk) = provider_stream.next().await {
            let chunk = match chunk {
                Ok(chunk) => chunk,
                Err(error) => {
                    tracing::error!(error = %error, "LLM stream failed");
                    metrics.record_provider(
                        &provider_name,
                        &provider_model,
                        None,
                        start.elapsed().as_millis() as u64,
                        true,
                    );
                    return Err(AgentError::LlmError(error.to_string()));
                }
            };
            if let Some(turn) = turn {
                let event = match &chunk {
                    ProviderChunk::Reasoning(delta) => Some(TurnEvent::ReasoningDelta {
                        iteration,
                        delta: delta.clone(),
                    }),
                    ProviderChunk::Text(delta) => Some(TurnEvent::TextDelta {
                        iteration,
                        delta: delta.clone(),
                    }),
                    _ => None,
                };
                if let Some(event) = event {
                    turn.emitter.emit(event).map_err(|error| {
                        AgentError::Other(format!("turn event rejected: {error}"))
                    })?;
                }
            }
            accumulator.push(chunk);
        }
        let response = accumulator.finish();
        let latency_ms = start.elapsed().as_millis() as u64;
        metrics.record_provider(&provider_name, &provider_model, None, latency_ms, false);
        metrics.record_provider_tokens(&provider_name, &response.usage);
        Ok(response)
    }

    fn annotate_message(
        message: &mut ChatMessage,
        turn: Option<&AgentTurnContext>,
        iteration: u32,
        final_response: bool,
    ) {
        message.iteration = Some(iteration);
        if let Some(turn) = turn {
            message.turn_id = Some(turn.turn_id.clone());
            if final_response {
                message.id = turn.message_id.clone();
            }
        }
    }

    fn chat_message_to_llm_message(&self, m: &ChatMessage, include_media: bool) -> Message {
        let content = if m.media_refs.is_empty() || !include_media {
            vec![ContentBlock::text(&m.content)]
        } else {
            let origin = match m.role.as_str() {
                "user" => MediaOrigin::User,
                "tool" => MediaOrigin::Tool(m.tool_name.as_deref().unwrap_or("unknown")),
                _ => MediaOrigin::Message,
            };
            // Provider APIs generally allow native image/audio blocks only in
            // user input (and, through provider-specific adaptation, current
            // tool results). Persisted assistant attachments are delivery
            // artifacts: replay their manifest as text, never as native media.
            let native_input_types = if matches!(m.role.as_str(), "user" | "tool") {
                self.input_types.as_slice()
            } else {
                &[]
            };
            build_content_blocks(
                &m.content,
                &m.media_refs,
                native_input_types,
                &self.media_registry,
                origin,
            )
        };

        Message {
            role: m.role.clone(),
            content,
            reasoning_content: m.reasoning_content.clone(),
            provider_state: m.provider_state.clone(),
            tool_call_id: m.tool_call_id.clone(),
            name: m.tool_name.clone(),
            tool_calls: m.tool_calls.clone(),
        }
    }

    fn messages_for_llm(&self, messages: &[ChatMessage]) -> Vec<Message> {
        messages
            .iter()
            .enumerate()
            .map(|(index, message)| {
                let include_media = should_include_message_media(messages, index);
                self.chat_message_to_llm_message(message, include_media)
            })
            .collect()
    }

    /// Process a message using the provided conversation history.
    /// History management is handled externally by SessionManager.
    ///
    /// This method supports multi-round tool calling: after executing tools,
    /// it loops back to the LLM with the tool results until either:
    /// - The LLM returns no more tool calls (final response)
    /// - Maximum iterations are reached
    pub async fn process(
        &self,
        messages: Vec<ChatMessage>,
    ) -> Result<AgentProcessResult, AgentError> {
        self.process_inner(messages, None, ToolExecutionContext::default())
            .await
    }

    pub async fn process_with_context(
        &self,
        messages: Vec<ChatMessage>,
        tool_context: ToolExecutionContext,
    ) -> Result<AgentProcessResult, AgentError> {
        self.process_inner(messages, None, tool_context).await
    }

    pub async fn process_streaming(
        &self,
        messages: Vec<ChatMessage>,
        turn: AgentTurnContext,
    ) -> Result<AgentProcessResult, AgentError> {
        let tool_context = ToolExecutionContext::default().with_turn_id(turn.turn_id.clone());
        self.process_inner(messages, Some(turn), tool_context).await
    }

    pub async fn process_streaming_with_context(
        &self,
        messages: Vec<ChatMessage>,
        turn: AgentTurnContext,
        mut tool_context: ToolExecutionContext,
    ) -> Result<AgentProcessResult, AgentError> {
        if tool_context.turn_id.is_none() {
            tool_context.turn_id = Some(turn.turn_id.clone());
        }
        self.process_inner(messages, Some(turn), tool_context).await
    }

    async fn process_inner(
        &self,
        mut messages: Vec<ChatMessage>,
        turn: Option<AgentTurnContext>,
        tool_context: ToolExecutionContext,
    ) -> Result<AgentProcessResult, AgentError> {
        let turn_start = Instant::now();

