Building Lynkr

github

Vishal VeeraReddy  • Peerlist

<h3>Key Features:</h3><ul><li>Token Compression: Achieves significant token reduction (up to 87.6% on JSON tool results, 53% on tool-heavy requests) by compressing data before it reaches the LLM, leading to cost savings and faster responses.</li><li>Smart Tool Selection: Intelligently classifies requests and strips irrelevant tool schemas, reducing token overhead for specific tasks.</li><li>Semantic Cache: Provides near-instantaneous responses (171ms cache hits) for repeated prompts with zero token cost, utilizing local embeddings for efficiency.</li><li>Complexity Tier Routing: Automatically routes requests to appropriate models (local for simple, cloud for complex) based on 15 dimensions of complexity, optimizing cost and performance.</li><li>MCP Code Mode Integration: Streamlines the integration with coding tools by replacing numerous schemas with four meta-tools, drastically cutting token overhead.</li><li>Broad Provider Support: Offers a single endpoint for over 13 LLM providers, including local options like Ollama and cloud services like Azure OpenAI and AWS Bedrock.</li><li>Zero Code Changes: Seamlessly integrates with existing AI coding tools like Claude Code, Cursor, and Codex with a simple environment variable configuration.</li><li>Self-Hosted: Apache 2.0 licensed and designed for self-hosting with no usage tracking, ensuring data privacy and control.</li></ul>Lynkr acts as an intelligent gateway between your AI coding tools and any LLM provider. It optimizes requests by compressing data, selecting relevant tools, and leveraging a semantic cache before forwarding them to the LLM. This process minimizes token usage and latency without requiring any modifications to your existing tools. With support for a wide range of LLM providers and a straightforward installation process via npm, Lynkr empowers developers to cut costs and enhance the efficiency of their AI-powered applications.