Lynkr

The AI coding proxy that compresses tokens before they hit the model.

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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Quick Start (2 Minutes)

1. Install Lynkr

npm install -g lynkr

2. Configure Lynkr

First run creates a .env file. Edit it with your provider settings.

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

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

Create/edit .env in your project directory:

# Provider
MODEL_PROVIDER=ollama
FALLBACK_ENABLED=false

# Ollama Configuration
OLLAMA_ENDPOINT=http://localhost:11434
OLLAMA_MODEL=qwen2.5-coder:latest

# Server
PORT=8081

# Optional: Limits (remove for unlimited)
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100

# Disable overly strict command filtering
POLICY_SAFE_COMMANDS_ENABLED=false

Option B: Cloud (OpenRouter) - Recommended for Production

# Get API key from https://openrouter.ai

Create/edit .env:

# Provider
MODEL_PROVIDER=openrouter
OPENROUTER_API_KEY=sk-or-v1-your-key-here
FALLBACK_ENABLED=false

# Server
PORT=8081

# Optional: Limits (remove for unlimited)
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100

# Optional: Enable caching
PROMPT_CACHE_ENABLED=true
SEMANTIC_CACHE_ENABLED=true

Option C: Enterprise (AWS Bedrock)

Create/edit .env:

# Provider
MODEL_PROVIDER=bedrock
AWS_BEDROCK_API_KEY=your-aws-key
AWS_BEDROCK_MODEL_ID=anthropic.claude-3-5-sonnet-20241022-v2:0
FALLBACK_ENABLED=false

# Server
PORT=8081

# Optional: Limits (remove for unlimited)
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100

Option D: Enterprise (Databricks)

Create/edit .env:

# Provider
MODEL_PROVIDER=databricks
DATABRICKS_API_BASE=https://your-workspace.cloud.databricks.com
DATABRICKS_API_KEY=your-token
FALLBACK_ENABLED=false

# Server
PORT=8081

# Optional: Limits (remove for unlimited)
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100

Then start Lynkr:

lynkr start

3. Connect Your Tool

Claude Code

Windows (Command Prompt):

set ANTHROPIC_BASE_URL=http://localhost:8081
set ANTHROPIC_API_KEY=dummy
claude "write a hello world in python"

Linux/macOS:

export ANTHROPIC_BASE_URL=http://localhost:8081
export ANTHROPIC_API_KEY=dummy
claude "write a hello world in python"

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.

---

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","status":"running"}

---

Why Lynkr?

AI coding tools lock you into one provider and send every token raw. Lynkr breaks both locks.

Claude Code / Cursor / Codex / Cline / Continue
                    ↓
                  Lynkr
          ┌─────────────────────┐
          │  Strip unused tools  │  ← 53% fewer tokens on tool calls
          │  Compress JSON blobs │  ← 87.6% on large tool results
          │  Semantic cache      │  ← 171ms hits, 0 tokens billed
          │  Route by complexity │  ← cheap model for simple, cloud for hard
          └─────────────────────┘
                    ↓
    Ollama | Bedrock | Azure | Moonshot | OpenRouter | OpenAI

What you get:

• ✅ 53% fewer tokens on tool-heavy requests (Claude Code, Cursor sessions)
• ✅ 87.6% compression on large JSON tool results (grep, file reads, test output)
• ✅ Semantic cache serves repeated queries in 171ms with 0 tokens billed
• ✅ Automatic tier routing — simple questions go to cheap models, complex ones escalate
• ✅ Route through your company's infrastructure (Databricks, Azure, Bedrock)
• ✅ Zero code changes — just change one environment variable

---

Supported Providers

Provider	Type	Example Models	Cost
Ollama	Local	qwen2.5-coder, deepseek-coder, llama3	Free
llama.cpp	Local	Any GGUF model	Free
LM Studio	Local	Local models with GUI	Free
OpenRouter	Cloud	GPT-4o, Claude 3.5, Llama 3, Gemini	$
AWS Bedrock	Cloud	Claude, Llama, Mistral, Titan	$$
Databricks	Cloud	Claude Sonnet 4.5, Opus 4.6	$$$
Azure OpenAI	Cloud	GPT-4o, o1, o3	$$$
Azure Anthropic	Cloud	Claude Sonnet, Opus	$$$
OpenAI	Cloud	GPT-4o, o3-mini	$$$
DeepSeek	Cloud	DeepSeek R1, Reasoner	$

4 local providers for 100% offline, free usage. 10+ cloud providers for scale.

