Gigacode – Use OpenCode's UI with Claude Code/Codex/Amp

Gigacode provides a unified web interface (OpenCode's UI) that abstracts away differences between multiple LLM code generation backends (Claude, Codex, Amp). The framework implements a provider-agnostic adapter pattern that translates UI interactions into model-specific API calls, handling differences in prompt formatting, token limits, and response parsing across heterogeneous model APIs without requiring users to switch interfaces.

Gigacode extends OpenCode's web-based code editor with pluggable LLM backend adapters that implement a standardized interface for code generation requests. The framework uses a request/response translation layer that converts OpenCode's internal API calls into model-specific formats (e.g., Anthropic's Messages API, OpenAI's Completions API) and normalizes responses back into OpenCode's expected schema, enabling any compatible LLM to power the editor.

Gigacode automatically collects relevant code context (open files, selected code, project structure) from the OpenCode editor and constructs optimized prompts for each LLM backend. The framework implements context windowing logic that respects each model's token limits, prioritizes relevant files based on import relationships or edit history, and formats context according to each backend's preferred prompt structure (e.g., system messages for Claude vs. instruction-following for Codex).

Gigacode implements streaming response handling for code generation across multiple LLM backends, translating each model's streaming protocol (Anthropic's Server-Sent Events, OpenAI's streaming format, etc.) into a unified stream that updates the OpenCode editor in real-time. The framework buffers partial tokens, handles mid-stream errors, and provides fallback behavior if a backend's stream is interrupted, enabling responsive code completion without waiting for full response generation.

Gigacode maintains a configuration layer that maps model-specific capabilities (e.g., Claude supports function calling, Codex supports fine-tuning, Amp supports custom instructions) and constraints (token limits, rate limits, supported languages) to a unified interface. The framework uses capability detection to enable or disable UI features based on the active backend and automatically adjusts request parameters (temperature, max_tokens, top_p) according to each model's supported ranges and defaults.

Gigacode allows users to switch between LLM backends (Claude, Codex, Amp) mid-session while preserving the current code context, editor state, and generation history. The framework implements a state abstraction layer that decouples the editor's internal state from backend-specific session management, enabling seamless backend switching without losing work or requiring re-authentication.

Gigacode tracks API usage (tokens, requests, costs) and latency metrics for each LLM backend, aggregating this data to help users understand the cost-performance tradeoff of different models. The framework logs request/response metadata (tokens used, time to first token, total generation time) and provides dashboards or reports showing which models are most cost-effective or fastest for different code generation tasks.

Gigacode implements automatic fallback and retry mechanisms that detect when an LLM backend is unavailable, rate-limited, or experiencing errors, and automatically retry the request with an alternative backend or with exponential backoff. The framework monitors backend health (API response times, error rates, availability) and can proactively switch to a healthy backend before the user's request fails, improving reliability without requiring manual intervention.