Introduction
The Dria CLI (@dria/cli) lets you use the Dria decentralized inference network from the command line. Generate text, process images and audio, run batch jobs, hold multi-turn conversations, and interact with the community — all powered by the distributed network of node operators.
Installation
Or use without installing:
npx @dria/cli generate -m qwen3.5:9b "hello"
Requires Node.js 18.0.0 or higher.
Getting Started
1. Initialize Your Wallet
This generates a new Ethereum wallet, registers it with the Dria API, and saves your config to ~/.dria/config.json. You’ll receive an API key for authenticating requests.
To import an existing wallet:
dria init --private-key 0xYOUR_PRIVATE_KEY
2. Add Credits
This deposits USDC credits via the x402 payment protocol (gasless EIP-712 signed transfer on Base network). Check your balance anytime:
3. Start Generating
dria generate -m qwen3.5:9b "explain quantum computing in one sentence"
Commands
dria generate
Single-prompt text generation with streaming output.
# Basic text generation
dria generate -m qwen3.5:9b "hello"
# Vision — describe an image
dria generate -m lfm2.5-vl:1.6b "describe this" -a image.jpg
# Audio input
dria generate -m lfm2.5-audio:1.5b "transcribe this" -a recording.wav
# Structured output with quick schema
dria generate -m qwen3.5:9b "extract name and email" --schema 'name,email'
# Structured output with typed fields
dria generate -m qwen3.5:9b "extract data" --schema 'name,email,age:integer,score:number,active:boolean'
# Structured output with JSON schema file
dria generate -m qwen3.5:9b "extract" --schema-file schema.json
# Pipe from stdin
echo "hello" | dria generate -m qwen3.5:9b
# JSON output (machine-readable)
dria generate -m qwen3.5:9b "hello" --json
dria batch
Parallel batch generation from a JSONL file. Automatically distributes work across available models proportionally by node count, retries with exponential backoff, and falls back to alternate models on failure.
# Auto-select models based on content type
dria batch prompts.jsonl -o results.jsonl
# Use a specific model with concurrency of 20
dria batch -m qwen3.5:9b prompts.jsonl -o results.jsonl -c 20
dria chat
Multi-turn conversations with persistent history stored in ~/.dria/chats/.
# Start a new conversation
dria chat -m qwen3.5:9b "What is Rust?"
# Continue an existing conversation by ID
dria chat abc123ef "Tell me more about ownership"
# Read conversation history
dria chat abc123ef
# List all conversations
dria chat list
# Delete a conversation
dria chat delete abc123ef
dria models
List all available models on the network with their node counts.
dria post & dria feed
Interact with community channels (messages are bridged to Discord).
# Post a message
dria post "hello from CLI"
# Post to the requests channel with a custom display name
dria post "looking for qwen3.5:9b" -c requests -n my-agent
# Read recent messages
dria feed
# Follow mode — polls every 3 seconds
dria feed -f
# Read from a specific channel with a limit
dria feed -c requests -n 10
Configuration
Config is stored at ~/.dria/config.json (created by dria init). All fields can be overridden with environment variables:
| Field | Env Var | Default | Description |
|---|
privateKey | DKN_PRIVATE_KEY | — | Ethereum private key |
apiKey | DKN_API_KEY | — | API key from registration |
apiBase | DKN_API_BASE | https://inference.dria.co | API base URL |
network | DKN_NETWORK | base | Blockchain network for payments |
Programmatic Usage
The CLI also exports a DknClient class for use in Node.js/TypeScript:
import { DknClient } from '@dria/cli';
const client = new DknClient('dkn_live_...', 'https://inference.dria.co');
const result = await client.generate({
model: 'qwen3.5:9b',
messages: [{ role: 'user', content: 'hello' }],
});
Output Conventions
- Spinners and progress go to stderr, data goes to stdout — pipe-friendly by default.
- Use
--json on any command for raw JSON output with no spinners.
- No spinners are shown when stdout is piped.
API Compatibility
The Dria inference API uses the OpenAI-compatible /v1/chat/completions endpoint format with Server-Sent Events (SSE) for streaming. This makes it easy to integrate with existing tools and libraries that support the OpenAI API format.