How to Use Qwen 3.5 Flash API with OpenClaw in 2026

OpenClaw is a popular open-source automation framework that developers use to build pipelines for content generation, data processing, and workflow orchestration. Pairing it with Qwen 3.5 Flash -- Alibaba's ultra-fast, budget-friendly coding model -- gives you a powerful combination of automation and AI intelligence at minimal cost.

This guide walks you through setting up Qwen 3.5 Flash as the LLM backend for your OpenClaw workflows using the Hypereal API.

Why Qwen 3.5 Flash for OpenClaw?

OpenClaw workflows often involve high-volume, repetitive LLM calls -- exactly the scenario where Qwen 3.5 Flash shines:

128K context window -- process large documents and codebases in a single pass
Ultra-fast inference -- keep your automation pipelines running without bottlenecks
Low cost -- at $0.20/$1.80 per 1M input/output tokens via Hypereal, even high-volume workflows stay affordable
OpenAI-compatible API -- drop-in replacement for any existing OpenAI integration

Prerequisites

Before you begin, make sure you have:

Python 3.8+ installed on your system
OpenClaw installed and configured (see the OpenClaw setup guide)
A Hypereal API key -- sign up at hypereal.ai for 35 free credits, no credit card required

Install the required Python packages:

pip install openclaw openai python-dotenv

Step 1: Configure Your Environment

Create a .env file in your project root with your Hypereal API credentials:

HYPEREAL_API_KEY=your-hypereal-key-here
HYPEREAL_BASE_URL=https://hypereal.tech/api/v1
OPENCLAW_LLM_MODEL=qwen-3.5-flash

Step 2: Set Up the LLM Client

Create a reusable client module that OpenClaw tasks can import:

# llm_client.py
import os
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

client = OpenAI(
    api_key=os.environ["HYPEREAL_API_KEY"],
    base_url=os.environ["HYPEREAL_BASE_URL"]
)

def chat(prompt: str, system: str = "You are a helpful assistant.", temperature: float = 0.7, max_tokens: int = 2048) -> str:
    """Send a chat completion request to Qwen 3.5 Flash via Hypereal."""
    response = client.chat.completions.create(
        model=os.environ.get("OPENCLAW_LLM_MODEL", "qwen-3.5-flash"),
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": prompt}
        ],
        temperature=temperature,
        max_tokens=max_tokens
    )
    return response.choices[0].message.content


def chat_stream(prompt: str, system: str = "You are a helpful assistant."):
    """Stream a chat completion response from Qwen 3.5 Flash."""
    stream = client.chat.completions.create(
        model=os.environ.get("OPENCLAW_LLM_MODEL", "qwen-3.5-flash"),
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": prompt}
        ],
        stream=True
    )
    for chunk in stream:
        content = chunk.choices[0].delta.content
        if content:
            yield content

Step 3: Create an OpenClaw Task

Now wire the LLM client into an OpenClaw task. Here is an example that uses Qwen 3.5 Flash to generate code documentation:

# tasks/document_code.py
from openclaw import Task, Pipeline
from llm_client import chat

class DocumentCodeTask(Task):
    """Generate documentation for source code files."""

    def run(self, context):
        source_code = context.get("source_code")
        language = context.get("language", "Python")

        prompt = f"""Analyze the following {language} code and generate comprehensive documentation.
Include:
- A brief summary of what the code does
- Parameter descriptions
- Return value descriptions
- Usage examples

Code:
```{language.lower()}
{source_code}
```"""

        documentation = chat(
            prompt=prompt,
            system="You are a senior software engineer who writes clear, concise documentation.",
            temperature=0.3
        )

        context["documentation"] = documentation
        return context

Step 4: Build a Pipeline

Chain multiple tasks together into an OpenClaw pipeline:

# pipeline.py
from openclaw import Pipeline
from tasks.document_code import DocumentCodeTask

def create_documentation_pipeline():
    pipeline = Pipeline("code-documentation")

    pipeline.add_task(DocumentCodeTask(name="generate-docs"))

    return pipeline


if __name__ == "__main__":
    pipeline = create_documentation_pipeline()

    result = pipeline.execute({
        "source_code": """
def fibonacci(n: int) -> list[int]:
    if n <= 0:
        return []
    elif n == 1:
        return [0]
    fib = [0, 1]
    for i in range(2, n):
        fib.append(fib[i-1] + fib[i-2])
    return fib
""",
        "language": "Python"
    })

    print(result["documentation"])

Step 5: Advanced -- Batch Processing with Streaming

For workflows that process many items, use batch calls with streaming to maximize throughput:

# tasks/batch_summarize.py
import asyncio
from openclaw import Task
from openai import AsyncOpenAI
from dotenv import load_dotenv
import os

load_dotenv()

async_client = AsyncOpenAI(
    api_key=os.environ["HYPEREAL_API_KEY"],
    base_url=os.environ["HYPEREAL_BASE_URL"]
)

class BatchSummarizeTask(Task):
    """Summarize multiple documents concurrently using Qwen 3.5 Flash."""

    def run(self, context):
        documents = context.get("documents", [])
        summaries = asyncio.run(self._process_batch(documents))
        context["summaries"] = summaries
        return context

    async def _process_batch(self, documents):
        tasks = [self._summarize(doc) for doc in documents]
        return await asyncio.gather(*tasks)

    async def _summarize(self, document):
        response = await async_client.chat.completions.create(
            model="qwen-3.5-flash",
            messages=[
                {"role": "system", "content": "Summarize the following document in 2-3 sentences."},
                {"role": "user", "content": document}
            ],
            temperature=0.3,
            max_tokens=256
        )
        return response.choices[0].message.content

Step 6: Add Error Handling and Retries

Production OpenClaw workflows should include retry logic for API calls:

# llm_client_robust.py
import os
import time
from openai import OpenAI, APIError, RateLimitError
from dotenv import load_dotenv

load_dotenv()

client = OpenAI(
    api_key=os.environ["HYPEREAL_API_KEY"],
    base_url=os.environ["HYPEREAL_BASE_URL"]
)

def chat_with_retry(prompt: str, system: str = "You are a helpful assistant.", max_retries: int = 3) -> str:
    """Chat completion with exponential backoff retry."""
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="qwen-3.5-flash",
                messages=[
                    {"role": "system", "content": system},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.7,
                max_tokens=2048
            )
            return response.choices[0].message.content
        except RateLimitError:
            wait = 2 ** attempt
            print(f"Rate limited. Retrying in {wait}s...")
            time.sleep(wait)
        except APIError as e:
            if attempt == max_retries - 1:
                raise
            print(f"API error: {e}. Retrying...")
            time.sleep(1)

    raise RuntimeError("Max retries exceeded")

TypeScript Alternative

If your OpenClaw setup uses TypeScript, here is the equivalent client:

import OpenAI from "openai";

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

export async function chat(
  prompt: string,
  system: string = "You are a helpful assistant."
): Promise<string> {
  const response = await client.chat.completions.create({
    model: "qwen-3.5-flash",
    messages: [
      { role: "system", content: system },
      { role: "user", content: prompt },
    ],
    temperature: 0.7,
    max_tokens: 2048,
  });

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

Cost Estimation for OpenClaw Workflows

Running Qwen 3.5 Flash through Hypereal is extremely affordable for automation:

Workflow Volume	Estimated Monthly Cost
100 tasks/day (short prompts)	~$1-3
1,000 tasks/day (medium prompts)	~$10-25
10,000 tasks/day (mixed)	~$80-200

Compare this to GPT-4o at roughly 10-20x the cost per token, and the savings add up fast for high-volume OpenClaw pipelines.

Wrapping Up

Qwen 3.5 Flash is an ideal LLM backend for OpenClaw workflows. Its combination of fast inference, 128K context, and rock-bottom pricing through Hypereal makes it perfect for automation pipelines that need to make thousands of LLM calls without breaking the budget. The OpenAI-compatible API means you can swap it into any existing integration with a one-line configuration change.

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