goose: Local AI agent for engineering task automation
LLM-powered agent automating local software engineering workflows.
Learn more about Goose
Goose is an LLM-powered automation agent designed to execute multi-step software engineering workflows on local development environments. The system operates through a session-based architecture where users configure provider and model preferences via profile objects, then submit natural language prompts that the agent interprets and executes as concrete development tasks. It supports sequential workflow orchestration, allowing complex operations like code analysis, refactoring, and test generation to be chained together while maintaining context across steps. The agent can interact with the local filesystem, read and modify source code, generate artifacts, and produce structured reports based on completed work. This approach enables automated execution of routine engineering tasks while keeping all operations within the developer's local environment rather than requiring cloud-based execution.
Multi-LLM support
Accepts any LLM as a backend and supports multi-model configuration, allowing users to route different tasks to different models based on requirements or cost considerations.
MCP server integration
Integrates with Model Context Protocol servers, enabling the agent to connect with external tools and services beyond its core capabilities.
Dual interface options
Available as both a desktop application and command-line tool, providing flexibility in how developers interact with the agent depending on their workflow preferences.
from goose import Session
session = Session(
provider="openai",
model="gpt-4o"
)
result = session.run("Analyze the error handling in src/utils.py")
print(result.output)See how people are using Goose
Top in AI & ML
Related Repositories
Discover similar tools and frameworks used by developers
ByteTrack
Multi-object tracker associating low-confidence detections across frames.
KoboldCpp
Self-contained llama.cpp distribution with KoboldAI API for running LLMs on consumer hardware.
DeepFace
Python library wrapping multiple face recognition deep learning models.
X Recommendation Algorithm
Open source implementation of X's recommendation algorithm for timeline and notification ranking.
OpenClaw
Personal AI assistant that runs on your own devices and connects to messaging platforms like WhatsApp, Telegram, and Slack.