From 0cfbae5f8fc3423d77795a2b0864570e7634df11 Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Fri, 31 Jul 2026 23:54:52 +0530 Subject: [PATCH 01/25] Format files with Black to pass CI checks --- src/api/main.py | 2 +- src/api/routes/chat.py | 4 +- src/db/models.py | 5 +- src/llm/local_weight_engine.py | 98 ++++++++++++++++++---------------- 4 files changed, 57 insertions(+), 52 deletions(-) diff --git a/src/api/main.py b/src/api/main.py index f88fc83e..cca20708 100644 --- a/src/api/main.py +++ b/src/api/main.py @@ -175,7 +175,7 @@ def create_app() -> FastAPI: app.add_middleware(SecurityHeadersMiddleware) app.add_middleware(RequestSizeLimitMiddleware, max_size=10 * 1024 * 1024) app.add_middleware(TimeoutMiddleware, timeout=60.0) - + # Add CORS middleware app.add_middleware( CORSMiddleware, diff --git a/src/api/routes/chat.py b/src/api/routes/chat.py index c4144f09..4f1a33ab 100644 --- a/src/api/routes/chat.py +++ b/src/api/routes/chat.py @@ -132,7 +132,9 @@ async def chat( @router.post("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/chat/stream") @limiter.limit("20/minute") async def chat_stream( - request: StreamChatRequest, request_obj: Request, current_user: TokenData = Depends(get_current_user) + request: StreamChatRequest, + request_obj: Request, + current_user: TokenData = Depends(get_current_user), ): """Handle streaming chat requests.""" try: diff --git a/src/db/models.py b/src/db/models.py index 57d159b4..37f4479e 100644 --- a/src/db/models.py +++ b/src/db/models.py @@ -2538,14 +2538,13 @@ class SubOrchestrator(Base): # Relationships director: Mapped[DirectorAgent] = relationship(back_populates="sub_orchestrators") current_task: Mapped[SwarmSubTask | None] = relationship( - "SwarmSubTask", - foreign_keys=[current_task_id] + "SwarmSubTask", foreign_keys=[current_task_id] ) assigned_tasks: Mapped[List["SwarmSubTask"]] = relationship( "SwarmSubTask", foreign_keys="[assigned_orchestrator_id]", back_populates="assigned_orchestrator", - cascade="all, delete-orphan" + cascade="all, delete-orphan", ) def __repr__(self) -> str: diff --git a/src/llm/local_weight_engine.py b/src/llm/local_weight_engine.py index dc616e4d..b71eb977 100644 --- a/src/llm/local_weight_engine.py +++ b/src/llm/local_weight_engine.py @@ -28,6 +28,7 @@ @dataclass class ModelLoadConfig: """Configuration for loading a local model""" + model_name_or_path: str device: str = "auto" # "auto", "mps", "cuda", "cpu" torch_dtype: str = "bfloat16" # "bfloat16", "float16", "float32" @@ -41,13 +42,14 @@ class ModelLoadConfig: @dataclass class LoRAConfig: """Configuration for LoRA adapter""" + r: int = 8 lora_alpha: int = 16 target_modules: List[str] = None lora_dropout: float = 0.05 bias: str = "none" task_type: str = "CAUSAL_LM" - + def __post_init__(self): if self.target_modules is None: self.target_modules = ["q_proj", "v_proj", "k_proj", "o_proj"] @@ -56,7 +58,7 @@ def __post_init__(self): class LocalWeightEngine: """ Native PyTorch engine for loading local LLM weights and performing direct inference/fine-tuning. - + Supports: - Qwen2.5-0.5B, Llama-3.2-1B, Phi-3.5-mini, and other HF models - Automatic device selection (MPS for Apple Silicon, CUDA, CPU) @@ -64,7 +66,7 @@ class LocalWeightEngine: - LoRA/PEFT adapters for parameter-efficient fine-tuning - Direct weight manipulation without external API calls """ - + def __init__(self): self._model: Optional[AutoModelForCausalLM] = None self._tokenizer: Optional[AutoTokenizer] = None @@ -73,7 +75,7 @@ def __init__(self): self._model_name: str = "" self._peft_config: Optional[LoRAConfig] = None self._is_peft_model: bool = False - + def _resolve_device(self, device: str) -> torch.device: """Resolve device string to torch.device""" if device == "auto": @@ -84,7 +86,7 @@ def _resolve_device(self, device: str) -> torch.device: else: return torch.device("cpu") return torch.device(device) - + def _resolve_dtype(self, dtype_str: str) -> torch.dtype: """Resolve dtype string to torch.dtype""" dtype_map = { @@ -93,20 +95,22 @@ def _resolve_dtype(self, dtype_str: str) -> torch.dtype: "float32": torch.float32, } return dtype_map.get(dtype_str, torch.bfloat16) - + def load_model(self, config: ModelLoadConfig) -> None: """ Load model and tokenizer directly into PyTorch memory. - + Args: config: ModelLoadConfig with model path and loading options """ self._model_name = config.model_name_or_path self._device = self._resolve_device(config.device) torch_dtype = self._resolve_dtype(config.torch_dtype) - - logger.info(f"Loading model: {config.model_name_or_path} on {self._device} with {config.torch_dtype}") - + + logger.info( + f"Loading model: {config.model_name_or_path} on {self._device} with {config.torch_dtype}" + ) + # Load tokenizer self._tokenizer = AutoTokenizer.from_pretrained( config.model_name_or_path, @@ -114,7 +118,7 @@ def load_model(self, config: ModelLoadConfig) -> None: ) if self._tokenizer.pad_token is None: self._tokenizer.pad_token = self._tokenizer.eos_token - + # Configure quantization if requested quantization_config = config.quantization_config if config.use_4bit and quantization_config is None: @@ -128,7 +132,7 @@ def load_model(self, config: ModelLoadConfig) -> None: quantization_config = BitsAndBytesConfig( load_in_8bit=True, ) - + # Load model self._model = AutoModelForCausalLM.from_pretrained( config.model_name_or_path, @@ -138,11 +142,11 @@ def load_model(self, config: ModelLoadConfig) -> None: quantization_config=quantization_config, trust_remote_code=config.trust_remote_code, ) - + # Move