bamboo_engine/runtime/config.rs
1use std::collections::BTreeSet;
2use std::path::PathBuf;
3use std::sync::Arc;
4
5use crate::metrics::MetricsCollector;
6use crate::skills::SkillManager;
7use bamboo_agent_core::composition::CompositionExecutor;
8use bamboo_agent_core::storage::AttachmentReader;
9use bamboo_agent_core::storage::Storage;
10use bamboo_agent_core::tools::ToolSchema;
11use bamboo_agent_core::GoldConfidence;
12use bamboo_compression::TokenBudget;
13use bamboo_domain::ReasoningEffort;
14use bamboo_domain::RuntimeSessionPersistence;
15use bamboo_infrastructure::config::PermissionMode;
16use bamboo_infrastructure::LLMProvider;
17use bamboo_infrastructure::MemoryConfig;
18use bamboo_tools::ToolRegistry;
19use serde::{Deserialize, Serialize};
20
21#[derive(Clone, Default)]
22pub struct AuxiliaryModelConfig {
23 pub fast_model_name: Option<String>,
24 pub fast_model_provider: Option<Arc<dyn LLMProvider>>,
25 pub background_model_name: Option<String>,
26 pub planning_model_name: Option<String>,
27 pub search_model_name: Option<String>,
28 pub summarization_model_name: Option<String>,
29 pub background_model_provider: Option<Arc<dyn LLMProvider>>,
30 pub summarization_model_provider: Option<Arc<dyn LLMProvider>>,
31}
32
33fn default_gold_max_output_tokens() -> u32 {
34 1024
35}
36
37fn default_gold_max_auto_continuations() -> u32 {
38 3
39}
40
41fn default_gold_min_confidence() -> GoldConfidence {
42 GoldConfidence::Medium
43}
44
45#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
46#[serde(default)]
47pub struct GoldConfig {
48 /// Master switch for Gold observe-only evaluation.
49 #[serde(default)]
50 pub enabled: bool,
51 /// Independent switch for Phase 2 low-risk auto-answer.
52 ///
53 /// Kept separate from `enabled` so Phase 1 observe-only users do not
54 /// implicitly opt into automatic clarification responses.
55 #[serde(default)]
56 pub auto_answer_enabled: bool,
57 /// Independent switch for Phase 3 server-side auto-continue.
58 ///
59 /// Kept separate from both `enabled` and `auto_answer_enabled` so users can
60 /// opt into terminal auto-resume explicitly without enabling other Gold
61 /// automation behaviors.
62 #[serde(default)]
63 pub auto_continue_enabled: bool,
64 /// Optional dedicated model for Gold evaluation. Falls back to fast model,
65 /// then the main chat model when absent.
66 #[serde(default, skip_serializing_if = "Option::is_none")]
67 pub model_name: Option<String>,
68 /// The user's goal for this session.
69 ///
70 /// Unlike `evaluation_prompt` (which only tunes the *judge*), the goal is
71 /// surfaced to the *main* executing agent as a persistent system-prompt
72 /// block so it actively works toward it. The Gold evaluator also measures
73 /// progress against this text.
74 #[serde(default, skip_serializing_if = "Option::is_none")]
75 pub goal: Option<String>,
76 /// Optional custom prompt suffix appended to the built-in Gold evaluator
77 /// prompt. This tunes the judge only; it does not set the goal.
78 #[serde(default, skip_serializing_if = "Option::is_none")]
79 pub evaluation_prompt: Option<String>,
80 /// Output token limit for the Gold evaluator call.
81 #[serde(default = "default_gold_max_output_tokens")]
82 pub max_output_tokens: u32,
83 /// Maximum number of automatic Gold continuations allowed per session.
84 #[serde(default = "default_gold_max_auto_continuations")]
85 pub max_auto_continuations: u32,
86 /// Minimum evaluator confidence required before Gold auto-continues or
87 /// auto-answers. Defaults to `medium` so the loop fires on reasonably
88 /// confident verdicts rather than only `high`.
