aiXplain 0.2.44

aiXplain SDK adds AI functions to software.

aiXplain SDK

Build, deploy, and govern autonomous AI agents for your business operations.

aiXplain SDK provides Python and REST APIs for agents that plan, use tools, call models and data, run code, and adapt at runtime. It also works natively with MCP-compatible coding agents and IDEs.

Become an agentic-first organization

Designed for business operations: autonomous, governed, MCP-compatible, and built for context management. Your interactive AI assistant is a click away.

We operate our business with aiXplain agents, using them across product, business development, and marketing.

Why aiXplain

• Autonomous runtime loop — plan, call tools and models, reflect, and continue without fixed flowcharts.
• Multi-agent execution — delegate work to specialized subagents at runtime.
• Governance by default — runtime access and policy enforcement on every run.
• Production observability — inspect step-level traces, tool calls, and outcomes for debugging.
• Model and tool portability — swap assets without rewriting application glue code.
• MCP-native access — connect MCP clients to 900+ aiXplain-hosted assets with one PAYG API key.
• Flexible deployment — run the same agent definition serverless or private.

	aiXplain SDK	Other agent frameworks
Governance	Runtime access and policy enforcement built in	Usually custom code or external guardrails
Models and tools	900+ models and tools with one API key	Provider-by-provider setup
Deployment	Cloud (instant) or on-prem	Usually self-assembled runtime and infra
Observability	Built-in traces and dashboards	Varies by framework
Coding-agent workflows	Works natively with MCP-compatible coding agents and IDEs	Usually not a first-class workflow target

AgenticOS

AgenticOS is the portable runtime platform behind aiXplain agents. AgentEngine orchestrates planning, execution, and delegation for autonomous agents. AssetServing connects agents to models, tools, and data through a governed runtime layer. Observability captures traces, metrics, and monitoring for every production run across Cloud (instant) and on-prem deployments.

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MCP Server Marketplace

aiXplain Marketplace now also exposes MCP servers for 900+ models and tools, allowing external clients to access selected tool, integration, and model assets, for example Opus 4.6, Kimi, Qwen, Airtable, and Slack, through aiXplain-hosted MCP endpoints with a single API key 🔑.

Read the full MCP setup guide in the MCP servers docs.

{
  "ms1": {
    "url": "https://models-mcp.aixplain.com/mcp/<AIXPLAIN_ASSET_ID>",
    "headers": {
      "Authorization": "Bearer <AIXPLAIN_APIKEY>",
      "Accept": "application/json, text/event-stream"
    }
  }
}

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Quick start

pip install aixplain

Get your API key from your aiXplain account.

v2 (default)

Create and run your first agent (v2)

from uuid import uuid4
from aixplain import Aixplain

aix = Aixplain(api_key="<AIXPLAIN_API_KEY>")

search_tool = aix.Tool.get("tavily/tavily-web-search/tavily")
search_tool.allowed_actions = ["search"]

agent = aix.Agent(
    name=f"Research agent {uuid4().hex[:8]}",
    description="Answers questions with concise web-grounded findings.",
    instructions="Use the search tool when needed and cite key findings.",
    tools=[search_tool],
)
agent.save()

result = agent.run(
    query="Who is the CEO of OpenAI? Answer in one sentence.",
)
print(result.data.output)

Build a multi-agent team (v2)

from uuid import uuid4
from aixplain import Aixplain
from aixplain.v2 import EditorConfig, EvaluatorConfig, EvaluatorType, Inspector, InspectorAction, InspectorActionConfig, InspectorSeverity, InspectorTarget

aix = Aixplain(api_key="<AIXPLAIN_API_KEY>")
search_tool = aix.Tool.get("tavily/tavily-web-search/tavily")
search_tool.allowed_actions = ["search"]

def never_edit(text: str) -> bool:
    return False

def passthrough(text: str) -> str:
    return text

noop_inspector = Inspector(
    name=f"noop-output-inspector-{uuid4().hex[:8]}",
    severity=InspectorSeverity.LOW,
    targets=[InspectorTarget.OUTPUT],
    action=InspectorActionConfig(type=InspectorAction.EDIT),
    evaluator=EvaluatorConfig(
        type=EvaluatorType.FUNCTION,
        function=never_edit,
    ),
    editor=EditorConfig(
        type=EvaluatorType.FUNCTION,
        function=passthrough,
    ),
)

researcher = aix.Agent(
    name=f"Researcher {uuid4().hex[:8]}",
    instructions="Find and summarize reliable sources.",
    tools=[search_tool],
)

team_agent = aix.Agent(
    name=f"Research team {uuid4().hex[:8]}",
    instructions="Research the topic and return exactly 5 concise bullet points.",
    subagents=[researcher],
    inspectors=[noop_inspector],
)
team_agent.save(save_subcomponents=True)

response = team_agent.run(
    query="Compare OpenAI and Anthropic in exactly 5 concise bullet points.",
)
print(response.data.output)

Execution order:

Human prompt: "Compare OpenAI and Anthropic in exactly 5 concise bullet points."

Team agent
├── Planner: breaks the goal into research and synthesis steps
├── Orchestrator: routes work to the right subagent
├── Researcher subagent
│   └── Tavily search tool: finds and summarizes reliable sources
├── Inspector: checks the final output through a simple runtime policy
├── Orchestrator: decides whether another pass is needed
└── Responder: returns one final answer

v1 (legacy)

Create and run your first agent (v1)

from aixplain.factories import AgentFactory, ModelFactory

weather_tool = ModelFactory.get("66f83c216eb563266175e201")

agent = AgentFactory.create(
    name="Weather Agent",
    description="Answers weather queries.",
    instructions="Use the weather tool to answer user questions.",
    tools=[weather_tool],
)

result = agent.run("What is the weather in Liverpool, UK?")
print(result["data"]["output"])

You can still access legacy docs at docs.aixplain.com/1.0.

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Data handling and deployment

aiXplain applies runtime governance and enterprise controls by default:

• We do not train on your data — your data is not used to train foundation models.
• No data retained by default — agent memory is opt-in (short-term and long-term).
• SOC 2 Type II certified — enterprise security and compliance posture.
• Runtime policy enforcement — Inspector and Bodyguard govern every agent execution.
• Portable deployment options — Cloud (instant) or on-prem (including VPC and air-gapped environments).
• Encryption — TLS 1.2+ in transit and encrypted storage at rest.

Learn more at aiXplain Security and aiXplain pricing.

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Pricing

Start free, then scale with usage-based pricing.

• Pay as you go — prepaid usage with no surprise overage bills.
• Subscription plans — reduce effective consumption-based rates.
• Custom enterprise pricing — available for advanced scale and deployment needs.

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.