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@agentic-conexus/mcp

Context-aware semantic search MCP server for OpenCode and other MCP clients

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Conexus - The Agentic Context Engine

Version: 0.0.5 (Phase 5 - Integration & Documentation)
Status: 🚧 Active Development
Go Version: 1.23.4

Go Tests Coverage License

🎯 Overview

Conexus is an agentic context engine that transforms Large Language Models (LLMs) into expert engineering assistants. It provides a multi-agent system for analyzing codebases, with built-in validation, profiling, and workflow orchestration.

Key Features

  • 🤖 Multi-Agent Architecture: Specialized agents for locating and analyzing code
  • 🔌 MCP Integration: First-class Model Context Protocol support for AI assistants
  • Evidence-Backed Validation: 100% evidence traceability for all agent outputs
  • 📊 Performance Profiling: Real-time metrics and bottleneck detection
  • 🔄 Workflow Orchestration: Complex multi-agent workflows with state management
  • 🏗️ AGENT_OUTPUT_V1: Standardized JSON schema for agent communication
  • 🧪 Comprehensive Testing: 53+ integration tests with real-world validation

🚀 Quick Start

Prerequisites

  • Node.js 18+ or Bun (for npm/bunx installation)
  • Git

Installation

Option 1: NPM/Bunx (Recommended - Pre-built Binaries)

# Install globally with npm
npm install -g @agentic-conexus/mcp

# Or use with bunx (no installation needed)
bunx @agentic-conexus/mcp

# Or use with npx
npx @agentic-conexus/mcp

Note: Pre-built binaries are included for:

  • macOS (Intel & Apple Silicon)
  • Linux (amd64 & arm64)
  • Windows (amd64)

Option 2: From Source (For Development)

# Clone the repository
git clone https://github.com/ferg-cod3s/conexus.git
cd conexus

# Requires Go 1.23.4+ - https://go.dev/dl/

# Build from source
go build -o conexus ./cmd/conexus

# Run tests
go test ./...

Basic Usage

# Run the MCP server (stdio mode - default)
npx @agentic-conexus/mcp

# Run with environment variables
CONEXUS_DB_PATH=./data/db.sqlite CONEXUS_LOG_LEVEL=debug npx @agentic-conexus/mcp

# Run in HTTP mode (for testing)
CONEXUS_PORT=3000 npx @agentic-conexus/mcp

🔌 MCP Integration

Conexus provides first-class support for the Model Context Protocol (MCP), enabling seamless integration with AI assistants like Claude Desktop and Cursor.

Why Use Conexus with AI Assistants?

  • 🔍 Intelligent Context Retrieval: Search your codebase using natural language
  • 🎯 Precise Results: Vector similarity search + filtering for relevant findings
  • 🔄 Real-time Indexing: Keep your code context fresh and up-to-date
  • 🛠️ Built-in Tools: 4 powerful MCP tools for code understanding

Quick MCP Setup (<5 minutes)

Option 1: NPM/Bunx (Recommended for MCP clients)

# Install globally with npm
npm install -g @ferg-cod3s/conexus

# Or use with bunx (no installation needed)
bunx @ferg-cod3s/conexus

Configure in your MCP client (OpenCode, Claude Desktop, etc.):

{
  "mcpServers": {
    "conexus": {
      "command": "bunx",
      "args": ["@ferg-cod3s/conexus"],
      "env": {
        "CONEXUS_DB_PATH": "/path/to/your/project/.conexus/db.sqlite"
      }
    }
  }
}

Option 2: Go Install (For development)

# Install Conexus
go install github.com/ferg-cod3s/conexus/cmd/conexus@latest

# Start the MCP server (stdio mode by default)
conexus

# Or run in HTTP mode
CONEXUS_PORT=3000 conexus

Configure for stdio mode (recommended for MCP):

{
  "mcpServers": {
    "conexus": {
      "command": "conexus",
      "env": {
        "CONEXUS_DB_PATH": "/path/to/your/project/.conexus/db.sqlite"
      }
    }
  }
}

Test the integration:

In your MCP client (OpenCode, Claude Desktop, etc.):

You: "Search for HTTP handler functions in this codebase"

AI Assistant: [Uses context.search tool]
Found 5 HTTP handlers:
- HandleRequest in internal/server/handler.go:42-68
- HandleHealth in internal/server/health.go:15-22
...

