First Chat

Zero-config chat

After installing, just run:

mycellm chat

This automatically discovers available models:

1. Local node — checks localhost:8420 for loaded models
2. LAN bootstrap — reads MYCELLM_BOOTSTRAP_PEERS from config
3. Public network — falls back to api.mycellm.dev

No configuration needed for first use.

Chat REPL

  mycellm_  chat
  ────────────────────────────────────────
  Model: Qwen2.5-3B-Instruct-Q8_0
  Node:  http://10.1.1.210:8420
  Type /help for commands, /q to exit

╭──
│ What is distributed computing?
╰──

Distributed computing is a model where multiple computers work
together to solve a problem...

  Qwen2.5-3B-Instruct-Q8_0 · via node 99e58f4c · 485ms

Features:

• Streaming — tokens appear as they’re generated
• Markdown rendering — code blocks with syntax highlighting
• Per-message attribution — model name, node hash, latency
• Multi-turn — conversation context maintained
• Slash commands — manage your node inline

Slash Commands

Command	Description
/help	Show all commands
/status	Node status (name, peers, models, uptime)
/models	List available models
/credits	Credit balance (earned/spent)
/fleet	Fleet nodes with online status
/config	Runtime configuration
/use <model>	Switch to a specific model
/clear	Clear conversation history
/q	Exit

Use as an API

Any tool that speaks the OpenAI protocol works:

Python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8420/v1",
    api_key="your-key",  # optional
)
response = client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

curl

curl http://localhost:8420/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Environment variables

export OPENAI_BASE_URL=http://localhost:8420/v1
export OPENAI_API_KEY=your-key
export OPENAI_MODEL=auto