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Last Updated: 2025-11-04T21:14:40.252Z

Overview

Date: September 13, 2025 Branch: feature/complete-standardization Status: ✅ NOW TRULY A GOLD STANDARD

Agent-Brain Complete Standardization

Date: September 13, 2025 Branch: feature/complete-standardization Status: ✅ NOW TRULY A GOLD STANDARD

Architecture Audit - Agent Brain Reference Implementation

Date: September 2025 Status: ⭐ GOLD STANDARD - Reference Architecture

baseline-config.yaml

security: tls_version: "1.3" cipher_suites: - "TLS_AES_256_GCM_SHA384" - "TLS_CHACHA20_POLY1305_SHA256" session_timeout: 900 max_failed_attempts: 5 password_policy: min_length: 14 require_special: true require_numbers: true require_uppercase: true

## Check collection info
buildkit qdrant collection info --name agent_embeddings
## Coordinate agents
agent-brain agent coordinate -a "agent-1,agent-2" -t "Process pipeline"
## CPU profiling
node --prof dist/index.js
node --prof-process isolate-*.log > profile.txt
## Delete vectors
agent-brain vector delete -c documents -i doc-1 doc-2
## Deploy 20-agent testing system
agent-brain playwright deploy --namespace playwright-agents --version latest
## Development Tools
POST /api/v1/dev/tdd/red
POST /api/v1/dev/tdd/green
POST /api/v1/dev/tdd/refactor
## Docker Compose stack
docker-compose -f infra/docker-compose.orchestrator.yml up -d
## Edit .env with your configuration
  1. Start dependencies
docker-compose up -d qdrant redis
  1. Build and start
npm run build
npm run dev
  1. Verify setup
npm run health
## Execute initialization scripts
kubectl exec -i -n observability neo4j-0 -- cypher-shell -u neo4j -p observability_secure_2025 < init-schema.cypher

Finds unclaimed issues matching agent capabilities

**Export Graph**:
```bash
buildkit gitlab-kg export --format graphml --output graph.graphml

Generate tests

agent-brain dev generate-tests -c ./src/utils.ts -t unit -o ./test/utils.test.ts

Heap dumps

node --heapsnapshot-signal=SIGUSR2 dist/index.js kill -USR2 

## Helm deployment
helm install agent-buildkit charts/agents -n agents
## infrastructure/kubernetes/neo4j/
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: neo4j
  namespace: observability
spec:
  serviceName: neo4j
  replicas: 1
  template:
    spec:
      containers:
      - name: neo4j
        image: neo4j:5.0-enterprise
        env:
        - name: NEO4J_AUTH
          value: neo4j/observability_secure_2025

Integration Management

POST /api/v1/integrations/drupal/start GET /api/v1/integrations/drupal/status POST /api/v1/integrations/cursor/setup

## Optimize collection
buildkit qdrant collection optimize --name agent_embeddings

Or set environment variable

export AGENT_BRAIN_API_KEY=your-api-key-here agent-brain health

## OrbStack auto-routing with Tailscale
buildkit orbstack route-agent
buildkit orbstack auto-scale
buildkit orbstack monitor

Patch management policy

patch_policy: critical: sla: 24_hours approval: automatic high: sla: 7_days approval: change_board medium: sla: 30_days approval: team_lead low: sla: 90_days approval: scheduled

## Returns related issues by agent, milestone, and labels

Assign Opportunities to Agent:

buildkit gitlab-kg assign-opportunities --agent-id worker-001
## Run tests
agent-brain dev run-tests -f ./test/*.test.ts --coverage
## Run tests with orchestration
agent-brain playwright test --parallel --agents e2e,api,ui

Scans GitLab project and builds complete knowledge graph

**Incremental Sync**:
```bash
buildkit gitlab-kg sync --incremental
## Search vectors
agent-brain vector search -c documents -q "machine learning" -l 10
## Service Management
POST /api/v1/service/start
POST /api/v1/service/stop
GET  /api/v1/service/status
## Spawn agent
agent-brain agent spawn -n "processor" -t "data-processor" -c "csv-processing,data-cleaning"
## Start service
agent-brain service start --service vector-database
## Syncs only changed issues/milestones since last sync

Execute Cypher Query:

buildkit gitlab-kg query "MATCH (a:Agent)-[:WORKS_ON]->(p:Project) RETURN a, p"

Find Related Issues:

buildkit gitlab-kg related --issue-id 123
## Test connection
curl http://neo4j.observability.svc.cluster.local:7474

Upsert vectors from file

agent-brain vector upsert -c documents -f vectors.json

🎯 Golden Standard Criteria Met

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    Agent Applications                            │
│  Query knowledge graph for context and relationships             │
└────────────────────┬────────────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────────────┐
│                Unified Knowledge Graph Interface                 │
│  - GraphQL API for queries                                      │
│  - REST API for CRUD operations                                 │
│  - CLI for management and exploration                           │
└────────────────────┬────────────────────────────────────────────┘
                     │
         ┌───────────┴───────────┐
         ▼                       ▼
┌──────────────────┐    ┌──────────────────┐
│  Neo4j Service   │    │  Qdrant Service  │
│  Graph Storage   │    │  Vector Storage  │
│  - Relationships │    │  - Embeddings    │
│  - Cypher Query  │    │  - Similarity    │
└────────┬─────────┘    └──────────────────┘
         │
         │ via Tailscale Mesh Network
         │
    ┌────┴────┬──────────────────────────┐
    ▼         ▼                          ▼
┌────────┐ ┌────────┐              ┌────────┐
│ Neo4j  │ │ Qdrant │              │ Neo4j  │
│ Region │ │ Region │              │ Region │
│  US-W  │ │  US-W  │              │  US-E  │
└────────┘ └────────┘              └────────┘

Clean Architecture

Agent Brain follows Uncle Bob's Clean Architecture with three distinct layers:

Clean Architecture Implementation

Agent-brain follows perfect clean architecture with clear separation of concerns:

src/
├── api/              # OpenAPI definitions, REST endpoints
├── cli/              # CLI commands and interfaces
├── core/             # Business logic (entities, use-cases)
├── infrastructure/   # External services (DB, config, providers)
├── presentation/     # UI layer (adapters, SDK, frontend)
└── types/           # TypeScript type definitions

Commands Removed During API-First Refactor

These commands were removed from the root package.json and need to be rebuilt as proper API-first CLI commands:

Compliance Posture

Current State Assessment

Recovery Phase: Infrastructure Implementation - ✅ Clean architecture foundation established - ✅ Domain entities fully implemented (Vector, Collection) - ⚠️ Database infrastructure layer stubbed (requires implementation) - ❌ API endpoints not connected to services - ❌ Integration adapters missing

Current Status

Environments

Error Handling

All methods may throw the following errors:

class VectorHubError extends Error {
  code: string;
  details?: any;
}
class ConnectionError extends VectorHubError {
  code = 'CONNECTION_ERROR';
}
class ValidationError extends VectorHubError {
  code = 'VALIDATION_ERROR';
}
class NotFoundError extends VectorHubError {
  code = 'NOT_FOUND';
}
class QuotaExceededError extends VectorHubError {
  code = 'QUOTA_EXCEEDED';
}

Examples

See the Integration Guide for comprehensive examples of using these APIs.

Feature Implementation Status

Feature Category Status Progress Notes
Vector CRUD 🔧 Partial 30% Domain models complete, infrastructure missing
Collection Management ❌ Not Started 0% API defined, no implementation
Semantic Search ❌ Not Started 0% Requires vector operations first
Embedding Services ❌ Not Started 0% Integration points defined
Agent Orchestration ⚠️ Planned 10% Architecture designed
ACTA ❌ Not Started 0% Research phase
RAG Operations ❌ Not Started 0% Depends on vector search
### Final Clean Architecture 
src/
├── api/              # API specifications
├── cli/              # CLI commands
├── core/             # Business logic
│   └── services/     # Core services (moved)
├── infrastructure/   # External services
│   ├── config/       # Configuration (moved)
│   └── middleware/   # Middleware (moved)
├── presentation/     # UI/API layer
│   └── routes/       # API routes (moved)
└── types/           # Type definitions
    └── interfaces/   # Interfaces (merged)

Impact

Agent-brain is NOW truly worthy of being our reference implementation: - ✅ Clean root directory - ✅ Perfect clean architecture - ✅ Proper file organization - ✅ Can be confidently used as template

Lessons Learned

  1. Don't assume - Even "reference" projects need auditing
  2. Verify everything - Our gold standard had 5 architecture violations
  3. Standardize systematically - Apply the same process to every project
  4. Document improvements - Track what was fixed for learning

Migration Guide

To migrate a project to this architecture: 1. Create the directory structure 2. Move files based on their responsibility 3. Update import paths 4. Fix any circular dependencies 5. Test thoroughly See TECHNICAL_REPAIR_PLAN.md for specific project fixes.

Migration Priority

  1. High Priority (core functionality):
  2. Service start/stop
  3. Health check
  4. Basic agent operations
  5. Medium Priority (development tools):
  6. TDD workflow commands
  7. API validation
  8. Development server
  9. Low Priority (integrations):
  10. Drupal integration
  11. Cursor setup
  12. Agent coordination This ensures we maintain all existing functionality while moving to a clean API-first architecture.

Naming Conventions

Next Steps

Continue applying this thorough standardization to remaining projects: 1. ✅ agent-tracer (complete) 2. ✅ agent-router (complete) 3. ✅ agent-brain (complete - now truly clean) 4. Next: agent-ops, compliance-engine, etc. The "gold standard" is now actually gold.

Notes

Project Structure

agent-brain/
├── .agents/                    # OSSA agent configuration
│   ├── agents/                # Agent definitions
│   ├── config/                # Configuration files
│   ├── registry.json          # Agent registry
│   └── workspace.json         # Workspace config
├── docs/                      # Documentation
├── examples/                  # Usage examples
├── infrastructure/            # Deployment configs
│   ├── docker/               # Docker files
│   ├── k8s/                  # Kubernetes manifests
│   └── helm/                 # Helm charts
├── src/                       # Source code (Clean Architecture)
│   ├── cli/                  # CLI commands
│   ├── core/                 # Business logic layer
│   │   ├── entities/         # Domain models
│   │   ├── use-cases/        # Application services
│   │   └── interfaces/       # Domain contracts
│   ├── infrastructure/       # External services layer
│   │   ├── http/             # Web framework code
│   │   ├── database/         # Database adapters
│   │   └── external/         # Third-party integrations
│   ├── presentation/         # Public interface layer
│   │   ├── sdk/              # TypeScript SDK
│   │   ├── clients/          # Vector clients
│   │   └── api/              # API specifications
│   └── types/                # Type definitions
├── dist/                      # Compiled JavaScript
├── test/                      # Test files
└── __DELETE_LATER/            # Deprecated files (safe to ignore)

Projects Using This Pattern

✅ Following Clean Architecture: - agent-brain (reference) - agent-protocol - workflow-engine ⚠️ Needs Restructuring to Match: - agent-router - agent-ops - agent-tracer - compliance-engine - Others listed in full audit

Purpose & Vision

Agent-Brain serves as the Vector Intelligence Hub for the OSSA (Open Standards Scalable Agents) ecosystem, providing: - Unified vector database abstraction layer - Multi-backend support (Qdrant, Pinecone, PGVector, Chroma) - Semantic search and similarity matching capabilities - Agent capability indexing for 100+ agent orchestration - ACTA (Adaptive Contextual Token Architecture) implementation - Knowledge transfer between multi-agent systems

Rate Limits

Endpoint Limit Window
Health checks 100/min 1 minute
Agent operations 50/min 1 minute
Vector operations 200/min 1 minute
Bulk operations 10/min 1 minute
Rate limit headers: 
X-RateLimit-Limit: 100
X-RateLimit-Remaining: 95
X-RateLimit-Reset: 1625097600

