Lynkr

An LLM Gateway which optimises your token usage.

87.6% fewer tokens on JSON tool results. 53% fewer tokens on tool-heavy requests. 171ms semantic cache hits. Zero code changes.

87.6% JSON Compression

53% Tool Token Reduction

171ms Semantic Cache Hits

13+ LLM Providers

0 Code Changes Required

Numbers from a live benchmark against LiteLLM on identical workloads. See full report →

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🚀 New: Wrap Mode for AI Coding Tools

Use Lynkr's routing with your AI coding assistant — maximize your subscription value:

npm install -g lynkr


# Claude Code Pro/Max
lynkr wrap claude

Wrapping gives you:

• ✅ Tier routing (send simple tasks to free Ollama, complex to your subscription/API)
• ✅ TOON/RTK compression (87% token reduction on tool outputs)
• ✅ Semantic caching (171ms cache hits)
• ✅ 3-5x more usage from the same subscription limits
• ✅ Works with OAuth (Claude, Copilot, Cursor) or API keys (Aider, Codex)

Full wrap guide →

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Quick Start (2 Minutes)

1. Install Lynkr

npm install -g lynkr

2. Configure Lynkr

The fastest path is the interactive wizard:

lynkr init

It asks four questions — usage mode (Claude Pro/Max via wrap, or direct API keys), tier picks for SIMPLE/MEDIUM/COMPLEX/REASONING across the 12 supported providers, credentials for what you chose, and a few routing-intelligence knobs — then writes a fully-populated .env with sensible production defaults for everything else (caching, compression, policy budgets, MCP sandbox, agents, rate limiting).

Useful flags:

lynkr init --force                # overwrite an existing .env

See docs/init.md for the full wizard reference.

If you'd rather configure by hand, the manual options below still work — copy .env.example to .env and edit it directly:

Option A: Free & Local (Ollama) - Recommended for Testing

# Install Ollama first: https://ollama.com
ollama pull qwen2.5-coder:latest

Then start Lynkr:

lynkr start

3. Connect Your Tool

Cursor IDE

• Settings → Models → Override Base URL
• Set to: http://localhost:8081/v1
• API Key: any-value

Codex CLI

Edit ~/.codex/config.toml:

model_provider = "lynkr"

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

✅ Done! Your AI tool now uses your chosen provider.

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Common Startup Errors

Error: unable to determine transport target for "pino-pretty"

Problem: You're running an older version (< 9.3.0).

Solution: Update to the latest version:

npm install -g lynkr@latest

If you must use an older version, set NODE_ENV=production before starting.

Warning: Missing tier configuration: TIER_SIMPLE, TIER_MEDIUM...

This is just a warning - you can ignore it. Tier routing is optional.

To remove the warning, add to .env:

TIER_SIMPLE=ollama:qwen2.5-coder:latest
TIER_MEDIUM=ollama:qwen2.5-coder:latest
TIER_COMPLEX=ollama:qwen2.5-coder:latest
TIER_REASONING=ollama:qwen2.5-coder:latest

Warning: FALLBACK_PROVIDER='databricks' is enabled but missing credentials

Solution: Add to .env:

FALLBACK_ENABLED=false

Error: connect ECONNREFUSED ::1:11434 (Ollama)

Problem: Ollama is not running.

Solution:

ollama serve

Keep this terminal open, and start Lynkr in a new terminal.

Error: Connection refused or 404 Not Found

Problem: Lynkr is not running or wrong port.

Solution: Check Lynkr is running on the correct port:

curl http://localhost:8081/

Should return: `{"service":"Lynkr","version":"9.x.x","