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Basic Information

Forge is an AI-enhanced terminal development environment and coding agent designed to help developers work with their codebase from the terminal. It runs interactively via npx and connects to a Forge web app for OAuth-based account setup, then provides AI assistance without sending your code off your machine. The project integrates multiple LLM providers (Forgecode.dev, OpenAI, Anthropic, OpenRouter, Requesty, x-ai, Google Vertex AI, Groq, Amazon Bedrock gateways and other OpenAI-compatible providers) and supports configuration through environment variables and a forge.yaml file. It includes a Model Context Protocol (MCP) implementation to let agents call external tools and services, and it is intended to be extensible and community-driven with documentation and a Discord community. The README emphasizes zero-configuration startup, secure-by-design local code handling, and command-line and workflow modes for using AI in everyday development tasks.

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4052
App URL
https://github.com/antinomyhq/forge
Github Repository
https://github.com/antinomyhq/forge/blob/main/README.md

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App Details

Features
Forge offers an interactive CLI and non-interactive modes with command-line flags for prompts, scripts, workflows, events and conversations. It supports many LLM providers and model selection in forge.yaml. Advanced configuration options include model, temperature, max_walker_depth, custom_rules, commands, tool timeouts, max_requests_per_turn and tool failure limits. Environment variables let you tune retry logic, HTTP client behavior, API endpoints and tool timeouts. MCP support allows registering local or remote MCP servers and managing them via forge mcp list/add/get/remove or a .mcp.json file. The tool also provides project-aware code exploration, scaffolding, debugging help, refactoring, database schema suggestions and git conflict assistance. Documentation is hosted in the docs folder and community support is available on Discord.
Use Cases
Forge helps developers by bringing AI assistance directly into the terminal so common development tasks are faster and less context-switching is required. It can analyze a repository to explain code flows, suggest implementations for new features, scaffold components, help debug errors with code-aware suggestions, perform code reviews, refactor legacy code, teach integration steps for new technologies, and propose database schema designs. Configuration options let teams enforce custom rules, define reusable commands, and control model behavior and resource limits. MCP and multi-agent workflow support enable integration with external tools, browser automation and API calls as part of automated problem-solving. The README highlights security, extensibility and multi-provider flexibility so teams can use preferred models and keep code local while leveraging AI assistance.

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