mirror of
https://github.com/leonvanzyl/autocoder.git
synced 2026-02-02 07:23:35 +00:00
feat: add multi-feature batching for coding agents
Enable the orchestrator to assign 1-3 features per coding agent subprocess, selected via dependency chain extension + same-category fill. This reduces cold-start overhead and leverages shared context across related features. Orchestrator (parallel_orchestrator.py): - Add batch tracking: _batch_features and _feature_to_primary data structures - Add build_feature_batches() with dependency chain + category fill algorithm - Add start_feature_batch() and _spawn_coding_agent_batch() methods - Update _on_agent_complete() for batch cleanup across all features - Update stop_feature() with _feature_to_primary lookup - Update get_ready_features() to exclude all batch feature IDs - Update main loop to build batches then spawn per available slot CLI and agent layer: - Add --feature-ids (comma-separated) and --batch-size CLI args - Add feature_ids parameter to run_autonomous_agent() with batch prompt selection - Add get_batch_feature_prompt() with sequential workflow instructions WebSocket layer (server/websocket.py): - Add BATCH_CODING_AGENT_START_PATTERN and BATCH_FEATURES_COMPLETE_PATTERN - Add _handle_batch_agent_start() and _handle_batch_agent_complete() methods - Add featureIds field to all agent_update messages - Track current_feature_id updates as agent moves through batch Frontend (React UI): - Add featureIds to ActiveAgent and WSAgentUpdateMessage types - Update KanbanColumn and DependencyGraph agent-feature maps for batch - Update AgentCard to show "Batch: #X, #Y, #Z" with active feature highlight - Add "Features per Agent" segmented control (1-3) in SettingsModal Settings integration (full stack): - Add batch_size to schemas, settings router, agent router, process manager - Default batch_size=3, user-configurable 1-3 via settings UI - batch_size=1 is functionally identical to pre-batching behavior Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Auto
@@ -17,11 +17,11 @@ from ..utils.project_helpers import get_project_path as _get_project_path
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from ..utils.validation import validate_project_name
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def _get_settings_defaults() -> tuple[bool, str, int, bool]:
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def _get_settings_defaults() -> tuple[bool, str, int, bool, int]:
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"""Get defaults from global settings.
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Returns:
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Tuple of (yolo_mode, model, testing_agent_ratio, playwright_headless)
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Tuple of (yolo_mode, model, testing_agent_ratio, playwright_headless, batch_size)
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"""
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import sys
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root = Path(__file__).parent.parent.parent
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@@ -42,7 +42,12 @@ def _get_settings_defaults() -> tuple[bool, str, int, bool]:
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playwright_headless = (settings.get("playwright_headless") or "true").lower() == "true"
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return yolo_mode, model, testing_agent_ratio, playwright_headless
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try:
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batch_size = int(settings.get("batch_size", "3"))
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except (ValueError, TypeError):
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batch_size = 3
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return yolo_mode, model, testing_agent_ratio, playwright_headless, batch_size
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router = APIRouter(prefix="/api/projects/{project_name}/agent", tags=["agent"])
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@@ -91,19 +96,22 @@ async def start_agent(
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manager = get_project_manager(project_name)
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# Get defaults from global settings if not provided in request
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default_yolo, default_model, default_testing_ratio, playwright_headless = _get_settings_defaults()
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default_yolo, default_model, default_testing_ratio, playwright_headless, default_batch_size = _get_settings_defaults()
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yolo_mode = request.yolo_mode if request.yolo_mode is not None else default_yolo
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model = request.model if request.model else default_model
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max_concurrency = request.max_concurrency or 1
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testing_agent_ratio = request.testing_agent_ratio if request.testing_agent_ratio is not None else default_testing_ratio
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batch_size = default_batch_size
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success, message = await manager.start(
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yolo_mode=yolo_mode,
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model=model,
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max_concurrency=max_concurrency,
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testing_agent_ratio=testing_agent_ratio,
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playwright_headless=playwright_headless,
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batch_size=batch_size,
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)
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# Notify scheduler of manual start (to prevent auto-stop during scheduled window)
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@@ -92,6 +92,7 @@ async def get_settings():
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ollama_mode=_is_ollama_mode(),
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testing_agent_ratio=_parse_int(all_settings.get("testing_agent_ratio"), 1),
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playwright_headless=_parse_bool(all_settings.get("playwright_headless"), default=True),
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batch_size=_parse_int(all_settings.get("batch_size"), 3),
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)
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@@ -110,6 +111,9 @@ async def update_settings(update: SettingsUpdate):
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if update.playwright_headless is not None:
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set_setting("playwright_headless", "true" if update.playwright_headless else "false")
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if update.batch_size is not None:
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set_setting("batch_size", str(update.batch_size))
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# Return updated settings
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all_settings = get_all_settings()
