🖥 amsh (Ape Machine Shell)

Technical Architecture

Memory Systems

Neo4j Integration

// Graph-based memory system
package tools

type Neo4j struct {
    client    neo4j.DriverWithContext
    Operation string // query/write
    Cypher    string
}

• Persistent graph memory storage
• Schema-driven query interface
• Supports both read (query) and write operations
• JSON-based response format

Qdrant Integration

• Vector-based similarity search
• Semantic memory storage
• Embedding management
• Nearest neighbor search capabilities

Process Architecture

Abstract Processes

Fractal Process

package fractal

type Process struct {
    BasePattern    Pattern  // Fundamental repeating pattern
    Scales         []Scale  // Multi-level manifestation
    Iterations     int      // Recursion depth
    SelfSimilarity float64  // Pattern preservation metric
}

• Pattern-based reasoning
• Scale-invariant processing
• Self-similar structure recognition
• Hierarchical pattern matching

Tensor Process

package tensor

type Process struct {
    Dimensions   []Dimension   // Relationship space aspects
    TensorFields []TensorField // Multi-dimensional patterns
    Projections  []Projection  // Dimensional reductions
}

• Multi-dimensional relationship modeling
• Field-based pattern recognition
• Dimensional projection capabilities
• Dynamic tensor manipulation

Agent Architecture

Main Agent

package marvin

type Agent struct {
    ID        string
    ctx       context.Context
    buffer    *Buffer
    processes map[string]Process
    prompt    *Prompt
}

Sidekick Agents (Tool Handlers)

• Dedicated process execution
• Tool-specific operations
• Focused context management
• Independent buffer spaces

Process Layering System

1. Base Layer (Abstract Processes)

   • Fractal pattern recognition
   • Tensor relationship modeling
   • Pattern extraction and matching

2. Tool Layer

   // Tool interface implementation
   func (tool *Tool) Use(ctx context.Context, args map[string]any) string
   func (tool *Tool) GenerateSchema() string

   • Neo4j graph operations
   • Qdrant vector operations
   • Schema-driven tool execution

3. Integration Layer

   • Process output transformation
   • Memory system synchronization
   • Context propagation
   • Event stream management

Context Management

Buffer System

package marvin

type Buffer struct {
    messages         []provider.Message
    maxContextTokens int  // 128k token window
}

• Token-aware truncation
• Priority message preservation
• Smart context window management
• Message role structuring

Process Context Flow

1. Abstract process execution
2. Memory system integration
3. Tool-based augmentation
4. Context window management

Event System

Generation Pipeline

package marvin

func (agent *Agent) Generate() <-chan provider.Event {
    // Event stream setup
    // Process execution
    // Context management
    // Response accumulation
}

Provider Integration

• Balanced provider selection
• Event stream accumulation
• Asynchronous processing
• Response aggregation

Schema-Driven Integration

Tool Schema Generation

func GenerateSchema[T any]() string {
    // JSON schema reflection
    // Tool interface definition
    // Operation specification
}

Process Schema Layering

1. Base process schema definition
2. Tool operation schema
3. Response format schema
4. Integration validation schema

Implementation Notes

1. Process Layering Strategy

   • Abstract processes feed into tool processes
   • Memory systems provide context enrichment
   • Sidekick agents handle specialized operations
   • Main agent orchestrates overall flow

2. Memory Integration Pattern

   • Graph-based relationship storage (Neo4j)
   • Vector-based similarity matching (Qdrant)
   • Hybrid memory access patterns
   • Context-aware memory operations

3. Agent Collaboration Model

   • Main agent delegates to sidekicks
   • Tool-specific context isolation
   • Shared memory access patterns
   • Event-based communication

Agent-Provider Interaction Model

The system implements a sophisticated provider abstraction layer that manages interactions between agents and various AI providers. This architecture is designed for reliability, scalability, and fault tolerance.

Provider Interface

• Common interface abstracting all AI providers (OpenAI, Anthropic, Google, Cohere)
• Event-driven streaming communication pattern
• Structured message format with role-based content modeling
• Support for multiple event types:
  • Token events (streaming responses)
  • Tool call events (function execution)
  • Error events (failure handling)
  • Done events (completion signals)

Balanced Provider System

The system implements an intelligent load balancer for AI provider management:

type ProviderStatus struct {
    name     string
    provider Provider
    occupied bool
    lastUsed time.Time
    failures int
    mu       sync.Mutex
}

Key Features:

• Provider status tracking and health monitoring
• Failure detection with cooldown periods
• Occupation tracking to prevent overload
• Intelligent provider selection based on:
  • Current availability
  • Historical performance
  • Failure count
  • Last usage time

Event-Driven Architecture

• Asynchronous communication using Go channels
• Structured event types for different response categories
• Accumulator pattern for response aggregation
• Non-blocking provider selection

Container-Based Isolation

The system uses a robust container-based isolation model to ensure secure and reproducible execution environments.

Builder System

type Builder struct {
    client *client.Client
}

Features:

• Docker client abstraction
• Context-aware build process
• Output streaming and error handling
• Build option management

Environment Management

The environment system provides:

• Container lifecycle management
• Command execution isolation
• Environment variable handling
• Workspace management

Security Model

1. Container Configuration:

   • Base: bitnami/minideb (minimal attack surface)
   • Dynamic user creation
   • Privilege separation
   • Workspace isolation

2. Security Features:

   # Dynamic user creation with controlled privileges
   USERNAME=${USERNAME:-devuser}
   useradd -m -s /bin/bash "$USERNAME"
   echo "$USERNAME ALL=(ALL) NOPASSWD:ALL" > /etc/sudoers.d/$USERNAME

   • Non-root execution
   • Controlled sudo access
   • SSH security configuration
   • Isolated workspace at /tmp/workspace/amsh

Integration Points

The system integrates these components through several key mechanisms:

1. Provider Integration:

   • Container-isolated provider calls
   • Load-balanced provider selection
   • Failure recovery and retry logic

2. Security Boundaries:

   • Container isolation for execution
   • Provider API key management
   • Workspace separation

3. Communication Flow:

   Agent -> Balanced Provider -> Container -> AI Provider
      ^            |               |            |
      |            v               v            v
   Response <- Event Stream <- Execution <- API Call
   

Development Considerations

1. Provider Management:

   • Monitor provider health and performance
   • Implement circuit breakers for failing providers
   • Consider adding provider-specific retry strategies

2. Container Security:

   • Regular security audits of base images
   • Implementation of resource limits
   • Proper secret management

3. Scalability:

   • Provider pool management
   • Container orchestration
   • Resource allocation strategies