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bb25c7881a5a8e0ea9177e146ba50f2c18000eb2
g3/crates/g3-core/src/lib.rs
Dhanji R. Prasanna bb25c7881a Change agent mode header text 
From: 🤖 Running as agent: fowler
To: >> agent mode | fowler
2026-01-11 17:24:26 +05:30

2822 lines
126 KiB
Rust
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pub mod context_window;
pub mod background_process;
pub mod compaction;
pub mod code_search;
pub mod error_handling;
pub mod feedback_extraction;
pub mod paths;
pub mod project;
pub mod provider_registration;
pub mod provider_config;
pub mod retry;
pub mod session;
pub mod session_continuation;
pub mod streaming_parser;
pub mod task_result;
pub mod tool_dispatch;
pub mod tool_definitions;
pub mod tools;
pub mod ui_writer;
pub mod streaming;
pub mod utils;
pub mod webdriver_session;
pub use task_result::TaskResult;
pub use retry::{RetryConfig, RetryResult, execute_with_retry, retry_operation};
pub use feedback_extraction::{ExtractedFeedback, FeedbackSource, FeedbackExtractionConfig, extract_coach_feedback};
pub use session_continuation::{SessionContinuation, load_continuation, save_continuation, clear_continuation, has_valid_continuation, get_session_dir, load_context_from_session_log, find_incomplete_agent_session, list_sessions_for_directory, format_session_time};
// Re-export context window types
pub use context_window::{ContextWindow, ThinScope};
// Export agent prompt generation for CLI use
pub use prompts::get_agent_system_prompt;
#[cfg(test)]
mod task_result_comprehensive_tests;
use crate::ui_writer::UiWriter;
#[cfg(test)]
mod tilde_expansion_tests;
#[cfg(test)]
mod error_handling_test;
mod prompts;
use anyhow::Result;
use g3_config::Config;
use g3_providers::{CacheControl, CompletionRequest, Message, MessageRole, ProviderRegistry};
use prompts::{get_system_prompt_for_native, SYSTEM_PROMPT_FOR_NON_NATIVE_TOOL_USE};
#[allow(unused_imports)]
use regex::Regex;
use serde::{Deserialize, Serialize};
use std::time::{Duration, Instant};
use tokio_util::sync::CancellationToken;
use tracing::{debug, error, warn};
// Re-export path utilities for backward compatibility
pub use paths::{
G3_WORKSPACE_PATH_ENV, ensure_session_dir, get_context_summary_file, get_g3_dir, get_logs_dir,
get_session_file, get_session_logs_dir, get_session_todo_path, get_thinned_dir, logs_dir,
};
use paths::get_todo_path;
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ToolCall {
pub tool: String,
pub args: serde_json::Value, // Should be a JSON object with tool-specific arguments
}
// Re-export WebDriverSession from its own module
pub use webdriver_session::WebDriverSession;
/// Options for fast-start discovery execution
#[derive(Debug, Clone)]
pub struct DiscoveryOptions<'a> {
pub messages: &'a [Message],
pub fast_start_path: Option<&'a str>,
}
#[derive(Debug, Clone)]
pub enum StreamState {
Generating,
ToolDetected(ToolCall),
Executing,
Resuming,
}
// Re-export StreamingToolParser from its own module
pub use streaming_parser::StreamingToolParser;
pub struct Agent<W: UiWriter> {
providers: ProviderRegistry,
context_window: ContextWindow,
thinning_events: Vec<usize>, // chars saved per thinning event
pending_90_compaction: bool, // flag to trigger compaction at 90%
auto_compact: bool, // whether to auto-compact at 90% before tool calls
compaction_events: Vec<usize>, // chars saved per compaction event
first_token_times: Vec<Duration>, // time to first token for each completion
config: Config,
session_id: Option<String>,
tool_call_metrics: Vec<(String, Duration, bool)>, // (tool_name, duration, success)
ui_writer: W,
is_autonomous: bool,
quiet: bool,
computer_controller: Option<Box<dyn g3_computer_control::ComputerController>>,
todo_content: std::sync::Arc<tokio::sync::RwLock<String>>,
webdriver_session: std::sync::Arc<
tokio::sync::RwLock<
Option<std::sync::Arc<tokio::sync::Mutex<WebDriverSession>>>,
>,
>,
webdriver_process: std::sync::Arc<tokio::sync::RwLock<Option<tokio::process::Child>>>,
tool_call_count: usize,
/// Tool calls made in the current turn (reset after each turn)
tool_calls_this_turn: Vec<String>,
requirements_sha: Option<String>,
/// Working directory for tool execution (set by --codebase-fast-start)
working_dir: Option<String>,
background_process_manager: std::sync::Arc<background_process::BackgroundProcessManager>,
/// Pending images to attach to the next user message
pending_images: Vec<g3_providers::ImageContent>,
/// Whether this agent is running in agent mode (--agent flag)
is_agent_mode: bool,
/// Name of the agent if running in agent mode (e.g., "fowler", "pike")
agent_name: Option<String>,
/// Whether auto-memory reminders are enabled (--auto-memory flag)
auto_memory: bool,
}
impl<W: UiWriter> Agent<W> {
pub async fn new(config: Config, ui_writer: W) -> Result<Self> {
Self::new_with_mode(config, ui_writer, false, false).await
}
pub async fn new_with_readme(
config: Config,
ui_writer: W,
readme_content: Option<String>,
) -> Result<Self> {
Self::new_with_mode_and_readme(config, ui_writer, false, readme_content, false, None).await
}
pub async fn new_autonomous_with_readme(
config: Config,
ui_writer: W,
readme_content: Option<String>,
) -> Result<Self> {
Self::new_with_mode_and_readme(config, ui_writer, true, readme_content, false, None).await
}
pub async fn new_autonomous(config: Config, ui_writer: W) -> Result<Self> {
Self::new_with_mode(config, ui_writer, true, false).await
}
pub async fn new_with_quiet(config: Config, ui_writer: W, quiet: bool) -> Result<Self> {
Self::new_with_mode(config, ui_writer, false, quiet).await
}
pub async fn new_with_readme_and_quiet(
config: Config,
ui_writer: W,
readme_content: Option<String>,
quiet: bool,
) -> Result<Self> {
Self::new_with_mode_and_readme(config, ui_writer, false, readme_content, quiet, None).await
}
pub async fn new_autonomous_with_readme_and_quiet(
config: Config,
ui_writer: W,
readme_content: Option<String>,
quiet: bool,
) -> Result<Self> {
Self::new_with_mode_and_readme(config, ui_writer, true, readme_content, quiet, None).await
}
/// Create a new agent with a custom system prompt (for agent mode)
/// The custom_system_prompt replaces the default G3 system prompt entirely
pub async fn new_with_custom_prompt(
config: Config,
ui_writer: W,
custom_system_prompt: String,
readme_content: Option<String>,
) -> Result<Self> {
Self::new_with_mode_and_readme(config, ui_writer, false, readme_content, false, Some(custom_system_prompt)).await
}
async fn new_with_mode(
config: Config,
ui_writer: W,
is_autonomous: bool,
quiet: bool,
) -> Result<Self> {
Self::new_with_mode_and_readme(config, ui_writer, is_autonomous, None, quiet, None).await
}
async fn new_with_mode_and_readme(
config: Config,
ui_writer: W,
is_autonomous: bool,
readme_content: Option<String>,
quiet: bool,
custom_system_prompt: Option<String>,
) -> Result<Self> {
// Register providers using the extracted module
let providers_to_register = provider_registration::determine_providers_to_register(&config, is_autonomous);
let providers = provider_registration::register_providers(&config, &providers_to_register).await?;
// Determine context window size based on active provider
let mut context_warnings = Vec::new();
let context_length =
Self::get_configured_context_length(&config, &providers, &mut context_warnings)?;
let mut context_window = ContextWindow::new(context_length);
// Surface any context warnings to the user via UI
for warning in context_warnings {
ui_writer.print_context_status(&format!("⚠️ {}", warning));
}
// Add system prompt as the FIRST message (before README)
// This ensures the agent always has proper tool usage instructions
let provider = providers.get(None)?;
let provider_has_native_tool_calling = provider.has_native_tool_calling();
let _ = provider; // Drop provider reference to avoid borrowing issues
let system_prompt = if let Some(custom_prompt) = custom_system_prompt {
// Use custom system prompt (for agent mode)
custom_prompt
} else {
// Use default system prompt based on provider capabilities
if provider_has_native_tool_calling {
// For native tool calling providers, use a more explicit system prompt
get_system_prompt_for_native()
} else {
// For non-native providers (embedded models), use JSON format instructions
SYSTEM_PROMPT_FOR_NON_NATIVE_TOOL_USE.to_string()
}
};
let system_message = Message::new(MessageRole::System, system_prompt);
context_window.add_message(system_message);
// If README content is provided, add it as a second system message (after the main system prompt)
if let Some(readme) = readme_content {
let readme_message = Message::new(MessageRole::System, readme);
context_window.add_message(readme_message);
}
// NOTE: TODO lists are now session-scoped and stored in .g3/sessions/<session_id>/todo.g3.md
// We don't load any TODO at initialization since we don't have a session_id yet.
// The agent will use todo_read to load the TODO once a session is established.
// Initialize computer controller if enabled
let computer_controller = if config.computer_control.enabled {
match g3_computer_control::create_controller() {
Ok(controller) => Some(controller),
Err(e) => {
warn!("Failed to initialize computer control: {}", e);
None
}
}
} else {
None
};
Ok(Self {
providers,
context_window,
auto_compact: config.agent.auto_compact,
pending_90_compaction: false,
thinning_events: Vec::new(),
compaction_events: Vec::new(),
first_token_times: Vec::new(),
config,
session_id: None,
tool_call_metrics: Vec::new(),
ui_writer,
// TODO content starts empty - session-scoped TODOs are loaded via todo_read
todo_content: std::sync::Arc::new(tokio::sync::RwLock::new(String::new())),
is_autonomous,
quiet,
computer_controller,
webdriver_session: std::sync::Arc::new(tokio::sync::RwLock::new(None)),
webdriver_process: std::sync::Arc::new(tokio::sync::RwLock::new(None)),
tool_call_count: 0,
tool_calls_this_turn: Vec::new(),
requirements_sha: None,
working_dir: None,
background_process_manager: std::sync::Arc::new(
background_process::BackgroundProcessManager::new(
paths::get_logs_dir().join("background_processes")
)),
pending_images: Vec::new(),
is_agent_mode: false,
agent_name: None,
auto_memory: false,
})
}
/// Validate that the system prompt is the first message in the conversation history.
/// This is a critical invariant that must be maintained for proper agent operation.
///
/// # Panics
/// Panics if:
/// - The conversation history is empty
/// - The first message is not a System message
/// - The first message doesn't contain the system prompt markers
fn validate_system_prompt_is_first(&self) {
if self.context_window.conversation_history.is_empty() {
panic!(
"FATAL: Conversation history is empty. System prompt must be the first message."
