aiXplain 0.2.41

aiXplain SDK adds AI functions to software.

Welcome to aiXplain

The Agentic Operating System for Enterprise AI

aiXplain is a full-stack platform for building, deploying, and governing mission-critical AI agents at scale. With the aiXplain SDK, you can ship production-grade agents faster:

• Discover & connect — Access hundreds of LLMs, tools, and integrations with a unified API, or bring your own.
• Build & orchestrate — Start from simple automations to adaptive multi-agent systems that reason, plan, and use tools, with a built-in memory.
• Ground & retrieve — Enhance agents with vector- and graph-based retrieval for accurate, context-aware responses.
• Deploy anywhere — Deploy with a click and let aiXplain handle the infrastructure (SaaS, on-prem, VPC) and MLOps so your agents can scale and evolve seamlessly.
• Observe & improve — Track usage and performance with tracing and audit trails, with enterprise-grade governance and compliance.

aiXplain combines developer agility with enterprise-grade reliability in a platform where data sovereignty and compliance are non-negotiable.

Check out this benchmark: aiXplain's orchestration engine outperforms other agentic frameworks on complex tasks while balancing speed and cost.

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aiXplain agents

aiXplain agents are designed with built-in intelligence, a.k.a microagents, that handle the operational complexity of agents at runtime — such as planning, monitoring, validation, routing, and formatting. This frees you to focus on tuning your agents for your use case instead of rebuilding the basics.

The diagram illustrates how the orchestration engine coordinates agents at runtime, enabling agents that are modular, traceable, and production-ready.

Microagents

Microagents are specialized components that manage core operational functions:

• Mentalist — planning and goal decomposition
• Orchestrator — task routing and role assignment
• Inspector — validation and policy enforcement (e.g., PII redaction)
• Bodyguard — data access, privacy, and security enforcement
• Responder — formatting and output delivery

Microagents are highly configurable — from lightweight automations to complex, iterative systems — and appear in agent traces for easier debugging, auditing, and explainability.

Meta-agents

Meta-agents boost adaptability by improving agent performance. The Evolver (in private beta) attaches to any agent, monitors KPIs and feedback, and refines behavior — also serving as a powerful benchmarking tool by simulating users and environments.

Orchestration modes

aiXplain agents support two orchestration modes:

• Static — define tasks (AgentTasks) and order for deterministic, repeatable execution.
• Dynamic (default) — the Mentalist generates the execution plan at runtime for adaptive, context-aware responses.

aiXplain also supports pipelines — sequential workflows that connect models and tools in a fixed order.

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How to start?

• For technical teams → Install the SDK and start building:

pip install aixplain

• For business teams without technical resources → Contact aiXplain. Our aiXperts will help you develop your agentic solutions and deploy them on your choice of infrastructure.

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Quick start

Installation

pip install aixplain

Authentication

import os
os.environ["AIXPLAIN_API_KEY"] = "<API_KEY>"

Get your API key from your aiXplain account.

Create and Run Your First Agent

Example: A weather agent powered by the Open Weather API from the aiXplain marketplace.

By default, aiXplain agents run on GPT-4o-mini as the reasoning model. You can swap it with any other model from the aiXplain marketplace at any time.

from aixplain.factories import AgentFactory, ModelFactory

# Add tools
weather_tool = ModelFactory.get("66f83c216eb563266175e201") # Tool ID for Open Weather API tools

# Create the agent
agent = AgentFactory.create(
name="Weather Agent",
description="An agent that answers queries about the current weather.",
instructions="Use the provided tool to answer weather queries.",
tools=[weather_tool],
)

# Run and test your agent
query = "What is the weather in Liverpool, UK?"
agent_response = agent.run(query)

print(agent_response['data']['output'])

Find a wide selection of LLMs and tools to power your agents by browsing our marketplace.

Access your deployed agent and API integration code

Once your agent is deployed, you can view its API integration details and generated code by visiting:

https://platform.aixplain.com/discover/agent/<AGENT_ID>

Just replace <AGENT_ID> in the URL with your actual agent identifier (agent.id).

Build and deploy a Team Agent

A team agent orchestrates multiple specialized agents to solve complex problems.

from aixplain.factories import TeamAgentFactory, AgentFactory
from aixplain.modules.agent.agent_task import AgentTask

# Define tasks for specialized agents
scrape_task = AgentTask(name="scrape_website", description="Scrapes websites to extract information", expected_output="Scraped website output.")

wiki_task = AgentTask(name="wiki_query", description="Queries wikipedia to answer user questions", expected_output="Queried results from wikipedia.")

#Scrape tool
scrape_tool = ModelFactory.get("66f423426eb563fa213a3531")

# Create specialized agents
scraper_agent = AgentFactory.create(
    name="Scraper Agent",
    description="An agent that answers queries using website scraping.",
    tasks=[scrape_task],
    tools=[scrape_tool]
)

#Wiki tool
wiki_tool = ModelFactory.get("6633fd59821ee31dd914e232")

wiki_agent = AgentFactory.create(
    name="Wiki Agent",
    description="An agent that answers queries using wikipedia.",
    tasks=[wiki_task],
    tools=[wiki_tool]
)

# Create the team agent to orchestrate them
team_agent = TeamAgentFactory.create(
    name="Wiki and Web Team Agent",
    description="You search using wiki or by web scraping URLs if appropriate.",
    instructions="You take user queries and search them using wiki or by web scraping URLs if appropriate.",
    agents=[scraper_agent, wiki_agent]
)

# Run and test the team agent
query = "Tell me about OpenAI. They have a website, https://openai.com/."
result = team_agent.run(query)

print(result['data']['output'])

# Deploy the team agent for a permanent API endpoint
team_agent.deploy()

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Security, compliance, and privacy

aiXplain takes a governance-first approach to enterprise trust:

• SOC 2 compliant — audited for security, confidentiality, and privacy.
• No data used for training — prompts, responses, and fine-tuned models stay private.
• Data sovereignty — full control with OnEdge and OnPrem options.
• End-to-end encryption — in transit (TLS 1.2+) and at rest.

Learn more at aiXplain Security.

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Pricing

Start with our Builder plan — free credits at signup.

• Unlimited agents — create and run without limits.
• Pay as you go — usage-based pricing only.
• No idle costs — pay nothing when agents aren't running.

Learn more at aiXplain Pricing.

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Community & support

• Documentation: docs.aixplain.com
• Example agents: https://github.com/aixplain/cookbook
• Learn how to build agents: https://academy.aixplain.com/student-registration/
• Meet us in Discord: discord.gg/aixplain
• Talk with our team: [e-mail]

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License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.