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return SettingsResponse(
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@@ -119,4 +123,5 @@ async def update_settings(update: SettingsUpdate):
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ollama_mode=_is_ollama_mode(),
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testing_agent_ratio=_parse_int(all_settings.get("testing_agent_ratio"), 1),
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playwright_headless=_parse_bool(all_settings.get("playwright_headless"), default=True),
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batch_size=_parse_int(all_settings.get("batch_size"), 3),
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)
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@@ -399,6 +399,7 @@ class SettingsResponse(BaseModel):
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ollama_mode: bool = False # True if Ollama API is configured via .env
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testing_agent_ratio: int = 1 # Regression testing agents (0-3)
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playwright_headless: bool = True
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batch_size: int = 3 # Features per coding agent batch (1-3)
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class ModelsResponse(BaseModel):
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@@ -413,6 +414,7 @@ class SettingsUpdate(BaseModel):
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model: str | None = None
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testing_agent_ratio: int | None = None # 0-3
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playwright_headless: bool | None = None
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batch_size: int | None = None # Features per agent batch (1-3)
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@field_validator('model')
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@classmethod
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@@ -428,6 +430,13 @@ class SettingsUpdate(BaseModel):
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raise ValueError("testing_agent_ratio must be between 0 and 3")
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return v
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@field_validator('batch_size')
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@classmethod
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def validate_batch_size(cls, v: int | None) -> int | None:
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if v is not None and (v < 1 or v > 3):
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raise ValueError("batch_size must be between 1 and 3")
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return v
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# ============================================================================
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# Dev Server Schemas
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@@ -298,6 +298,7 @@ class AgentProcessManager:
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max_concurrency: int | None = None,
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testing_agent_ratio: int = 1,
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playwright_headless: bool = True,
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batch_size: int = 3,
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) -> tuple[bool, str]:
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"""
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Start the agent as a subprocess.
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@@ -349,6 +350,9 @@ class AgentProcessManager:
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# Add testing agent configuration
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cmd.extend(["--testing-ratio", str(testing_agent_ratio)])
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# Add --batch-size flag for multi-feature batching
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cmd.extend(["--batch-size", str(batch_size)])
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try:
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# Start subprocess with piped stdout/stderr
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# Use project_dir as cwd so Claude SDK sandbox allows access to project files
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@@ -39,6 +39,14 @@ TESTING_AGENT_START_PATTERN = re.compile(r'Started testing agent for feature #(\
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# Matches: "Feature #123 testing completed" or "Feature #123 testing failed"
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TESTING_AGENT_COMPLETE_PATTERN = re.compile(r'Feature #(\d+) testing (completed|failed)')
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# Pattern to detect batch coding agent start message
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# Matches: "Started coding agent for features #5, #8, #12"
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BATCH_CODING_AGENT_START_PATTERN = re.compile(r'Started coding agent for features (#\d+(?:,\s*#\d+)*)')
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# Pattern to detect batch completion
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# Matches: "Features #5, #8, #12 completed" or "Features #5, #8, #12 failed"
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BATCH_FEATURES_COMPLETE_PATTERN = re.compile(r'Features (#\d+(?:,\s*#\d+)*)\s+(completed|failed)')
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# Patterns for detecting agent activity and thoughts
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THOUGHT_PATTERNS = [
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# Claude's tool usage patterns (actual format: [Tool: name])
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@@ -64,9 +72,9 @@ ORCHESTRATOR_PATTERNS = {
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'capacity_check': re.compile(r'\[DEBUG\] Spawning loop: (\d+) ready, (\d+) slots'),
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'at_capacity': re.compile(r'At max capacity|at max testing agents|At max total agents'),
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'feature_start': re.compile(r'Starting feature \d+/\d+: #(\d+) - (.+)'),
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'coding_spawn': re.compile(r'Started coding agent for feature #(\d+)'),
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'coding_spawn': re.compile(r'Started coding agent for features? #(\d+)'),
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'testing_spawn': re.compile(r'Started testing agent for feature #(\d+)'),
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'coding_complete': re.compile(r'Feature #(\d+) (completed|failed)'),
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'coding_complete': re.compile(r'Features? #(\d+)(?:,\s*#\d+)* (completed|failed)'),
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'testing_complete': re.compile(r'Feature #(\d+) testing (completed|failed)'),
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'all_complete': re.compile(r'All features complete'),
