llmkit-cli 0.1.0

Run any LLM with one config file. No framework lock-in.

llmkit

Run any LLM — local or via API — with one config file. No framework lock-in. Works on Windows, Mac, Linux.

llmkit run "explain this codebase in one sentence"

Install

# Linux
bash install.sh

# Mac
bash install.mac.sh

# Windows (PowerShell as admin)
./install.ps1

Each script installs deps, sets up Ollama if needed, and adds llmkit to your PATH.

Configure

Edit llm.yaml (or run llmkit init for a guided setup):

provider: groq          # local | openai | anthropic | groq | together | deepseek | mistral
model: llama-3.3-70b-versatile
mode: chat

For API providers, copy .env.example to .env and add your key.

Commands

# One-shot prompt
llmkit run "explain this codebase in one sentence"

# Validate config + check provider reachability
llmkit check

# Interactive wizard to create llm.yaml
llmkit init

# Pin current model to llm.lock (commit this file)
llmkit lock

# Generate a commit message from staged changes
llmkit commit

# Generate a PR title + body vs main/master
llmkit pr

# Review staged/unstaged diff for bugs
llmkit review

# Run a coding agent on a task
llmkit agent "refactor this module to use dataclasses"

# Plan only — no tools executed, just a numbered plan
llmkit agent --plan "add pagination to the API"

# Approve mode — confirm each shell command before it runs
llmkit agent --approve "run the test suite and fix any failures"

Examples

# Chat
python examples/chat.py
node examples/chat.js          # all providers including Anthropic

# Streaming
python examples/stream.py
node examples/stream.js        # all providers including Anthropic

# Function calling / tools
python examples/tools.py

# Vision (image input)
python examples/vision.py

# Multi-round conversation
python examples/multiround.py

# Embeddings + cosine similarity
python examples/embed.py

# Coding agent
python examples/agent.py

# MCP agent (connects to MCP servers defined in llm.yaml)
python examples/mcp_agent.py

Local models (via Ollama)

Model	Config
Llama 4	model: llama4
Qwen 3	model: qwen3
DeepSeek R1	model: deepseek-r1
Mistral	model: mistral
Phi-4	model: phi4
Gemma 3	model: gemma3

API providers

Provider	Env key	Fast cheap model
OpenAI	OPENAI_API_KEY	gpt-4o-mini
Anthropic	ANTHROPIC_API_KEY	claude-3-5-haiku-20241022
Groq	GROQ_API_KEY	llama-3.1-8b-instant
Together	TOGETHER_API_KEY	meta-llama/Llama-3.3-70B-Instruct-Turbo
DeepSeek	DEEPSEEK_API_KEY	deepseek-chat
Mistral	MISTRAL_API_KEY	mistral-small-latest

5-minute team setup

# 1 — clone
git clone https://github.com/your-org/llmkit
cd llmkit

# 2 — install deps + register llmkit command
bash install.sh          # Mac: bash install.mac.sh  |  Windows: ./install.ps1

# 3 — set one API key (Groq free tier works)
echo "GROQ_API_KEY=your_key_here" > .env

# 4 — verify everything is wired up
llmkit check

# 5 — run your first prompt
llmkit run "explain this codebase in one sentence"

No server. No IDE extension. No code to write. Switch providers by editing one line in llm.yaml.

llm.lock

Run llmkit lock to pin your model runtime. Commit llm.lock alongside your code:

# Auto-generated — commit this file to pin your model runtime
locked_at: "2026-06-26T12:00:00Z"
provider: groq
model: llama-3.3-70b-versatile
mode: chat

Same idea as package-lock.json — reproducible environments, no surprise model swaps between teammates.

Switch providers

Change one line in llm.yaml, run llmkit check, done:

# was: provider: groq
provider: anthropic
model: claude-3-5-haiku-20241022

No code changes. No redeploy. Works in CI too.