        #[cfg(debug_assertions)]
        tracing::debug!(
            history_len = messages.len(),
            max_iterations = self.max_iterations,
            "Starting agent process"
        );

        // Build and inject system prompt if not present
        let has_system = messages.first().is_some_and(|m| m.role == "system");
        if !has_system {
            let system_prompt =
                build_system_prompt(&self.workspace_dir, &self.model_name, &self.tools);
            #[cfg(debug_assertions)]
            tracing::debug!("System prompt injected:\n{}", system_prompt);
            messages.insert(0, ChatMessage::system(system_prompt));
        }

        // Track tool calls for loop detection
        let mut loop_detector = LoopDetector::new(LoopDetectorConfig::default());
        let mut emitted_messages = Vec::new();
        let mut accumulated_tokens: u32 = 0;
        let mut accumulated_usage = crate::providers::Usage::default();
        let mut last_request_usage = None;

        for iteration in 0..self.max_iterations {
            #[cfg(debug_assertions)]
            tracing::debug!(iteration, "Agent iteration started");

            // Preemptive context check: trim old tool results if token estimate
            // exceeds 80% of context window to prevent mid-loop overflow.
            if self.context_window > 0 {
                let estimated = estimate_tokens(&messages);
                let danger = (self.context_window as f64 * 0.8) as usize;
                if estimated > danger {
                    let trimmed = self.preemptive_trim_old_tool_results(&mut messages, 2000, 4);
                    if trimmed > 0 {
                        #[cfg(debug_assertions)]
                        tracing::debug!(
                            estimated,
                            danger,
                            trimmed_msgs = trimmed,
                            "Preemptive tool-result trim applied in loop"
                        );
                    }
                }
            }

            // Convert messages to LLM format
            let messages_for_llm = self.messages_for_llm(&messages);

            // Build request
            let tools = if self.tools.has_tools() {
                Some(self.tools.get_definitions())
            } else {
                None
            };

            let request = ChatCompletionRequest {
                messages: messages_for_llm,
                temperature: None,
                max_tokens: None,
                tools,
            };

            // Call LLM
            let iteration = u32::try_from(iteration)
                .map_err(|_| AgentError::Other("tool iteration exceeds u32".to_string()))?;
            let response = self
                .stream_completion(request, iteration, turn.as_ref())
                .await?;

            accumulated_tokens = accumulated_tokens.saturating_add(response.usage.total_tokens);
            merge_usage(&mut accumulated_usage, &response.usage);
            last_request_usage = Some(response.usage.clone());

            #[cfg(debug_assertions)]
            tracing::debug!(
                iteration,
                response_len = response.content.len(),
                tool_calls_len = response.tool_calls.len(),
                "LLM response received"
            );

            // If no tool calls, this is the final response
            if response.tool_calls.is_empty() {
                let mut assistant_message = ChatMessage::assistant(response.content);
                assistant_message.reasoning_content = response.reasoning_content;
                assistant_message.provider_state = response.provider_state;
                Self::annotate_message(&mut assistant_message, turn.as_ref(), iteration, true);
                emitted_messages.push(assistant_message.clone());
                crate::observability::metrics::global_metrics().record_turn(
                    Some(&accumulated_usage),
                    turn_start.elapsed().as_millis() as u64,
                );
                return Ok(AgentProcessResult {
                    final_response: assistant_message,
                    emitted_messages,
                    total_tokens: Some(accumulated_tokens),
                    usage: Some(accumulated_usage),
                    last_request_usage,
                });
            }

            if let Some(turn) = turn.as_ref() {
                turn.emitter
                    .emit(TurnEvent::TextSegmentFinished { iteration })
                    .map_err(|error| AgentError::Other(format!("turn event rejected: {error}")))?;
            }

            // Execute tool calls. User-visible progress is emitted through the
            // structured TurnEvent stream, not a second notification channel.
            {
                let tools_info: Vec<String> = response
                    .tool_calls
                    .iter()
                    .map(|tc| {
                        let args = serde_json::to_string(&tc.arguments).unwrap_or_default();
                        let s = format!("{}:{}", tc.name, args);
                        s
                    })
                    .collect();
                tracing::info!(iteration, count = response.tool_calls.len(), tools = %tools_info.join(", "), "Tool calls detected, executing tools");
            }

