How to Use Kimi K2.5 API with OpenClaw in 2026

How to Use Kimi K2.5 API with OpenClaw in 2026

OpenClaw is an open-source automation framework that lets developers build powerful workflows by chaining together API calls, data transformations, and conditional logic. Pairing it with Kimi K2.5 -- Moonshot AI's balanced coding and reasoning model -- creates a flexible pipeline for content generation, data processing, and automated analysis.

This guide walks you through integrating Kimi K2.5 into your OpenClaw setup using the Hypereal API as the provider.

Prerequisites

Before starting, make sure you have the following ready:

• OpenClaw installed and running (v2.0+ recommended)
• Python 3.9+ or Node.js 18+
• A Hypereal API key -- sign up at hypereal.ai to get 35 free credits (no credit card required)

Step 1: Get Your Hypereal API Key

1. Visit hypereal.ai and create a free account
2. Go to the API section in your dashboard
3. Generate a new API key
4. Copy the key -- you will need it for the OpenClaw configuration

Step 2: Configure the Hypereal Provider in OpenClaw

OpenClaw supports custom LLM providers through its configuration file. Add Hypereal as a provider to route Kimi K2.5 requests through the OpenAI-compatible endpoint.

openclaw.config.yaml

providers:
  hypereal:
    type: openai-compatible
    base_url: "https://hypereal.tech/api/v1/chat"
    api_key: "${HYPEREAL_API_KEY}"
    models:
      - kimi-k2.5

workflows:
  kimi-analysis:
    provider: hypereal
    model: kimi-k2.5
    max_tokens: 4096
    temperature: 0.7

Set Your Environment Variable

export HYPEREAL_API_KEY="your-hypereal-api-key"

Step 3: Create a Basic Workflow

Here is a simple OpenClaw workflow that uses Kimi K2.5 to analyze text input and produce a structured summary.

workflow.py

from openclaw import Workflow, Step
from openai import OpenAI

# Initialize the Hypereal client
client = OpenAI(
    api_key="your-hypereal-api-key",
    base_url="https://hypereal.tech/api/v1/chat"
)

def analyze_with_kimi(input_text: str) -> str:
    """Send text to Kimi K2.5 for analysis."""
    response = client.chat.completions.create(
        model="kimi-k2.5",
        messages=[
            {"role": "system", "content": "You are a technical analyst. Provide structured, concise analysis."},
            {"role": "user", "content": f"Analyze the following and provide key insights:\n\n{input_text}"}
        ],
        max_tokens=2048,
        temperature=0.5
    )
    return response.choices[0].message.content

# Define the workflow
workflow = Workflow(name="kimi-analysis")

workflow.add_step(Step(
    name="fetch-data",
    action="http_get",
    config={"url": "https://example.com/api/data"}
))

workflow.add_step(Step(
    name="analyze",
    action=analyze_with_kimi,
    input_from="fetch-data"
))

workflow.add_step(Step(
    name="save-results",
    action="file_write",
    config={"path": "./output/analysis.json"},
    input_from="analyze"
))

workflow.run()

Step 4: Build a Content Generation Pipeline

A more advanced use case chains Kimi K2.5 into a multi-step content pipeline.

from openclaw import Workflow, Step
from openai import OpenAI

client = OpenAI(
    api_key="your-hypereal-api-key",
    base_url="https://hypereal.tech/api/v1/chat"
)

def generate_outline(topic: str) -> str:
    response = client.chat.completions.create(
        model="kimi-k2.5",
        messages=[
            {"role": "system", "content": "You are a content strategist."},
            {"role": "user", "content": f"Create a detailed article outline for: {topic}"}
        ],
        max_tokens=1024,
        temperature=0.7
    )
    return response.choices[0].message.content

def write_section(outline_section: str) -> str:
    response = client.chat.completions.create(
        model="kimi-k2.5",
        messages=[
            {"role": "system", "content": "You are a technical writer. Write detailed, accurate content."},
            {"role": "user", "content": f"Write the following section in detail:\n\n{outline_section}"}
        ],
        max_tokens=2048,
        temperature=0.7
    )
    return response.choices[0].message.content

def review_and_edit(draft: str) -> str:
    response = client.chat.completions.create(
        model="kimi-k2.5",
        messages=[
            {"role": "system", "content": "You are an editor. Improve clarity, fix errors, and tighten the prose."},
            {"role": "user", "content": f"Review and improve this draft:\n\n{draft}"}
        ],
        max_tokens=2048,
        temperature=0.3
    )
    return response.choices[0].message.content

