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AMX Workspace Technical Specifications

AMX Workspace Technical Specifications

AMX Workspace is a sophisticated version-controlled repository system designed specifically for managing agent workspaces, knowledge artifacts, and AI-related resources. It provides git-like functionality optimized for the unique requirements of AI agents and knowledge management.

Architecture Overview

AMX Workspace implements a distributed version control system with specialized handling for knowledge artifacts, maintaining compatibility with standard git operations while extending functionality for AI-specific use cases.

Core Components

  • Workspace Engine: Core repository management and version control
  • Knowledge Store: Specialized storage for capsules, volumes, and libraries
  • Agent Registry: Agent definition management and configuration
  • Sync Protocol: Hub synchronization and collaboration features
  • Cache System: Performance optimization and offline capabilities
  • Security Layer: Encryption and access control

Workspace Directory Structure 

.amx/                           # AMX metadata directory (similar to .git)
├── config                      # Workspace configuration
├── objects/                    # Content-addressed object store
│   ├── capsules/              # Knowledge capsules
│   ├── volumes/               # Knowledge volumes
│   ├── libraries/             # Code libraries
│   └── blobs/                 # Binary data
├── refs/                      # References and branches
│   ├── heads/                 # Local branches
│   ├── remotes/               # Remote tracking branches
│   └── tags/                  # Tagged versions
├── index                      # Staging area
├── hooks/                     # Event hooks
├── logs/                      # Operation logs
└── cache/                     # Local cache
    ├── objects/               # Cached objects
    ├── metadata/              # Cached metadata
    └── search/                # Search indices

agents/                        # Agent definitions
├── definitions/               # Agent configuration files
│   ├── agent-id.yaml         # Agent definition
│   └── metadata.json         # Agent metadata
└── templates/                 # Agent templates

knowledge/                     # Knowledge artifacts
├── capsules/                  # Discrete knowledge units
│   ├── capsule-id/
│   │   ├── content.md        # Primary content
│   │   ├── metadata.yaml     # Capsule metadata
│   │   ├── embeddings.bin    # Vector embeddings
│   │   └── relations.json    # Knowledge relationships
├── volumes/                   # Knowledge collections
│   ├── volume-id/
│   │   ├── manifest.yaml     # Volume manifest
│   │   ├── capsules/         # Referenced capsules
│   │   └── index.json        # Volume index
└── libraries/                 # Code libraries
    ├── library-id/
    │   ├── package.json      # Library metadata
    │   ├── src/              # Source code
    │   └── docs/             # Documentation

workspace.yaml                 # Workspace configuration
.amxignore                    # Ignore patterns (similar to .gitignore)

Knowledge Artifact Storage Formats

Capsule Format

Knowledge capsules are discrete units of information stored with rich metadata:

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# metadata.yaml
id: "capsule-uuid-v4"
version: "1.0.0"
title: "Machine Learning Fundamentals"
description: "Core concepts of machine learning"
author: "agent-id"
created: "2024-01-15T10:30:00Z"
modified: "2024-01-16T14:20:00Z"
tags: ["ml", "fundamentals", "supervised-learning"]
category: "knowledge"
access_level: "public"
content_type: "markdown"
language: "en"
size: 15420
checksum: "sha256:abc123..."
relations:
  - type: "references"
    target: "another-capsule-id"
  - type: "contains"
    target: "sub-concept-id"
embeddings:
  model: "sentence-transformers/all-MiniLM-L6-v2"
  dimensions: 384
  file: "embeddings.bin"

Volume Format

Knowledge volumes are collections of related capsules:

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# manifest.yaml
id: "volume-uuid-v4"
version: "2.1.0"
title: "Data Science Handbook"
description: "Comprehensive data science knowledge collection"
author: "team-id"
created: "2024-01-01T00:00:00Z"
modified: "2024-01-20T16:45:00Z"
capsules:
  - id: "ml-basics-capsule"
    version: "1.0.0"
    weight: 1.0
  - id: "statistics-capsule"
    version: "2.0.1"
    weight: 0.8
structure:
  - chapter: "Introduction"
    capsules: ["intro-capsule"]
  - chapter: "Machine Learning"
    capsules: ["ml-basics-capsule", "supervised-learning-capsule"]
metadata:
  category: "handbook"
  difficulty: "intermediate"
  estimated_read_time: 240

Library Format

Code libraries with AI-specific metadata:

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{
  "id": "library-uuid-v4",
  "name": "nlp-toolkit",
  "version": "1.2.0",
  "description": "Natural language processing utilities",
  "author": "developer-id",
  "license": "MIT",
  "created": "2024-01-10T08:00:00Z",
  "modified": "2024-01-18T12:30:00Z",
  "dependencies": {
    "transformers": "^4.21.0",
    "torch": "^1.12.0"
  },
  "ai_metadata": {
    "model_compatibility": ["bert", "gpt", "t5"],
    "frameworks": ["pytorch", "tensorflow"],
    "capabilities": ["tokenization", "embedding", "classification"]
  },
  "entry_point": "src/main.py",
  "documentation": "docs/README.md",
  "tests": "tests/",
  "examples": "examples/"
}