Environment Variables:

  • CONEXUS_DB_PATH: Path to SQLite database (default: ~/.conexus/db.sqlite)
  • CONEXUS_LOG_LEVEL: Log level: debug, info, warn, error (default: info)
  • CONEXUS_PORT: Run in HTTP mode instead of stdio (for development)

Available MCP Tools

ToolStatusDescription
context.search✅ Fully ImplementedSearch code with filters (type, language, file patterns)
context.get_related_info✅ Fully ImplementedGet related files, functions, and context
context.index_control⏳ PartialIndexing operations (status available, reindex planned)
context.connector_management⏳ PartialData source management (list available, CRUD planned)

Example Queries

Code Understanding:

"Show me all database query functions"
"Find the authentication middleware implementation"
"What functions handle user registration?"

Bug Investigation:

"Search for error handling in the payment module"
"Find all functions that access the user database"
"Show panic or fatal calls in the codebase"

Feature Development:

"Locate API endpoint handlers"
"Find all struct definitions related to orders"
"Search for configuration loading functions"

Project-Specific Installation

For using Conexus with specific projects, you can configure it to work with your existing codebase structure:

1. Per-Project MCP Server Configuration

Create a project-specific MCP configuration:

{
  "mcpServers": {
    "conexus-myproject": {
      "command": "conexus",
      "args": ["mcp", "--root", "/path/to/your/project"],
      "env": {
        "CONEXUS_LOG_LEVEL": "info",
        "CONEXUS_CONFIG": "/path/to/your/project/conexus.yml"
      }
    }
  }
}

2. Project Configuration File

Create a conexus.yml file in your project root:

# conexus.yml - Project-specific configuration
project:
  name: "my-project"
  description: "Web application backend"

# Codebase settings
codebase:
  root: "."
  include_patterns:
    - "**/*.go"
    - "**/*.js"
    - "**/*.ts"
    - "**/*.py"
  exclude_patterns:
    - "**/node_modules/**"
    - "**/vendor/**"
    - "**/dist/**"
    - "**/.git/**"

# Search configuration
search:
  max_results: 50
  similarity_threshold: 0.7
  enable_fts: true

# Indexing settings
indexing:
  auto_reindex: true
  reindex_interval: "1h"
  chunk_size: 1000

3. Docker Integration for Teams

For team environments, use Docker to ensure consistent configuration:

# docker-compose.conexus.yml
version: '3.8'
services:
  conexus:
    image: conexus:latest
    container_name: conexus-myproject
    restart: unless-stopped
    ports:
      - "3000:3000"
    volumes:
      - ./:/workspace:ro
      - ./data:/data
    environment:
      - CONEXUS_ROOT_PATH=/workspace
      - CONEXUS_LOG_LEVEL=info
      - CONEXUS_CONFIG=/workspace/conexus.yml
    working_dir: /workspace
# Start for your project
docker-compose -f docker-compose.conexus.yml up -d

# Test the connection
curl http://localhost:3000/health

4. Project Type Examples

Node.js Project:

codebase:
  include_patterns:
    - "**/*.js"
    - "**/*.ts"
    - "**/*.json"
    - "**/*.md"
  exclude_patterns:
    - "**/node_modules/**"
    - "**/coverage/**"
    - "**/dist/**"

Python Project:

codebase:
  include_patterns:
    - "**/*.py"
    - "**/*.md"
    - "**/requirements*.txt"
    - "**/pyproject.toml"
  exclude_patterns:
    - "**/__pycache__/**"
    - "**/venv/**"
    - "**/env/**"
    - "**/.pytest_cache/**"

