Build and deploy autonomous AI agents using the OpenServ SDK (@openserv-labs/sdk). IMPORTANT - Always read the companion skill openserv-client alongside this skill, as both packages are required to build and run agents. openserv-client covers the full Platform API for multi-agent workflows and ERC-8004 on-chain identity. Read reference.md for the full API reference.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Build and deploy autonomous AI agents using the OpenServ SDK (@openserv-labs/sdk). IMPORTANT - Always read the companion skill openserv-client alongside this skill, as both packages are required to build and run agents. openserv-client covers the full Platform API for multi-agent workflows and ERC-8004 on-chain identity. Read reference.md for the full API reference.
OpenServ Agent SDK
Build and deploy custom AI agents for the OpenServ platform using TypeScript.
Why build an agent?
An OpenServ agent is a service that runs your code and exposes it on the OpenServ platform—so it can be triggered by workflows, other agents, or paid calls (e.g. x402). The platform sends tasks to your agent; your agent runs your capabilities (APIs, tools, file handling) and returns results. You don't have to use an LLM—e.g. it could be a static API that just returns data. If you need LLM reasoning, you have two options: use runless capabilities (the platform handles the AI call for you—no API key needed) or use generate() (delegates the LLM call to the platform); alternatively, bring your own LLM (any provider you have access to).
How it works (the flow)
Define your agent — System prompt plus capabilities. Capabilities come in two flavors: runnable (with a Zod schema and a run handler) and runless (just a name and description—the platform handles the AI call automatically). You can also use generate() inside runnable capabilities to delegate LLM calls to the platform.
Register with the platform — You need an account on the platform; often the easiest way is to let provision() create one for you automatically by creating a wallet and signing up with it (that account is reused on later runs). Call provision() (from @openserv-labs/client): it creates or reuses a wallet, registers the agent, and writes API key and auth token into your env (or you pass to bind them directly). In development you can skip setting an endpoint URL; the SDK can use a built-in tunnel to the platform.
agent.instance
Start the agent — Call run(agent). The agent listens for tasks, runs your capabilities (and your LLM if you use one), and responds. Use reference.md and troubleshooting.md for details; examples/ has full runnable code.
What your agent can do
Runless Capabilities — Just a name and description. The platform handles the AI call automatically—no API key, no run() function needed. Optionally define inputSchema and outputSchema for structured I/O.
Runnable Capabilities — The tools your agent can run (e.g. search, transform data, call APIs). Each has a name, description, inputSchema, and run() function.
generate() method — Delegate LLM calls to the platform from inside any runnable capability. No API key needed—the platform performs the call and records usage. Supports text and structured output.
Task context — When running in a task, the agent can attach logs and uploads to that task via methods like addLogToTask() and uploadFile().
Multi-agent workflows — Your agent can be part of workflows with other agents; see the openserv-client skill for the Platform API, workflows, and ERC-8004 on-chain identity.
Note:openai is only needed if you use the process() method for direct OpenAI calls. Most agents don't need it—use runless capabilities or generate() instead.
Minimal Agent
See examples/basic-agent.ts for a complete runnable example.
The pattern is simple:
Create an Agent with a system prompt
Add capabilities with agent.addCapability()
Call provision() to register on the platform (pass agent.instance to bind credentials)
npm init -y && npm pkg settype=module
npm i @openserv-labs/sdk @openserv-labs/client dotenv zod
npm i -D @types/node tsx typescript
Note: The project must use "type": "module" in package.json. Add a "dev": "tsx src/agent.ts" script for local development. Only install openai if you use the process() method for direct OpenAI calls.
.env
Most agents don't need any LLM API key—use runless capabilities or generate() and the platform handles LLM calls for you. If you use process() for direct OpenAI calls, set OPENAI_API_KEY. The rest is filled by provision().
# Only needed if you use process() for direct OpenAI calls:
# OPENAI_API_KEY=your-openai-key
# ANTHROPIC_API_KEY=your_anthropic_key # If using Claude directly
# Required for deploy (get from OpenServ platform dashboard)
OPENSERV_USER_API_KEY=your-user-api-key
# Auto-populated by provision():
WALLET_PRIVATE_KEY=
OPENSERV_API_KEY=
OPENSERV_AUTH_TOKEN=
PORT=7378
# Production: skip tunnel and run HTTP server only
# DISABLE_TUNNEL=true
# Force tunnel even when endpointUrl is set
# FORCE_TUNNEL=true
Capabilities
Capabilities come in two flavors:
Runless Capabilities (recommended for most use cases)
Runless capabilities don't need a run function—the platform handles the AI call automatically. Just provide a name and description:
// Simplest form — just name + description
agent.addCapability({
name: 'generate_haiku',
description: 'Generate a haiku poem (5-7-5 syllables) about the given input.'
