Lynkr

Run Claude Code, Cursor, and Codex on any model. One proxy, every provider.

The Problem

AI coding tools lock you into one provider. Claude Code requires Anthropic. Codex requires OpenAI. You can't use your company's Databricks endpoint, your local Ollama models, or your AWS Bedrock account — at least, not without Lynkr.

The real costs:

• Anthropic API at $15/MTok output adds up fast for daily coding
• No way to use free local models (Ollama, llama.cpp) with Claude Code
• Enterprise teams can't route through their own cloud infrastructure
• Provider outages take your entire workflow down

The Solution

Lynkr is a self-hosted proxy that sits between your AI coding tools and any LLM provider. One environment variable change, and your tools work with any model.

Claude Code / Cursor / Codex / Cline / Continue / Vercel AI SDK
                        |
                      Lynkr
                        |
    Ollama | Bedrock | Databricks | OpenRouter | Azure | OpenAI | llama.cpp

# That's it. Three lines.
npm install -g lynkr
export ANTHROPIC_BASE_URL=http://localhost:8081
lynkr start

Quick Start

Install

npm install -g pino-pretty && npm install -g lynkr

Pick a Provider

Free & Local (Ollama)

export MODEL_PROVIDER=ollama
export OLLAMA_MODEL=qwen2.5-coder:latest
lynkr start

AWS Bedrock (100+ models)

export MODEL_PROVIDER=bedrock
export AWS_BEDROCK_API_KEY=your-key
export AWS_BEDROCK_MODEL_ID=anthropic.claude-3-5-sonnet-20241022-v2:0
lynkr start

OpenRouter (cheapest cloud)

export MODEL_PROVIDER=openrouter
export OPENROUTER_API_KEY=sk-or-v1-your-key
lynkr start

Connect Your Tool

Claude Code

export ANTHROPIC_BASE_URL=http://localhost:8081
export ANTHROPIC_API_KEY=dummy
claude "Your prompt here"

Codex CLI — edit ~/.codex/config.toml:

model_provider = "lynkr"
model = "gpt-4o"

[model_providers.lynkr]
name = "Lynkr Proxy"
base_url = "http://localhost:8081/v1"
wire_api = "responses"

Cursor IDE

• Settings > Features > Models
• Base URL: http://localhost:8081/v1
• API Key: sk-lynkr

Vercel AI SDK

import { generateText } from "ai";
import { createOpenAICompatible } from "@ai-sdk/openai-compatible";

const lynkr = createOpenAICompatible({
  baseURL: "http://localhost:8081/v1",
  name: "lynkr",
  apiKey: "sk-lynkr",
});

const { text } = await generateText({
  model: lynkr.chatModel("auto"),
  prompt: "Hello!",
});

Works with any OpenAI-compatible client: Cline, Continue.dev, ClawdBot, KiloCode, and more.

Supported Providers

4 local providers for 100% offline, free usage. 6+ cloud providers for scale.

Why Lynkr Over Alternatives

Lynkr's edge: Purpose-built for AI coding tools. Not a general LLM gateway — a proxy that understands Claude Code, Cursor, and Codex natively, with built-in token optimization, complexity-based routing, and a memory system designed for coding workflows. Installs in one command, runs on Node.js, zero infrastructure required.

Cost Comparison

With token optimization enabled, Lynkr's smart tool selection, prompt caching, and memory deduplication reduce token usage by 60-80% on top of provider savings.

What's Under the Hood

Lynkr isn't just a passthrough proxy. It's an optimization layer.

Smart Routing

Routes requests to the right model based on task complexity. Simple questions go to fast/cheap models. Complex architectural tasks go to powerful models. You configure the tiers.

Token Optimization

• Smart tool selection — only sends tools relevant to the current task
• Prompt compression — removes redundant context before sending
• Memory deduplication — eliminates repeated information across turns
• TOON format — compact serialization that cuts token count

Enterprise Resilience

• Circuit breakers — automatic failover when a provider goes down
• Load shedding — graceful degradation under high load
• Prometheus metrics — full observability at /metrics
• Health checks — K8s-ready endpoints at /health

Memory System

Titans-inspired long-term memory with surprise-based filtering. The system remembers important context across sessions and forgets noise — reducing token waste from repeated context.

Semantic Cache

Cache responses for semantically similar prompts. Hit rate depends on your workflow, but repeat questions (common in coding) get instant responses.

SEMANTIC_CACHE_ENABLED=true
SEMANTIC_CACHE_THRESHOLD=0.95

MCP Integration

Automatic Model Context Protocol server discovery and orchestration. Your MCP tools work through Lynkr without configuration.

Deployment Options

NPM (recommended)

npm install -g lynkr && lynkr start

Docker

docker-compose up -d

Git Clone

git clone https://github.com/vishalveerareddy123/Lynkr.git
cd Lynkr && npm install && cp .env.example .env
npm start

Homebrew

brew tap vishalveerareddy123/lynkr
brew install lynkr

Documentation

Troubleshooting

Contributing

We welcome contributions. See the Contributing Guide and Testing Guide.

License

Apache 2.0 — See LICENSE.

Community

• GitHub Discussions — Questions and tips
• Report Issues — Bug reports and feature requests
• NPM Package — Official package
• DeepWiki — AI-powered docs search

Built by Vishal Veera Reddy — for developers who want control over their AI tools.