Code Puppy is an agentic AI designed for code generation and explanation, positioning itself as a cost-effective alternative to expensive IDE features. It targets developers seeking an intelligent coding assistant capable of understanding tasks, producing high-quality code, and detailing its reasoning. The primary benefit is providing advanced AI coding capabilities without the premium associated with some integrated development environments.

How It Works

Code Puppy integrates with a vast array of Large Language Models (LLMs) through the models.dev platform, supporting providers like OpenAI, Google (Gemini), Anthropic (Claude), Cerebras, and many others. Its core architecture features a flexible agent system, allowing users to define custom agents via Python classes or JSON configurations. These agents can leverage a suite of tools, including file system operations, shell command execution, and reasoning sharing. The project also incorporates DBOS for durable execution, enabling checkpointing and recovery of agent interactions.

Quick Start & Requirements

The recommended installation and execution method is via uvx:

uvx code-puppy -i

This requires Python 3.11 or newer. uv will manage Python versions if necessary. Users will need API keys for the specific LLM providers they intend to use (e.g., OpenAI, Gemini, Anthropic). Links to official quick-start guides are not explicitly provided, but the installation instructions are detailed.

Highlighted Details

• Extensive LLM Support: Integrates with over 65 providers and 1000+ models via models.dev, including many with OpenAI-compatible APIs.
• Pluggable Agent System: Supports custom Python agents and user-defined JSON agents for specialized tasks, with tools for file management, shell execution, and more.
• Durable Execution: Leverages DBOS for automatic checkpointing and recovery of agent workflows, enhancing reliability.
• Round Robin Model Distribution: Facilitates load balancing and rate limit management across multiple API keys for the same model.
• Privacy Commitment: Emphasizes a zero-telemetry, zero-prompt-logging policy, with options for fully local LLM execution.

Maintenance & Community

The README indicates direct developer contact for feature requests and bug reports. Specific details regarding maintainers, community channels (like Discord/Slack), or a public roadmap are not provided.

Licensing & Compatibility

The project is licensed under the MIT License, which generally permits commercial use and integration into closed-source projects.

Limitations & Caveats

While supporting numerous LLMs, users must provide their own API keys and manage associated costs. Some LLM providers requiring special authentication (e.g., Amazon Bedrock, Google Vertex) are noted as unsupported or require manual configuration. The project's "sassy" tone suggests a focus on rapid development and functionality, potentially implying less emphasis on formal QA or extensive documentation for all features.