})
// With custom input schema
agent.addCapability({
name: 'translate',
description: 'Translate text to the target language.',
inputSchema: z.object({
text: z.string(),
targetLanguage: z.string()
})
})
// With structured output
agent.addCapability({
name: 'analyze_sentiment',
description: 'Analyze the sentiment of the given text.',
outputSchema: z.object({
sentiment: z.enum(['positive', 'negative', 'neutral']),
confidence: z.number().min(0).max(1)
})
})
No run function — the platform performs the LLM call
No API key needed — the platform handles it
inputSchema is optional — defaults to z.object({ input: z.string() }) if omitted
outputSchema is optional — define it for structured output from the platform
See examples/haiku-poet-agent.ts for a complete runless example.
Runnable Capabilities
Runnable capabilities have a run function for custom logic. Each requires:
name - Unique identifier
description - What it does (helps AI decide when to use it)
inputSchema - Zod schema defining parameters
run - Function returning a string
agent.addCapability({
name: 'greet',
description: 'Greet a user by name',
inputSchema: z.object({ name: z.string() }),
asyncrun({ args }) {
return`Hello, ${args.name}!`
}
})
See examples/capability-example.ts for basic capabilities.
Note: The schema property still works as an alias for inputSchema but is deprecated. Use inputSchema for new code.
Using Agent Methods
Access this in capabilities to use agent methods like addLogToTask(), uploadFile(), generate(), etc.
See examples/capability-with-agent-methods.ts for logging and file upload patterns.
Agent Methods
generate() — Platform-Delegated LLM Calls
The generate() method lets you make LLM calls without any API key. The platform performs the call and records usage to the workspace.
// Text generationconst poem = awaitthis.generate({
prompt: `Write a short poem about ${args.topic}`,
action
})
// Structured output (returns validated object matching the schema)const metadata = awaitthis.generate({
prompt: `Suggest a title and 3 tags for: ${poem}`,
outputSchema: z.object({
title: z.string(),
tags: z.array(z.string()).length(3)
}),
action
})
// With conversation historyconst followUp = awaitthis.generate({
prompt: 'Suggest a related topic.',
messages, // conversation history from run function
action
})
Parameters:
prompt (string) — The prompt for the LLM
action (ActionSchema) — The action context (passed into your run function)
outputSchema (Zod schema, optional) — When provided, returns a validated structured output
messages (array, optional) — Conversation history for multi-turn generation
The action parameter is required because it identifies the workspace/task for billing. Use it inside runnable capabilities where action is available from the run function arguments.
The action parameter in capabilities is a union type — task only exists on the 'do-task' variant. Always narrow with a type guard before accessing action.task:
asyncrun({ args, action }) {
// action.task does NOT exist on all action types — you must narrow firstif (action?.type === 'do-task' && action.task) {
const { workspace, task } = action
workspace.id// Workspace ID
workspace.goal// Workspace goal
task.id// Task ID
task.description// Task description
task.input// Task input
action.me.id// Current agent ID
}
}
Do not extract action?.task?.id before the type guard — TypeScript will error with Property 'task' does not exist on type 'ActionSchema'.
Workflow Name & Goal
The workflow object in provision() requires two important properties:
name (string) - This becomes the agent name in ERC-8004. Make it polished, punchy, and memorable — this is the public-facing brand name users see. Think product launch, not variable name. Examples: 'Crypto Alpha Scanner', 'AI Video Studio', 'Instant Blog Machine'.
goal (string, required) - A detailed description of what the workflow accomplishes. Must be descriptive and thorough — short or vague goals will cause API calls to fail. Write at least a full sentence explaining the workflow's purpose.
workflow: {
name: 'Haiku Poetry Generator', // Polished display name — the ERC-8004 agent name users seegoal: 'Transform any theme or emotion into a beautiful traditional 5-7-5 haiku poem using AI',
trigger: triggers.x402({ ... }),
task: { description: 'Generate a haiku about the given topic' }
}
Important: Always set timeout to at least 600 seconds (10 minutes) for webhook and x402 triggers. Agents often take significant time to process requests — especially when performing research, content generation, or other complex tasks. A low timeout will cause premature failures. For multi-agent pipelines with many sequential steps, consider 900 seconds or more.
