Add comprehensive development roadmap via GitHub Issues
Created 10 detailed GitHub issues covering: - Project activation and management UI (#1-2) - Worker node coordination and visualization (#3-4) - Automated GitHub repository scanning (#5) - Intelligent model-to-issue matching (#6) - Multi-model task execution system (#7) - N8N workflow integration (#8) - Hive-Bzzz P2P bridge (#9) - Peer assistance protocol (#10) Each issue includes detailed specifications, acceptance criteria, technical implementation notes, and dependency mapping. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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anthonyrawlins
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backend/app/services/__pycache__/__init__.cpython-310.pyc
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backend/app/services/__pycache__/task_service.cpython-310.pyc
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backend/app/services/capability_detector.py
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backend/app/services/capability_detector.py
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"""
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Capability Detection Service for Hive Agents
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This service automatically detects agent capabilities and specializations based on
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the models installed on each Ollama endpoint. It replaces hardcoded specializations
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with dynamic detection based on actual model capabilities.
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"""
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import httpx
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import asyncio
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from typing import Dict, List, Set, Optional, Tuple
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from enum import Enum
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import logging
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logger = logging.getLogger(__name__)
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class ModelCapability(str, Enum):
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"""Model capability categories based on model characteristics"""
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CODE_GENERATION = "code_generation"
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CODE_REVIEW = "code_review"
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REASONING = "reasoning"
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DOCUMENTATION = "documentation"
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TESTING = "testing"
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VISUAL_ANALYSIS = "visual_analysis"
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GENERAL_AI = "general_ai"
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KERNEL_DEV = "kernel_dev"
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PYTORCH_DEV = "pytorch_dev"
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PROFILER = "profiler"
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class AgentSpecialty(str, Enum):
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"""Dynamic agent specializations based on model capabilities"""
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ADVANCED_CODING = "advanced_coding" # starcoder2, deepseek-coder-v2, devstral
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REASONING_ANALYSIS = "reasoning_analysis" # phi4-reasoning, granite3-dense
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CODE_REVIEW_DOCS = "code_review_docs" # codellama, qwen2.5-coder
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GENERAL_AI = "general_ai" # llama3, gemma, mistral
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MULTIMODAL = "multimodal" # llava, vision models
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LIGHTWEIGHT = "lightweight" # small models < 8B
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# Model capability mapping based on model names and characteristics
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MODEL_CAPABILITIES = {
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# Advanced coding models
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"starcoder2": [ModelCapability.CODE_GENERATION, ModelCapability.KERNEL_DEV],
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"deepseek-coder": [ModelCapability.CODE_GENERATION, ModelCapability.CODE_REVIEW],
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"devstral": [ModelCapability.CODE_GENERATION, ModelCapability.PROFILER],
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"codellama": [ModelCapability.CODE_GENERATION, ModelCapability.CODE_REVIEW],
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"qwen2.5-coder": [ModelCapability.CODE_GENERATION, ModelCapability.CODE_REVIEW],
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"qwen3": [ModelCapability.CODE_GENERATION, ModelCapability.REASONING],
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# Reasoning and analysis models
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"phi4-reasoning": [ModelCapability.REASONING, ModelCapability.PROFILER],
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"phi4": [ModelCapability.REASONING, ModelCapability.GENERAL_AI],
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"granite3-dense": [ModelCapability.REASONING, ModelCapability.PYTORCH_DEV],
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"deepseek-r1": [ModelCapability.REASONING, ModelCapability.CODE_REVIEW],
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# General purpose models
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"llama3": [ModelCapability.GENERAL_AI, ModelCapability.DOCUMENTATION],
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"gemma": [ModelCapability.GENERAL_AI, ModelCapability.TESTING],
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"mistral": [ModelCapability.GENERAL_AI, ModelCapability.DOCUMENTATION],
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"dolphin": [ModelCapability.GENERAL_AI, ModelCapability.REASONING],
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# Multimodal models
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"llava": [ModelCapability.VISUAL_ANALYSIS, ModelCapability.DOCUMENTATION],
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# Tool use models
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"llama3-groq-tool-use": [ModelCapability.CODE_GENERATION, ModelCapability.TESTING],
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}
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# Specialization determination based on capabilities
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SPECIALTY_MAPPING = {
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frozenset([ModelCapability.CODE_GENERATION, ModelCapability.KERNEL_DEV]): AgentSpecialty.ADVANCED_CODING,
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frozenset([ModelCapability.CODE_GENERATION, ModelCapability.PROFILER]): AgentSpecialty.ADVANCED_CODING,
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frozenset([ModelCapability.REASONING, ModelCapability.PROFILER]): AgentSpecialty.REASONING_ANALYSIS,
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frozenset([ModelCapability.REASONING, ModelCapability.PYTORCH_DEV]): AgentSpecialty.REASONING_ANALYSIS,
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frozenset([ModelCapability.CODE_REVIEW, ModelCapability.DOCUMENTATION]): AgentSpecialty.CODE_REVIEW_DOCS,
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frozenset([ModelCapability.VISUAL_ANALYSIS]): AgentSpecialty.MULTIMODAL,
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}
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class CapabilityDetector:
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"""Detects agent capabilities by analyzing available models"""
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def __init__(self, timeout: int = 10):
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self.timeout = timeout
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self.client = httpx.AsyncClient(timeout=timeout)
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async def get_available_models(self, endpoint: str) -> List[Dict]:
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"""Get list of available models from Ollama endpoint"""
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try:
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# Handle endpoints with or without protocol
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if not endpoint.startswith(('http://', 'https://')):
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endpoint = f"http://{endpoint}"
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# Ensure endpoint has port if not specified
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if ':' not in endpoint.split('//')[-1]:
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endpoint = f"{endpoint}:11434"
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response = await self.client.get(f"{endpoint}/api/tags")
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response.raise_for_status()
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data = response.json()
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return data.get('models', [])
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except Exception as e:
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logger.error(f"Failed to get models from {endpoint}: {e}")
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return []
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def analyze_model_capabilities(self, model_name: str) -> List[ModelCapability]:
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"""Analyze a single model to determine its capabilities"""
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capabilities = []
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# Normalize model name for matching
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normalized_name = model_name.lower().split(':')[0] # Remove version tags
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# Check for exact matches first
