aiXplain

aiXplain is an AI agent development and deployment SDK for Python programmers, aiming to quickly add AI capabilities to software. Its official repository describes it as "the fastest way to turn AI agents into APIs": describe an agent in Python, call .save(), and it goes live, and you pay only when someone uses it.

Main capabilities

• Rapid deployment as an API:.save() means deployment, with no need to manage servers, containers, or queues; agents keep their endpoints unchanged when updated.
• Rich models and tools: provides 1000+ models, tools, and integrations sharing one API key. Tools cover web search, Slack, Gmail, MCP servers, or use your own Python code.
• Controllable: use Budgets to limit the cost, time, and steps of each run; Inspectors check each step.
• Observable: each run returns its steps, cost, and status, displayed in code and on the dashboard.
• Multilingual invocation: after an agent is deployed as an API, it can be called using agent.id from any language, and the response includes a URL for polling the answer.

How to use

1. Get an API key at app.aixplain.com.
2. Describe and deploy an agent in Python (call .save()).
3. Call the API from any language via agent.id , and poll the result according to the URL in the response.

Deployment options include:

• Cloud (default): aixplain hosts and scales the agent.
• On-prem: run the same agent on your own infrastructure, including air-gapped environments.
• Local: run on the user's own machine.

In addition, Koder can help build and deploy aixplain agents: after describing the agent, Koder uses the agent builder skill to write, test, and deploy it, and users can review each change. If using Claude Code, you can install the aixplain plugin from this repository, which bundles the agent builder skill and marketplace search.

Control and security

• Budgets can limit the cost, time, and steps of each run.
• Inspectors can check inputs, intermediate steps, and final answers using custom rules or LLM judging.
• Each run returns steps, cost, and status, and appears on the dashboard.
• Run metadata: each agent run sends a metaData object, which contains an approximate location obtained through a single ipinfo.io query.

Applicable scenarios and limitations

Applicable scenarios: Python programmers need to add AI capabilities to software, quickly deploy AI agents as APIs, uniformly call multiple models and tools, or need budget control and step checking for agent runs.

In terms of limitations: official materials do not provide specific charges, price lists, detailed model lists, performance benchmarks, or detailed comparisons of each deployment method; this content is not yet available. The documentation mentions a migration guide for upgrading old code to 0.3.0 (in which this API is the only API).