Implement OpenRouter LLM Client Infrastructure #100

New Issue

tegwick · 2025-10-03T22:26:33Z

tegwick commented

2025-10-03 22:26:33 +00:00

Priority: HIGH | Effort: 2 days | Dependencies: None

User Story

As a developer, I want a robust OpenRouter client so that MarkiTect can connect to and interact with various LLM models through the OpenRouter API.

Description

Create the foundational OpenRouter client infrastructure that will enable all LLM-powered features in MarkiTect. This includes API communication, model management, rate limiting, and error handling.

Technical Implementation

New Files: markitect/llm/init.py, markitect/llm/openrouter_client.py, markitect/llm/exceptions.py, tests/test_openrouter_client.py
Modified Files: requirements.txt, markitect/config_manager.py

Acceptance Criteria

OpenRouterClient class with async API communication
Support for multiple models (GPT-4, Claude, etc.)
Rate limiting and retry logic with exponential backoff
Comprehensive error handling for API failures
Token usage tracking and cost estimation
Unit tests with >90% coverage
Integration tests with mock API responses
Documentation with usage examples

Part of LLM Integration implementation from gameplan. Supports Issues #98 and #99.

**Priority**: HIGH | **Effort**: 2 days | **Dependencies**: None ## User Story As a developer, I want a robust OpenRouter client so that MarkiTect can connect to and interact with various LLM models through the OpenRouter API. ## Description Create the foundational OpenRouter client infrastructure that will enable all LLM-powered features in MarkiTect. This includes API communication, model management, rate limiting, and error handling. ## Technical Implementation - **New Files**: markitect/llm/__init__.py, markitect/llm/openrouter_client.py, markitect/llm/exceptions.py, tests/test_openrouter_client.py - **Modified Files**: requirements.txt, markitect/config_manager.py ## Acceptance Criteria - [ ] OpenRouterClient class with async API communication - [ ] Support for multiple models (GPT-4, Claude, etc.) - [ ] Rate limiting and retry logic with exponential backoff - [ ] Comprehensive error handling for API failures - [ ] Token usage tracking and cost estimation - [ ] Unit tests with >90% coverage - [ ] Integration tests with mock API responses - [ ] Documentation with usage examples ## Related Issues Part of LLM Integration implementation from gameplan. Supports Issues #98 and #99.

tegwick added the type:feature priority:high labels 2025-10-03 22:26:33 +00:00

tegwick added this to the LLM Chat and Agent Assistance project 2025-10-03 22:28:30 +00:00

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Reference: coulomb/markitect-main#100