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for pattern, caps in MODEL_CAPABILITIES.items():
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if pattern in normalized_name:
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capabilities.extend(caps)
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break
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# If no specific match, determine by model size and type
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if not capabilities:
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if any(size in normalized_name for size in ['3b', '7b']):
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capabilities.append(ModelCapability.LIGHTWEIGHT)
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capabilities.append(ModelCapability.GENERAL_AI)
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return list(set(capabilities)) # Remove duplicates
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def determine_agent_specialty(self, all_capabilities: List[ModelCapability]) -> AgentSpecialty:
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"""Determine agent specialty based on combined model capabilities"""
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capability_set = frozenset(all_capabilities)
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# Check for exact specialty matches
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for caps, specialty in SPECIALTY_MAPPING.items():
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if caps.issubset(capability_set):
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return specialty
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# Fallback logic based on dominant capabilities
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if ModelCapability.CODE_GENERATION in all_capabilities:
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if ModelCapability.REASONING in all_capabilities:
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return AgentSpecialty.ADVANCED_CODING
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elif ModelCapability.CODE_REVIEW in all_capabilities:
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return AgentSpecialty.CODE_REVIEW_DOCS
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else:
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return AgentSpecialty.ADVANCED_CODING
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elif ModelCapability.REASONING in all_capabilities:
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return AgentSpecialty.REASONING_ANALYSIS
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elif ModelCapability.VISUAL_ANALYSIS in all_capabilities:
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return AgentSpecialty.MULTIMODAL
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else:
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return AgentSpecialty.GENERAL_AI
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async def detect_agent_capabilities(self, endpoint: str) -> Tuple[List[str], AgentSpecialty, List[ModelCapability]]:
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"""
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Detect agent capabilities and determine specialty
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Returns:
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Tuple of (model_names, specialty, capabilities)
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"""
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models = await self.get_available_models(endpoint)
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if not models:
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return [], AgentSpecialty.GENERAL_AI, [ModelCapability.GENERAL_AI]
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model_names = [model['name'] for model in models]
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all_capabilities = []
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# Analyze each model
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for model in models:
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model_caps = self.analyze_model_capabilities(model['name'])
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all_capabilities.extend(model_caps)
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# Remove duplicates and determine specialty
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unique_capabilities = list(set(all_capabilities))
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specialty = self.determine_agent_specialty(unique_capabilities)
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return model_names, specialty, unique_capabilities
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async def scan_cluster_capabilities(self, endpoints: List[str]) -> Dict[str, Dict]:
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"""Scan multiple endpoints and return capabilities for each"""
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results = {}
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tasks = []
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for endpoint in endpoints:
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task = self.detect_agent_capabilities(endpoint)
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tasks.append((endpoint, task))
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# Execute all scans concurrently
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for endpoint, task in tasks:
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try:
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models, specialty, capabilities = await task
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results[endpoint] = {
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'models': models,
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'model_count': len(models),
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'specialty': specialty,
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'capabilities': capabilities,
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'status': 'online' if models else 'offline'
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}
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except Exception as e:
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logger.error(f"Failed to scan {endpoint}: {e}")
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results[endpoint] = {
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'models': [],
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'model_count': 0,
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'specialty': AgentSpecialty.GENERAL_AI,
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'capabilities': [],
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'status': 'error',
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'error': str(e)
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}
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return results
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async def close(self):
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"""Close the HTTP client"""
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await self.client.aclose()
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# Convenience function for quick capability detection
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async def detect_capabilities(endpoint: str) -> Dict:
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"""Quick capability detection for a single endpoint"""
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detector = CapabilityDetector()
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try:
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models, specialty, capabilities = await detector.detect_agent_capabilities(endpoint)
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return {
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'endpoint': endpoint,
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'models': models,
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'model_count': len(models),
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'specialty': specialty.value,
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'capabilities': [cap.value for cap in capabilities],
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'status': 'online' if models else 'offline'
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}
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finally:
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await detector.close()
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if __name__ == "__main__":
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# Test the capability detector
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async def test_detection():
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endpoints = [
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"192.168.1.27:11434", # WALNUT
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"192.168.1.113:11434", # IRONWOOD
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"192.168.1.72:11434", # ACACIA
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]
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detector = CapabilityDetector()
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try:
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results = await detector.scan_cluster_capabilities(endpoints)
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for endpoint, data in results.items():
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print(f"\n{endpoint}:")
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print(f" Models: {data['model_count']}")
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print(f" Specialty: {data['specialty']}")
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print(f" Capabilities: {data['capabilities']}")
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print(f" Status: {data['status']}")
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finally:
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await detector.close()
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asyncio.run(test_detection())
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