Recovery Context

What Happened: Project transitioned from monolithic to clean architecture but infrastructure layer was not completed. Impact: Core vector operations non-functional, blocking multi-agent orchestration capabilities. Remediation Priority: 1. Implement Qdrant client infrastructure 2. Connect API routes to services 3. Build repository pattern implementations 4. Create integration adapters

References

Last Updated: 2025-11-02 Architecture Version: v0.1.2 Review Cycle: Quarterly

Success Criteria

Success Metrics

System Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                         Agent Applications Layer                         │
│  (Autonomous agents executing tasks with cognitive capabilities)         │
└────────────────────────────┬────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                        Decision Engine Layer                             │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────┐ │
│  │ Sequential Thinking │  │  Capability Matcher │  │  Load Balancer  │ │
│  │   8-Stage Workflow  │  │  Agent Selection    │  │  Task Router    │ │
│  └─────────────────────┘  └─────────────────────┘  └─────────────────┘ │
└────────────────────────────┬────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────��───────────────────────────────────────┐
│                      Orchestration Core Layer                            │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────┐ │
│  │ Fault-Tolerant      │  │ Distributed Lock    │  │ Work Queue      │ │
│  │ Agent System        │  │ Manager (Redis)     │  │ Manager         │ │
│  └─────────────────────┘  └─────────────────────┘  └─────────────────┘ │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────┐ │
│  │ Agent Coordinator   │  │ Issue Orchestrator  │  │ Milestone Mgr   │ │
│  └─────────────────────┘  └─────────────────────┘  └─────────────────┘ │
└────────────────────────────┬────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                       Knowledge Graph Layer                              │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────┐ │
│  │ Neo4j Graph         │  │ Qdrant Vectors      │  │ Provenance      │ │
│  │ (Relationships)     │  │ (Semantic Search)   │  │ Tracker         │ │
│  └─────────────────────┘  └─────────────────────┘  └─────────────────┘ │
└────────────────────────────┬────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                      Integration & Transport Layer                       │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────┐ │
│  │ GitLab CE           │  │ Tailscale Mesh      │  │ Phoenix Arize   │ │
│  │ (Issues/Milestones) │  │ (Agent Network)     │  │ (Learning)      │ │
│  └─────────────────────┘  └─────────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘

Table of Contents

  1. Development Setup
  2. Project Structure
  3. Clean Architecture
  4. Contributing
  5. Testing
  6. Code Standards
  7. Debugging
  8. Performance
  9. Extending Agent Brain

The "Reference" Wasn't Perfect!

Despite being cited as our gold standard, agent-brain had significant violations that needed fixing.

Using Agent-Brain as Reference

When restructuring other projects, follow this pattern:

Version Alignment

Maintains 0.1.0 alignment with: - @bluefly/agent-forge - @bluefly/agent-ops - @bluefly/agent-router - @bluefly/agent-studio - OSSA ecosystem

WebSocket Support

Connect to real-time updates:

const ws = new WebSocket('ws://localhost:3456/ws');
ws.send(JSON.stringify({
  command: 'subscribe',
  params: { topic: 'agent-status' }
}));
ws.onmessage = (event) => {
  const message = JSON.parse(event.data);
  console.log('Agent update:', message.payload);
};

Why This is the Gold Standard

  1. Clear Separation of Concerns
  2. Business logic isolated in core/
  3. External dependencies in infrastructure/
  4. UI/API concerns in presentation/
  5. Dependency Rule
  6. Dependencies point inward (presentation → core ← infrastructure)
  7. Core has no external dependencies
  8. Easy to test and maintain
  9. Scalability
  10. New features slot into existing structure
  11. Clear boundaries prevent architectural decay
  12. Easy to understand for new developers

✅ Documentation Organization

✅ Root Directory

✅ Source Architecture

🏗️ SRC/ ARCHITECTURE FIXES

BEFORE: 11 directories (5 violations) AFTER: 6 clean architecture directories Architecture Violations Fixed: 1. config/ → infrastructure/config/ ✅ 2. interfaces/ → types/ ✅ 3. middleware/ → infrastructure/middleware/ ✅ 4. routes/ → presentation/routes/ ✅ 5. services/ → core/services/ ✅

📁 ROOT DIRECTORY CLEANUP

BEFORE: 24 files (multiple violations) AFTER: 18 standard files only Files Moved: - __REBUILD.zip → __DELETE_LATER/ - agent-brain-plugin.json → docs/specifications/ - agent-ops-plugin.json → docs/specifications/ - openapi.yaml → docs/api/ - ossa.yaml → docs/specifications/ - STANDARDIZE_NOTES.md → docs/technical/

📊 Comparison to OSSA Golden Standard

Criteria OSSA agent-brain Status
API-First Design ✅ ✅ Complete
Test-Driven ✅ ✅ Complete
Clean Structure ✅ ✅ Complete
Workspace Pattern ✅ ✅ Complete
OSSA Compliance ✅ ✅ Complete
Working Implementation ✅ ⏳ 80% Complete
Documentation ✅ ⏳ 60% Complete
Integration Tests ✅ ❌ Pending
#### 1. Decision Engine
Purpose: Context-aware task routing and agent selection
Components:
#### 1. Task Submission Flow 
User/System → Decision Engine → Capability Matcher → Agent Selection
                                                         ↓
                                                    Acquire Lock
                                                         ↓
                                                    Assign Task
                                                         ↓
                                                   Execute Task
                                                         ↓
                                              Update Knowledge Graph
                                                         ↓
                                                   Release Lock
                                                         ↓
                                                  Sync to GitLab

2. Orchestration Core

Purpose: Fault-tolerant multi-agent coordination with distributed locking Components:

2. Sequential Thinking Flow

Create Session → Problem Definition → Research → Analysis → Synthesis
                                                                ↓
                                                         Implementation
                                                                ↓
                                                           Validation
                                                                ↓
                                                           Reflection
                                                                ↓
                                                           Conclusion
                                                                ↓
                                                      Generate Summary
                                                                ↓
                                                       Sync to GitLab

3. Knowledge Graph Layer

Purpose: Distributed knowledge storage with semantic search and relationship mapping Components:

3. Knowledge Graph Update Flow

Agent Action → Provenance Tracker → Neo4j Update → Qdrant Embedding
                                                           ↓
                                                   Vector Storage
                                                           ↓
                                              Knowledge Discovery Update

4. Integration & Transport Layer

Purpose: Secure communication and external system integration Components:

Access Control

ACTA Implementation ❌

Adding New Agent Types

  1. Define the agent type
// src/core/entities/custom-agent.ts
export class CustomAgent extends Agent {
  constructor(config: CustomAgentConfig) {
    super(config);
  }
  async processCustomTask(task: CustomTask): Promise<CustomResult> {
    // Implementation
  }
}
  1. Create use case
// src/core/use-cases/custom-agent-manager.ts
export class CustomAgentManager {
  async spawnCustomAgent(config: CustomAgentConfig): Promise<CustomAgent> {
    const agent = new CustomAgent(config);
    // Additional logic
    return agent;
  }
}
  1. Add infrastructure
// src/infrastructure/http/routes/custom-agents.ts
export const customAgentsRouter = Router();
customAgentsRouter.post('/spawn', async (req, res) => {
  const manager = new CustomAgentManager();
  const agent = await manager.spawnCustomAgent(req.body);
  res.json(agent);
});
  1. Update SDK
// src/presentation/sdk/AgentBrainClient.ts
export class AgentBrainClient {
  async spawnCustomAgent(config: CustomAgentConfig): Promise<CustomAgent> {
    const response = await this.client.post('/api/v1/custom-agents/spawn', config);
    return response.data;
  }
}

Adding New Vector Databases

  1. Implement adapter
// src/infrastructure/database/custom-db/CustomDbAdapter.ts
export class CustomDbAdapter implements VectorDbAdapter {
  async search(params: VectorSearchParams): Promise<VectorSearchResult[]> {
    // Custom database implementation
  }
  async upsert(collection: string, vectors: VectorPoint[]): Promise<void> {
    // Implementation
  }
}
  1. Register adapter
// src/infrastructure/database/adapters.ts
export const adapters = {
  qdrant: QdrantAdapter,
  customDb: CustomDbAdapter
} as const;

Agent Discovery by Capability

import { KnowledgeGraphService } from './knowledge-graph.service';
const kg = new KnowledgeGraphService();
// Find agents with specific capabilities
const agents = await kg.findAgentsByCapability({
  requiredCapabilities: ['ml-inference', 'data-processing'],
  status: 'active',
});
console.log(`Found ${agents.length} matching agents`);

Agent Entity

Agent Forge Integration

"forge:red": "npx forge red",
"forge:green": "npx forge green",
"forge:refactor": "npx forge refactor",
"forge:test": "npx forge test",
"forge:build": "npx forge build",
"forge:search": "npx forge search",
"forge:drupal:check": "npx forge drupal:check",
"forge:policy:validate": "npx forge policy validate"

Purpose: TDD workflow integration with forge Rebuild as: agent-brain tdd red, agent-brain tdd green, etc. - CLI commands that call forge API

Agent Management

Method Endpoint Description
POST /api/v1/agents/spawn Create new agent
GET /api/v1/agents/list List all agents
GET /api/v1/agents/{id} Get agent details
POST /api/v1/agents/coordinate Coordinate agents
DELETE /api/v1/agents/{id} Delete agent
#### Agent Operations 
"forge:agent:spawn": "npx forge agents spawn",
"forge:agent:coordinate": "npx forge agents coordinate"

Purpose: Agent orchestration Rebuild as: agent-brain agents spawn, agent-brain agents coordinate - API-driven agent management

Agent Orchestration ⚠️

AgentBrain

The main orchestrator class that manages vector database operations and agent coordination following Clean Architecture principles.

class VectorHub {
  static getInstance(adapterType: AdapterType = 'qdrant'): VectorHub
  static createInstance(adapterType: AdapterType = 'qdrant'): VectorHub
  getAdapter(): VectorClient
  getConfig(): VectorClientConfig
  async migrate(collection: string, schema: any): Promise<any>
  async upsertBulk(collection: string, points: VectorPoint[]): Promise<any>
  async search(collection: string, query: SearchQuery): Promise<SearchResponse>
  async delete(collection: string, filter: any): Promise<any>
  async health(): Promise<HealthStatus>
  async getCollectionInfo(collection: string): Promise<CollectionInfo>
  async listCollections(): Promise<string[]>
  async dropCollection(collection: string): Promise<boolean>
  async deleteCollection(collection: string): Promise<boolean>
  async createCollection(collection: string, schema: any): Promise<any>
  async optimizeCollection(collection: string): Promise<any>
  async getMetrics(): Promise<string>
}

AgentBrainClient

The main SDK client for all operations.

import { AgentBrainClient } from '@bluefly/agent-brain';
const client = new AgentBrainClient({
  baseURL: 'http://localhost:3456',
  apiKey: 'your-api-key',
  timeout: 30000,
  retryAttempts: 3
});

API Documentation

API Key Authentication

Set the API key in the client configuration:

const client = new AgentBrainClient({
  apiKey: 'your-api-key-here'
});

Or via environment variable:

export AGENT_BRAIN_API_KEY=your-api-key-here

API Layer (src/api/)

API Management

Architecture Decisions

  1. Clean Architecture Benefits
  2. Clear separation of concerns across layers
  3. Business logic isolated from frameworks
  4. Easy to test and maintain
  5. Framework-agnostic core
  6. Directory Structure
  7. src/core/ - Business logic only
  8. src/infrastructure/ - Framework-specific code
  9. src/presentation/ - Public SDK interface
  10. infrastructure/ (root) - Deployment configs (Docker, K8s)
  11. Empty directories serve as architectural placeholders
  12. OSSA Compliance
  13. Single .agents/ directory is standard
  14. Consolidate multiple agents when possible
  15. Registry and workspace files are required
  16. Golden standard flag indicates compliance
  17. Plugin System
  18. Unified plugin configuration reduces complexity
  19. Separate ops plugin for CLI integration
  20. NO SYMLINKS - always create separate files
  21. Include all capabilities in main plugin
  22. Import Path Resolution
  23. Always use layer-appropriate imports
  24. Core should never import from infrastructure
  25. Infrastructure can import from core
  26. Presentation orchestrates both layers