Go Project:

codebase:
  include_patterns:
    - "**/*.go"
    - "**/go.mod"
    - "**/go.sum"
    - "**/*.md"
  exclude_patterns:
    - "**/vendor/**"

Monorepo:

codebase:
  include_patterns:
    - "packages/**/*.ts"
    - "packages/**/*.js"
    - "apps/**/*.ts"
    - "apps/**/*.js"
  exclude_patterns:
    - "**/node_modules/**"
    - "**/dist/**"
    - "**/build/**"

5. Claude Desktop Project Templates

Create reusable templates for different project types:

{
  "mcpServers": {
    "conexus-nodejs": {
      "command": "conexus",
      "args": ["mcp", "--root", "$PROJECT_ROOT"],
      "env": {
        "CONEXUS_CONFIG": "$PROJECT_ROOT/.conexus/nodejs.yml"
      }
    },
    "conexus-python": {
      "command": "conexus", 
      "args": ["mcp", "--root", "$PROJECT_ROOT"],
      "env": {
        "CONEXUS_CONFIG": "$PROJECT_ROOT/.conexus/python.yml"
      }
    }
  }
}

Advanced Configuration

For production deployments, custom embedding providers, and advanced search optimization, see the MCP Integration Guide.

Topics covered:

  • Custom embedding providers (OpenAI, Anthropic, Ollama, Cohere)
  • Vector store backends (SQLite, PostgreSQL, memory)
  • Search optimization strategies
  • Security configuration (RBAC, API keys, audit logging)
  • Troubleshooting common issues
  • Multiple instance support (monorepos)

📚 Architecture

High-Level Overview

┌─────────────────────────────────────────────────────┐
│                   Orchestrator                      │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │   Intent    │  │  Workflow   │  │    State    │ │
│  │   Parser    │  │   Engine    │  │  Manager    │ │
│  └─────────────┘  └─────────────┘  └─────────────┘ │
└─────────────────────────────────────────────────────┘
                        │
        ┌───────────────┼───────────────┐
        │               │               │
  ┌─────▼─────┐   ┌────▼─────┐   ┌────▼─────┐
  │  Locator  │   │ Analyzer │   │  Future  │
  │   Agent   │   │  Agent   │   │  Agents  │
  └─────┬─────┘   └────┬─────┘   └──────────┘
        │               │
        └───────┬───────┘
                │
  ┌─────────────▼─────────────┐
  │      Validation Layer     │
  │  ┌──────────┐ ┌─────────┐ │
  │  │ Evidence │ │ Schema  │ │
  │  │Validator │ │Validator│ │
  │  └──────────┘ └─────────┘ │
  └───────────────────────────┘
                │
  ┌─────────────▼─────────────┐
  │     Profiling Layer       │
  │  ┌──────────┐ ┌─────────┐ │
  │  │Collector │ │Reporter │ │
  │  └──────────┘ └─────────┘ │
  └───────────────────────────┘

Core Components

ComponentDescriptionStatus
OrchestratorWorkflow engine, intent parsing, state management✅ Complete
Locator AgentFind files/functions matching patterns✅ Complete
Analyzer AgentExtract control flow and data dependencies✅ Complete
Evidence ValidatorVerify 100% evidence backing✅ Complete
Schema ValidatorValidate AGENT_OUTPUT_V1 format✅ Complete
ProfilerPerformance metrics and reporting✅ Complete
Integration FrameworkEnd-to-end testing harness✅ Complete

🧪 Testing

Test Suite Overview

Conexus has 53 integration tests covering real-world scenarios:

