From 6f8c567ee62e442060224b664a6efef348a5d1d8 Mon Sep 17 00:00:00 2001 From: Cursor Agent Date: Wed, 26 Nov 2025 23:14:31 +0000 Subject: [PATCH 1/4] feat: Add Product Requirements Document for JARVIS Neural Mesh Co-authored-by: djamesr23 --- PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md | 1053 ++++++++++++++++++++++++++ 1 file changed, 1053 insertions(+) create mode 100644 PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md diff --git a/PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md b/PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md new file mode 100644 index 0000000000..36d166ff09 --- /dev/null +++ b/PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md @@ -0,0 +1,1053 @@ +# Product Requirements Document: JARVIS Autonomous Neural Mesh System + +**Document Version:** 1.0 +**Date:** November 26, 2025 +**Project:** JARVIS Multi-Agent System - Autonomy Infrastructure +**Status:** Ready for Implementation + +--- + +## Executive Summary + +This PRD defines the requirements for implementing the **Neural Mesh Core Infrastructure** that will enable JARVIS to operate autonomously. Currently, JARVIS has 60+ specialized AI agents across three tiers, but lacks the fundamental communication and coordination infrastructure needed for autonomous operation. Many critical "brain" agents (Goal Inference, Activity Recognition, Workflow Pattern, Autonomous Decision) are dormant and disconnected. + +**Goal:** Transform JARVIS from a reactive system into an autonomous, proactive AI assistant by implementing core infrastructure and activating intelligent agents. + +**Target Timeline:** 3-6 months (Phases 1-2) + +--- + +## Background & Context + +### Current State +- **60+ specialized AI agents** organized in 3 tiers (Master Intelligence, Core Domain, Specialized Sub-Agents) +- **Active Components:** UAE (Unified Awareness Engine), SAI (Situational Awareness Intelligence), Claude Vision, VSMS (Visual State Management), Voice Pipeline +- **Dormant "Brain" Agents:** Goal Inference System, Activity Recognition Engine, Workflow Pattern Engine, Predictive Precomputation Engine, Autonomous Decision Engine, Autonomous Behaviors Manager +- **Missing Infrastructure:** No inter-agent communication system, no shared memory, no orchestration layer + +### Problem Statement +JARVIS agents operate in isolation without: +1. A way to communicate with each other (no messaging bus) +2. Shared memory to store and retrieve learned patterns (no knowledge graph) +3. Coordination mechanism for multi-agent workflows (no orchestrator) +4. Discovery mechanism for agent capabilities (no registry) + +### Technology Stack +- **Language:** Python 3.x +- **Framework:** FastAPI +- **Database:** SQLite (local), Cloud SQL (cloud) +- **Voice:** Whisper +- **Vision:** YOLO, Claude Vision API +- **Platform:** macOS (Yabai, Core Graphics API, AppleScript) +- **Cloud:** GCP (project: jarvis-473803) + +--- + +## Goals & Objectives + +### Primary Goals +1. **Enable Inter-Agent Communication:** Implement a pub/sub messaging bus for asynchronous agent communication +2. **Create Shared Memory:** Build a knowledge graph for persistent, queryable agent memory +3. **Coordinate Multi-Agent Tasks:** Implement an orchestrator for task decomposition and agent coordination +4. **Dynamic Agent Discovery:** Build a registry for agent registration and capability discovery +5. **Activate Intelligent Agents:** Wire up dormant brain agents to the neural mesh + +### Success Metrics +- ✅ All 60+ agents migrated to BaseAgent standard +- ✅ Communication Bus handles 1000+ messages/sec with <10ms latency +- ✅ Knowledge Graph stores and retrieves patterns with <50ms query time +- ✅ Orchestrator coordinates 5+ agents in parallel workflows +- ✅ Goal Inference System predicts user intent with >70% accuracy +- ✅ Activity Recognition Engine identifies workflows with >80% accuracy +- ✅ System operates autonomously for 4+ hours without user intervention + +--- + +## Functional Requirements + +### FR-1: Agent Communication Bus +**Priority:** P0 (Critical) + +**Description:** A publish-subscribe messaging system for asynchronous inter-agent communication. + +**Requirements:** +- FR-1.1: Support pub/sub pattern with topic-based routing +- FR-1.2: Handle message types: `TASK_ASSIGNED`, `TASK_COMPLETED`, `TASK_FAILED`, `QUERY`, `RESPONSE`, `EVENT`, `CUSTOM` +- FR-1.3: Support message priorities: `CRITICAL`, `HIGH`, `NORMAL`, `LOW` +- FR-1.4: Guarantee at-least-once delivery for CRITICAL/HIGH priority messages +- FR-1.5: Store message history for debugging (last 1000 messages per agent) +- FR-1.6: Support both local (in-process) and distributed (Redis/RabbitMQ) backends +- FR-1.7: Provide async API: `publish()`, `subscribe()`, `unsubscribe()` + +**Acceptance Criteria:** +- Agent A can publish a message that Agent B receives within 10ms +- Message bus handles 1000 messages/sec without dropping messages +- Failed deliveries are retried 3 times with exponential backoff +- Message history is queryable via `get_message_history(agent_name, limit)` + +--- + +### FR-2: Shared Knowledge Graph +**Priority:** P0 (Critical) + +**Description:** A centralized, persistent memory system for storing and retrieving learned patterns, facts, and solutions. + +**Requirements:** +- FR-2.1: Store knowledge types: `workflow_pattern`, `ui_pattern`, `user_preference`, `error_solution`, `optimization`, `automation_rule` +- FR-2.2: Support vector embeddings for semantic search (using sentence-transformers) +- FR-2.3: Support graph relationships for connected knowledge (using networkx) +- FR-2.4: Provide async API: `add_knowledge()`, `query_knowledge()`, `update_knowledge()`, `delete_knowledge()` +- FR-2.5: Return similarity scores for semantic queries +- FR-2.6: Support filtering by knowledge type, timestamp, source agent +- FR-2.7: Persist to disk (ChromaDB for vectors, SQLite for graph) +- FR-2.8: Support hybrid local/cloud storage + +**Data Schema:** +```python +KnowledgeEntry { + id: str, + type: str, # workflow_pattern, ui_pattern, etc. + data: Dict[str, Any], + embedding: np.ndarray, + timestamp: datetime, + source_agent: str, + confidence: float, + usage_count: int, + relationships: List[str] # IDs of related knowledge +} +``` + +**Acceptance Criteria:** +- Can store 10,000+ knowledge entries +- Semantic queries return results in <50ms +- Knowledge persists across system restarts +- Agents can discover related knowledge via graph relationships + +--- + +### FR-3: Multi-Agent Orchestrator +**Priority:** P0 (Critical) + +**Description:** A central coordinator for decomposing tasks, selecting agents, and managing multi-agent workflows. + +**Requirements:** +- FR-3.1: Accept high-level tasks from UAE/SAI +- FR-3.2: Decompose tasks into subtasks based on agent capabilities +- FR-3.3: Select optimal agents for each subtask (via registry) +- FR-3.4: Coordinate parallel agent execution +- FR-3.5: Handle task failures with retry logic and fallback agents +- FR-3.6: Aggregate results from multiple agents +- FR-3.7: Track task execution state: `PENDING`, `RUNNING`, `COMPLETED`, `FAILED` +- FR-3.8: Provide async API: `submit_task()`, `get_task_status()`, `cancel_task()` + +**Orchestration Flow:** +``` +1. Receive task from UAE/SAI +2. Query registry for capable agents +3. Decompose task into subtasks +4. Assign subtasks to agents (via Communication Bus) +5. Monitor progress and handle failures +6. Aggregate results +7. Return to requesting agent +``` + +**Acceptance Criteria:** +- Can coordinate 5+ agents in parallel +- Task failures trigger automatic retries (max 3 attempts) +- If primary agent fails, fallback agent is selected automatically +- Task execution time is tracked and optimized + +--- + +### FR-4: Agent Registry +**Priority:** P0 (Critical) + +**Description:** A system for dynamic agent registration, health monitoring, and capability discovery. + +**Requirements:** +- FR-4.1: Agents register on startup with name, type, capabilities, backend (local/cloud) +- FR-4.2: Track agent state: `INITIALIZING`, `ACTIVE`, `BUSY`, `IDLE`, `ERROR`, `OFFLINE` +- FR-4.3: Monitor agent health via periodic heartbeats (every 30 seconds) +- FR-4.4: Mark agents as `OFFLINE` if heartbeat missed for 60 seconds +- FR-4.5: Support agent capability queries: "Find agents with capability X" +- FR-4.6: Track agent load (0.0 = idle, 1.0 = fully loaded) +- FR-4.7: Provide async API: `register()`, `unregister()`, `update_status()`, `find_agents()`, `get_agent_info()` + +**Agent Metadata:** +```python +AgentInfo { + name: str, + type: str, # vision, voice, system, ml, etc. + capabilities: Set[str], + backend: str, # "local" or "cloud" + state: AgentState, + load: float, # 0.0 to 1.0 + last_heartbeat: datetime, + metadata: Dict[str, Any] +} +``` + +**Acceptance Criteria:** +- Registry tracks all 60+ JARVIS agents +- Offline agents are detected within 60 seconds +- Capability queries return agents sorted by load (least loaded first) +- Registry persists to disk and restores on restart + +--- + +### FR-5: BaseAgent Standard +**Priority:** P0 (Critical) + +**Description:** A base class that all JARVIS agents inherit from, providing standardized lifecycle and communication. + +**Requirements:** +- FR-5.1: All agents inherit from `BaseAgent` abstract class +- FR-5.2: Provide lifecycle methods: `initialize()`, `start()`, `stop()` +- FR-5.3: Provide communication methods: `publish()`, `subscribe()` +- FR-5.4: Provide knowledge methods: `query_knowledge()`, `add_knowledge()` +- FR-5.5: Handle heartbeat transmission automatically +- FR-5.6: Abstract method `execute_task()` for task handling +- FR-5.7: Abstract methods `on_initialize()`, `on_start()`, `on_stop()` for custom logic +- FR-5.8: Support both local and cloud backends + +**Class Structure:** +```python +class BaseAgent(ABC): + def __init__(self, agent_name: str, agent_type: str, + capabilities: Set[str], backend: str = "local") + + # Lifecycle + async def initialize(self) + async def start(self) + async def stop(self) + + # Communication + async def publish(self, to_agent: str, message_type: MessageType, + payload: Dict[str, Any], priority: MessagePriority) + async def subscribe(self, message_type: MessageType, handler: Callable) + + # Knowledge + async def query_knowledge(self, query: str, + knowledge_types: List[str], limit: int) + async def add_knowledge(self, knowledge_type: str, data: Dict[str, Any]) + + # Abstract methods to implement + @abstractmethod + async def execute_task(self, task_payload: Dict[str, Any]) -> Any + @abstractmethod + async def on_initialize(self) + @abstractmethod + async def on_start(self) + @abstractmethod + async def on_stop(self) +``` + +**Acceptance Criteria:** +- All existing agents migrated to inherit from BaseAgent +- Agents can communicate via publish/subscribe without direct coupling +- Agents can query and add knowledge without direct database access +- Agent lifecycle is managed consistently across the system + +--- + +### FR-6: Goal Inference System +**Priority:** P1 (High) + +**Description:** Predict user intent and next actions based on context, history, and patterns. + +**Requirements:** +- FR-6.1: Analyze current context (screen, voice, recent actions) via SAI/UAE +- FR-6.2: Query knowledge graph for similar past workflows +- FR-6.3: Use Transformer model for intent classification +- FR-6.4: Predict next 1-3 likely user actions with confidence scores +- FR-6.5: Publish predictions to Predictive Precomputation Engine +- FR-6.6: Learn from user corrections (when prediction is wrong) +- FR-6.7: Support confidence threshold configuration (default: 0.7) + +**Inference Pipeline:** +``` +1. Receive context from SAI (screen state, voice command, etc.) +2. Query knowledge graph for similar workflows +3. Use Transformer model to classify intent +4. Predict next actions with confidence scores +5. If confidence > threshold, publish to Predictive Precomputation +6. If confidence > high_threshold (0.85), publish to Autonomous Decision +7. Store prediction and outcome for learning +``` + +**Acceptance Criteria:** +- Predicts user intent with >70% accuracy +- Responds within 200ms +- Learns from corrections and improves over time +- Stores prediction history in knowledge graph + +--- + +### FR-7: Activity Recognition Engine +**Priority:** P1 (High) + +**Description:** Detect and classify user activities and workflows in real-time. + +**Requirements:** +- FR-7.1: Monitor screen changes, keyboard/mouse events, application switches +- FR-7.2: Classify activities: `coding`, `debugging`, `browsing`, `writing`, `meeting`, `idle` +- FR-7.3: Detect workflow patterns (sequences of activities) +- FR-7.4: Store recognized workflows in knowledge graph +- FR-7.5: Trigger Workflow Pattern Engine when new pattern detected +- FR-7.6: Support custom activity definitions via config + +**Activity Recognition:** +``` +1. Subscribe to VSMS for screen changes +2. Subscribe to system events for keyboard/mouse/app switches +3. Extract features: active app, window title, focused element, typing speed, etc. +4. Use ML model to classify current activity +5. Track activity sequences to detect workflows +6. Store workflows in knowledge graph +``` + +**Acceptance Criteria:** +- Identifies user activities with >80% accuracy +- Detects workflow patterns after 2-3 repetitions +- Stores workflow patterns for future automation +- Minimal performance impact (<5% CPU) + +--- + +### FR-8: Workflow Pattern Engine +**Priority:** P1 (High) + +**Description:** Learn and automate repetitive user workflows. + +**Requirements:** +- FR-8.1: Receive workflow patterns from Activity Recognition Engine +- FR-8.2: Identify repetitive workflows (occurred 3+ times) +- FR-8.3: Create automation rules for repetitive workflows +- FR-8.4: Store automation rules in knowledge graph +- FR-8.5: Publish automation suggestions to UAE (for user confirmation) +- FR-8.6: Execute automated workflows when triggered +- FR-8.7: Support workflow parameterization (e.g., "open file X" where X varies) + +**Automation Flow:** +``` +1. Receive workflow pattern from Activity Recognition +2. Check if pattern is repetitive (occurred 3+ times) +3. Create automation rule with trigger conditions +4. Ask user for confirmation via UAE +5. If confirmed, store rule and monitor for trigger conditions +6. When triggered, execute workflow via Multi-Agent Orchestrator +7. Track success/failure and adjust rule +``` + +**Acceptance Criteria:** +- Detects repetitive workflows after 3 occurrences +- Creates automation rules with >90% accuracy +- Executes automated workflows successfully >85% of the time +- Allows user to approve/reject automation suggestions + +--- + +### FR-9: Predictive Precomputation Engine +**Priority:** P2 (Medium) + +**Description:** Pre-compute likely next actions for performance optimization. + +**Requirements:** +- FR-9.1: Receive predictions from Goal Inference System +- FR-9.2: Pre-load resources (files, data, models) for predicted actions +- FR-9.3: Warm up agents likely to be needed +- FR-9.4: Cache computation results for predicted queries +- FR-9.5: Track hit rate (how often predictions are correct) +- FR-9.6: Discard pre-computed results after timeout (default: 5 minutes) + +**Precomputation Strategy:** +``` +1. Receive prediction from Goal Inference (e.g., "User likely to open file X") +2. Pre-load file X into memory +3. Warm up relevant agents (e.g., code analysis agent) +4. When user actually opens file X, serve from cache (instant) +5. Track prediction accuracy +``` + +**Acceptance Criteria:** +- Pre-computation hit rate >60% +- Reduces action latency by 50%+ for predicted actions +- Memory usage stays under 500MB for pre-computed data +- Automatically adjusts strategy based on hit rate + +--- + +### FR-10: Autonomous Decision Engine +**Priority:** P2 (Medium) + +**Description:** Make autonomous decisions and execute actions without user input. + +**Requirements:** +- FR-10.1: Receive high-confidence predictions from Goal Inference (confidence >0.85) +- FR-10.2: Evaluate if action is safe to execute autonomously +- FR-10.3: Execute action via Multi-Agent Orchestrator +- FR-10.4: Log all autonomous actions for audit trail +- FR-10.5: Support undo mechanism for autonomous actions +- FR-10.6: Respect user-defined autonomy level: `OFF`, `LOW`, `MEDIUM`, `HIGH` +- FR-10.7: Never execute destructive actions (delete, overwrite) without confirmation + +**Safety Rules:** +```python +SAFE_AUTONOMOUS_ACTIONS = { + "LOW": ["open_file", "switch_app", "scroll", "search"], + "MEDIUM": ["open_file", "switch_app", "scroll", "search", "navigate", "run_test"], + "HIGH": ["*"], # All except destructive +} + +NEVER_AUTONOMOUS = ["delete_file", "overwrite_file", "commit", "push", "deploy"] +``` + +**Acceptance Criteria:** +- Only executes actions when confidence >85% +- Respects user-defined autonomy level +- Never executes destructive actions autonomously +- Provides undo for last 10 autonomous actions +- All actions are logged with timestamp, trigger, result + +--- + +### FR-11: Autonomous Behaviors Manager +**Priority:** P2 (Medium) + +**Description:** Manage and coordinate autonomous behavior patterns. + +**Requirements:** +- FR-11.1: Define behavior patterns: `proactive_assistance`, `error_prevention`, `performance_optimization` +- FR-11.2: Monitor system state and trigger behaviors when conditions met +- FR-11.3: Coordinate multiple behaviors to avoid conflicts +- FR-11.4: Learn behavior effectiveness and adjust trigger conditions +- FR-11.5: Support user-defined custom behaviors via config + +**Example Behaviors:** +```yaml +behaviors: + - name: proactive_error_detection + trigger: "Vision detects error message on screen" + actions: + - Query knowledge graph for solution + - If solution found, suggest to user + - If not found, search online and store solution + + - name: performance_optimization + trigger: "System detects slow response time" + actions: + - Profile current agents + - Offload heavy tasks to cloud + - Cache frequently accessed data + + - name: workflow_suggestion + trigger: "Repetitive pattern detected" + actions: + - Create automation rule + - Ask user for confirmation +``` + +**Acceptance Criteria:** +- Supports 5+ behavior patterns out of the box +- Behaviors trigger correctly based on conditions +- No behavior conflicts (managed via priority system) +- Users can enable/disable behaviors individually +- Behavior effectiveness is tracked and displayed + +--- + +### FR-12: Configuration System +**Priority:** P1 (High) + +**Description:** Centralized configuration for autonomous features. + +**Requirements:** +- FR-12.1: Configuration file: `config/autonomous_settings.yaml` +- FR-12.2: Support hierarchical config (global, agent-specific) +- FR-12.3: Hot-reload on config changes (no restart needed) +- FR-12.4: Validate config on load (fail fast on errors) +- FR-12.5: Provide defaults for all settings + +**Configuration Schema:** +```yaml +autonomous: + enabled: true + level: "MEDIUM" # OFF, LOW, MEDIUM, HIGH + + goal_inference: + enabled: true + confidence_threshold: 0.7 + high_confidence_threshold: 0.85 + model: "facebook/bart-large-mnli" + + activity_recognition: + enabled: true + activities: ["coding", "debugging", "browsing", "writing", "meeting", "idle"] + min_pattern_repetitions: 3 + + workflow_automation: + enabled: true + require_user_confirmation: true + max_automated_workflows: 20 + + predictive_precomputation: + enabled: true + cache_timeout_seconds: 300 + max_cache_size_mb: 500 + + autonomous_decision: + enabled: false # Disabled by default for safety + allowed_actions: ["open_file", "switch_app", "scroll", "search"] + + behaviors: + proactive_error_detection: true + performance_optimization: true + workflow_suggestion: true + +communication_bus: + backend: "local" # "local" or "redis" + redis_url: "redis://localhost:6379" + message_history_size: 1000 + +knowledge_graph: + backend: "local" # "local" or "cloud" + embedding_model: "sentence-transformers/all-MiniLM-L6-v2" + vector_db_path: "backend/data/chroma" + graph_db_path: "backend/data/knowledge_graph.db" + max_entries: 100000 +``` + +**Acceptance Criteria:** +- Configuration loads on system startup +- Config changes are detected and applied within 5 seconds +- Invalid config triggers clear error message +- All settings have sensible defaults + +--- + +## Non-Functional Requirements + +### NFR-1: Performance +- Communication Bus: <10ms message latency, 1000+ msg/sec throughput +- Knowledge Graph: <50ms query time for semantic search +- Agent heartbeats: <5ms processing time +- Goal Inference: <200ms prediction time +- Activity Recognition: <5% CPU usage +- Total system overhead: <10% CPU, <1GB RAM + +### NFR-2: Reliability +- Communication Bus: At-least-once delivery for CRITICAL/HIGH priority messages +- Agent failures: Automatic recovery and fallback +- Knowledge Graph: Persistent storage with backup/restore +- Uptime: 99.9% availability for core infrastructure + +### NFR-3: Scalability +- Support 60+ agents (current) to 100+ agents (future) +- Knowledge Graph: 100,000+ entries +- Communication Bus: 10,000+ messages/hour +- Orchestrator: Coordinate 10+ agents in parallel + +### NFR-4: Security +- No destructive actions executed autonomously +- All autonomous actions logged for audit +- User can disable autonomous features at any time +- Sensitive data (passwords, API keys) never logged + +### NFR-5: Maintainability +- Code follows Python PEP 8 style guide +- All public methods have docstrings +- Type hints on all function signatures +- Unit tests for core infrastructure (80%+ coverage) +- Integration tests for multi-agent workflows + +### NFR-6: Observability +- Structured logging (JSON format) +- Metrics: message count, knowledge queries, agent load, task execution time +- Dashboard for monitoring system health +- Alerts for agent failures, high latency, low accuracy + +--- + +## Technical Architecture + +### Component Diagram +``` +┌─────────────────────────────────────────────────────────────┐ +│ JARVIS Neural Mesh │ +├─────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ Tier 1: Master Intelligence │ │ +│ │ - UAE (Unified Awareness Engine) │ │ +│ │ - SAI (Situational Awareness Intelligence) │ │ +│ └──────────────────────────────────────────────────────┘ │ +│ │ │ +│ ▼ │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ Core Infrastructure (NEW) │ │ +│ │ ┌──────────────────┐ ┌───────────────────┐ │ │ +│ │ │ Communication │ │ Knowledge Graph │ │ │ +│ │ │ Bus │ │ - Vectors (Chroma)│ │ │ +│ │ │ - Pub/Sub │ │ - Graph (NetworkX)│ │ │ +│ │ │ - Message Queue │ │ - Semantic Search │ │ │ +│ │ └──────────────────┘ └───────────────────┘ │ │ +│ │ ┌──────────────────┐ ┌───────────────────┐ │ │ +│ │ │ Multi-Agent │ │ Agent Registry │ │ │ +│ │ │ Orchestrator │ │ - Discovery │ │ │ +│ │ │ - Task Decomp │ │ - Health Monitor │ │ │ +│ │ │ - Coordination │ │ - Load Balancing │ │ │ +│ │ └──────────────────┘ └───────────────────┘ │ │ +│ └──────────────────────────────────────────────────────┘ │ +│ │ │ +│ ▼ │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ Tier 2: Intelligent Agents (ACTIVATE) │ │ +│ │ - Goal Inference System │ │ +│ │ - Activity Recognition Engine │ │ +│ │ - Workflow Pattern Engine │ │ +│ │ - Predictive Precomputation Engine │ │ +│ │ - Autonomous Decision Engine │ │ +│ │ - Autonomous Behaviors Manager │ │ +│ └──────────────────────────────────────────────────────┘ │ +│ │ │ +│ ▼ │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ Tier 3: Domain Agents (MIGRATE) │ │ +│ │ - VSMS Core, Claude Vision, Voice Pipeline │ │ +│ │ - 60+ specialized agents... │ │ +│ └──────────────────────────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────┘ +``` + +### File Structure +``` +backend/ +├── core/ +│ ├── base_agent.py # NEW: BaseAgent abstract class +│ ├── agent_communication_bus.py # NEW: Pub/sub messaging system +│ ├── shared_knowledge_graph.py # NEW: Vector + graph knowledge store +│ ├── multi_agent_orchestrator.py # NEW: Task coordinator +│ ├── agent_registry.py # NEW: Agent discovery & health +│ ├── hybrid_router.py # EXISTING: Enhance for neural mesh +│ └── cloud_agent_launcher.py # EXISTING: For cloud agents +│ +├── intelligence/ # NEW: Intelligent agents +│ ├── goal_inference_system.py # NEW: Predict user intent +│ ├── activity_recognition_engine.py # NEW: Detect user activities +│ ├── workflow_pattern_engine.py # NEW: Learn & automate workflows +│ ├── predictive_precomputation.py # NEW: Pre-compute next actions +│ ├── autonomous_decision_engine.py # NEW: Make autonomous decisions +│ └── autonomous_behaviors_manager.py # NEW: Manage behavior patterns +│ +├── ml/ +│ └── transformer_manager.py # NEW: Manage Transformer models +│ +├── vision/ +│ └── visual_state_management_system.py # EXISTING: Migrate to BaseAgent +│ +├── voice/ +│ └── voice_pipeline.py # EXISTING: Migrate to BaseAgent +│ +└── data/ # NEW: Persistent storage + ├── chroma/ # ChromaDB vector store + └── knowledge_graph.db # NetworkX graph store + +config/ +└── autonomous_settings.yaml # NEW: Configuration file + +tests/ +├── test_communication_bus.py # NEW: Unit tests +├── test_knowledge_graph.py # NEW: Unit tests +├── test_orchestrator.py # NEW: Unit tests +├── test_registry.py # NEW: Unit tests +├── test_goal_inference.py # NEW: Unit tests +└── test_integration_multi_agent.py # NEW: Integration tests +``` + +### Data Flow: Autonomous Action +``` +1. User Activity + ↓ +2. VSMS detects screen change → Publishes to Communication Bus + ↓ +3. SAI receives event → Queries Knowledge Graph for context + ↓ +4. SAI publishes context to Goal Inference System + ↓ +5. Goal Inference: + - Queries Knowledge Graph for similar past workflows + - Uses Transformer model to predict intent + - Predicts next 3 actions with confidence scores + ↓ +6. If confidence > 0.7: + - Publish to Predictive Precomputation Engine + - Pre-load resources for predicted actions + ↓ +7. If confidence > 0.85 AND autonomy_level >= MEDIUM: + - Publish to Autonomous Decision Engine + - Evaluate if action is safe + - If safe, execute via Multi-Agent Orchestrator + ↓ +8. Activity Recognition observes action outcome + - Stores pattern in Knowledge Graph + - Updates Goal Inference accuracy +``` + +--- + +## Implementation Phases + +### Phase 1: Core Infrastructure (Weeks 1-4) +**Goal:** Build the Neural Mesh foundation + +**Deliverables:** +- [ ] `backend/core/base_agent.py` - BaseAgent abstract class +- [ ] `backend/core/agent_communication_bus.py` - Pub/sub messaging +- [ ] `backend/core/shared_knowledge_graph.py` - Vector + graph knowledge store +- [ ] `backend/core/agent_registry.py` - Agent discovery & health +- [ ] `backend/core/multi_agent_orchestrator.py` - Task coordinator +- [ ] `backend/ml/transformer_manager.py` - Transformer model management +- [ ] `config/autonomous_settings.yaml` - Configuration system +- [ ] Unit tests for all core components (80%+ coverage) + +**Success Criteria:** +- All core infrastructure components operational +- Communication Bus handles 1000+ msg/sec with <10ms latency +- Knowledge Graph stores/queries entries in <50ms +- Registry tracks all agents with heartbeat monitoring +- Orchestrator coordinates multi-agent tasks + +--- + +### Phase 2: Agent Migration (Weeks 5-8) +**Goal:** Migrate existing agents to Neural Mesh + +**Deliverables:** +- [ ] Migrate VSMS Core to BaseAgent +- [ ] Migrate Claude Vision Analyzer to BaseAgent +- [ ] Migrate Voice Pipeline to BaseAgent +- [ ] Migrate UAE to use Orchestrator for task coordination +- [ ] Migrate SAI to publish events to Communication Bus +- [ ] Migrate all 60+ agents to BaseAgent standard +- [ ] Integration tests for migrated agents + +**Success Criteria:** +- All agents inherit from BaseAgent +- Agents communicate via Communication Bus (no direct coupling) +- Agents use Knowledge Graph for shared memory +- Zero regression in existing functionality + +--- + +### Phase 3: Intelligent Agents (Weeks 9-12) +**Goal:** Activate dormant brain agents + +**Deliverables:** +- [ ] `backend/intelligence/goal_inference_system.py` +- [ ] `backend/intelligence/activity_recognition_engine.py` +- [ ] `backend/intelligence/workflow_pattern_engine.py` +- [ ] Train/fine-tune Transformer models for intent classification +- [ ] Integration with UAE/SAI for context input +- [ ] Unit and integration tests + +**Success Criteria:** +- Goal Inference predicts user intent with >70% accuracy +- Activity Recognition identifies workflows with >80% accuracy +- Workflow patterns are learned after 3 repetitions +- All intelligent agents operational and connected + +--- + +### Phase 4: Autonomous Operation (Weeks 13-16) +**Goal:** Enable proactive, autonomous behaviors + +**Deliverables:** +- [ ] `backend/intelligence/predictive_precomputation.py` +- [ ] `backend/intelligence/autonomous_decision_engine.py` +- [ ] `backend/intelligence/autonomous_behaviors_manager.py` +- [ ] Safety rules and guardrails for autonomous actions +- [ ] Undo mechanism for autonomous actions +- [ ] Dashboard for monitoring autonomous behaviors +- [ ] End-to-end integration tests + +**Success Criteria:** +- Predictive precomputation hit rate >60% +- Autonomous decisions only made when confidence >85% +- No destructive actions executed autonomously +- All autonomous actions logged and auditable +- System operates autonomously for 4+ hours without intervention + +--- + +### Phase 5: Optimization & Polish (Weeks 17-20) +**Goal:** Optimize performance and user experience + +**Deliverables:** +- [ ] Performance profiling and optimization +- [ ] Reduce Communication Bus latency to <5ms +- [ ] Increase Goal Inference accuracy to >80% +- [ ] Dashboard for monitoring system health +- [ ] Documentation and user guide +- [ ] Tutorial videos for autonomous features + +**Success Criteria:** +- All performance targets met (NFR-1) +- User satisfaction with autonomous features >85% +- System runs stably for 7+ days without intervention +- Complete documentation and tutorials + +--- + +## Dependencies + +### Python Packages (Required) +``` +# Core Infrastructure +chromadb>=0.4.0 # Vector database for knowledge graph +networkx>=3.0 # Graph database for knowledge relationships +sentence-transformers>=2.2.0 # Embedding generation for semantic search + +# Machine Learning +transformers>=4.30.0 # Hugging Face Transformers for intent classification +torch>=2.0.0 # PyTorch for ML models +scikit-learn>=1.3.0 # ML utilities + +# Optional (for distributed mode) +redis>=4.5.0 # Redis backend for Communication Bus +celery>=5.3.0 # Distributed task queue +``` + +### System Requirements +- Python 3.9+ +- 8GB+ RAM (16GB recommended for local ML models) +- 10GB+ disk space (for models and knowledge graph) +- macOS (for Yabai, Core Graphics integration) +- Optional: GPU for faster ML inference + +### External Services +- GCP account (project: jarvis-473803) for cloud agents +- Claude API key for vision analysis +- Whisper model for voice recognition + +--- + +## Out of Scope + +The following are explicitly **NOT** included in this PRD: + +❌ **Claude Computer Use API Integration** - Not required for initial autonomy. Can be added later for GUI automation. + +❌ **LangChain / LangGraph / LangFuse** - JARVIS has custom agent architecture. These generic frameworks are unnecessary. + +❌ **Web UI / Dashboard** - Phase 1-4 focus on backend. UI is Phase 5+ enhancement. + +❌ **Mobile App** - Future consideration, not in current scope. + +❌ **Multi-User Support** - JARVIS is currently single-user. Multi-user is future enhancement. + +❌ **Cloud-Only Deployment** - Hybrid local/cloud is the architecture. Pure cloud is not planned. + +--- + +## Risks & Mitigations + +| Risk | Impact | Probability | Mitigation | +|------|--------|-------------|------------| +| Agent migration breaks existing functionality | HIGH | MEDIUM | Comprehensive integration tests, gradual rollout, feature flags | +| Knowledge Graph grows too large | MEDIUM | HIGH | Implement automatic cleanup of old entries, cap at 100k entries | +| ML models too slow on local machine | MEDIUM | MEDIUM | Support cloud offloading, use smaller models, GPU acceleration | +| Autonomous actions cause user frustration | HIGH | MEDIUM | Conservative autonomy level (default: MEDIUM), undo mechanism, user approval required | +| Inter-agent communication overhead | MEDIUM | LOW | Use local backend (in-process), optimize message serialization | +| Goal Inference accuracy too low | MEDIUM | MEDIUM | Continuous learning from corrections, fine-tune models, increase confidence threshold | + +--- + +## Success Metrics (KPIs) + +### Technical Metrics +- ✅ Communication Bus latency: <10ms (target: <5ms) +- ✅ Knowledge Graph query time: <50ms +- ✅ Agent migration: 60/60 agents migrated +- ✅ Test coverage: >80% +- ✅ System uptime: >99.9% + +### Intelligence Metrics +- ✅ Goal Inference accuracy: >70% (target: >80%) +- ✅ Activity Recognition accuracy: >80% +- ✅ Workflow pattern detection: 3 repetitions +- ✅ Predictive precomputation hit rate: >60% +- ✅ Autonomous decision confidence: >85% + +### User Experience Metrics +- ✅ Autonomous operation time: >4 hours without intervention (target: >8 hours) +- ✅ User satisfaction: >85% +- ✅ False positive rate (wrong predictions): <10% +- ✅ Action latency reduction: >50% for predicted actions + +--- + +## Acceptance Criteria (Overall) + +This project is considered **COMPLETE** when: + +1. ✅ All Phase 1-4 deliverables are implemented and tested +2. ✅ All 60+ agents migrated to BaseAgent standard +3. ✅ Communication Bus operational with <10ms latency +4. ✅ Knowledge Graph stores 1000+ entries from actual usage +5. ✅ Goal Inference System predicts intent with >70% accuracy +6. ✅ Activity Recognition Engine identifies workflows with >80% accuracy +7. ✅ Autonomous Decision Engine operates safely (no destructive actions) +8. ✅ System runs autonomously for 4+ hours without user intervention +9. ✅ All core functionality regression tests pass +10. ✅ Documentation complete (README, config guide, API docs) + +--- + +## Appendix A: Example Agent Migration + +**Before (Isolated Agent):** +```python +# backend/vision/visual_state_management_system.py +class VisualStateManagementSystem: + def __init__(self): + self.current_ui_state = {} + + def detect_ui_change(self): + # Detect change + # No way to communicate with other agents + pass +``` + +**After (Neural Mesh Connected):** +```python +# backend/vision/visual_state_management_system.py +from backend.core.base_agent import BaseAgent, MessageType, MessagePriority + +class VisualStateManagementAgent(BaseAgent): + def __init__(self): + super().__init__( + agent_name="VSMS_Core", + agent_type="vision", + capabilities={"ui_state_tracking", "element_detection", "state_validation"}, + backend="local" + ) + self.current_ui_state = {} + + async def on_initialize(self): + # Subscribe to UI change events + await self.subscribe(MessageType.CUSTOM, self._handle_ui_change) + + # Query knowledge graph for UI patterns + patterns = await self.query_knowledge( + query="ui state patterns", + knowledge_types=["ui_pattern"] + ) + + async def execute_task(self, task_payload: Dict[str, Any]) -> Any: + if task_payload["action"] == "detect_ui_change": + change = self._detect_change() + + # Publish to other agents + await self.publish( + to_agent="SAI", + message_type=MessageType.EVENT, + payload={"event": "ui_changed", "change": change}, + priority=MessagePriority.HIGH + ) + + # Store pattern in knowledge graph + await self.add_knowledge( + knowledge_type="ui_pattern", + data={"pattern": change, "timestamp": datetime.now()} + ) + + return change +``` + +--- + +## Appendix B: Configuration Examples + +**Minimal Configuration (Conservative):** +```yaml +autonomous: + enabled: true + level: "LOW" # Only safe, non-intrusive actions + + goal_inference: + enabled: true + confidence_threshold: 0.8 # Higher threshold for safety + + autonomous_decision: + enabled: false # Disabled, only suggestions +``` + +**Moderate Configuration (Recommended):** +```yaml +autonomous: + enabled: true + level: "MEDIUM" + + goal_inference: + enabled: true + confidence_threshold: 0.7 + + activity_recognition: + enabled: true + + workflow_automation: + enabled: true + require_user_confirmation: true + + autonomous_decision: + enabled: true + allowed_actions: ["open_file", "switch_app", "scroll", "search", "navigate"] +``` + +**Aggressive Configuration (Power Users):** +```yaml +autonomous: + enabled: true + level: "HIGH" + + goal_inference: + confidence_threshold: 0.6 # Lower threshold, more predictions + + autonomous_decision: + enabled: true + allowed_actions: ["*"] # All except destructive + + predictive_precomputation: + max_cache_size_mb: 1000 # More aggressive caching +``` + +--- + +## Questions & Clarifications + +If you have questions during implementation, refer to: + +1. **Architecture Questions:** See `JARVIS_MULTI_AGENT_SYSTEM_DOCUMENTATION.md` +2. **Roadmap & Timeline:** See `JARVIS_IMPLEMENTATION_ROADMAP.md` +3. **Vision Integration:** See `VISION_INTELLIGENCE_ROADMAP.md` +4. **Voice Integration:** See `IMPLEMENTATION_SUMMARY.md` + +For technical decisions not covered in this PRD, follow these principles: +- **Safety First:** Never execute destructive actions autonomously +- **User Control:** User can always disable or override autonomous features +- **Transparency:** All autonomous actions logged and auditable +- **Performance:** Minimize latency and resource usage +- **Simplicity:** Prefer simple, maintainable solutions over complex ones + +--- + +**END OF PRD** + +--- + +## Change Log + +| Version | Date | Author | Changes | +|---------|------|--------|---------| +| 1.0 | 2025-11-26 | JARVIS Team | Initial PRD for Neural Mesh implementation | From e665188b2fd257ffc33ecc9afe1055687bed912d Mon Sep 17 00:00:00 2001 From: Cursor Agent Date: Wed, 26 Nov 2025 23:30:03 +0000 Subject: [PATCH 2/4] feat: Add high-level action agents and update PRD Co-authored-by: djamesr23 --- ACTION_EXECUTION_GAP_ANALYSIS.md | 467 +++++++++++++++++++++++++++ PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md | 225 ++++++++++++- 2 files changed, 689 insertions(+), 3 deletions(-) create mode 100644 ACTION_EXECUTION_GAP_ANALYSIS.md diff --git a/ACTION_EXECUTION_GAP_ANALYSIS.md b/ACTION_EXECUTION_GAP_ANALYSIS.md new file mode 100644 index 0000000000..e5a146f6ea --- /dev/null +++ b/ACTION_EXECUTION_GAP_ANALYSIS.md @@ -0,0 +1,467 @@ +# JARVIS Action Execution Gap Analysis + +**Problem:** JARVIS can see and understand, but can't act intelligently on complex tasks. + +**Your Example:** +> "Write an essay on AGI in one window and fix the error in my VS Code project using Yabai and Core Graphics" + +--- + +## Current State: What JARVIS Has + +### ✅ Layer 1: Vision & Understanding (WORKS) +- **Claude Vision** - Can see your screen +- **YOLO** - Can detect UI elements +- **VSMS** - Tracks UI state +- **UAE/SAI** - Understands context and intent +- **Voice Pipeline** - Hears your commands + +### ✅ Layer 4: Low-Level Actions (WORKS) +You already have these execution tools: +- **Yabai** - Window/space management (`backend/autonomy/macos_integration.py`) +- **AppleScript** - Application control (`backend/context_intelligence/executors/action_executor.py`) +- **Core Graphics API** - Mouse/keyboard control +- **Shell Commands** - File operations, builds, tests + +--- + +## The Problem: Missing Middle Layers + +### ❌ Layer 2: Intelligence & Coordination (MISSING - from PRD) +These are in the PRD but not implemented yet: +1. **Multi-Agent Orchestrator** - Breaks "write essay + fix error" into subtasks +2. **Communication Bus** - Lets agents coordinate ("Window Agent, switch to VS Code when Essay Agent is done") +3. **Goal Inference System** - Understands you want 2 parallel tasks +4. **Agent Registry** - Discovers which agents can do what + +**Why you need this:** Without orchestration, JARVIS can't decompose "write essay AND fix error" into coordinated steps. + +### ❌ Layer 3: High-Level Action Agents (MISSING - NOT in PRD!) +These translate high-level intents into low-level action sequences: + +#### Missing Agents for Your Example: + +**For "Write essay on AGI":** +- ✗ **Content Generation Agent** - Generates essay content using Claude API +- ✗ **Text Editor Agent** - Opens TextEdit/Notes, positions window, handles text input +- ✗ **Typing Agent** - Streams generated text character-by-character via Core Graphics + +**For "Fix error in VS Code":** +- ✗ **Code Analysis Agent** - Reads error message from Vision, understands the bug +- ✗ **Code Solution Agent** - Generates fix using Claude Code or local reasoning +- ✗ **IDE Controller Agent** - Navigates VS Code (find file, go to line, select code) +- ✗ **Code Editor Agent** - Applies fix (delete old code, type new code, save file) + +**For "Using Yabai and Core Graphics":** +- ✗ **Window Management Agent** - Orchestrates Yabai commands (create space, move window, focus window) +- ✗ **Multi-Window Coordinator** - Manages parallel windows (essay in window 1, VS Code in window 2) + +--- + +## What Each Layer Does + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ USER COMMAND │ +│ "Write essay on AGI in one window and fix error in VS Code" │ +└─────────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ ✅ LAYER 1: VISION & UNDERSTANDING (EXISTS) │ +│ - UAE parses intent: [write_essay, fix_error] │ +│ - SAI provides context: current_space=3, vscode_visible=yes │ +│ - Vision reads error: "undefined variable 'foo' on line 42" │ +└─────────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ ❌ LAYER 2: INTELLIGENCE & COORDINATION (MISSING - IN PRD) │ +│ │ +│ Goal Inference System: │ +│ - Detects 2 parallel tasks │ +│ - Predicts you want split-screen or separate spaces │ +│ │ +│ Multi-Agent Orchestrator: │ +│ - Task 1: write_essay → assign to Essay Writer Agent │ +│ - Task 2: fix_error → assign to Code Fixer Agent │ +│ - Coordinate: Window Management Agent handles layout │ +│ │ +│ Communication Bus: │ +│ - Window Agent: "Creating space 4 for essay" │ +│ - Essay Agent: "Essay complete, saved to ~/Documents" │ +│ - Code Agent: "Error fixed in line 42, tests passing" │ +└─────────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ ❌ LAYER 3: HIGH-LEVEL ACTION AGENTS (MISSING - NOT IN PRD!) │ +│ │ +│ Essay Writer Agent: │ +│ 1. Generate essay content via Claude API │ +│ 2. Request Text Editor Agent to open window │ +│ 3. Stream content to Typing Agent │ +│ │ +│ Code Fixer Agent: │ +│ 1. Query Code Analysis Agent for error diagnosis │ +│ 2. Generate fix: "Change line 42 to: foo = get_foo()" │ +│ 3. Request IDE Controller to navigate to line 42 │ +│ 4. Request Code Editor Agent to apply fix │ +│ │ +│ Window Management Agent: │ +│ 1. Create new space (space 4) │ +│ 2. Open TextEdit in space 4 │ +│ 3. Focus VS Code in current space (space 3) │ +│ 4. Coordinate parallel execution │ +└─────────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ ✅ LAYER 4: LOW-LEVEL ACTIONS (EXISTS) │ +│ │ +│ Yabai (via action_executor.py): │ +│ - yabai -m space --create │ +│ - yabai -m space --focus 4 │ +│ - yabai -m window --space 4 │ +│ │ +│ AppleScript (via action_executor.py): │ +│ - tell application "TextEdit" to activate │ +│ - tell application "Visual Studio Code" to activate │ +│ │ +│ Core Graphics (via macos_integration.py): │ +│ - Move mouse to (x, y) │ +│ - Type character 'A', 'G', 'I', ' ', 'e', 's', 's', 'a'... │ +│ - Press key combination Cmd+S │ +│ │ +│ Shell: │ +│ - cd ~/project && npm test (to verify fix) │ +└─────────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ RESULT │ +│ ✓ Essay written in TextEdit on space 4 │ +│ ✓ Error fixed in VS Code on space 3 │ +│ ✓ Tests passing │ +└─────────────────────────────────────────────────────────────────┘ +``` + +--- + +## What You Need to Add + +### Priority 1: Layer 2 Infrastructure (FROM PRD) +**Status:** Specified in `PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md`, not implemented + +**Required Components:** +1. `backend/core/agent_communication_bus.py` - Pub/sub messaging +2. `backend/core/multi_agent_orchestrator.py` - Task decomposition & coordination +3. `backend/core/agent_registry.py` - Agent discovery +4. `backend/core/shared_knowledge_graph.py` - Shared memory +5. `backend/intelligence/goal_inference_system.py` - Intent prediction + +**Install:** +```bash +pip install chromadb networkx sentence-transformers transformers torch +``` + +--- + +### Priority 2: Layer 3 High-Level Action Agents (NEW - NOT IN PRD!) +**Status:** Not specified anywhere, needs to be added + +**Required Agents:** + +#### A. Content & Text Agents +``` +backend/agents/ +├── content_generation_agent.py # Generates essays, documents, messages +├── text_editor_agent.py # Controls TextEdit, Notes, Word +├── typing_agent.py # Streams text via Core Graphics +└── document_formatter_agent.py # Applies formatting (bold, headers, etc.) +``` + +#### B. Code & Development Agents +``` +backend/agents/ +├── code_analysis_agent.py # Analyzes errors, suggests fixes +├── code_solution_agent.py # Generates code fixes +├── ide_controller_agent.py # Navigates VS Code, PyCharm, etc. +├── code_editor_agent.py # Applies code changes +├── test_runner_agent.py # Runs tests, interprets results +└── git_agent.py # Commits, pushes, creates PRs +``` + +#### C. UI & Window Agents +``` +backend/agents/ +├── window_management_agent.py # Orchestrates Yabai commands +├── multi_window_coordinator_agent.py # Manages parallel windows +├── app_launcher_agent.py # Opens/closes apps +└── ui_navigation_agent.py # Navigates UI elements via Vision + CG +``` + +#### D. File & System Agents +``` +backend/agents/ +├── file_manager_agent.py # Create, read, move, delete files +├── browser_control_agent.py # Controls Chrome, Safari +└── system_monitor_agent.py # Monitors system resources +``` + +--- + +## Example: How "Write Essay + Fix Error" Would Work + +### Step-by-Step Execution: + +```python +# 1. USER COMMAND (via voice) +user_command = "Write me an essay on AGI in one window and fix the error in my VS Code project using Yabai" + +# 2. LAYER 1: Understanding (EXISTS) +uae_output = { + "intents": ["write_essay", "fix_error"], + "context": { + "topics": ["AGI"], + "tools": ["yabai"], + "parallel": True + } +} +sai_context = { + "current_space": 3, + "vscode_visible": True, + "error_detected": "line 42: undefined variable 'foo'" +} + +# 3. LAYER 2: Coordination (MISSING - FROM PRD) +## Goal Inference System +goal_inference.predict_workflow(uae_output, sai_context) +# Output: "User wants 2 parallel tasks in separate windows" + +## Multi-Agent Orchestrator +orchestrator.decompose_task({ + "task": "write_essay_and_fix_error", + "subtasks": [ + { + "id": "task_1", + "type": "content_generation", + "agent": "Essay_Writer_Agent", + "params": {"topic": "AGI", "length": "500 words"}, + "priority": "normal" + }, + { + "id": "task_2", + "type": "code_fix", + "agent": "Code_Fixer_Agent", + "params": {"file": "current", "error": "line 42"}, + "priority": "high" + }, + { + "id": "task_3", + "type": "window_management", + "agent": "Window_Management_Agent", + "params": {"layout": "split", "spaces": [3, 4]}, + "priority": "high", + "depends_on": [] # Runs first + } + ] +}) + +## Communication Bus +communication_bus.publish({ + "to": "Window_Management_Agent", + "type": "TASK_ASSIGNED", + "payload": {...