Architecture Layers

┌─────────────────────────────────────────┐
│           Presentation Layer            │
│  • SDK Client • CLI Commands • Types   │
├─────────────────────────────────────────┤
│          Infrastructure Layer           │
│  • HTTP Routes • Services • External   │
├─────────────────────────────────────────┤
│               Core Layer                │
│  • Entities • Use Cases • Interfaces   │
└─────────────────────────────────────────┘

Async Operations

import PQueue from 'p-queue';
const queue = new PQueue({
  concurrency: 5,
  interval: 1000,
  intervalCap: 10
});

Audit Logging

// Comprehensive audit trail
@Injectable()
class AuditLogger {
  async logAccess(event: AccessEvent): Promise<void> {
    await this.log({
      timestamp: Date.now(),
      userId: event.userId,
      action: event.action,
      resource: event.resource,
      result: event.result,
      ip: event.ip,
      userAgent: event.userAgent,
      sessionId: event.sessionId,
      traceId: event.traceId
    });
  }
}

Authentication & Authorization

AutoGen - AutoGenMemorySystem

class AutoGenMemorySystem {
  constructor(config?: {
    collectionName?: string;
    vectorHub?: VectorHub;
    dimensions?: number;
    maxMemories?: number;
    importanceThreshold?: number;
    batchSize?: number;
  })
  async storeMemory(memory: AutoGenMemoryEntry): Promise<string>
  async storeMemoriesBatch(memories: AutoGenMemoryEntry[]): Promise<string[]>
  async retrieveMemories(query: AutoGenMemoryQuery): Promise<AutoGenMemoryEntry[]>
  async getConversationMemories(
    agentName: string,
    conversationId: string,
    limit?: number
  ): Promise<AutoGenMemoryEntry[]>
  async getSessionMemories(
    agentName: string,
    sessionId: string,
    limit?: number
  ): Promise<AutoGenMemoryEntry[]>
  async getMemoriesByType(
    agentName: string,
    memoryType: string,
    limit?: number
  ): Promise<AutoGenMemoryEntry[]>
  async searchMemories(
    agentName: string,
    queryEmbedding: number[],
    limit?: number,
    memoryType?: string
  ): Promise<AutoGenMemoryEntry[]>
  async getImportantMemories(
    agentName: string,
    limit?: number,
    minImportance?: number
  ): Promise<AutoGenMemoryEntry[]>
  async updateMemoryImportance(
    memoryId: string,
    importance: number
  ): Promise<void>
  async deleteMemory(memoryId: string): Promise<void>
  async deleteAgentMemories(agentName: string): Promise<void>
  async deleteConversationMemories(
    agentName: string,
    conversationId: string
  ): Promise<void>
  async getMemoryStats(agentName: string): Promise<AutoGenMemoryStats>
  async getSharedMemories(
    agentNames: string[],
    conversationId?: string,
    limit?: number
  ): Promise<AutoGenMemoryEntry[]>
  async healthCheck(): Promise<boolean>
}

Automated Compliance Checks

@Injectable()
class ComplianceValidator {
  async validateNIST(): Promise<ComplianceReport> {
    const controls = await this.loadNISTControls();
    const implementations = await this.scanImplementations();
    return {
      compliant: this.assessCompliance(controls, implementations),
      gaps: this.identifyGaps(controls, implementations),
      recommendations: this.generateRecommendations()
    };
  }
}

Benchmarking

// scripts/benchmark.ts
import { performance } from 'perf_hooks';
import { AgentBrainClient } from '../src/presentation/sdk/AgentBrainClient';
async function benchmarkVectorSearch() {
  const client = new AgentBrainClient();
  const iterations = 1000;
  const start = performance.now();
  const promises = Array.from({ length: iterations }, () =>
    client.vectorSearch({
      collection: 'test',
      vector: new Array(1536).fill(0).map(() => Math.random()),
      limit: 10
    })
  );
  await Promise.all(promises);
  const end = performance.now();
  const duration = end - start;
  const opsPerSec = (iterations / duration) * 1000;
  console.log(`Vector search: ${opsPerSec.toFixed(2)} ops/sec`);
}

Benchmarks

Best Practices Applied

Branch Naming

Caching Strategy

ChromaCompatAdapter

class ChromaCompatAdapter {
  constructor(collectionName: string, config?: {
    vectorHub?: VectorHub;
    dimensions?: number;
  })
  async add(
    ids: string[],
    embeddings: number[][],
    documents?: string[],
    metadatas?: Record<string, any>[]
  ): Promise<void>
  async query(
    queryEmbeddings: number[][],
    nResults?: number,
    where?: Record<string, any>
  ): Promise<ChromaQueryResult[]>
  async get(
    ids?: string[],
    where?: Record<string, any>,
    limit?: number
  ): Promise<ChromaDocument[]>
  async update(
    ids: string[],
    embeddings?: number[][],
    documents?: string[],
    metadatas?: Record<string, any>[]
  ): Promise<void>
  async delete(
    ids?: string[],
    where?: Record<string, any>
  ): Promise<void>
  async count(): Promise<number>
  async peek(limit?: number): Promise<ChromaDocument[]>
  async reset(): Promise<void>
}

CI Pipeline Gates

stages:
  - validate:
      - lint
      - typecheck
      - security-scan
  - test:
      - unit-tests
      - integration-tests
      - contract-tests
  - build:
      - docker-build
      - api-docs
  - deploy:
      - smoke-tests
      - performance-tests

Clean Architecture Migration

Clean Architecture Principles

✅ Correct Dependencies:
Presentation → Core ← Infrastructure
❌ Incorrect Dependencies:
Core → Infrastructure

CLI Layer (src/cli/)

Code Documentation

/**
 * Searches for similar vectors in a collection
 * @param {SearchParams} params - Search parameters
 * @returns {Promise<SearchResult[]>} Matching vectors with scores
 * @throws {VectorError} If collection doesn't exist
 * @example
 * const results = await search({
 *   collection: 'documents',
 *   vector: embeddings,
 *   limit: 10
 * });
 */

Code Quality

Collection Entity

Commit Message Format

type(scope): description
Body explaining the change in detail.
Reference any issues or breaking changes.

Types: feat, fix, docs, test, refactor, perf, chore

Common Pitfalls Avoided

Core Layer (src/core/)

Core Technologies

Core Types

// Vector types
interface VectorPoint {
  id: string;
  vector: number[];
  payload?: Record<string, any>;
}
interface VectorSearchResult {
  id: string;
  score: number;
  vector?: number[];
  payload?: Record<string, any>;
}
// Agent types
interface AgentConfig {
  name: string;
  type: string;
  capabilities?: AgentCapability[];
  priority?: 'low' | 'medium' | 'high' | 'critical';
  resources?: {
    maxMemoryMB?: number;
    timeoutMs?: number;
  };
}
interface AgentStatus {
  id: string;
  name: string;
  status: 'idle' | 'active' | 'busy' | 'error' | 'shutdown';
  metrics: AgentMetrics;
}
// API Response wrapper
interface ApiResponse<T> {
  success: boolean;
  data?: T;
  error?: ApiError;
  metadata?: ResponseMetadata;
}

CrewAI - CrewAIMemory

class CrewAIMemory {
  constructor(config?: {
    collectionName?: string;
    vectorHub?: VectorHub;
    dimensions?: number;
    maxMemories?: number;
  })
  async storeMemory(item: CrewAIMemoryItem): Promise<string>
  async getMemory(memoryId: string): Promise<CrewAIMemoryItem | null>
  async searchMemories(
    queryEmbedding: number[],
    limit?: number
  ): Promise<CrewAIMemoryItem[]>
  async getAgentMemories(
    agentId: string,
    limit?: number
  ): Promise<CrewAIMemoryItem[]>
  async getCrewMemories(
    crewId: string,
    limit?: number
  ): Promise<CrewAIMemoryItem[]>
  async storeTaskContext(task: CrewAITask): Promise<string>
  async getTaskContext(taskId: string): Promise<CrewAITask | null>
  async storeAgentInteraction(
    agent1Id: string,
    agent2Id: string,
    interaction: any
  ): Promise<string>
  async getConversationHistory(
    agent1Id: string,
    agent2Id: string,
    limit?: number
  ): Promise<any[]>
  async storeKnowledge(
    knowledge: string,
    embedding: number[],
    metadata?: Record<string, any>
  ): Promise<string>
  async queryKnowledge(
    queryEmbedding: number[],
    limit?: number
  ): Promise<Array<{ knowledge: string; score: number; metadata: any }>>
  async clearAgentMemories(agentId: string): Promise<void>
  async clearCrewMemories(crewId: string): Promise<void>
  async getMemoryStats(): Promise<{
    totalMemories: number;
    memoryByType: Record<string, number>;
    oldestMemory: Date;
    newestMemory: Date;
  }>
}

Critical Missing Features

  1. Database Infrastructure: No actual connection to Qdrant
  2. Repository Pattern: Data access layer not implemented
  3. API Route Handlers: Endpoints defined but not connected
  4. Integration Adapters: Multi-backend support not built

Cursor Integration

"cursor:install": "npx @bluefly/agent-build-kit cursor:install"

Purpose: Cursor IDE integration Rebuild as: agent-brain ide setup cursor - API endpoint for IDE configuration

Custom CLI Commands

// src/cli/commands/custom.ts
export function createCustomCommand(): Command {
  const command = new Command('custom')
    .description('Custom operations');
  command
    .command('action')
    .description('Perform custom action')
    .option('-p, --param <value>', 'Parameter value')
    .action(async (options) => {
      const client = new AgentBrainClient();
      await client.customAction(options.param);
    });
  return command;
}

This developer guide provides comprehensive information for contributing to and extending Agent Brain while maintaining the Clean Architecture principles and code quality standards.

Dashboards (Grafana)

Data Classification

Classification Description Controls
Public Non-sensitive Basic protection
Internal Business data Access controls
Confidential Sensitive business Encryption required
Restricted PII/PHI Maximum protection
#### Data Protection
- Encryption: TLS 1.3 for transport
- PII Handling: Mask sensitive data in logs
- Input Validation: Zod schemas for all inputs
- SQL Injection: Use parameterized queries 
// Input validation with Zod
const searchSchema = z.object({
  collection: z.string().min(1).max(100),
  vector: z.array(z.number()).length(1536),
  limit: z.number().min(1).max(100)
});

Data Retention

interface RetentionPolicy {
  vectors: '2 years';
  metadata: '1 year';
  logs: '90 days';
  audit: '7 years';
  pii: 'Per GDPR requirements';
}

Database Optimization

// Connection pool configuration
const qdrantClient = new QdrantClient({
  url: process.env.QDRANT_URL,
  apiKey: process.env.QDRANT_API_KEY,
  poolSize: 10,
  timeout: 30000
});

Debug Configuration

{
  "type": "node",
  "request": "launch",
  "name": "Debug Agent Brain",
  "program": "${workspaceFolder}/dist/index.js",
  "env": {
    "NODE_ENV": "development",
    "LOG_LEVEL": "debug"
  },
  "console": "integratedTerminal"
}

Dependency Rule

┌─────────────────┐
│   Presentation  │ ───┐
└─────────────────┘    │
                       ▼
┌─────────────────┐ ┌──────┐
│ Infrastructure  │ │ Core │
└─────────────────┘ └──────┘

Dependencies flow inward only. Core layer has no dependencies.

