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middleware_chain 从 Option<MiddlewareChain> 改为 MiddlewareChain: - 移除 6 处 use_middleware 分支 + 2 处 legacy loop_guard inline path - 移除 loop_guard field + Mutex import + circuit_breaker_triggered 变量 - 空 chain (Default) 行为等价于 middleware path 中的 no-op - 1154行 → 1023行，净减 131 行 - cargo check --workspace ✓ | cargo test ✓ (排除 desktop 预存编译问题)
1024 lines
49 KiB
Rust
1024 lines
49 KiB
Rust
//! Agent loop implementation
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use std::sync::Arc;
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use futures::StreamExt;
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use tokio::sync::mpsc;
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use zclaw_types::{AgentId, SessionId, Message, Result};
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use crate::driver::{LlmDriver, CompletionRequest, ContentBlock};
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use crate::stream::StreamChunk;
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use crate::tool::{ToolRegistry, ToolContext, SkillExecutor};
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use crate::tool::builtin::PathValidator;
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use crate::growth::GrowthIntegration;
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use crate::compaction::{self, CompactionConfig};
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use crate::middleware::{self, MiddlewareChain};
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use crate::prompt::{PromptBuilder, PromptContext};
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use zclaw_memory::MemoryStore;
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/// Agent loop runner
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pub struct AgentLoop {
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agent_id: AgentId,
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driver: Arc<dyn LlmDriver>,
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tools: ToolRegistry,
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memory: Arc<MemoryStore>,
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model: String,
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system_prompt: Option<String>,
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/// Custom agent personality for prompt assembly
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soul: Option<String>,
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max_tokens: u32,
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temperature: f32,
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skill_executor: Option<Arc<dyn SkillExecutor>>,
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path_validator: Option<PathValidator>,
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/// Growth system integration (optional)
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growth: Option<GrowthIntegration>,
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/// Compaction threshold in tokens (0 = disabled)
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compaction_threshold: usize,
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/// Compaction behavior configuration
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compaction_config: CompactionConfig,
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/// Middleware chain — cross-cutting concerns are delegated to the chain.
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/// An empty chain (Default) is a no-op: all `run_*` methods return Continue/Allow.
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middleware_chain: MiddlewareChain,
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/// Chat mode: extended thinking enabled
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thinking_enabled: bool,
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/// Chat mode: reasoning effort level
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reasoning_effort: Option<String>,
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/// Chat mode: plan mode
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plan_mode: bool,
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}
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impl AgentLoop {
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pub fn new(
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agent_id: AgentId,
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driver: Arc<dyn LlmDriver>,
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tools: ToolRegistry,
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memory: Arc<MemoryStore>,
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) -> Self {
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Self {
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agent_id,
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driver,
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tools,
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memory,
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model: String::new(), // Must be set via with_model()
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system_prompt: None,
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soul: None,
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max_tokens: 16384,
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temperature: 0.7,
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skill_executor: None,
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path_validator: None,
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growth: None,
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compaction_threshold: 0,
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compaction_config: CompactionConfig::default(),
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middleware_chain: MiddlewareChain::default(),
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thinking_enabled: false,
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reasoning_effort: None,
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plan_mode: false,
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}
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}
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/// Set the skill executor for tool execution
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pub fn with_skill_executor(mut self, executor: Arc<dyn SkillExecutor>) -> Self {
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self.skill_executor = Some(executor);
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self
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}
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/// Set the path validator for file system operations
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pub fn with_path_validator(mut self, validator: PathValidator) -> Self {
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self.path_validator = Some(validator);
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self
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}
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/// Set the model to use
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pub fn with_model(mut self, model: impl Into<String>) -> Self {
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self.model = model.into();
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self
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}
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/// Set the system prompt
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pub fn with_system_prompt(mut self, prompt: impl Into<String>) -> Self {
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self.system_prompt = Some(prompt.into());
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self
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}
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/// Set the agent personality (SOUL.md equivalent)
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pub fn with_soul(mut self, soul: impl Into<String>) -> Self {
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self.soul = Some(soul.into());
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self
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}
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/// Enable extended thinking/reasoning mode
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pub fn with_thinking_enabled(mut self, enabled: bool) -> Self {
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self.thinking_enabled = enabled;
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self
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}
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/// Set reasoning effort level (low/medium/high)
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pub fn with_reasoning_effort(mut self, effort: impl Into<String>) -> Self {
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self.reasoning_effort = Some(effort.into());
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self
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}
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/// Enable plan mode
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pub fn with_plan_mode(mut self, enabled: bool) -> Self {
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self.plan_mode = enabled;
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self
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}
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/// Set max tokens
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pub fn with_max_tokens(mut self, max_tokens: u32) -> Self {
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self.max_tokens = max_tokens;
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self
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}
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/// Set temperature
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pub fn with_temperature(mut self, temperature: f32) -> Self {
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self.temperature = temperature;
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self
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}
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/// Enable growth system integration
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pub fn with_growth(mut self, growth: GrowthIntegration) -> Self {
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self.growth = Some(growth);
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self
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}
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/// Set growth system (mutable)
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pub fn set_growth(&mut self, growth: GrowthIntegration) {
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self.growth = Some(growth);
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}
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/// Set compaction threshold in tokens (0 = disabled)
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///
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/// When the estimated token count of conversation history exceeds this
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/// threshold, older messages are summarized into a single system message
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/// and only recent messages are sent to the LLM.