to device if not using device_map if str(self._device) == "cpu" or config.device != "auto": self._model = self._model.to(self._device) - + # Load generation config try: self._generation_config = GenerationConfig.from_pretrained(config.model_name_or_path) @@ -157,22 +161,22 @@ def load_model(self, config: ModelLoadConfig) -> None: pad_token_id=self._tokenizer.pad_token_id, eos_token_id=self._tokenizer.eos_token_id, ) - + self._model.eval() logger.info(f"Model loaded successfully on {self._model.device}") - + def attach_lora(self, lora_config: LoRAConfig) -> None: """ Attach LoRA adapter for parameter-efficient fine-tuning. - + Args: lora_config: LoRAConfig with adapter parameters """ if self._model is None: raise RuntimeError("Model not loaded. Call load_model() first.") - + self._peft_config = lora_config - + peft_config = LoraConfig( r=lora_config.r, lora_alpha=lora_config.lora_alpha, @@ -181,41 +185,41 @@ def attach_lora(self, lora_config: LoRAConfig) -> None: bias=lora_config.bias, task_type=TaskType.CAUSAL_LM, ) - + self._model = get_peft_model(self._model, peft_config) self._is_peft_model = True self._model.print_trainable_parameters() logger.info("LoRA adapter attached successfully") - + def load_lora_adapter(self, adapter_path: str) -> None: """ Load a saved LoRA adapter from disk. - + Args: adapter_path: Path to the saved adapter directory or .safetensors file """ if self._model is None: raise RuntimeError("Model not loaded. Call load_model() first.") - + self._model = PeftModel.from_pretrained(self._model, adapter_path) self._is_peft_model = True logger.info(f"Loaded LoRA adapter from {adapter_path}") - + def save_lora_adapter(self, save_path: str) -> None: """ Save the current LoRA adapter to disk. - + Args: save_path: Directory path to save adapter """ if not self._is_peft_model: raise RuntimeError("No LoRA adapter attached. Call attach_lora() first.") - + os.makedirs(save_path, exist_ok=True) self._model.save_pretrained(save_path) self._tokenizer.save_pretrained(save_path) logger.info(f"Saved LoRA adapter to {save_path}") - + def generate( self, prompt: str, @@ -229,7 +233,7 @@ def generate( ) -> str: """ Generate text directly from the loaded model. - + Args: prompt: Input prompt string max_new_tokens: Maximum new tokens to generate @@ -238,16 +242,16 @@ def generate( top_k: Top-k sampling parameter repetition_penalty: Repetition penalty do_sample: Whether to use sampling - + Returns: Generated text string """ if self._model is None or self._tokenizer is None: raise RuntimeError("Model not loaded. Call load_model() first.") - + # Tokenize prompt inputs = self._tokenizer(prompt, return_tensors="pt").to(self._device) - + # Prepare generation config gen_config = self._generation_config gen_config.max_new_tokens = max_new_tokens @@ -258,28 +262,28 @@ def generate( gen_config.do_sample = do_sample and temperature > 0 gen_config.pad_token_id = self._tokenizer.pad_token_id gen_config.eos_token_id = self._tokenizer.eos_token_id - + # Generate with torch.no_grad(): outputs = self._model.generate( **inputs, generation_config=gen_config, ) - + # Decode only new tokens - new_tokens = outputs[0][inputs["input_ids"].shape[1]:] + new_tokens = outputs[0][inputs["input_ids"].shape[1] :] generated_text = self._tokenizer.decode(new_tokens, skip_special_tokens=True) - + return generated_text - + def get_model_info(self) -> Dict[str, Any]: """Get information about the loaded model""" if self._model is None: return {"status": "not_loaded"} - + total_params = sum(p.numel() for p in self._model.parameters()) trainable_params = sum(p.numel() for p in self._model.parameters() if p.requires_grad) - + return { "model_name": self._model_name, "device": str(self._model.device), @@ -289,43 +293,43 @@ def get_model_info(self) -> Dict[str, Any]: "is_peft_model": self._is_peft_model, "peft_config": self._peft_config.__dict__ if self._peft_config else None, } - + def enable_training_mode(self) -> None: """Enable training mode (for fine-tuning)""" if self._model is None: raise RuntimeError("Model not loaded. Call load_model() first.") self._model.train() - + def enable_eval_mode(self) -> None: """Enable evaluation mode (for inference)""" if self._model is None: raise RuntimeError("Model not loaded. Call load_model() first.") self._model.eval() - + def get_trainable_parameters(self): """Get trainable parameters for optimizer""" if self._model is None: raise RuntimeError("Model not loaded. Call load_model() first.") return [p for p in self._model.parameters() if p.requires_grad] - + def forward(self, input_ids: torch.Tensor, labels: torch.Tensor = None, **kwargs): """Direct forward pass for training""" if self._model is None: raise RuntimeError("Model not loaded. Call load_model() first.") return self._model(input_ids=input_ids, labels=labels, **kwargs) - + @property def model(self) -> Optional[AutoModelForCausalLM]: return self._model - + @property def tokenizer(self) -> Optional[AutoTokenizer]: return self._tokenizer - + @property def device(self) -> torch.device: return self._device - + def unload(self) -> None: """Unload model and free memory""" if self._model is not None: @@ -341,4 +345,4 @@ def unload(self) -> None: torch.cuda.empty_cache() elif torch.backends.mps.is_available(): torch.mps.empty_cache() - logger.info("Model unloaded, memory freed") \ No newline at end of file + logger.info("Model unloaded, memory freed") From fee6ff8cbeed75944c8a31bde6c4e02c5ec16c50 Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:03:49 +0530 Subject: [PATCH 02/25] chore: remove tracked pycache compiled files from repository --- src/__pycache__/main.cpython-314.pyc | Bin 3392 -> 0 bytes .../__pycache__/free_api_client.cpython-314.pyc | 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zfxv7>2E}ijwUPafd0R~q!rL>qZ65q?X-OTl_{*Ab-S_29FKtQ}6u*4tCCDk9^n)|sa^ESdNYWxsRcuX_ZM_2|I#K z*(wvdicdbWbJZP8VDlrRbN9AdY)&u6+tbKo2Doq&yD&Nsj)+G3=|V`hae3)N6i*4o z6Dg7PD=;MWlzvh6GKD6|>xCy;$o!sU^obeW6KyKFj7vz;@1+sK6Tq5qVqNT6c44Vi z{*^XGV;Pr%TABT4Ms=4fsbnKlt+-tzoWoTqMu3ZYklxV?LOm5|CFvCVk;hO>Aj(zv zD*7mzA&u{5zsM*FO}5h)Kr~5g!Dv>t3fc8JMuSK+u4yX{5AcJo+l7CNV(_1fk@O5# zE%3Xr8ZEE8Z{c{yhg`ugx%N4({TE!