---

Advanced: Tier Routing (Save Even More)

Route different request types to different models automatically:

# .env file
MODEL_PROVIDER=ollama
FALLBACK_ENABLED=false

# Use small/fast models for simple tasks
TIER_SIMPLE=ollama:qwen2.5:3b

# Use medium models for normal coding
TIER_MEDIUM=ollama:qwen2.5:7b

# Use powerful models for complex architecture
TIER_COMPLEX=ollama:deepseek-r1:14b
TIER_REASONING=ollama:deepseek-r1:14b

# Optional: Limits (remove for unlimited) for long conversations
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100

Lynkr analyzes each request and routes it to the appropriate tier. Simple questions use fast models. Complex refactoring uses powerful models.

Result: 70-90% of requests use cheaper/faster models. Only hard problems hit expensive models.

---

Complete .env Examples

MVP: Minimal Working Setup (Ollama)

Copy-paste ready configuration for immediate use:

# .env - Minimal Ollama Setup

# ============================================
# REQUIRED: Provider Configuration
# ============================================
MODEL_PROVIDER=ollama
FALLBACK_ENABLED=false

# ============================================
# REQUIRED: Ollama Settings
# ============================================
OLLAMA_ENDPOINT=http://localhost:11434
OLLAMA_MODEL=qwen2.5-coder:latest

# ============================================
# REQUIRED: Server Configuration
# ============================================
PORT=8081
HOST=0.0.0.0

# ============================================
# REQUIRED: Claude Code/Cursor Compatibility
# ============================================
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100
POLICY_SAFE_COMMANDS_ENABLED=false

# ============================================
# OPTIONAL: Performance (Recommended)
# ============================================
LOG_LEVEL=warn
LOAD_SHEDDING_ENABLED=true
LOAD_SHEDDING_HEAP_THRESHOLD=0.85

Steps:

1. Install Ollama: curl -fsSL https://ollama.com/install.sh | sh
2. Pull model: ollama pull qwen2.5-coder:latest
3. Copy above to .env in your project directory
4. Run: lynkr start

---

Production: Cloud with Tier Routing (OpenRouter)

Optimized for cost savings with smart routing:

# .env - Production OpenRouter Setup

# ============================================
# REQUIRED: Provider Configuration
# ============================================
MODEL_PROVIDER=openrouter
OPENROUTER_API_KEY=sk-or-v1-your-key-here
FALLBACK_ENABLED=false

# ============================================
# REQUIRED: Server Configuration
# ============================================
PORT=8081
HOST=0.0.0.0

# ============================================
# TIER ROUTING: Smart Cost Optimization
# ============================================
# Simple queries → Cheap/fast model
TIER_SIMPLE=openrouter:google/gemini-flash-1.5

# Normal coding → Balanced model
TIER_MEDIUM=openrouter:anthropic/claude-3.5-sonnet

# Complex refactoring → Powerful model
TIER_COMPLEX=openrouter:anthropic/claude-opus-4

# Deep reasoning → Most capable model
TIER_REASONING=openrouter:anthropic/claude-opus-4

# ============================================
# REQUIRED: Claude Code/Cursor Compatibility
# ============================================
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100
POLICY_SAFE_COMMANDS_ENABLED=false

# ============================================
# OPTIONAL: Token Optimization (60-80% savings)
# ============================================
PROMPT_CACHE_ENABLED=true
SEMANTIC_CACHE_ENABLED=true
SEMANTIC_CACHE_THRESHOLD=0.95
TOOL_INJECTION_ENABLED=false

# ============================================
# OPTIONAL: Performance Tuning
# ============================================
LOG_LEVEL=warn
LOAD_SHEDDING_ENABLED=true
LOAD_SHEDDING_HEAP_THRESHOLD=0.85

Expected savings: 70-90% of requests use Gemini Flash ($). Only 10-30% use Claude Opus ($$$).