            // Add assistant message with tool calls
            let mut assistant_message = ChatMessage::assistant_with_tool_calls(
                response.content.clone(),
                response.tool_calls.clone(),
            );
            assistant_message.reasoning_content = response.reasoning_content;
            assistant_message.provider_state = response.provider_state;
            Self::annotate_message(&mut assistant_message, turn.as_ref(), iteration, false);
            messages.push(assistant_message.clone());
            emitted_messages.push(assistant_message);

            // Execute tools and add results to messages
            let tool_results = self
                .execute_tools(
                    &response.tool_calls,
                    iteration,
                    turn.as_ref(),
                    &tool_context,
                )
                .await?;

            for (tool_call, result) in response.tool_calls.iter().zip(tool_results.iter()) {
                // Log function call with name and arguments
                let args_str = match &tool_call.arguments {
                    serde_json::Value::Object(obj) if obj.is_empty() => "{}".to_string(),
                    other => {
                        serde_json::to_string_pretty(other).unwrap_or_else(|_| other.to_string())
                    }
                };
                tracing::info!(tool = %tool_call.name, args = %args_str, "Calling tool");

                // Truncate tool result if too large
                let truncated_output = truncate_tool_result(&result.output);

                // Record tool call and check for loops
                let loop_result = loop_detector.record(&tool_call.name, &tool_call.arguments);

                match loop_result {
                    LoopDetectionResult::Warning(msg) => {
                        // Add warning and proceed
                        tracing::warn!(
                            tool = %tool_call.name,
                            "Loop warning: {}",
                            msg
                        );
                        let mut tool_message = ChatMessage::tool_with_media(
                            tool_call.id.clone(),
                            tool_call.name.clone(),
                            format!("{}\n\n[上一条结果]\n{}", msg, truncated_output),
                            result.media_refs.clone(),
                        );
                        Self::annotate_message(&mut tool_message, turn.as_ref(), iteration, false);
                        messages.push(tool_message.clone());
                        emitted_messages.push(tool_message);
                    }
                    LoopDetectionResult::Ok => {
                        let mut tool_message = ChatMessage::tool_with_media(
                            tool_call.id.clone(),
                            tool_call.name.clone(),
                            truncated_output,
                            result.media_refs.clone(),
                        );
                        Self::annotate_message(&mut tool_message, turn.as_ref(), iteration, false);
                        messages.push(tool_message.clone());
                        emitted_messages.push(tool_message);
                    }
                }
            }

            // Loop continues to next iteration with updated messages
            #[cfg(debug_assertions)]
            tracing::debug!(
                iteration,
                message_count = messages.len(),
                "Tool execution complete, continuing to next iteration"
            );
        }

        // Max iterations reached - ask LLM for a summary based on completed work
        tracing::warn!("Max iterations reached, requesting final summary from LLM");

        // Add a message asking for summary
        let summary_request = ChatMessage::user(
            "You have reached the maximum number of tool call iterations. \
            Please provide your best answer based on the work completed so far.",
        );
        messages.push(summary_request);

        // Convert messages to LLM format
        let messages_for_llm = self.messages_for_llm(&messages);

        let request = ChatCompletionRequest {
            messages: messages_for_llm,
            temperature: None,
            max_tokens: None,
            tools: None, // No tools in final summary call
        };

