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Python SDK Quickstart

Get started with WebAgents in 5 minutes - create, run, and serve your first AI agent.

Beta Software Notice

WebAgents is currently in beta stage. While the core functionality is stable and actively used, APIs and features may change. We recommend testing thoroughly before deploying to critical environments.

Installation

# Core framework
pip install webagents

# With ecosystem skills (optional)
pip install webagents[ecosystem]

Create Your First Agent

from webagents.agents.core.base_agent import BaseAgent

# Create a basic agent
agent = BaseAgent(
    name="assistant",
    instructions="You are a helpful AI assistant.",
    model="openai/gpt-4o-mini"  # Automatically creates LLM skill
)

# Run chat completion
messages = [{"role": "user", "content": "Hello! What can you help me with?"}]
response = await agent.run(messages=messages)
print(response.content)

Serve Your Agent

Deploy your agent as an OpenAI-compatible API server:

from webagents.server.core.app import create_server
import uvicorn

# Create server with your agent
server = create_server(agents=[agent])

# Run the server
uvicorn.run(server.app, host="0.0.0.0", port=8000)

Test your agent API: 

curl -X POST http://localhost:8000/assistant/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Hello!"}]}'

Environment Setup

Set up your API keys for LLM providers:

# Required for OpenAI models
export OPENAI_API_KEY="your-openai-key"

# Optional for other providers
export ANTHROPIC_API_KEY="your-anthropic-key"
export WEBAGENTS_API_KEY="your-robutler-key"

Add Skills

Enhance your agent with platform capabilities:

from webagents.agents.core.base_agent import BaseAgent
from webagents.agents.skills.robutler.nli.skill import NLISkill
from webagents.agents.skills.robutler.auth.skill import AuthSkill
from webagents.agents.skills.robutler.discovery.skill import DiscoverySkill
from webagents.agents.skills.robutler.payments.skill import PaymentSkill

# Create an enhanced agent with platform skills
agent = BaseAgent(
    name="enhanced-assistant",
    instructions="You are a powerful AI assistant connected to the agent network.",
    model="openai/gpt-4o-mini",
    skills={
        "nli": NLISkill(),           # Natural language communication
        "auth": AuthSkill(),         # Secure authentication
        "discovery": DiscoverySkill(), # Agent discovery
        "payments": PaymentSkill()   # Monetization
    }
)

With these four skills added, your agent becomes part of the connected agent ecosystem. The NLI skill enables natural language communication with other agents - your agent can delegate tasks by simply describing what it needs. The Auth skill provides secure authentication and scope-based access control for agent-to-agent interactions.

The Discovery skill acts like DNS for agents, allowing real-time discovery of other agents through intent matching without manual integration. Finally, the Payment skill enables automatic monetization with billing, credits, and micropayments handled seamlessly by the platform.

Learn More