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zesdex/apps/application/src/agent/turn_service.rs
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use std::collections::VecDeque;
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::{Arc, Mutex};
use tracing::{debug, info, warn};
use zesdex_domain::agent::{AgentTurnParams, TurnEvent};
use zesdex_domain::core::{ChatMessage, StreamEvent, ToolDef};
use zesdex_domain::main_agent_prompt;
use crate::ports::ProviderService;
use super::{ExploreService, ToolExecutor};
/// Maximum tool-call iterations per agent turn before forcing termination.
const MAX_TURN_ITERATIONS: u32 = 50;
// ---------------------------------------------------------------------------
// Helper: push a TurnEvent onto the shared queue.
// ---------------------------------------------------------------------------
fn push_event(queue: &Arc<Mutex<VecDeque<TurnEvent>>>, event: TurnEvent) {
if let Ok(mut q) = queue.lock() {
q.push_back(event);
}
}
// ---------------------------------------------------------------------------
// Helper: stream-event callback that forwards tokens to the turn-event queue
// and checks the abort flag on each emission.
// ---------------------------------------------------------------------------
fn make_stream_callback(
abort: &Arc<AtomicBool>,
turn_events: &Arc<Mutex<VecDeque<TurnEvent>>>,
) -> Box<dyn FnMut(&StreamEvent) -> bool + Send> {
let abort_clone = Arc::clone(abort);
let events_clone = Arc::clone(turn_events);
Box::new(move |event: &StreamEvent| -> bool {
if abort_clone.load(Ordering::SeqCst) {
return false;
}
match event {
StreamEvent::Token(s) => {
push_event(&events_clone, TurnEvent::StreamToken(s.clone()));
}
StreamEvent::Reasoning(s) => {
push_event(&events_clone, TurnEvent::StreamReasoning(s.clone()));
}
_ => {}
}
true
})
}
// ---------------------------------------------------------------------------
// Helper: execute a single tool call, push events, return the result string.
// ---------------------------------------------------------------------------
async fn execute_tool_call<T: ToolExecutor>(
tool_executor: &T,
turn_events: &Arc<Mutex<VecDeque<TurnEvent>>>,
tc: &zesdex_domain::core::ToolCall,
) -> String {
let name = &tc.function.name;
let args = zesdex_domain::core::tool_call::sanitize_tool_arguments(&tc.function.arguments);
debug!("executing tool: {name}");
let output = match tool_executor.execute(name, &args).await {
Ok(o) => o,
Err(e) => format!("Error: {e}"),
};
let is_error = output.starts_with("Error:");
push_event(
turn_events,
TurnEvent::ToolResult {
tool_call_id: tc.id.clone(),
tool_name: name.clone(),
output: output.clone(),
is_error,
path: None,
},
);
output
}
// ---------------------------------------------------------------------------
// Helper: emit usage event from optional LLM response metadata.
// ---------------------------------------------------------------------------
fn emit_usage(turn_events: &Arc<Mutex<VecDeque<TurnEvent>>>, usage: Option<(u64, u64)>) {
if let Some((tokens_in, tokens_out)) = usage {
push_event(
turn_events,
TurnEvent::Usage {
tokens_in,
tokens_out,
},
);
}
}
// ---------------------------------------------------------------------------
// Service implementation
// ---------------------------------------------------------------------------
/// Service implementation for executing an agent turn asynchronously.
///
/// # Explore phase
///
/// Before the main LLM loop begins, [`AgentTurnServiceImpl`] runs a mandatory
/// explore phase that spawns ≥3 parallel subagents (code structure, symbol
/// index, semantic context) and injects their consolidated findings as a
/// system message. See [`ExploreService`] for the trait contract.
pub struct AgentTurnServiceImpl<P: ProviderService, T: ToolExecutor> {
provider: Arc<P>,
tool_executor: Arc<T>,
tool_defs: Vec<ToolDef>,
/// Optional explore-phase service. When `Some`, the explore phase runs
/// before every turn; when `None` it is skipped (tests, daemon mode).
explore_service: Option<Arc<dyn ExploreService>>,
}
impl<P: ProviderService, T: ToolExecutor> AgentTurnServiceImpl<P, T> {
pub fn new(
provider: Arc<P>,
tool_executor: Arc<T>,
tool_defs: Vec<ToolDef>,
) -> Self {
Self {
provider,
tool_executor,
tool_defs,
explore_service: None,
}
}
/// Attach an optional explore-phase service.
///
/// When set, every call to `run_turn` will first run the explore phase
/// and inject the consolidated context as a system message.
pub fn with_explore(mut self, service: Arc<dyn ExploreService>) -> Self {
self.explore_service = Some(service);
self
}
/// Execute a single LLM call with the current message list, handling
/// streaming events and error reporting.
async fn call_llm(
&self,
messages: &[ChatMessage],
abort: &Arc<AtomicBool>,
turn_events: &Arc<Mutex<VecDeque<TurnEvent>>>,
) -> Result<(ChatMessage, Option<(u64, u64)>), String> {
let on_event = make_stream_callback(abort, turn_events);
self.provider
.chat_stream(
messages,
Some(self.tool_defs.clone()),
Some(4096),
Some(0.7),
on_event,
)
.await
.map_err(|e| format!("LLM error: {e}"))
}
}
impl<P: ProviderService, T: ToolExecutor> super::AgentTurnService for AgentTurnServiceImpl<P, T> {
async fn run_turn(&self, mut params: AgentTurnParams) -> anyhow::Result<()> {
info!(
"Starting async agent turn with {} messages (model: {})",
params.messages.len(),
params.model
);
// ── Phase 0: Mandatory explore ──────────────────────────────────
// Spawn ≥3 parallel subagents to discover code structure, symbols,
// and semantic context. The consolidated summary is injected as a
// system message before the main agent prompt.
if let Some(ref explorer) = self.explore_service {
// Determine workspace root from the first message's context or
// the first workspace root in params.
let user_query = params
.messages
.last()
.map(|m| m.content.clone().unwrap_or_default())
.unwrap_or_default();
let workspace_root = params
.workspace_roots
.first()
.map(|p| p.to_string_lossy().to_string())
.unwrap_or_else(|| ".".to_string());
push_event(
&params.turn_events,
TurnEvent::SystemNote {
kind: "info".into(),
message: "🔍 Exploring codebase structure...".into(),
},
);
match explorer.explore(&user_query, &workspace_root, &params.turn_events).await {
Ok(output) => {
// Insert each context message as a system message.