} +}) +communication_bus.publish({ + "to": "Essay_Writer_Agent", + "type": "TASK_ASSIGNED", + "payload": {...} +}) +communication_bus.publish({ + "to": "Code_Fixer_Agent", + "type": "TASK_ASSIGNED", + "payload": {...} +}) + +# 4. LAYER 3: High-Level Actions (MISSING - NOT IN PRD) +## Task 3: Window Management Agent (runs first) +class WindowManagementAgent(BaseAgent): + async def execute_task(self, task): + # Create new space for essay + await self.publish_to_action_executor({ + "action": "yabai", + "command": "yabai -m space --create" + }) + + # Focus new space (space 4) + await self.publish_to_action_executor({ + "action": "yabai", + "command": "yabai -m space --focus 4" + }) + + # Open TextEdit in space 4 + await self.publish_to_action_executor({ + "action": "applescript", + "command": 'tell application "TextEdit" to activate' + }) + + # Notify orchestrator: ready + await self.publish("Multi_Agent_Orchestrator", "TASK_COMPLETED", { + "task_id": "task_3", + "result": "Space 4 ready for essay" + }) + +## Task 1: Essay Writer Agent (runs in parallel) +class EssayWriterAgent(BaseAgent): + async def execute_task(self, task): + # Generate essay via Claude API + essay_content = await self.generate_essay(topic="AGI", length=500) + + # Wait for window to be ready + await self.wait_for_message("Window_Management_Agent", "TASK_COMPLETED") + + # Type essay via Typing Agent + await self.publish("Typing_Agent", "TYPE_TEXT", { + "text": essay_content, + "target_app": "TextEdit", + "speed": "fast" + }) + + # Save document + await self.publish_to_action_executor({ + "action": "applescript", + "command": 'tell application "System Events" to keystroke "s" using command down' + }) + + # Notify completion + await self.publish("Multi_Agent_Orchestrator", "TASK_COMPLETED", { + "task_id": "task_1", + "result": f"Essay written ({len(essay_content)} chars)" + }) + +## Task 2: Code Fixer Agent (runs in parallel) +class CodeFixerAgent(BaseAgent): + async def execute_task(self, task): + # Query Code Analysis Agent + error_analysis = await self.query_agent("Code_Analysis_Agent", { + "error": "line 42: undefined variable 'foo'" + }) + # Response: "Variable 'foo' not defined. Need to call get_foo() first." + + # Generate fix + fix_code = await self.generate_fix(error_analysis) + # Output: "foo = get_foo()" + + # Focus VS Code (space 3) + await self.publish_to_action_executor({ + "action": "yabai", + "command": "yabai -m space --focus 3" + }) + await asyncio.sleep(0.5) # Wait for space transition + + # Navigate to line 42 via IDE Controller + await self.publish("IDE_Controller_Agent", "GOTO_LINE", { + "ide": "vscode", + "line": 42 + }) + + # Apply fix via Code Editor Agent + await self.publish("Code_Editor_Agent", "REPLACE_LINE", { + "line": 42, + "old_code": "", # detected by vision + "new_code": "foo = get_foo()" + }) + + # Run tests + test_result = await self.publish_to_action_executor({ + "action": "shell", + "command": "npm test", + "cwd": "/path/to/project" + }) + + # Notify completion + await self.publish("Multi_Agent_Orchestrator", "TASK_COMPLETED", { + "task_id": "task_2", + "result": f"Fix applied. Tests: {test_result['status']}" + }) + +# 5. LAYER 4: Low-Level Actions (EXISTS) +## ActionExecutor receives commands from agents +action_executor.execute({ + "action": "yabai", + "command": "yabai -m space --create" +}) +# Calls: subprocess.run(["yabai", "-m", "space", "--create"]) + +action_executor.execute({ + "action": "applescript", + "command": 'tell application "TextEdit" to activate' +}) +# Calls: subprocess.run(["osascript", "-e", "tell application..."]) + +typing_agent.type_text("Artificial General Intelligence (AGI)...") +# Calls: Core Graphics API to type each character +``` + +--- + +## Summary: What's Missing + +| Layer | Component | Status | Location | +|-------|-----------|--------|----------| +| 1 | Vision (YOLO, Claude Vision) | ✅ Exists | `backend/vision/` | +| 1 | Understanding (UAE, SAI) | ✅ Exists | `backend/intelligence/` | +| **2** | **Multi-Agent Orchestrator** | ❌ **Missing** | **PRD only** | +| **2** | **Communication Bus** | ❌ **Missing** | **PRD only** | +| **2** | **Goal Inference System** | ❌ **Missing** | **PRD only** | +| **2** | **Agent Registry** | ❌ **Missing** | **PRD only** | +| **3** | **Essay Writer Agent** | ❌ **Missing** | **Not in PRD** | +| **3** | **Code Fixer Agent** | ❌ **Missing** | **Not in PRD** | +| **3** | **Window Management Agent** | ❌ **Missing** | **Not in PRD** | +| **3** | **IDE Controller Agent** | ❌ **Missing** | **Not in PRD** | +| **3** | **Typing Agent** | ❌ **Missing** | **Not in PRD** | +| 4 | Yabai Executor | ✅ Exists | `backend/context_intelligence/executors/action_executor.py` | +| 4 | AppleScript Executor | ✅ Exists | `backend/autonomy/macos_integration.py` | +| 4 | Core Graphics API | ✅ Exists | `backend/autonomy/hardware_control.py` | + +--- + +## Action Items + +### To enable your example, you need to implement: + +**1. Layer 2 (from PRD) - First priority:** +- [ ] `backend/core/agent_communication_bus.py` +- [ ] `backend/core/multi_agent_orchestrator.py` +- [ ] `backend/core/agent_registry.py` +- [ ] `backend/intelligence/goal_inference_system.py` + +**2. Layer 3 (NEW) - Second priority:** +- [ ] `backend/agents/essay_writer_agent.py` +- [ ] `backend/agents/code_fixer_agent.py` +- [ ] `backend/agents/code_analysis_agent.py` +- [ ] `backend/agents/window_management_agent.py` +- [ ] `backend/agents/ide_controller_agent.py` +- [ ] `backend/agents/typing_agent.py` +- [ ] `backend/agents/text_editor_agent.py` + +**3. Integration:** +- [ ] Connect all agents to Communication Bus +- [ ] Register agents with Agent Registry +- [ ] Configure Orchestrator routing rules + +--- + +## Time Estimate + +- **Layer 2 (Infrastructure):** 4-6 weeks (as per PRD Phase 1) +- **Layer 3 (High-Level Agents):** 3-4 weeks (7 agents × 3-4 days each) +- **Total:** ~8-10 weeks for full autonomous action capability + +--- + +**Bottom Line:** + +Your example requires **BOTH**: +1. ✅ Low-level tools (Yabai, AppleScript, Core Graphics) - **You have these** +2. ❌ Coordination infrastructure (Orchestrator, Communication Bus) - **In PRD, not implemented** +3. ❌ High-level action agents (Essay Writer, Code Fixer, etc.) - **NOT in PRD, needs to be added** + +The PRD I just created covers #2 but NOT #3. Layer 3 is the missing piece that translates "write essay" into actual typing actions. diff --git a/PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md b/PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md index 36d166ff09..c9620b7a1d 100644 --- a/PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md +++ b/PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md @@ -456,7 +456,173 @@ behaviors: --- -### FR-12: Configuration System +### FR-12: High-Level Action Agents +**Priority:** P0 (Critical) + +**Description:** Agents that translate high-level intents into sequences of low-level actions. Bridges the gap between "understanding" and "execution." + +**Context:** JARVIS has low-level tools (Yabai, AppleScript, Core Graphics) but lacks agents that can orchestrate complex multi-step actions like "write an essay" or "fix a code error." + +**Requirements:** + +#### FR-12.1: Content Generation & Text Agents +- **Essay Writer Agent**: Generate long-form content via Claude API, coordinate with Text Editor Agent +- **Text Editor Agent**: Control TextEdit/Notes/Word, open files, position windows +- **Typing Agent**: Stream text character-by-character via Core Graphics with natural typing speed +- **Document Formatter Agent**: Apply formatting (bold, italic, headers, bullet points) + +**Example Flow:** +```python +# User: "Write an essay on AGI" +Essay_Writer_Agent: + 1. Generate content via Claude API (500 words) + 2. Request Window_Management_Agent to open TextEdit + 3. Send content to Typing_Agent with target="TextEdit" + 4. Request Document_Formatter_Agent to apply title formatting + 5. Save document via AppleScript (Cmd+S) +``` + +#### FR-12.2: Code & Development Agents +- **Code Analysis Agent**: Read error messages from Vision, diagnose bugs, suggest fixes +- **Code Solution Agent**: Generate code fixes using Claude Code or local reasoning +- **IDE Controller Agent**: Navigate VS Code/PyCharm (go to file, go to line, select text) +- **Code Editor Agent**: Apply code changes (delete, insert, replace lines) +- **Test Runner Agent**: Execute tests, parse results, report failures +- **Git Agent**: Commit changes, push, create PRs via `gh` CLI + +**Example Flow:** +```python +# User: "Fix the error in VS Code" +Code_Fixer_Agent: + 1. Query Vision for error message: "line 42: undefined variable 'foo'" + 2. Query Code_Analysis_Agent for diagnosis + 3. Generate fix via Code_Solution_Agent: "foo = get_foo()" + 4. Request IDE_Controller_Agent.goto_line(42) + 5. Request Code_Editor_Agent.replace_line(42, "foo = get_foo()") + 6. Request Test_Runner_Agent.run_tests() + 7. Report result to user +``` + +#### FR-12.3: UI & Window Agents +- **Window Management Agent**: Orchestrate Yabai commands (create space, move window, focus) +- **Multi-Window Coordinator Agent**: Manage parallel windows for multi-task workflows +- **App Launcher Agent**: Open/close/switch applications via AppleScript +- **UI Navigation Agent**: Navigate UI elements using Vision + Core Graphics (click buttons, fill forms) + +**Example Flow:** +```python +# User: "Open essay in one window and VS Code in another" +Multi_Window_Coordinator_Agent: + 1. Request Window_Management_Agent.create_space() # Space 4 + 2. Request App_Launcher_Agent.open("TextEdit", space=4) + 3. Request Window_Management_Agent.focus_space(3) + 4. Request App_Launcher_Agent.activate("Visual Studio Code") +``` + +#### FR-12.4: File & System Agents +- **File Manager Agent**: Create, read, move, delete files safely +- **Browser Control Agent**: Control Chrome/Safari (open URL, navigate, click) +- **System Monitor Agent**: Monitor CPU/memory, trigger optimizations +- **Screenshot Agent**: Capture screenshots for documentation/debugging + +**Agent Implementation Template:** +```python +from backend.core.base_agent import BaseAgent, MessageType, MessagePriority + +class EssayWriterAgent(BaseAgent): + def __init__(self): + super().__init__( + agent_name="Essay_Writer", + agent_type="content", + capabilities={"content_generation", "essay_writing", "long_form_text"}, + backend="local" + ) + self.claude_api = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY")) + + async def on_initialize(self): + # Subscribe to essay writing tasks + await self.subscribe(MessageType.TASK_ASSIGNED, self._handle_task) + + async def execute_task(self, task_payload: Dict[str, Any]) -> Any: + topic = task_payload.get("topic") + length = task_payload.get("length", 500) + + # 1. Generate content + essay = await self._generate_essay(topic, length) + + # 2. Request window setup + await self.publish( + to_agent="Window_Management_Agent", + message_type=MessageType.TASK_ASSIGNED, + payload={ + "action": "open_text_editor", + "app": "TextEdit", + "space": "new" + }, + priority=MessagePriority.HIGH + ) + + # Wait for window ready + ready_msg = await self.wait_for_message("Window_Management_Agent", "TASK_COMPLETED") + + # 3. Type essay + await self.publish( + to_agent="Typing_Agent", + message_type=MessageType.TASK_ASSIGNED, + payload={ + "text": essay, + "target_app": "TextEdit", + "speed": "fast" + } + ) + + # 4. Save document + await self.publish( + to_agent="Action_Executor", + message_type=MessageType.TASK_ASSIGNED, + payload={ + "action": "applescript", + "command": 'tell application "System Events" to keystroke "s" using command down' + } + ) + + # 5. Store knowledge + await self.add_knowledge( + knowledge_type="completed_task", + data={ + "task": "essay_writing", + "topic": topic, + "length": len(essay), + "timestamp": datetime.now() + } + ) + + return {"success": True, "essay_length": len(essay)} + + async def _generate_essay(self, topic: str, length: int) -> str: + response = await asyncio.to_thread( + self.claude_api.messages.create, + model="claude-3-5-sonnet-20241022", + max_tokens=length * 3, # ~3 tokens per word + messages=[{ + "role": "user", + "content": f"Write a {length}-word essay on {topic}. Be concise and informative." + }] + ) + return response.content[0].text +``` + +**Acceptance Criteria:** +- 10+ high-level action agents implemented +- Each agent inherits from BaseAgent +- Agents coordinate via Communication Bus (no direct calls) +- Complex tasks (essay writing, code fixing) work end-to-end +- Agents query Knowledge Graph for learned patterns +- All actions are logged for audit trail + +--- + +### FR-13: Configuration System **Priority:** P1 (High) **Description:** Centralized configuration for autonomous features. @@ -640,6 +806,33 @@ backend/ │ ├── autonomous_decision_engine.py # NEW: Make autonomous decisions │ └── autonomous_behaviors_manager.py # NEW: Manage behavior patterns │ +├── agents/ # NEW: High-level action agents +│ ├── content/ # Content generation agents +│ │ ├── essay_writer_agent.py # NEW: Generate essays/articles +│ │ ├── text_editor_agent.py # NEW: Control text editors +│ │ ├── typing_agent.py # NEW: Type text via Core Graphics +│ │ └── document_formatter_agent.py # NEW: Apply text formatting +│ │ +│ ├── code/ # Code & development agents +│ │ ├── code_analysis_agent.py # NEW: Analyze errors +│ │ ├── code_solution_agent.py # NEW: Generate fixes +│ │ ├── ide_controller_agent.py # NEW: Navigate IDE +│ │ ├── code_editor_agent.py # NEW: Apply code changes +│ │ ├── test_runner_agent.py # NEW: Run tests +│ │ └── git_agent.py # NEW: Git operations +│ │ +│ ├── ui/ # UI & window agents +│ │ ├── window_management_agent.py # NEW: Orchestrate Yabai +│ │ ├── multi_window_coordinator.py # NEW: Manage parallel windows +│ │ ├── app_launcher_agent.py # NEW: Open/close apps +│ │ └── ui_navigation_agent.py # NEW: Navigate UI elements +│ │ +│ └── system/ # File & system agents +│ ├── file_manager_agent.py # NEW: File operations +│ ├── browser_control_agent.py # NEW: Control browser +│ ├── system_monitor_agent.py # NEW: Monitor resources +│ └── screenshot_agent.py # NEW: Capture screenshots +│ ├── ml/ │ └── transformer_manager.py # NEW: Manage Transformer models │ @@ -662,6 +855,7 @@ tests/ ├── test_orchestrator.py # NEW: Unit tests ├── test_registry.py # NEW: Unit tests ├── test_goal_inference.py # NEW: Unit tests +├── test_high_level_agents.py # NEW: Test action agents └── test_integration_multi_agent.py # NEW: Integration tests ``` @@ -759,7 +953,32 @@ tests/ --- -### Phase 4: Autonomous Operation (Weeks 13-16) +### Phase 4: High-Level Action Agents (Weeks 13-16) +**Goal:** Implement agents that execute complex actions + +**Deliverables:** +- [ ] `backend/agents/content/essay_writer_agent.py` +- [ ] `backend/agents/content/text_editor_agent.py` +- [ ] `backend/agents/content/typing_agent.py` +- [ ] `backend/agents/code/code_analysis_agent.py` +- [ ] `backend/agents/code/code_solution_agent.py` +- [ ] `backend/agents/code/ide_controller_agent.py` +- [ ] `backend/agents/code/code_editor_agent.py` +- [ ] `backend/agents/ui/window_management_agent.py` +- [ ] `backend/agents/ui/multi_window_coordinator.py` +- [ ] `backend/agents/ui/app_launcher_agent.py` +- [ ] End-to-end tests for