# Chain the steps
pipeline = Workflow(name="content-pipeline")
pipeline.add_step(Step(name="outline", action=generate_outline))
pipeline.add_step(Step(name="draft", action=write_section, input_from="outline"))
pipeline.add_step(Step(name="edit", action=review_and_edit, input_from="draft"))
pipeline.add_step(Step(name="publish", action="file_write", config={"path": "./output/article.md"}, input_from="edit"))

pipeline.run(input_data="How to optimize PostgreSQL queries for large datasets")

Step 5: Add Error Handling and Retries

Production workflows need resilience. Add retry logic and error handling for API calls.

import time
from openai import OpenAI, APIError, RateLimitError

client = OpenAI(
    api_key="your-hypereal-api-key",
    base_url="https://hypereal.tech/api/v1/chat"
)

def call_kimi_with_retry(messages: list, max_retries: int = 3) -> str:
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="kimi-k2.5",
                messages=messages,
                max_tokens=2048,
                temperature=0.7
            )
            return response.choices[0].message.content
        except RateLimitError:
            wait_time = 2 ** attempt
            print(f"Rate limited. Retrying in {wait_time}s...")
            time.sleep(wait_time)
        except APIError as e:
            print(f"API error: {e}. Retrying...")
            time.sleep(1)
    raise Exception("Max retries exceeded")

Step 6: TypeScript Integration

If your OpenClaw setup uses Node.js, here is the equivalent TypeScript configuration.

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.HYPEREAL_API_KEY,
  baseURL: "https://hypereal.tech/api/v1/chat",
});

interface WorkflowStep {
  name: string;
  execute: (input: string) => Promise<string>;
}

async function runKimiStep(systemPrompt: string, userInput: string): Promise<string> {
  const response = await client.chat.completions.create({
    model: "kimi-k2.5",
    messages: [
      { role: "system", content: systemPrompt },
      { role: "user", content: userInput },
    ],
    max_tokens: 2048,
    temperature: 0.7,
  });

  return response.choices[0].message.content ?? "";
}

// Define workflow steps
const steps: WorkflowStep[] = [
  {
    name: "analyze",
    execute: (input) => runKimiStep("You are a data analyst.", `Analyze this data:\n${input}`),
  },
  {
    name: "summarize",
    execute: (input) => runKimiStep("You are a summarizer.", `Summarize these findings:\n${input}`),
  },
];

// Run the pipeline
async function runPipeline(initialInput: string) {
  let data = initialInput;
  for (const step of steps) {
    console.log(`Running step: ${step.name}`);
    data = await step.execute(data);
  }
  return data;
}

runPipeline("Your input data here").then(console.log);

Benefits of Using Kimi K2.5 with OpenClaw

Tips for Production Workflows

1. Set explicit temperature values -- use 0.1-0.3 for deterministic tasks (data extraction, formatting) and 0.7-1.0 for creative generation
2. Use system prompts consistently -- Kimi K2.5 follows system-level instructions closely, which is essential for maintaining output quality across automated runs
3. Implement token budgets -- monitor your usage with Hypereal's dashboard to avoid unexpected costs
4. Cache repeated queries -- if your workflow processes the same inputs frequently, add a caching layer to reduce API calls
5. Log all responses -- store raw API responses for debugging and quality review

Get Started

Combining OpenClaw with Kimi K2.5 through Hypereal gives you a production-ready automation stack at a fraction of the cost of frontier models. Sign up for free and start building.

Try Hypereal AI free -- 35 credits, no credit card required.