        let summary_iteration = u32::try_from(self.max_iterations)
            .map_err(|_| AgentError::Other("tool iteration exceeds u32".to_string()))?;
        match self
            .stream_completion(request, summary_iteration, turn.as_ref())
            .await
        {
            Ok(response) => {
                accumulated_tokens = accumulated_tokens.saturating_add(response.usage.total_tokens);
                merge_usage(&mut accumulated_usage, &response.usage);
                last_request_usage = Some(response.usage.clone());
                let mut assistant_message = ChatMessage::assistant(response.content);
                assistant_message.reasoning_content = response.reasoning_content;
                assistant_message.provider_state = response.provider_state;
                Self::annotate_message(
                    &mut assistant_message,
                    turn.as_ref(),
                    summary_iteration,
                    true,
                );
                emitted_messages.push(assistant_message.clone());
                crate::observability::metrics::global_metrics().record_turn(
                    Some(&accumulated_usage),
                    turn_start.elapsed().as_millis() as u64,
                );
                Ok(AgentProcessResult {
                    final_response: assistant_message,
                    emitted_messages,
                    total_tokens: Some(accumulated_tokens),
                    usage: Some(accumulated_usage),
                    last_request_usage,
                })
            }
            Err(e) => {
                // Fallback if summary call fails
                tracing::error!(error = %e, "Failed to get summary from LLM");
                let fallback = format!(
                    "I reached the maximum number of tool call iterations ({}) without completing the task. The work done so far has been lost due to an error. Please try breaking the task into smaller steps.",
                    self.max_iterations
                );
                if let Some(turn) = turn.as_ref() {
                    turn.emitter
                        .emit(TurnEvent::TextSegmentFinished {
                            iteration: summary_iteration,
                        })
                        .and_then(|()| {
                            turn.emitter.emit(TurnEvent::TextDelta {
                                iteration: summary_iteration,
                                delta: fallback.clone(),
                            })
                        })
                        .map_err(|error| {
                            AgentError::Other(format!("turn event rejected: {error}"))
                        })?;
                }
                let mut final_message = ChatMessage::assistant(fallback);
                Self::annotate_message(&mut final_message, turn.as_ref(), summary_iteration, true);
                emitted_messages.push(final_message.clone());
                let turn_usage = (accumulated_usage.total_tokens > 0).then_some(&accumulated_usage);
                crate::observability::metrics::global_metrics()
                    .record_turn(turn_usage, turn_start.elapsed().as_millis() as u64);
                Ok(AgentProcessResult {
                    final_response: final_message,
                    emitted_messages,
                    total_tokens: if accumulated_tokens > 0 {
                        Some(accumulated_tokens)
                    } else {
                        None
                    },
                    usage: (accumulated_usage.total_tokens > 0).then_some(accumulated_usage),
                    last_request_usage,
                })
            }
        }
    }

    /// Determine whether to execute tools in parallel or sequentially.
    ///
    /// Returns true if:
    /// - There are multiple tool calls
    /// - None of the tools require sequential execution (tool_search, non-concurrency-safe)
    fn should_execute_in_parallel(&self, tool_calls: &[ToolCall]) -> bool {
        if tool_calls.len() <= 1 {
            return false;
        }

        // tool_search must run sequentially to avoid MCP activation race conditions
        if tool_calls.iter().any(|tc| tc.name == "tool_search") {
            return false;
        }

        // All tools must be concurrency-safe to run in parallel
        tool_calls.iter().all(|tc| {
            self.tools
                .get(&tc.name)
                .map(|t| t.concurrency_safe())
                .unwrap_or(false)
        })
    }

    /// Execute multiple tool calls, choosing parallel or sequential based on conditions.
    async fn execute_tools(
        &self,
        tool_calls: &[ToolCall],
        iteration: u32,
        turn: Option<&AgentTurnContext>,
        context: &ToolExecutionContext,
    ) -> Result<Vec<ToolExecutionOutcome>, AgentError> {
        if self.should_execute_in_parallel(tool_calls) {
            tracing::debug!("Executing {} tools in parallel", tool_calls.len());
            self.execute_tools_parallel(tool_calls, iteration, turn, context)
                .await
        } else {
            tracing::debug!("Executing {} tools sequentially", tool_calls.len());
            self.execute_tools_sequential(tool_calls, iteration, turn, context)
                .await
        }
    }

    /// Execute tools in parallel using join_all.
    async fn execute_tools_parallel(
        &self,
        tool_calls: &[ToolCall],
        iteration: u32,
        turn: Option<&AgentTurnContext>,
        context: &ToolExecutionContext,
    ) -> Result<Vec<ToolExecutionOutcome>, AgentError> {
        let futures: Vec<_> = tool_calls
            .iter()
            .map(|tool_call| self.execute_one_tool(tool_call, iteration, turn, context))
            .collect();

        futures_util::future::join_all(futures)
            .await
            .into_iter()
            .collect()
    }

    /// Execute tools sequentially.
    async fn execute_tools_sequential(
        &self,
        tool_calls: &[ToolCall],
        iteration: u32,
        turn: Option<&AgentTurnContext>,
        context: &ToolExecutionContext,
    ) -> Result<Vec<ToolExecutionOutcome>, AgentError> {
        let mut outcomes = Vec::with_capacity(tool_calls.len());

        for tool_call in tool_calls {
            outcomes.push(
                self.execute_one_tool(tool_call, iteration, turn, context)
                    .await?,
            );
        }

        Ok(outcomes)
    }

    /// Execute a single tool and return the outcome with event tracking.
    async fn execute_one_tool(
        &self,
        tool_call: &ToolCall,
        iteration: u32,
        turn: Option<&AgentTurnContext>,
        context: &ToolExecutionContext,
    ) -> Result<ToolExecutionOutcome, AgentError> {
        let start = Instant::now();
        let tool_name = tool_call.name.clone();

        if let Some(turn) = turn {
            turn.emitter
                .emit(TurnEvent::ToolStarted {
                    iteration,
                    call: tool_call.clone(),
                })
                .map_err(|error| AgentError::Other(format!("turn event rejected: {error}")))?;
        }

