Local AI Programming Assistant: VSCode + Continue/Cline + vLLM + Kimi-K2.5

Published on Jan 1, 2025 Updated on Jan 15, 2026 LLM Inference Engines LLM Inference Engines, VSCode, Continue, Cline, VLLM, OpenAI Compatible 3 minutes

Contents

Introduction

This document provides a comprehensive guide to integrating vLLM with Continue and Cline to build a high-performance, low-latency local LLM programming assistant environment.

vLLM leverages advanced inference technologies including PagedAttention for efficient memory management
Continue and Cline provide powerful AI-assisted coding capabilities directly within the VSCode environment through OpenAI-compatible API integration.

Architecture

Component Description

Component	Role	Key Features
vLLM	LLM Inference Service	PagedAttention, Streaming, Prefix Caching
Continue	VSCode AI Copilot	Code Completion, Summarization, Diagnostics, Refactoring
Cline	AI Programming Assistant	Task Execution, Conversation, Multi-file Operations
OpenAI API	Communication Protocol	Standardized Interface, Good Compatibility

Installation & Configuration

vLLM Installation & Configuration

Continue & Cline Installation & Configuration

Install Continue & Cline Extensions

Open VSCode Extension Market (Ctrl+Shift+X)
Search for “Continue” or “Cline”
Click to install the extension

Configure Continue & Cline

Continue Configuration
- API Provider: OpenAI Compatible
- Base URL: http://localhost:8000/v1
- API Key: YOUR_API_KEY
- Model ID: Qwen3-Coder-Next (same as the model name when starting the vLLM server)
- Context Window Size: 8192-16384
Cline Configuration
- API Provider: OpenAI Compatible
- Base URL: http://localhost:8000/v1
- API Key: YOUR_API_KEY
- Model ID: Qwen3-Coder-Next (same as the model name when starting the vLLM server)
- Context Window Size: 8192-16384

Model Selection Strategy

Task Type	Recommended Model	Reason
Code Completion	~7B	Fast speed, low resource usage
Code Generation	7B+	Balanced performance and quality

Custom Prompt Templates [to review]

Continue Prompt Template

Create ~/.continue/prompt.py with a custom system prompt configuration:

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from typing import List
from continue_core.config.config import Config

def build_prompt(context: List[str], user_query: str, config: Config) -> str:
    context_str = "\n\n".join(context) if context else "No context provided."
    
    return f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are an expert full-stack developer proficient in Python, TypeScript, and Go.

**Code Quality Standards:**
1. Follow PEP 8 / Google Style Guide
2. Include type annotations
3. Implement error handling
4. Write unit tests

**Output Format:**
- Brief explanation first
- Then provide code implementation
- Finally, include usage examples

<|eot_id|><|start_header_id|>user<|end_header_id|>
**Context Code:**
{context_str}

**Question:**
{user_query}

Please follow the above specifications:<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""

Cline Prompt Template

Cline supports custom system prompts via ~/.cline/config.yaml:

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models:
  - name: Qwen-Coder
    provider: openai
    model: Qwen-Coder
    api_base: http://localhost:8000/v1
    api_key: EMPTY
    context_length: 8192
    system_prompt: |
      You are an expert full-stack developer proficient in Python, TypeScript, and Go.
      
      **Code Quality Standards:**
      1. Follow PEP 8 / Google Style Guide
      2. Include type annotations
      3. Implement error handling
      4. Write unit tests
      
      **Output Format:**
      - Brief explanation first
      - Then provide code implementation
      - Finally, include usage examples      