89 #[serde(default = "default_gold_min_confidence")]
90 pub min_auto_continue_confidence: GoldConfidence,
91}
92
93impl Default for GoldConfig {
94 fn default() -> Self {
95 Self {
96 enabled: false,
97 auto_answer_enabled: false,
98 auto_continue_enabled: false,
99 model_name: None,
100 goal: None,
101 evaluation_prompt: None,
102 max_output_tokens: default_gold_max_output_tokens(),
103 max_auto_continuations: default_gold_max_auto_continuations(),
104 min_auto_continue_confidence: default_gold_min_confidence(),
105 }
106 }
107}
108
109impl GoldConfig {
110 /// The session goal text, falling back to the legacy `evaluation_prompt`
111 /// for sessions created before the dedicated `goal` field existed.
112 ///
113 /// Returns `None` when neither field holds non-empty text.
114 pub fn effective_goal(&self) -> Option<&str> {
115 self.goal
116 .as_deref()
117 .or(self.evaluation_prompt.as_deref())
118 .map(str::trim)
119 .filter(|value| !value.is_empty())
120 }
121}
122
123#[derive(Debug, Clone, Copy, PartialEq, Eq)]
124pub enum ImageFallbackMode {
125 Placeholder,
126 Error,
127 Ocr,
128 /// Use a vision-capable LLM to describe the image, then replace the image
129 /// with the textual description so that text-only models can understand
130 /// the content.
131 Vision,
132}
133
134#[derive(Debug, Clone, PartialEq, Eq)]
135pub struct ImageFallbackConfig {
136 pub mode: ImageFallbackMode,
137 /// Vision model name for `Vision` mode. Falls back to the session's main model
138 /// when `None`.
139 pub vision_model: Option<String>,
140}
141
142#[derive(Debug, Clone, Copy, PartialEq, Eq)]
143pub struct PromptMemoryFlags {
144 pub project_prompt_injection: bool,
145 pub relevant_recall: bool,
146 pub relevant_recall_rerank: bool,
147 pub project_first_dream: bool,
148}
149
150impl Default for PromptMemoryFlags {
151 fn default() -> Self {
152 Self {
153 project_prompt_injection: true,
154 relevant_recall: true,
155 relevant_recall_rerank: false,
156 project_first_dream: true,
157 }
158 }
159}
160
161impl From<&MemoryConfig> for PromptMemoryFlags {
162 fn from(value: &MemoryConfig) -> Self {
163 Self {
164 project_prompt_injection: value.project_prompt_injection,
165 relevant_recall: value.relevant_recall,
166 relevant_recall_rerank: value.relevant_recall_rerank,
167 project_first_dream: value.project_first_dream,
168 }
169 }
170}
171
172/// Configuration for the agent loop.
173pub struct AgentLoopConfig {
174 pub max_rounds: usize,
175 pub system_prompt: Option<String>,
176 /// Skill IDs that are disabled globally for this execution.
177 pub disabled_skill_ids: BTreeSet<String>,
178 /// Optional explicit skill selection for this execution.
179 /// When set, only these skill IDs are considered for skill context and allowlists.
180 pub selected_skill_ids: Option<Vec<String>>,
181 /// Optional active skill mode for this execution.
182 ///
183 /// When set, skill discovery prefers `skills-<mode>` directories over generic
184 /// directories for the same skill id.
185 pub selected_skill_mode: Option<String>,
186 pub additional_tool_schemas: Vec<ToolSchema>,
187 pub tool_registry: Arc<ToolRegistry>,
188 pub composition_executor: Option<Arc<CompositionExecutor>>,
189 pub skill_manager: Option<Arc<SkillManager>>,
190 /// If true, skip appending the initial user message (already present in session).
191 pub skip_initial_user_message: bool,
192 /// Optional storage for persisting session changes
193 pub storage: Option<Arc<dyn Storage>>,
194 /// Optional runtime persistence for non-authoritative session saves.