);
}
let first_message = &self.context_window.conversation_history[0];
if !matches!(first_message.role, MessageRole::System) {
panic!(
"FATAL: First message is not a System message. Found: {:?}",
first_message.role
);
}
// Check for system prompt markers that are present in both standard and agent mode
// Agent mode replaces the identity line but keeps all other instructions
let has_tool_instructions = first_message.content.contains("IMPORTANT: You must call tools to achieve goals");
if !has_tool_instructions {
panic!("FATAL: First system message does not contain the system prompt. This likely means the README was added before the system prompt.");
}
}
/// Convert cache config string to CacheControl enum
fn parse_cache_control(cache_config: &str) -> Option<CacheControl> {
match cache_config {
"ephemeral" => Some(CacheControl::ephemeral()),
"5minute" => Some(CacheControl::five_minute()),
"1hour" => Some(CacheControl::one_hour()),
_ => {
warn!(
"Invalid cache_config value: '{}'. Valid values are: ephemeral, 5minute, 1hour",
cache_config
);
None
}
}
}
/// Count how many cache_control annotations exist in the conversation history
fn count_cache_controls_in_history(&self) -> usize {
self.context_window
.conversation_history
.iter()
.filter(|msg| msg.cache_control.is_some())
.count()
}
/// Resolve the max_tokens to use for a given provider, applying fallbacks.
fn resolve_max_tokens(&self, provider_name: &str) -> u32 {
provider_config::resolve_max_tokens(&self.config, provider_name)
}
/// Get the thinking budget tokens for Anthropic provider, if configured.
/// Pre-flight check to validate max_tokens for thinking.budget_tokens constraint.
fn preflight_validate_max_tokens(&self, provider_name: &str, proposed_max_tokens: u32) -> (u32, bool) {
provider_config::preflight_validate_max_tokens(&self.config, provider_name, proposed_max_tokens)
}
/// Calculate max_tokens for a summary request.
fn calculate_summary_max_tokens(&self, provider_name: &str) -> (u32, bool) {
provider_config::calculate_summary_max_tokens(
&self.config,
provider_name,
self.context_window.total_tokens,
self.context_window.used_tokens,
)
}
/// Apply the fallback sequence to free up context space for thinking budget.
fn apply_max_tokens_fallback_sequence(&mut self, provider_name: &str, initial_max_tokens: u32, hard_coded_minimum: u32) -> u32 {
self.apply_fallback_sequence_impl(provider_name, Some(initial_max_tokens), hard_coded_minimum)
}
/// Unified implementation of the fallback sequence for freeing context space.
/// If `initial_max_tokens` is Some, uses preflight_validate_max_tokens for validation.
/// If `initial_max_tokens` is None, uses calculate_summary_max_tokens for validation.
fn apply_fallback_sequence_impl(
&mut self,
provider_name: &str,
initial_max_tokens: Option<u32>,
hard_coded_minimum: u32,
) -> u32 {
// Initial validation
let (mut max_tokens, needs_reduction) = match initial_max_tokens {
Some(initial) => self.preflight_validate_max_tokens(provider_name, initial),
None => self.calculate_summary_max_tokens(provider_name),
};
if !needs_reduction {
return max_tokens;
}
self.ui_writer.print_context_status(
"⚠️ Context window too full for thinking budget. Applying fallback sequence...\n",
);
// Step 1: Try thinnify (first third of context)
self.ui_writer.print_context_status("🥒 Step 1: Trying thinnify...\n");
let thin_msg = self.do_thin_context();
self.ui_writer.print_context_thinning(&thin_msg);
// Recalculate after thinnify
let (new_max, still_needs_reduction) = self.recalculate_max_tokens(provider_name, initial_max_tokens.is_some());
max_tokens = new_max;
if !still_needs_reduction {
self.ui_writer.print_context_status("✅ Thinnify resolved capacity issue. Continuing...\n");
return max_tokens;
}
// Step 2: Try skinnify (entire context)
self.ui_writer.print_context_status("🦴 Step 2: Trying skinnify...\n");
let skinny_msg = self.do_thin_context_all();
self.ui_writer.print_context_thinning(&skinny_msg);
// Recalculate after skinnify
let (final_max, final_needs_reduction) = self.recalculate_max_tokens(provider_name, initial_max_tokens.is_some());
if !final_needs_reduction {
self.ui_writer.print_context_status("✅ Skinnify resolved capacity issue. Continuing...\n");
return final_max;
}
// Step 3: Nothing worked, use hard-coded minimum
self.ui_writer.print_context_status(&format!(
"⚠️ Step 3: Context reduction insufficient. Using hard-coded max_tokens={} as last resort...\n",
hard_coded_minimum
));
hard_coded_minimum
}
/// Helper to recalculate max_tokens after context reduction.
fn recalculate_max_tokens(&self, provider_name: &str, use_preflight: bool) -> (u32, bool) {
if use_preflight {
let recalc_max = self.resolve_max_tokens(provider_name);
self.preflight_validate_max_tokens(provider_name, recalc_max)
} else {
self.calculate_summary_max_tokens(provider_name)
}
}
/// Resolve the temperature to use for a given provider, applying fallbacks.
fn resolve_temperature(&self, provider_name: &str) -> f32 {
provider_config::resolve_temperature(&self.config, provider_name)
}
/// Print provider diagnostics through the UiWriter for visibility
pub fn print_provider_banner(&self, role_label: &str) {
if let Ok((provider_name, model)) = self.get_provider_info() {
let max_tokens = self.resolve_max_tokens(&provider_name);
let context_len = self.context_window.total_tokens;
let mut details = vec![
format!("provider={}", provider_name),
format!("model={}", model),
format!("max_tokens={}", max_tokens),
format!("context_window_length={}", context_len),
];
if let Ok(provider) = self.providers.get(None) {
details.push(format!(
"native_tools={}",
if provider.has_native_tool_calling() {
"yes"
} else {
"no"
}
));
if provider.supports_cache_control() {
details.push("cache_control=yes".to_string());
}
}
self.ui_writer
.print_context_status(&format!("{}: {}", role_label, details.join(", ")));
}
}
fn get_configured_context_length(
config: &Config,
providers: &ProviderRegistry,
warnings: &mut Vec<String>,
) -> Result<u32> {
// First, check if there's a global max_context_length override in agent config
if let Some(max_context_length) = config.agent.max_context_length {
debug!(
"Using configured agent.max_context_length: {}",
max_context_length
);
return Ok(max_context_length);
}
// Get the active provider to determine context length
let provider = providers.get(None)?;
let provider_name = provider.name();
let model_name = provider.model();
// Parse provider name to get type and config name
let (provider_type, config_name) = provider_config::parse_provider_ref(provider_name);
// Use provider-specific context length if available
let context_length = match provider_type {
"embedded" | "embedded." => {
// For embedded models, use the configured context_length or model-specific defaults
if let Some(embedded_config) = config.providers.embedded.get(config_name) {
embedded_config.context_length.unwrap_or_else(|| {
// Model-specific defaults for embedded models
match &embedded_config.model_type.to_lowercase()[..] {
"codellama" => 16384, // CodeLlama supports 16k context
"llama" => 4096, // Base Llama models
"mistral" => 8192, // Mistral models
"qwen" => 32768, // Qwen2.5 supports 32k context
_ => 4096, // Conservative default
}
})
} else {
config.agent.fallback_default_max_tokens as u32
}
}
"openai" => {
// OpenAI models have varying context windows
if let Some(max_tokens) = provider_config::get_max_tokens(config, provider_name) {
warnings.push(format!(
"Context length falling back to max_tokens ({}) for provider={}",
max_tokens, provider_name
));
max_tokens
} else {
400000
}
}
"anthropic" => {
// Claude models have large context windows
if let Some(max_tokens) = provider_config::get_max_tokens(config, provider_name) {
warnings.push(format!(
"Context length falling back to max_tokens ({}) for provider={}",
max_tokens, provider_name
));
max_tokens
} else {
200000
}
}
"databricks" => {
// Databricks models have varying context windows depending on the model
if let Some(max_tokens) = provider_config::get_max_tokens(config, provider_name) {
warnings.push(format!(
"Context length falling back to max_tokens ({}) for provider={}",
max_tokens, provider_name
));
max_tokens
} else if model_name.contains("claude") {
200000 // Claude models on Databricks have large context windows
} else if model_name.contains("llama") || model_name.contains("dbrx") {
32768 // DBRX supports 32k context
} else {
16384 // Conservative default for other Databricks models
}
}
_ => config.agent.fallback_default_max_tokens as u32,
};
debug!(
"Using context length: {} tokens for provider: {} (model: {})",
context_length, provider_name, model_name
);
Ok(context_length)
}
pub fn get_provider_info(&self) -> Result<(String, String)> {
let provider = self.providers.get(None)?;
Ok((provider.name().to_string(), provider.model().to_string()))
}
/// Get the default LLM provider
pub fn get_provider(&self) -> Result<&dyn g3_providers::LLMProvider> {
self.providers.get(None)
}
/// Get the current session ID for this agent
pub fn get_session_id(&self) -> Option<&str> {
self.session_id.as_deref()
}
pub async fn execute_task(
&mut self,
description: &str,
language: Option<&str>,
_auto_execute: bool,
) -> Result<TaskResult> {
self.execute_task_with_options(description, language, false, false, false, None)
.await
}
pub async fn execute_task_with_options(
&mut self,
description: &str,
language: Option<&str>,
_auto_execute: bool,
show_prompt: bool,
show_code: bool,
discovery_options: Option<DiscoveryOptions<'_>>,
) -> Result<TaskResult> {
self.execute_task_with_timing(
description,
language,
_auto_execute,
show_prompt,
show_code,
false,
discovery_options,
)
.await
}
pub async fn execute_task_with_timing(
&mut self,
description: &str,
language: Option<&str>,
_auto_execute: bool,
show_prompt: bool,
show_code: bool,
show_timing: bool,
discovery_options: Option<DiscoveryOptions<'_>>,
) -> Result<TaskResult> {
// Create a cancellation token that never cancels for backward compatibility
let cancellation_token = CancellationToken::new();
self.execute_task_with_timing_cancellable(
description,
language,
_auto_execute,
show_prompt,
show_code,
show_timing,
cancellation_token,
discovery_options,
)
.await
}
#[allow(clippy::too_many_arguments)]
pub async fn execute_task_with_timing_cancellable(
&mut self,
description: &str,
_language: Option<&str>,
_auto_execute: bool,
show_prompt: bool,
show_code: bool,
show_timing: bool,
cancellation_token: CancellationToken,
discovery_options: Option<DiscoveryOptions<'_>>,
) -> Result<TaskResult> {
// Execute the task directly without splitting
self.execute_single_task(
description,
show_prompt,
show_code,
show_timing,
cancellation_token,
discovery_options,
)
.await
}
async fn execute_single_task(
&mut self,
description: &str,
_show_prompt: bool,
_show_code: bool,
show_timing: bool,
cancellation_token: CancellationToken,
discovery_options: Option<DiscoveryOptions<'_>>,
) -> Result<TaskResult> {