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pub fn with_compaction_threshold(mut self, threshold: usize) -> Self {
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self.compaction_threshold = threshold;
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self
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}
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/// Set compaction configuration (LLM mode, memory flushing, etc.)
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pub fn with_compaction_config(mut self, config: CompactionConfig) -> Self {
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self.compaction_config = config;
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self
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}
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/// Inject a middleware chain. Cross-cutting concerns (compaction,
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/// loop guard, token calibration, etc.) are delegated to the chain.
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pub fn with_middleware_chain(mut self, chain: MiddlewareChain) -> Self {
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self.middleware_chain = chain;
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self
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}
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/// Get growth integration reference
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pub fn growth(&self) -> Option<&GrowthIntegration> {
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self.growth.as_ref()
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}
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/// Create tool context for tool execution
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fn create_tool_context(&self, session_id: SessionId) -> ToolContext {
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// If no path_validator is configured, create a default one with user home as workspace.
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// This allows file_read/file_write tools to work without explicit workspace config,
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// while still restricting access to the user's home directory for security.
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let path_validator = self.path_validator.clone().unwrap_or_else(|| {
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let home = std::env::var("USERPROFILE")
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.or_else(|_| std::env::var("HOME"))
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.unwrap_or_else(|_| ".".to_string());
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let home_path = std::path::PathBuf::from(&home);
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tracing::info!(
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"[AgentLoop] No path_validator configured, using user home as workspace: {}",
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home_path.display()
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);
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PathValidator::new().with_workspace(home_path)
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});
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let working_dir = path_validator.workspace_root()
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.map(|p| p.to_string_lossy().to_string());
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ToolContext {
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agent_id: self.agent_id.clone(),
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working_directory: working_dir,
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session_id: Some(session_id.to_string()),
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skill_executor: self.skill_executor.clone(),
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path_validator: Some(path_validator),
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event_sender: None,
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}
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}
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/// Execute a tool with the given input
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async fn execute_tool(&self, tool_name: &str, input: serde_json::Value, context: &ToolContext) -> Result<serde_json::Value> {
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let tool = self.tools.get(tool_name)
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.ok_or_else(|| zclaw_types::ZclawError::ToolError(format!("Unknown tool: {}", tool_name)))?;
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tool.execute(input, context).await
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}
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/// Run the agent loop with a single message
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/// Implements complete agent loop: LLM → Tool Call → Tool Result → LLM → Final Response
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pub async fn run(&self, session_id: SessionId, input: String) -> Result<AgentLoopResult> {
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// Add user message to session
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let user_message = Message::user(input.clone());
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self.memory.append_message(&session_id, &user_message).await?;
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// Get all messages for context
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let mut messages = self.memory.get_messages(&session_id).await?;
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// Enhance system prompt via PromptBuilder (middleware may further modify)
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let prompt_ctx = PromptContext {
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base_prompt: self.system_prompt.clone(),
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soul: self.soul.clone(),
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thinking_enabled: self.thinking_enabled,
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plan_mode: self.plan_mode,
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tool_definitions: self.tools.definitions(),
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agent_name: None,
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};
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let mut enhanced_prompt = PromptBuilder::new().build(&prompt_ctx);
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// Run middleware before_completion hooks (compaction, memory inject, etc.)
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{
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let mut mw_ctx = middleware::MiddlewareContext {
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agent_id: self.agent_id.clone(),
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session_id: session_id.clone(),
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user_input: input.clone(),
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system_prompt: enhanced_prompt.clone(),
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messages,
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response_content: Vec::new(),
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input_tokens: 0,
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output_tokens: 0,
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};
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match self.middleware_chain.run_before_completion(&mut mw_ctx).await? {
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middleware::MiddlewareDecision::Continue => {
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messages = mw_ctx.messages;
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enhanced_prompt = mw_ctx.system_prompt;
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}
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middleware::MiddlewareDecision::Stop(reason) => {
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return Ok(AgentLoopResult {
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response: reason,
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input_tokens: 0,
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output_tokens: 0,
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iterations: 1,
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});
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}
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}
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}
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let max_iterations = 10;
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let mut iterations = 0;
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let mut total_input_tokens = 0u32;
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let mut total_output_tokens = 0u32;
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let result = loop {
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iterations += 1;
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if iterations > max_iterations {
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// Save the state before returning
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let error_msg = "达到最大迭代次数，请简化请求";
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self.memory.append_message(&session_id, &Message::assistant(error_msg)).await?;
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break AgentLoopResult {
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response: error_msg.to_string(),
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input_tokens: total_input_tokens,
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output_tokens: total_output_tokens,
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iterations,
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};
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}
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// Build completion request
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let request = CompletionRequest {
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model: self.model.clone(),
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system: Some(enhanced_prompt.clone()),
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messages: messages.clone(),
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tools: self.tools.definitions(),
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max_tokens: Some(self.max_tokens),
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temperature: Some(self.temperature),
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stop: Vec::new(),
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stream: false,
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thinking_enabled: self.thinking_enabled,
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reasoning_effort: self.reasoning_effort.clone(),
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plan_mode: self.plan_mode,
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};
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// Call LLM
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let response = self.driver.complete(request).await?;
