Cline Integration

AVELIN API works with Cline — an open-source VS Code extension that transforms your editor into an autonomous AI coding agent. Cline can read and write files, execute terminal commands, use the browser, and work through complex tasks step-by-step with user approval.

Why AVELIN + Cline? Cline's autonomous agentic workflow benefits from AVELIN's reliable tool calling, 1M-token context windows, and deep reasoning models — all at competitive per-token pricing. AVELIN supports both the Anthropic Messages API and OpenAI Chat Completions API, giving you flexibility in how you configure Cline.

Table of Contents

• Quick Start
• Prerequisites
• Installation
• Configuration Methods
  • Method 1: Anthropic Provider (RECOMMENDED)
  • Method 2: OpenAI Compatible Provider
• Model Selection
• Model Configuration Options
• Verification
• Cline-Specific Features with AVELIN
  • Plan & Act Mode
  • Auto-Approve & YOLO Mode
  • Checkpoints
  • Memory Bank
  • Custom Instructions
  • Subagents
  • Context Management
• Advanced Configuration
  • Switching Models Mid-Session
  • Multiple Model Strategy
  • Extended Thinking
  • Using Cline CLI
• Example Workflows
• Troubleshooting
• Performance Tips
• Why AVELIN for Cline?
• Related Guides

Quick Start

Get Cline working with AVELIN in under 3 minutes:

Step 1: Get your AVELIN API key

Sign up at avelin.ai and generate an API key from your dashboard. Your key will look like:

sk-avelin-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Step 2: Install Cline in VS Code

1. Open VS Code
2. Press Cmd+Shift+X (macOS) or Ctrl+Shift+X (Windows/Linux) to open Extensions
3. Search for Cline
4. Click Install

Step 3: Configure Cline with AVELIN (Anthropic — Recommended)

1. Click the Cline icon in the VS Code sidebar
2. Click the ⚙️ Settings gear icon in the Cline panel
3. Select Anthropic from the "API Provider" dropdown
4. Check "Use custom base URL"
5. Enter the base URL: https://api.avelin.ai
6. Paste your AVELIN API key in the Anthropic API Key field
7. Select or type your model: avelin-coding-pro
8. Click Done

Step 4: Verify the connection

Send a simple test message in the Cline chat panel:

Say "Hello from AVELIN!" and list the current working directory.

If Cline responds and executes the command, your setup is complete.

Prerequisites

Before you begin, ensure you have:

Note: Cline works on macOS, Windows, and Linux. All configuration is done through the Cline settings UI panel within VS Code — there is no separate configuration file to edit.

Installation

From VS Code Marketplace

1. Open VS Code
2. Go to Extensions (Cmd+Shift+X / Ctrl+Shift+X)
3. Search for Cline
4. Install the extension by Cline
5. The Cline icon will appear in your Activity Bar (left sidebar)

From Command Line

code --install-extension saoudrizwan.claude-dev

Verify Installation

After installing, open the Cline panel by clicking its icon in the sidebar. You should see the initial setup screen prompting you to configure an API provider.

Configuration Methods

Cline supports multiple API provider types. For AVELIN, you have two options:

Important: Cline stores all settings (API keys, provider selection, model preferences) in VS Code's internal storage (GlobalState and SecretStorage). There is no external JSON configuration file to edit — all configuration is done through the Cline settings UI panel.

Method 1: Anthropic Provider (RECOMMENDED)

The Anthropic provider type gives you the best experience with AVELIN because it enables full support for extended thinking, streaming responses, and Anthropic-native tool calling formats.