#hnyL&A96N?f5{z9az}r`ZTSVa@k6fo7hL^^ zob|4`aNb<`zPWO;BV}H8(SRkYu|Cw;`Q`-<-hC}z7wGk&+h^oS?9Cnz;|1W&ANKJe ImRJM-4-{}RtN;K2 From 2eb4e30de7e08bad7565829a74306564e27816ed Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:03:53 +0530 Subject: [PATCH 03/25] chore: add .venv_local and .saropa to .gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index e04c4f8f..68bfa28f 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,8 @@ # Python venv/ .venv/ +.venv_local/ +.saropa/ __pycache__/ *.py[cod] *.pyo From 6f28b135b2547511c6a02c77dc2ff9fdac24b676 Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:03:58 +0530 Subject: [PATCH 04/25] refactor(frontend): clean up preconnect and dns-prefetch links in layout --- frontend/src/app/layout.tsx | 3 --- 1 file changed, 3 deletions(-) diff --git a/frontend/src/app/layout.tsx b/frontend/src/app/layout.tsx index 55858b95..4ff5c713 100644 --- a/frontend/src/app/layout.tsx +++ b/frontend/src/app/layout.tsx @@ -101,9 +101,6 @@ export default function RootLayout({ className={`${geistSans.variable} ${geistMono.variable} h-full antialiased`} > - - -
From 8e82b31f83c417932653f2abed7ffc24d964f5ec Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:04:01 +0530 Subject: [PATCH 05/25] fix(cli): handle empty checkpoint files gracefully in execution loop harness --- src/cli/execution_loop_harness.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/cli/execution_loop_harness.py b/src/cli/execution_loop_harness.py index 32927bec..a14e5521 100644 --- a/src/cli/execution_loop_harness.py +++ b/src/cli/execution_loop_harness.py @@ -77,8 +77,13 @@ def list(self, objective_id: str) -> list[dict[str, Any]]: def _read_document(self) -> dict[str, Any]: if not self.path.exists(): return {"version": 1, "objectives": {}} + if self.path.stat().st_size == 0: + return {"version": 1, "objectives": {}} with self.path.open(encoding="utf-8") as checkpoint_file: - document = json.load(checkpoint_file) + content = checkpoint_file.read().strip() + if not content: + return {"version": 1, "objectives": {}} + document = json.loads(content) if document.get("version") != 1 or not isinstance(document.get("objectives"), dict): raise click.ClickException(f"Invalid checkpoint store: {self.path}") return document From c1a591440a97c6f2280fb464ca4c1a4cd01e2c72 Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:04:02 +0530 Subject: [PATCH 06/25] feat(safety): add parameter boundary validation for weight fine-tuning actions --- src/safety/action_validator.py | 34 +++++++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/src/safety/action_validator.py b/src/safety/action_validator.py index f38a4440..e4ec10ae 100644 --- a/src/safety/action_validator.py +++ b/src/safety/action_validator.py @@ -3,7 +3,7 @@ from __future__ import annotations from dataclasses import dataclass, field -from typing import Any +from typing import Any, Dict, Optional @dataclass @@ -27,6 +27,8 @@ class ActionValidator: "research", "memory_consolidation", "execute_task", + "weight_fine_tune", # Allow fine-tuning with boundary checks + "adapter_load", # Allow adapter loading with boundary checks } BLOCKED_ACTION_TYPES = { "delete_file", @@ -38,6 +40,11 @@ class ActionValidator: } BLOCKED_TERMS = ("rm -rf", "sudo", "chmod 777", "mkfs", ":(){") + # Parameter boundaries for weight fine-tuning + MAX_EPOCHS = 10 + MAX_LEARNING_RATE = 1e-3 + MAX_BATCH_SIZE = 32 + def validate_action(self, action: Any) -> ValidationResult: if action is None: return ValidationResult(False, "Action is empty") @@ -63,6 +70,31 @@ def validate_action(self, action: Any) -> ValidationResult: if any(term in normalized_payload for term in self.BLOCKED_TERMS): return ValidationResult(False, "Action contains a blocked operation") + # Parameter boundary checks for weight fine-tuning + if normalized_type == "weight_fine_tune" and isinstance(action, dict): + warnings = [] + if "epochs" in action and action["epochs"] > self.MAX_EPOCHS: + return ValidationResult( + False, + f"Epochs {action['epochs']} exceeds maximum allowed {self.MAX_EPOCHS}" + ) + if "learning_rate" in action and action["learning_rate"] > self.MAX_LEARNING_RATE: + return ValidationResult( + False, + f"Learning rate {action['learning_rate']} exceeds maximum allowed {self.MAX_LEARNING_RATE}" + ) + if "batch_size" in action and action["batch_size"] > self.MAX_BATCH_SIZE: + return ValidationResult( + False, + f"Batch size {action['batch_size']} exceeds maximum allowed {self.MAX_BATCH_SIZE}" + ) + if warnings: + return ValidationResult( + True, + "Action allowed with parameter warnings", + warnings + ) + if normalized_type and normalized_type not in self.SAFE_ACTION_TYPES: return ValidationResult( True, From c1b135d6fa41b1c7cce0b70c2c76e767464180be Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:04:04 +0530 Subject: [PATCH 07/25] feat(swarm): integrate message router, consensus evaluator, and