// Reset the JSON tool call filter state at the start of each new task
// This prevents the filter from staying in suppression mode between user interactions
self.ui_writer.reset_json_filter();
// Validate that the system prompt is the first message (critical invariant)
self.validate_system_prompt_is_first();
// Generate session ID based on the initial prompt if this is a new session
if self.session_id.is_none() {
self.session_id = Some(self.generate_session_id(description));
}
// Add user message to context window
let mut user_message = {
// Check if we should use cache control (every 10 tool calls)
// But only if we haven't already added 4 cache_control annotations
let provider = self.providers.get(None)?;
let provider_name = provider.name();
let (provider_type, config_name) = provider_config::parse_provider_ref(provider_name);
if let Some(cache_config) = match provider_type {
"anthropic" => {
self.config
.providers
.anthropic
.get(config_name)
.and_then(|c| c.cache_config.as_ref())
.and_then(|config| Self::parse_cache_control(config))
}
_ => None,
} {
Message::with_cache_control_validated(
MessageRole::User,
format!("Task: {}", description),
cache_config,
provider,
)
} else {
Message::new(MessageRole::User, format!("Task: {}", description))
}
};
// Attach any pending images to this user message
if !self.pending_images.is_empty() {
user_message.images = std::mem::take(&mut self.pending_images);
}
self.context_window.add_message(user_message);
// Execute fast-discovery tool calls if provided (immediately after user message)
if let Some(ref options) = discovery_options {
self.ui_writer
.println("▶️ Playing back discovery commands...");
// Store the working directory for subsequent tool calls in the streaming loop
if let Some(path) = options.fast_start_path {
self.working_dir = Some(path.to_string());
}
let provider = self.providers.get(None)?;
let supports_cache = provider.supports_cache_control();
let message_count = options.messages.len();
for (idx, discovery_msg) in options.messages.iter().enumerate() {
if let Ok(tool_call) = serde_json::from_str::<ToolCall>(&discovery_msg.content) {
self.add_message_to_context(discovery_msg.clone());
let result = self
.execute_tool_call_in_dir(&tool_call, options.fast_start_path)
.await
.unwrap_or_else(|e| format!("Error: {}", e));
// Add cache_control to the last user message if provider supports it (anthropic)
let is_last = idx == message_count - 1;
let result_message = if supports_cache
&& is_last
&& self.count_cache_controls_in_history() < 4
{
Message::with_cache_control(
MessageRole::User,
format!("Tool result: {}", result),
CacheControl::ephemeral(),
)
} else {
Message::new(MessageRole::User, format!("Tool result: {}", result))
};
self.add_message_to_context(result_message);
}
}
}
// Use the complete conversation history for the request
let messages = self.context_window.conversation_history.clone();
// Check if provider supports native tool calling and add tools if so
let provider = self.providers.get(None)?;
let provider_name = provider.name().to_string();
let _has_native_tool_calling = provider.has_native_tool_calling();
let _supports_cache_control = provider.supports_cache_control();
// Check if we should exclude the research tool (scout agent to prevent recursion)
let exclude_research = self.agent_name.as_deref() == Some("scout");
let tools = if provider.has_native_tool_calling() {
let mut tool_config = tool_definitions::ToolConfig::new(
self.config.webdriver.enabled,
self.config.computer_control.enabled,
);
if exclude_research {
tool_config = tool_config.with_research_excluded();
}
Some(tool_definitions::create_tool_definitions(tool_config))
} else {
None
};
let _ = provider; // Drop the provider reference to avoid borrowing issues
// Get max_tokens from provider configuration with preflight validation
// This ensures max_tokens > thinking.budget_tokens for Anthropic with extended thinking
let initial_max_tokens = self.resolve_max_tokens(&provider_name);
let max_tokens = Some(self.apply_max_tokens_fallback_sequence(
&provider_name,
initial_max_tokens,
16000, // Hard-coded minimum for main API calls (higher than summary's 5000)
));
let request = CompletionRequest {
messages,
max_tokens,
temperature: Some(self.resolve_temperature(&provider_name)),
stream: true, // Enable streaming
tools,
disable_thinking: false,
};
// Time the LLM call with cancellation support and streaming
let llm_start = Instant::now();
let result = tokio::select! {
result = self.stream_completion(request, show_timing) => result,
_ = cancellation_token.cancelled() => {
// Save context window on cancellation
self.save_context_window("cancelled");
Err(anyhow::anyhow!("Operation cancelled by user"))
}
};
let task_result = match result {
Ok(result) => result,
Err(e) => {
// Save context window on error
self.save_context_window("error");
return Err(e);
}
};
let response_content = task_result.response.clone();
let _llm_duration = llm_start.elapsed();
// Create a mock usage for now (we'll need to track this during streaming)
let mock_usage = g3_providers::Usage {
prompt_tokens: 100, // Estimate
completion_tokens: response_content.len() as u32 / 4, // Rough estimate
total_tokens: 100 + (response_content.len() as u32 / 4),
};
// Update context window with estimated token usage
self.context_window.update_usage(&mock_usage);
// Add assistant response to context window only if not empty
// This prevents the "Skipping empty message" warning when only tools were executed
// Also strip timing footer - it's display-only and shouldn't be in context
let content_for_context = if let Some(timing_pos) = response_content.rfind("\n\n⏱️") {
response_content[..timing_pos].to_string()
} else {
response_content.clone()
};
if !content_for_context.trim().is_empty() {
let assistant_message = Message::new(MessageRole::Assistant, content_for_context);
self.context_window.add_message(assistant_message);
} else {
debug!("Assistant response was empty (likely only tool execution), skipping message addition");
}
// Save context window at the end of successful interaction
self.save_context_window("completed");
// Check if we need to do 90% auto-compaction
if self.pending_90_compaction {
self.ui_writer
.print_context_status("\n⚡ Context window reached 90% - auto-compacting...\n");
if let Err(e) = self.force_compact().await {
warn!("Failed to auto-compact at 90%: {}", e);
} else {
self.ui_writer.println("");
}
self.pending_90_compaction = false;
}
// Return the task result which already includes timing if needed
Ok(task_result)
}
/// Generate a session ID based on the initial prompt
fn generate_session_id(&self, description: &str) -> String {
session::generate_session_id(description, self.agent_name.as_deref())
}
/// Save the entire context window to a per-session file
fn save_context_window(&self, status: &str) {
if self.quiet {
return;
}
session::save_context_window(self.session_id.as_deref(), &self.context_window, status);
}
/// Write context window summary to file
/// Format: date&time, token_count, message_id, role, first_100_chars
fn write_context_window_summary(&self) {
if self.quiet {
return;
}
if let Some(ref session_id) = self.session_id {
session::write_context_window_summary(session_id, &self.context_window);
}
}
pub fn get_context_window(&self) -> &ContextWindow {
&self.context_window
}
/// Add a message directly to the context window.
/// Used for injecting discovery messages before the first LLM turn.
pub fn add_message_to_context(&mut self, message: Message) {
self.context_window.add_message(message);
}
/// Execute a tool call and return the result.
/// This is a public wrapper around execute_tool for use by external callers
/// like the planner's fast-discovery feature.
pub async fn execute_tool_call(&mut self, tool_call: &ToolCall) -> Result<String> {
self.execute_tool(tool_call).await
}
/// Execute a tool call with an optional working directory (for discovery commands)
pub async fn execute_tool_call_in_dir(
&mut self,
tool_call: &ToolCall,
working_dir: Option<&str>,
) -> Result<String> {
self.execute_tool_in_dir(tool_call, working_dir).await
}
/// Log an error message to the session JSON file as the last message
/// This is used in autonomous mode to record context length exceeded errors
pub fn log_error_to_session(
&self,
error: &anyhow::Error,
role: &str,
forensic_context: Option<String>,
) {
if self.quiet {
return;
}
match &self.session_id {
Some(id) => session::log_error_to_session(id, error, role, forensic_context),
None => {
error!("Cannot log error to session: no session ID");
}
}
}
/// Manually trigger context compaction regardless of context window size
/// Returns Ok(true) if compaction was successful, Ok(false) if it failed
pub async fn force_compact(&mut self) -> Result<bool> {
use crate::compaction::{CompactionConfig, perform_compaction};
debug!("Manual compaction triggered");
self.ui_writer.print_context_status(&format!(
"\n🗜️ Manual compaction requested (current usage: {}%)...",
self.context_window.percentage_used() as u32
));
let provider = self.providers.get(None)?;
let provider_name = provider.name().to_string();
let _ = provider; // Release borrow early
// Get the latest user message to preserve it
let latest_user_msg = self
.context_window
.conversation_history
.iter()
.rev()
.find(|m| matches!(m.role, MessageRole::User))
.map(|m| m.content.clone());
let compaction_config = CompactionConfig {
provider_name: &provider_name,
latest_user_msg,
};
let result = perform_compaction(
&self.providers,
&mut self.context_window,
&self.config,
compaction_config,
&self.ui_writer,
&mut self.thinning_events,
).await?;
if result.success {
self.ui_writer.print_context_status("✅ Context compacted successfully.\n");
self.compaction_events.push(result.chars_saved);
Ok(true)
} else {
self.ui_writer.print_context_status(
"⚠️ Unable to create summary. Please try again or start a new session.\n",
);
Ok(false)
}
}
/// Manually trigger context thinning regardless of thresholds
pub fn force_thin(&mut self) -> String {
debug!("Manual context thinning triggered");
self.do_thin_context()
}
/// Manually trigger context thinning for the ENTIRE context window
/// Unlike force_thin which only processes the first third, this processes all messages
pub fn force_thin_all(&mut self) -> String {
debug!("Manual full context skinnifying triggered");
self.do_thin_context_all()
}
/// Internal helper: thin context and track the event
fn do_thin_context(&mut self) -> String {
let (message, chars_saved) = self.context_window.thin_context(self.session_id.as_deref());
self.thinning_events.push(chars_saved);
message
}
/// Internal helper: thin all context and track the event
fn do_thin_context_all(&mut self) -> String {
let (message, chars_saved) = self.context_window.thin_context_all(self.session_id.as_deref());
self.thinning_events.push(chars_saved);
message
}
/// Check if a tool call is a duplicate of the last tool call in the previous assistant message.