        // Record ToolCallStart event
        if let Some(ref observer) = self.observer {
            observer.record_event(&ObserverEvent::ToolCallStart {
                tool: tool_name.clone(),
                arguments: Some(truncate_args(&tool_call.arguments, 300)),
            });
        }

        let result = self.execute_tool_internal(tool_call, context).await;
        let duration = start.elapsed();

        if let Some(turn) = turn {
            turn.emitter
                .emit(TurnEvent::ToolFinished {
                    iteration,
                    call_id: tool_call.id.clone(),
                    success: result.success,
                    preview: Some(tool_result_preview(&truncate_tool_result(&result.output))),
                })
                .map_err(|error| AgentError::Other(format!("turn event rejected: {error}")))?;
        }

        // Record ToolCall event
        if let Some(ref observer) = self.observer {
            observer.record_event(&ObserverEvent::ToolCall {
                tool: tool_name.clone(),
                duration,
                success: result.success,
            });
        }

        crate::observability::metrics::global_metrics()
            .record_tool_call(&tool_name, result.success);

        // Apply duration
        Ok(ToolExecutionOutcome { duration, ..result })
    }

    /// Internal tool execution without event tracking.
    async fn execute_tool_internal(
        &self,
        tool_call: &ToolCall,
        context: &ToolExecutionContext,
    ) -> ToolExecutionOutcome {
        let tool = match self.tools.get(&tool_call.name) {
            Some(t) => t,
            None => {
                tracing::warn!(tool = %tool_call.name, "Tool not found");
                return ToolExecutionOutcome::failure(
                    format!("Error: Tool '{}' not found", tool_call.name),
                    Some(format!("Tool '{}' not found", tool_call.name)),
                );
            }
        };

        match tool
            .execute_with_context(context, tool_call.arguments.clone())
            .await
        {
            Ok(result_with_media) => {
                let result = result_with_media.result;
                if result.success {
                    ToolExecutionOutcome::success_with_media(
                        result.output,
                        result_with_media.media_refs,
                    )
                } else {
                    let error = result.error.unwrap_or_default();
                    ToolExecutionOutcome::failure(format!("Error: {}", error), Some(error))
                }
            }
            Err(e) => {
                tracing::error!(tool = %tool_call.name, error = %e, "Tool execution failed");
                ToolExecutionOutcome::failure(format!("Error: {}", e), Some(e.to_string()))
            }
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::observability::{MultiObserver, Observer};
    use crate::providers::{
        ChatCompletionResponse, FinishReason, ProviderChunk, ProviderStream, Usage,
    };
    use crate::session::{TurnBlock, TurnController};
    use crate::tools::FileReadTool;

    struct TestObserver {
        events: std::sync::Mutex<Vec<ObserverEvent>>,
    }

    struct StreamingTextProvider;

    #[async_trait::async_trait]
    impl LLMProvider for StreamingTextProvider {
        async fn stream(
            &self,
            _request: ChatCompletionRequest,
        ) -> Result<ProviderStream, crate::providers::DynProviderError> {
            let chunks = vec![
                ProviderChunk::Metadata {
                    id: "response".into(),
                    model: "streaming-test".into(),
                },
                ProviderChunk::Reasoning("because ".into()),
                ProviderChunk::Reasoning("facts".into()),
                ProviderChunk::Text("hello ".into()),
                ProviderChunk::Text("world".into()),
                ProviderChunk::ProviderState(crate::bus::ProviderReasoningState {
                    provider: "test".into(),
                    payload: serde_json::json!({"opaque":"state"}),
                }),
                ProviderChunk::Usage(Usage {
                    prompt_tokens: 2,
                    completion_tokens: 3,
                    total_tokens: 5,
                    ..Usage::default()
                }),
                ProviderChunk::Done(FinishReason::Stop),
            ];
            Ok(Box::pin(futures_util::stream::iter(
                chunks.into_iter().map(Ok),
            )))
        }

        fn ptype(&self) -> &str {
            "test"
        }

        fn name(&self) -> &str {
            "streaming-test"
        }

        fn model_id(&self) -> &str {
            "streaming-test"
        }
    }

    #[tokio::test]
    async fn process_streaming_emits_turn_blocks_and_stamps_durable_message() {
        let agent = AgentLoop::with_provider(
            Arc::new(StreamingTextProvider),
            1,
            "streaming-test".into(),
            PathBuf::from("."),
            Vec::new(),
        );
        let (controller, emitter, _) = TurnController::start("session", "assistant-id");
        let initial = controller.snapshot();
        let context =
            AgentTurnContext::new(initial.id.0.clone(), initial.message_id.clone(), emitter);