Version Control Implementation

Object Storage

AMX Workspace uses a content-addressed storage system similar to git but optimized for knowledge artifacts:

objects/
├── ab/
│   └── cd1234... (capsule object)
├── ef/
│   └── gh5678... (volume object)
└── ij/
    └── kl9012... (library object)

Commit Structure 

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# Commit object
id: "commit-sha256"
author: "user-id"
timestamp: "2024-01-20T10:30:00Z"
message: "Add machine learning capsules"
parent: "parent-commit-sha"
tree: "tree-sha256"
changes:
  - type: "add"
    path: "knowledge/capsules/ml-basics"
    object: "object-sha256"
  - type: "modify"
    path: "knowledge/volumes/data-science"
    object: "new-object-sha256"
    previous: "old-object-sha256"
signature: "cryptographic-signature"

Branching and Merging

  • Feature Branches: Isolated development environments
  • Knowledge Branches: Specialized branches for knowledge curation
  • Agent Branches: Agent-specific development branches
  • Merge Strategies: Custom merge algorithms for knowledge conflicts

Metadata and Indexing

Search Index Structure 

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{
  "index_version": "1.0",
  "created": "2024-01-20T10:00:00Z",
  "objects": {
    "capsules": {
      "capsule-id": {
        "title": "Machine Learning Basics",
        "content_hash": "sha256:...",
        "embeddings": [0.1, 0.2, ...],
        "keywords": ["ml", "supervised", "unsupervised"],
        "last_modified": "2024-01-15T10:30:00Z"
      }
    },
    "volumes": {},
    "libraries": {}
  },
  "relationships": [
    {
      "source": "capsule-a",
      "target": "capsule-b",
      "type": "references",
      "strength": 0.85
    }
  ]
}

Metadata Database Schema 

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-- Capsules table
CREATE TABLE capsules (
  id VARCHAR(36) PRIMARY KEY,
  version VARCHAR(20),
  title TEXT,
  content_hash VARCHAR(64),
  author VARCHAR(36),
  created TIMESTAMP,
  modified TIMESTAMP,
  size INTEGER,
  embedding_model VARCHAR(100),
  embedding_dimensions INTEGER
);

-- Relationships table
CREATE TABLE relationships (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  source_id VARCHAR(36),
  target_id VARCHAR(36),
  relationship_type VARCHAR(50),
  strength REAL,
  created TIMESTAMP
);

-- Full-text search index
CREATE VIRTUAL TABLE capsules_fts USING fts5(
  title, content, tags, 
  content=capsules, 
  content_rowid=id
);

Synchronization Protocols

Hub Synchronization

AMX Workspace synchronizes with AMX Hub using a custom protocol built on HTTP/2:

Push Operation 

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POST /api/v1/workspaces/{workspace-id}/push
Authorization: Bearer {access-token}
Content-Type: application/amx-pack

{
  "commits": [
    {
      "id": "commit-sha",
      "objects": ["object-sha1", "object-sha2"]
    }
  ],
  "objects": {
    "object-sha1": {
      "type": "capsule",
      "data": "compressed-binary-data"
    }
  }
}

Pull Operation 

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GET /api/v1/workspaces/{workspace-id}/pull?since={commit-sha}
Authorization: Bearer {access-token}

Response:
{
  "commits": [...],
  "objects": {...},
  "refs": {
    "main": "latest-commit-sha"
  }
}

Conflict Resolution 

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conflict_resolution:
  strategies:
    - type: "auto_merge"
      applicable: ["metadata_only_changes"]
    - type: "manual_merge"
      applicable: ["content_conflicts"]
    - type: "semantic_merge"
      applicable: ["knowledge_relationships"]
  
  merge_tools:
    - name: "knowledge_differ"
      command: "amx-merge-tool"
      args: ["--semantic", "{base}", "{local}", "{remote}"]

Cache Management

Multi-Level Caching 

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cache_levels:
  l1_memory:
    size: "256MB"
    ttl: "30m"
    items: ["recent_capsules", "search_results"]
  
  l2_disk:
    size: "2GB"
    ttl: "24h"
    location: ".amx/cache/objects"
    items: ["embeddings", "processed_content"]
  
  l3_remote:
    provider: "amx_hub"
    ttl: "7d"
    items: ["shared_knowledge", "public_libraries"]

Cache Policies 

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class CachePolicy:
    def should_cache(self, object_type, size, access_frequency):
        if object_type == "capsule" and access_frequency > 0.1:
            return True
        if object_type == "embedding" and size < 10MB:
            return True
        return False
    
    def eviction_strategy(self):
        return "LRU_with_semantic_importance"