/// Returns Some("DUP IN MSG") if it's a duplicate, None otherwise.
fn check_duplicate_in_previous_message(&self, tool_call: &ToolCall) -> Option<String> {
// Helper to check if two tool calls are duplicates
let are_duplicates = |tc1: &ToolCall, tc2: &ToolCall| -> bool {
tc1.tool == tc2.tool && tc1.args == tc2.args
};
// Find the most recent assistant message
for msg in self.context_window.conversation_history.iter().rev() {
if !matches!(msg.role, MessageRole::Assistant) {
continue;
}
let content = &msg.content;
// Look for the last occurrence of a tool call pattern
let last_tool_start = content.rfind(r#"{"tool""#)
.or_else(|| content.rfind(r#"{ "tool""#))?;
// Find the end of this JSON object
let end_offset = StreamingToolParser::find_complete_json_object_end(&content[last_tool_start..])?;
let end_idx = last_tool_start + end_offset + 1;
let tool_json = &content[last_tool_start..end_idx];
// Check if there's any non-whitespace text after this tool call
let text_after = content[end_idx..].trim();
if !text_after.is_empty() {
// There's text after the tool call, so it's not a trailing duplicate
return None;
}
// Parse and compare the tool call
if let Ok(prev_tool) = serde_json::from_str::<ToolCall>(tool_json) {
if are_duplicates(&prev_tool, tool_call) {
return Some("DUP IN MSG".to_string());
}
}
// Only check the most recent assistant message
break;
}
None
}
/// Reload README.md and AGENTS.md and replace the first system message
/// Returns Ok(true) if README was found and reloaded, Ok(false) if no README was present initially
pub fn reload_readme(&mut self) -> Result<bool> {
debug!("Manual README reload triggered");
// Check if the second message in conversation history is a system message with README content
// (The first message should always be the system prompt)
let has_readme = self
.context_window
.conversation_history
.get(1) // Check the SECOND message (index 1)
.map(|m| {
matches!(m.role, MessageRole::System)
&& (m.content.contains("Project README")
|| m.content.contains("Agent Configuration"))
})
.unwrap_or(false);
// Validate that the system prompt is still first
self.validate_system_prompt_is_first();
if !has_readme {
return Ok(false);
}
// Try to load README.md and AGENTS.md
let mut combined_content = String::new();
let mut found_any = false;
if let Ok(agents_content) = std::fs::read_to_string("AGENTS.md") {
combined_content.push_str("# Agent Configuration\n\n");
combined_content.push_str(&agents_content);
combined_content.push_str("\n\n");
found_any = true;
}
if let Ok(readme_content) = std::fs::read_to_string("README.md") {
combined_content.push_str("# Project README\n\n");
combined_content.push_str(&readme_content);
found_any = true;
}
if found_any {
// Replace the second message (README) with the new content
if let Some(first_msg) = self.context_window.conversation_history.get_mut(1) {
first_msg.content = combined_content;
debug!("README content reloaded successfully");
Ok(true)
} else {
Ok(false)
}
} else {
Ok(false)
}
}
/// Get detailed context statistics
pub fn get_stats(&self) -> String {
let mut stats = String::new();
use std::time::Duration;
stats.push_str("\n📊 Context Window Statistics\n");
stats.push_str(&"=".repeat(60));
stats.push_str("\n\n");
// Context window usage
stats.push_str("🗂️ Context Window:\n");
stats.push_str(&format!(
" • Used Tokens: {:>10} / {}\n",
self.context_window.used_tokens, self.context_window.total_tokens
));
stats.push_str(&format!(
" • Usage Percentage: {:>10.1}%\n",
self.context_window.percentage_used()
));
stats.push_str(&format!(
" • Remaining Tokens: {:>10}\n",
self.context_window.remaining_tokens()
));
stats.push_str(&format!(
" • Cumulative Tokens: {:>10}\n",
self.context_window.cumulative_tokens
));
stats.push_str(&format!(
" • Last Thinning: {:>10}%\n",
self.context_window.last_thinning_percentage
));
stats.push('\n');
// Context optimization metrics
stats.push_str("🗜️ Context Optimization:\n");
stats.push_str(&format!(
" • Thinning Events: {:>10}\n",
self.thinning_events.len()
));
if !self.thinning_events.is_empty() {
let total_thinned: usize = self.thinning_events.iter().sum();
let avg_thinned = total_thinned / self.thinning_events.len();
stats.push_str(&format!(" • Total Chars Saved: {:>10}\n", total_thinned));
stats.push_str(&format!(" • Avg Chars/Event: {:>10}\n", avg_thinned));
}
stats.push_str(&format!(
" • Compactions: {:>10}\n",
self.compaction_events.len()
));
if !self.compaction_events.is_empty() {
let total_compacted: usize = self.compaction_events.iter().sum();
let avg_compacted = total_compacted / self.compaction_events.len();
stats.push_str(&format!(
" • Total Chars Saved: {:>10}\n",
total_compacted
));
stats.push_str(&format!(" • Avg Chars/Event: {:>10}\n", avg_compacted));
}
stats.push('\n');
// Performance metrics
stats.push_str("⚡ Performance:\n");
if !self.first_token_times.is_empty() {
let avg_ttft = self.first_token_times.iter().sum::<Duration>()
/ self.first_token_times.len() as u32;
let mut sorted_times = self.first_token_times.clone();
sorted_times.sort();
let median_ttft = sorted_times[sorted_times.len() / 2];
stats.push_str(&format!(
" • Avg Time to First Token: {:>6.3}s\n",
avg_ttft.as_secs_f64()
));
stats.push_str(&format!(
" • Median Time to First Token: {:>6.3}s\n",
median_ttft.as_secs_f64()
));
}
stats.push('\n');
// Conversation history
stats.push_str("💬 Conversation History:\n");
stats.push_str(&format!(
" • Total Messages: {:>10}\n",
self.context_window.conversation_history.len()
));
// Count messages by role
let mut system_count = 0;
let mut user_count = 0;
let mut assistant_count = 0;
for msg in &self.context_window.conversation_history {
match msg.role {
MessageRole::System => system_count += 1,
MessageRole::User => user_count += 1,
MessageRole::Assistant => assistant_count += 1,
}
}
stats.push_str(&format!(" • System Messages: {:>10}\n", system_count));
stats.push_str(&format!(" • User Messages: {:>10}\n", user_count));
stats.push_str(&format!(
" • Assistant Messages:{:>10}\n",
assistant_count
));
stats.push('\n');
// Tool call metrics
stats.push_str("🔧 Tool Call Metrics:\n");
stats.push_str(&format!(
" • Total Tool Calls: {:>10}\n",
self.tool_call_metrics.len()
));
let successful_calls = self
.tool_call_metrics
.iter()
.filter(|(_, _, success)| *success)
.count();
let failed_calls = self.tool_call_metrics.len() - successful_calls;
stats.push_str(&format!(
" • Successful: {:>10}\n",
successful_calls
));
stats.push_str(&format!(" • Failed: {:>10}\n", failed_calls));
if !self.tool_call_metrics.is_empty() {
let total_duration: Duration = self
.tool_call_metrics
.iter()
.map(|(_, duration, _)| *duration)
.sum();
let avg_duration = total_duration / self.tool_call_metrics.len() as u32;
stats.push_str(&format!(
" • Total Duration: {:>10.2}s\n",
total_duration.as_secs_f64()
));
stats.push_str(&format!(
" • Average Duration: {:>10.2}s\n",
avg_duration.as_secs_f64()
));
}
stats.push('\n');
// Provider info
stats.push_str("🔌 Provider:\n");
if let Ok((provider, model)) = self.get_provider_info() {
stats.push_str(&format!(" • Provider: {}\n", provider));
stats.push_str(&format!(" • Model: {}\n", model));
}
stats.push_str(&"=".repeat(60));
stats.push('\n');
stats
}
pub fn get_tool_call_metrics(&self) -> &Vec<(String, Duration, bool)> {
&self.tool_call_metrics
}
pub fn get_config(&self) -> &Config {
&self.config
}
pub fn set_requirements_sha(&mut self, sha: String) {
self.requirements_sha = Some(sha);
}
/// Save a session continuation artifact
/// Save session continuation for potential resumption
pub fn save_session_continuation(&self, summary: Option<String>) {
use crate::session_continuation::{save_continuation, SessionContinuation};
let session_id = match &self.session_id {
Some(id) => id.clone(),
None => {
debug!("No session ID, skipping continuation save");
return;
}
};
// Get the session log path (now in .g3/sessions/<session_id>/session.json)
let session_log_path = get_session_file(&session_id);
// Get current TODO content - try session-specific path first, then workspace path
let session_todo_path = crate::paths::get_session_todo_path(&session_id);
let todo_snapshot = if session_todo_path.exists() {
std::fs::read_to_string(&session_todo_path).ok()
} else {
// Fall back to workspace TODO path for backwards compatibility
std::fs::read_to_string(get_todo_path()).ok()
};
// Get working directory
let working_directory = std::env::current_dir()
.map(|p| p.to_string_lossy().to_string())
.unwrap_or_else(|_| ".".to_string());
// Get description from first user message (strip "Task: " prefix if present)
let description = self.context_window.conversation_history.iter()
.find(|m| matches!(m.role, g3_providers::MessageRole::User))
.map(|m| {
let content = m.content.strip_prefix("Task: ").unwrap_or(&m.content);
// Truncate to ~60 chars for display, ending at word boundary
truncate_to_word_boundary(content, 60)
});
let continuation = SessionContinuation::new(
self.is_agent_mode,
self.agent_name.clone(),
session_id,
description,
summary,
session_log_path.to_string_lossy().to_string(),
self.context_window.percentage_used(),
todo_snapshot,
working_directory,
);
if let Err(e) = save_continuation(&continuation) {
error!("Failed to save session continuation: {}", e);
} else {
debug!("Saved session continuation artifact");
}
}
/// Set agent mode information for session tracking
/// Called when running with --agent flag to enable agent-specific session resume
pub fn set_agent_mode(&mut self, agent_name: &str) {
self.is_agent_mode = true;
self.agent_name = Some(agent_name.to_string());
debug!("Agent mode enabled for agent: {}", agent_name);
}
/// Enable auto-memory reminders after turns with tool calls
pub fn set_auto_memory(&mut self, enabled: bool) {
self.auto_memory = enabled;
debug!("Auto-memory reminders: {}", if enabled { "enabled" } else { "disabled" });
}
/// Send an auto-memory reminder to the LLM if tools were called during the turn.
/// This prompts the LLM to call the `remember` tool if it discovered any key code locations.