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total_input_tokens += response.input_tokens;
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total_output_tokens += response.output_tokens;
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// Calibrate token estimation on first iteration
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if iterations == 1 {
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compaction::update_calibration(
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compaction::estimate_messages_tokens(&messages),
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response.input_tokens,
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);
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}
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// Extract tool calls from response
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let tool_calls: Vec<(String, String, serde_json::Value)> = response.content.iter()
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.filter_map(|block| match block {
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ContentBlock::ToolUse { id, name, input } => Some((id.clone(), name.clone(), input.clone())),
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_ => None,
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})
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.collect();
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// Extract text and thinking separately
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let text_parts: Vec<String> = response.content.iter()
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.filter_map(|block| match block {
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ContentBlock::Text { text } => Some(text.clone()),
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_ => None,
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})
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.collect();
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let thinking_parts: Vec<String> = response.content.iter()
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.filter_map(|block| match block {
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ContentBlock::Thinking { thinking } => Some(thinking.clone()),
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_ => None,
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})
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.collect();
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let text_content = text_parts.join("\n");
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let thinking_content = if thinking_parts.is_empty() { None } else { Some(thinking_parts.join("")) };
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// If no tool calls, we have the final response
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if tool_calls.is_empty() {
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// Save final assistant message with thinking
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let msg = if let Some(thinking) = &thinking_content {
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Message::assistant_with_thinking(&text_content, thinking)
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} else {
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Message::assistant(&text_content)
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};
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self.memory.append_message(&session_id, &msg).await?;
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break AgentLoopResult {
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response: text_content,
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input_tokens: total_input_tokens,
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output_tokens: total_output_tokens,
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iterations,
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};
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}
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// There are tool calls - push assistant message with thinking before tool calls
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// (required by Kimi and other thinking-enabled APIs)
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let assistant_msg = if let Some(thinking) = &thinking_content {
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Message::assistant_with_thinking(&text_content, thinking)
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} else {
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Message::assistant(&text_content)
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};
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messages.push(assistant_msg);
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for (id, name, input) in &tool_calls {
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messages.push(Message::tool_use(id, zclaw_types::ToolId::new(name), input.clone()));
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}
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// Create tool context and execute all tools
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let tool_context = self.create_tool_context(session_id.clone());
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let mut abort_result: Option<AgentLoopResult> = None;
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let mut clarification_result: Option<AgentLoopResult> = None;
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for (id, name, input) in tool_calls {
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// Check if loop was already aborted
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if abort_result.is_some() {
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break;
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}
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// Check tool call safety — via middleware chain
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{
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let mw_ctx_ref = middleware::MiddlewareContext {
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agent_id: self.agent_id.clone(),
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session_id: session_id.clone(),
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user_input: input.to_string(),
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system_prompt: enhanced_prompt.clone(),
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messages: messages.clone(),
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response_content: Vec::new(),
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input_tokens: total_input_tokens,
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output_tokens: total_output_tokens,
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};
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match self.middleware_chain.run_before_tool_call(&mw_ctx_ref, &name, &input).await? {
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middleware::ToolCallDecision::Allow => {}
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middleware::ToolCallDecision::Block(msg) => {
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tracing::warn!("[AgentLoop] Tool '{}' blocked by middleware: {}", name, msg);
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let error_output = serde_json::json!({ "error": msg });
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messages.push(Message::tool_result(id, zclaw_types::ToolId::new(&name), error_output, true));
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continue;
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}
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middleware::ToolCallDecision::ReplaceInput(new_input) => {
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// Execute with replaced input (with timeout)
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let tool_result = match tokio::time::timeout(
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std::time::Duration::from_secs(30),
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self.execute_tool(&name, new_input, &tool_context),
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).await {
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Ok(Ok(result)) => result,
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Ok(Err(e)) => serde_json::json!({ "error": e.to_string() }),
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Err(_) => {
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tracing::warn!("[AgentLoop] Tool '{}' (replaced input) timed out after 30s", name);
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serde_json::json!({ "error": format!("工具 '{}' 执行超时（30秒），请重试", name) })
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}
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};
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messages.push(Message::tool_result(id, zclaw_types::ToolId::new(&name), tool_result, false));
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continue;
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}
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middleware::ToolCallDecision::AbortLoop(reason) => {
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tracing::warn!("[AgentLoop] Loop aborted by middleware: {}", reason);
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let msg = format!("{}\n已自动终止", reason);
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self.memory.append_message(&session_id, &Message::assistant(&msg)).await?;
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abort_result = Some(AgentLoopResult {
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response: msg,
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input_tokens: total_input_tokens,
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output_tokens: total_output_tokens,
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iterations,
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});
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}
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}
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}
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let tool_result = match tokio::time::timeout(
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std::time::Duration::from_secs(30),
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self.execute_tool(&name, input, &tool_context),
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).await {
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Ok(Ok(result)) => result,
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Ok(Err(e)) => serde_json::json!({ "error": e.to_string() }),
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Err(_) => {
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tracing::warn!("[AgentLoop] Tool '{}' timed out after 30s", name);
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serde_json::json!({ "error": format!("工具 '{}' 执行超时（30秒），请重试", name) })
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}
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};
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// Check if this is a clarification response — terminate loop immediately
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// so the LLM waits for user input instead of continuing to generate.