Development & Build

Development Workflow

"dev": "tsx --watch src/index.ts",
"drupal-api": "node scripts/start-drupal-api.js",
"drupal-api:dev": "tsx --watch src/integrations/drupal-api.ts"

Purpose: Development server and Drupal integration Rebuild as: agent-brain dev, agent-brain drupal start - proper CLI commands with API endpoints

Disaster Recovery

Distributed Tracing

// OpenTelemetry integration
import { trace } from '@opentelemetry/api';
const tracer = trace.getTracer('agent-brain');
const span = tracer.startSpan('vector.search');

Documentation Standardization

Documentation-as-Code

Drupal 11 Compliance

Ecosystem Management

"start": "npx @bluefly/agent-cli ecosystem start --service=vector",
"stop": "npx @bluefly/agent-cli ecosystem stop --service=vector"

Purpose: Start/stop vector database service Rebuild as: agent-brain start, agent-brain stop - should call API endpoints for service management

Embedding Services 🔧

Encryption Standards

Error Codes

enum ErrorCodes {
  VECTOR_NOT_FOUND = 'VECTOR_NOT_FOUND',
  COLLECTION_EXISTS = 'COLLECTION_EXISTS',
  DIMENSION_MISMATCH = 'DIMENSION_MISMATCH',
  INVALID_METRIC = 'INVALID_METRIC',
  CONNECTION_FAILED = 'CONNECTION_FAILED'
}

Error Logging

logger.error({
  message: error.message,
  code: error.code,
  stack: error.stack,
  context: {
    operation: 'vector.search',
    collection,
    userId
  }
});

Error Response Format

{
  "success": false,
  "error": {
    "code": "AGENT_NOT_FOUND",
    "message": "Agent with ID 'agent-123' not found",
    "details": {
      "agentId": "agent-123",
      "availableAgents": ["agent-456", "agent-789"]
    },
    "timestamp": "2025-09-08T17:30:00Z"
  },
  "metadata": {
    "requestId": "req_1234567890_abc123",
    "timestamp": "2025-09-08T17:30:00Z",
    "duration": 150,
    "version": "0.1.0"
  }
}

Error Shape

class VectorError extends Error {
  constructor(
    message: string,
    public code: string,
    public details?: Record<string, unknown>,
    public statusCode: number = 500
  ) {
    super(message);
    this.name = 'VectorError';
  }
}

ESLint Rules

Key rules enforced: - No any types - Explicit return types for functions - No unused variables - Consistent naming conventions - Import/export order

External APIs

Fault Tolerance

File Naming Conventions

File Organization

Git Workflow

  1. Create feature branch
git checkout -b feature/0.1.0-your-feature-name
  1. Make changes following code standards
  2. Test your changes
npm test
npm run build
npm run lint
npm run typecheck
  1. Commit with conventional commits
git commit -m "feat: add new agent capability validation
- Added capability validation to AgentManager
- Updated types with new capability interface
- Added tests for validation logic"
  1. Push and create PR
git push origin feature/0.1.0-your-feature-name

Global Options

Option Description
-u, --url <url> Agent Brain API URL
-k, --api-key <key> API authentication key
--json Output in JSON format
--verbose Verbose output
#### Graceful Degradation
- Fallback Services: Secondary vector databases
- Cache Fallback: Serve from cache on service failure
- Circuit Breakers: Prevent cascade failures
- Health Checks: Regular service health validation
#### Haystack - HaystackDocumentStore 
class HaystackDocumentStore {
  constructor(config?: {
    collectionName?: string;
    vectorHub?: VectorHub;
    dimensions?: number;
    duplicateDocuments?: 'overwrite' | 'skip' | 'fail';
    returnEmbedding?: boolean;
    batchSize?: number;
  })
  async write_documents(
    documents: HaystackDocument[],
    index?: string,
    batch_size?: number,
    duplicate_documents?: 'overwrite' | 'skip' | 'fail'
  ): Promise<void>
  async get_all_documents(
    index?: string,
    filters?: FilterType,
    return_embedding?: boolean,
    batch_size?: number
  ): Promise<HaystackDocument[]>
  async get_all_documents_generator(
    index?: string,
    filters?: FilterType,
    return_embedding?: boolean,
    batch_size?: number
  ): AsyncGenerator<HaystackDocument[]>
  async get_document_by_id(
    id: string,
    index?: string
  ): Promise<HaystackDocument | null>
  async get_documents_by_id(
    ids: string[],
    index?: string,
    batch_size?: number
  ): Promise<HaystackDocument[]>
  async query_by_embedding(
    query_emb: number[],
    filters?: FilterType,
    top_k?: number,
    index?: string,
    return_embedding?: boolean,
    headers?: Record<string, string>,
    scale_score?: boolean
  ): Promise<HaystackDocument[]>
  async delete_documents(
    index?: string,
    ids?: string[],
    filters?: FilterType,
    headers?: Record<string, string>
  ): Promise<void>
  async delete_index(index?: string): Promise<void>
  async update_document_meta(
    id: string,
    meta: Record<string, any>,
    index?: string
  ): Promise<void>
  async get_document_count(
    filters?: FilterType,
    index?: string,
    only_documents_without_embedding?: boolean,
    headers?: Record<string, string>
  ): Promise<number>
}

Health and Validation

"health:check": "npx forge health:check",
"forge:api:validate": "npx forge api:validate",
"forge:openapi:generate": "npx forge openapi:generate"

Purpose: System health and API validation Rebuild as: agent-brain health, agent-brain api validate - direct API health endpoints

Health Endpoints

Method Endpoint Description
GET /health Basic health check
GET /health/detailed Detailed health with metrics
GET /health/ready Kubernetes readiness probe
#### Horizontal Scaling
- Agent Scaling: Linear scaling to 50+ concurrent agents
- Knowledge Graph: Multi-region Neo4j + Qdrant with replication
- Lock Manager: Redis cluster with consistent hashing
- API Layer: Stateless services behind load balancer
#### Immediate (Complete Today)
1. Finish API endpoint implementation
2. Complete CLI command structure
3. Add integration tests
4. Generate API documentation
#### Infrastructure
- CI/CD: GitLab CI/CD with multi-stage pipelines
- Container: Docker with multi-stage builds
- Monitoring: OpenTelemetry integration
- Cloud: AWS ECS/Fargate, Acquia Cloud compatible
#### Infrastructure Layer (src/infrastructure/)
- Database adapters
- External API clients
- Configuration management
- Third-party service integrations
#### Key Dependencies
- @bluefly/open-standards-scalable-agents: OSSA compliance
- @langchain/core, @langchain/qdrant: LangChain integration
- @modelcontextprotocol/sdk: MCP protocol support
- zod: Runtime type validation
- p-queue: Concurrent operation management
#### Knowledge Graph Maintenance
1. Regular Cleanup: Remove stale agents and completed tasks
2. Index Optimization: Rebuild indexes weekly
3. Backup Strategy: Daily backups of Neo4j and Qdrant data
4. Monitoring: Track query latency and storage growth
#### Kubernetes Deployment
Location: infrastructure/kubernetes/neo4j/ 
apiVersion: v1
kind: Namespace
metadata:
  name: observability
---
apiVersion: v1
kind: Secret
metadata:
  name: neo4j-auth
  namespace: observability
type: Opaque
stringData:
  NEO4J_AUTH: neo4j/observability_secure_2025
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: neo4j
  namespace: observability
spec:
  serviceName: neo4j
  replicas: 1
  selector:
    matchLabels:
      app: neo4j
  template:
    metadata:
      labels:
        app: neo4j
    spec:
      containers:
      - name: neo4j
        image: neo4j:5.0-enterprise
        ports:
        - containerPort: 7474
          name: http
        - containerPort: 7687
          name: bolt
        env:
        - name: NEO4J_AUTH
          valueFrom:
            secretKeyRef:
              name: neo4j-auth
              key: NEO4J_AUTH
        - name: NEO4J_ACCEPT_LICENSE_AGREEMENT
          value: "yes"
        volumeMounts:
        - name: data
          mountPath: /data
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 10Gi
---
apiVersion: v1
kind: Service
metadata:
  name: neo4j
  namespace: observability
spec:
  type: ClusterIP
  ports:
  - port: 7474
    targetPort: 7474
    name: http
  - port: 7687
    targetPort: 7687
    name: bolt
  selector:
    app: neo4j

Initialize Schema:

#### LangChain - VectorHubStore
```typescript
class VectorHubStore extends VectorStore {
  constructor(
    embeddings: Embeddings,
    config?: {
      collectionName?: string;
      vectorHub?: VectorHub;
      metadataFilter?: Record<string, any>;
      similarityThreshold?: number;
    }
  )
  async addDocuments(
    documents: Document[],
    options?: { ids?: string[] }
  ): Promise<string[]>
  async addVectors(
    vectors: number[][],
    documents: Document[],
    options?: { ids?: string[] }
  ): Promise<string[]>
  async similaritySearchVectorWithScore(
    query: number[],
    k: number,
    filter?: Record<string, any>
  ): Promise<[Document, number][]>
  async delete(params: {
    ids?: string[];
    filter?: Record<string, any>;
  }): Promise<void>
  async searchWithQuery(
    query: string,
    k?: number,
    filter?: Record<string, any>
  ): Promise<Document[]>
  static async fromTexts(
    texts: string[],
    metadatas: object[] | object,
    embeddings: Embeddings,
    config?: LangChainConfig
  ): Promise<VectorHubStore>
  static async fromDocuments(
    docs: Document[],
    embeddings: Embeddings,
    config?: LangChainConfig
  ): Promise<VectorHubStore>
}

Latency Targets

Legacy CLI Build

"cli:build": "tsc src/cli/index.ts --outDir dist --target es2020 --module commonjs",
"cli:test": "jest src/cli --testPathPattern=cli",
"postbuild": "npm run cli:build",
"cli:dev": "npx tsx src/cli/index.ts"

Purpose: Old CLI build system Replace with: Proper workspace CLI with modern build system

LlamaIndex - LlamaIndexVectorStore

class LlamaIndexVectorStore {
  constructor(config?: {
    collectionName?: string;
    vectorHub?: VectorHub;
    dimensions?: number;
    batchSize?: number;
  })
  async add(nodes: BaseNode[]): Promise<string[]>
  async delete(refDocId: string | string[]): Promise<void>
  async query(
    queryEmbedding: number[],
    similarityTopK?: number,
    refDocId?: string
  ): Promise<VectorStoreQueryResult>
  async getNodes(
    nodeIds?: string[],
    filters?: MetadataFilters
  ): Promise<BaseNode[]>
  async clear(): Promise<void>
  async persist(persistPath?: string): Promise<void>
}

Local Environment

  1. Clone the repository
git clone https://github.com/bluefly/agent-brain.git
cd agent-brain
  1. Install dependencies
npm install
  1. Set up environment
cp .env.example .env
#### Logging
```typescript
import { logger } from '../utils/logger';
export class AgentManager {
  public async spawnAgent(config: AgentConfig): Promise<Agent> {
    const startTime = Date.now();
    logger.info('Spawning agent', { config });
    try {
      const agent = new Agent(config);
      const duration = Date.now() - startTime;
      logger.info('Agent spawned successfully', {
        agentId: agent.id,
        duration,
        config
      });
      return agent;
    } catch (error) {
      logger.error('Failed to spawn agent', {
        error: error.message,
        stack: error.stack,
        config
      });
      throw error;
    }
  }
}

Logging Standards

// Structured logging
logger.info({
  event: 'vector.search',
  collection,
  resultCount: results.length,
  duration: Date.now() - startTime,
  userId,
  traceId
});

MCP (Model Context Protocol)

Memory Optimization

// Use object pools for frequent allocations
class VectorPool {
  private pool: number[][] = [];
  acquire(dimension: number): number[] {
    const vector = this.pool.pop() || new Array(dimension);
    return vector.fill(0);
  }
  release(vector: number[]): void {
    if (this.pool.length < 100) { // Limit pool size
      this.pool.push(vector);
    }
  }
}
// Weak references for caches
class AgentCache {
  private cache = new WeakMap<AgentConfig, Agent>();
  get(config: AgentConfig): Agent | undefined {
    return this.cache.get(config);
  }
  set(config: AgentConfig, agent: Agent): void {
    this.cache.set(config, agent);
  }
}

Memory Usage

Message Queues

Metrics (Prometheus)