        let result = agent
            .process_streaming(vec![ChatMessage::user("hi")], context)
            .await
            .unwrap();

        assert_eq!(result.final_response.id, "assistant-id");
        assert_eq!(result.final_response.content, "hello world");
        assert_eq!(
            result.final_response.reasoning_content.as_deref(),
            Some("because facts")
        );
        assert_eq!(result.final_response.turn_id, Some(initial.id.0.clone()));
        assert_eq!(result.final_response.iteration, Some(0));
        assert_eq!(
            result
                .final_response
                .provider_state
                .as_ref()
                .map(|state| state.provider.as_str()),
            Some("test")
        );
        assert_eq!(
            result.usage.as_ref().map(|usage| usage.total_tokens),
            Some(5)
        );

        let snapshot = controller.snapshot();
        assert_eq!(snapshot.blocks.len(), 2);
        assert!(matches!(
            &snapshot.blocks[0],
            TurnBlock::Reasoning { text, .. } if text == "because facts"
        ));
        assert!(matches!(
            &snapshot.blocks[1],
            TurnBlock::Assistant { text, .. } if text == "hello world"
        ));
    }

    impl TestObserver {
        fn new() -> Self {
            Self {
                events: std::sync::Mutex::new(Vec::new()),
            }
        }
    }

    impl Observer for TestObserver {
        fn record_event(&self, event: &ObserverEvent) {
            self.events.lock().unwrap().push(event.clone());
        }

        fn name(&self) -> &str {
            "test_observer"
        }
    }

    #[tokio::test]
    async fn test_observer_receives_tool_events() {
        // Verify MultiObserver works
        let mut multi = MultiObserver::new();
        multi.add_observer(Box::new(TestObserver::new()));

        let event = ObserverEvent::ToolCallStart {
            tool: "test".to_string(),
            arguments: Some("{}".to_string()),
        };
        multi.record_event(&event);

        // Just verify the structure works
        assert_eq!(multi.len(), 1);
    }

    struct ToolMediaProvider {
        image_path: String,
        requests: std::sync::Mutex<Vec<ChatCompletionRequest>>,
    }

    #[async_trait::async_trait]
    impl LLMProvider for ToolMediaProvider {
        async fn stream(
            &self,
            request: ChatCompletionRequest,
        ) -> Result<crate::providers::ProviderStream, crate::providers::DynProviderError> {
            let call_number = {
                let mut requests = self.requests.lock().unwrap();
                requests.push(request);
                requests.len()
            };
            let response = ChatCompletionResponse {
                id: format!("response-{call_number}"),
                model: "vision-test".to_string(),
                content: if call_number == 1 {
                    String::new()
                } else {
                    "image seen".to_string()
                },
                reasoning_content: None,
                provider_state: None,
                tool_calls: if call_number == 1 {
                    vec![ToolCall {
                        id: "call-image".to_string(),
                        name: "file_read".to_string(),
                        arguments: serde_json::json!({ "path": self.image_path }),
                    }]
                } else {
                    Vec::new()
                },
                usage: Usage {
                    prompt_tokens: 1,
                    completion_tokens: 1,
                    total_tokens: 2,
                    cached_tokens: None,
                    cache_read_input_tokens: None,
                    cache_creation_input_tokens: None,
                },
            };
            Ok(crate::providers::provider_stream_for_test(response))
        }

        fn ptype(&self) -> &str {
            "test"
        }

        fn name(&self) -> &str {
            "tool-media-test"
        }

        fn model_id(&self) -> &str {
            "vision-test"
        }
    }

    #[tokio::test]
    async fn file_read_media_reaches_the_next_model_iteration() {
        use std::io::Write;

        let mut image = tempfile::Builder::new().suffix(".png").tempfile().unwrap();
        image.write_all(b"\x89PNG\r\n\x1a\nminimal").unwrap();
        let provider = Arc::new(ToolMediaProvider {
            image_path: image.path().to_string_lossy().into_owned(),
            requests: std::sync::Mutex::new(Vec::new()),
        });
        let tools = Arc::new(ToolRegistry::new());
        tools.register(FileReadTool::new());
        let agent = AgentLoop::with_provider_and_tools(
            provider.clone(),
            tools,
            2,
            "vision-test".to_string(),
            std::env::current_dir().unwrap(),
            vec!["text".to_string(), "image".to_string()],
        );

        let (controller, emitter, _) = TurnController::start("session", "assistant-id");
        let turn = controller.snapshot();
        let result = agent
            .process_streaming(
                vec![ChatMessage::user("inspect the image")],
                AgentTurnContext::new(turn.id.0.clone(), turn.message_id.clone(), emitter),
            )
            .await
            .unwrap();