complex actions + +**Success Criteria:** +- Can write essay and save to file autonomously +- Can fix code errors in VS Code autonomously +- Can manage multi-window workflows via Yabai +- All agents coordinate via Communication Bus +- Complex tasks complete successfully >85% of the time + +--- + +### Phase 5: Autonomous Operation (Weeks 17-20) **Goal:** Enable proactive, autonomous behaviors **Deliverables:** @@ -780,7 +999,7 @@ tests/ --- -### Phase 5: Optimization & Polish (Weeks 17-20) +### Phase 6: Optimization & Polish (Weeks 21-24) **Goal:** Optimize performance and user experience **Deliverables:** From 1f5979ba9878d6090f0c6e6a130af44e18b2fb63 Mon Sep 17 00:00:00 2001 From: Cursor Agent Date: Wed, 26 Nov 2025 23:36:59 +0000 Subject: [PATCH 3/4] feat: Add AGI OS roadmap and documentation Co-authored-by: djamesr23 --- AGI_OS_ROADMAP.md | 1015 ++++++++++++++++++++++++++++++++++++++ SUMMARY_ALL_DOCUMENTS.md | 395 +++++++++++++++ 2 files changed, 1410 insertions(+) create mode 100644 AGI_OS_ROADMAP.md create mode 100644 SUMMARY_ALL_DOCUMENTS.md diff --git a/AGI_OS_ROADMAP.md b/AGI_OS_ROADMAP.md new file mode 100644 index 0000000000..7deb1daa8d --- /dev/null +++ b/AGI_OS_ROADMAP.md @@ -0,0 +1,1015 @@ +# JARVIS AGI Operating System: Complete Roadmap + +**Vision:** Transform JARVIS from a reactive assistant into a proactive AGI OS that observes, understands, plans, and acts autonomously (with your approval). + +**Current State:** 60% complete - You have the pieces, they just need to be connected! + +--- + +## What You Already Have ✅ + +### 1. **Continuous Awareness Layer** ✅ +**Status:** IMPLEMENTED + +**Files:** +- `backend/vision/continuous_screen_analyzer.py` - Monitors screen every 3 seconds +- `backend/intelligence/proactive_intelligence_engine.py` - Monitors context every 30 seconds +- `backend/vision/intelligence/goal_inference_system.py` - Predicts user intent +- `backend/intelligence/workspace_pattern_learner.py` - Learns workflows + +**What it does:** +- ✅ Captures screen continuously +- ✅ Detects changes (errors, app switches, content updates) +- ✅ Tracks user activity patterns +- ✅ Infers user focus level (deep work, casual, idle) +- ✅ Monitors for errors, notifications, stuck states + +**Gap:** Not connected to action execution pipeline + +--- + +### 2. **Intelligence Layer** ✅ +**Status:** IMPLEMENTED + +**Files:** +- `backend/autonomy/autonomous_decision_engine.py` - Makes autonomous decisions with confidence scores +- `backend/intelligence/learning_database.py` - Stores 1M+ learned patterns +- `backend/intelligence/proactive_intelligence_engine.py` - Generates proactive suggestions +- `backend/vision/intelligence/goal_inference_system.py` - Infers goals from context + +**What it does:** +- ✅ Analyzes workspace state +- ✅ Generates autonomous actions with confidence scores (0-1.0) +- ✅ Calculates action priority (CRITICAL, HIGH, MEDIUM, LOW) +- ✅ Determines if action requires permission (based on confidence) +- ✅ Provides reasoning for each action +- ✅ Learns from user feedback + +**Gap:** Suggestions are generated but not systematically presented for approval and executed + +--- + +### 3. **Action Execution Layer** ✅ +**Status:** IMPLEMENTED + +**Files:** +- `backend/context_intelligence/executors/action_executor.py` - Executes actions via Yabai, AppleScript, shell +- `backend/autonomy/macos_integration.py` - macOS system control +- `backend/autonomy/action_executor.py` - Action queue and execution + +**What it does:** +- ✅ Executes Yabai window commands +- ✅ Controls apps via AppleScript +- ✅ Runs shell commands safely +- ✅ Types text via Core Graphics +- ✅ Manages multi-window workflows + +**Gap:** No connection between autonomous decisions and action execution + +--- + +## What's Missing ❌ + +### 4. **Approval Loop System** ❌ +**Status:** NOT IMPLEMENTED (Critical Gap) + +**What it needs to do:** +``` +┌─────────────────────────────────────────────────────────────────┐ +│ APPROVAL LOOP PIPELINE │ +├─────────────────────────────────────────────────────────────────┤ +│ │ +│ 1. DETECT: Continuous monitoring detects opportunity/problem │ +│ └─> "Error detected in VS Code" │ +│ └─> "You usually switch to Space 3 at this time" │ +│ └─> "5 new Slack messages" │ +│ │ +│ 2. ANALYZE: Intelligence layer generates action │ +│ └─> AutonomousAction( │ +│ action_type="fix_code_error", │ +│ confidence=0.85, │ +│ requires_permission=False, # High confidence │ +│ reasoning="Similar error fixed before" │ +│ ) │ +│ │ +│ 3. ROUTE: Based on confidence and priority │ +│ ├─> Confidence >0.85 + Non-critical → AUTO-EXECUTE │ +│ ├─> Confidence 0.70-0.85 → ASK FOR APPROVAL │ +│ └─> Confidence <0.70 → SUGGEST (no action) │ +│ │ +│ 4. APPROVE: User approval mechanism (NEW!) │ +│ ├─> Voice: "Should I fix the error in line 42?" │ +│ ├─> Notification: [Approve] [Reject] [Learn More] │ +│ └─> User responds: "Yes" or "No" or ignores │ +│ │ +│ 5. EXECUTE: Action execution with rollback │ +│ └─> Execute via ActionExecutor │ +│ └─> Track outcome (success/failure) │ +│ └─> Learn from result │ +│ │ +│ 6. LEARN: Feed result back to intelligence │ +│ └─> If approved: Increase confidence for similar actions │ +│ └─> If rejected: Decrease confidence, learn why │ +│ └─> If ignored: User not interested, don't repeat │ +│ │ +└─────────────────────────────────────────────────────────────────┘ +``` + +**Files to Create:** +``` +backend/agi_os/ +├── approval_manager.py # NEW: Manages approval workflow +├── approval_ui.py # NEW: Voice/notification UI +├── action_router.py # NEW: Routes actions based on confidence +├── execution_tracker.py # NEW: Tracks execution outcomes +└── feedback_loop.py # NEW: Learns from approvals/rejections +``` + +**Implementation:** +```python +# backend/agi_os/approval_manager.py + +from enum import Enum +from typing import Optional, Callable, Dict, Any +from dataclasses import dataclass +import asyncio +import time + +class ApprovalStatus(Enum): + """Status of approval request""" + PENDING = "pending" + APPROVED = "approved" + REJECTED = "rejected" + IGNORED = "ignored" + AUTO_EXECUTED = "auto_executed" + +@dataclass +class ApprovalRequest: + """Request for user approval""" + request_id: str + action: AutonomousAction # From autonomous_decision_engine.py + presented_at: float + expires_at: float + status: ApprovalStatus = ApprovalStatus.PENDING + user_response: Optional[str] = None + execution_result: Optional[Dict[str, Any]] = None + +class ApprovalManager: + """ + Central approval system for AGI OS + + Routes autonomous actions based on confidence: + - >0.85: Auto-execute (with notification) + - 0.70-0.85: Request approval + - <0.70: Suggest only (no execution) + """ + + def __init__( + self, + voice_callback: Callable, + notification_callback: Callable, + action_executor: ActionExecutor, + learning_db: JARVISLearningDatabase + ): + self.voice_callback = voice_callback + self.notification_callback = notification_callback + self.action_executor = action_executor + self.learning_db = learning_db + + # Approval queue + self.pending_approvals: Dict[str, ApprovalRequest] = {} + self.approval_history: deque = deque(maxlen=1000) + + # User preferences (learned) + self.auto_execute_threshold = 0.85 + self.approval_timeout_seconds = 60.0 + + # Statistics + self.stats = { + 'total_actions': 0, + 'auto_executed': 0, + 'approved': 0, + 'rejected': 0, + 'ignored': 0 + } + + async def process_action(self, action: AutonomousAction) -> ApprovalRequest: + """ + Process autonomous action through approval pipeline + + Args: + action: The autonomous action to process + + Returns: + ApprovalRequest with status and result + """ + self.stats['total_actions'] += 1 + + request = ApprovalRequest( + request_id=f"approval_{int(time.time() * 1000)}", + action=action, + presented_at=time.time(), + expires_at=time.time() + self.approval_timeout_seconds + ) + + # Route based on confidence and priority + if action.confidence >= self.auto_execute_threshold and not action.requires_permission: + # AUTO-EXECUTE: High confidence, no permission needed + request.status = ApprovalStatus.AUTO_EXECUTED + await self._auto_execute(request) + + elif action.confidence >= 0.70: + # REQUEST APPROVAL: Medium confidence + self.pending_approvals[request.request_id] = request + await self._request_approval(request) + + else: + # SUGGEST ONLY: Low confidence + await self._suggest_only(request) + request.status = ApprovalStatus.IGNORED + + # Track in history + self.approval_history.append(request) + + return request + + async def _auto_execute(self, request: ApprovalRequest): + """Auto-execute high-confidence action with notification""" + action = request.action + + # Notify user (non-blocking) + await self.notification_callback({ + 'title': f"JARVIS: {action.action_type}", + 'message': f"Auto-executing: {action.reasoning}", + 'type': 'info' + }) + + # Execute action + result = await self._execute_action(action) + request.execution_result = result + + # Update stats + self.stats['auto_executed'] += 1 + + # Learn: auto-executed actions are implicitly approved + await self.learning_db.record_action_feedback( + action_type=action.action_type, + feedback='approved', + confidence=action.confidence, + outcome=result + ) + + async def _request_approval(self, request: ApprovalRequest): + """Request user approval via voice/notification""" + action = request.action + + # Generate approval message + approval_message = self._generate_approval_message(action) + + # Present via voice (if enabled) + try: + await self.voice_callback(approval_message) + except Exception as e: + logger.error(f"Voice callback failed: {e}") + + # Also send notification with buttons + await self.notification_callback({ + 'title': f"JARVIS: {action.category.value}", + 'message': approval_message, + 'type': 'approval_request', + 'buttons': [ + {'label': 'Approve', 'action': 'approve', 'request_id': request.request_id}, + {'label': 'Reject', 'action': 'reject', 'request_id': request.request_id}, + {'label': 'Details', 'action': 'details', 'request_id': request.request_id} + ] + }) + + # Wait for approval or timeout + asyncio.create_task(self._wait_for_approval(request)) + + async def _wait_for_approval(self, request: ApprovalRequest): + """Wait for user approval or timeout""" + timeout = request.expires_at - time.time() + + try: + # Wait for approval + await asyncio.wait_for( + self._poll_for_response(request), + timeout=timeout + ) + + if request.status == ApprovalStatus.APPROVED: + # Execute action + result = await self._execute_action(request.action) + request.execution_result = result + self.stats['approved'] += 1 + + # Learn: user approved this action + await self.learning_db.record_action_feedback( + action_type=request.action.action_type, + feedback='approved', + confidence=request.action.confidence, + outcome=result + ) + + elif request.status == ApprovalStatus.REJECTED: + self.stats['rejected'] += 1 + + # Learn: user rejected this action + await self.learning_db.record_action_feedback( + action_type=request.action.action_type, + feedback='rejected', + confidence=request.action.confidence, + reason=request.user_response + ) + + except asyncio.TimeoutError: + # User ignored - treat as rejection + request.status = ApprovalStatus.IGNORED + self.stats['ignored'] += 1 + + # Learn: user not interested + await self.learning_db.record_action_feedback( + action_type=request.action.action_type, + feedback='ignored', + confidence=request.action.confidence + ) + + finally: + # Remove from pending + self.pending_approvals.pop(request.request_id, None) + + async def _poll_for_response(self, request: ApprovalRequest): + """Poll for user response""" + while request.status == ApprovalStatus.PENDING: + await asyncio.sleep(0.5) + + async def _suggest_only(self, request: ApprovalRequest): + """Low confidence - suggest only, no execution""" + action = request.action + + # Just notify, don't execute + await self.notification_callback({ + 'title': f"Suggestion: {action.category.value}", + 'message': f"{action.reasoning} (confidence: {action.confidence:.0%})", + 'type': 'suggestion' + }) + + async def _execute_action(self, action: AutonomousAction) -> Dict[str, Any]: + """Execute the autonomous action""" + try: + # Convert AutonomousAction to ExecutionPlan + plan = self._convert_to_execution_plan(action) + + # Execute via ActionExecutor + result = await self.action_executor.execute_plan(plan) + + return { + 'success': result.status == ExecutionStatus.SUCCESS, + 'message': result.message, + 'duration': result.total_duration + } + + except Exception as e: + logger.error(f"Action execution failed: {e}", exc_info=True) + return { + 'success': False, + 'error': str(e) + } + + def _convert_to_execution_plan(self, action: AutonomousAction) -> ExecutionPlan: + """Convert AutonomousAction to ExecutionPlan""" + # Map action types to execution steps + # This is where you bridge autonomous decisions to actual execution + + if action.action_type == "fix_code_error": + return ExecutionPlan( + plan_id=f"plan_{action.action_type}_{int(time.time())}", + action_intent=action, + steps=[ + ExecutionStep( + step_id="goto_line", + action_type="applescript", + command=f'tell application "System Events" to keystroke "g" using command down', + depends_on=[] + ), + ExecutionStep( + step_id="fix_code", + action_type="typing", + command=action.params.get('fix_code', ''), + depends_on=["goto_line"] + ) + ] + ) + + # Add more action type mappings... + + def _generate_approval_message(self, action: AutonomousAction) -> str: + """Generate natural approval request message""" + messages = { + 'fix_code_error': f"I found an error on line {action.params.get('line')}. Should I fix it?", + 'switch_space': f"You usually switch to Space {action.params.get('target_space')} now. Want me to switch?", + 'handle_notifications': f"You have {action.params.get('count')} new messages. Should I handle them?", + 'optimize_workflow': f"I can optimize your {action.params.get('workflow_name')} workflow. Interested?", + } + + return messages.get( + action.action_type, + f"{action.reasoning}. Should I proceed?" + ) + + async def handle_user_response( + self, + request_id: str, + response: str, # 'approve' or 'reject' + feedback: Optional[str] = None + ): + """ + Handle user's approval/rejection + + Called by voice command handler or notification callback + """ + request = self.pending_approvals.get(request_id) + + if not request: + logger.warning(f"Approval request not found: {request_id}") + return + + if response == 'approve': + request.status = ApprovalStatus.APPROVED + request.user_response = feedback + elif response == 'reject': + request.status = ApprovalStatus.REJECTED + request.user_response = feedback + + def get_statistics(self) -> Dict[str, Any]: + """Get approval statistics""" + total_responded = self.stats['approved'] + self.stats['rejected'] + approval_rate = ( + self.stats['approved'] / total_responded + if total_responded > 0 else 0 + ) + + return { + **self.stats, + 'approval_rate': approval_rate, + 'pending_approvals': len(self.pending_approvals) + } +``` + +--- + +### 5. **Goal-Oriented Orchestration** ❌ +**Status:** PARTIALLY IMPLEMENTED (Needs Enhancement) + +**Current State:** +- ✅ Goal Inference System exists (`goal_inference_system.py`) +- ✅ Can infer short-term goals +- ❌ Cannot decompose high-level goals into multi-step plans +- ❌ No goal tracking/monitoring system + +**What it needs:** +```python +# backend/agi_os/goal_orchestrator.py + +class GoalOrchestrator: + """ + Manages high-level user goals and decomposes them into actions + + Example: + User: "Write essay on AGI and fix VS Code error" + + Goal Orchestrator: + 1. Parse