/// Returns true if a reminder was sent and processed.
pub async fn send_auto_memory_reminder(&mut self) -> Result<bool> {
if !self.auto_memory {
return Ok(false);
}
// Check if any tools were called this turn
if self.tool_calls_this_turn.is_empty() {
debug!("Auto-memory: No tools called, skipping reminder");
self.ui_writer.print_context_status("📝 Auto-memory: No tools called this turn, skipping reminder.\n");
return Ok(false);
}
// Check if remember was already called this turn - no need to remind
if self.tool_calls_this_turn.iter().any(|t| t == "remember") {
debug!("Auto-memory: 'remember' was already called this turn, skipping reminder");
self.ui_writer.print_context_status("📝 Auto-memory: 'remember' already called, skipping reminder.\n");
self.tool_calls_this_turn.clear();
return Ok(false);
}
// Take the tools list and reset for next turn
let tools_called = std::mem::take(&mut self.tool_calls_this_turn);
debug!("Auto-memory: Sending reminder to LLM ({} tools called this turn: {:?})", tools_called.len(), tools_called);
self.ui_writer.print_context_status("\n*memory checkpoint:* ");
let reminder = "SYSTEM REMINDER: You used tools during this turn. If you discovered any key code locations, patterns, or entry points that aren't already in Project Memory, please call the `remember` tool now to save them. If you didn't discover anything new worth remembering, you can skip this. Respond briefly after deciding.";
// Add the reminder as a user message and get a response
self.context_window.add_message(Message::new(
MessageRole::User,
reminder.to_string(),
));
// Build the completion request
let messages = self.context_window.conversation_history.clone();
// Get provider and tools
let provider = self.providers.get(None)?;
let provider_name = provider.name().to_string();
let tools = if provider.has_native_tool_calling() {
let tool_config = tool_definitions::ToolConfig::new(
self.config.webdriver.enabled,
self.config.computer_control.enabled,
);
Some(tool_definitions::create_tool_definitions(tool_config))
} else {
None
};
let _ = provider; // Drop the provider reference
let max_tokens = Some(self.resolve_max_tokens(&provider_name));
let request = CompletionRequest {
messages,
max_tokens,
temperature: Some(self.resolve_temperature(&provider_name)),
stream: true,
tools,
disable_thinking: true, // Keep it brief
};
// Execute the reminder turn (show_timing = false to keep it quiet)
self.stream_completion_with_tools(request, false).await?;
Ok(true)
}
/// Initialize session ID manually (primarily for testing).
/// This allows tests to verify session ID generation without calling execute_task,
/// which would require an LLM provider.
pub fn init_session_id_for_test(&mut self, description: &str) {
if self.session_id.is_none() {
self.session_id = Some(self.generate_session_id(description));
}
}
/// Clear session state and continuation artifacts (for /clear command)
pub fn clear_session(&mut self) {
use crate::session_continuation::clear_continuation;
// Clear the context window (keep system prompt)
self.context_window.clear_conversation();
// Clear continuation artifacts
if let Err(e) = clear_continuation() {
error!("Failed to clear continuation artifacts: {}", e);
}
debug!("Session cleared");
}
/// Restore session from a continuation artifact
/// Returns true if full context was restored, false if only summary was used
pub fn restore_from_continuation(
&mut self,
continuation: &crate::session_continuation::SessionContinuation,
) -> Result<bool> {
use std::path::PathBuf;
let session_log_path = PathBuf::from(&continuation.session_log_path);
// If context < 80%, try to restore full context
if continuation.can_restore_full_context() && session_log_path.exists() {
// Load the session log
let json = std::fs::read_to_string(&session_log_path)?;
let session_data: serde_json::Value = serde_json::from_str(&json)?;
// Extract conversation history
if let Some(context_window) = session_data.get("context_window") {
if let Some(history) = context_window.get("conversation_history") {
if let Some(messages) = history.as_array() {
// Clear current conversation (keep system messages)
self.context_window.clear_conversation();
// Restore messages from session log (skip system messages as they're preserved)
for msg in messages {
let role_str = msg.get("role").and_then(|r| r.as_str()).unwrap_or("user");
let content = msg.get("content").and_then(|c| c.as_str()).unwrap_or("");
let role = match role_str {
"system" => continue, // Skip system messages, already preserved
"assistant" => MessageRole::Assistant,
_ => MessageRole::User,
};
self.context_window.add_message(Message {
role,
id: String::new(),
images: Vec::new(),
content: content.to_string(),
cache_control: None,
});
}
debug!("Restored full context from session log");
return Ok(true);
}
}
}
}
// Fall back to using session summary + TODO
let mut context_msg = String::new();
if let Some(ref summary) = continuation.summary {
context_msg.push_str(&format!("Previous session summary:\n{}\n\n", summary));
}
if let Some(ref todo) = continuation.todo_snapshot {
context_msg.push_str(&format!("Current TODO state:\n{}\n", todo));
}
if !context_msg.is_empty() {
self.context_window.add_message(Message {
role: MessageRole::User,
id: String::new(),
images: Vec::new(),
content: format!("[Session Resumed]\n\n{}", context_msg),
cache_control: None,
});
}
debug!("Restored session from summary");
Ok(false)
}
/// Switch to a different session, saving the current one first.
/// This discards the current in-memory state and loads the new session.
pub fn switch_to_session(
&mut self,
continuation: &crate::session_continuation::SessionContinuation,
) -> Result<bool> {
// Save current session first (so it can be resumed later)
self.save_session_continuation(None);
// Reset session-specific metrics
self.thinning_events.clear();
self.compaction_events.clear();
self.first_token_times.clear();
self.tool_call_metrics.clear();
self.tool_call_count = 0;
self.pending_90_compaction = false;
// Update session ID to the new session
self.session_id = Some(continuation.session_id.clone());
// Update agent mode info from continuation
self.is_agent_mode = continuation.is_agent_mode;
self.agent_name = continuation.agent_name.clone();
// Load TODO content from the new session if available
if let Some(ref todo) = continuation.todo_snapshot {
// Use blocking write since we're in a sync context
if let Ok(mut guard) = self.todo_content.try_write() {
*guard = todo.clone();
}
}
// Restore context from the continuation
self.restore_from_continuation(continuation)
}
async fn stream_completion(
&mut self,
request: CompletionRequest,
show_timing: bool,
) -> Result<TaskResult> {
self.stream_completion_with_tools(request, show_timing)
.await
}
/// Create tool definitions for native tool calling providers
/// Helper method to stream with retry logic
async fn stream_with_retry(
&self,
request: &CompletionRequest,
error_context: &error_handling::ErrorContext,
) -> Result<g3_providers::CompletionStream> {
use crate::error_handling::{calculate_retry_delay, classify_error, ErrorType};
let mut attempt = 0;
let max_attempts = if self.is_autonomous {
self.config.agent.autonomous_max_retry_attempts
} else {
self.config.agent.max_retry_attempts
};
loop {
attempt += 1;
let provider = self.providers.get(None)?;
match provider.stream(request.clone()).await {
Ok(stream) => {
if attempt > 1 {
debug!("Stream started successfully after {} attempts", attempt);
}
debug!("Stream started successfully");
debug!(
"Request had {} messages, tools={}, max_tokens={:?}",
request.messages.len(),
request.tools.is_some(),
request.max_tokens
);
return Ok(stream);
}
Err(e) if attempt < max_attempts => {
if matches!(classify_error(&e), ErrorType::Recoverable(_)) {
let delay = calculate_retry_delay(attempt, self.is_autonomous);
warn!(
"Recoverable error on attempt {}/{}: {}. Retrying in {:?}...",
attempt, max_attempts, e, delay
);
tokio::time::sleep(delay).await;
} else {
error_context.clone().log_error(&e);
return Err(e);
}
}
Err(e) => {
error_context.clone().log_error(&e);
return Err(e);
}
}
}
}
async fn stream_completion_with_tools(
&mut self,
mut request: CompletionRequest,
show_timing: bool,
) -> Result<TaskResult> {
use crate::error_handling::ErrorContext;
use tokio_stream::StreamExt;
debug!("Starting stream_completion_with_tools");
let mut full_response = String::new();
let mut first_token_time: Option<Duration> = None;
let stream_start = Instant::now();
let mut iteration_count = 0;
const MAX_ITERATIONS: usize = 400; // Prevent infinite loops
let mut response_started = false;
let mut any_tool_executed = false; // Track if ANY tool was executed across all iterations
let mut auto_summary_attempts = 0; // Track auto-summary prompt attempts
const MAX_AUTO_SUMMARY_ATTEMPTS: usize = 5; // Limit auto-summary retries (increased from 2 for better recovery)
//
// Note: Session-level duplicate tracking was removed - we only prevent sequential duplicates (DUP IN CHUNK, DUP IN MSG)
let mut turn_accumulated_usage: Option<g3_providers::Usage> = None; // Track token usage for timing footer
// Check if we need to compact before starting
if self.context_window.should_compact() {
// First try thinning if we are at capacity, don't call the LLM for compaction (might fail)
if self.context_window.percentage_used() > 90.0 && self.context_window.should_thin() {
self.ui_writer.print_context_status(&format!(
"\n🥒 Context window at {}%. Trying thinning first...",
self.context_window.percentage_used() as u32
));
let thin_summary = self.do_thin_context();
self.ui_writer.print_context_thinning(&thin_summary);
// Check if thinning was sufficient
if !self.context_window.should_compact() {
self.ui_writer.print_context_status(
"✅ Thinning resolved capacity issue. Continuing...\n",
);
// Continue with the original request without compaction
} else {
self.ui_writer.print_context_status(
"⚠️ Thinning insufficient. Proceeding with compaction...\n",
);
}
}
// Only proceed with compaction if still needed after thinning
if self.context_window.should_compact() {
use crate::compaction::{CompactionConfig, perform_compaction};
// Notify user about compaction
self.ui_writer.print_context_status(&format!(
"\n🗜️ Context window reaching capacity ({}%). Compacting...",
self.context_window.percentage_used() as u32
));
let provider = self.providers.get(None)?;
let provider_name = provider.name().to_string();
let _ = provider; // Release borrow early
// Extract the latest user message from the request (not context_window)
let latest_user_msg = request
.messages
.iter()
.rev()
.find(|m| matches!(m.role, MessageRole::User))
.map(|m| m.content.clone());
let compaction_config = CompactionConfig {
provider_name: &provider_name,
latest_user_msg,
};
let result = perform_compaction(
&self.providers,
&mut self.context_window,
&self.config,
compaction_config,
&self.ui_writer,
&mut self.thinning_events,
).await?;
if result.success {
self.ui_writer.print_context_status(
"✅ Context compacted successfully. Continuing...\n",
);
self.compaction_events.push(result.chars_saved);
// Update the request with new context
request.messages = self.context_window.conversation_history.clone();
} else {
self.ui_writer.print_context_status("⚠️ Unable to compact context. Consider starting a new session if you continue to see errors.\n");
// Don't continue with the original request if compaction failed
// as we're likely at token limit
return Err(anyhow::anyhow!("Context window at capacity and compaction failed. Please start a new session."));
}
}
}
loop {
iteration_count += 1;
debug!("Starting iteration {}", iteration_count);
if iteration_count > MAX_ITERATIONS {
warn!("Maximum iterations reached, stopping stream");
break;
}
// Add a small delay between iterations to prevent "model busy" errors
if iteration_count > 1 {
tokio::time::sleep(tokio::time::Duration::from_millis(50)).await;
}
// Get provider info for logging, then drop it to avoid borrow issues
let (provider_name, provider_model) = {
let provider = self.providers.get(None)?;
(provider.name().to_string(), provider.model().to_string())
};
debug!("Got provider: {}", provider_name);
// Create error context for detailed logging
let last_prompt = request
.messages
.iter()
.rev()
.find(|m| matches!(m.role, MessageRole::User))
.map(|m| m.content.clone())
.unwrap_or_else(|| "No user message found".to_string());
let error_context = ErrorContext::new(
"stream_completion".to_string(),
provider_name.clone(),
provider_model.clone(),
last_prompt,
self.session_id.clone(),
self.context_window.used_tokens,
self.quiet,
)
.with_request(
serde_json::to_string(&request)
.unwrap_or_else(|_| "Failed to serialize request".to_string()),
