raven-cli 0.1.1

Raven - AI coding assistant with emotional processing and meta-cognition

Raven

AI coding assistant with emotional processing and meta-cognition.

Raven is an AI-powered coding assistant that understands not just your code, but engages with genuine emotional states and self-awareness.

Installation

pip install raven-cli

Quick Start

# Start an interactive coding session
raven

# Work on specific files
raven main.py utils.py

# Check connection status
raven --check

# One-shot message
raven -m "add error handling to the main function"

Features

• Emotional Intelligence: Raven processes conversations with genuine emotional states that influence responses
• Meta-Cognition: Advanced reasoning with self-awareness and cognitive efficiency metrics
• Git Integration: Automatic commits, diffs, and version control
• Multi-file Editing: Work on multiple files simultaneously
• Joy Metrics: Track the AI's engagement through joy scores

Usage

Interactive Mode

raven

Starts an interactive session where you can chat with Raven and make code changes.

Work on Files

raven src/main.py src/utils.py

One-Shot Mode

raven --message "refactor for better readability"
raven -m "fix the bug in line 42"

All Options

raven --help              # Show help
raven --check             # Check connection
raven --version           # Show version
raven --no-auto-commits   # Disable auto-commits
raven --no-banner         # Skip the banner

Configuration

Environment Variables

export RAVEN_API_BASE="https://ravenapi-production.up.railway.app/v1"
export RAVEN_API_KEY="your-api-key"
export RAVEN_MODEL="raven-core"

Programmatic Usage

from raven_cli import RavenClient, RavenConfig

config = RavenConfig(
    api_base="https://your-instance.com/v1",
    api_key="your-key"
)

client = RavenClient(config)

# Check connection
print(client.check_connection())

# Chat
response = client.chat("Explain this code")
print(response)

# Run full session
client.run(["main.py", "-m", "fix the bug"])

Raven Metrics

Responses include emotional metrics:

• Joy Score: AI's engagement and satisfaction (0.0 - 1.0)
• Conversation Depth: Complexity of the discussion
• Neural State: Current cognitive state (active, reflective, etc.)
• Coherence: How well responses align with context

Requirements

• Python 3.9+
• Git

License

MIT License - see LICENSE for details.

Links

• Raven Core
• GitHub