# Run all tests
go test ./...

# Run integration tests only
go test ./internal/testing/integration

# Run with verbose output
go test -v ./internal/testing/integration

# Run with coverage
go test -cover ./...

# Run specific test
go test -run TestLocatorAnalyzerIntegration ./internal/testing/integration

Test Categories

CategoryTestsCoverage
Framework Tests13Core test infrastructure
Duration Tests7Performance regression detection
E2E Fixture Tests4Workflow execution with test fixtures
Advanced Workflows7Complex multi-step scenarios
Coordination Tests5Multi-agent communication
Real-World Tests5Actual Conexus source code analysis

Performance Benchmarks

  • Full Test Suite: <1 second
  • Single Agent Execution: <50ms
  • Multi-Agent Workflow: <100ms
  • Real Codebase Analysis: <100ms per file

📖 Documentation

User Guides

Architecture Documentation

Development Resources

🔧 Configuration

Agent Configuration

Conexus agents use environment variables for configuration:

# Enable verbose logging
export CONEXUS_LOG_LEVEL=debug

# Set profiling interval (ms)
export CONEXUS_PROFILE_INTERVAL=100

# Enable evidence validation
export CONEXUS_VALIDATE_EVIDENCE=true

# Set cache directory
export CONEXUS_CACHE_DIR=~/.cache/conexus

Validation Configuration

# Require 100% evidence backing (default: true)
export CONEXUS_REQUIRE_FULL_EVIDENCE=true

# Schema validation mode (strict|lenient)
export CONEXUS_SCHEMA_MODE=strict

# Max validation errors before failing
export CONEXUS_MAX_VALIDATION_ERRORS=10

🎯 AGENT_OUTPUT_V1 Schema

All agents produce standardized output following the AGENT_OUTPUT_V1 schema:

{
  "schema_version": "AGENT_OUTPUT_V1",
  "task_description": "Locate all HTTP handler functions",
  "result_summary": "Found 5 HTTP handlers in 3 files",
  "confidence_score": 0.95,
  "items": [
    {
      "type": "function",
      "name": "HandleRequest",
      "file_path": "/internal/server/handler.go",
      "line_start": 42,
      "line_end": 68,
      "evidence_file_path": "/internal/server/handler.go",
      "evidence_line_start": 42,
      "evidence_line_end": 68,
      "classification": "primary",
      "explanation": "HTTP handler implementing request processing logic"
    }
  ],
  "files_examined": ["/internal/server/handler.go"],
  "metadata": {
    "agent_name": "locator",
    "execution_time_ms": 45,
    "timestamp": "2025-01-15T10:30:00Z"
  }
}

Key Requirements:

  • 100% Evidence Backing: Every item must have valid file/line references
  • Schema Compliance: All required fields must be present
  • Confidence Score: Between 0.0 and 1.0
  • Structured Items: Typed items with classification

See API Reference for complete schema documentation.

🔄 Workflow Integration

Overview

Conexus provides a powerful workflow integration system that combines validation, profiling, and quality gates into coordinated multi-agent workflows.

Basic Orchestrator Usage

package main

import (
    "context"
    "github.com/ferg-cod3s/conexus/internal/orchestrator"
    "github.com/ferg-cod3s/conexus/internal/process"
    "github.com/ferg-cod3s/conexus/internal/tool"
    "github.com/ferg-cod3s/conexus/internal/validation/evidence"
)

func main() {
    // Create orchestrator with default configuration
    config := orchestrator.OrchestratorConfig{
        ProcessManager:    process.NewManager(),
        ToolExecutor:      tool.NewExecutor(),
        EvidenceValidator: evidence.NewValidator(false), // false = non-strict mode
        QualityGates:      orchestrator.DefaultQualityGates(),
        EnableProfiling:   true,
    }
    orch := orchestrator.NewWithConfig(config)
    
    // Execute a workflow
    ctx := context.Background()
    result, err := orch.HandleRequest(ctx, "find all HTTP handlers", permissions)
    if err != nil {
        log.Fatal(err)
    }
    
