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Get Started with Python

This guide will walk you through creating and deploying your first AI agent using the aiXplain Python SDK. You'll learn how to specify a Large Language Model (LLM), equip the agent with tools, and integrate the agent into your application.

Setup​

Set an aiXplain API Key​

Generate an API key from the Integrations page. Once generated, you can use it in one of two ways:

  1. Export it as an environment variable in your terminal or
  2. Set it in your Python project using either environment variables or a .env file with python-dotenv.
export AIXPLAIN_API_KEY="<KEY>"
import os
os.environ["AIXPLAIN_API_KEY"] = "<KEY>"

Install the aiXplain SDK​

pip install aixplain

Create an AI Agent​

Agents are defined using a name, description, and instructions.

from aixplain.factories import AgentFactory

post_agent = AgentFactory.create(
    name="Post Generating Agent",
    description="An agent that creates social media posts based on a given topic.",
    instructions="You generate engaging, platform-ready social media posts complete with suggested images. Keep the tone informative and audience-focused."
)

Only the instructions field influences an agent's behavior. The name and description are metadata and do not affect execution.

note

aiXplain agents are currently optimized for OpenAI (GPT-4o Mini default) and Meta models. However, you can override the default llm_id with your preferred model from a collection of 170+ LLMs in the aiXplain marketplace.

Run the agent​

response = post_agent.run("What's an AI agent?")
response.data.output

Enable conversational memory​

Retrieve the session_id from the first query and include it in subsequent requests to maintain the agent’s conversation history.

session_id = response.data.session_id
response = post_agent.run("What makes this text interesting?", session_id=session_id)
response.data.output

Deploy the agent​

Creating an agent with create generates a draft agent with a temporary API endpoint that expires after 24 hours. Draft agents are ideal for development and debugging.

To make an agent permanent, you need to deploy it. By default, agents are deployed on aiXplain’s multi-tenant AWS cloud environment.

post_agent.deploy()

Add tools​

The agentic framework supports different types of tools. This section demonstrates how to list, try and add an AI model as an agent tool.

List models​

from aixplain.factories import ModelFactory
from aixplain.enums import Function

# List utility models 
model_list = ModelFactory.list(function=Function.UTILITIES, page_size=50)["results"]

for model in model_list:
  print(model.id, model.name, model.supplier)

Alternatively, browse models and other assets in the marketplace.

Try the model​

In this example, we will use the Google Search by Scale SERP model.

model = ModelFactory.get("65c51c556eb563350f6e1bb1") # or model_list[#]
response = model.run("Latest news about AI agents")
response.data.output

Add to the agent​

First convert the model asset into a tool. Then add it to the agent.

from aixplain.factories import AgentFactory

google_search_tool = AgentFactory.create_model_tool(model="65c51c556eb563350f6e1bb1")
post_agent.tools.append(google_search_tool) 
post_agent.save() # To deploy changes

Create a team agent​

A Team Agent orchestrates multiple agents to execute multi-step plans collaboratively.

from aixplain.factories import TeamAgentFactory

team_agent = TeamAgentFactory.create(
    name="Social Media Agent",
    description="An AI-powered agent that generates engaging social media posts, complete with suggested images and SEO-optimized captions.",
    agents=[post_agent]
)

# Run the team agent for the first time
# response = team_agent.run("Generate a post about eco-friendly travel.")

# Use the same session ID to maintain conversational context
# team_agent.run("Make it more concise.", session_id=response.data.session_id)

# Deploy the team agent to make it production-ready
# team_agent.deploy()

Next – Customize your agent​

Now that you’ve seen how agents can collaborate as a team, let’s dive deeper into how you can customize individual agents even further. From refining instructions and selecting tools to configuring memory, guardrails, and model behavior—agents on aiXplain are highly adaptable to your specific use case.

Learn more about agents →