);
// Log initial request details
debug!("Starting stream with provider={}, model={}, messages={}, tools={}, max_tokens={:?}",
provider_name,
provider_model,
request.messages.len(),
request.tools.is_some(),
request.max_tokens
);
// Try to get stream with retry logic
let mut stream = match self.stream_with_retry(&request, &error_context).await {
Ok(s) => s,
Err(e) => {
error!("Failed to start stream: {}", e);
// Additional retry for "busy" errors on subsequent iterations
if iteration_count > 1 && e.to_string().contains("busy") {
warn!(
"Model busy on iteration {}, attempting one more retry in 500ms",
iteration_count
);
tokio::time::sleep(tokio::time::Duration::from_millis(500)).await;
match self.stream_with_retry(&request, &error_context).await {
Ok(s) => s,
Err(e2) => {
error!("Failed to start stream after retry: {}", e2);
error_context.clone().log_error(&e2);
return Err(e2);
}
}
} else {
return Err(e);
}
}
};
// Write context window summary every time we send messages to LLM
self.write_context_window_summary();
let mut parser = StreamingToolParser::new();
let mut current_response = String::new();
let mut tool_executed = false;
let mut chunks_received = 0;
let mut raw_chunks: Vec<String> = Vec::new(); // Store raw chunks for debugging
let mut _last_error: Option<String> = None;
let mut accumulated_usage: Option<g3_providers::Usage> = None;
let mut stream_stop_reason: Option<String> = None; // Track why the stream stopped
while let Some(chunk_result) = stream.next().await {
match chunk_result {
Ok(chunk) => {
// Notify UI about SSE received (including pings)
self.ui_writer.notify_sse_received();
// Capture usage data if available
if let Some(ref usage) = chunk.usage {
accumulated_usage = Some(usage.clone());
turn_accumulated_usage = Some(usage.clone());
debug!(
"Received usage data - prompt: {}, completion: {}, total: {}",
usage.prompt_tokens, usage.completion_tokens, usage.total_tokens
);
}
// Store raw chunk for debugging (limit to first 20 and last 5)
if chunks_received < 20 || chunk.finished {
raw_chunks.push(format!(
"Chunk #{}: content={:?}, finished={}, tool_calls={:?}",
chunks_received + 1,
chunk.content,
chunk.finished,
chunk.tool_calls
));
} else if raw_chunks.len() == 20 {
raw_chunks.push("... (chunks 21+ omitted for brevity) ...".to_string());
}
// Record time to first token
if first_token_time.is_none() && !chunk.content.is_empty() {
first_token_time = Some(stream_start.elapsed());
// Record in agent metrics
if let Some(ttft) = first_token_time {
self.first_token_times.push(ttft);
}
}
chunks_received += 1;
if chunks_received == 1 {
debug!(
"First chunk received: content_len={}, finished={}",
chunk.content.len(),
chunk.finished
);
}
// Process chunk with the new parser
let completed_tools = parser.process_chunk(&chunk);
// Handle completed tool calls - process all if multiple calls enabled
// Always process all tool calls - they will be executed after stream ends
let tools_to_process: Vec<ToolCall> = completed_tools;
// Helper function to check if two tool calls are duplicates
let are_duplicates = |tc1: &ToolCall, tc2: &ToolCall| -> bool {
tc1.tool == tc2.tool && tc1.args == tc2.args
};
// De-duplicate tool calls and track duplicates
let mut last_tool_in_chunk: Option<ToolCall> = None;
let mut deduplicated_tools: Vec<(ToolCall, Option<String>)> = Vec::new();
for tool_call in tools_to_process {
let mut duplicate_type = None;
// Check for IMMEDIATELY SEQUENTIAL duplicate in current chunk
// Only the immediately previous tool call counts as a duplicate
if let Some(ref last_tool) = last_tool_in_chunk {
if are_duplicates(last_tool, &tool_call) {
duplicate_type = Some("DUP IN CHUNK".to_string());
}
} else {
// Check for duplicate against previous message
duplicate_type = self.check_duplicate_in_previous_message(&tool_call);
}
// Track the last tool call for sequential duplicate detection
last_tool_in_chunk = Some(tool_call.clone());
deduplicated_tools.push((tool_call, duplicate_type));
}
// Process each tool call
for (tool_call, duplicate_type) in deduplicated_tools {
debug!("Processing completed tool call: {:?}", tool_call);
// If it's a duplicate, log it and skip - don't set tool_executed!
// Setting tool_executed for duplicates would trigger auto-continue
// even when no actual tool execution occurred.
if let Some(dup_type) = &duplicate_type {
// Log the duplicate with red prefix
let prefixed_tool_name =
format!("🟥 {} {}", tool_call.tool, dup_type);
let warning_msg = format!(
"⚠️ Duplicate tool call detected ({}): Skipping execution of {} with args {}",
dup_type,
tool_call.tool,
serde_json::to_string(&tool_call.args).unwrap_or_else(|_| "<unserializable>".to_string())
);
// Log to tool log with red prefix
let mut modified_tool_call = tool_call.clone();
modified_tool_call.tool = prefixed_tool_name;
debug!("{}", warning_msg);
// NOTE: Do NOT call parser.reset() here!
// Resetting the parser clears the entire text buffer, which would
// lose any subsequent (non-duplicate) tool calls that haven't been
// processed yet.
continue; // Skip execution of duplicate
}
// Check if we should auto-compact at 90% BEFORE executing the tool
// We need to do this before any borrows of self
if self.auto_compact && self.context_window.percentage_used() >= 90.0 {
// Set flag to trigger compaction after this turn completes
// We can't do it now due to borrow checker constraints
self.pending_90_compaction = true;
}
// Check if we should thin the context BEFORE executing the tool
if self.context_window.should_thin() {
let thin_summary = self.do_thin_context();
// Print the thinning summary
self.ui_writer.print_context_thinning(&thin_summary);
}
// Track what we've already displayed before getting new text
// This prevents re-displaying old content after tool execution
let already_displayed_chars = current_response.chars().count();
// Get the text content accumulated so far
let text_content = parser.get_text_content();
// Clean the content
let clean_content = streaming::clean_llm_tokens(&text_content);
// Store the raw content BEFORE filtering for the context window log
let raw_content_for_log = clean_content.clone();
// Filter out JSON tool calls from the display
let filtered_content =
self.ui_writer.filter_json_tool_calls(&clean_content);
let final_display_content = filtered_content.trim();
// Display any new content before tool execution
// We need to skip what was already shown (tracked in current_response)
// but also account for the fact that parser.text_buffer accumulates
// across iterations and is never cleared until reset()
let new_content =
if current_response.len() <= final_display_content.len() {
// Only show content that hasn't been displayed yet
final_display_content
.chars()
.skip(already_displayed_chars)
.collect::<String>()
} else {
// Nothing new to display
String::new()
};
// Display any new text content
if !new_content.trim().is_empty() {
#[allow(unused_assignments)]
if !response_started {
self.ui_writer.print_agent_prompt();
response_started = true;
}
self.ui_writer.print_agent_response(&new_content);
self.ui_writer.flush();
// Update current_response to track what we've displayed
current_response.push_str(&new_content);
}
// Execute the tool with formatted output
// Finish streaming markdown before showing tool output
self.ui_writer.finish_streaming_markdown();
// Tool call header
self.ui_writer.print_tool_header(&tool_call.tool, Some(&tool_call.args));
if let Some(args_obj) = tool_call.args.as_object() {
for (key, value) in args_obj {
let value_str = match value {
serde_json::Value::String(s) => {
if tool_call.tool == "shell" && key == "command" {
if let Some(first_line) = s.lines().next() {
if s.lines().count() > 1 {
format!("{}...", first_line)
} else {
first_line.to_string()
}
} else {
s.clone()
}
} else if s.chars().count() > 100 {
streaming::truncate_for_display(s, 100)
} else {
s.clone()
}
}
_ => value.to_string(),
};
self.ui_writer.print_tool_arg(key, &value_str);
}
}
self.ui_writer.print_tool_output_header();
// Clone working_dir to avoid borrow checker issues
let working_dir = self.working_dir.clone();
let exec_start = Instant::now();
// Add 8-minute timeout for tool execution
let tool_result = match tokio::time::timeout(
Duration::from_secs(8 * 60), // 8 minutes
// Use working_dir if set (from --codebase-fast-start)
self.execute_tool_in_dir(&tool_call, working_dir.as_deref()),
)
.await
{
Ok(result) => result?,
Err(_) => {
warn!("Tool call {} timed out after 8 minutes", tool_call.tool);
"❌ Tool execution timed out after 8 minutes".to_string()
}
};
let exec_duration = exec_start.elapsed();
// Track tool call metrics
let tool_success = !tool_result.contains("❌");
self.tool_call_metrics.push((
tool_call.tool.clone(),
exec_duration,
tool_success,
));
// Display tool execution result with proper indentation
{
let output_lines: Vec<&str> = tool_result.lines().collect();
// Check if UI wants full output (machine mode) or truncated (human mode)
let wants_full = self.ui_writer.wants_full_output();
const MAX_LINES: usize = 5;
const MAX_LINE_WIDTH: usize = 80;
let output_len = output_lines.len();
// Skip printing for todo tools - they already print their content
let is_todo_tool =
tool_call.tool == "todo_read" || tool_call.tool == "todo_write";
if !is_todo_tool {
let max_lines_to_show = if wants_full { output_len } else { MAX_LINES };
for (idx, line) in output_lines.iter().enumerate() {
if !wants_full && idx >= max_lines_to_show {
break;
}
let clipped_line = streaming::truncate_line(line, MAX_LINE_WIDTH, !wants_full);
self.ui_writer.update_tool_output_line(&clipped_line);
}
if !wants_full && output_len > MAX_LINES {
self.ui_writer.print_tool_output_summary(output_len);
}
}
}
// Add the tool call and result to the context window using RAW unfiltered content
// This ensures the log file contains the true raw content including JSON tool calls
let tool_message = if !raw_content_for_log.trim().is_empty() {
Message::new(
MessageRole::Assistant,
format!(
"{}\n\n{{\"tool\": \"{}\", \"args\": {}}}",
raw_content_for_log.trim(),
tool_call.tool,
tool_call.args
),
)
} else {
// No text content before tool call, just include the tool call
Message::new(
MessageRole::Assistant,
format!(
"{{\"tool\": \"{}\", \"args\": {}}}",
tool_call.tool, tool_call.args
),
)
};
let mut result_message = {
// Check if we should use cache control (every 10 tool calls)
// But only if we haven't already added 4 cache_control annotations
if self.tool_call_count > 0
&& self.tool_call_count % 10 == 0
&& self.count_cache_controls_in_history() < 4
{
let provider = self.providers.get(None)?;
let provider_name = provider.name();
let (provider_type, config_name) = provider_config::parse_provider_ref(provider_name);
if let Some(cache_config) = match provider_type {
"anthropic" => {
self.config
.providers
.anthropic
.get(config_name)
.and_then(|c| c.cache_config.as_ref())
.and_then(|config| Self::parse_cache_control(config))
}
_ => None,
} {
Message::with_cache_control_validated(
MessageRole::User,
format!("Tool result: {}", tool_result),
cache_config,
provider,
)
} else {
Message::new(
MessageRole::User,
format!("Tool result: {}", tool_result),
)
}
} else {
Message::new(
MessageRole::User,
format!("Tool result: {}", tool_result),
)
}
};
// Attach any pending images to the result message
// (images loaded via read_image tool)
if !self.pending_images.is_empty() {
result_message.images = std::mem::take(&mut self.pending_images);
}
// Track tokens before adding messages
let tokens_before = self.context_window.used_tokens;
self.context_window.add_message(tool_message);
self.context_window.add_message(result_message);
// Closure marker with timing
let tokens_delta = self.context_window.used_tokens.saturating_sub(tokens_before);
self.ui_writer
.print_tool_timing(&Self::format_duration(exec_duration),
tokens_delta,
self.context_window.percentage_used());
self.ui_writer.print_agent_prompt();
// Update the request with the new context for next iteration
request.messages = self.context_window.conversation_history.clone();
// Ensure tools are included for native providers in subsequent iterations
let provider_for_tools = self.providers.get(None)?;
if provider_for_tools.has_native_tool_calling() {
let mut tool_config = tool_definitions::ToolConfig::new(
self.config.webdriver.enabled,
self.config.computer_control.enabled,
);
// Exclude research tool for scout agent to prevent recursion
if self.agent_name.as_deref() == Some("scout") {
tool_config = tool_config.with_research_excluded();
}
request.tools = Some(tool_definitions::create_tool_definitions(tool_config));
}
// DO NOT add final_display_content to full_response here!
