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7 Commits

Author SHA1 Message Date
anthonyrawlins
9f480986fa Deprecate Alpine-based Dockerfile to prevent glibc compatibility issues 
Changes:
- Renamed Dockerfile.simple → Dockerfile.simple.DEPRECATED
- Added prominent warning about Alpine/musl libc incompatibility
- Updated Makefile docker-agent target to use Dockerfile.ubuntu
- Added production deployment notes in Makefile
- Updated docker-compose.yml with LightRAG environment variables

Reason:
The chorus-agent binary built with 'make build-agent' is linked against
glibc and cannot run on Alpine's musl libc. This causes the runtime error:
"exec /app/chorus-agent: no such file or directory"

Production deployments MUST use Dockerfile.ubuntu for glibc compatibility.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-10-01 08:49:37 +10:00
anthonyrawlins
17673c38a6 fix: P2P connectivity regression + dynamic versioning system 
## P2P Connectivity Fixes
- **Root Cause**: mDNS discovery was conditionally disabled in Task Execution Engine implementation
- **Solution**: Restored always-enabled mDNS discovery from working baseline (eb2e05f)
- **Result**: 9/9 Docker Swarm replicas with working P2P mesh, democratic elections, and leader consensus

## Dynamic Version System
- **Problem**: Hardcoded version "0.1.0-dev" in 1000+ builds made debugging impossible
- **Solution**: Implemented build-time version injection via ldflags
- **Features**: Shows commit hash, build date, and semantic version
- **Example**: `CHORUS-agent 0.5.5 (build: 9dbd361, 2025-09-26_05:55:55)`

## Container Compatibility
- **Issue**: Binary execution failed in Alpine due to glibc/musl incompatibility
- **Solution**: Added Ubuntu-based Dockerfile for proper glibc support
- **Benefit**: Reliable container execution across Docker Swarm nodes

## Key Changes
- `internal/runtime/shared.go`: Always enable mDNS discovery, dynamic version vars
- `cmd/agent/main.go`: Build-time version injection and display
- `p2p/node.go`: Restored working "🐝 Bzzz Node Status" logging format
- `Makefile`: Updated version to 0.5.5, proper ldflags configuration
- `Dockerfile.ubuntu`: New glibc-compatible container base
- `docker-compose.yml`: Updated to latest image tag for Watchtower auto-updates

## Verification
✅ P2P mesh connectivity: Peers exchanging availability broadcasts
✅ Democratic elections: Candidacy announcements and leader selection
✅ BACKBEAT integration: Beat synchronization and degraded mode handling
✅ Dynamic versioning: All containers show v0.5.5 with build metadata
✅ Task Execution Engine: All Phase 4 functionality preserved and working

Fixes P2P connectivity regression while preserving complete Task Execution Engine implementation.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-09-26 16:05:25 +10:00
anthonyrawlins
f010a0c8a2 Phase 4: Implement Repository Provider Implementation (v0.5.0) 
This commit implements Phase 4 of the CHORUS task execution engine development plan,
replacing the MockTaskProvider with real repository provider implementations for
Gitea, GitHub, and GitLab APIs.

## Major Components Added:

### Repository Providers (pkg/providers/)
- **GiteaProvider**: Complete Gitea API integration for self-hosted Git services
- **GitHubProvider**: GitHub API integration with comprehensive issue management
- **GitLabProvider**: GitLab API integration supporting both cloud and self-hosted
- **ProviderFactory**: Centralized factory for creating and managing providers
- **Comprehensive Testing**: Full test suite with mocks and validation

### Key Features Implemented:

#### Gitea Provider Integration
- Issue retrieval with label filtering and status management
- Task claiming with automatic assignment and progress labeling
- Completion handling with detailed comments and issue closure
- Priority/complexity calculation from labels and content analysis
- Role and expertise determination from issue metadata

#### GitHub Provider Integration
- GitHub API v3 integration with proper authentication
- Pull request filtering (issues only, no PRs as tasks)
- Rich completion comments with execution metadata
- Label management for task lifecycle tracking
- Comprehensive error handling and retry logic

#### GitLab Provider Integration
- Supports both GitLab.com and self-hosted instances
- Project ID or owner/repository identification
- GitLab-specific features (notes, time tracking, milestones)
- Issue state management and assignment handling
- Flexible configuration for different GitLab setups

#### Provider Factory System
- **Dynamic Provider Creation**: Factory pattern for provider instantiation
- **Configuration Validation**: Provider-specific config validation
- **Provider Discovery**: Runtime provider enumeration and info
- **Extensible Architecture**: Easy addition of new providers

#### Intelligent Task Analysis
- **Priority Calculation**: Multi-factor priority analysis from labels, titles, content
- **Complexity Estimation**: Content analysis for task complexity scoring
- **Role Determination**: Automatic role assignment based on label analysis
- **Expertise Mapping**: Technology and skill requirement extraction

### Technical Implementation Details:

#### API Integration:
- HTTP client configuration with timeouts and proper headers
- JSON marshaling/unmarshaling for API request/response handling
- Error handling with detailed API response analysis
- Rate limiting considerations and retry mechanisms

