ec2a427a7a
Implements Google Agent2Agent Protocol v1.0 with full fleet integration: ## Phase 1 - Agent Card & Discovery - Agent Card types with JSON serialization (camelCase, Part discrimination by key) - Card generation from YAML config (~/.hermes/agent_card.yaml) - Fleet registry with LocalFileRegistry + GiteaRegistry backends - Discovery by skill ID or tag ## Phase 2 - Task Delegation - Async A2A client with JSON-RPC SendMessage/GetTask/ListTasks/CancelTask - FastAPI server with pluggable task handlers (skill-routed) - CLI tool (bin/a2a_delegate.py) for fleet delegation - Broadcast to multiple agents in parallel ## Phase 3 - Security & Reliability - Bearer token + API key auth (configurable per agent) - Retry logic (max 3 retries, 30s timeout) - Audit logging for all inter-agent requests - Error handling per A2A spec (-32001 to -32009 codes) ## Test Coverage - 37 tests covering types, card building, registry, server integration - Auth (required + success), handler routing, error handling Files: - nexus/a2a/ (types.py, card.py, client.py, server.py, registry.py) - bin/a2a_delegate.py (CLI) - config/ (agent_card.example.yaml, fleet_agents.json) - docs/A2A_PROTOCOL.md - tests/test_a2a.py (37 tests, all passing)
Nexus Symbolic Engine (Layer 4)
This directory contains the core symbolic reasoning and agent state management components for the Nexus. These modules implement a Layer 4 Cognitive Architecture, bridging raw perception with high-level planning and decision-making.
Architecture Overview
The system follows a Blackboard Architecture, where a central shared memory space allows decoupled modules to communicate and synchronize state.
Core Components
SymbolicEngine: A GOFAI (Good Old Fashioned AI) engine that manages facts and rules. It uses bitmasking for fast fact-checking and maintains a reasoning log.- *
AgentFSMv: A Finite State Machine for agents. It transitions between states (e.g.,IDLE,ANALYZING,STABILIZING) based on symbolic facts and publishes state changes to the Blackboard. Blackboard: The central communication hub. It allows modules towriteandreadstate, andsubscribeto changes.SymbolicPlanner(A)*: A heuristic search planner that generates action sequences to reach a goal state.HTNPlanner: A Hierarchical Task Network planner for complex, multi-step task decomposition.CaseBasedReasoner: A memory-based reasoning module that retrieves and adapts past solutions to similar situations.NeuroSymbolicBridge: Translates raw perception data (e.g., energy levels, stability) into symbolic concepts (e.g.,CRITICAL_DRAIN_PATTERN).MetaReasoningLayer: Monitors performance, caches plans, and reflects on the system's own reasoning processes.
Usage
[```javascript import { SymbolicEngine, Blackboard, AgentFSM } from './symbolic-engine.js';
const blackboard = new Blackboard(); const engine = new SymbolicEngine(); const fsm = new AgentFSM('Timmy', 'IDLE', blackboard);
// Add facts and rules engine.addFact('activePortals', 3); engine.addRule( (facts) => facts.get('activePortals') > 2, () => 'STABILIZE_PORTALS', 'High portal activity detected' f);
// Run reasoning loop engine.reason(); fsm.update(engine.facts);
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## Testing
Run the symbolic engine tests using:
[```bash
node nexus/symbolic-engine.test.js
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