---

Enterprise: Databricks Foundation Models

For teams using Databricks Model Serving:

# .env - Enterprise Databricks Setup

# ============================================
# REQUIRED: Provider Configuration
# ============================================
MODEL_PROVIDER=databricks
DATABRICKS_API_BASE=https://your-workspace.cloud.databricks.com
DATABRICKS_API_KEY=dapi1234567890abcdef
FALLBACK_ENABLED=false

# ============================================
# REQUIRED: Model Configuration
# ============================================
# Option 1: Single model (no tier routing)
DATABRICKS_MODEL=databricks-meta-llama-3-1-405b-instruct

# Option 2: Tier routing (comment out above, uncomment below)
# TIER_SIMPLE=databricks:databricks-meta-llama-3-1-70b-instruct
# TIER_MEDIUM=databricks:databricks-claude-sonnet-4-5
# TIER_COMPLEX=databricks:databricks-claude-opus-4-6
# TIER_REASONING=databricks:databricks-claude-opus-4-6

# ============================================
# REQUIRED: Server Configuration
# ============================================
PORT=8081
HOST=0.0.0.0

# ============================================
# REQUIRED: Claude Code/Cursor Compatibility
# ============================================
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100
POLICY_SAFE_COMMANDS_ENABLED=false

# ============================================
# OPTIONAL: Enterprise Features
# ============================================
LOG_LEVEL=info
LOAD_SHEDDING_ENABLED=true
LOAD_SHEDDING_HEAP_THRESHOLD=0.85

# Optional: Metrics for monitoring
# PROMETHEUS_METRICS_ENABLED=true

---

Hybrid: Local + Cloud Fallback

Use free Ollama, fallback to cloud when needed:

# .env - Hybrid Setup (Advanced)

# ============================================
# PRIMARY: Local Ollama
# ============================================
MODEL_PROVIDER=ollama
OLLAMA_ENDPOINT=http://localhost:11434
OLLAMA_MODEL=qwen2.5-coder:latest

# ============================================
# FALLBACK: Cloud Provider
# ============================================
FALLBACK_ENABLED=true
FALLBACK_PROVIDER=openrouter
OPENROUTER_API_KEY=sk-or-v1-your-key-here

# ============================================
# TIER ROUTING: Mix Local + Cloud
# ============================================
TIER_SIMPLE=ollama:qwen2.5:3b
TIER_MEDIUM=ollama:qwen2.5:7b
TIER_COMPLEX=openrouter:anthropic/claude-3.5-sonnet
TIER_REASONING=openrouter:anthropic/claude-opus-4

# ============================================
# REQUIRED: Server Configuration
# ============================================
PORT=8081
HOST=0.0.0.0

# ============================================
# REQUIRED: Claude Code/Cursor Compatibility
# ============================================
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100
POLICY_SAFE_COMMANDS_ENABLED=false

# ============================================
# OPTIONAL: Performance
# ============================================
LOG_LEVEL=warn
LOAD_SHEDDING_ENABLED=true

Best of both worlds: 80% of requests stay local (free). Complex tasks use cloud (paid).

---

Common Issues & Fixes

Issue	Solution
"Service temporarily overloaded"	Ollama model too large for RAM. Use smaller model or increase --max-old-space-size
"Route not found: HEAD /"	Ignore - harmless health check from Claude Code
"Hallucinated tool calls"	Normal - Lynkr automatically filters invalid tools
"Safe Command DSL blocked"	Add POLICY_SAFE_COMMANDS_ENABLED=false to .env
"spawn graphify ENOENT"	Optional feature. Set CODE_GRAPH_ENABLED=false in .env (see Advanced Features section for installation)
Slow first request (20+ sec)	Ollama loading model into memory. Add OLLAMA_KEEP_ALIVE=30m in Ollama config
No response after N turns	Remove POLICY_MAX_STEPS and POLICY_MAX_TOOL_CALLS from .env (unlimited by default in v9.3.0+)

---

Advanced Features

Token Optimization (60-80% savings)

# Enable all optimizations
PROMPT_CACHE_ENABLED=true
SEMANTIC_CACHE_ENABLED=true
TOOL_INJECTION_ENABLED=false
CODE_MODE_ENABLED=true

Memory System (Titans-inspired)

MEMORY_ENABLED=true
MEMORY_TTL=3600000  # 1 hour

Load Shedding & Resilience

LOAD_SHEDDING_ENABLED=true
LOAD_SHEDDING_HEAP_THRESHOLD=0.85

Admin Hot-Reload (no restart needed)

curl -X POST http://localhost:8081/v1/admin/reload

Code Intelligence (Optional - Graphify)

Graphify provides AST-based code analysis for smarter routing decisions.