// They go at index 0 and are removed after the turn
// like the main agent prompt.
for ctx_msg in &output.context_messages {
params
.messages
.insert(0, ChatMessage::system(ctx_msg.clone()));
}
info!(
"Explore phase complete: {} context messages, {}",
output.context_messages.len(),
output.summary
);
}
Err(e) => {
warn!("Explore phase failed (non-fatal): {e}");
push_event(
&params.turn_events,
TurnEvent::SystemNote {
kind: "warn".into(),
message: format!("Explore phase failed: {e}"),
},
);
}
}
}
// Insert system prompt at position 0 once and keep it there for the
// entire turn, avoiding per-iteration clones of the full message list.
// It is removed before emitting the Compacted event so persistence
// does not store the prompt redundantly.
params.messages.insert(0, ChatMessage::system(main_agent_prompt()));
let original_count = params.messages.len();
for iteration in 0..MAX_TURN_ITERATIONS {
// ── Check abort flag ────────────────────────────────────────
if params.abort.load(Ordering::SeqCst) {
params.abort.store(false, Ordering::SeqCst);
push_event(
&params.turn_events,
TurnEvent::SystemNote {
kind: "info".into(),
message: "Turn aborted by user".into(),
},
);
break;
}
debug!("agent turn iteration {iteration}");
// ── Stream start + call LLM ─────────────────────────────────
push_event(&params.turn_events, TurnEvent::StreamStart);
// Uses params.messages directly (sys_msg[0] already in place
// from the insert above) — no per-iteration clone needed.
let result = self
.call_llm(&params.messages, &params.abort, &params.turn_events)
.await;
match result {
Ok((assistant_msg, usage)) => {
let content = assistant_msg.content.clone().unwrap_or_default();
let tool_calls = assistant_msg.tool_calls.clone().unwrap_or_default();
push_event(
&params.turn_events,
TurnEvent::StreamDone(assistant_msg.clone()),
);
emit_usage(&params.turn_events, usage);
// ── No tool calls → assistant is done ──────────────
if tool_calls.is_empty() {
params.messages.push(ChatMessage::assistant(Some(content)));
break;
}
params.messages.push(assistant_msg);
// ── Execute each tool call ──────────────────────────
for tc in &tool_calls {
let output =
execute_tool_call(self.tool_executor.as_ref(), &params.turn_events, tc).await;
params
.messages
.push(ChatMessage::tool(tc.id.clone(), output));
}
}
Err(e) => {
warn!("{e}");
push_event(
&params.turn_events,
TurnEvent::Error(e),
);
break;
}
}
}
// Remove the synthetic sys_msg before shipping events to the TUI
// so the transcript shows only the actual user/assistant/tool exchange.
let compacted: Vec<ChatMessage> = params.messages.drain(original_count - 1..).collect();
push_event(
&params.turn_events,
TurnEvent::Compacted(compacted),
);
push_event(&params.turn_events, TurnEvent::Done);
params.in_flight.store(false, Ordering::SeqCst);
Ok(())
}
}
// ---------------------------------------------------------------------------
// Conversation compaction
// ---------------------------------------------------------------------------
/// Maximum number of recent messages to preserve during compaction.
const COMPACT_KEEP_TAIL: usize = 6;
/// Compacts conversation history using AI summarisation.
///
/// Flow: if the message count exceeds `KEEP_TAIL + 2`, the oldest messages
/// are drained and summarised by the LLM. The summary is inserted as a
/// system message at the head of the remaining history.
pub async fn compact_messages_with_ai<P: ProviderService>(
messages: &mut Vec<ChatMessage>,
provider: &P,
) -> anyhow::Result<()> {
if messages.len() <= COMPACT_KEEP_TAIL + 2 {
return Ok(()); // Not enough messages to compact
}
let split_idx = messages.len() - COMPACT_KEEP_TAIL;
let evicted: Vec<_> = messages.drain(..split_idx).collect();
let mut summary_prompt = vec![
ChatMessage::system(zesdex_domain::compaction_prompt()),
];
summary_prompt.extend(evicted);
summary_prompt.push(ChatMessage::user(
"Please summarise our previous conversation above for context continuity.".to_string(),
));
match provider.chat(&summary_prompt, None, Some(1024), Some(0.3)).await {
Ok((summary_msg, _)) => {
let summary_text = summary_msg
.content
.unwrap_or_else(|| "Previous context summarised.".to_string());
let summary_node = ChatMessage::system(format!(
"[AI Summary of Previous Conversation]\n{}",
summary_text.trim()
));
messages.insert(0, summary_node);
Ok(())
}
Err(e) => {
warn!("AI summarisation failed during compact, falling back to simple notice: {e}");
messages.insert(
0,
ChatMessage::system(
"[Earlier conversation messages compacted to save context window]".to_string(),
),
);
Ok(())
}
}
}