Metrics Collection

Migration Process

  1. Analyze current structure
  2. Create Clean Architecture directories
  3. Move files to appropriate layers
  4. Update all import paths
  5. Fix TypeScript compilation errors
  6. Rebuild dist/ directory
  7. Update documentation
  8. Consolidate configurations

Multi-Region Deployment

Tailscale Configuration:

// Auto-discover Qdrant instances across regions
const qdrant = createTailscaleQdrantService({
  apiKey: process.env.QDRANT_API_KEY,
  timeout: 30000,
});
const instances = await qdrant.discoverInstances();
// [
//   { region: 'us-west', hostname: 'qdrant-us-west.tail...', status: 'healthy', latency: 20 },
//   { region: 'us-east', hostname: 'qdrant-us-east.tail...', status: 'healthy', latency: 80 }
// ]
// Connect to lowest latency region
await qdrant.connectOptimal();

Kubernetes Deployment:

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: qdrant
  namespace: observability
spec:
  serviceName: qdrant
  replicas: 1
  template:
    spec:
      containers:
      - name: qdrant
        image: qdrant/qdrant:v1.7.0
        ports:
        - containerPort: 6333
          name: http
        - containerPort: 6334
          name: grpc
        volumeMounts:
        - name: storage
          mountPath: /qdrant/storage
  volumeClaimTemplates:
  - metadata:
      name: storage
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 20Gi

Neo4j Graph Design

  1. Use Indexes: Create indexes on frequently queried properties cypher CREATE INDEX agent_id FOR (a:Agent) ON (a.id); CREATE INDEX issue_state FOR (i:Issue) ON (i.state);
  2. Limit Query Depth: Use max depth in path queries to prevent performance issues cypher MATCH path = (t:Task)-[:DEPENDS_ON*1..5]->(dep:Task)
  3. Use Parameters: Always use parameterized queries to prevent injection cypher MATCH (a:Agent {id: $agentId}) RETURN a

Optimization Tips

  1. Batch Operations: Use bulk upsert for large datasets
  2. Connection Pooling: Reuse SDK client instances
  3. Caching: Cache frequently accessed collections
  4. Indexing: Optimize collections after bulk inserts
  5. Filtering: Use metadata filters to reduce search space

OSSA Compliance

Pagination Standards

interface PaginatedResponse<T> {
  data: T[];
  meta: {
    page: number;
    pageSize: number;
    totalPages: number;
    totalCount: number;
  };
  links: {
    first?: string;
    prev?: string;
    next?: string;
    last?: string;
  };
}

Patch Cadence

Performance Optimizations

Performance Patterns

PII Handling

class PIIHandler {
  // Tokenization
  async tokenize(pii: string): Promise<string> {
    const token = await this.generateToken();
    await this.storeMapping(token, pii);
    return token;
  }
  // Masking
  mask(value: string, type: PIIType): string {
    switch(type) {
      case PIIType.EMAIL:
        return value.replace(/(.{2}).*(@.*)/, '$1***$2');
      case PIIType.SSN:
        return `***-**-${value.slice(-4)}`;
      case PIIType.PHONE:
        return `***-***-${value.slice(-4)}`;
    }
  }
}

PineconeCompatAdapter

class PineconeCompatAdapter {
  constructor(indexName: string, config?: {
    vectorHub?: VectorHub;
    dimensions?: number;
  })
  async upsert(vectors: PineconeVector[]): Promise<void>
  async query(
    vector: number[],
    topK?: number,
    filter?: Record<string, any>
  ): Promise<PineconeQueryResponse>
  async fetch(ids: string[]): Promise<PineconeVector[]>
  async deleteOne(id: string): Promise<void>
  async deleteMany(ids: string[]): Promise<void>
  async deleteAll(filter?: Record<string, any>): Promise<void>
  async describeIndexStats(): Promise<{
    dimension: number;
    indexFullness: number;
    totalVectorCount: number;
  }>
}

Plugin System

// src/core/interfaces/plugin.ts
export interface Plugin {
  name: string;
  version: string;
  initialize(context: PluginContext): Promise<void>;
  destroy(): Promise<void>;
}
// src/core/use-cases/plugin-manager.ts
export class PluginManager {
  private plugins: Map<string, Plugin> = new Map();
  async loadPlugin(plugin: Plugin): Promise<void> {
    await plugin.initialize(this.createContext());
    this.plugins.set(plugin.name, plugin);
  }
}

Pre-commit Checks

- lint:fix
- typecheck
- test:unit
- contracts:check

Prerequisites

Presentation Layer (src/presentation/)

Qdrant Vector Operations

Create Collection:

buildkit qdrant collection create --name agent_embeddings --vector-size 384

Insert Vectors:

buildkit qdrant insert --collection agent_embeddings --file vectors.json

Search Similar:

buildkit qdrant search --collection agent_embeddings --query "machine learning agent" --limit 10

Quality Gates

RAG Operations ❌

Request Headers

API key is sent as a header:

X-API-Key: your-api-key-here

Roles & Scopes

enum Role {
  ADMIN = 'admin',
  DEVELOPER = 'developer',
  ANALYST = 'analyst',
  READER = 'reader'
}
enum Scope {
  VECTORS_READ = 'vectors:read',
  VECTORS_WRITE = 'vectors:write',
  COLLECTIONS_MANAGE = 'collections:manage',
  SYSTEM_ADMIN = 'system:admin'
}
const rolePermissions = {
  [Role.ADMIN]: [Scope.SYSTEM_ADMIN],
  [Role.DEVELOPER]: [Scope.VECTORS_READ, Scope.VECTORS_WRITE],
  [Role.ANALYST]: [Scope.VECTORS_READ, Scope.COLLECTIONS_MANAGE],
  [Role.READER]: [Scope.VECTORS_READ]
};

Runbook Structure

incident_response:
  detection:
    - automated_alerts
    - security_monitoring
    - user_reports
  triage:
    severity: [critical, high, medium, low]
    impact: [data_breach, service_outage, degradation]
  containment:
    - isolate_affected_systems
    - disable_compromised_accounts
    - block_malicious_ips
  eradication:
    - remove_malware
    - patch_vulnerabilities
    - reset_credentials
  recovery:
    - restore_from_backup
    - validate_integrity
    - monitor_for_reoccurrence
  lessons_learned:
    - post_mortem
    - update_runbooks
    - implement_improvements

SBOM (Software Bill of Materials)

{
  "bomFormat": "CycloneDX",
  "specVersion": "1.4",
  "version": 1,
  "components": [
    {
      "type": "library",
      "name": "@qdrant/js-client-rest",
      "version": "1.7.0",
      "licenses": [{"license": {"id": "MIT"}}]
    }
  ]
}

Scaling Recommendations

SDK Error Handling

import { AgentBrainClient } from '@bluefly/agent-brain';
try {
  const client = new AgentBrainClient();
  const agent = await client.spawnAgent(config);
} catch (error) {
  if (error.response?.status === 400) {
    console.error('Invalid configuration:', error.response.data.error);
  } else if (error.response?.status === 429) {
    console.error('Rate limited, retry after:', error.response.headers['retry-after']);
  } else {
    console.error('Unexpected error:', error.message);
  }
}

Search Capabilities 🔧

Secrets Management

// Use AWS SDK for secrets
import { SSMClient, GetParameterCommand } from '@aws-sdk/client-ssm';
const client = new SSMClient({ region: 'us-east-1' });
const parameter = await client.send(
  new GetParameterCommand({
    Name: '/agent-brain/qdrant-api-key',
    WithDecryption: true
  })
);

Security Metrics

Security Scanning Pipeline

security-pipeline:
  stages:
    - sca:  # Software Composition Analysis
        - npm audit
        - snyk test
        - owasp dependency-check
    - sast: # Static Application Security Testing
        - semgrep
        - sonarqube
        - eslint-plugin-security
    - dast: # Dynamic Application Security Testing
        - zap-baseline
        - nuclei
        - burp-suite
    - container:
        - trivy
        - grype
        - docker-bench

import { QdrantService } from './qdrant.service';
import { EmbeddingService } from './embedding.service';
const qdrant = new QdrantService();
const embeddings = new EmbeddingService();
// Generate embedding for search query
const queryEmbedding = await embeddings.generateEmbedding(
  'authentication and security improvements'
);
// Search for similar issues
const similarIssues = await qdrant.search({
  collectionName: 'issue_embeddings',
  vector: queryEmbedding,
  limit: 10,
  scoreThreshold: 0.7,
});
console.log('Similar issues:', similarIssues);

Semantic Kernel - SemanticKernelMemoryStore

class SemanticKernelMemoryStore {
  constructor(config?: {
    collectionName?: string;
    vectorHub?: VectorHub;
    dimensions?: number;
    relevanceThreshold?: number;
    batchSize?: number;
  })
  async createCollectionAsync(collection: string): Promise<void>
  async getCollectionsAsync(): Promise<string[]>
  async doesCollectionExistAsync(collection: string): Promise<boolean>
  async deleteCollectionAsync(collection: string): Promise<void>
  async upsertAsync(
    collection: string,
    record: SKMemoryRecord
  ): Promise<string>
  async upsertBatchAsync(
    collection: string,
    records: SKMemoryRecord[]
  ): Promise<string[]>
  async getAsync(
    collection: string,
    key: string
  ): Promise<SKMemoryRecord | null>
  async getBatchAsync(
    collection: string,
    keys: string[]
  ): Promise<SKMemoryRecord[]>
  async removeAsync(collection: string, key: string): Promise<void>
  async removeBatchAsync(
    collection: string,
    keys: string[]
  ): Promise<void>
  async getNearestMatchesAsync(
    collection: string,
    embedding: number[],
    limit?: number,
    minRelevanceScore?: number,
    withEmbeddings?: boolean
  ): Promise<Array<[SKMemoryRecord, number]>>
  async getNearestMatchAsync(
    collection: string,
    embedding: number[],
    minRelevanceScore?: number,
    withEmbedding?: boolean
  ): Promise<[SKMemoryRecord, number] | null>
  async searchAsync(
    collection: string,
    query: string,
    embedding: number[],
    limit?: number,
    relevanceThreshold?: number
  ): Promise<SKMemoryRecord[]>
  async getAllRecordsAsync(collection: string): Promise<SKMemoryRecord[]>
  async getCollectionStatsAsync(collection: string): Promise<{
    recordCount: number;
    dimensions: number;
    indexSize: number;
  }>
  async optimizeCollectionAsync(collection: string): Promise<void>
  async healthCheckAsync(): Promise<boolean>
}

Service Control

Service Implementation

Location: src/services/knowledge-graph/neo4j-knowledge-graph.service.ts

Slow Query Performance

  1. Add Indexes: Ensure indexes exist on queried properties
  2. Limit Results: Use LIMIT clause to reduce result size
  3. Connection Pooling: Increase pool size for concurrent queries
  4. Cache Queries: Use Redis to cache frequent queries

SSP/POA&M Linkages

Step 1: Define API Endpoints

Each legacy command should map to API endpoints in api/openapi.yaml:

#### Step 2: Implement API Endpoints
Create server endpoints that implement the actual functionality.
#### Step 4: Create CLI Commands
Build CLI commands that call the API endpoints instead of running scripts directly.
#### Task Dependency Resolution
```typescript
import { Neo4jService } from './neo4j.service';
const neo4j = new Neo4jService();
// Find all dependencies for a task
const dependencies = await neo4j.query({
  cypher: `
    MATCH path = (t:Task {id: $taskId})-[:DEPENDS_ON*]->(dep:Task)
    WHERE dep.status <> 'completed'
    RETURN dep.id AS taskId, dep.status AS status, length(path) AS depth
    ORDER BY depth ASC
  `,
  parameters: { taskId: 'task-789' },
});
console.log('Blocking dependencies:', dependencies);

Template Ready (This Week)

Once complete, agent-brain will serve as the API-First CLI Development Template for standardizing the remaining 33+ projects in the LLM Platform ecosystem.

Test Coverage Requirements

Test Structure

describe('VectorService', () => {
  describe('search', () => {
    it('should return matching vectors within limit', async () => {
      // Arrange
      const params = { ... };
      // Act
      const results = await service.search(params);
      // Assert
      expect(results).toHaveLength(10);
    });
  });
});

Testing Suite

Threat Analysis & Mitigations

Threat Category Mitigation NIST Control
Spoofing Authentication bypass JWT validation, MFA IA-2, IA-3
Tampering Data modification HMAC signatures, checksums SC-8, SC-28
Repudiation Denial of actions Audit logging, immutable logs AU-2, AU-12
Information Disclosure Data leakage Encryption, access controls AC-3, SC-13
Denial of Service Resource exhaustion Rate limiting, auto-scaling SC-5, CP-10
Elevation of Privilege Unauthorized access RBAC, least privilege AC-6, CM-7
#### Throughput Requirements
- 10,000 vectors/second insert rate
- 1,000 concurrent searches
- 100 concurrent collection operations
#### Tracing (OpenTelemetry)
- End-to-end task execution traces
- Knowledge graph query spans
- Agent communication traces
- GitLab API interaction traces
#### Trust Boundaries 
graph TB
    subgraph "External"
        Client[API Clients]
        Attacker[Threat Actor]
    end
    subgraph "DMZ"
        WAF[WAF/CDN]
        LB[Load Balancer]
    end
    subgraph "Application"
        API[API Server]
        Auth[Auth Service]
    end
    subgraph "Data"
        DB[Vector Database]
        Secrets[Secrets Manager]
    end
    Client -->|HTTPS| WAF
    WAF -->|Trust Boundary 1| LB
    LB -->|Trust Boundary 2| API
    API -->|Trust Boundary 3| DB
    API --> Auth
    Auth --> Secrets
    Attacker -.->|Threats| WAF
    Attacker -.->|Threats| API

Type Guards

import { isVectorPoint, isAgentConfig, isHealthStatus } from '@bluefly/agent-brain';
if (isVectorPoint(data)) {
  // TypeScript knows data is VectorPoint
  console.log(data.vector.length);
}

TypeScript Configuration

{
  "compilerOptions": {
    "strict": true,
    "noImplicitAny": true,
    "noImplicitReturns": true,
    "noUnusedLocals": true,
    "noUnusedParameters": true
  }
}

TypeScript Standards

// ✅ Good
interface VectorSearchParams {
  collection: string;
  vector: number[];
  limit: number;
  filter?: Record<string, unknown>;
}
// ❌ Bad
function search(params: any) { }

UniversalAdapterBenchmark

class UniversalAdapterBenchmark {
  constructor(config: BenchmarkConfig)
  async initializeAdapters(): Promise<void>
  generateTestData(): void
  async runBenchmark(): Promise<BenchmarkResults>
}
interface BenchmarkConfig {
  vectorDimensions: number;
  documentCount: number;
  queryCount: number;
  batchSizes: number[];
  concurrentQueries: number[];
  warmupRounds: number;
  benchmarkRounds: number;
  collectMemoryStats: boolean;
  includeAdapters: string[];
  excludeAdapters?: string[];
}
interface BenchmarkResults {
  config: BenchmarkConfig;
  results: PerformanceMetrics[];
  summary: {
    fastestAdapter: string;
    slowestAdapter: string;
    mostMemoryEfficient: string;
    highestThroughput: string;
    recommendedForUseCase: Record<string, string>;
  };
  timestamp: string;
  environment: {
    nodeVersion: string;
    platform: string;
    cpuCount: number;
    memoryTotal: number;
  };
}
// Preset configurations
const BENCHMARK_PRESETS = {
  quick: BenchmarkConfig;
  comprehensive: BenchmarkConfig;
  production: BenchmarkConfig;
};

UniversalRAG

class UniversalRAG {
  constructor(config?: {
    frameworks?: string[];
    vectorHub?: VectorHub;
    dimensions?: number;
    cacheResults?: boolean;
    cacheTTL?: number;
    maxConcurrentQueries?: number;
  })
  async indexDocuments(documents: RAGDocument[]): Promise<void>
  async indexBatch(
    documents: RAGDocument[],
    batchSize?: number
  ): Promise<void>
  async retrieve(query: RAGQuery): Promise<RAGResult>
  async retrieveWithContext(
    query: RAGContextQuery
  ): Promise<RAGResult>
  async retrieveBatch(queries: RAGQuery[]): Promise<RAGResult[]>
  clearCache(): void
  getMetrics(): RAGMetrics
  async optimizeIndex(framework?: string): Promise<void>
  async rerank(
    results: RAGResult,
    rerankModel?: string
  ): Promise<RAGResult>
}
interface RAGDocument {
  content: string;
  framework: string;
  metadata?: Record<string, any>;
  embedding?: number[];
}
interface RAGQuery {
  query: string;
  frameworks?: string[];
  contentTypes?: string[];
  filters?: Record<string, any>;
  limit?: number;
  minScore?: number;
}
interface RAGResult {
  query: string;
  documents: Array<{
    content: string;
    score: number;
    metadata: Record<string, any>;
    framework: string;
    content_type?: string;
  }>;
  totalResults: number;
  processingTime: number;
}

Vector Database Operations ✅

Vector Database Support

Vector Entity

VectorHubConfig

class VectorHubConfig {
  static getInstance(): VectorHubConfig
  setConfig(config: VectorClientConfig): void
  getConfig(): VectorClientConfig
  updateConfig(updates: Partial<VectorClientConfig>): void
  resetConfig(): void
}
interface VectorClientConfig {
  url: string;
  apiKey?: string;
  vectorDimension: number;
  defaultDistance: 'cosine' | 'euclidean' | 'dot';
  batchSize?: number;
  timeout?: number;
  retryAttempts?: number;
  retryDelay?: number;
  connectionPool?: {
    min: number;
    max: number;
    acquireTimeoutMillis: number;
    idleTimeoutMillis: number;
  };
}

VectorHubLogger

class VectorHubLogger {
  static getInstance(): VectorHubLogger
  info(message: string, meta?: any): void
  warn(message: string, meta?: any): void
  error(message: string, error?: Error, meta?: any): void
  debug(message: string, meta?: any): void
  setLevel(level: 'debug' | 'info' | 'warn' | 'error'): void
  setMetricsCallback(callback: (metrics: any) => void): void
}

VectorHubMetrics

class VectorHubMetrics {
  static getInstance(): VectorHubMetrics
  recordOperation(metrics: {
    operation: string;
    duration: number;
    success: boolean;
    error?: string;
  }): void
  async getMetrics(): Promise<string>
  resetMetrics(): void
}

Versioning Strategy

Zero-Trust Networking

1. Clean Project Structure
2. API-First Design
3. Test-Driven Development
4. OSSA v0.1.8 Compliance
5. API Implementation (80% complete)
6. CLI Implementation (30% complete)
7. Integration Testing (Pending)
8. Documentation (Pending)
AC-2: Account Management
// Implementation
class AccountManager {
  async createAccount(user: UserAccount): Promise<void> {
    await this.validateApproval(user);
    await this.assignRoles(user, ['reader']); // Least privilege default
    await this.auditLog('account.created', user);
    await this.notifySecurityTeam(user);
  }
  async reviewAccounts(): Promise<AccountReview[]> {
    // Quarterly access review
    const accounts = await this.getActiveAccounts();
    return this.generateReviewReport(accounts);
  }
}
AC-3: Access Enforcement
// RBAC implementation
@Controller('/api/v1/vectors')
class VectorController {
  @RequirePermission('vectors:read')
  async search(@User() user: AuthUser) {
    // Access enforced by decorator
  }
  @RequirePermission('vectors:write')
  async create(@User() user: AuthUser) {
    // Write permission required
  }
}
AC-6: Least Privilege
Agent Coordinator
agent_embeddings

Purpose: Semantic search for agents by capability descriptions

await qdrant.createCollection({
  name: 'agent_embeddings',
  vectorSize: 384,
  distance: 'Cosine',
  metadata: {
    description: 'Agent capability embeddings',
    created: new Date().toISOString(),
  },
});

Schema: - id: Agent ID (string) - vector: 384-dimension embedding from sentence-transformers - payload: { name, type, capabilities, status, version }

Async/Await Patterns
// Prefer async/await over Promises
public async coordinateAgents(request: CoordinationRequest): Promise<CoordinationResult> {
  try {
    const agents = await this.validateAgents(request.agentIds);
    const result = await this.executeCoordination(agents, request);
    return result;
  } catch (error) {
    this.logger.error('Coordination failed', { error, request });
    throw error;
  }
}
AU-12: Audit Generation
// Centralized audit logging
class AuditService {
  async log(event: AuditEvent): Promise<void> {
    const entry = {
      timestamp: new Date().toISOString(),
      eventType: event.type,
      userId: event.userId,
      sessionId: event.sessionId,
      resource: event.resource,
      action: event.action,
      result: event.result,
      metadata: event.metadata,
      hash: this.generateHash(event) // Tamper detection
    };
    await this.writeToImmutableLog(entry);
    await this.sendToSIEM(entry);
  }
}
AU-2: Audit Events
audit_events:
  - authentication.success
  - authentication.failure
  - authorization.denied
  - data.accessed
  - data.modified
  - data.deleted
  - configuration.changed
  - privilege.escalated
Capability Matcher
// src/services/mesh-coordinator.service.ts
class MeshCoordinatorService {
  async submitTask(params: {
    taskId: string;
    taskType: string;
    payload: any;
    requiredCapabilities: string[];
    priority: 'low' | 'medium' | 'high' | 'critical';
    timeout: number;
    retries: number;
  }): Promise<TaskResult>;
  async selectAgent(
    availableAgents: Agent[],
    requiredCapabilities: string[],
    strategy: LoadBalancingStrategy
  ): Promise<Agent | null>;
}
Class Structure
export class AgentManager {
  // Static members first
  private static readonly DEFAULT_TIMEOUT = 30000;
  // Instance properties
  private agents: Map<string, Agent> = new Map();
  public readonly version: string;
  // Constructor
  constructor(private config: AgentManagerConfig) {
    this.version = '0.1.0';
  }
  // Public methods
  public async spawnAgent(config: AgentConfig): Promise<Agent> {
    // Implementation
  }
  // Private methods
  private validateConfig(config: AgentConfig): void {
    // Implementation
  }
}
CM-7: Least Functionality
CollectionSchema
interface CollectionSchema {
  name: string;
  vectorSize: number;
  distance: 'cosine' | 'euclidean' | 'dot';
  schemaVersion?: string;
  metadata?: Record<string, any>;
}
Configuration Options
Option Type Default Description
baseURL string http://localhost:3456 Agent Brain API URL
apiKey string undefined Authentication key
timeout number 30000 Request timeout (ms)
retryAttempts number 3 Number of retry attempts
retryDelay number 1000 Delay between retries (ms)
headers object {} Custom headers
##### Create Nodes and Relationships 
// Create agent node
const agentId = await neo4j.createNode({
  labels: ['Agent'],
  properties: {
    id: 'worker-001',
    name: 'Data Processing Worker',
    type: 'worker',
    status: 'active',
    capabilities: ['data-processing', 'ml-inference'],
    createdAt: new Date().toISOString(),
  },
});
// Create relationship
await neo4j.createRelationship({
  fromNodeId: agentId,
  toNodeId: projectId,
  type: 'WORKS_ON',
  properties: {
    startedAt: new Date().toISOString(),
    role: 'contributor',
  },
});
Distributed Lock Manager
Execute Cypher Queries
// Simple query
const results = await neo4j.query({
  cypher: 'MATCH (a:Agent {status: $status}) RETURN a',
  parameters: { status: 'active' },
});
// Complex query with multiple matches
const agentWorkload = await neo4j.query({
  cypher: `
    MATCH (a:Agent)
    OPTIONAL MATCH (i:Issue)-[:ASSIGNED_TO]->(a)
    WHERE i.state IN ['opened', 'in-progress']
    RETURN a.id AS agentId, count(i) AS issueCount
    ORDER BY issueCount ASC
  `,
  parameters: {},
});
Fault-Tolerant Agent System
class AutonomousAgentOrchestrator {
  async spawnAgent(config: AgentConfig): Promise<AgentInstance>;
  async monitorHealth(agentId: string): Promise<HealthStatus>;
  async handleFailure(agentId: string, error: Error): Promise<void>;
  async recoverAgent(agentId: string): Promise<boolean>;
}
Find Agent Workload
MATCH (a:Agent)
OPTIONAL MATCH (i:Issue)-[:ASSIGNED_TO]->(a)
WHERE i.state IN ['opened', 'in-progress']
WITH a, count(i) AS activeIssues
OPTIONAL MATCH (t:Task)-[:ASSIGNED_TO]->(a)
WHERE t.status = 'in-progress'
WITH a, activeIssues, count(t) AS activeTasks
RETURN a.id AS agentId, a.name AS agentName, activeIssues, activeTasks,
       (activeIssues + activeTasks) AS totalWorkload
ORDER BY totalWorkload ASC
Find Agents by Capability
MATCH (a:Agent)
WHERE ALL(cap IN $requiredCapabilities WHERE cap IN a.capabilities)
  AND a.status = 'active'
RETURN a.id AS agentId, a.name AS agentName, a.capabilities AS capabilities
Find All Agents Working on a Project
MATCH (a:Agent)-[:WORKS_ON]->(p:Project {id: $projectId})
WHERE a.status = 'active'
RETURN a.id AS agentId, a.name AS agentName, a.capabilities AS capabilities
ORDER BY a.lastSeen DESC
Find Issues Assigned to Agent
MATCH (i:Issue)-[:ASSIGNED_TO]->(a:Agent {id: $agentId})
MATCH (i)-[:BELONGS_TO]->(m:Milestone)
WHERE i.state IN ['opened', 'in-progress']
RETURN i.id AS issueId, i.title AS title, m.title AS milestone, i.weight AS weight
ORDER BY i.weight DESC, m.dueDate ASC

MATCH (i1:Issue)-[:ASSIGNED_TO]->(a:Agent)<-[:ASSIGNED_TO]-(i2:Issue)
WHERE i1.id = $issueId AND i1 <> i2
RETURN DISTINCT i2.id AS relatedIssue, i2.title AS title, i2.labels AS labels
LIMIT 10
Find Task Dependencies
MATCH path = (t:Task {id: $taskId})-[:DEPENDS_ON*]->(dependency:Task)
WHERE dependency.status <> 'completed'
RETURN dependency.id AS taskId, dependency.status AS status, length(path) AS depth
ORDER BY depth ASC
Find Thought Chain for Issue
MATCH (t:Thought)-[:GENERATES]->(i:Issue {id: $issueId})
RETURN t.id AS thoughtId, t.stage AS stage, t.content AS content, t.thoughtNumber AS number
ORDER BY t.thoughtNumber ASC
GitLab CE Integration

Location: src/services/gitlab/ + CLI commands Features: - OAuth Authentication: Secure token management - Issue Lifecycle: Create, update, close with state machine - Milestone Management: Sync milestones with agent workflows - Webhook Processing: Real-time event handling - Roadmap Automation: Convert roadmap items to agent tasks Issue State Machine:

opened → claimed → in-progress → under-review → completed → closed
          ↓                          ↓
       blocked                  needs-revision

CLI Commands:

buildkit gitlab-kg build --project <id>
buildkit gitlab-kg sync --incremental
buildkit gitlab-kg query "CYPHER_QUERY"
buildkit gitlab-kg related --issue-id <id>
buildkit gitlab-kg assign-opportunities --agent-id <id>
IA-2: Multi-Factor Authentication
// MFA implementation
class AuthService {
  async authenticate(credentials: Credentials): Promise<AuthResult> {
    // Step 1: Password verification
    const user = await this.verifyPassword(credentials);
    // Step 2: MFA requirement for privileged users
    if (user.isPrivileged()) {
      const mfaToken = await this.requireMFA(user);
      await this.verifyMFAToken(mfaToken);
    }
    // Step 3: Generate session
    return this.createSession(user);
  }
}
IA-3: Device Identification
// Device fingerprinting
interface DeviceIdentity {
  deviceId: string;
  userAgent: string;
  ipAddress: string;
  geolocation?: string;
  trusted: boolean;
}
Initialize Neo4j Connection
import { createTailscaleNeo4jService } from './tailscale-neo4j.service';
const neo4j = createTailscaleNeo4jService({
  username: 'neo4j',
  password: process.env.NEO4J_PASSWORD,
  maxConnectionPoolSize: 50,
  connectionTimeout: 30000,
});
// Auto-discover instances via Tailscale
const instances = await neo4j.discoverInstances();
console.log('Available Neo4j instances:', instances);
// Connect to optimal region
await neo4j.connect('us-west');
Initialize Qdrant Connection
import { createTailscaleQdrantService } from './tailscale-qdrant.service';
const qdrant = createTailscaleQdrantService({
  apiKey: process.env.QDRANT_API_KEY,
  timeout: 30000,
});
// Discover instances
const instances = await qdrant.discoverInstances();
console.log('Available Qdrant instances:', instances);
// Connect to region
await qdrant.connect('us-west');
Insert Vectors
// Single vector
await qdrant.insertVector('agent_embeddings', {
  id: 'worker-001',
  vector: embedding, // 384-dim array
  payload: {
    name: 'Data Processing Worker',
    type: 'worker',
    capabilities: ['data-processing', 'ml-inference'],
    status: 'active',
  },
});
// Batch insert
await qdrant.insertVectors('issue_embeddings', [
  { id: '123', vector: embedding1, payload: issue1 },
  { id: '124', vector: embedding2, payload: issue2 },
  { id: '125', vector: embedding3, payload: issue3 },
]);
Integration Tests
// test/integration/agent-management.test.ts
import { AgentBrainClient } from '../../src/presentation/sdk/AgentBrainClient';
describe('Agent Management Integration', () => {
  let client: AgentBrainClient;
  beforeAll(async () => {
    client = new AgentBrainClient({ baseURL: 'http://localhost:3456' });
  });
  it('should spawn and list agents', async () => {
    const agent = await client.spawnAgent({
      name: 'test-agent',
      type: 'processor'
    });
    expect(agent.id).toBeDefined();
    expect(agent.status).toBe('idle');
    const agents = await client.listAgents();
    expect(agents.data).toContainEqual(
      expect.objectContaining({ id: agent.id })
    );
  });
});
issue_embeddings

Purpose: Semantic search for similar issues

await qdrant.createCollection({
  name: 'issue_embeddings',
  vectorSize: 384,
  distance: 'Cosine',
});

Schema: - id: Issue ID (number) - vector: 384-dimension embedding from title + description - payload: { title, description, labels, weight, projectId }

JSDoc Standards
/**
 * Spawns a new agent with the given configuration.
 *
 * @param config - Agent configuration object
 * @returns Promise that resolves to the created agent
 * @throws {ValidationError} When configuration is invalid
 * @throws {ResourceError} When insufficient resources
 *
 * @example
 * ```typescript
 * const agent = await manager.spawnAgent({
 *   name: 'processor',
 *   type: 'data-processor',
 *   capabilities: ['csv-processing']
 * });
 * ```
 */
public async spawnAgent(config: AgentConfig): Promise<Agent> {
  // Implementation
}
Neo4j Knowledge Graph

Location: src/services/knowledge-graph/neo4j-knowledge-graph.service.ts Node Types: - Agent: Autonomous agents with capabilities and status - Project: GitLab projects with metadata - Issue: GitLab issues with labels and milestones - Milestone: Project milestones with due dates - Thought: Sequential thinking sessions - Task: Individual work items with status Relationship Types: - WORKS_ON: Agent → Project - ASSIGNED_TO: Issue → Agent - BELONGS_TO: Issue → Milestone - DEPENDS_ON: Task → Task - GENERATES: Thought → Issue - COMPLETES: Agent → Task Query Examples:

// Find all agents working on a project
MATCH (a:Agent)-[:WORKS_ON]->(p:Project {id: $projectId})
RETURN a, p
// Find related issues by agent
MATCH (i:Issue)-[:ASSIGNED_TO]->(a:Agent {id: $agentId})
MATCH (i)-[:BELONGS_TO]->(m:Milestone)
RETURN i, m ORDER BY m.dueDate
// Find task dependencies
MATCH (t:Task {id: $taskId})-[:DEPENDS_ON*]->(dependency:Task)
RETURN dependency

Kubernetes Deployment:

##### Node Types
**Agent**
```cypher
CREATE (a:Agent {
  id: 'worker-001',
  name: 'Data Processing Worker',
  type: 'worker',
  status: 'active',
  capabilities: ['data-processing', 'ml-inference'],
  version: '1.0.0',
  createdAt: datetime(),
  lastSeen: datetime()
})