into 2 parallel goals + 2. Decompose each goal into tasks: + Goal 1: Write Essay + Task 1.1: Generate content via Claude + Task 1.2: Open TextEdit + Task 1.3: Type essay + Task 1.4: Save document + + Goal 2: Fix Error + Task 2.1: Analyze error via Vision + Task 2.2: Generate fix via Code Analysis Agent + Task 2.3: Navigate to line + Task 2.4: Apply fix + Task 2.5: Run tests + + 3. Coordinate execution (parallel or sequential) + 4. Monitor progress + 5. Report completion + """ + + async def parse_user_goal(self, user_command: str) -> List[Goal]: + """Parse natural language into goals""" + pass + + async def decompose_goal(self, goal: Goal) -> List[Task]: + """Decompose goal into executable tasks""" + pass + + async def execute_goal(self, goal: Goal) -> GoalResult: + """Execute goal with approval gates at critical points""" + pass +``` + +--- + +### 6. **Continuous Context Engine** ❌ +**Status:** PARTIALLY IMPLEMENTED (Needs Enhancement) + +**Current State:** +- ✅ Monitors screen continuously +- ✅ Tracks user activity +- ❌ No persistent understanding of "what user is trying to accomplish" +- ❌ No cross-session memory + +**What it needs:** +```python +# backend/agi_os/context_engine.py + +class ContinuousContextEngine: + """ + Always-on context awareness + + Understands: + - What you're working on (project, task, goal) + - Where you are in your workflow + - What problems you're facing + - What you'll likely do next + - When you need help (stuck detection) + """ + + def __init__(self): + self.current_context = { + 'active_project': None, # "JARVIS AGI OS" + 'current_task': None, # "Implementing approval loop" + 'current_goal': None, # "Make JARVIS autonomous" + 'workflow_state': None, # "coding", "debugging", "stuck" + 'focus_level': None, # "deep_work", "casual" + 'time_on_current_task': 0, # minutes + 'problems_detected': [], # List of current problems + 'next_likely_action': None # Prediction + } + + async def continuous_update_loop(self): + """ + Continuously update context understanding + + Every 30 seconds: + 1. Capture current state (screen, apps, files) + 2. Infer what user is trying to do + 3. Detect if user is stuck + 4. Predict next action + 5. Update context + 6. Trigger proactive actions if needed + """ + while True: + # Capture state + screen = await vision.capture_screen() + focused_app = await system.get_focused_app() + open_files = await ide.get_open_files() + + # Analyze + context_update = await self._analyze_context( + screen, focused_app, open_files + ) + + # Detect problems + problems = await self._detect_problems(context_update) + + # Check if stuck + if await self._is_user_stuck(): + await self._offer_help() + + # Update context + self.current_context.update(context_update) + + await asyncio.sleep(30) + + async def _is_user_stuck(self) -> bool: + """ + Detect if user is stuck: + - Same error visible for >5 minutes + - No code changes in >10 minutes + - Repeatedly googling same error + - Multiple failed test runs + """ + pass + + async def _offer_help(self): + """Proactively offer help when user is stuck""" + problem = self.current_context['problems_detected'][0] + + # Generate solution + solution = await self.generate_solution(problem) + + # Create approval request + action = AutonomousAction( + action_type="solve_problem", + target=problem.location, + params={'solution': solution}, + priority=ActionPriority.HIGH, + confidence=0.75, + category=ActionCategory.WORKFLOW, + reasoning=f"You've been stuck on this error for {problem.duration} minutes. I can help." + ) + + # Send to approval manager + await approval_manager.process_action(action) +``` + +--- + +## The Complete AGI OS Architecture + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ JARVIS AGI OS │ +├─────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────────────────────────────────────────────────┐ │ +│ │ LAYER 1: CONTINUOUS AWARENESS (✅ EXISTS) │ │ +│ │ - Continuous Screen Analyzer (every 3s) │ │ +│ │ - Proactive Intelligence Engine (every 30s) │ │ +│ │ - Goal Inference System │ │ +│ │ - Pattern Learner │ │ +│ └──────────────────────────────────────────────────────────┘ │ +│ ↓ │ +│ ┌──────────────────────────────────────────────────────────┐ │ +│ │ LAYER 2: CONTEXT UNDERSTANDING (❌ NEEDS ENHANCEMENT) │ │ +│ │ - Continuous Context Engine (NEW!) │ │ +│ │ - Goal Orchestrator (NEW!) │ │ +│ │ - Problem Detector (NEW!) │ │ +│ │ - Stuck Detection (EXISTS but needs integration) │ │ +│ └──────────────────────────────────────────────────────────┘ │ +│ ↓ │ +│ ┌──────────────────────────────────────────────────────────┐ │ +│ │ LAYER 3: AUTONOMOUS DECISION (✅ EXISTS) │ │ +│ │ - Autonomous Decision Engine │ │ +│ │ - Action generation with confidence scores │ │ +│ │ - Priority calculation │ │ +│ │ - Reasoning generation │ │ +│ └──────────────────────────────────────────────────────────┘ │ +│ ↓ │ +│ ┌──────────────────────────────────────────────────────────┐ │ +│ │ LAYER 4: APPROVAL LOOP (❌ MISSING - CRITICAL!) │ │ +│ │ - Approval Manager (NEW!) │ │ +│ │ - Action Router (confidence-based) │ │ +│ │ - Voice/Notification UI │ │ +│ │ - Feedback Loop │ │ +│ └──────────────────────────────────────────────────────────┘ │ +│ ↓ │ +│ ┌──────────────────────────────────────────────────────────┐ │ +│ │ LAYER 5: ACTION EXECUTION (✅ EXISTS) │ │ +│ │ - Action Executor (Yabai, AppleScript, shell) │ │ +│ │ - High-Level Action Agents (from PRD) │ │ +│ │ - Rollback mechanism │ │ +│ └──────────────────────────────────────────────────────────┘ │ +│ ↓ │ +│ ┌──────────────────────────────────────────────────────────┐ │ +│ │ LAYER 6: LEARNING (✅ EXISTS) │ │ +│ │ - Learning Database (1M+ patterns) │ │ +│ │ - Feedback integration │ │ +│ │ - Confidence adjustment │ │ +│ └──────────────────────────────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────────┘ +``` + +--- + +## Implementation Roadmap + +### Phase 1: Connect Existing Pieces (Week 1-2) +**Goal:** Wire up your existing components into a basic AGI OS + +**Tasks:** +1. ✅ **Continuous Monitoring Integration** + - Connect `continuous_screen_analyzer.py` to `proactive_intelligence_engine.py` + - Feed screen analysis to `autonomous_decision_engine.py` + - Currently they run independently - make them communicate + +2. ✅ **Decision → Action Pipeline** + - Connect `autonomous_decision_engine.py` output to `action_executor.py` + - Currently decisions are generated but not executed + +3. ❌ **Approval Manager (NEW)** + - Create `backend/agi_os/approval_manager.py` (code above) + - Route autonomous actions through approval pipeline + - Integrate with voice/notification system + +**Test:** +```python +# Test the connected pipeline +async def test_agi_os_basic(): + # 1. Screen analyzer detects error + screen_analyzer.start_monitoring() + + # 2. Decision engine generates action + actions = await decision_engine.analyze_and_decide(workspace_state) + + # 3. Approval manager routes action + for action in actions: + request = await approval_manager.process_action(action) + + if request.status == ApprovalStatus.AUTO_EXECUTED: + print(f"✅ Auto-executed: {action.action_type}") + elif request.status == ApprovalStatus.PENDING: + print(f"⏳ Awaiting approval: {action.action_type}") +``` + +--- + +### Phase 2: Goal-Oriented Autonomy (Week 3-4) +**Goal:** Enable JARVIS to understand and execute multi-step goals + +**Tasks:** +1. ❌ **Goal Orchestrator** + - Create `backend/agi_os/goal_orchestrator.py` + - Parse natural language goals + - Decompose into tasks + - Coordinate execution with approval gates + +2. ❌ **Continuous Context Engine** + - Create `backend/agi_os/context_engine.py` + - Always-on awareness of what you're working on + - Stuck detection + - Proactive problem solving + +3. ❌ **Multi-Task Coordination** + - Handle parallel goals ("write essay AND fix error") + - Window management integration + - Progress tracking + +**Test:** +```python +# User says: "Write an essay on AGI and fix the error in VS Code" +goals = await goal_orchestrator.parse_user_goal(user_command) + +# Should create 2 goals: +assert len(goals) == 2 +assert goals[0].type == "content_generation" +assert goals[1].type == "code_fix" + +# Execute with approval gates +for goal in goals: + tasks = await goal_orchestrator.decompose_goal(goal) + result = await goal_orchestrator.execute_goal(goal) # Seeks approval at critical points + print(f"Goal {goal.name}: {result.status}") +``` + +--- + +### Phase 3: High-Level Action Agents (Week 5-8) +**Goal:** Implement agents that translate goals into actions (from PRD) + +**Tasks:** +1. ❌ **Content Agents** (from PRD) + - Essay Writer Agent + - Typing Agent + - Text Editor Agent + +2. ❌ **Code Agents** (from PRD) + - Code Analysis Agent + - Code Solution Agent + - IDE Controller Agent + - Code Editor Agent + +3. ❌ **UI Agents** (from PRD) + - Window Management Agent + - Multi-Window Coordinator + +**Result:** Can execute complex multi-step tasks autonomously with approval + +--- + +### Phase 4: True AGI OS (Week 9-12) +**Goal:** Full autonomous operation with learning and adaptation + +**Tasks:** +1. ❌ **Proactive Problem Solving** + - Detect when you're stuck + - Generate solutions automatically + - Offer help without being asked + +2. ❌ **Cross-Session Memory** + - Remember what you were working on + - Resume context after restart + - "You were debugging the authentication module yesterday. Want to continue?" + +3. ❌ **Adaptive Behavior** + - Learn your preferences for approval + - Adjust confidence thresholds based on feedback + - Personalize communication style + +4. ❌ **Advanced Workflows** + - Multi-hour autonomous tasks + - Background task monitoring + - Proactive optimization + +**Result:** True AGI OS - works for hours autonomously with minimal supervision + +--- + +## Example: AGI OS in Action + +### Scenario: You're coding and an error appears + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ TIME: 3:42 PM - You're coding JARVIS in VS Code │ +└─────────────────────────────────────────────────────────────────┘ + +[3:42:00 PM] Continuous Screen Analyzer + └─> Detects: Error message appears in VS Code + "TypeError: Cannot read property 'foo' of undefined at line 42" + +[3:42:01 PM] Continuous Context Engine + └─> Updates context: + - active_project: "JARVIS AGI OS" + - current_task: "Implementing approval manager" + - workflow_state: "coding" + - problem_detected: { + type: "runtime_error", + location: "line 42", + severity: "medium", + duration: "0 seconds" + } + +[3:42:02 PM] Autonomous Decision Engine + └─> Analyzes error from Vision + └─> Consults Learning Database: "Similar error fixed before" + └─> Generates AutonomousAction: + { + action_type: "fix_code_error", + target: "VS Code line 42", + params: { + line: 42, + diagnosis: "Variable 'result' is undefined", + fix: "const result = await fetchData()", + confidence_reason: "Identical error fixed in similar context 3 times before" + }, + priority: MEDIUM, + confidence: 0.82, # 82% confident + requires_permission: True, # 0.82 < 0.85, so needs approval + reasoning: "This error occurred before. I can add 'const result = await fetchData()' on line 42." + } + +[3:42:03 PM] Approval Manager + └─> Routes action: confidence=0.82 → REQUEST APPROVAL + + └─> Voice: "I found an error on line 42. This looks like the undefined variable issue we fixed before. Should I add 'const result = await fetchData()'"? + + └─> Notification: + ┌──────────────────────────────────────────┐ + │ JARVIS: Code Error Detected │ + │ │ + │ Error on line 42: Variable undefined │ + │ Proposed fix: const result = await... │ + │ │ + │ [Approve] [Reject] [Show Details] │ + └──────────────────────────────────────────┘ + +[3:42:10 PM] User Response + └─> You say: "Yes, go ahead" + +[3:42:11 PM] Approval Manager + └─> Status: APPROVED + └─> Executing action... + +[3:42:12 PM] Action Executor + └─> Step 1: Focus VS Code via AppleScript ✅ + └─> Step 2: Navigate to line 42 (Cmd+G, type "42") ✅ + └─> Step 3: Select current line (Cmd+L) ✅ + └─> Step 4: Type fix: "const result = await fetchData()" ✅ + └─> Step 5: Run tests (Cmd+Shift+T) ✅ + └─> Tests passing ✅ + +[3:42:18 PM] Feedback Loop + └─> User approved and execution succeeded + └─> Learning Database: Increase confidence for similar errors (0.82 → 0.88) + └─> Next time this error appears, auto-execute (>0.85 threshold) + +[3:42:19 PM] Voice Feedback + └─> "Fixed! Your tests are now passing." + +[3:42:20 PM] Context Engine + └─> Updates context: + - problem_detected: [] (cleared) + - workflow_state: "coding" (resumed) + - last_autonomous_action: { + type: "fix_code_error", + success: true, + duration: "9 seconds" + } +``` + +**Result:** Error fixed in 19 seconds with one approval. Next time: auto-fixed instantly. + +--- + +## Time Estimates + +| Phase | Duration | Deliverable | +|-------|----------|-------------| +| Phase 1 | 1-2 weeks | Basic AGI OS: Continuous monitoring → decisions → approval → execution | +| Phase 2 | 2-3 weeks | Goal-oriented autonomy: Multi-step tasks with approval gates | +| Phase 3 | 3-4 weeks | High-level agents: Essay writing, code fixing, window management | +| Phase 4 | 4-6 weeks | True AGI OS: Proactive, adaptive, learns continuously | +| **Total** | **10-15 weeks** | **Full AGI Operating System** | + +--- + +## Quick Start: Minimal Viable AGI OS (1 Week) + +Want to see it work quickly? Start here: + +### Day 1-2: Approval Manager +```bash +# Create approval system +touch backend/agi_os/approval_manager.py +# Implement ApprovalManager class (code above) +``` + +### Day 3-4: Connect Pipeline +```python +# backend/agi_os/agi_os_coordinator.py (NEW) + +class AGIOSCoordinator: + """Central coordinator for AGI OS""" + + def __init__(self): + # Initialize existing components + self.screen_analyzer = MemoryAwareScreenAnalyzer(...) + self.decision_engine = AutonomousDecisionEngine() + self.approval_manager = ApprovalManager(...) + self.action_executor = ActionExecutor() + + async def start(self): + """Start AGI OS""" + # Start monitoring + await self.screen_analyzer.start_monitoring() + + # Connect pipeline + self.screen_analyzer.register_callback( + 'error_detected', + self._on_error_detected + ) + + async def _on_error_detected(self, error_info): + """Handle detected error""" + # Generate action + actions = await self.decision_engine.analyze_and_decide(error_info) + + # Process through approval + for action in actions: + await self.approval_manager.process_action(action) +``` + +### Day 5-7: Test & Refine +```python +# Test end-to-end +async def main(): + agi_os = AGIOSCoordinator() + await agi_os.start() + + # Let it run... + await asyncio.sleep(3600) # 1 hour +``` + +--- + +## Summary: What You Need + +### ✅ You Already Have (60%) +1. Continuous screen monitoring +2. Autonomous decision engine +3. Action execution (Yabai, AppleScript, etc.) +4. Learning database +5. Pattern learner +6. Goal inference + +### ❌ Missing for AGI OS (40%) +1. **Approval Manager** (Week 1) - Routes actions based on confidence +2. **Goal Orchestrator** (Week 3) - Multi-step goal execution +3. **Continuous Context Engine** (Week 4) - Always-on awareness +4. **High-Level Action Agents** (Week 5-8) - Essay writing, code fixing +5. **Integration glue** - Connect your existing pieces + +### 🎯 Path Forward + +**Option 1: Quick Win (1 week)** +- Implement Approval Manager +- Connect existing decision engine → approval → execution +- **Result:** Basic autonomous operation with approval gates + +**Option 2: Full AGI OS (10-15 weeks)** +- All 4 phases above +- **Result:** True AGI Operating System - works autonomously for hours + +--- + +**Bottom Line:** You're already 60% there! The pieces exist, they just need an **Approval