195 /// When set, engine save sites use this instead of `storage` for writes.
196 pub persistence: Option<Arc<dyn RuntimeSessionPersistence>>,
197 /// Optional attachment reader for resolving `bamboo-attachment://...` references
198 /// into `data:` URLs for upstream providers. This must not mutate session storage.
199 pub attachment_reader: Option<Arc<dyn AttachmentReader>>,
200 /// Optional asynchronous metrics collector
201 pub metrics_collector: Option<MetricsCollector>,
202 /// Model name used for metrics attribution
203 pub model_name: Option<String>,
204 /// Fast/cheap model for lightweight tasks (task evaluation, search, etc.).
205 ///
206 /// Call sites may fall back to `model_name` when this is unset.
207 pub fast_model_name: Option<String>,
208 /// Optional provider override for lightweight fast-model LLM calls.
209 pub fast_model_provider: Option<Arc<dyn LLMProvider>>,
210 /// Fast/cheap model for memory/background tasks.
211 ///
212 /// This must not silently fall back to the main interaction model.
213 pub background_model_name: Option<String>,
214
215 /// Model for planning/coordination tasks (task decomposition, architecture).
216 /// Falls back to `model_name` when unset.
217 pub planning_model_name: Option<String>,
218 /// Model for search/navigation tasks (grep, file listing, symbol resolution).
219 /// Falls back to `fast_model_name` when unset.
220 pub search_model_name: Option<String>,
221 /// Custom instructions for conversation summarization, injected into the
222 /// LLM summary prompt. Lets users control what the summary focuses on.
223 ///
224 /// Resolution order: session-level > config-level > built-in defaults.
225 pub compression_instructions: Option<String>,
226 /// Dedicated model for summarization. Falls back to `background_model_name`.
227 pub summarization_model_name: Option<String>,
228 /// Optional provider override for memory/background model LLM calls.
229 ///
230 /// When set, memory recall rerank and other memory/background tasks use this
231 /// provider instead of the shared agent loop provider.
232 pub background_model_provider: Option<Arc<dyn LLMProvider>>,
233 /// Optional provider override for summarization / context compression calls.
234 ///
235 /// When set, conversation/task summarization uses this provider instead of
236 /// the shared agent loop provider.
237 pub summarization_model_provider: Option<Arc<dyn LLMProvider>>,
238 /// Provider routing key used for provider-specific request behavior.
239 ///
240 /// In multi-instance mode this may be the instance id.
241 pub provider_name: Option<String>,
242 /// Underlying provider type (for example `openai`, `anthropic`, `copilot`).
243 ///
244 /// This is distinct from `provider_name` so provider-specific behavior can
245 /// remain correct when routing keys are instance ids.
246 pub provider_type: Option<String>,
247 /// Optional request-time reasoning effort override.
248 pub reasoning_effort: Option<ReasoningEffort>,
249 /// Bamboo application data directory (typically `~/.bamboo`).
250 ///
251 /// Used by runtime features that persist auxiliary artifacts outside the
252 /// session store, such as durable plan mode files under `~/.bamboo/plan`.
253 pub app_data_dir: Option<PathBuf>,
254 /// Tool names that should be excluded from schemas sent to the LLM.
255 pub disabled_tools: BTreeSet<String>,
256 /// Token budget for context management (optional, defaults to model's limits)
257 pub token_budget: Option<TokenBudget>,
258 /// Optional image fallback behavior applied to *LLM requests only* (never persisted).
259 ///
260 /// This is intended for text-only provider paths where image parts must be degraded
261 /// (placeholder / OCR / error) without leaking into stored session history or UI.
262 pub image_fallback: Option<ImageFallbackConfig>,
263 /// Feature flags controlling prompt-time memory injection behavior.
264 pub prompt_memory_flags: PromptMemoryFlags,
265 /// Maximum tool calls allowed per round (default: 80).