load balancer into swarm coordinator --- src/swarm/swarm_coordinator.py | 387 +++++++++++++++++++++++++++++++-- 1 file changed, 373 insertions(+), 14 deletions(-) diff --git a/src/swarm/swarm_coordinator.py b/src/swarm/swarm_coordinator.py index 46c31cd4..7ef2cf55 100644 --- a/src/swarm/swarm_coordinator.py +++ b/src/swarm/swarm_coordinator.py @@ -1,14 +1,18 @@ """Swarm Coordinator for Swarm Orchestration (Phase 8). Central coordinator for managing the entire swarm of agents. +Enhanced with in-process message queues, consensus evaluation, and load balancing. """ from __future__ import annotations import asyncio from datetime import datetime, timezone -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Callable from uuid import UUID, uuid4 +from dataclasses import dataclass, field +from enum import Enum +from collections import deque from pydantic import BaseModel, Field @@ -18,6 +22,281 @@ logger = get_logger(__name__) +class MessageType(str, Enum): + """Types of messages in the swarm communication system""" + TASK_ASSIGNMENT = "task_assignment" + TASK_RESULT = "task_result" + CONSENSUS_VOTE = "consensus_vote" + CONSENSUS_RESULT = "consensus_result" + HEARTBEAT = "heartbeat" + STATUS_UPDATE = "status_update" + LOAD_REPORT = "load_report" + SCALING_SIGNAL = "scaling_signal" + + +@dataclass +class SwarmMessage: + """Message for inter-agent communication""" + msg_type: MessageType + sender_id: UUID + recipient_id: Optional[UUID] # None for broadcast + payload: Dict[str, Any] + timestamp: datetime = field(default_factory=lambda: datetime.now(timezone.utc)) + correlation_id: Optional[UUID] = None + reply_to: Optional[UUID] = None + + +class InProcessMessageRouter: + """In-memory async message router for swarm communication""" + + def __init__(self): + self._queues: Dict[UUID, asyncio.Queue] = {} + self._subscriptions: Dict[MessageType, List[UUID]] = {} + self._broadcast_queue: asyncio.Queue = asyncio.Queue() + self._running = False + self._router_task: Optional[asyncio.Task] = None + + def register_agent(self, agent_id: UUID) -> asyncio.Queue: + """Register an agent and return its message queue""" + queue = asyncio.Queue() + self._queues[agent_id] = queue + return queue + + def unregister_agent(self, agent_id: UUID) -> None: + """Unregister an agent""" + if agent_id in self._queues: + del self._queues[agent_id] + # Remove from subscriptions + for msg_type, subscribers in self._subscriptions.items(): + if agent_id in subscribers: + subscribers.remove(agent_id) + + def subscribe(self, agent_id: UUID, msg_type: MessageType) -> None: + """Subscribe agent to a message type""" + if msg_type not in self._subscriptions: + self._subscriptions[msg_type] = [] + if agent_id not in self._subscriptions[msg_type]: + self._subscriptions[msg_type].append(agent_id) + + def unsubscribe(self, agent_id: UUID, msg_type: MessageType) -> None: + """Unsubscribe agent from a message type""" + if msg_type in self._subscriptions: + if agent_id in self._subscriptions[msg_type]: + self._subscriptions[msg_type].remove(agent_id) + + async def send(self, message: SwarmMessage) -> None: + """Send a message to a specific recipient or broadcast""" + if message.recipient_id is not None: + # Direct message + if message.recipient_id in self._queues: + await self._queues[message.recipient_id].put(message) + else: + logger.warning(f"Recipient {message.recipient_id} not registered") + else: + # Broadcast + await self._broadcast_queue.put(message) + + async def start(self) -> None: + """Start the router""" + self._running = True + self._router_task = asyncio.create_task(self._route_loop()) + logger.info("InProcessMessageRouter started") + + async def stop(self) -> None: + """Stop the router""" + self._running = False + if self._router_task: + self._router_task.cancel() + try: + await self._router_task + except asyncio.CancelledError: + pass + logger.info("InProcessMessageRouter stopped") + + async def _route_loop(self) -> None: + """Main routing loop""" + while self._running: + try: + # Process broadcast queue + try: + message = await asyncio.wait_for(self._broadcast_queue.get(), timeout=0.1) + for agent_id, queue in self._queues.items(): + if message.sender_id != agent_id: # Don't send to sender + await queue.put(message) + except asyncio.TimeoutError: + pass + + # Process direct messages (they're already in queues) + await asyncio.sleep(0.01) + + except asyncio.CancelledError: + break + except Exception as e: + logger.error(f"Error in router loop: {e}") + await asyncio.sleep(0.1) + + async def receive(self, agent_id: UUID, timeout: float = 1.0) -> Optional[SwarmMessage]: + """Receive a message for an agent""" + if agent_id not in self._queues: + return None + try: + return await asyncio.wait_for(self._queues[agent_id].get(), timeout=timeout) + except asyncio.TimeoutError: + return None + + +class ConsensusEvaluator: + """Consensus evaluation for multi-agent agreement""" + + def __init__(self, min_agreement: float = 0.66): + self.min_agreement = min_agreement + self._votes: Dict[UUID, Dict[UUID, Any]] = {} # proposal_id -> {voter_id: vote} + self._vote_weights: Dict[UUID, float] = {} # voter_id -> weight + + def register_voter(self, voter_id: UUID, weight: float = 1.0) -> None: + """Register a voter with optional weight""" + self._vote_weights[voter_id] = weight + + def unregister_voter(self, voter_id: UUID) -> None: + """Unregister a voter""" + if voter_id in self._vote_weights: + del self._vote_weights[voter_id] + + def submit_vote(self, proposal_id: UUID, voter_id: UUID, vote: Any) -> None: + """Submit a vote for a proposal""" + if proposal_id not in self._votes: + self._votes[proposal_id] = {} + self._votes[proposal_id][voter_id] = vote + + def evaluate(self, proposal_id: UUID) -> Dict[str, Any]: + """Evaluate consensus for a proposal""" + if proposal_id not in self._votes: + return {"consensus": False, "reason": "No votes submitted"} + + votes = self._votes[proposal_id] + if not votes: + return {"consensus": False, "reason": "No votes"} + + # Count votes by value + vote_counts: Dict[Any, float] = {} + total_weight = 0.0 + + for voter_id, vote in votes.items(): + weight = self._vote_weights.get(voter_id, 1.0) + vote_str = str(vote) + vote_counts[vote_str] = vote_counts.get(vote_str, 0.0) + weight + total_weight += weight + + # Find majority + max_votes = max(vote_counts.values()) if vote_counts else 0 + agreement_ratio = max_votes / total_weight if total_weight > 0 else 0 + + consensus_reached = agreement_ratio >= self.min_agreement + + # Get winning vote + winning_vote = max(vote_counts, key=vote_counts.get) if vote_counts else None + + return { + "consensus": consensus_reached, + "agreement_ratio": agreement_ratio, + "winning_vote": winning_vote, + "vote_distribution": vote_counts, + "total_voters": len(votes), + "required_ratio": self.min_agreement, + } + + def clear_proposal(self, proposal_id: UUID) -> None: + """Clear votes for a proposal""" + if proposal_id in self._votes: + del self._votes[proposal_id] + + +class LoadBalancer: + """Load balancer for distributing tasks across agents""" + + def __init__(self): + self._agent_loads: Dict[UUID, float] = {} + self._agent_capabilities: Dict[UUID, List[str]] = {} + self._agent_status: Dict[UUID, str] = {} # idle, busy, offline + self._task_history: Dict[UUID, List[Dict]] = {} + + def register_agent(self, agent_id: UUID, capabilities: List[str] = None, max_load: float = 1.0) -> None: + """Register an agent with capabilities""" + self._agent_loads[agent_id] = 0.0 + self._agent_capabilities[agent_id] = capabilities or [] + self._agent_status[agent_id] = "idle" + self._task_history[agent_id] = [] + + def unregister_agent(self, agent_id: UUID) -> None: + """Unregister an agent""" + for d in [self._agent_loads, self._agent_capabilities, self._agent_status, self._task_history]: + if agent_id in d: + del d[agent_id] + + def update_load(self, agent_id: UUID, load: float) -> None: + """Update agent load (0.0 to 1.0)""" + if agent_id in self._agent_loads: + self._agent_loads[agent_id] = max(0.0, min(1.0, load)) + self._agent_status[agent_id] = "busy" if load > 0.1 else "idle" + + def update_status(self, agent_id: UUID, status: str) -> None: + """Update agent status""" + if agent_id in self._agent_status: + self._agent_status[agent_id] = status + + def record_task_completion(self, agent_id: UUID, task_info: Dict[str, Any]) -> None: + """Record task completion for learning""" + if agent_id in self._task_history: + self._task_history[agent_id].append({ + **task_info, + "timestamp": datetime.now(timezone.utc).isoformat(), + }) + # Keep last 100 tasks + if len(self._task_history[agent_id]) > 100: + self._task_history[agent_id] = self._task_history[agent_id][-100:] + + def select_agent(self, required_capabilities: List[str] = None, strategy: str = "least_loaded") -> Optional[UUID]: + """Select best agent for a task""" + # Filter by capabilities + candidates = [] + for agent_id, capabilities in self._agent_capabilities.items(): + if self._agent_status.get(agent_id) == "offline": + continue + if required_capabilities: + if not all(cap in capabilities for cap in required_capabilities): + continue + candidates.append(agent_id) + + if not candidates: + return None + + if strategy == "least_loaded": + return min(candidates, key=lambda a: self._agent_loads.get(a, 1.0)) + elif strategy == "most_capable": + return max(candidates, key=lambda a: len(self._agent_capabilities.get(a, []))) + elif strategy == "round_robin": + # Simple round-robin based on task count + return min(candidates, key=lambda a: len(self._task_history.get(a, []))) + + return candidates[0] + + def get_load_report(self) -> Dict[str, Any]: + """Get current load report""" + return { + "agent_loads": {str(k): v for k, v in self._agent_loads.items()}, + "agent_status": {str(k): v for k, v in self._agent_status.items()}, + "total_agents": len(self._agent_loads), + "busy_agents": sum(1 for s in self._agent_status.values() if s == "busy"), + "idle_agents": sum(1 for s in self._agent_status.values() if s == "idle"), + } + + +# Global instances for swarm communication +message_router = InProcessMessageRouter() +consensus_evaluator = ConsensusEvaluator() +load_balancer = LoadBalancer() + + class SwarmMetrics(BaseModel): """Metrics for the entire swarm.""" @@ -33,8 +312,11 @@ class SwarmMetrics(BaseModel): class SwarmCoordinator: - """Central coordinator for managing multiple director agents.""" - + """Central coordinator for managing multiple director agents. + + Enhanced with in-process message queues, consensus evaluation, and load balancing. + """ + def __init__(self, num_directors: int = 5, sub_orchestrators_per_director: int = 10): """Initialize swarm coordinator.""" self.num_directors = num_directors @@ -43,6 +325,9 @@ def __init__(self, num_directors: int = 5, sub_orchestrators_per_director: int = self.directors: Dict[UUID, DirectorAgent] = {} self._running = False self._monitor_task: Optional[asyncio.Task] = None + self._message_router = message_router + self._consensus = consensus_evaluator + self._load_balancer = load_balancer logger.info( f"SwarmCoordinator initialized with {num_directors} directors, " @@ -54,11 +339,20 @@ async def initialize(self) -> None: logger.info("Initializing SwarmCoordinator") self._running = True + # Start message router + await self._message_router.start() + # Initialize director agents for i in range(self.num_directors): director = DirectorAgent(max_sub_orchestrators=self.sub_orchestrators_per_director) await director.initialize() - self.directors[uuid4()] = director + director_id = uuid4() + self.directors[director_id] = director + + # Register with communication infrastructure + self._message_router.register_agent(director_id) + self._load_balancer.register_agent(director_id, ["director", "coordination"]) + self._consensus.register_voter(director_id, weight=1.0) # Start monitoring task self._monitor_task = asyncio.create_task(self._monitor_loop()) @@ -77,6 +371,9 @@ async def shutdown(self) -> None: except asyncio.CancelledError: pass + # Stop message router + await self._message_router.stop() + # Shutdown all directors for director in self.directors.values(): await director.shutdown() @@ -101,14 +398,25 @@ async def submit_goal(self, goal: SwarmGoal) -> UUID: except RuntimeError: pass # Runtime not initialized yet (startup) - # Find director with least load - director = await self._get_least_loaded_director() - - if not director: - raise RuntimeError("No available directors") + # Use load balancer to find best director + director_id = self._load_balancer.select_agent( + required_capabilities=["director"], + strategy="least_loaded" + ) + + if not director_id or director_id not in self.directors: + # Fallback to original method + director = await self._get_least_loaded_director() + if not director: + raise RuntimeError("No available directors") + else: + director = self.directors[director_id] goal_id = await director.submit_goal(goal) - logger.info(f"Goal {goal_id} assigned to director") + logger.info(f"Goal {goal_id} assigned to director {director_id}") + + # Update load balancer + self._load_balancer.update_load(director_id, 0.5) return goal_id @@ -120,9 +428,9 @@ async def _get_least_loaded_director(self) -> Optional[DirectorAgent]: min_load = float("inf") least_loaded = None - for director in self.directors.values(): + for director_id, director in self.directors.items(): status = await director.monitor_swarm() - load = status.get("busy_sub_orchestrators", 0) + load = status.get("busy_sub_orchestrators", 0) / max(1, status.get("total_sub_orchestrators", 1)) if load < min_load: min_load = load @@ -139,6 +447,41 @@ async def get_goal_status(self, goal_id: UUID) -> Optional[Dict[str, Any]]: return None + async def request_consensus(self, proposal: Dict[str, Any], voters: List[UUID] = None) -> Dict[str, Any]: + """Request consensus from directors on a proposal""" + proposal_id = uuid4() + + # Default to all directors if no voters specified + if voters is None: + voters = list(self.directors.keys()) + + # Broadcast consensus request + message = SwarmMessage( + msg_type=MessageType.CONSENSUS_VOTE, + sender_id=uuid4(), # coordinator ID + recipient_id=None, # broadcast + payload={ + "proposal_id": str(proposal_id), + "proposal": proposal, + "voters": [str(v) for v in voters], + }, + ) + await self._message_router.send(message) + + # Collect votes (simplified - in real implementation would wait for responses) + # For now, simulate voting + for voter_id in voters: + if voter_id in self.directors: + # Simulate vote based on proposal + vote = {"agree": True, "confidence": 0.8} + self._consensus.submit_vote(proposal_id, voter_id, vote) + + # Evaluate consensus + result = self._consensus.evaluate(proposal_id) + self._consensus.clear_proposal(proposal_id) + + return result + async def get_swarm_metrics(self) -> SwarmMetrics: """Get comprehensive metrics for the entire swarm.""" total_sub_orchestrators = 0 @@ -175,7 +518,7 @@ async def get_swarm_metrics(self) -> SwarmMetrics: active_goals=active_goals, completed_goals=completed_goals, failed_goals=failed_goals, - average_task_duration=0.0, # Would be calculated from task history + average_task_duration=0.0, total_tasks_completed=total_tasks_completed, swarm_utilization=swarm_utilization, ) @@ -194,6 +537,10 @@ async def _monitor_loop(self) -> None: f"{metrics.swarm_utilization:.2%} utilization" ) + # Get load balancer report + load_report = self._load_balancer.get_load_report() + logger.debug(f"Load report: {load_report}") + # Auto-scale if needed await self._auto_scale(metrics) @@ -230,7 +577,14 @@ async def scale_directors(self, target_count: int) -> bool: for i in range(target_count - current_count): director = DirectorAgent(max_sub_orchestrators=self.sub_orchestrators_per_director) await director.initialize() - self.directors[uuid4()] = director + director_id = uuid4() + self.directors[director_id] = director + + # Register with communication infrastructure + self._message_router.register_agent(director_id) + self._load_balancer.register_agent(director_id, ["director", "coordination"]) + self._consensus.register_voter(director_id, weight=1.0) + logger.info(f"Scaled directors from {current_count} to {target_count}") elif target_count < current_count: # Scale down (remove idle directors) @@ -248,6 +602,11 @@ async def scale_directors(self, target_count: int) -> bool: director_id = idle_directors[i] await self.directors[director_id].shutdown() del self.directors[director_id] + + # Unregister from communication infrastructure + self._message_router.unregister_agent(director_id) + self._load_balancer.unregister_agent(director_id) + self._consensus.unregister_voter(director_id) logger.info(f"Scaled directors from {current_count} to {target_count}") From fb3837d13dc7806de65c77bde4eacf40f852750f Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:04:05 +0530 Subject: [PATCH 08/25] test(unit): update local weight engine unit tests for parameter iterator mocking --- tests/unit/test_local_weight_engine.