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if name == "ask_clarification"
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&& tool_result.get("status").and_then(|v| v.as_str()) == Some("clarification_needed")
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{
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tracing::info!("[AgentLoop] Clarification requested, terminating loop");
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let question = tool_result.get("question")
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.and_then(|v| v.as_str())
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.unwrap_or("需要更多信息")
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.to_string();
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messages.push(Message::tool_result(
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id,
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zclaw_types::ToolId::new(&name),
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tool_result,
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false,
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));
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self.memory.append_message(&session_id, &Message::assistant(&question)).await?;
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clarification_result = Some(AgentLoopResult {
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response: question,
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input_tokens: total_input_tokens,
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output_tokens: total_output_tokens,
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iterations,
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});
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break;
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}
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// Add tool result to messages
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messages.push(Message::tool_result(
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id,
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zclaw_types::ToolId::new(&name),
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tool_result,
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false, // is_error - we include errors in the result itself
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));
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}
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// Continue the loop - LLM will process tool results and generate final response
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// If middleware aborted the loop, return immediately
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if let Some(result) = abort_result {
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break result;
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}
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// If clarification was requested, return immediately
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if let Some(result) = clarification_result {
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break result;
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}
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};
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// Post-completion processing — middleware chain
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{
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let mw_ctx = middleware::MiddlewareContext {
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agent_id: self.agent_id.clone(),
|
||
session_id: session_id.clone(),
|
||
user_input: input.clone(),
|
||
system_prompt: enhanced_prompt.clone(),
|
||
messages: self.memory.get_messages(&session_id).await.unwrap_or_default(),
|
||
response_content: Vec::new(),
|
||
input_tokens: total_input_tokens,
|
||
output_tokens: total_output_tokens,
|
||
};
|
||
if let Err(e) = self.middleware_chain.run_after_completion(&mw_ctx).await {
|
||
tracing::warn!("[AgentLoop] Middleware after_completion failed: {}", e);
|
||
}
|
||
}
|
||
|
||
Ok(result)
|
||
}
|
||
|
||
/// Run the agent loop with streaming
|
||
/// Implements complete agent loop with multi-turn tool calling support
|
||
pub async fn run_streaming(
|
||
&self,
|
||
session_id: SessionId,
|
||
input: String,
|
||
) -> Result<mpsc::Receiver<LoopEvent>> {
|
||
let (tx, rx) = mpsc::channel(100);
|
||
|
||
// Add user message to session
|
||
let user_message = Message::user(input.clone());
|
||
self.memory.append_message(&session_id, &user_message).await?;
|
||
|
||
// Get all messages for context
|
||
let mut messages = self.memory.get_messages(&session_id).await?;
|
||
|
||
// Enhance system prompt via PromptBuilder (middleware may further modify)
|
||
let prompt_ctx = PromptContext {
|
||
base_prompt: self.system_prompt.clone(),
|
||
soul: self.soul.clone(),
|
||
thinking_enabled: self.thinking_enabled,
|
||
plan_mode: self.plan_mode,
|
||
tool_definitions: self.tools.definitions(),
|
||
agent_name: None,
|
||
};
|
||
let mut enhanced_prompt = PromptBuilder::new().build(&prompt_ctx);
|
||
|
||
// Run middleware before_completion hooks (compaction, memory inject, etc.)