    // Access results with profiling data
    fmt.Printf("Completed in %v\n", result.Duration)
    fmt.Printf("Evidence coverage: %.1f%%\n", result.Profile.EvidenceCoverage)
}

Quality Gate Presets

Conexus provides three quality gate configurations:

1. Default Quality Gates (Balanced)

config := orchestrator.OrchestratorConfig{
    QualityGates: orchestrator.DefaultQualityGates(),
}
  • ✅ 100% evidence backing required
  • ✅ 5-minute max workflow time
  • ✅ 1-minute max agent execution time
  • ✅ Blocks on validation failures

2. Relaxed Quality Gates (Development)

config := orchestrator.OrchestratorConfig{
    QualityGates: orchestrator.RelaxedQualityGates(),
}
  • ⚠️ 80% evidence coverage minimum
  • ⚠️ 10-minute max workflow time
  • ⚠️ Allows up to 5 unbacked claims

3. Strict Quality Gates (Production)

config := orchestrator.OrchestratorConfig{
    QualityGates: orchestrator.StrictQualityGates(),
}
  • 🔒 100% evidence backing enforced
  • 🔒 2-minute max workflow time
  • 🔒 30-second max agent execution time
  • 🔒 Blocks on all failures (validation + performance)

Custom Quality Gates

config := orchestrator.OrchestratorConfig{
    QualityGates: &orchestrator.QualityGateConfig{
        RequireEvidenceBacking:    true,
        MinEvidenceCoverage:       95.0,
        AllowUnbackedClaims:       2,
        MaxExecutionTime:          3 * time.Minute,
        MaxAgentExecutionTime:     30 * time.Second,
        BlockOnValidationFailure:  true,
        BlockOnPerformanceFailure: false,
    },
}

Profiling Integration

Enable automatic profiling to capture performance metrics:

config := orchestrator.OrchestratorConfig{
    EnableProfiling: true,
}

result, _ := orch.ExecuteWorkflow(ctx, workflow, permissions)

// Access profiling data
profile := result.Profile
fmt.Printf("Total duration: %v\n", profile.TotalDuration)
fmt.Printf("Agent time: %v\n", profile.AgentExecutionTime)
fmt.Printf("Validation time: %v\n", profile.ValidationTime)
fmt.Printf("Profiling overhead: %.2f%%\n", profile.ProfilingOverheadPercent)

Validation Integration

Evidence validation is automatically integrated:

// Strict mode - requires 100% evidence backing
validator := evidence.NewValidator(true)

// Non-strict mode - allows partial evidence
validator := evidence.NewValidator(false)

config := orchestrator.OrchestratorConfig{
    EvidenceValidator: validator,
}

Workflow Reports

Generate comprehensive workflow reports:

result, _ := orch.ExecuteWorkflow(ctx, workflow, permissions)

// Generate workflow report
report := orchestrator.GenerateWorkflowReport(result)

fmt.Println(report.ExecutionSummary)
fmt.Println(report.ValidationReport)
fmt.Println(report.PerformanceReport)

Example report output:

=== Workflow Execution Report ===

Execution Summary:
  Duration: 127ms
  Agents Executed: 2
  Status: ✅ Success

Validation Report:
  Evidence Coverage: 100.0%
  Backed Claims: 15
  Unbacked Claims: 0
  Status: ✅ Passed

Performance Report:
  Agent Execution: 85ms (66.9%)
  Validation: 12ms (9.4%)
  Profiling Overhead: 1.2%
  Status: ✅ Within Limits

Best Practices

  • Use Default Gates for Most Cases: Balanced performance and quality
  • Enable Profiling in Development: Identify bottlenecks early
  • Strict Mode for Production: Maximum confidence in production workflows
  • Monitor Profiling Overhead: Keep under 10% for production systems
  • Review Validation Reports: Ensure evidence backing meets standards

See Testing Strategy for workflow testing patterns.

