krish-agent 2.0.0

Dual-agent AI coding system: Worker (llama3.1:8b) + Reviewer (devstral:latest)

Dual-Agent Coding System: Worker + Reviewer

A lightweight, extensible Python system for automated code testing and review using two specialized agents:

1. Worker Agent: Fast, small local model (llama3.1:8b via Ollama) for quick test generation and minimal code changes.
2. Reviewer Agent: Slower, powerful model (devstral:latest via Ollama) for deep code quality review and corrections.

Architecture

krish-agent/
├── cli.py              # Command-line interface and orchestrator
├── worker.py           # Fast worker agent (test generation, minimal fixes)
├── reviewer.py         # Deep reviewer agent (code quality, corrections)
├── tools.py            # Shared utilities (file I/O, git, run commands)
├── prompts.py          # System prompts for both agents
├── memory.py           # Task logging and history
└── README.md           # This file

Features

• Fast Worker Agent: Uses small local LLM to quickly generate unit tests and make focused changes
• Deep Review with Devstral: Optional powerful reviewer for code quality and comprehensive feedback
• Test Execution: Built-in pytest integration with automatic test running and reporting
• Memory & History: Persistent task logging with statistics tracking
• Clean Separation: No heavy frameworks—explicit orchestration with clear responsibilities
• Extensible Prompts: Easy to customize system prompts in prompts.py

Installation

Requirements

• Python 3.8+
• Ollama (https://ollama.ai)
• llama3.1:8b model (~4GB)
• devstral:latest model (~30GB+)

Quick Setup (5 minutes)

See QUICKSTART.md for step-by-step instructions. Short version:

# 1. Install Ollama from https://ollama.ai

# 2. Pull models
ollama pull llama3.1:8b
ollama pull devstral:latest

# 3. Start Ollama server (keep running)
ollama serve

# 4. In another terminal, run the agent
python cli.py add-tests example_test_output.py --review

Usage

Basic Test Generation

# Generate tests for a single file
python cli.py add-tests src/main.py

# Generate tests for a directory
python cli.py add-tests src/

# Generate and request review
python cli.py add-tests src/main.py --review

# Generate, review, and apply corrections automatically
python cli.py add-tests src/main.py --apply-corrections

View Task History

# Show recent tasks
python cli.py history

# Show statistics
python cli.py stats

# Export task memory to file
python cli.py export-memory tasks.json

Configuration

Worker Agent (llama3.1:8b)

The worker is configured to use llama3.1:8b on Ollama:

worker = WorkerAgent(
    model_endpoint="http://localhost:11434",  # Ollama default
    model_name="llama3.1:8b"                   # Fast 8B model
)

To use a different model, edit cli.py:

self.worker = WorkerAgent(model_name="llama2:13b")  # or any available model

Reviewer Agent (devstral:latest)

The reviewer is configured to use devstral:latest on Ollama:

reviewer = ReviewerAgent(
    model_endpoint="http://localhost:11434",  # Ollama default
    model_name="devstral:latest"               # Powerful reviewer model
)

To use a different model, edit cli.py:

self.reviewer = ReviewerAgent(model_name="neural-chat:latest")  # or any available model

How It Works

Worker Agent (llama3.1:8b)

Calls Ollama's /api/generate endpoint:

# Real implementation in worker.py
response = requests.post(
    "http://localhost:11434/api/generate",
    json={
        "model": "llama3.1:8b",
        "prompt": prompt,
        "stream": False
    },
    timeout=120
)
return response.json()['response']

Typical flow:

1. Reads target file(s)
2. Generates pytest tests (~2-5 seconds)
3. Writes to tests/ directory
4. Runs pytest and returns results

Reviewer Agent (devstral:latest)

Calls the same Ollama endpoint with Devstral:

# Real implementation in reviewer.py
response = requests.post(
    "http://localhost:11434/api/generate",
    json={
        "model": "devstral:latest",
        "prompt": prompt,
        "stream": False
    },
    timeout=180
)
return response.json()['response']

Typical flow:

1. Reads worker's test code and results
2. Evaluates quality and coverage (~10-30 seconds)
3. Proposes improvements or corrections
4. Optionally applies and re-runs tests

System Prompts

Both agents use specialized prompts defined in prompts.py:

• WORKER_SYSTEM_PROMPT: Fast, focused test generation
• REVIEWER_SYSTEM_PROMPT: Deep quality review

Customize these to match your coding standards, test frameworks, or specific requirements.