Security and Encryption

Encryption at Rest 

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encryption:
  algorithm: "AES-256-GCM"
  key_derivation: "PBKDF2"
  iterations: 100000
  salt_size: 32
  
  encrypted_objects:
    - capsules
    - volumes
    - agent_definitions
    - sensitive_metadata
  
  plaintext_objects:
    - public_metadata
    - index_structures
    - cache_data

Access Control 

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access_control:
  model: "RBAC"  # Role-Based Access Control
  
  roles:
    - name: "owner"
      permissions: ["read", "write", "delete", "admin"]
    - name: "contributor"
      permissions: ["read", "write"]
    - name: "reader"
      permissions: ["read"]
  
  policies:
    - resource: "capsules/*"
      role: "contributor"
      conditions:
        - "author == user.id OR workspace.visibility == 'public'"
    - resource: "agents/*"
      role: "owner"
      conditions: []

Digital Signatures 

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signing:
  algorithm: "Ed25519"
  required_for:
    - commits
    - capsule_publications
    - agent_definitions
  
  verification:
    - check_signature_validity
    - verify_author_identity
    - validate_timestamp
    - check_certificate_chain

API Specifications

Workspace Management API 

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openapi: "3.0.0"
info:
  title: "AMX Workspace API"
  version: "1.0.0"

paths:
  /workspaces:
    post:
      summary: "Create new workspace"
      requestBody:
        content:
          application/json:
            schema:
              type: object
              properties:
                name:
                  type: string
                description:
                  type: string
                template:
                  type: string
                  enum: ["empty", "basic", "ml-research", "agent-dev"]
      responses:
        201:
          description: "Workspace created"
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Workspace"
  
  /workspaces/{id}/capsules:
    get:
      summary: "List capsules in workspace"
      parameters:
        - name: id
          in: path
          required: true
          schema:
            type: string
        - name: category
          in: query
          schema:
            type: string
        - name: search
          in: query
          schema:
            type: string
      responses:
        200:
          description: "List of capsules"
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: "#/components/schemas/Capsule"

components:
  schemas:
    Workspace:
      type: object
      properties:
        id:
          type: string
        name:
          type: string
        description:
          type: string
        created:
          type: string
          format: date-time
        owner:
          type: string
        visibility:
          type: string
          enum: ["private", "public", "team"]
    
    Capsule:
      type: object
      properties:
        id:
          type: string
        title:
          type: string
        content:
          type: string
        metadata:
          type: object
        embeddings:
          type: array
          items:
            type: number

Knowledge Operations API 

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class KnowledgeAPI:
    def create_capsule(self, content: str, metadata: dict) -> str:
        """Create a new knowledge capsule."""
        pass
    
    def update_capsule(self, capsule_id: str, content: str, metadata: dict) -> bool:
        """Update existing capsule."""
        pass
    
    def search_capsules(self, query: str, filters: dict = None) -> List[Capsule]:
        """Search capsules using text and semantic search."""
        pass
    
    def create_volume(self, capsule_ids: List[str], metadata: dict) -> str:
        """Create a knowledge volume from capsules."""
        pass
    
    def link_capsules(self, source_id: str, target_id: str, relation_type: str) -> bool:
        """Create relationship between capsules."""
        pass

Agent Management API 

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class AgentAPI:
    def create_agent(self, definition: AgentDefinition) -> str:
        """Create new agent definition."""
        pass
    
    def deploy_agent(self, agent_id: str, environment: str) -> DeploymentStatus:
        """Deploy agent to specified environment."""
        pass
    
    def update_agent_knowledge(self, agent_id: str, volume_ids: List[str]) -> bool:
        """Update agent's knowledge base."""
        pass

Performance Specifications

Scalability Targets

  • Workspace Size: Up to 10GB per workspace
  • Capsule Count: Up to 100,000 capsules per workspace
  • Concurrent Users: Up to 100 users per workspace
  • Search Performance: <100ms for text search, <500ms for semantic search
  • Sync Performance: <10s for incremental sync, <60s for full sync

Memory Requirements

  • Minimum RAM: 4GB
  • Recommended RAM: 16GB
  • Storage: SSD recommended for optimal performance
  • Network: Broadband connection for hub synchronization

Optimization Features

  • Lazy Loading: Load content on demand
  • Incremental Indexing: Update search indices incrementally
  • Compression: LZ4 compression for storage efficiency
  • Deduplication: Content-based deduplication
  • Parallel Processing: Multi-threaded operations where possible

Integration Points

IDE Integration

  • VS Code Extension: AMX Workspace management in VS Code
  • JetBrains Plugin: IntelliJ/PyCharm integration
  • CLI Tools: Command-line interface for all operations

External Systems

  • Git Integration: Import/export from Git repositories
  • Cloud Storage: S3, Google Cloud Storage, Azure Blob Storage
  • Knowledge Graphs: Neo4j, Amazon Neptune integration
  • Vector Databases: Pinecone, Weaviate, Qdrant support

This technical specification provides a comprehensive foundation for implementing AMX Workspace as a sophisticated version control system optimized for AI agents and knowledge management workflows.

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