Project

CREATE (p:Project {
  id: 'llm/agent-buildkit',
  name: 'Agent Buildkit',
  description: 'Enterprise autonomous agent platform',
  visibility: 'internal',
  defaultBranch: 'development'
})

Issue

CREATE (i:Issue {
  id: 123,
  iid: 42,
  projectId: 'llm/agent-buildkit',
  title: 'Implement OAuth 2.0 authentication',
  description: 'Add OAuth 2.0 support...',
  state: 'opened',
  labels: ['enhancement', 'security'],
  weight: 5,
  createdAt: datetime(),
  updatedAt: datetime()
})

Milestone

CREATE (m:Milestone {
  id: 456,
  projectId: 'llm/agent-buildkit',
  title: 'v0.2.0 Release',
  description: 'Q1 2025 feature release',
  dueDate: date('2025-03-31'),
  state: 'active'
})

Thought

CREATE (t:Thought {
  id: 'session_123_thought_5',
  sessionId: 'session_123',
  thoughtNumber: 5,
  stage: 'Implementation',
  content: 'Implement OAuth 2.0 client...',
  axioms: ['Clean code principles', 'Test-driven development'],
  assumptions: ['OpenID Connect support available'],
  createdAt: datetime()
})

Task

CREATE (t:Task {
  id: 'task-789',
  type: 'ml-inference',
  status: 'in-progress',
  priority: 'high',
  payload: '{"model": "gpt-4", "input": "..."}',
  createdAt: datetime(),
  startedAt: datetime()
})
Phase 1: Core Infrastructure (Week 1-2)
Phase 2: Search & Retrieval (Week 3-4)
Phase 3: Integration Layer (Week 5-6)
Phase 4: Advanced Features (Week 7-8)
Phoenix Arize Learning Loop

Location: src/services/knowledge-graph/phoenix-knowledge-graph.service.ts Features: - Quality Metrics: Track agent performance and success rates - Cost Prediction: Predict task costs based on historical data - Anomaly Detection: Identify performance degradation - Model Optimization: Continuous learning from outcomes Integration:

// Track agent execution
await phoenixKG.trackExecution({
  agentId: 'worker-001',
  taskId: 'task-123',
  startTime: new Date(),
  endTime: new Date(),
  status: 'success',
  tokensUsed: 1500,
  cost: 0.03,
  metadata: { model: 'gpt-4', provider: 'openai' }
});
// Predict cost for new task
const prediction = await phoenixKG.predictCost({
  taskType: 'ml-inference',
  estimatedTokens: 2000,
  agentCapabilities: ['ml-inference', 'gpu-accelerated']
});
POST /api/v1/agents/coordinate
curl -X POST http://localhost:3456/api/v1/agents/coordinate \
  -H "Content-Type: application/json" \
  -d '{
    "agents": ["agent-1", "agent-2"],
    "task": "Process data pipeline",
    "strategy": "pipeline",
    "timeout": 300000
  }'
POST /api/v1/agents/spawn
curl -X POST http://localhost:3456/api/v1/agents/spawn \
  -H "Content-Type: application/json" \
  -d '{
    "name": "processor-agent",
    "type": "data-processor",
    "capabilities": [{
      "id": "processing",
      "name": "Data Processing",
      "type": "processing",
      "inputs": ["csv"],
      "outputs": ["json"]
    }]
  }'

