feat(runtime): implement streaming in AgentLoop
- Implement run_streaming() method with async channel - Stream chunks from LLM driver and emit LoopEvent - Save assistant message to memory on completion Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -1,10 +1,12 @@
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//! 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};
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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;
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use crate::loop_guard::LoopGuard;
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use zclaw_memory::MemoryStore;
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@@ -16,6 +18,10 @@ pub struct AgentLoop {
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tools: ToolRegistry,
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memory: Arc<MemoryStore>,
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loop_guard: LoopGuard,
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model: String,
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system_prompt: Option<String>,
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max_tokens: u32,
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temperature: f32,
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}
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impl AgentLoop {
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@@ -31,9 +37,37 @@ impl AgentLoop {
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tools,
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memory,
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loop_guard: LoopGuard::default(),
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model: String::new(), // Must be set via with_model()
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system_prompt: None,
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max_tokens: 4096,
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temperature: 0.7,
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}
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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 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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/// Run the agent loop with a single message
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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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@@ -43,14 +77,14 @@ impl AgentLoop {
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// Get all messages for context
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let messages = self.memory.get_messages(&session_id).await?;
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// Build completion request
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// Build completion request with configured model
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let request = CompletionRequest {
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model: "claude-sonnet-4-20250514".to_string(), // TODO: Get from agent config
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system: None, // TODO: Get from agent config
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model: self.model.clone(),
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system: self.system_prompt.clone(),
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messages,
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tools: self.tools.definitions(),
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max_tokens: Some(4096),
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temperature: Some(0.7),
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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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};
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@@ -58,14 +92,24 @@ impl AgentLoop {
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// Call LLM
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let response = self.driver.complete(request).await?;
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// Process response and handle tool calls
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let mut iterations = 0;
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let max_iterations = 10;
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// Extract text content from response
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let response_text = response.content
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.iter()
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.filter_map(|block| match block {
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ContentBlock::Text { text } => Some(text.clone()),
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ContentBlock::Thinking { thinking } => Some(format!("[思考] {}", thinking)),
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ContentBlock::ToolUse { name, input, .. } => {
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Some(format!("[工具调用] {}({})", name, serde_json::to_string(input).unwrap_or_default()))
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}
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})
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.collect::<Vec<_>>()
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.join("\n");
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// TODO: Implement full loop with tool execution
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// Process response and handle tool calls
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let iterations = 0;
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Ok(AgentLoopResult {
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response: "Response placeholder".to_string(),
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response: response_text,
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input_tokens: response.input_tokens,
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output_tokens: response.output_tokens,
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iterations,
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@@ -80,7 +124,92 @@ impl AgentLoop {
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) -> Result<mpsc::Receiver<LoopEvent>> {
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let (tx, rx) = mpsc::channel(100);
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// TODO: Implement streaming
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// Add user message to session
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let user_message = Message::user(input);
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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 messages = self.memory.get_messages(&session_id).await?;
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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: self.system_prompt.clone(),
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messages,
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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: true,
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};
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// Clone necessary data for the async task
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let session_id_clone = session_id.clone();
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let memory = self.memory.clone();
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let driver = self.driver.clone();
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tokio::spawn(async move {
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let mut full_response = String::new();
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let mut input_tokens = 0u32;
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let mut output_tokens = 0u32;
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let mut stream = driver.stream(request);
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while let Some(chunk_result) = stream.next().await {
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match chunk_result {
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Ok(chunk) => {
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// Track response and tokens
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match &chunk {
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StreamChunk::TextDelta { delta } => {
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full_response.push_str(delta);
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let _ = tx.send(LoopEvent::Delta(delta.clone())).await;
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}
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StreamChunk::ThinkingDelta { delta } => {
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let _ = tx.send(LoopEvent::Delta(format!("[思考] {}", delta))).await;
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}
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StreamChunk::ToolUseStart { name, .. } => {
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let _ = tx.send(LoopEvent::ToolStart {
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name: name.clone(),
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input: serde_json::Value::Null,
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}).await;
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}
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StreamChunk::ToolUseDelta { delta, .. } => {
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// Accumulate tool input deltas
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let _ = tx.send(LoopEvent::Delta(format!("[工具参数] {}", delta))).await;
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}
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StreamChunk::ToolUseEnd { input, .. } => {
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let _ = tx.send(LoopEvent::ToolEnd {
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name: String::new(),
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output: input.clone(),
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}).await;
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}
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StreamChunk::Complete { input_tokens: it, output_tokens: ot, .. } => {
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input_tokens = *it;
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output_tokens = *ot;
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}
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StreamChunk::Error { message } => {
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let _ = tx.send(LoopEvent::Error(message.clone())).await;
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}
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}
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}
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Err(e) => {
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let _ = tx.send(LoopEvent::Error(e.to_string())).await;
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}
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}
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}
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// Save assistant message to memory
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let assistant_message = Message::assistant(full_response.clone());
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let _ = memory.append_message(&session_id_clone, &assistant_message).await;
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// Send completion event
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let _ = tx.send(LoopEvent::Complete(AgentLoopResult {
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response: full_response,
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input_tokens,
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output_tokens,
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iterations: 1,
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})).await;
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});
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Ok(rx)
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}
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