|
||
{
|
||
let mut mw_ctx = middleware::MiddlewareContext {
|
||
agent_id: self.agent_id.clone(),
|
||
session_id: session_id.clone(),
|
||
user_input: input.clone(),
|
||
system_prompt: enhanced_prompt.clone(),
|
||
messages,
|
||
response_content: Vec::new(),
|
||
input_tokens: 0,
|
||
output_tokens: 0,
|
||
};
|
||
match self.middleware_chain.run_before_completion(&mut mw_ctx).await? {
|
||
middleware::MiddlewareDecision::Continue => {
|
||
messages = mw_ctx.messages;
|
||
enhanced_prompt = mw_ctx.system_prompt;
|
||
}
|
||
middleware::MiddlewareDecision::Stop(reason) => {
|
||
if let Err(e) = tx.send(LoopEvent::Complete(AgentLoopResult {
|
||
response: reason,
|
||
input_tokens: 0,
|
||
output_tokens: 0,
|
||
iterations: 1,
|
||
})).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Complete event: {}", e);
|
||
}
|
||
return Ok(rx);
|
||
}
|
||
}
|
||
}
|
||
|
||
// Clone necessary data for the async task
|
||
let session_id_clone = session_id.clone();
|
||
let memory = self.memory.clone();
|
||
let driver = self.driver.clone();
|
||
let tools = self.tools.clone();
|
||
let middleware_chain = self.middleware_chain.clone();
|
||
let skill_executor = self.skill_executor.clone();
|
||
let path_validator = self.path_validator.clone();
|
||
let agent_id = self.agent_id.clone();
|
||
let model = self.model.clone();
|
||
let max_tokens = self.max_tokens;
|
||
let temperature = self.temperature;
|
||
let thinking_enabled = self.thinking_enabled;
|
||
let reasoning_effort = self.reasoning_effort.clone();
|
||
let plan_mode = self.plan_mode;
|
||
|
||
tokio::spawn(async move {
|
||
let mut messages = messages;
|
||
let max_iterations = 10;
|
||
let mut iteration = 0;
|
||
let mut total_input_tokens = 0u32;
|
||
let mut total_output_tokens = 0u32;
|
||
|
||
'outer: loop {
|
||
iteration += 1;
|
||
if iteration > max_iterations {
|
||
if let Err(e) = tx.send(LoopEvent::Error("达到最大迭代次数".to_string())).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Error event: {}", e);
|
||
}
|
||
break;
|
||
}
|
||
|
||
// Notify iteration start
|
||
if let Err(e) = tx.send(LoopEvent::IterationStart {
|
||
iteration,
|
||
max_iterations,
|
||
}).await {
|
||
tracing::warn!("[AgentLoop] Failed to send IterationStart event: {}", e);
|
||
}
|
||
|
||
// Build completion request
|
||
let request = CompletionRequest {
|
||
model: model.clone(),
|
||
system: Some(enhanced_prompt.clone()),
|
||
messages: messages.clone(),
|
||
tools: tools.definitions(),
|
||
max_tokens: Some(max_tokens),
|
||
temperature: Some(temperature),
|
||
stop: Vec::new(),
|
||
stream: true,
|
||
thinking_enabled,
|
||
reasoning_effort: reasoning_effort.clone(),
|
||
plan_mode,
|
||
};
|
||
|
||
let mut stream = driver.stream(request);
|
||
let mut pending_tool_calls: Vec<(String, String, serde_json::Value)> = Vec::new();
|
||
let mut iteration_text = String::new();
|
||
let mut reasoning_text = String::new(); // Track reasoning separately for API requirement
|
||
|
||
// Process stream chunks
|
||
tracing::debug!("[AgentLoop] Starting to process stream chunks");
|
||
let mut chunk_count: usize = 0;
|
||
let mut text_delta_count: usize = 0;
|
||
let mut thinking_delta_count: usize = 0;
|
||
let mut stream_errored = false;
|
||
// 180s per-chunk timeout — thinking models (Kimi, DeepSeek R1) can have
|
||
// long gaps between reasoning_content and content phases (observed: ~60s).
|
||
// The SaaS relay sends SSE heartbeat comments during idle periods, but these
|
||
// are filtered out by the OpenAI driver and don't yield StreamChunks.
|
||
let chunk_timeout = std::time::Duration::from_secs(180);
|
||
|
||
loop {
|
||
match tokio::time::timeout(chunk_timeout, stream.next()).await {
|
||
Ok(Some(Ok(chunk))) => {
|
||
chunk_count += 1;
|
||
match &chunk {
|
||
StreamChunk::TextDelta { delta } => {
|
||
text_delta_count += 1;
|
||
tracing::debug!("[AgentLoop] TextDelta #{}: {} chars", text_delta_count, delta.len());
|
||
iteration_text.push_str(delta);
|
||
if let Err(e) = tx.send(LoopEvent::Delta(delta.clone())).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Delta event: {}", e);
|
||
}
|
||
}
|
||
StreamChunk::ThinkingDelta { delta } => {
|
||
thinking_delta_count += 1;
|
||
tracing::debug!("[AgentLoop] ThinkingDelta #{}: {} chars", thinking_delta_count, delta.len());
|
||
reasoning_text.push_str(delta);
|
||
if let Err(e) = tx.send(LoopEvent::ThinkingDelta(delta.clone())).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ThinkingDelta event: {}", e);
|
||
}
|
||
}
|
||
StreamChunk::ToolUseStart { id, name } => {
|
||
tracing::debug!("[AgentLoop] ToolUseStart: id={}, name={}", id, name);
|
||