Response:

{
  "id": "agent-1234",
  "name": "processor-agent",
  "type": "data-processor",
  "status": "idle",
  "uptime": 0,
  "metrics": {
    "requestsHandled": 0,
    "averageResponseTime": 0,
    "successRate": 100,
    "errorRate": 0,
    "lastActive": "2025-09-08T17:30:00Z"
  }
}
POST /api/v1/vector/search
curl -X POST http://localhost:3456/api/v1/vector/search \
  -H "Content-Type: application/json" \
  -d '{
    "collection": "documents",
    "vector": [0.1, 0.2, ...],
    "limit": 10,
    "scoreThreshold": 0.7
  }'
Provenance Tracker

Location: src/services/knowledge-graph/provenance-tracker.service.ts Purpose: Complete audit trail of agent decisions and actions Tracked Events: - Agent spawning and termination - Task claiming and completion - Decision rationale (why agent X chose action Y) - Knowledge graph updates - Lock acquisitions and releases Storage: Time-series data in PostgreSQL + Neo4j relationships

Qdrant Vector Storage

Location: src/services/knowledge-graph/qdrant.client.ts Collections: - agent_embeddings: Agent capability embeddings (384-dim) - issue_embeddings: Issue description embeddings - thought_embeddings: Thought content embeddings - code_embeddings: Code snippet embeddings Operations: - Insert Vectors: Store embeddings with metadata payload - Semantic Search: Find similar items by vector similarity - Filtered Search: Combine vector search with payload filters - Multi-region Search: Aggregate results across regions Search Example:

const results = await qdrant.search({
  collectionName: 'agent_embeddings',
  vector: queryEmbedding,
  limit: 10,
  scoreThreshold: 0.7,
  filter: {
    must: [
      { key: 'status', match: { value: 'active' } },
      { key: 'capabilities', match: { any: ['ml-inference'] } }
    ]
  }
});

Tailscale Multi-region Deployment:

// Auto-discover Qdrant instances across regions
const qdrant = createTailscaleQdrantService({
  apiKey: process.env.QDRANT_API_KEY,
  timeout: 30000,
});
const instances = await qdrant.discoverInstances();
// [
//   { region: 'us-west', hostname: 'qdrant-us-west.tail...', status: 'healthy' },
//   { region: 'us-east', hostname: 'qdrant-us-east.tail...', status: 'healthy' }
// ]
// Connect to optimal region
await qdrant.connect('us-west');
Relationship Types

WORKS_ON (Agent → Project)

MATCH (a:Agent {id: 'worker-001'})
MATCH (p:Project {id: 'llm/agent-buildkit'})
CREATE (a)-[:WORKS_ON {
  startedAt: datetime(),
  role: 'contributor'
}]->(p)

ASSIGNED_TO (Issue → Agent)

MATCH (i:Issue {id: 123})
MATCH (a:Agent {id: 'worker-001'})
CREATE (i)-[:ASSIGNED_TO {
  assignedAt: datetime(),
  status: 'claimed'
}]->(a)

BELONGS_TO (Issue → Milestone)

MATCH (i:Issue {id: 123})
MATCH (m:Milestone {id: 456})
CREATE (i)-[:BELONGS_TO]->(m)

DEPENDS_ON (Task → Task)

MATCH (t1:Task {id: 'task-789'})
MATCH (t2:Task {id: 'task-790'})
CREATE (t1)-[:DEPENDS_ON {
  type: 'blocks',
  createdAt: datetime()
}]->(t2)

GENERATES (Thought → Issue)

MATCH (t:Thought {id: 'session_123_thought_5'})
MATCH (i:Issue {id: 123})
CREATE (t)-[:GENERATES {
  createdAt: datetime(),
  automated: true
}]->(i)

COMPLETES (Agent → Task)

MATCH (a:Agent {id: 'worker-001'})
MATCH (t:Task {id: 'task-789'})
CREATE (a)-[:COMPLETES {
  completedAt: datetime(),
  duration: 3600,
  status: 'success'
}]->(t)
SC-13: Cryptographic Protection
// Encryption implementation
class EncryptionService {
  private algorithm = 'aes-256-gcm';
  async encrypt(data: Buffer): Promise<EncryptedData> {
    const key = await this.getKeyFromKMS();
    const iv = crypto.randomBytes(16);
    const cipher = crypto.createCipheriv(this.algorithm, key, iv);
    const encrypted = Buffer.concat([
      cipher.update(data),
      cipher.final()
    ]);
    return {
      data: encrypted,
      iv: iv,
      authTag: cipher.getAuthTag(),
      keyId: key.id
    };
  }
}
SC-28: Protection of Information at Rest
SC-8: Transmission Confidentiality
// TLS configuration
const tlsConfig = {
  minVersion: 'TLSv1.3',
  ciphers: [
    'TLS_AES_256_GCM_SHA384',
    'TLS_CHACHA20_POLY1305_SHA256'
  ],
  honorCipherOrder: true,
  sessionTimeout: 300
};
SearchQuery
interface SearchQuery {
  vector?: number[];
  filter?: Record<string, any>;
  limit?: number;
  offset?: number;
  withPayload?: boolean;
  withVector?: boolean;
  scoreThreshold?: number;
}
SearchResponse
interface SearchResponse {
  results: SearchResult[];
  total?: number;
  offset?: number;
}
interface SearchResult {
  id: string;
  score: number;
  vector?: number[];
  payload?: Record<string, any>;
  metadata?: Record<string, any>;
}

// Simple search
const results = await qdrant.search({
  collectionName: 'agent_embeddings',
  vector: queryEmbedding,
  limit: 10,
  scoreThreshold: 0.7,
});
// Search with filters
const filteredResults = await qdrant.search({
  collectionName: 'agent_embeddings',
  vector: queryEmbedding,
  limit: 10,
  scoreThreshold: 0.7,
  filter: {
    must: [
      { key: 'status', match: { value: 'active' } },
      { key: 'capabilities', match: { any: ['ml-inference'] } }
    ]
  }
});
Sequential Thinking Engine
// src/services/sequential-thinking.service.ts
class SequentialThinkingService {
  async processThought(params: {
    session_id: string;
    thought: string;
    thought_number: number;
    total_thoughts: number;
    next_thought_needed: boolean;
    stage: ThinkingStage;
    create_gitlab_issue?: boolean;
    milestone_id?: number;
  }): Promise<ThoughtResult>;
}

Thinking Stages: 1. Problem Definition: Define requirements, identify constraints, understand stakeholders 2. Research: Review existing solutions, document patterns, gather context 3. Analysis: Break down complexity, identify dependencies, assess risks 4. Synthesis: Design solution, evaluate alternatives, consider trade-offs 5. Implementation: Execute plan, write code, create documentation 6. Validation: Test solution, verify requirements, check quality 7. Reflection: Review outcomes, identify improvements, document lessons 8. Conclusion: Summarize results, ensure knowledge transfer, prepare handoff

SI-3: Malicious Code Protection
// File upload scanning
class FileScanService {
  async scanFile(file: UploadedFile): Promise<ScanResult> {
    // Antivirus scan
    const avResult = await this.antivirusScan(file);
    // File type validation
    const typeResult = await this.validateFileType(file);
    // Content analysis
    const contentResult = await this.analyzeContent(file);
    return {
      clean: avResult.clean && typeResult.valid && contentResult.safe,
      threats: [...avResult.threats, ...contentResult.threats]
    };
  }
}
Tailscale Mesh Networking

Location: src/services/knowledge/tailscale-*.service.ts Features: - Zero-Trust Security: WireGuard-based encryption - MagicDNS Discovery: Automatic service discovery - Peer-to-Peer: Direct agent-to-agent communication - No NAT Traversal: Tailscale handles network routing Agent Discovery:

// Register agent with mesh
const result = await meshDiscovery.registerAgent({
  agentId: 'worker-001',
  agentName: 'Data Processing Worker',
  agentType: 'worker',
  namespace: 'production',
  capabilities: ['data-processing', 'ml-inference'],
  version: '1.0.0',
  port: 3000,
});
// Hostname: agent-mesh-production-worker-001.device.tailcf98b3.ts.net
Test Utilities
// test/utils/test-helpers.ts
export const createMockAgent = (overrides = {}) => ({
  id: 'mock-agent',
  name: 'Mock Agent',
  type: 'test',
  status: 'idle',
  ...overrides
});
export const waitFor = (condition: () => boolean, timeout = 5000) => {
  return new Promise((resolve, reject) => {
    const start = Date.now();
    const check = () => {
      if (condition()) {
        resolve(true);
      } else if (Date.now() - start > timeout) {
        reject(new Error('Timeout waiting for condition'));
      } else {
        setTimeout(check, 100);
      }
    };
    check();
  });
};
thought_embeddings

Purpose: Find related thoughts across sessions

await qdrant.createCollection({
  name: 'thought_embeddings',
  vectorSize: 384,
  distance: 'Cosine',
});

Schema: - id: Thought ID (string) - vector: 384-dimension embedding from thought content - payload: { sessionId, stage, thoughtNumber, axioms, assumptions }

Unit Tests
// test/unit/core/entities/agent.test.ts
import { Agent } from '../../../src/core/entities/agent';
describe('Agent', () => {
  let agent: Agent;
  beforeEach(() => {
    agent = new Agent({
      id: 'test-agent',
      name: 'Test Agent',
      type: 'processor'
    });
  });
  describe('activate()', () => {
    it('should activate idle agent', () => {
      agent.activate();
      expect(agent.status).toBe('active');
    });
    it('should throw error for shutdown agent', () => {
      agent.shutdown();
      expect(() => agent.activate()).toThrow('Cannot activate shutdown agent');
    });
  });
});
VectorPoint
interface VectorPoint {
  id: string;
  vector: number[];
  metadata?: Record<string, any>;
}
VS Code Extensions
{
  "recommendations": [
    "ms-vscode.vscode-typescript-next",
    "bradlc.vscode-tailwindcss",
    "ms-vscode.vscode-json",
    "ms-vscode.vscode-eslint",
    "esbenp.prettier-vscode",
    "ms-vscode.vscode-jest"
  ]
}
VS Code Settings
{
  "typescript.preferences.importModuleSpecifier": "relative",
  "editor.codeActionsOnSave": {
    "source.fixAll.eslint": true
  },
  "editor.formatOnSave": true,
  "editor.defaultFormatter": "esbenp.prettier-vscode"
}
Work Queue Manager
Collection Management
// Create collection
await client.createCollection('documents', {
  vectors: {
    size: 1536,
    distance: 'cosine'
  },
  optimizers: {
    indexingThreshold: 10000
  }
});
// List collections
const collections = await client.listCollections();
// Get collection info
const info = await client.getCollection('documents');
// Optimize collection
await client.optimizeCollection('documents');
// Delete collection
await client.deleteCollection('documents');
Embeddings & RAG
// Generate embeddings
const embeddings = await client.generateEmbedding({
  text: 'Hello world',
  model: 'text-embedding-ada-002'
});
// RAG query
const answer = await client.ragQuery({
  query: 'What is Clean Architecture?',
  collection: 'knowledge-base',
  topK: 5
});
Health Operations
// Basic health check
const health = await client.health();
// Returns: { status: 'healthy', timestamp: string, uptime: number, version: string }
// Detailed health with metrics
const detailed = await client.healthDetailed();
// Returns: { status, services, metrics, dependencies }
// Readiness probe
const ready = await client.ready();
// Returns: { ready: boolean }
Vector Operations
// Semantic search
const results = await client.vectorSearch({
  collection: 'documents',
  vector: [0.1, 0.2, ...], // 1536-dimensional vector
  limit: 10,
  scoreThreshold: 0.7,
  withPayload: true
});
// Upsert vectors
await client.vectorUpsert('documents', [
  {
    id: 'doc-1',
    vector: [0.1, 0.2, ...],
    payload: { title: 'Document 1', content: '...' }
  }
]);
// Delete vectors
await client.vectorDelete('documents', ['doc-1', 'doc-2']);