aixplain SDK

Build, deploy, and run autonomous AI agents — governed by default, in a few lines of Python.

aixplain is the operating system for autonomous AI: multi-agent orchestration with runtime governance on every action, across cloud, on-prem, edge, and local. The full lifecycle — build → evaluate → deploy → monitor → evolve — on one runtime, instead of stitching tools together.

On your terms — your data in your perimeter, your cost free on local models and tools, pay as you go in the cloud, your independence across any model or infrastructure, no lock-in.

Build any agent — knowledge (RAG), data, custom-logic, integration, and team — via SDK, API, CLI, or MCP, on a marketplace of 900+ models, tools, and integrations.

Why aixplain

Less to build, less to operate:

• Deploy with one call — agent.save() promotes an agent to a persistent, versioned endpoint; no Dockerfiles, queues, or autoscaling to manage.
• No integration glue — reach 900+ models, tools, and integrations through one key; skip per-provider SDKs, auth, and rate-limit handling.
• Guardrails you don't have to build — allow-lists, per-asset permissions, rate and usage limits, and access control enforced at runtime.
• Self-debugging — step-level traces of every plan, tool call, and outcome.
• Run it anywhere — the same definition runs in the cloud, on-prem, at the edge, or locally.
• Works with your coding agent — native MCP support for MCP-compatible IDEs and coding agents.

How it works

The portable runtime behind aixplain agents: orchestration, governed asset serving, and observability across cloud, on-prem, edge, and local. See the documentation for the full architecture.

---

Quick start

This README documents SDK v2, the default API. SDK v1 (the legacy factory API) keeps working until August 1, 2026, after which v2 is the only supported surface.

pip install aixplain

Get your API key from your aixplain account, then expose it to the SDK:

export AIXPLAIN_API_KEY=<your-key>

Create and run your first agent

from aixplain import Aixplain

aix = Aixplain()  # reads AIXPLAIN_API_KEY from the environment

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

agent = aix.Agent(
    name="Research agent",
    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)

Runs return typed objects — read outputs with result.data.output, not dict indexing.

Build a multi-agent team

from aixplain import Aixplain
from aixplain.v2 import Inspector

aix = Aixplain()  # reads AIXPLAIN_API_KEY from the environment
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

# Config is plain data — strings for action/targets/severity, and a Metric,
# asset-id string, or callable for the `metric` (the universal judge).
noop_inspector = Inspector(
    name="noop-output-inspector",
    severity="low",
    targets=["output"],
    action="edit",
    metric=never_edit,
    editor=passthrough,
)

researcher = aix.Agent(
    name="Researcher",
    instructions="Find and summarize reliable sources.",
    tools=[search_tool],
)

team_agent = aix.Agent(
    name="Research team",
    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: validates the output against a runtime policy
└── Orchestrator: composes and returns the final answer

SDK v1 (legacy): available until August 1, 2026 — see the SDK v1 docs.

---

Marketplace

The aixplain Marketplace is a catalog of 900+ models, tools, and integrations. Every asset is reachable through the same three outlets — SDK, API, and MCP — with a single API key 🔑.

For MCP-compatible clients and IDEs, assets (for example Opus 4.6, Kimi, Qwen, Airtable, Slack) are served through aixplain-hosted MCP endpoints. See 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"
    }
  }
}

---

Data handling and deployment

• Your data stays yours — never used to train foundation models; agent memory is opt-in. SOC 2 Type II; TLS 1.2+ in transit, encrypted at rest.
• Governed at runtime — Inspector and Bodyguard enforce allow-lists, per-asset permissions, rate and usage limits, and access control on every execution.
• Deploy anywhere — cloud, on-prem, edge, or local; air-gapped and VPC available on-prem or local.

Learn more at aixplain Security and aixplain pricing.

---

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.

---

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: care@aixplain.com

---

License

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