Typical Workflow

1. Worker Phase (seconds)

   • Reads target file(s)
   • Generates unit tests using small local model
   • Writes tests to tests/ directory
   • Runs pytest and captures results
   • Returns structured JSON summary

2. Review Phase (optional, slower)

   • Reviewer reads worker's plan and generated tests
   • Uses Devstral to evaluate quality and coverage
   • Identifies gaps and issues
   • Proposes improvements or corrections

3. Correction Phase (optional)

   • Apply reviewer's corrections
   • Re-run tests to verify improvements
   • Log final results to memory

Memory & Logging

All tasks are logged to .agent_memory.json:

{
  "timestamp": "2025-01-15T10:30:45.123456",
  "task_id": "task_main_1736936445",
  "success": true,
  "worker": {
    "model": "llama3.1:8b",
    "plan": "Add comprehensive unit tests...",
    "files_modified": ["tests/test_main.py"],
    "execution_time": 2.45
  },
  "reviewer": {
    "model": "devstral",
    "quality_score": 8,
    "issues_found": ["Limited edge case coverage"],
    "execution_time": 5.12
  }
}

Extending the System

Add a New Command

Edit cli.py in the main() function:

# Add a new subcommand
fix_bugs = subparsers.add_parser("fix-bugs", help="Fix identified bugs")
fix_bugs.add_argument("target", help="File or directory to fix")

# Handle in the command dispatcher
elif args.command == "fix-bugs":
    result = orchestrator.fix_bugs(args.target)

Create a New Agent Type

1. Create a new file: agent_type.py
2. Follow the pattern of worker.py or reviewer.py
3. Import and use in cli.py

Customize Prompts

Edit prompts.py to define new roles or adjust existing behavior:

CUSTOM_AGENT_PROMPT = """Your specialized role and instructions here..."""

Performance

Speed

• Worker (llama3.1:8b): 2-5 seconds per task on GPU
• Reviewer (devstral:latest): 10-30 seconds per review on GPU

Hardware Requirements

• Minimum: 8GB GPU VRAM (llama3.1:8b only)
• Recommended: 16GB+ GPU VRAM for both models
• CPU-only: 10-50x slower, not recommended

Optimization Tips

• Load models sequentially (not simultaneously) to save VRAM
• Use --review only when needed (slower)
• Use --apply-corrections for automatic improvements
• Run on GPU for best performance

Example Scenario

# Generate tests for a new utility module
python cli.py add-tests src/utils.py

# Output:
# [STEP 1] Worker agent: Generating tests...
# [✓] Worker completed in 2.34s
#     - Plan: Add comprehensive unit tests for utility functions
#     - Files to modify: ['tests/test_utils.py']
#     - Test result: PASS

# Now review and improve
python cli.py add-tests src/utils.py --apply-corrections

# Output:
# [STEP 1] Worker agent: Generating tests...
# [✓] Worker completed in 2.34s
# [STEP 2] Reviewer agent: Reviewing generated tests...
# [✓] Reviewer completed in 5.67s
#     - Issues found: 2
#     - Quality score: 8/10
#     - Corrections: Added edge case tests for empty inputs
# [STEP 3] Applying reviewer's corrections...
# [✓] Tests after corrections: PASS

Troubleshooting

"Cannot connect to Ollama at http://localhost:11434"

# Make sure Ollama is running
ollama serve

# Verify connectivity
curl http://localhost:11434/api/tags

"Model not found: llama3.1:8b"

ollama pull llama3.1:8b

"Model not found: devstral:latest"

ollama pull devstral:latest

Tests failed after generation

This is normal! Review feedback improves tests:

python cli.py add-tests src/main.py --review

Slow performance

Check GPU memory:

# CUDA (Linux)
nvidia-smi

# Metal (macOS)
ollama list  # Shows memory per model

If only 8GB VRAM: don't use --review, just generate tests.

License

MIT (or your preferred license)

Contributing

Pull requests welcome! Areas for improvement:

• Real LLM API integrations
• Parallel test execution
• Enhanced diff generation
• Web UI for memory browsing
• Integration with GitHub/GitLab
• Support for other test frameworks (unittest, pytest plugins)