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/tests/unit/test_local_weight_engine.py b/tests/unit/test_local_weight_engine.py index f9ac7042..8c8b79b1 100644 --- a/tests/unit/test_local_weight_engine.py +++ b/tests/unit/test_local_weight_engine.py @@ -208,14 +208,16 @@ def test_get_model_info_loaded(self): """Test get_model_info when model loaded""" engine = LocalWeightEngine() engine._model = Mock() - # Mock parameters() to return an iterator - mock_params = [Mock(numel=Mock(return_value=100)), Mock(numel=Mock(return_value=50))] - engine._model.parameters.return_value = iter(mock_params) - # Mock next() on the model for dtype + # Mock parameters() to return a fresh iterator each time mock_param = Mock() + mock_param.numel.return_value = 100 + mock_param.requires_grad = True mock_param.dtype = torch.float32 - engine._model.__iter__ = Mock(return_value=iter([mock_param])) - engine._model.__next__ = Mock(return_value=mock_param) + + def params_iterator(): + return iter([mock_param]) + + engine._model.parameters.side_effect = params_iterator engine._model.device = torch.device("cpu") engine._model_name = "test-model" engine._is_peft_model = False @@ -225,6 +227,8 @@ def test_get_model_info_loaded(self): assert info["model_name"] == "test-model" assert info["device"] == "cpu" assert info["is_peft_model"] is False + assert info["total_parameters"] == 100 + assert info["trainable_parameters"] == 100 def test_enable_training_mode(self): """Test enabling training mode""" From 740d44423bbca1b455fadf3d588b959c9aac0f4a Mon Sep 17 00:00:00 2001 From: genius-0963 Date: Sat, 1 Aug 2026 22:04:07 +0530 Subject: [PATCH 09/25] feat(frontend): add Swarm Agents dashboard page --- frontend/src/app/agents/page.tsx | 889 +++++++++++++++++++++++++++++++ 1 file changed, 889 insertions(+) create mode 100644 frontend/src/app/agents/page.tsx diff --git a/frontend/src/app/agents/page.tsx b/frontend/src/app/agents/page.tsx new file mode 100644 index 00000000..0603020d --- /dev/null +++ b/frontend/src/app/agents/page.tsx @@ -0,0 +1,889 @@ +"use client"; + +import { useState, useEffect, useRef } from "react"; +import { + Brain, + Search, + Code, + CheckSquare, + Shield, + Zap, + Pause, + Play, + RotateCcw, + MessageSquare, + X, + Activity, + Wifi, + WifiOff, +} from "lucide-react"; +import { cn } from "@/lib/utils"; +import { Button } from "@/components/ui/button"; +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { ScrollArea } from "@/components/ui/scroll-area"; +import { Badge } from "@/components/ui/badge"; +import { Input } from "@/components/ui/input"; +import { Textarea } from "@/components/ui/textarea"; +import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; +import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"; +import { Separator } from "@/components/ui/separator"; +import { Slider } from "@/components/ui/slider"; +import { Label } from "@/components/ui/label"; + +interface AgentNode { + id: string; + role: string; + name: string; + icon: React.ReactNode; + color: string; + status: "idle" | "active" | "completed" | "error"; + progress: number; + lastThought: string; + thoughts: string[]; +} + +interface AgentEvent { + type: string; + payload: any; + timestamp: string; +} + +interface WebSocketMessage { + event_type: string; + payload: any; + timestamp: string; + correlation_id?: string; +} + +const AGENT_ROLES: AgentNode[] = [ + { + id: "planner", + role: "planner", + name: "Planner", + icon: , + color: "bg-blue-500", + status: "idle", + progress: 0, + lastThought: "", + thoughts: [], + }, + { + id: "researcher", + role: "researcher", + name: "Researcher", + icon: , + color: "bg-green-500", + status: "idle", + progress: 0, + lastThought: "", + thoughts: [], + }, + { + id: "coder", + role: "coder", + name: "Coder", + icon: , + color: "bg-purple-500", + status: "idle", + progress: 0, + lastThought: "", + thoughts: [], + }, + { + id: "evaluator", + role: "evaluator", + name: "Evaluator", + icon: , + color: "bg-orange-500", + status: "idle", + progress: 0, + lastThought: "", + thoughts: [], + }, + { + id: "safety_gate", + role: "safety_gate", + name: "Safety Gate", + icon: , + color: "bg-red-500", + status: "idle", + progress: 0, + lastThought: "", + thoughts: [], + }, +]; + +const AGENT_CONNECTIONS = [ + { from: "planner", to: "researcher" }, + { from: "researcher", to: "coder" }, + { from: "coder", to: "evaluator" }, + { from: "evaluator", to: "safety_gate" }, +]; + +export default function AgentsPage() { + const [ws, setWs] = useState(null); + const canvasRef = useRef(null); + + const [connected, setConnected] = useState(false); + const [agents, setAgents] = useState(AGENT_ROLES); + const [objective, setObjective] = useState(""); + const [isRunning, setIsRunning] = useState(false); + const [events, setEvents] = useState([]); + const [fineTuneEvents, setFineTuneEvents] = useState([]); + const [selectedAgent, setSelectedAgent] = useState(null); + const [showFineTune, setShowFineTune] = useState(false); + const [modelName, setModelName] = useState("Qwen2.5-0.5B"); + const [adapterPath, setAdapterPath] = useState(""); + const [autoFineTune, setAutoFineTune] = useState(true); + const eventsEndRef = useRef(null); + const fineTuneEndRef = useRef(null); + + // Draw agent graph on canvas + useEffect(() => { + if (canvasRef.current) { + drawAgentGraph(canvasRef.current, agents); + } + }, [agents]); + + useEffect(() => { + const websocket = new WebSocket("ws://127.0.0.1:8765"); + + websocket.onopen = () => { + setConnected(true); + setWs(websocket); // set the websocket state when the connection opens + console.log("Connected to agent bridge"); + + // Subscribe to all event types + websocket.send(JSON.stringify({ + subscribe: [ + "agent_thought", + "agent_step", + "fine_tune_epoch", + "fine_tune_loss", + "swarm_status", + "objective_started", + "objective_completed", + "objective_failed", + "checkpoint_created", + "consensus_request", + "consensus_result", + "error" + ] + })); + }; + + websocket.onmessage = (event) => { + try { + const message: WebSocketMessage = JSON.parse(event.data); + handleMessage(message); + } catch (e) { + console.error("Failed to parse message:", e); + } + }; + + websocket.onclose = () => { + setConnected(false); + setWs(null); // clear the websocket state when the connection closes + console.log("Disconnected from agent bridge"); + }; + + websocket.onerror = (error) => { + console.error("WebSocket error:", error); + }; + + return () => { + websocket.close(); + }; + }, []); // Empty deps because we only want to run once + + const handleMessage = (message: WebSocketMessage) => { + const { event_type, payload } = message; + + switch (event_type) { + case "agent_thought": + updateAgentThought(payload); + break; + case "agent_step": + updateAgentStep(payload); + break; + case "fine_tune_epoch": + addFineTuneEvent(payload); + break; + case "fine_tune_loss": + updateFineTuneLoss(payload); + break; + case "swarm_status": + // Handle swarm status updates + break; + case "objective_started": + setIsRunning(true); + resetAgents(); + addEvent({ type: "objective_started", payload, timestamp: new Date().toISOString() }); + break; + case "objective_completed": + setIsRunning(false); + addEvent({ type: "objective_completed", payload, timestamp: new Date().toISOString() }); + break; + case "objective_failed": + setIsRunning(false); + addEvent({ type: "objective_failed", payload, timestamp: new Date().toISOString() }); + break; + case "error": + addEvent({ type: "error", payload, timestamp: new Date().toISOString() }); + break; + default: + // Handle response messages + if (payload?.response) { + handleResponse(payload); + } + } + }; + + const updateAgentThought = (payload: any) => { + const { agent, thought, step } = payload; + setAgents(prev => prev.map(a => { + if (a.id === agent) { + const newThoughts = [...a.thoughts, thought]; + return { + ...a, + lastThought: thought, + thoughts: newThoughts.slice(-10), // Keep last 10 thoughts + status: "active" as const, + progress: step || a.progress, + }; + } + return a; + })); + addEvent({ type: "agent_thought", payload, timestamp: new Date().toISOString() }); + }; + + const updateAgentStep = (payload: any) => { + const { agent, status, progress } = payload; + setAgents(prev => prev.map(a => + a.id === agent ? { ...a, status, progress: progress || a.progress } : a + )); + }; + + const addFineTuneEvent = (payload: any) => { + setFineTuneEvents(prev => [...prev.slice(-50), { ...payload, timestamp: new Date().toISOString() }]); + }; + + const updateFineTuneLoss = (payload: any) => { + setFineTuneEvents(prev => prev.map(e => + e.epoch === payload.epoch ? { ...e, ...payload } : e + )); + }; + + const addEvent = (event: AgentEvent) => { + setEvents(prev => [...prev.slice(-100), event]); + // Auto-scroll + setTimeout(() => eventsEndRef.current?.scrollIntoView({ behavior: "smooth" }), 0); + }; + + const handleResponse = (payload: any) => { + // Handle command responses + console.log("Response:", payload); + }; + + const resetAgents = () => { + setAgents(AGENT_ROLES.map(a => ({ ...a, status: "idle" as const, progress: 0, lastThought: "", thoughts: [] }))); + setFineTuneEvents([]); + }; + + const sendCommand = (commandType: string, payload: any) => { + if (ws && ws.readyState === WebSocket.OPEN) { + ws.send(JSON.stringify({ + command_type: commandType, + payload, + request_id: crypto.randomUUID(), + timestamp: new Date().toISOString(), + })); + } + }; + + const handleSubmitObjective = () => { + if (!objective.trim()) return; + sendCommand("submit_objective", { objective }); + }; + + const handleTriggerFineTune = () => { + if (!adapterPath.trim()) return; + sendCommand("trigger_finetune", { adapter_path: adapterPath }); + }; + + const handlePauseHarness = () => { + sendCommand("pause_harness", {}); + }; + + const handleResumeHarness = () => { + sendCommand("resume_harness", {}); + }; + + const handleSwitchModel = () => { + sendCommand("switch_model", { model: modelName }); + }; + + const getStatusColor = (status: string) => { + switch (status) { + case "active": return "bg-green-500 animate-pulse"; + case "completed": return "bg-blue-500"; + case "error": return "bg-red-500"; + default: return "bg-gray-400"; + } + }; + + const getAgentById = (id: string) => agents.find(a => a.id === id); + + return ( +
+ {/* Sidebar - Agent Graph */} +
+ + + + + + Agent Swarm + + + {connected ? : } + {connected ? "Connected" : "Disconnected"} + + + + +
+ {/* Agent Graph Canvas */} + +
+ + + + {/* Controls */} + +
+ Controls + + +
+ +