API Keys: Agent vs User
provision() creates two types of credentials. They are not interchangeable:
OPENSERV_API_KEY (Agent API key) — Used internally by the SDK to authenticate when receiving tasks. Set automatically by provision() when you pass agent.instance. Do not use this key with PlatformClient.
WALLET_PRIVATE_KEY / OPENSERV_USER_API_KEY (User credentials) — Used with PlatformClient to make management calls (list tasks, debug workflows, etc.). Authenticate with client.authenticate(walletKey) or pass apiKey to the constructor.
If you need to debug tasks or inspect workflows, use wallet authentication:
Starts the agent HTTP server (port 7378, with automatic fallback)
Connects via WebSocket to agents-proxy.openserv.ai
Routes platform requests to your local machine
No need for ngrok or other tunneling tools - run() handles this seamlessly. Just call run(agent) and your local agent is accessible to the platform.
Deploy to OpenServ Cloud
Deploy your agent to the OpenServ managed cloud with a single command:
npx @openserv-labs/client deploy [path]
Where [path] is the directory containing your agent code (defaults to current directory).
Prerequisites
OPENSERV_USER_API_KEY in .env — Your .env file in the agent directory must contain OPENSERV_USER_API_KEY. Get this from the OpenServ platform dashboard. This key is required by the deploy command (and by PlatformClient for management operations). Note that provision() itself does not need this key — it creates its own wallet, authenticates, and persists credentials to .openserv.json independently. The user API key is also saved to .openserv.json after provision if present.
Call provision() first — provision() must run at least once before deploying. It registers the agent on the platform and persists credentials to .openserv.json. The recommended agent template already calls provision() before run(agent) in main(), so starting the agent locally (npm run dev or npx tsx src/agent.ts) is enough. If your code does not call provision() (e.g., you only call run(agent) in a custom script), you must add an explicit provision() call and run it once before deploying.
Deploy Workflow
1. Set OPENSERV_USER_API_KEY in .env
2. Call provision() during local startup (npm run dev) — registers the agent and writes .openserv.json
3. npx @openserv-labs/client deploy .
Self-Hosted Production
When deploying to a hosting provider like Cloud Run, set DISABLE_TUNNEL=true as an environment variable. This makes run() start only the HTTP server without opening a WebSocket tunnel — the platform reaches your agent directly at its public URL.
awaitprovision({
agent: {
name: 'my-agent',
description: '...',
endpointUrl: 'https://my-agent.example.com'// Required for production
},
workflow: {
name: 'Lightning Service Pro',
goal: 'Describe in detail what this workflow does — be thorough, vague goals cause failures',
trigger: triggers.webhook({ waitForCompletion: true, timeout: 600 }),
task: { description: 'Process incoming requests' }
}
})
// With DISABLE_TUNNEL=true, run() starts only the HTTP server (no tunnel)awaitrun(agent)
ERC-8004: On-Chain Agent Identity
After provisioning, register your agent on-chain for discoverability via the Identity Registry.
Requires ETH on Base. Registration calls register() on the ERC-8004 contract on Base mainnet (chain 8453), which costs gas. The wallet created by provision() starts with a zero balance. Fund it with a small amount of ETH on Base before the first registration attempt. The wallet address is logged during provisioning (Created new wallet: 0x...).
Always wrap in try/catch so a registration failure (e.g. unfunded wallet) doesn't prevent run(agent) from starting.
Two important patterns:
Use dotenv programmatically (not import 'dotenv/config') so you can reload .env after provision() writes WALLET_PRIVATE_KEY.
Call dotenv.config({ override: true }) after provision() to pick up the freshly written key before ERC-8004 registration.
First run mints a new identity NFT. Re-runs update the URI — agent ID stays the same.
Never clear the wallet state unless you intentionally want a new agent ID. To update metadata, just re-run.
Default chain: Base mainnet (8453). Pass chainId / rpcUrl for others.
See openserv-client skill for the full ERC-8004 API reference and troubleshooting.
DO NOT USE
this.process() inside capabilities — Legacy method requiring an OpenAI API key. Use this.generate() instead (platform-delegated, no key needed), or use runless capabilities
doTask override — The SDK handles task execution automatically
this.completeTask() — Task completion is handled by the Runtime API
Updating This Skill
This skill may be outdated. The OpenServ team ships updates regularly—new features, API changes, and bug fixes won't be reflected here until you update. If something isn't working as expected, or you want to make sure you have the latest information, update the skill before proceeding.
# Check if updates are available
npx skills check
# Update all installed skills to latest versions
npx skills update
Or reinstall the OpenServ skills directly:
npx skills add openserv-labs/skills
Related Skills
openserv-client - Full Platform Client API reference