// The content was already displayed during streaming and added to current_response.
// Adding it again would cause duplication when the agent message is printed.
// The only time we should add to full_response is:
// 1. At the end when no tools were executed
// 2. At the end when no tools were executed (handled in the "no tool executed" branch)
tool_executed = true;
any_tool_executed = true; // Track across all iterations
// Reset auto-continue attempts after successful tool execution
// This gives the LLM fresh attempts since it's making progress
auto_summary_attempts = 0;
// Reset the JSON tool call filter state after each tool execution
// This ensures the filter doesn't stay in suppression mode for subsequent streaming content
self.ui_writer.reset_json_filter();
// Only reset parser if there are no more unexecuted tool calls in the buffer
// This handles the case where the LLM emits multiple tool calls in one response
if parser.has_unexecuted_tool_call() {
debug!("Parser still has unexecuted tool calls, not resetting buffer");
// Mark current tool as consumed so we don't re-detect it
parser.mark_tool_calls_consumed();
} else {
// Reset parser for next iteration - this clears the text buffer
parser.reset();
}
// Clear current_response for next iteration to prevent buffered text
// from being incorrectly displayed after tool execution
current_response.clear();
// Reset response_started flag for next iteration
response_started = false;
// Continue processing - don't break mid-stream
} // End of for loop processing each tool call
// Note: We no longer break mid-stream after tool execution.
// All tool calls are collected and executed after the stream ends.
// If no tool calls were completed, continue streaming normally
if !tool_executed {
let clean_content = streaming::clean_llm_tokens(&chunk.content);
if !clean_content.is_empty() {
let filtered_content =
self.ui_writer.filter_json_tool_calls(&clean_content);
if !filtered_content.is_empty() {
if !response_started {
self.ui_writer.print_agent_prompt();
response_started = true;
}
self.ui_writer.print_agent_response(&filtered_content);
self.ui_writer.flush();
current_response.push_str(&filtered_content);
// Mark parser buffer as consumed up to current position
// This prevents tool-call-like patterns in displayed text
// from triggering false positives in has_unexecuted_tool_call()
parser.mark_tool_calls_consumed();
}
}
}
if chunk.finished {
debug!("Stream finished: tool_executed={}, current_response_len={}, full_response_len={}, chunks_received={}",
tool_executed, current_response.len(), full_response.len(), chunks_received);
// Capture the stop reason from the final chunk
if let Some(ref reason) = chunk.stop_reason {
debug!("Stream stop_reason: {}", reason);
stream_stop_reason = Some(reason.clone());
}
// Stream finished - check if we should continue or return
if !tool_executed {
// No tools were executed in this iteration
// Check if we got any meaningful response at all
// We need to check the parser's text buffer as well, since the LLM
// might have responded with text but no tool calls
let text_content = parser.get_text_content();
let has_text_response = !text_content.trim().is_empty()
|| !current_response.trim().is_empty();
// Don't re-add text from parser buffer if we already displayed it
// The parser buffer contains ALL accumulated text, but current_response
// already has what was displayed during streaming
if current_response.is_empty() && !text_content.trim().is_empty() {
// Only use parser text if we truly have no response
// This should be rare - only if streaming failed to display anything
debug!("Warning: Using parser buffer text as fallback - this may duplicate output");
// Extract only the undisplayed portion from parser buffer
// Parser buffer accumulates across iterations, so we need to be careful
let clean_text = streaming::clean_llm_tokens(&text_content);
let filtered_text =
self.ui_writer.filter_json_tool_calls(&clean_text);
// Only use this if we truly have nothing else
if !filtered_text.trim().is_empty() && full_response.is_empty()
{
debug!(
"Using filtered parser text as last resort: {} chars",
filtered_text.len()
);
// Note: This assignment is currently unused but kept for potential future use
let _ = filtered_text;
}
}
if !has_text_response && full_response.is_empty() {
streaming::log_stream_error(
iteration_count,
&provider_name,
&provider_model,
chunks_received,
&parser,
&request,
&self.context_window,
self.session_id.as_deref(),
&raw_chunks,
);
// No response received - this is an error condition
warn!("Stream finished without any content or tool calls");
warn!("Chunks received: {}", chunks_received);
return Err(anyhow::anyhow!(
"No response received from the model. The model may be experiencing issues or the request may have been malformed."
));
}
// If tools were executed in previous iterations,
// break to let the outer loop's auto-continue logic handle it
if any_tool_executed {
debug!("Tools were executed, continuing - breaking to auto-continue");
// IMPORTANT: Save any text response to context window before breaking
// This ensures text displayed after tool execution is not lost
if !current_response.trim().is_empty() {
debug!("Saving current_response ({} chars) to context before auto-continue", current_response.len());
let assistant_msg = Message::new(
MessageRole::Assistant,
current_response.clone(),
);
self.context_window.add_message(assistant_msg);
}
// NOTE: We intentionally do NOT set full_response here.
// The content was already displayed during streaming.
// Setting full_response would cause duplication when the
// function eventually returns.
// Context window is updated separately via add_message().
break;
}
// Set full_response to current_response (don't append)
// current_response already contains everything that was displayed
// Don't set full_response here - it would duplicate the output
// The text was already displayed during streaming
// Return empty string to avoid duplication
full_response = String::new();
// Finish the streaming markdown formatter before returning
self.ui_writer.finish_streaming_markdown();
// Save context window BEFORE returning
self.save_context_window("completed");
let _ttft =
first_token_time.unwrap_or_else(|| stream_start.elapsed());
// Add timing if needed
let final_response = if show_timing {
let turn_tokens = turn_accumulated_usage.as_ref().map(|u| u.total_tokens);
let timing_footer = Self::format_timing_footer(
stream_start.elapsed(),
_ttft,
turn_tokens,
self.context_window.percentage_used(),
);
format!(
"{}\n\n{}",
full_response,
timing_footer
)
} else {
full_response
};
return Ok(TaskResult::new(
final_response,
self.context_window.clone(),
));
}
break; // Tool was executed, break to continue outer loop
}
}
Err(e) => {
// Capture detailed streaming error information
let error_msg = e.to_string();
let error_details = format!(
"Streaming error at chunk {}: {}",
chunks_received + 1,
error_msg
);
error!("Error type: {}", std::any::type_name_of_val(&e));
error!("Parser state at error: text_buffer_len={}, has_incomplete={}, message_stopped={}",
parser.text_buffer_len(), parser.has_incomplete_tool_call(), parser.is_message_stopped());
// Store the error for potential logging later
_last_error = Some(error_details.clone());
// Check if this is a recoverable connection error
let is_connection_error = streaming::is_connection_error(&error_msg);
if is_connection_error {
warn!(
"Connection error at chunk {}, treating as end of stream",
chunks_received + 1
);
// If we have any content or tool calls, treat this as a graceful end
if chunks_received > 0
&& (!parser.get_text_content().is_empty()
|| parser.has_unexecuted_tool_call())
{
warn!("Stream terminated unexpectedly but we have content, continuing");
break; // Break to process what we have
}
}
if tool_executed {
error!("{}", error_details);
warn!("Stream error after tool execution, attempting to continue");
break; // Break to outer loop to start new stream
} else {
// Log raw chunks before failing
error!("Fatal streaming error. Raw chunks received before error:");
for chunk_str in raw_chunks.iter().take(10) {
error!(" {}", chunk_str);
}
return Err(e);
}
}
}
}
// Update context window with actual usage if available
if let Some(usage) = accumulated_usage {
debug!("Updating context window with actual usage from stream");
self.context_window.update_usage_from_response(&usage);
} else {
// Fall back to estimation if no usage data was provided
debug!("No usage data from stream, using estimation");
let estimated_tokens = ContextWindow::estimate_tokens(&current_response);
self.context_window.add_streaming_tokens(estimated_tokens);
}
// If we get here and no tool was executed, we're done
if !tool_executed {
// IMPORTANT: Do NOT add parser text_content here!
// The text has already been displayed during streaming via current_response.