        assert_eq!(result.final_response.content, "image seen");
        let requests = provider.requests.lock().unwrap();
        assert_eq!(requests.len(), 2);
        let tool_result = requests[1]
            .messages
            .iter()
            .find(|message| message.role == "tool")
            .unwrap();
        assert!(
            tool_result
                .content
                .iter()
                .any(|block| matches!(block, ContentBlock::ImageUrl { .. }))
        );
        assert!(result.emitted_messages.iter().any(|message| {
            message.role == "tool"
                && message
                    .media_refs
                    .iter()
                    .any(|media| media.media_type == "image")
        }));
        assert!(controller.snapshot().blocks.iter().any(|block| matches!(
            block,
            TurnBlock::Tool {
                id,
                status: crate::session::ToolStatus::Completed,
                ..
            } if id == "call-image"
        )));
    }

    #[test]
    fn test_should_execute_in_parallel_single_tool() {
        // Would need a proper setup with AgentLoop to test fully
        // For now, just verify the logic: single tool should return false
        let calls = [ToolCall {
            id: "1".to_string(),
            name: "test".to_string(),
            arguments: serde_json::json!({}),
        }];

        // If there's only 1 tool, should return false regardless
        assert!(calls.len() <= 1);
    }

    #[test]
    fn test_chat_message_to_llm_message_preserves_assistant_tool_calls() {
        use crate::providers::Message;

        let chat_message = ChatMessage::assistant_with_tool_calls(
            "calling tool",
            vec![ToolCall {
                id: "call_1".to_string(),
                name: "calculator".to_string(),
                arguments: serde_json::json!({ "expression": "2+2" }),
            }],
        );

        let content = vec![ContentBlock::text(&chat_message.content)];
        let provider_message = Message {
            role: chat_message.role.clone(),
            content,
            reasoning_content: None,
            provider_state: None,
            tool_call_id: chat_message.tool_call_id.clone(),
            name: chat_message.tool_name.clone(),
            tool_calls: chat_message.tool_calls.clone(),
        };

        assert_eq!(provider_message.role, "assistant");
        assert_eq!(provider_message.tool_calls.as_ref().unwrap().len(), 1);
        assert_eq!(
            provider_message.tool_calls.as_ref().unwrap()[0].id,
            "call_1"
        );
        assert_eq!(
            provider_message.tool_calls.as_ref().unwrap()[0].name,
            "calculator"
        );
    }

    #[test]
    fn test_build_content_blocks_keeps_text_with_media() {
        let registry = MediaHandlerRegistry::new();
        let blocks = build_content_blocks(
            "先看这段文字",
            &[MediaRef {
                path: "missing.png".to_string(),
                media_type: "image".to_string(),
            }],
            &[],
            &registry,
            MediaOrigin::User,
        );

        assert_eq!(blocks.len(), 1);
        assert!(matches!(blocks.first(), Some(ContentBlock::Text { text })
                if text.starts_with("先看这段文字\n\n")
                    && text.contains("随本条用户消息同时提交的附件")
                    && text.contains("missing.png")
                    && text.contains("\"media_type\":\"image\"")
                    && text.contains("content is not embedded in this model request")));
    }

    #[test]
    fn test_build_content_blocks_describes_attachment_only_message() {
        let registry = MediaHandlerRegistry::new();
        let blocks = build_content_blocks(
            "",
            &[MediaRef {
                path: "/tmp/report.docx".to_string(),
                media_type: "file".to_string(),
            }],
            &[],
            &registry,
            MediaOrigin::User,
        );

        assert_eq!(blocks.len(), 1);
        assert!(matches!(blocks.first(), Some(ContentBlock::Text { text })
                if text.starts_with("[用户发送了以下附件")
                    && text.contains("report.docx")
                    && text.contains("application/vnd.openxmlformats-officedocument.wordprocessingml.document")
                    && text.contains("\"extension\":\"docx\"")
                    && text.contains("\"size_bytes\":null")
                    && text.contains("content is not embedded in this model request")));
    }

    #[test]
    fn test_build_content_blocks_includes_path_for_supported_images() {
        use std::io::Write;

        let mut image = tempfile::Builder::new().suffix(".png").tempfile().unwrap();
        image.write_all(b"\x89PNG\r\n\x1a\nminimal").unwrap();
        let path = image.path().to_string_lossy().into_owned();
        let registry = MediaHandlerRegistry::with_defaults();

        let blocks = build_content_blocks(
            "分析图片",
            &[MediaRef {
                path: path.clone(),
                media_type: "image".to_string(),
            }],
            &["image".to_string()],
            &registry,
            MediaOrigin::User,
        );

        assert!(matches!(blocks.first(), Some(ContentBlock::Text { text })
                if text.starts_with("分析图片\n\n")
                    && text.contains("随本条用户消息同时提交的附件")
                    && text.contains(&path)
                    && text.contains("model-native content block")));
        assert!(matches!(blocks.get(1), Some(ContentBlock::ImageUrl { .. })));
    }