pending_tool_calls.push((id.clone(), name.clone(), serde_json::Value::Null));
|
||
}
|
||
StreamChunk::ToolUseDelta { id, delta } => {
|
||
// Accumulate tool input delta (internal processing, not sent to user)
|
||
if let Some(tool) = pending_tool_calls.iter_mut().find(|(tid, _, _)| tid == id) {
|
||
// Try to accumulate JSON string
|
||
match &mut tool.2 {
|
||
serde_json::Value::String(s) => s.push_str(delta),
|
||
serde_json::Value::Null => tool.2 = serde_json::Value::String(delta.clone()),
|
||
_ => {}
|
||
}
|
||
}
|
||
}
|
||
StreamChunk::ToolUseEnd { id, input } => {
|
||
tracing::debug!("[AgentLoop] ToolUseEnd: id={}, input={:?}", id, input);
|
||
// Update with final parsed input and emit ToolStart event
|
||
if let Some(tool) = pending_tool_calls.iter_mut().find(|(tid, _, _)| tid == id) {
|
||
tool.2 = input.clone();
|
||
if let Err(e) = tx.send(LoopEvent::ToolStart { name: tool.1.clone(), input: input.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolStart event: {}", e);
|
||
}
|
||
}
|
||
}
|
||
StreamChunk::Complete { input_tokens: it, output_tokens: ot, .. } => {
|
||
tracing::debug!("[AgentLoop] Stream complete: input_tokens={}, output_tokens={}", it, ot);
|
||
total_input_tokens += *it;
|
||
total_output_tokens += *ot;
|
||
// Calibrate token estimation on first iteration
|
||
if iteration == 1 {
|
||
compaction::update_calibration(
|
||
compaction::estimate_messages_tokens(&messages),
|
||
*it,
|
||
);
|
||
}
|
||
}
|
||
StreamChunk::Error { message } => {
|
||
tracing::error!("[AgentLoop] Stream error: {}", message);
|
||
if let Err(e) = tx.send(LoopEvent::Error(message.clone())).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Error event: {}", e);
|
||
}
|
||
stream_errored = true;
|
||
}
|
||
}
|
||
}
|
||
Ok(Some(Err(e))) => {
|
||
tracing::error!("[AgentLoop] Chunk error: {}", e);
|
||
if let Err(e) = tx.send(LoopEvent::Error(format!("LLM 响应错误: {}", e.to_string()))).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Error event: {}", e);
|
||
}
|
||
stream_errored = true;
|
||
}
|
||
Ok(None) => break, // Stream ended normally
|
||
Err(_) => {
|
||
tracing::error!("[AgentLoop] Stream chunk timeout ({}s)", chunk_timeout.as_secs());
|
||
if let Err(e) = tx.send(LoopEvent::Error("LLM 响应超时，请重试".to_string())).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Error event: {}", e);
|
||
}
|
||
stream_errored = true;
|
||
}
|
||
}
|
||
if stream_errored {
|
||
break;
|
||
}
|
||
}
|
||
tracing::info!("[AgentLoop] Stream ended: {} total chunks (text={}, thinking={}, tools={}), iteration_text={} chars",
|
||
chunk_count, text_delta_count, thinking_delta_count, pending_tool_calls.len(),
|
||
iteration_text.len());
|
||
|
||
// Fallback: if model generated reasoning but no text content,
|
||
// use reasoning as text response. This happens with some thinking models
|
||
// (DeepSeek R1, QWQ) that put the answer in reasoning_content instead of content.
|
||
// Safe now because: (1) context is clean (no stale user_profile/memory injection),
|
||
// (2) max_tokens=16384 prevents truncation, (3) reasoning is about the correct topic.
|
||
if iteration_text.is_empty() && !reasoning_text.is_empty() {
|
||
tracing::info!("[AgentLoop] Model generated {} chars of reasoning but no text — using reasoning as response",
|
||
reasoning_text.len());
|
||
if let Err(e) = tx.send(LoopEvent::Delta(reasoning_text.clone())).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Delta event: {}", e);
|
||
}
|
||
iteration_text = reasoning_text.clone();
|
||
} else if iteration_text.is_empty() {
|
||
tracing::warn!("[AgentLoop] No text content after {} chunks (thinking_delta={})",
|
||
chunk_count, thinking_delta_count);
|
||
}
|
||
|
||
// If no tool calls, we have the final response
|
||
if pending_tool_calls.is_empty() {
|
||
tracing::info!("[AgentLoop] No tool calls, returning final response: {} chars (reasoning: {} chars)", iteration_text.len(), reasoning_text.len());
|
||
// Save final assistant message with reasoning
|
||
if let Err(e) = memory.append_message(&session_id_clone, &Message::assistant_with_thinking(
|
||
&iteration_text,
|
||
&reasoning_text,
|
||
)).await {
|
||
tracing::warn!("[AgentLoop] Failed to save final assistant message: {}", e);
|
||
}
|
||
|
||
if let Err(e) = tx.send(LoopEvent::Complete(AgentLoopResult {
|
||
response: iteration_text.clone(),
|
||
input_tokens: total_input_tokens,
|
||
output_tokens: total_output_tokens,
|
||
iterations: iteration,
|
||
})).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Complete event: {}", e);
|
||
}
|
||
|
||
// Post-completion: middleware after_completion (memory extraction, etc.)