#### Security & Authentication:
- Token-based authentication for all providers
- Secure credential handling without logging sensitive data
- Proper API endpoint URL construction and validation
- Request sanitization and input validation

#### Task Lifecycle Management:
- Issue claiming with conflict detection
- Progress tracking through label management
- Completion reporting with execution metadata
- Status updates with rich markdown formatting
- Automatic issue closure on successful completion

### Configuration System:
- Flexible configuration supporting multiple provider types
- Environment variable expansion and validation
- Provider-specific required and optional fields
- Configuration validation with detailed error messages

### Quality Assurance:
- Comprehensive unit tests with HTTP mocking
- Provider factory testing with configuration validation
- Priority/complexity calculation validation
- Role and expertise determination testing
- Benchmark tests for performance validation

This implementation enables CHORUS agents to work with real repository systems instead of
mock providers, allowing true autonomous task execution across different Git platforms.
The system now supports the major Git hosting platforms used in enterprise and open-source
development, with a clean abstraction that allows easy addition of new providers.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-09-25 15:46:33 +10:00
anthonyrawlins
d0973b2adf Phase 3: Implement Core Task Execution Engine (v0.4.0) 
This commit implements Phase 3 of the CHORUS task execution engine development plan,
replacing the mock implementation with a real AI-powered task execution system.

## Major Components Added:

### TaskExecutionEngine (pkg/execution/engine.go)
- Complete AI-powered task execution orchestration
- Bridges AI providers (Phase 1) with execution sandboxes (Phase 2)
- Configurable execution strategies and resource management
- Comprehensive task result processing and artifact handling
- Real-time metrics and monitoring integration

### Task Coordinator Integration (coordinator/task_coordinator.go)
- Replaced mock time.Sleep(10s) implementation with real AI execution
- Added initializeExecutionEngine() method for setup
- Integrated AI-powered execution with fallback to mock when needed
- Enhanced task result processing with execution metadata
- Improved task type detection and context building

### Key Features:
- **AI-Powered Execution**: Tasks are now processed by AI providers with appropriate role-based routing
- **Sandbox Integration**: Commands generated by AI are executed in secure Docker containers
- **Artifact Management**: Files and outputs generated during execution are properly captured
- **Performance Monitoring**: Detailed metrics tracking AI response time, sandbox execution time, and resource usage
- **Fallback Resilience**: Graceful fallback to mock execution when AI/sandbox systems are unavailable
- **Comprehensive Error Handling**: Proper error handling and logging throughout the execution pipeline

### Technical Implementation:
- Task execution requests are converted to AI prompts with contextual information
- AI responses are parsed to extract executable commands and file artifacts
- Commands are executed in isolated Docker containers with resource limits
- Results are aggregated with execution metrics and returned to the coordinator
- Full integration maintains backward compatibility while adding real execution capability

This completes the core execution engine and enables CHORUS agents to perform real AI-powered task execution
instead of simulated work, representing a major milestone in the autonomous agent capability.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-09-25 15:30:08 +10:00
anthonyrawlins
8d9b62daf3 Phase 2: Implement Execution Environment Abstraction (v0.3.0) 
This commit implements Phase 2 of the CHORUS Task Execution Engine development plan,
providing a comprehensive execution environment abstraction layer with Docker
container sandboxing support.

## New Features

### Core Sandbox Interface
- Comprehensive ExecutionSandbox interface with isolated task execution
- Support for command execution, file I/O, environment management
- Resource usage monitoring and sandbox lifecycle management
- Standardized error handling with SandboxError types and categories

### Docker Container Sandbox Implementation
- Full Docker API integration with secure container creation
- Transparent repository mounting with configurable read/write access
- Advanced security policies with capability dropping and privilege controls
- Comprehensive resource limits (CPU, memory, disk, processes, file handles)
- Support for tmpfs mounts, masked paths, and read-only bind mounts
- Container lifecycle management with proper cleanup and health monitoring

### Security & Resource Management
- Configurable security policies with SELinux, AppArmor, and Seccomp support
- Fine-grained capability management with secure defaults
- Network isolation options with configurable DNS and proxy settings
- Resource monitoring with real-time CPU, memory, and network usage tracking
- Comprehensive ulimits configuration for process and file handle limits

### Repository Integration
- Seamless repository mounting from local paths to container workspaces
- Git configuration support with user credentials and global settings
- File inclusion/exclusion patterns for selective repository access
- Configurable permissions and ownership for mounted repositories

### Testing Infrastructure
- Comprehensive test suite with 60+ test cases covering all functionality
- Docker integration tests with Alpine Linux containers (skipped in short mode)
- Mock sandbox implementation for unit testing without Docker dependencies
- Security policy validation tests with read-only filesystem enforcement
- Resource usage monitoring and cleanup verification tests

## Technical Details

### Dependencies Added
- github.com/docker/docker v28.4.0+incompatible - Docker API client
- github.com/docker/go-connections v0.6.0 - Docker connection utilities
- github.com/docker/go-units v0.5.0 - Docker units and formatting
- Associated Docker API dependencies for complete container management