    #[test]
    fn test_build_content_blocks_labels_tool_media_without_user_wording() {
        use std::io::Write;

        let mut image = tempfile::Builder::new().suffix(".png").tempfile().unwrap();
        image.write_all(b"\x89PNG\r\n\x1a\nminimal").unwrap();
        let path = image.path().to_string_lossy().into_owned();
        let registry = MediaHandlerRegistry::with_defaults();

        let blocks = build_content_blocks(
            "Image file ready for visual inspection.",
            &[MediaRef {
                path,
                media_type: "image".to_string(),
            }],
            &["image".to_string()],
            &registry,
            MediaOrigin::Tool("file_read"),
        );

        assert!(matches!(blocks.first(), Some(ContentBlock::Text { text })
                if text.contains("工具 file_read 返回了以下附件")
                    && !text.contains("用户消息")));
        assert!(matches!(blocks.get(1), Some(ContentBlock::ImageUrl { .. })));
    }

    #[test]
    fn assistant_attachments_replay_as_text_without_native_image_blocks() {
        use std::io::Write;

        let mut image = tempfile::Builder::new().suffix(".png").tempfile().unwrap();
        image.write_all(b"\x89PNG\r\n\x1a\nminimal").unwrap();
        let path = image.path().to_string_lossy().into_owned();
        let provider = Arc::new(ToolMediaProvider {
            image_path: path.clone(),
            requests: std::sync::Mutex::new(Vec::new()),
        });
        let agent = AgentLoop::with_provider_and_tools(
            provider,
            Arc::new(ToolRegistry::new()),
            1,
            "vision-test".to_string(),
            std::env::current_dir().unwrap(),
            vec!["text".to_string(), "image".to_string()],
        );
        let mut assistant = ChatMessage::assistant("截图已发送");
        assistant.media_refs = vec![MediaRef {
            path: path.clone(),
            media_type: "image".to_string(),
        }];

        let converted = agent.chat_message_to_llm_message(&assistant, true);

        assert_eq!(converted.role, "assistant");
        assert_eq!(converted.content.len(), 1);
        assert!(
            matches!(converted.content.first(), Some(ContentBlock::Text { text })
                if text.contains("截图已发送")
                    && text.contains(&path)
                    && text.contains("not embedded in this model request"))
        );
        assert!(
            !converted
                .content
                .iter()
                .any(|block| matches!(block, ContentBlock::ImageUrl { .. }))
        );
    }

    #[test]
    fn only_the_trailing_tool_batch_replays_tool_media() {
        let mut messages = vec![
            ChatMessage::assistant_with_tool_calls(
                "",
                vec![ToolCall {
                    id: "old-call".to_string(),
                    name: "file_read".to_string(),
                    arguments: serde_json::json!({}),
                }],
            ),
            ChatMessage::tool_with_media(
                "old-call",
                "file_read",
                "old",
                vec![MediaRef {
                    path: "/tmp/old.png".to_string(),
                    media_type: "image".to_string(),
                }],
            ),
            ChatMessage::assistant("continue"),
            ChatMessage::assistant_with_tool_calls(
                "",
                vec![ToolCall {
                    id: "new-call".to_string(),
                    name: "file_read".to_string(),
                    arguments: serde_json::json!({}),
                }],
            ),
            ChatMessage::tool_with_media(
                "new-call",
                "file_read",
                "new",
                vec![MediaRef {
                    path: "/tmp/new.png".to_string(),
                    media_type: "image".to_string(),
                }],
            ),
        ];

        assert!(!should_include_message_media(&messages, 1));
        assert!(should_include_message_media(&messages, 4));

        messages.push(ChatMessage::user("next turn"));
        assert!(!should_include_message_media(&messages, 4));
    }
}

#[derive(Debug)]
pub enum AgentError {
    ProviderCreation(String),
    LlmError(String),
    Other(String),
}

impl std::fmt::Display for AgentError {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            AgentError::ProviderCreation(e) => write!(f, "Provider creation error: {}", e),
            AgentError::LlmError(e) => write!(f, "LLM error: {}", e),
            AgentError::Other(e) => write!(f, "{}", e),
        }
    }
}

impl std::error::Error for AgentError {}