|
||
{
|
||
let mw_ctx = middleware::MiddlewareContext {
|
||
agent_id: agent_id.clone(),
|
||
session_id: session_id_clone.clone(),
|
||
user_input: String::new(),
|
||
system_prompt: enhanced_prompt.clone(),
|
||
messages: memory.get_messages(&session_id_clone).await.unwrap_or_default(),
|
||
response_content: Vec::new(),
|
||
input_tokens: total_input_tokens,
|
||
output_tokens: total_output_tokens,
|
||
};
|
||
if let Err(e) = middleware_chain.run_after_completion(&mw_ctx).await {
|
||
tracing::warn!("[AgentLoop] Streaming middleware after_completion failed: {}", e);
|
||
}
|
||
}
|
||
|
||
break 'outer;
|
||
}
|
||
|
||
// Skip tool processing if stream errored or timed out
|
||
if stream_errored {
|
||
tracing::debug!("[AgentLoop] Stream errored, skipping tool processing and breaking");
|
||
break 'outer;
|
||
}
|
||
|
||
tracing::debug!("[AgentLoop] Processing {} tool calls (reasoning: {} chars)", pending_tool_calls.len(), reasoning_text.len());
|
||
|
||
// Push assistant message with reasoning before tool calls (required by Kimi and other thinking-enabled APIs)
|
||
messages.push(Message::assistant_with_thinking(
|
||
&iteration_text,
|
||
&reasoning_text,
|
||
));
|
||
|
||
// There are tool calls - add to message history
|
||
for (id, name, input) in &pending_tool_calls {
|
||
tracing::debug!("[AgentLoop] Adding tool_use to history: id={}, name={}, input={:?}", id, name, input);
|
||
messages.push(Message::tool_use(id, zclaw_types::ToolId::new(name), input.clone()));
|
||
}
|
||
|
||
// Execute tools
|
||
for (id, name, input) in pending_tool_calls {
|
||
tracing::debug!("[AgentLoop] Executing tool: name={}, input={:?}", name, input);
|
||
|
||
// Check tool call safety — via middleware chain
|
||
{
|
||
let mw_ctx = middleware::MiddlewareContext {
|
||
agent_id: agent_id.clone(),
|
||
session_id: session_id_clone.clone(),
|
||
user_input: input.to_string(),
|
||
system_prompt: enhanced_prompt.clone(),
|
||
messages: messages.clone(),
|
||
response_content: Vec::new(),
|
||
input_tokens: total_input_tokens,
|
||
output_tokens: total_output_tokens,
|
||
};
|
||
match middleware_chain.run_before_tool_call(&mw_ctx, &name, &input).await {
|
||
Ok(middleware::ToolCallDecision::Allow) => {}
|
||
Ok(middleware::ToolCallDecision::Block(msg)) => {
|
||
tracing::warn!("[AgentLoop] Tool '{}' blocked by middleware: {}", name, msg);
|
||
let error_output = serde_json::json!({ "error": msg });
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: error_output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
messages.push(Message::tool_result(id, zclaw_types::ToolId::new(&name), error_output, true));
|
||
continue;
|
||
}
|
||
Ok(middleware::ToolCallDecision::AbortLoop(reason)) => {
|
||
tracing::warn!("[AgentLoop] Loop aborted by middleware: {}", reason);
|
||
if let Err(e) = tx.send(LoopEvent::Error(reason)).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Error event: {}", e);
|
||
}
|
||
break 'outer;
|
||
}
|
||
Ok(middleware::ToolCallDecision::ReplaceInput(new_input)) => {
|
||
// Execute with replaced input (same path_validator logic below)
|
||
let pv = path_validator.clone().unwrap_or_else(|| {
|
||
let home = std::env::var("USERPROFILE")
|
||
.or_else(|_| std::env::var("HOME"))
|
||
.unwrap_or_else(|_| ".".to_string());
|
||
PathValidator::new().with_workspace(std::path::PathBuf::from(&home))
|
||
});
|
||
let working_dir = pv.workspace_root()
|
||
.map(|p| p.to_string_lossy().to_string());
|
||
let tool_context = ToolContext {
|
||
agent_id: agent_id.clone(),
|
||
working_directory: working_dir,
|
||
session_id: Some(session_id_clone.to_string()),
|
||
skill_executor: skill_executor.clone(),
|
||
path_validator: Some(pv),
|
||
event_sender: Some(tx.clone()),
|
||
};
|
||
let (result, is_error) = if let Some(tool) = tools.get(&name) {
|
||
match tool.execute(new_input, &tool_context).await {
|
||
Ok(output) => {
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
(output, false)
|
||
}
|
||
Err(e) => {
|
||
let error_output = serde_json::json!({ "error": e.to_string() });
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: error_output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
(error_output, true)
|
||
}
|
||
}
|
||
} else {
|
||
let error_output = serde_json::json!({ "error": format!("Unknown tool: {}", name) });
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: error_output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
(error_output, true)
|
||
};
|
||
messages.push(Message::tool_result(id, zclaw_types::ToolId::new(&name), result, is_error));
|
||
continue;
|
||
}
|
||
Err(e) => {
|
||
tracing::error!("[AgentLoop] Middleware error for tool '{}': {}", name, e);
|
||
let error_output = serde_json::json!({ "error": e.to_string() });
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: error_output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