// The parser buffer accumulates ALL text and would cause duplication.
debug!("Stream completed without tool execution. Response already displayed during streaming.");
debug!(
"Current response length: {}, Full response length: {}",
current_response.len(),
full_response.len()
);
let has_response = !current_response.is_empty() || !full_response.is_empty();
// Check if the response is essentially empty (just whitespace or timing lines)
// This detects cases where the LLM outputs nothing substantive
let response_text = if !current_response.is_empty() {
&current_response
} else {
&full_response
};
let is_empty_response = streaming::is_empty_response(response_text);
// Check if there's an incomplete tool call in the buffer
let has_incomplete_tool_call = parser.has_incomplete_tool_call();
// Check if there's a complete but unexecuted tool call in the buffer
let has_unexecuted_tool_call = parser.has_unexecuted_tool_call();
// Log when we detect unexecuted or incomplete tool calls for debugging
if has_incomplete_tool_call {
debug!("Detected incomplete tool call in buffer (buffer_len={}, consumed_up_to={})",
parser.text_buffer_len(), parser.text_buffer_len());
}
if has_unexecuted_tool_call {
debug!("Detected unexecuted tool call in buffer - this may indicate a parsing issue");
warn!("Unexecuted tool call detected in buffer after stream ended");
}
// Check if the response was truncated due to max_tokens
let was_truncated_by_max_tokens = stream_stop_reason.as_deref() == Some("max_tokens");
if was_truncated_by_max_tokens {
debug!("Response was truncated due to max_tokens limit");
warn!("LLM response was cut off due to max_tokens limit - will auto-continue");
}
// Auto-continue if tools were executed and we are in autonomous mode
// OR if the LLM emitted an incomplete tool call (truncated JSON)
// OR if the LLM emitted a complete tool call that wasn't executed
// OR if the response was truncated due to max_tokens
// This ensures we don't return control when the LLM clearly intended to call a tool
// Note: We removed the redundant condition (any_tool_executed && is_empty_response)
// because it's already covered by (any_tool_executed )
// Auto-continue is only enabled in autonomous mode - in interactive mode,
// the user may be asking questions and we should return control to them
let should_auto_continue = self.is_autonomous && ((any_tool_executed )
|| has_incomplete_tool_call
|| has_unexecuted_tool_call
|| was_truncated_by_max_tokens);
if should_auto_continue {
if auto_summary_attempts < MAX_AUTO_SUMMARY_ATTEMPTS {
auto_summary_attempts += 1;
if has_incomplete_tool_call {
warn!(
"LLM emitted incomplete tool call ({} iterations, auto-continue attempt {}/{})",
iteration_count, auto_summary_attempts, MAX_AUTO_SUMMARY_ATTEMPTS
);
self.ui_writer.print_context_status(
"\n🔄 Model emitted incomplete tool call. Auto-continuing...\n"
);
} else if has_unexecuted_tool_call {
warn!(
"LLM emitted unexecuted tool call ({} iterations, auto-continue attempt {}/{})",
iteration_count, auto_summary_attempts, MAX_AUTO_SUMMARY_ATTEMPTS
);
self.ui_writer.print_context_status(
"\n🔄 Model emitted tool call that wasn't executed. Auto-continuing...\n"
);
} else if is_empty_response {
warn!(
"LLM emitted empty/trivial response ({} iterations, auto-continue attempt {}/{})",
iteration_count, auto_summary_attempts, MAX_AUTO_SUMMARY_ATTEMPTS
);
self.ui_writer.print_context_status(
"\n🔄 Model emitted empty response. Auto-continuing...\n"
);
} else {
warn!(
"LLM stopped after executing tools ({} iterations, auto-continue attempt {}/{})",
iteration_count, auto_summary_attempts, MAX_AUTO_SUMMARY_ATTEMPTS
);
self.ui_writer.print_context_status(
"\n🔄 Model stopped without providing summary. Auto-continuing...\n"
);
}
// Add any text response to context before prompting for continuation
if has_response {
let response_text = if !current_response.is_empty() {
current_response.clone()
} else {
full_response.clone()
};
if !response_text.trim().is_empty() {
let assistant_msg = Message::new(
MessageRole::Assistant,
response_text.trim().to_string(),
);
self.context_window.add_message(assistant_msg);
}
}
// Add a follow-up message asking for continuation
let continue_prompt = if has_incomplete_tool_call {
Message::new(
MessageRole::User,
"Your previous response was cut off mid-tool-call. Please complete the tool call and continue.".to_string(),
)
} else {
Message::new(
MessageRole::User,
"Please continue until you are done. Provide a summary when complete.".to_string(),
)
};
self.context_window.add_message(continue_prompt);
request.messages = self.context_window.conversation_history.clone();
// Continue the loop
continue;
} else {
// Max attempts reached, give up gracefully
warn!(
"Max auto-continue attempts ({}) reached after {} iterations. Conditions: any_tool_executed={}, has_incomplete={}, has_unexecuted={}, is_empty_response={}",
MAX_AUTO_SUMMARY_ATTEMPTS,
iteration_count,
any_tool_executed,
has_incomplete_tool_call,
has_unexecuted_tool_call,
is_empty_response
);
self.ui_writer.print_agent_response(
&format!("\n⚠️ The model stopped without providing a summary after {} auto-continue attempts.\n", MAX_AUTO_SUMMARY_ATTEMPTS)
);
}
} else if has_response {
// Only set full_response if it's empty (first iteration without tools)
// This prevents duplication when the agent responds
// NOTE: We intentionally do NOT set full_response here anymore.
// The content was already displayed during streaming via print_agent_response().
// Setting full_response would cause the CLI to print it again.
// We only need full_response for the context window (handled separately).
debug!(
"Response already streamed, not setting full_response. current_response: {} chars",
current_response.len()
);
}
let _ttft = first_token_time.unwrap_or_else(|| stream_start.elapsed());
// Add the RAW unfiltered response to context window before returning
// This ensures the log contains the true raw content including any JSON
if !full_response.trim().is_empty() {
// Get the raw text from the parser (before filtering)
let raw_text = parser.get_text_content();
let raw_clean = streaming::clean_llm_tokens(&raw_text);
if !raw_clean.trim().is_empty() {
let assistant_message = Message::new(MessageRole::Assistant, raw_clean);
self.context_window.add_message(assistant_message);
}
}
// Save context window BEFORE returning
self.save_context_window("completed");
// Add timing if needed
let final_response = if show_timing {
let turn_tokens = turn_accumulated_usage.as_ref().map(|u| u.total_tokens);
let timing_footer = Self::format_timing_footer(
stream_start.elapsed(),
_ttft,
turn_tokens,
self.context_window.percentage_used(),
);
format!(
"{}\n\n{}",
full_response,
timing_footer
)
} else {
full_response
};
return Ok(TaskResult::new(final_response, self.context_window.clone()));
}
// Continue the loop to start a new stream with updated context
}
// If we exit the loop due to max iterations
let _ttft = first_token_time.unwrap_or_else(|| stream_start.elapsed());
// Add timing if needed
let final_response = if show_timing {
let turn_tokens = turn_accumulated_usage.as_ref().map(|u| u.total_tokens);
let timing_footer = Self::format_timing_footer(
stream_start.elapsed(),
_ttft,
turn_tokens,
self.context_window.percentage_used(),
);
format!(
"{}\n\n{}",
full_response,
timing_footer
)
} else {
full_response
};
Ok(TaskResult::new(final_response, self.context_window.clone()))
}
pub async fn execute_tool(&mut self, tool_call: &ToolCall) -> Result<String> {
// Tool tracking is handled by execute_tool_in_dir
self.execute_tool_in_dir(tool_call, None).await
}
/// Execute a tool with an optional working directory (for discovery commands)
pub async fn execute_tool_in_dir(
&mut self,
tool_call: &ToolCall,
working_dir: Option<&str>,
) -> Result<String> {
// Always track tool calls for auto-memory feature
self.tool_call_count += 1;
self.tool_calls_this_turn.push(tool_call.tool.clone());
let result = self.execute_tool_inner_in_dir(tool_call, working_dir).await;
let log_str = match &result {
Ok(s) => s.clone(),
Err(e) => format!("ERROR: {}", e),
};
debug!("Tool {} completed: {}", tool_call.tool, &log_str.chars().take(100).collect::<String>());
result
}
async fn execute_tool_inner_in_dir(
&mut self,
tool_call: &ToolCall,
working_dir: Option<&str>,
) -> Result<String> {
debug!("=== EXECUTING TOOL ===");
debug!("Tool name: {}", tool_call.tool);
debug!(
"Working directory passed to execute_tool_inner_in_dir: {:?}",
working_dir
);
debug!("Tool args (raw): {:?}", tool_call.args);
debug!(
"Tool args (JSON): {}",
serde_json::to_string(&tool_call.args)
.unwrap_or_else(|_| "failed to serialize".to_string())
);
debug!("======================");
// Create tool context for dispatch
let mut ctx = tools::executor::ToolContext {
config: &self.config,
ui_writer: &self.ui_writer,
session_id: self.session_id.as_deref(),
working_dir,
computer_controller: self.computer_controller.as_ref(),
webdriver_session: &self.webdriver_session,
webdriver_process: &self.webdriver_process,
background_process_manager: &self.background_process_manager,
todo_content: &self.todo_content,
pending_images: &mut self.pending_images,
is_autonomous: self.is_autonomous,
requirements_sha: self.requirements_sha.as_deref(),
context_total_tokens: self.context_window.total_tokens,
context_used_tokens: self.context_window.used_tokens,
};
// Dispatch to the appropriate tool handler
let result = tool_dispatch::dispatch_tool(tool_call, &mut ctx).await?;
Ok(result)
}
fn format_duration(duration: Duration) -> String {
streaming::format_duration(duration)
}
fn format_timing_footer(
elapsed: Duration,
ttft: Duration,
turn_tokens: Option<u32>,
context_percentage: f32,
) -> String {
streaming::format_timing_footer(elapsed, ttft, turn_tokens, context_percentage)
}
}
// Re-export utility functions
pub use utils::apply_unified_diff_to_string;
/// Truncate a string to approximately max_len characters, ending at a word boundary
fn truncate_to_word_boundary(s: &str, max_len: usize) -> String {
if s.len() <= max_len {
return s.to_string();
}
// Find the last space before max_len
let truncated = &s[..max_len];
if let Some(last_space) = truncated.rfind(' ') {
if last_space > max_len / 2 {
// Only use word boundary if it's not too short
return format!("{}...", &s[..last_space]);
}
}
// Fall back to character truncation
format!("{}...", truncated)
}
// Implement Drop to clean up safaridriver process
impl<W: UiWriter> Drop for Agent<W> {
fn drop(&mut self) {
// Validate system prompt invariant on drop (agent exit)
// This catches any bugs where the conversation history was corrupted during execution
if !self.context_window.conversation_history.is_empty() {
if let Err(e) = std::panic::catch_unwind(std::panic::AssertUnwindSafe(|| {
self.validate_system_prompt_is_first();
})) {
eprintln!(
"\n⚠️ FATAL ERROR ON EXIT: System prompt validation failed: {:?}",
e
);
}
}
// Try to kill safaridriver process if it's still running
// We need to use try_lock since we can't await in Drop
if let Ok(mut process_guard) = self.webdriver_process.try_write() {
if let Some(process) = process_guard.take() {
// Use blocking kill since we can't await in Drop
// This is a best-effort cleanup
let _ = std::process::Command::new("kill")
.arg("-9")
.arg(process.id().unwrap_or(0).to_string())
.output();
debug!("Attempted to clean up safaridriver process on Agent drop");
}
}
}
}
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