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'blocked_features': re.compile(r'(\d+) blocked by dependencies'),
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@@ -96,7 +104,17 @@ class AgentTracker:
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# Check for orchestrator status messages first
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# These don't have [Feature #X] prefix
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# Coding agent start: "Started coding agent for feature #X"
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# Batch coding agent start: "Started coding agent for features #5, #8, #12"
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batch_start_match = BATCH_CODING_AGENT_START_PATTERN.match(line)
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if batch_start_match:
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try:
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feature_ids = [int(x.strip().lstrip('#')) for x in batch_start_match.group(1).split(',')]
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if feature_ids:
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return await self._handle_batch_agent_start(feature_ids, "coding")
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except ValueError:
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pass
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# Single coding agent start: "Started coding agent for feature #X"
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if line.startswith("Started coding agent for feature #"):
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m = re.search(r'#(\d+)', line)
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if m:
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@@ -119,6 +137,17 @@ class AgentTracker:
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is_success = testing_complete_match.group(2) == "completed"
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return await self._handle_agent_complete(feature_id, is_success, agent_type="testing")
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# Batch features complete: "Features #5, #8, #12 completed/failed"
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batch_complete_match = BATCH_FEATURES_COMPLETE_PATTERN.match(line)
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if batch_complete_match:
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try:
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feature_ids = [int(x.strip().lstrip('#')) for x in batch_complete_match.group(1).split(',')]
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is_success = batch_complete_match.group(2) == "completed"
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if feature_ids:
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return await self._handle_batch_agent_complete(feature_ids, is_success, "coding")
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except ValueError:
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pass
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# Coding agent complete: "Feature #X completed/failed" (without "testing" keyword)
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if line.startswith("Feature #") and ("completed" in line or "failed" in line) and "testing" not in line:
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m = re.search(r'#(\d+)', line)
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@@ -158,6 +187,7 @@ class AgentTracker:
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'name': AGENT_MASCOTS[agent_index % len(AGENT_MASCOTS)],
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'agent_index': agent_index,
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'agent_type': 'coding',
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'feature_ids': [feature_id],
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'state': 'thinking',
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'feature_name': f'Feature #{feature_id}',
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'last_thought': None,
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@@ -165,6 +195,10 @@ class AgentTracker:
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agent = self.active_agents[key]
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# Update current_feature_id for batch agents when output comes from a different feature
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if 'current_feature_id' in agent and feature_id in agent.get('feature_ids', []):
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agent['current_feature_id'] = feature_id
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# Detect state and thought from content
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state = 'working'
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thought = None
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@@ -188,6 +222,7 @@ class AgentTracker:
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'agentName': agent['name'],
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'agentType': agent['agent_type'],
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'featureId': feature_id,
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'featureIds': agent.get('feature_ids', [feature_id]),
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'featureName': agent['feature_name'],
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'state': state,
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'thought': thought,
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@@ -244,6 +279,7 @@ class AgentTracker:
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'name': AGENT_MASCOTS[agent_index % len(AGENT_MASCOTS)],
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'agent_index': agent_index,
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'agent_type': agent_type,
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'feature_ids': [feature_id],
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'state': 'thinking',
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'feature_name': feature_name,
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'last_thought': 'Starting work...',
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@@ -255,12 +291,55 @@ class AgentTracker:
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'agentName': AGENT_MASCOTS[agent_index % len(AGENT_MASCOTS)],
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'agentType': agent_type,
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'featureId': feature_id,
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'featureIds': [feature_id],
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'featureName': feature_name,
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'state': 'thinking',
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'thought': 'Starting work...',
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'timestamp': datetime.now().isoformat(),
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}
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async def _handle_batch_agent_start(self, feature_ids: list[int], agent_type: str = "coding") -> dict | None:
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"""Handle batch agent start message from orchestrator."""