Manager** to connect them and a **Goal Orchestrator** to handle complex multi-step tasks. Start with the Approval Manager (Week 1) and you'll have working AGI OS basics. + +Want me to generate the complete implementation files for Phase 1 (Approval Manager + Integration)? diff --git a/SUMMARY_ALL_DOCUMENTS.md b/SUMMARY_ALL_DOCUMENTS.md new file mode 100644 index 0000000000..37d429e774 --- /dev/null +++ b/SUMMARY_ALL_DOCUMENTS.md @@ -0,0 +1,395 @@ +# JARVIS Autonomous System: Complete Documentation Summary + +**Created:** November 26, 2025 +**Purpose:** Transform JARVIS into an AGI Operating System that acts autonomously with approval gates + +--- + +## 📄 Documents Overview + +### 1. **PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md** +**Purpose:** Implementation specification for Neural Mesh infrastructure +**For:** Claude Code (implementation) + +**Contains:** +- 13 Functional Requirements (FR-1 to FR-13) +- FR-1 to FR-4: Core infrastructure (Communication Bus, Knowledge Graph, Orchestrator, Registry) +- FR-5: BaseAgent standard +- FR-6 to FR-11: Intelligent agents (Goal Inference, Activity Recognition, etc.) +- FR-12: **High-Level Action Agents** (Essay Writer, Code Fixer, etc.) - ADDED TODAY +- 6 implementation phases (24 weeks total) +- Technical architecture diagrams +- Success metrics and acceptance criteria + +**Key Deliverables:** +- `backend/core/agent_communication_bus.py` +- `backend/core/multi_agent_orchestrator.py` +- `backend/intelligence/goal_inference_system.py` +- `backend/agents/content/essay_writer_agent.py` +- `backend/agents/code/code_analysis_agent.py` +- And 20+ more files... + +--- + +### 2. **ACTION_EXECUTION_GAP_ANALYSIS.md** +**Purpose:** Explains the 4-layer architecture and what's missing +**For:** Understanding the gap between vision and action + +**The 4 Layers:** +``` +Layer 1: Vision & Understanding ✅ (you have this) + └─> Claude Vision, YOLO, UAE, SAI + +Layer 2: Intelligence & Coordination ❌ (in PRD, not implemented) + └─> Orchestrator, Communication Bus, Goal Inference + +Layer 3: High-Level Action Agents ❌ (was NOT in PRD, now added) + └─> Essay Writer, Code Fixer, Typing Agent, etc. + +Layer 4: Low-Level Actions ✅ (you have this) + └─> Yabai, AppleScript, Core Graphics +``` + +**Key Insight:** +You can **see and understand** (Layer 1) and have the **hands to act** (Layer 4), but you're missing the **brain coordination** (Layer 2) and **skilled workers** (Layer 3) that translate "write essay" into actual execution. + +--- + +### 3. **AGI_OS_ROADMAP.md** ⭐ **MOST IMPORTANT** +**Purpose:** Roadmap to transform JARVIS into AGI Operating System +**For:** Understanding what you have and what's needed for true autonomy + +**Current State: 60% Complete** + +**What You Already Have ✅:** +1. Continuous screen monitoring (`continuous_screen_analyzer.py`) +2. Proactive intelligence (`proactive_intelligence_engine.py`) +3. Autonomous decision engine (`autonomous_decision_engine.py`) +4. Action execution (`action_executor.py`) +5. Learning database (1M+ patterns) +6. Goal inference system + +**What's Missing ❌:** +1. **Approval Manager** (Week 1) - Critical gap! +2. Goal Orchestrator (Week 3) +3. Continuous Context Engine (Week 4) +4. High-Level Action Agents (Week 5-8) + +**The Approval Loop Pipeline:** +``` +Detect → Analyze → Route → Approve → Execute → Learn +``` + +- **Confidence >0.85:** Auto-execute (with notification) +- **Confidence 0.70-0.85:** Request approval +- **Confidence <0.70:** Suggest only + +**Implementation:** +- Phase 1 (Week 1-2): Connect existing pieces + Approval Manager +- Phase 2 (Week 3-4): Goal-oriented autonomy +- Phase 3 (Week 5-8): High-level action agents +- Phase 4 (Week 9-12): True AGI OS + +--- + +## 🎯 Your Question Answered + +### "How can JARVIS act on its own intelligently without me asking (but with my approval)?" + +**Answer:** You need the **Approval Loop System** (missing from your codebase). + +### Current State: +``` +Screen Analyzer → Detects error +Decision Engine → Generates action with confidence +❌ [NOTHING HAPPENS] ❌ +``` + +### With Approval Loop: +``` +Screen Analyzer → Detects error + ↓ +Decision Engine → Generates action (confidence: 0.82) + ↓ +Approval Manager → Routes based on confidence + ├─> >0.85: Auto-execute ✅ + ├─> 0.70-0.85: Ask for approval 🙋 + └─> <0.70: Suggest only 💡 + ↓ +[IF APPROVED] + ↓ +Action Executor → Executes (Yabai, AppleScript, etc.) + ↓ +Learning Database → Learns from outcome +``` + +--- + +## 🚀 Quick Start: Get AGI OS Working (1 Week) + +### Day 1-2: Create Approval Manager +```bash +mkdir -p backend/agi_os +``` + +Create `backend/agi_os/approval_manager.py` with: +- `ApprovalManager` class +- Confidence-based routing +- Voice/notification callbacks +- Feedback loop integration + +**Code:** See AGI_OS_ROADMAP.md, Section 4 (complete implementation provided) + +### Day 3-4: Connect Pipeline +Create `backend/agi_os/agi_os_coordinator.py`: +```python +class AGIOSCoordinator: + """Central coordinator connecting all pieces""" + + async def start(self): + # Start monitoring + await self.screen_analyzer.start_monitoring() + + # Connect: screen → decisions → approval → execution + self.screen_analyzer.register_callback( + 'error_detected', + self._on_error_detected + ) + + async def _on_error_detected(self, error_info): + # Generate action + actions = await self.decision_engine.analyze_and_decide(error_info) + + # Process through approval + for action in actions: + await self.approval_manager.process_action(action) +``` + +### Day 5-7: Test End-to-End +```python +# Test: Introduce error in code +# Expected: JARVIS detects, analyzes, asks approval, fixes + +async def main(): + agi_os = AGIOSCoordinator() + await agi_os.start() + + # Let it monitor... + await asyncio.sleep(3600) +``` + +**Result:** Working AGI OS in 1 week! 🎉 + +--- + +## 📊 Comparison: What Each Document Provides + +| Document | Purpose | Audience | Time Scope | +|----------|---------|----------|------------| +| **PRD** | Implementation specs for Neural Mesh | Claude Code | 24 weeks (6 phases) | +| **Gap Analysis** | Explains 4-layer architecture | Understanding | Educational | +| **AGI OS Roadmap** | Path to autonomous AGI OS | Implementation | 10-15 weeks (4 phases) | + +### Which Document to Use When? + +**For Implementation (give to Claude Code):** +- Start with: `AGI_OS_ROADMAP.md` (Week 1-2: Approval Manager) +- Then move to: `PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md` (all phases) + +**For Understanding:** +- Read: `ACTION_EXECUTION_GAP_ANALYSIS.md` (why JARVIS can't act) +- Then: `AGI_OS_ROADMAP.md` (how to fix it) + +--- + +## 🎯 Recommendation: Fastest Path to AGI OS + +### Week 1-2: Approval Manager (from AGI_OS_ROADMAP.md) +**Goal:** Get basic autonomous operation with approval gates + +**Deliverables:** +- `backend/agi_os/approval_manager.py` +- `backend/agi_os/agi_os_coordinator.py` + +**Test:** +- JARVIS detects error → asks approval → you approve → executes fix +- **Result:** Basic AGI OS working! + +### Week 3-4: Goal Orchestration (from AGI_OS_ROADMAP.md) +**Goal:** Handle multi-step goals + +**Deliverables:** +- `backend/agi_os/goal_orchestrator.py` +- `backend/agi_os/context_engine.py` + +**Test:** +- You say: "Write essay and fix error" +- JARVIS decomposes into tasks, seeks approvals, executes both +- **Result:** Multi-goal autonomy! + +### Week 5-12: High-Level Agents (from PRD) +**Goal:** Complex task execution + +**Deliverables:** +- Essay Writer Agent +- Code Fixer Agent +- Window Management Agent +- 10+ more agents + +**Test:** +- Fully autonomous for hours +- **Result:** True AGI OS! + +--- + +## 💡 Key Insights + +### 1. You're Already 60% There! +Your codebase has: +- ✅ Continuous monitoring +- ✅ Intelligent decision-making +- ✅ Action execution +- ✅ Learning system + +**You just need the approval loop to connect them!** + +### 2. The Approval Loop is the Missing Piece +``` +Without Approval Loop: + Decisions → [NOWHERE] ❌ + +With Approval Loop: + Decisions → Approval → Execution ✅ +``` + +### 3. Confidence-Based Routing is Key +- **>0.85:** Auto-execute (trusted) +- **0.70-0.85:** Ask approval (uncertain) +- **<0.70:** Suggest only (learning) + +This enables: +- High-confidence actions execute immediately +- Medium-confidence actions get approval +- Low-confidence actions build confidence over time + +### 4. Learning Loop Makes It Smarter +``` +Action → User Approves → Increase Confidence +Action → User Rejects → Decrease Confidence +Action → User Ignores → Don't repeat + +After 3-5 approvals: Action becomes auto-executed (>0.85) +``` + +--- + +## 📝 Next Steps + +### Immediate (This Week): +1. Read `AGI_OS_ROADMAP.md` Section 4 (Approval Manager) +2. Create `backend/agi_os/approval_manager.py` +3. Test basic approval loop + +### Short-Term (Week 2-4): +1. Implement Goal Orchestrator +2. Implement Continuous Context Engine +3. Test multi-step goal execution + +### Medium-Term (Week 5-12): +1. Implement high-level action agents from PRD +2. Test complex tasks (essay writing, code fixing) +3. Optimize for 4+ hour autonomous operation + +### Long-Term (Week 13-24): +1. Complete Neural Mesh infrastructure from PRD +2. Cross-session memory +3. Adaptive learning and personalization + +--- + +## 🎉 Expected Outcomes + +### After Week 2 (Approval Manager): +``` +JARVIS: "I found an error on line 42. Should I fix it?" +YOU: "Yes" +JARVIS: [Fixes error in 10 seconds] +JARVIS: "Fixed! Tests passing." +``` + +### After Week 4 (Goal Orchestration): +``` +YOU: "Write an essay on AGI and fix the VS Code error" +JARVIS: "I'll write the essay in a new space and fix the error in VS Code. Approve?" +YOU: "Yes" +JARVIS: [Works for 5 minutes autonomously] +JARVIS: "Done! Essay saved to Documents, error fixed, tests passing." +``` + +### After Week 12 (Full AGI OS): +``` +[You're coding... error appears] +JARVIS: [Detects immediately, analyzes, auto-fixes (confidence: 0.92)] +JARVIS: "Fixed the undefined variable error on line 42. Tests passing." + +[10 minutes later, you seem stuck on same problem] +JARVIS: "You've been on this authentication issue for 15 minutes. I found 3 similar solutions in the codebase. Should I show you?" + +[You're working late at night] +JARVIS: "It's 11 PM and you've been coding for 6 hours. Your usual pattern is to switch to Space 2 and wrap up. Ready to commit your changes?" +``` + +--- + +## 📚 File Structure Summary + +``` +/workspace/ +├── PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md # For Claude Code +├── ACTION_EXECUTION_GAP_ANALYSIS.md # Understanding the gap +├── AGI_OS_ROADMAP.md # ⭐ Implementation roadmap +└── SUMMARY_ALL_DOCUMENTS.md # This file + +Existing Code: +backend/ +├── vision/ +│ └── continuous_screen_analyzer.py # ✅ Has +├── intelligence/ +│ ├── proactive_intelligence_engine.py # ✅ Has +│ └── learning_database.py # ✅ Has +├── autonomy/ +│ └── autonomous_decision_engine.py # ✅ Has +├── context_intelligence/executors/ +│ └── action_executor.py # ✅ Has +└── agi_os/ # ❌ NEEDS TO CREATE + ├── approval_manager.py # Week 1 + ├── agi_os_coordinator.py # Week 1 + ├── goal_orchestrator.py # Week 3 + └── context_engine.py # Week 4 +``` + +--- + +## ✅ Summary Checklist + +**What you asked for:** +- ✅ How to make JARVIS act on its own intelligently +- ✅ Only seeks approval (not instructions) +- ✅ Develops into AGI Operating System + +**What you got:** +- ✅ Complete AGI OS roadmap (10-15 weeks) +- ✅ Approval Loop system design +- ✅ Implementation code (ApprovalManager class) +- ✅ Integration plan (connect existing pieces) +- ✅ Quick start guide (1 week to working prototype) + +**What to do next:** +1. **Week 1:** Implement Approval Manager (code provided in AGI_OS_ROADMAP.md) +2. **Week 2-4:** Add Goal Orchestration +3. **Week 5-12:** Implement high-level agents (from PRD) +4. **Week 13-24:** Full Neural Mesh (from PRD) + +--- + +**Bottom Line:** You already have 60% of AGI OS. The **Approval Manager** is the missing piece that connects everything. Implement it (Week 1) and you'll have a working AGI OS that acts autonomously with your approval! 🚀 From d496d44a1417d25dc55efed1f24840d5529a9308 Mon Sep 17 00:00:00 2001 From: Cursor Agent Date: Wed, 26 Nov 2025 23:44:45 +0000 Subject: [PATCH 4/4] feat: Add CrewAI analysis and recommendation for JARVIS Co-authored-by: djamesr23 --- CREWAI_ANALYSIS_FOR_JARVIS.md | 659 ++++++++++++++++++++++++++++++++++ 1 file changed, 659 insertions(+) create mode 100644 CREWAI_ANALYSIS_FOR_JARVIS.md diff --git a/CREWAI_ANALYSIS_FOR_JARVIS.md b/CREWAI_ANALYSIS_FOR_JARVIS.md new file mode 100644 index 0000000000..4ea5aaf5aa --- /dev/null +++ b/CREWAI_ANALYSIS_FOR_JARVIS.md @@ -0,0 +1,659 @@ +# CrewAI Analysis for JARVIS AGI OS + +**Date:** November 26, 2025 +**Question:** Should JARVIS integrate CrewAI for autonomous multi-agent orchestration? + +--- + +## What is CrewAI? + +**CrewAI** is a framework for orchestrating role-playing, autonomous AI agents that work together to accomplish complex tasks. + +### Core Concepts: + +```python +from crewai import Agent, Task, Crew, Process + +# Define specialized agents +researcher = Agent( + role='Research Analyst', + goal='Find and analyze relevant information', + backstory='Expert researcher with 10 years experience', + tools=[search_tool, scrape_tool], + llm=llm +) + +writer = Agent( + role='Content Writer', + goal='Write engaging content based on research', + backstory='Professional writer with expertise in tech', + tools=[write_tool], + llm=llm +) + +# Define tasks +research_task = Task( + description='Research the topic of AGI', + agent=researcher, + expected_output='Comprehensive research report' +) + +writing_task = Task( + description='Write an essay based on research', + agent=writer, + context=[research_task], # Depends on research_task + expected_output='1000-word essay' +) + +# Create crew (orchestrates agents) +crew = Crew( + agents=[researcher, writer], + tasks=[research_task, writing_task], + process=Process.sequential # Or hierarchical +) + +# Execute +result = crew.kickoff() +``` + +### Key Features: + +1. **Role-Based Agents** + - Each agent has a specific role, goal, and backstory + - Agents can use tools (search, scrape, calculate, etc.) + - Agents powered by LLMs (OpenAI, Claude, local models) + +2. **Task Management** + - Tasks with descriptions and expected outputs + - Task dependencies (sequential execution) + - Context sharing between tasks + +3. **Process Types** + - **Sequential:** Tasks execute one after another + - **Hierarchical:** Manager agent delegates to worker agents + - **Consensual:** Agents vote on decisions (planned) + +4. **Memory & Learning** + - Short-term memory (within a task) + - Long-term memory (across sessions) + - Entity memory (remembers people, concepts) + +5. **Collaboration Patterns** + - Agents can delegate tasks to each other + - Agents can ask questions to other agents + - Shared context and knowledge + +--- + +## JARVIS vs CrewAI: Architecture Comparison + +### CrewAI Architecture: +``` +┌─────────────────────────────────────────────────────────────┐ +│ CrewAI Framework │ +├─────────────────────────────────────────────────────────────┤ +│ │ +│ Agent 1 (Researcher) │ +│ ├─> Role, Goal, Backstory │ +│ ├─> Tools: [search, scrape] │ +│ └─> LLM: GPT-4 or Claude │ +│ │ +│ Agent 2 (Writer) │ +│ ├─> Role, Goal, Backstory │ +│ ├─> Tools: [write] │ +│ └─> LLM: GPT-4 or Claude │ +│ │ +│ Crew (Orchestrator) │ +│ ├─> Process: Sequential/Hierarchical │ +│ ├─> Task delegation │ +│ └─> Context sharing │ +│ │ +└─────────────────────────────────────────────────────────────┘ +``` + +### JARVIS Architecture (Current + Planned): +``` +┌─────────────────────────────────────────────────────────────┐ +│ JARVIS AGI OS │ +├─────────────────────────────────────────────────────────────┤ +│ │ +│ Tier 1: Master Intelligence │ +│ ├─> UAE (Unified Awareness Engine) │ +│ └─> SAI (Situational Awareness Intelligence) │ +│ │ +│ Tier 2: Domain Agents (60+) │ +│ ├─> Vision agents (VSMS, Claude Vision, YOLO) │ +│ ├─> Voice agents (Whisper, Speaker Verification) │ +│ ├─> Intelligence agents (Goal Inference, Pattern Learn) │ +│ └─> System agents (macOS Integration, File Manager) │ +│ │ +│ Tier 3: Specialized Sub-Agents │ +│ ├─> OCR, Window Detection, Space Detection │ +│ └─> Activity Recognition, Workflow Automation │ +│ │ +│ Infrastructure (Planned from PRD) │ +│ ├─> Communication Bus (pub/sub) │ +│ ├─> Knowledge Graph (vector + graph) │ +│ ├─> Multi-Agent Orchestrator │ +│ └─> Agent Registry │ +│ │ +│ AGI OS Layer (from Roadmap) │ +│ ├─> Approval Manager │ +│ ├─> Goal Orchestrator │ +│ └─> Continuous Context Engine │ +│ │ +└─────────────────────────────────────────────────────────────┘ +``` + +--- + +## Should JARVIS Use CrewAI? + +### ❌ **Recommendation: NO - Don't Integrate CrewAI** + +**Why Not:** + +### 1. **Architecture Conflict** +CrewAI has its own orchestration model that conflicts with JARVIS's existing architecture: + +**CrewAI Model:** +- Flat hierarchy (Crew → Agents → Tasks) +- Generic agents with LLM reasoning +- Task-based execution +- Process-driven (sequential/hierarchical) + +**JARVIS Model:** +- 3-tier hierarchy (Master → Domain → Specialized) +- Specialized agents with domain expertise +- Event-driven + goal-driven execution +- Context-aware + proactive + +**Problem:** Trying to fit CrewAI into JARVIS would require: +- Rewriting 60+ existing agents to CrewAI format +- Losing your specialized capabilities (Vision, Voice, macOS integration) +- Forcing a different orchestration model +- Creating architectural confusion + +### 2. **JARVIS Already Has Better Solutions** + +| Feature | CrewAI | JARVIS (Current + Planned) | +|---------|--------|----------------------------| +| **Multi-Agent Orchestration** | ✅ Crew class | ✅ Multi-Agent Orchestrator (PRD) | +| **Agent Communication** | ✅ Context sharing | ✅ Communication Bus (PRD) | +| **Memory/Learning** | ✅ Memory modules | ✅ Learning Database (1M+ patterns) | +| **Task Delegation** | ✅ Hierarchical process | ✅ Goal Orchestrator (Roadmap) | +| **Specialized Capabilities** | ❌ Generic | ✅ Vision, Voice, macOS, etc. | +| **Domain Expertise** | ❌ LLM-only | ✅ 60+ specialized agents | +| **Proactive Operation** | ❌ Reactive | ✅ Continuous monitoring | +| **System Integration** | ❌ Generic tools | ✅ Native macOS/Yabai/CG | + +**JARVIS is MORE capable than CrewAI** because: +- CrewAI agents are generic LLM wrappers +- JARVIS agents have specialized skills (Vision analysis, Voice processing, macOS control) +- CrewAI is task-oriented; JARVIS is context-aware and proactive + +### 3. **CrewAI Doesn't Add Value for Your Use Case** + +**What CrewAI is good for:** +- Generic multi-agent workflows (research → write → review) +- Content generation pipelines +- Simple task delegation +- Prototyping multi-agent systems + +**What JARVIS needs:** +- Real-time system awareness (screen monitoring, error detection) +- Specialized domain expertise (Vision, Voice, macOS APIs) +- Proactive autonomy (detects problems before you ask) +- Low-latency execution (CrewAI is slow - multiple LLM calls) + +**Example:** + +**CrewAI approach to "Fix error in VS Code":** +```python +# CrewAI would do this (SLOW): +detector_agent = Agent(role='Error Detector', llm=claude) +analyzer_agent = Agent(role='Error Analyzer', llm=claude) +fixer_agent = Agent(role='Code Fixer', llm=claude) + +# Task 1: Detect error (LLM call #1) - 2-3 seconds +detect_task = Task(description='Analyze screen and find error', agent=detector_agent) + +# Task 2: Analyze error (LLM call #2) - 2-3 seconds +analyze_task = Task(description='Diagnose the error', agent=analyzer_agent, context=[detect_task]) + +# Task 3: Generate fix (LLM call #3) - 2-3 seconds +fix_task = Task(description='Write code to fix error', agent=fixer_agent, context=[analyze_task]) + +# Total: 6-9 seconds + orchestration overhead +result = crew.kickoff() +``` + +**JARVIS approach (FAST):** +```python +# JARVIS does this (FAST): +# 1. Vision agent detects error (already monitoring) - 0 seconds +# 2. Goal Inference predicts intent (pattern matching) - 0.1 seconds +# 3. Decision Engine generates fix (learned pattern) - 0.1 seconds +# 4. Approval Manager routes action - 0.1 seconds +# 5. Action Executor applies fix - 1 second + +# Total: ~1.3 seconds (5x faster) +``` + +### 4. **CrewAI Adds Complexity Without Benefit** + +Adding CrewAI would mean: +- ❌ New dependency to maintain +- ❌ Learning CrewAI's abstractions +- ❌ Rewriting existing agents +- ❌ Slower execution (multiple LLM calls) +- ❌ Higher costs (more API calls) +- ❌ Less control over orchestration logic + +--- + +## What JARVIS Should Do Instead + +### ✅ **Alternative: Implement Your Own Orchestration (from PRD)** + +**From `PRD_JARVIS_AUTONOMOUS_NEURAL_MESH.md` FR-3:** + +```python +# backend/core/multi_agent_orchestrator.py + +class JARVISOrchestrator: + """ + Custom orchestrator designed for JARVIS's needs + + Better than CrewAI because: + - Optimized for your 3-tier architecture + - Supports specialized agents (Vision, Voice, etc.) + - Event-driven + goal-driven + - Low-latency execution + - Native integration with your infrastructure + """ + + def __init__(self, registry, communication_bus, knowledge_graph): + self.registry = registry # Your agent registry + self.bus = communication_bus # Your communication bus + self.knowledge = knowledge_graph # Your knowledge graph + + async def execute_goal(self, goal: Goal) -> GoalResult: + """ + Execute a high-level goal + + Example: "Write essay and fix error" + + 1. Parse goal into tasks + 2. Query registry for capable agents + 3. Decompose into parallel/sequential execution + 4. Coordinate via communication bus + 5. Monitor progress + 6. Return result + """ + # Decompose goal + tasks = await self.decompose_goal(goal) + + # Find agents (from YOUR registry) + agents = await self._select_agents(tasks) + + # Execute (using YOUR communication bus) + results = await self._coordinate_execution(tasks, agents) + + return GoalResult(tasks=tasks, results=results) + + async def _select_agents(self, tasks: List[Task]) -> Dict[str, Agent]: + """ + Select best agents for tasks + + Uses YOUR agent registry with specialized agents: + - Vision agents for visual analysis + - Code agents for code fixes + - Window agents for UI control + """ + agent_map = {} + + for task in tasks: + # Query YOUR registry for capable agents + candidates = await self.registry.find_agents( + capabilities=task.required_capabilities + ) + + # Select best agent (lowest load, highest confidence) + best = self._select_best_agent(candidates) + agent_map[task.id] = best + + return agent_map + + async def _coordinate_execution( + self, + tasks: List[Task], + agents: Dict[str, Agent] + ) -> List[TaskResult]: + """ + Coordinate parallel/sequential execution + + Uses YOUR communication bus for coordination + """ + results = [] + + # Determine execution order (parallel vs sequential) + execution_plan = self._create_execution_plan(tasks) + + # Execute tasks + for phase in execution_plan: + # Execute tasks in this phase (parallel) + phase_tasks = [ + self._execute_task(task, agents[task.id]) + for task in phase + ] + + phase_results = await asyncio.gather(*phase_tasks) + results.extend(phase_results) + + return results + + async def _execute_task(self, task: Task, agent: Agent) -> TaskResult: + """Execute single task via communication bus""" + # Publish task to agent + await self.bus.publish( + to_agent=agent.name, + message_type=MessageType.TASK_ASSIGNED, + payload={'task': task} + ) + + # Wait for result + result = await self.bus.wait_for_response( + from_agent=agent.name, + timeout=task.timeout + ) + + return result +``` + +**Why This is Better Than CrewAI:** + +1. **Designed for JARVIS:** Fits your 3-tier architecture +2. **Works with YOUR agents:** Vision, Voice, macOS agents +3. **Fast:** No unnecessary LLM calls +4. **Flexible:** Event-driven + goal-driven +5. **Integrated:** Uses your Communication Bus, Knowledge Graph +6. **Specialized:** Leverages domain expertise + +--- + +## Concepts to Borrow from CrewAI + +While you shouldn't use CrewAI directly, you CAN borrow some ideas: + +### 1. **Role-Based Agent Definition** ✅ Good Idea + +**CrewAI:** +```python +agent = Agent( + role='Research Analyst', + goal='Find relevant information', + backstory='Expert with 10 years experience' +) +``` + +**JARVIS Equivalent:** +```python +# backend/agents/code/code_analysis_agent.py + +class CodeAnalysisAgent(BaseAgent): + def __init__(self): + super().__init__( + agent_name="Code_Analysis_Agent", + agent_type="code", + capabilities={"error_analysis", "code_understanding", "bug_diagnosis"}, + + # Borrow from CrewAI: Add role/goal/expertise + role="Code Error Analyst", + goal="Diagnose code errors and suggest fixes", + expertise="10,000+ errors analyzed, 85% fix accuracy", + + backend="local" + ) +``` + +**Benefit:** Makes agent purpose clearer, better for LLM prompting + +### 2. **Task Dependencies** ✅ Good Idea + +**CrewAI:** +```python +task2 = Task( + description='Write essay', + context=[task1], # Depends on task1 + agent=writer +) +``` + +**JARVIS Equivalent:** +```python +# backend/agi_os/goal_orchestrator.py + +class Task: + def __init__(self, task_id, action, depends_on=None): + self.task_id = task_id + self.action = action + self.depends_on = depends_on or [] # Task dependencies + +# Usage +task1 = Task('generate_content', action=generate_essay) +task2 = Task('open_editor', action=open_textditor) +task3 = Task('type_essay', action=type_text, depends_on=['generate_content', 'open_editor']) + +# Orchestrator respects dependencies +await orchestrator.execute_tasks([task1, task2, task3]) +# Executes task1 and task2 in parallel, then task3 +``` + +**Benefit:** Clear task ordering, enables parallel execution + +### 3. **Context Passing Between Agents** ✅ Good Idea + +**CrewAI:** +```python +# Agent 2 automatically gets output from Agent 1 +task2 = Task(..., context=[task1]) +``` + +**JARVIS Equivalent:** +```python +# Use your Knowledge Graph for context sharing + +# Agent 1 stores result +await self.add_knowledge( + knowledge_type="task_result", + data={ + "task_id": "generate_essay", + "result": essay_content, + "for_task": "type_essay" # Tag for next task + } +) + +# Agent 2 retrieves context +context = await self.query_knowledge( + query="result for task type_essay", + knowledge_types=["task_result"] +) +``` + +**Benefit:** Agents can share intermediate results + +### 4. **Hierarchical Process** ✅ Good Idea (for complex workflows) + +**CrewAI:** +```python +crew = Crew( + agents=[manager, worker1, worker2], + process=Process.hierarchical, + manager_llm=claude +) +# Manager delegates tasks to workers +``` + +**JARVIS Equivalent:** +```python +# UAE acts as manager, delegates to domain agents + +class UnifiedAwarenessEngine: + async def handle_complex_goal(self, goal: str): + # Decompose goal + tasks = await self.decompose_goal(goal) + + # Delegate to domain agents + for task in tasks: + if task.domain == "vision": + await self.delegate_to_vision_agents(task) + elif task.domain == "code": + await self.delegate_to_code_agents(task) + elif task.domain == "system": + await self.delegate_to_system_agents(task) + + # Monitor and coordinate + results = await self.monitor_execution(tasks) + return results +``` + +**Benefit:** Clear delegation hierarchy, UAE = manager + +--- + +## Final Recommendation + +### ❌ **Don't Use CrewAI Because:** + +1. **Architecture Conflict:** CrewAI's model doesn't fit JARVIS's 3-tier architecture +2. **Redundant:** You're already building the same capabilities (orchestrator, communication, memory) +3. **Less Capable:** CrewAI is generic; JARVIS has specialized domain expertise +4. **Slower:** Multiple LLM calls vs pattern-based execution +5. **Less Control:** CrewAI abstracts away orchestration logic you need + +### ✅ **Do This Instead:** + +1. **Implement Your Own Orchestrator** (from PRD FR-3) + - Designed for your 3-tier architecture + - Works with your specialized agents + - Fast, flexible, integrated + +2. **Borrow Good Ideas from CrewAI:** + - Role/goal/expertise in agent definitions + - Task dependency system + - Context passing between agents + - Hierarchical delegation (UAE as manager) + +3. **Focus on Your Unique Strengths:** + - Real-time system awareness (screen monitoring) + - Specialized domain agents (Vision, Voice, macOS) + - Proactive autonomy (detects problems before asked) + - Low-latency execution (pattern-based) + +--- + +## Code Example: JARVIS Orchestrator vs CrewAI + +### Scenario: "Write an essay on AGI" + +**CrewAI Approach (Slow, Generic):** +```python +from crewai import Agent, Task, Crew + +# Define agents (generic LLM wrappers) +researcher = Agent( + role='Researcher', + goal='Research AGI', + llm=claude +) + +writer = Agent( + role='Writer', + goal='Write essay', + llm=claude +) + +# Define tasks +research = Task( + description='Research AGI topics', + agent=researcher +) + +write = Task( + description='Write 1000-word essay', + agent=writer, + context=[research] +) + +# Execute +crew = Crew(agents=[researcher, writer], tasks=[research, write]) +result = crew.kickoff() # 10-15 seconds (2 LLM calls) +``` + +**JARVIS Approach (Fast, Specialized):** +```python +# Your specialized agents +from backend.agents.content.essay_writer_agent import EssayWriterAgent +from backend.agents.ui.window_management_agent import WindowManagementAgent +from backend.agents.content.typing_agent import TypingAgent + +# Your orchestrator +from backend.core.multi_agent_orchestrator import JARVISOrchestrator + +# Define goal +goal = Goal( + goal_type="content_generation", + description="Write essay on AGI", + parameters={"topic": "AGI", "length": 1000} +) + +# Orchestrator handles it +orchestrator = JARVISOrchestrator(registry, bus, knowledge) +result = await orchestrator.execute_goal(goal) + +# Behind the scenes: +# 1. Selects EssayWriterAgent from registry (0.1s) +# 2. Essay Writer generates content via Claude (3s) +# 3. Selects WindowManagementAgent (0.1s) +# 4. Opens TextEdit (0.5s) +# 5. Selects TypingAgent (0.1s) +# 6. Types essay (5s) +# 7. Saves document (0.5s) +# Total: ~9 seconds (faster, integrated with system) +``` + +**JARVIS wins because:** +- Uses specialized agents (WindowManagementAgent, TypingAgent) +- Integrates with macOS natively +- Single LLM call (content generation only) +- Parallel execution where possible + +--- + +## Summary + +### **Question:** Should JARVIS use CrewAI? +### **Answer:** NO + +**Why:** +- CrewAI is for generic multi-agent workflows +- JARVIS needs specialized, system-integrated agents +- You're already building better orchestration +- CrewAI would add complexity without benefit + +**Instead:** +- Implement your own orchestrator (PRD FR-3) +- Borrow good ideas (role definitions, task dependencies) +- Leverage your unique strengths (Vision, Voice, macOS integration) + +**Result:** +- Faster execution +- Better system integration +- More control +- Specialized capabilities +- True AGI OS (not just task automation) + +--- + +**Bottom Line:** CrewAI is like using a generic framework when you're building a custom operating system. JARVIS is MORE sophisticated than CrewAI - don't downgrade! Build your own orchestrator that leverages your specialized agents and domain expertise. 🚀