🐳 Docker Deployment

Quick Start with Docker

# Pull and run the latest image (when available)
docker pull conexus:latest
docker run -d -p 8080:8080 --name conexus conexus:latest

# Or build locally
docker build -t conexus:latest .
docker run -d -p 8080:8080 --name conexus conexus:latest

# Test the service
curl http://localhost:8080/health

Production deployment:

# Start the service
docker compose up -d

# View logs
docker compose logs -f

# Stop the service
docker compose down

# Rebuild after code changes
docker compose up -d --build

Development deployment:

# Use development configuration with debug logging
docker compose -f docker-compose.yml -f docker-compose.dev.yml up

# View debug logs
docker compose -f docker-compose.yml -f docker-compose.dev.yml logs -f

Configuration

Environment Variables:

# Server configuration
CONEXUS_HOST=0.0.0.0              # Server bind address
CONEXUS_PORT=8080                  # Server port

# Database configuration
CONEXUS_DB_PATH=/data/conexus.db   # SQLite database path

# Codebase configuration
CONEXUS_ROOT_PATH=/data/codebase   # Path to codebase to index

# Logging configuration
CONEXUS_LOG_LEVEL=info             # Log level (debug|info|warn|error)
CONEXUS_LOG_FORMAT=json            # Log format (json|text)

# Embedding configuration (optional)
CONEXUS_EMBEDDING_PROVIDER=openai  # Embedding provider (mock|openai)
CONEXUS_EMBEDDING_MODEL=text-embedding-3-small
OPENAI_API_KEY=sk-...              # OpenAI API key

Volume Mounts:

volumes:
  # Persistent database storage
  - ./data:/data
  
  # Optional: Mount your codebase for indexing
  - /path/to/your/code:/data/codebase:ro
  
  # Optional: Mount config file
  - ./config.yml:/app/config.yml:ro

Docker Image Details

Multi-stage build:

  • Builder: golang:1.24-alpine (CGO enabled for SQLite)
  • Runtime: alpine:3.19 (minimal base, ca-certificates + sqlite-libs)

Image specifications:

  • Size: ~19.5MB (optimized with multi-stage build)
  • User: Non-root conexus:1000
  • Port: 8080 (HTTP + MCP over JSON-RPC 2.0)
  • Health Check: GET /health every 30s

Security features:

  • Non-root execution (UID 1000)
  • Static binary (no dynamic linking)
  • Minimal attack surface (Alpine base)
  • Read-only config option
  • Health check monitoring

MCP Server Endpoints

Once running, the service exposes:

HTTP Endpoints:

# Health check
curl http://localhost:8080/health
# Response: {"status":"healthy","version":"0.1.0-alpha"}

# Service info
curl http://localhost:8080/
# Response: Service info with MCP endpoint

# MCP JSON-RPC endpoint
curl -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'

MCP Tools:

  • context.search - Comprehensive search with filters
  • context.get_related_info - File/ticket context retrieval
  • context.index_control - Indexing operations
  • context.connector_management - Data source management

Production Deployment

With Docker Compose:

# docker-compose.prod.yml
services:
  conexus:
    image: conexus:latest
    restart: always
    environment:
      - CONEXUS_LOG_LEVEL=info
      - CONEXUS_LOG_FORMAT=json
    volumes:
      - conexus-data:/data
      - /mnt/codebase:/data/codebase:ro
    ports:
      - "8080:8080"
    healthcheck:
      test: ["CMD", "wget", "--spider", "-q", "http://localhost:8080/health"]
      interval: 30s
      timeout: 5s
      retries: 3
      start_period: 10s

volumes:
  conexus-data:
    driver: local

Deploy:

docker compose -f docker-compose.prod.yml up -d

Monitoring

Check health:

# Container status
docker compose ps

# Health check status
docker inspect conexus | jq '.[0].State.Health'

# View logs
docker compose logs -f

# Check metrics
curl http://localhost:8080/health

Troubleshooting:

# View container logs
docker compose logs --tail=100

# Execute commands in container
docker compose exec conexus sh

# Check database
docker compose exec conexus ls -la /data/

# Restart service
docker compose restart

Building from Source

# Build Docker image
docker build -t conexus:custom .

# Build with specific Go version
docker build --build-arg GO_VERSION=1.24 -t conexus:custom .

# Build and tag
docker build -t conexus:v0.1.0 -t conexus:latest .

# Push to registry (configure your registry)
docker tag conexus:latest registry.example.com/conexus:latest
docker push registry.example.com/conexus:latest

Docker Best Practices

  • Use Docker Compose for orchestration
  • Mount volumes for data persistence
  • Configure environment variables for secrets
  • Enable health checks for monitoring
  • Use named volumes in production
  • Check logs regularly with docker compose logs
  • Backup database in /data directory regularly
  • Limit resources with Docker resource constraints if needed

🏗️ Development Workflow

Project Structure

conexus/
├── cmd/conexus/          # Main entry point
├── internal/
│   ├── agent/           # Agent implementations
│   │   ├── locator/     # File/function locator
│   │   └── analyzer/    # Code analyzer
│   ├── orchestrator/    # Workflow orchestration
│   │   ├── intent/      # Intent parsing
│   │   ├── workflow/    # Workflow engine
│   │   ├── state/       # State management
│   │   └── escalation/  # Error handling
│   ├── validation/      # Validation systems
│   │   ├── evidence/    # Evidence validation
│   │   └── schema/      # Schema validation
│   ├── profiling/       # Performance profiling
│   ├── protocol/        # JSON-RPC protocol
│   └── testing/         # Integration testing
├── pkg/schema/          # Public schemas
├── tests/fixtures/      # Test fixtures
└── docs/                # Documentation

Adding a New Agent

  • Create agent directory: internal/agent/myagent/
  • Implement agent interface:
    type Agent interface {
        Execute(ctx context.Context, req Request) (*schema.AgentOutput, error)
    }
    
  • Add tests in myagent_test.go
  • Register in orchestrator
  • Add integration tests

See Contributing Guide for details.

📊 Current Status

Phase 5 Progress (95% Complete)

  • Task 5.1: Integration Testing Framework (53 tests passing)
  • 🔄 Task 5.2: Documentation Updates (in progress)
  • Task 5.3: Workflow Integration (pending)
  • Task 5.4: Protocol Tests (optional)

Test Results

✅ All 53 integration tests passing
✅ Execution time: <1 second
✅ Evidence validation: 100%
✅ Schema compliance: 100%
✅ Real-world analysis: 5 scenarios validated

See PHASE5-STATUS.md for detailed status.

🛣️ Roadmap

Phase 6: Optimization (Planned)

  • ⏳ Advanced caching strategies
  • ⏳ Parallel agent execution
  • ⏳ Performance optimization
  • ⏳ Memory usage reduction

Phase 7: Production Readiness (Planned)

  • ⏳ CLI enhancements
  • ⏳ Configuration management
  • ⏳ Deployment automation
  • ⏳ Monitoring dashboards

Future Agents (Planned)

  • ⏳ Pattern recognition agent
  • ⏳ Thoughts analyzer agent
  • ⏳ Dependency analyzer agent
  • ⏳ Security audit agent

🤝 Contributing

We welcome contributions! Please see:

Quick Contribution Checklist

  • Fork the repository
  • Create a feature branch
  • Write tests for new features
  • Ensure all tests pass (go test ./...)
  • Follow code style guidelines
  • Update documentation
  • Submit a pull request

📄 License

This project is licensed under the MIT License - see LICENSE for details.

🙏 Acknowledgments

📞 Support & Contact

  • MCP - Model Context Protocol specification
  • Claude Code - AI-powered development assistant
  • OpenCode - Open-source AI coding tools

Built with ❤️ by the Conexus team

Keywords

mcp

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Package last updated on 20 Oct 2025

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