messages.push(Message::tool_result(id, zclaw_types::ToolId::new(&name), error_output, true));
|
||
continue;
|
||
}
|
||
}
|
||
}
|
||
// Use pre-resolved path_validator (already has default fallback from create_tool_context logic)
|
||
let pv = path_validator.clone().unwrap_or_else(|| {
|
||
let home = std::env::var("USERPROFILE")
|
||
.or_else(|_| std::env::var("HOME"))
|
||
.unwrap_or_else(|_| ".".to_string());
|
||
PathValidator::new().with_workspace(std::path::PathBuf::from(&home))
|
||
});
|
||
let working_dir = pv.workspace_root()
|
||
.map(|p| p.to_string_lossy().to_string());
|
||
let tool_context = ToolContext {
|
||
agent_id: agent_id.clone(),
|
||
working_directory: working_dir,
|
||
session_id: Some(session_id_clone.to_string()),
|
||
skill_executor: skill_executor.clone(),
|
||
path_validator: Some(pv),
|
||
event_sender: Some(tx.clone()),
|
||
};
|
||
|
||
let (result, is_error) = if let Some(tool) = tools.get(&name) {
|
||
tracing::debug!("[AgentLoop] Tool '{}' found, executing...", name);
|
||
match tool.execute(input.clone(), &tool_context).await {
|
||
Ok(output) => {
|
||
tracing::debug!("[AgentLoop] Tool '{}' executed successfully: {:?}", name, output);
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
(output, false)
|
||
}
|
||
Err(e) => {
|
||
tracing::error!("[AgentLoop] Tool '{}' execution failed: {}", name, e);
|
||
let error_output = serde_json::json!({ "error": e.to_string() });
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: error_output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
(error_output, true)
|
||
}
|
||
}
|
||
} else {
|
||
tracing::error!("[AgentLoop] Tool '{}' not found in registry", name);
|
||
let error_output = serde_json::json!({ "error": format!("Unknown tool: {}", name) });
|
||
if let Err(e) = tx.send(LoopEvent::ToolEnd { name: name.clone(), output: error_output.clone() }).await {
|
||
tracing::warn!("[AgentLoop] Failed to send ToolEnd event: {}", e);
|
||
}
|
||
(error_output, true)
|
||
};
|
||
|
||
// Check if this is a clarification response — break outer loop
|
||
if name == "ask_clarification"
|
||
&& result.get("status").and_then(|v| v.as_str()) == Some("clarification_needed")
|
||
{
|
||
tracing::info!("[AgentLoop] Streaming: Clarification requested, terminating loop");
|
||
let question = result.get("question")
|
||
.and_then(|v| v.as_str())
|
||
.unwrap_or("需要更多信息")
|
||
.to_string();
|
||
messages.push(Message::tool_result(
|
||
id,
|
||
zclaw_types::ToolId::new(&name),
|
||
result,
|
||
is_error,
|
||
));
|
||
// Send the question as final delta so the user sees it
|
||
if let Err(e) = tx.send(LoopEvent::Delta(question.clone())).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Delta event: {}", e);
|
||
}
|
||
if let Err(e) = tx.send(LoopEvent::Complete(AgentLoopResult {
|
||
response: question.clone(),
|
||
input_tokens: total_input_tokens,
|
||
output_tokens: total_output_tokens,
|
||
iterations: iteration,
|
||
})).await {
|
||
tracing::warn!("[AgentLoop] Failed to send Complete event: {}", e);
|
||
}
|
||
if let Err(e) = memory.append_message(&session_id_clone, &Message::assistant(&question)).await {
|
||
tracing::warn!("[AgentLoop] Failed to save clarification message: {}", e);
|
||
}
|
||
break 'outer;
|
||
}
|
||
|
||
// Add tool result to message history
|
||
tracing::debug!("[AgentLoop] Adding tool_result to history: id={}, name={}, is_error={}", id, name, is_error);
|
||
messages.push(Message::tool_result(
|
||
id,
|
||
zclaw_types::ToolId::new(&name),
|
||
result,
|
||
is_error,
|
||
));
|
||
}
|
||
|
||
tracing::debug!("[AgentLoop] Continuing to next iteration for LLM to process tool results");
|
||
// Continue loop - next iteration will call LLM with tool results
|
||
}
|
||
});
|
||
|
||
Ok(rx)
|
||
}
|
||
}
|
||
|
||
/// Result of an agent loop execution
|
||
#[derive(Debug, Clone)]
|
||
pub struct AgentLoopResult {
|
||
pub response: String,
|
||
pub input_tokens: u32,
|
||
pub output_tokens: u32,
|
||
pub iterations: usize,
|
||
}
|
||
|
||
/// Events emitted during streaming
|
||
#[derive(Debug, Clone)]
|
||
pub enum LoopEvent {
|
||
/// Text delta from LLM
|
||
Delta(String),
|
||
/// Thinking/reasoning delta from LLM (extended thinking)
|
||
ThinkingDelta(String),
|
||
/// Tool execution started
|
||
ToolStart { name: String, input: serde_json::Value },
|
||
/// Tool execution completed
|
||
ToolEnd { name: String, output: serde_json::Value },
|
||
/// Sub-agent task status update (started/running/completed/failed)
|
||
SubtaskStatus {
|
||
task_id: String,
|
||
description: String,
|
||
status: String,
|
||
detail: Option<String>,
|
||
},
|
||
/// New iteration started (multi-turn tool calling)
|
||
IterationStart { iteration: usize, max_iterations: usize },
|
||
/// Loop completed with final result
|
||
Complete(AgentLoopResult),
|
||
/// Error occurred
|
||
Error(String),
|
||
}
|