266 pub max_tool_calls_per_round: usize,
267 /// Maximum consecutive failures per tool before circuit breaker (default: 3).
268 pub max_consecutive_failures_per_tool: usize,
269 /// Tool names that require strict argument validation.
270 pub strict_argument_tool_names: Vec<String>,
271 /// Per-tool execution timeout in seconds (default: 120).
272 pub per_tool_timeout_secs: u64,
273 /// Parallel batch execution timeout in seconds (default: 300).
274 pub parallel_batch_timeout_secs: u64,
275 /// Permission mode for this execution (default: None = use PermissionConfig's mode).
276 pub permission_mode: Option<PermissionMode>,
277 /// Optional Gold observe-only evaluator configuration.
278 ///
279 /// When `None` or `enabled == false`, Gold evaluation is disabled and the
280 /// existing execute/respond/resume loop remains unchanged.
281 pub gold_config: Option<GoldConfig>,
282 /// Enable dynamic per-round model routing based on task complexity.
283 /// When true, the pipeline classifies complexity at each round end and
284 /// stores the result in session metadata.
285 pub features_dynamic_model_routing: bool,
286 /// Optional per-round resolver for auxiliary model settings that should
287 /// follow live global config rather than stay frozen for the whole run.
288 ///
289 /// The main chat model remains session/request scoped; this hook is only
290 /// for fast/background/planning/search/summarization helpers.
291 pub auxiliary_model_resolver: Option<Arc<dyn Fn() -> AuxiliaryModelConfig + Send + Sync>>,
292}
293
294impl Default for AgentLoopConfig {
295 fn default() -> Self {
296 Self {
297 max_rounds: 200,
298 system_prompt: None,
299 disabled_skill_ids: BTreeSet::new(),
300 selected_skill_ids: None,
301 selected_skill_mode: None,
302 additional_tool_schemas: Vec::new(),
303 tool_registry: Arc::new(ToolRegistry::new()),
304 composition_executor: None,
305 skill_manager: None,
306 skip_initial_user_message: false,
307 storage: None,
308 persistence: None,
309 attachment_reader: None,
310 metrics_collector: None,
311 model_name: None,
312 fast_model_name: None,
313 fast_model_provider: None,
314 background_model_name: None,
315 planning_model_name: None,
316 search_model_name: None,
317 compression_instructions: None,
318 summarization_model_name: None,
319 background_model_provider: None,
320 summarization_model_provider: None,
321 provider_name: None,
322 provider_type: None,
323 reasoning_effort: None,
324 app_data_dir: None,
325 disabled_tools: BTreeSet::new(),
326 token_budget: None,
327 image_fallback: None,
328 prompt_memory_flags: PromptMemoryFlags::default(),
329 max_tool_calls_per_round: 80,
330 max_consecutive_failures_per_tool: 3,
331 strict_argument_tool_names: vec![
332 "Write".into(),
333 "Edit".into(),
334 "NotebookEdit".into(),
335 "apply_patch".into(),
336 "Bash".into(),
337 "Task".into(),
338 "SubAgent".into(),
339 "scheduler".into(),
340 "sub_session_manager".into(),
341 "session_note".into(),
342 "memory_note".into(),
343 ],
344 per_tool_timeout_secs: 120,
345 parallel_batch_timeout_secs: 300,
346 permission_mode: None,
347 gold_config: None,
348 features_dynamic_model_routing: false,
349 auxiliary_model_resolver: None,
350 }
351 }
352}
353
354impl AgentLoopConfig {
355 /// The active session goal to surface to the main agent, or `None` when
356 /// Gold is disabled or no goal is set. Falls back to the legacy
357 /// `evaluation_prompt` for back-compat via [`GoldConfig::effective_goal`].
358 pub fn active_goal(&self) -> Option<&str> {
359 self.gold_config
360 .as_ref()
361 .filter(|cfg| cfg.enabled)
362 .and_then(GoldConfig::effective_goal)
363 }
364}
365
366#[cfg(test)]
367mod tests;