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 →

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:

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

Always-on (no config): smart tool selection (server mode), RTK tool-result compression (test/git/grep/lint/build/JSON output), MCP tool dedup (drops built-in WebSearch/WebFetch when an Exa/Tavily MCP tool is present), and request bypass (Claude CLI Warmup / title-extraction calls are answered locally, never hitting a provider).

Optional terse-output mode to cut output tokens:

CAVEMAN_ENABLED=true        # off by default — nudges the model to be concise
CAVEMAN_LEVEL=lite          # lite | full | ultra

Cost tracking & model pricing

Per-request cost is computed from a model-pricing registry (LiteLLM → models.dev, cached 24h) and recorded in telemetry. Models the registry doesn't know record cost_usd=null (logged once) rather than a fabricated price. Pin prices for unknown models:

# Per-1M-token USD prices, JSON keyed by model name
MODEL_PRICE_OVERRIDES={"my-model":{"input":0.5,"output":1.5}}

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

Head-to-head against LiteLLM on the same backends (Ollama minimax-m2.5, Moonshot, Azure OpenAI), 9 scenarios across 4 feature categories. Apples-to-apples comparison is Lynkr vs LiteLLM billed tokens on the same scenario. Run with node benchmark-tier-routing.js.

Run: June 5, 2026 · Lynkr v9.3.2 · LiteLLM v1.87.1 · macOS, Apple Silicon.

Token reduction (vs LiteLLM, same model & prompt)

Mechanism	Lynkr	LiteLLM	Result
Smart tool selection (14 tools)	959 tokens · $0.0044	2,085 tokens · $0.0091	53% fewer tokens, 52% cheaper
TOON compression (60-item grep JSON)	427 tokens · $0.009	3,458 tokens · $0.018	87.6% fewer tokens, 50% cheaper

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

Semantic cache

	Tokens billed	Response time
First call (cold)	2,857	1,891ms
Second call — paraphrased, cache hit	0 (served from cache)	171ms (11× faster)

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

Tier routing

Request	Lynkr routes to	LiteLLM routes to
"What does git stash do?"	`minimax-m2.5` (local, free)	Ollama (local)
JWT vs cookies security analysis	`moonshot` (cloud — correct)	Ollama (local — wrong call)

Lynkr scores each request on 15 dimensions (token count, code complexity, reasoning markers, risk signals, agentic patterns) and escalates automatically. LiteLLM's cost-based-routing sends everything to the cheapest model regardless of complexity.

Cost projection (100,000 requests/month, same backend)

	Monthly cost	vs LiteLLM
LiteLLM	~$818	baseline
Lynkr	~$409	~50% cheaper

Based on a tool-heavy agentic session (TOON scenario). On equal footing — same provider, same model — Lynkr is cheaper due to token optimization.

→ Full benchmark report with methodology

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

License

Apache 2.0 — See LICENSE.

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

Name		Name	Last commit message	Last commit date
Latest commit History 177 Commits
.github		.github
.impeccable/live		.impeccable/live
benchmark-configs		benchmark-configs
bin		bin
config		config
docker		docker
docs		docs
documentation		documentation
examples/vercel-ai-sdk		examples/vercel-ai-sdk
headroom-sidecar		headroom-sidecar
models		models
native		native
public		public
scripts		scripts
skills/lynkr		skills/lynkr
src		src
test		test
.env.example		.env.example
.eslintrc.cjs		.eslintrc.cjs
.gitignore		.gitignore
.npmignore		.npmignore
BENCHMARK_REPORT.md		BENCHMARK_REPORT.md
CITATIONS.bib		CITATIONS.bib
DEPLOYMENT.md		DEPLOYMENT.md
Dockerfile		Dockerfile
INSTALL.md		INSTALL.md
KRONAXIS_ANALYSIS.md		KRONAXIS_ANALYSIS.md
LICENSE		LICENSE
PERFORMANCE-REPORT.md		PERFORMANCE-REPORT.md
README.md		README.md
benchmark-tier-routing.js		benchmark-tier-routing.js
docker-compose.yml		docker-compose.yml
final-test.js		final-test.js
funding.json		funding.json
index.js		index.js
install.sh		install.sh
lynkr-skill.tar.gz		lynkr-skill.tar.gz
package-lock.json		package-lock.json
package.json		package.json
test-learning-unit.js		test-learning-unit.js
test-learning.js		test-learning.js
test-parallel-agents.sh		test-parallel-agents.sh
test-parallel-direct.js		test-parallel-direct.js

Uh oh!

Folders and files

Latest commit

History

Repository files navigation

Lynkr

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

Quick Start (2 Minutes)

1. Install Lynkr

2. Configure Lynkr

3. Connect Your Tool

Common Startup Errors

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

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

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

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

Error: Connection refused or 404 Not Found

Why Lynkr?

Supported Providers

Advanced: Tier Routing (Save Even More)

Complete .env Examples

MVP: Minimal Working Setup (Ollama)

Production: Cloud with Tier Routing (OpenRouter)

Enterprise: Databricks Foundation Models

Hybrid: Local + Cloud Fallback

Common Issues & Fixes

Advanced Features

Token Optimization (60-80% savings)

Cost tracking & model pricing

Memory System (Titans-inspired)

Load Shedding & Resilience

Admin Hot-Reload (no restart needed)

Code Intelligence (Optional - Graphify)

Installation Methods

Documentation

Benchmark Results

Token reduction (vs LiteLLM, same model & prompt)

Semantic cache

Tier routing

Cost projection (100,000 requests/month, same backend)

Cost Comparison

Why Lynkr vs Alternatives

Community

License

About

Topics

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases 1

Sponsor this project

Uh oh!

Packages 0

Uh oh!

Contributors

Uh oh!

Languages

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

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

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

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

Error: `Connection refused` or `404 Not Found`

Packages