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if not feature_ids:
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return None
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primary_id = feature_ids[0]
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async with self._lock:
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key = (primary_id, agent_type)
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agent_index = self._next_agent_index
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self._next_agent_index += 1
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feature_name = f'Features {", ".join(f"#{fid}" for fid in feature_ids)}'
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self.active_agents[key] = {
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'name': AGENT_MASCOTS[agent_index % len(AGENT_MASCOTS)],
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'agent_index': agent_index,
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'agent_type': agent_type,
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'feature_ids': list(feature_ids),
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'current_feature_id': primary_id,
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'state': 'thinking',
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'feature_name': feature_name,
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'last_thought': 'Starting batch work...',
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}
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# Register all feature IDs so output lines can find this agent
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for fid in feature_ids:
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secondary_key = (fid, agent_type)
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if secondary_key != key:
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self.active_agents[secondary_key] = self.active_agents[key]
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return {
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'type': 'agent_update',
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'agentIndex': agent_index,
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'agentName': AGENT_MASCOTS[agent_index % len(AGENT_MASCOTS)],
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'agentType': agent_type,
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'featureId': primary_id,
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'featureIds': list(feature_ids),
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'featureName': feature_name,
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'state': 'thinking',
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'thought': 'Starting batch work...',
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'timestamp': datetime.now().isoformat(),
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}
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async def _handle_agent_complete(self, feature_id: int, is_success: bool, agent_type: str = "coding") -> dict | None:
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"""Handle agent completion - ALWAYS emits a message, even if agent wasn't tracked.
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@@ -282,6 +361,7 @@ class AgentTracker:
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'agentName': agent['name'],
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'agentType': agent.get('agent_type', agent_type),
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'featureId': feature_id,
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'featureIds': agent.get('feature_ids', [feature_id]),
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'featureName': agent['feature_name'],
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'state': state,
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'thought': 'Completed successfully!' if is_success else 'Failed to complete',
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@@ -298,6 +378,7 @@ class AgentTracker:
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'agentName': 'Unknown',
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'agentType': agent_type,
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'featureId': feature_id,
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'featureIds': [feature_id],
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'featureName': f'Feature #{feature_id}',
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'state': state,
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'thought': 'Completed successfully!' if is_success else 'Failed to complete',
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@@ -305,6 +386,49 @@ class AgentTracker:
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'synthetic': True,
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}
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async def _handle_batch_agent_complete(self, feature_ids: list[int], is_success: bool, agent_type: str = "coding") -> dict | None:
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"""Handle batch agent completion."""
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if not feature_ids:
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return None
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primary_id = feature_ids[0]
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async with self._lock:
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state = 'success' if is_success else 'error'
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key = (primary_id, agent_type)
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if key in self.active_agents:
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agent = self.active_agents[key]
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result = {
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'type': 'agent_update',
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'agentIndex': agent['agent_index'],
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'agentName': agent['name'],
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'agentType': agent.get('agent_type', agent_type),
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'featureId': primary_id,
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'featureIds': agent.get('feature_ids', list(feature_ids)),
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'featureName': agent['feature_name'],
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'state': state,
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'thought': 'Batch completed successfully!' if is_success else 'Batch failed to complete',
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'timestamp': datetime.now().isoformat(),
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}
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# Clean up all keys for this batch
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for fid in feature_ids:
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self.active_agents.pop((fid, agent_type), None)
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return result
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else:
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# Synthetic completion
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return {
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'type': 'agent_update',
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'agentIndex': -1,
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'agentName': 'Unknown',
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'agentType': agent_type,
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'featureId': primary_id,
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'featureIds': list(feature_ids),
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'featureName': f'Features {", ".join(f"#{fid}" for fid in feature_ids)}',
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'state': state,
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'thought': 'Batch completed successfully!' if is_success else 'Batch failed to complete',
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'timestamp': datetime.now().isoformat(),
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'synthetic': True,
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}
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class OrchestratorTracker:
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"""Tracks orchestrator state for Mission Control observability.
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