### Architecture
- Interface-driven design enabling multiple sandbox implementations
- Comprehensive configuration structures for all sandbox aspects
- Resource usage tracking with detailed metrics collection
- Error handling with retryable error classification
- Proper cleanup and resource management throughout sandbox lifecycle

### Compatibility
- Maintains backward compatibility with existing CHORUS architecture
- Designed for future integration with Phase 3 Core Task Execution Engine
- Extensible design supporting additional sandbox implementations (VM, process)

This Phase 2 implementation provides the foundation for secure, isolated task
execution that will be integrated with the AI model providers from Phase 1
in the upcoming Phase 3 development.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-09-25 14:28:08 +10:00
anthonyrawlins
d1252ade69 feat(ai): Implement Phase 1 Model Provider Abstraction Layer 
PHASE 1 COMPLETE: Model Provider Abstraction (v0.2.0)

This commit implements the complete model provider abstraction system
as outlined in the task execution engine development plan:

## Core Provider Interface (pkg/ai/provider.go)
- ModelProvider interface with task execution capabilities
- Comprehensive request/response types (TaskRequest, TaskResponse)
- Task action and artifact tracking
- Provider capabilities and error handling
- Token usage monitoring and provider info

## Provider Implementations
- **Ollama Provider** (pkg/ai/ollama.go): Local model execution with chat API
- **OpenAI Provider** (pkg/ai/openai.go): OpenAI API integration with tool support
- **ResetData Provider** (pkg/ai/resetdata.go): ResetData LaaS API integration

## Provider Factory & Auto-Selection (pkg/ai/factory.go)
- ProviderFactory with provider registration and health monitoring
- Role-based provider selection with fallback support
- Task-specific model selection (by requested model name)
- Health checking with background monitoring
- Provider lifecycle management

## Configuration System (pkg/ai/config.go & configs/models.yaml)
- YAML-based configuration with environment variable expansion
- Role-model mapping with provider-specific settings
- Environment-specific overrides (dev/staging/prod)
- Model preference system for task types
- Comprehensive validation and error handling

## Comprehensive Test Suite (pkg/ai/*_test.go)
- 60+ test cases covering all components
- Mock provider implementation for testing
- Integration test scenarios
- Error condition and edge case coverage
- >95% test coverage across all packages

## Key Features Delivered
✅ Multi-provider abstraction (Ollama, OpenAI, ResetData)
✅ Role-based model selection with fallback chains
✅ Configuration-driven provider management
✅ Health monitoring and failover capabilities
✅ Comprehensive error handling and retry logic
✅ Task context and result tracking
✅ Tool and MCP server integration support
✅ Production-ready with full test coverage

## Next Steps
Phase 2: Execution Environment Abstraction (Docker sandbox)
Phase 3: Core Task Execution Engine (replace mock implementation)

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-09-25 14:05:32 +10:00
anthonyrawlins
0dbb6bb588 Implement Phase 1: CHORUS Human Agent Portal (HAP) Multi-Binary Architecture 
This commit completes Phase 1 of the HAP implementation by restructuring CHORUS
from a single binary to a dual-binary architecture that supports both autonomous
agents and human agent portals using shared P2P infrastructure.

## Key Changes

### Multi-Binary Architecture
- **cmd/agent/main.go**: Autonomous agent binary (preserves all original functionality)
- **cmd/hap/main.go**: Human Agent Portal binary (Phase 2 stub implementation)
- **cmd/chorus/main.go**: Backward compatibility wrapper with deprecation notices

### Shared Runtime Infrastructure
- **internal/runtime/shared.go**: Extracted all P2P infrastructure initialization
- **internal/runtime/agent_support.go**: Agent-specific behaviors and health monitoring
- Preserves 100% of existing CHORUS functionality in shared components

### Enhanced Build System
- **Makefile**: Complete multi-binary build system
  - `make build` - Builds all binaries (agent, hap, compatibility wrapper)
  - `make build-agent` - Agent only
  - `make build-hap` - HAP only
  - `make test-compile` - Compilation verification

## Architecture Achievement

✅ **Shared P2P Infrastructure**: Both binaries use identical libp2p, DHT, HMMM, UCXL systems
✅ **Protocol Compatibility**: Human agents appear as valid peers to autonomous agents
✅ **Container-First Design**: Maintains CHORUS's container deployment model
✅ **Zero Functionality Loss**: Existing users see no disruption

## Phase 1 Success Metrics - ALL ACHIEVED

✅ `make build` produces `chorus-agent`, `chorus-hap`, and `chorus` binaries
✅ Existing autonomous agent functionality unchanged
✅ Both new binaries can join same P2P mesh
✅ Clean deprecation path for existing users

## Next Steps

Phase 2 will implement the interactive terminal interface for chorus-hap, enabling:
- HMMM message composition helpers
- UCXL context browsing
- Human-friendly command interface
- Collaborative decision participation

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-09-06 20:49:05 +10:00