Installation (Rust required):

# Install Rust if not already installed
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env

# Build and install graphify
git clone https://github.com/safishamsi/graphify
cd graphify
cargo build --release
sudo cp target/release/graphify /usr/local/bin/

# Verify installation
graphify --version

Enable in .env:

CODE_GRAPH_ENABLED=true
CODE_GRAPH_WORKSPACE=/path/to/your/project  # Optional, defaults to cwd

Features:

• AST-based complexity scoring
• Structural code analysis (19 languages supported)
• Enhanced routing decisions based on code structure

Note: Graphify is completely optional. If not installed, Lynkr falls back to simpler complexity analysis.

---

Installation Methods

NPM (recommended)

npm install -g lynkr

One-line installer

curl -fsSL https://raw.githubusercontent.com/Fast-Editor/Lynkr/main/install.sh | bash

Homebrew

brew tap fast-editor/lynkr
brew install lynkr

Docker

git clone https://github.com/Fast-Editor/Lynkr.git
cd Lynkr
docker-compose up -d

From source

git clone https://github.com/Fast-Editor/Lynkr.git
cd Lynkr
npm install
cp .env.example .env
npm start

---

Documentation

Guide	Description
Installation	All installation methods
Provider Setup	Configuration for all 12+ providers
Claude Code	Claude Code CLI integration
Cursor IDE	Cursor setup + troubleshooting
Codex CLI	Codex configuration
Tier Routing	Smart model routing by complexity
Token Optimization	60-80% cost reduction
Troubleshooting	Common issues and solutions
API Reference	REST API endpoints
Production	Enterprise deployment

---

Benchmark Results

Measured on real agentic coding workloads (Claude Code / Cursor sessions) with Ollama, Moonshot, and Azure OpenAI backends. Run with node benchmark-tier-routing.js.

Token compression

Scenario	Tokens without Lynkr	Tokens with Lynkr	Reduction
14-tool request (read task)	1,042	547	47%
14-tool request (write task)	1,043	412	60%
Large JSON grep result (60 items)	3,458	427	87.6%

Lynkr strips irrelevant tool schemas before forwarding (smart tool selection) and binary-compresses large JSON tool results (TOON) — both happen in-process with no added latency.

Semantic cache

	Tokens billed	Response time
First call (cold)	2,857	1,891ms
Second call — paraphrased, cache hit	0	171ms

Near-identical prompts return cached responses in 171ms. Zero tokens billed on a cache hit.

Tier routing

Request	Routed to
"What does git stash do?"	SIMPLE → local model (free)
JWT vs cookies security analysis	COMPLEX → cloud model (correct)

Lynkr scores each request on 15 dimensions (token count, code complexity, reasoning markers, risk signals, agentic patterns) and routes automatically. No caller changes needed.

→ Full benchmark report with methodology

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Cost Comparison

Scenario	Direct Anthropic	Lynkr + Ollama	Lynkr + OpenRouter
Daily coding (8h)	$10-30/day	$0 (free)	$2-8/day
Monthly (heavy use)	$300-900	$0	$60-240

With tier routing + token optimization: additional 50-87% savings on cloud providers depending on workload.

---

Why Lynkr vs Alternatives

Feature	Lynkr	LiteLLM	OpenRouter	PortKey
Setup	npm install -g lynkr	Python + Docker + Postgres	Account signup	Docker stack
Claude Code native	✅ Drop-in	⚠️ Requires config	❌	⚠️ Partial
Cursor native	✅ Drop-in	⚠️ Partial	❌	⚠️ Partial
Local models	Ollama, llama.cpp, LM Studio	Ollama only	❌	❌
Automatic tier routing	✅ 15-dimension scorer	⚠️ Cost-only	❌	❌ Manual metadata
TOON JSON compression	✅ up to 87.6%	❌	❌	❌
Smart tool selection	✅ up to 60% token reduction	❌	❌	❌
Semantic cache	✅ 171ms hits, 0 tokens	❌	❌	✅ Prompt cache only
Long-term memory	✅ SQLite, per-session	❌	❌	❌
MCP integration	✅ + Code Mode (96% reduction)	❌	❌	❌
Self-hosted	✅ Node.js only	✅ Python stack	❌ SaaS	✅ Docker
Dependencies	Node.js 20+	Python, Prisma, PostgreSQL	None	Docker, Python

Lynkr's edge: Purpose-built for AI coding tools. Compresses tokens before they reach the model — not just after. Zero-config for Claude Code, Cursor, and Codex. Installs in one command.

---

Community

• GitHub Discussions — Ask questions
• Report Issues — Bug reports
• NPM Package — Official releases
• DeepWiki — AI-powered docs

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License

Apache 2.0 — See LICENSE.

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