Configuration Steps

1. Open Cline Settings

   • Click the Cline icon in the VS Code sidebar
   • Click the ⚙️ (gear) icon in the top-right of the Cline panel

2. Select Provider

   • In the "API Provider" dropdown, select Anthropic

3. Configure Custom Base URL

   • Check the box labeled "Use custom base URL"
   • In the URL field that appears, enter:

   https://api.avelin.ai
   

4. Enter API Key

   • Paste your AVELIN API key in the Anthropic API Key field:

   sk-avelin-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
   

5. Select Model

   • Choose from the dropdown or manually type: avelin-coding-pro

6. Configure Model Settings (optional but recommended)

   • Max Output Tokens: 65536
   • Context Window: 1000000 (1M tokens)
   • Image Support: Enable if your model supports vision
   • Computer Use: Enable if your model supports browser automation

7. Save

   • Click Done to save your configuration

Anthropic Provider — Complete Configuration Summary

Why Anthropic Provider is Recommended

• Extended Thinking: Full support for thinking/reasoning blocks, giving you visibility into the model's decision process
• Streaming: Token-by-token streaming works natively with the Anthropic Messages API format
• Prompt Caching: Anthropic's prompt caching protocol reduces costs on repeated system prompts (up to 90% savings)
• Tool Calling: Cline's tool use format is designed around Anthropic's native tool calling — fewer conversion layers means more reliable execution
• Image Support: Direct support for image inputs when using vision-capable models

Method 2: OpenAI Compatible Provider

If you prefer a simpler configuration or need compatibility with OpenAI-specific features, use the OpenAI Compatible provider.

Configuration Steps

1. Open Cline Settings

   • Click the Cline icon in the VS Code sidebar
   • Click the ⚙️ (gear) icon in the top-right of the Cline panel

2. Select Provider

   • In the "API Provider" dropdown, select OpenAI Compatible

3. Set Base URL

   • Enter the OpenAI-compatible base URL:

   https://api.avelin.ai/v1
   

4. Enter API Key

   • Paste your AVELIN API key in the API Key field:

   sk-avelin-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
   

5. Set Model ID

   • Enter the model identifier:

   avelin-coding-pro
   

6. Configure Model Settings (optional but recommended)

   • Click Model Configuration to expand advanced options
   • Max Output Tokens: 65536
   • Context Window: 1000000
   • Image Support: Enable if your model supports vision
   • Computer Use: Enable if needed
   • Input Price: Set your cost per million tokens (for tracking)
   • Output Price: Set your cost per million tokens (for tracking)

7. Save

   • Click Done to save your configuration

OpenAI Compatible Provider — Complete Configuration Summary

How the OpenAI Compatible Provider Works

When you select "OpenAI Compatible," Cline sends requests to:

POST https://api.avelin.ai/v1/chat/completions

This is the standard OpenAI Chat Completions endpoint format. AVELIN's API translates internally to provide the same models through this interface.

Model Selection

Primary Recommendation: avelin-coding-pro

For autonomous coding workflows, avelin-coding-pro is the optimal choice:

Alternative Models

Model Selection Strategy for Cline

Cline's autonomous loops generate many API calls. Consider this cost/performance strategy:

Planning Phase:      avelin-coding-pro or avelin-agentic-ultra
                     (Deep reasoning for architecture decisions)

Implementation:      avelin-coding-pro
                     (Reliable tool calling, fast iteration)

Quick Fixes:         avelin-coding-fast or avelin-fast
                     (Cost-effective for simple edits)

Review/Testing:      avelin-coding-ultra
                     (Thorough analysis of generated code)

Model Configuration Options

Cline provides detailed model configuration in its settings panel. These settings help Cline understand your model's capabilities and limits.

Available Settings

Configuring Model Settings

1. In the Cline settings panel, after selecting your provider and model
2. Expand the Model Configuration section
3. Adjust values as needed
4. These settings affect how Cline manages context and tracks costs

Tip: Setting accurate context window and max output token values prevents Cline from sending requests that exceed model limits, reducing errors and wasted API calls.

Verification

After configuring Cline, verify your setup works correctly before starting real work.

Step 1: Connection Test

Send a simple test message:

Hello! Confirm you are connected and tell me what model you are using.

Expected response: Cline responds with a greeting and confirms the model name.

Step 2: Tool Calling Test

Ask Cline to perform a basic file operation:

List the files in the current directory and tell me what kind of project this is.

Expected behavior:

1. Cline requests permission to run a terminal command (ls or dir)
2. Click Approve (or auto-approve handles it)
3. Cline reads the output and describes the project type

Step 3: File Read Test

Read the main entry point file of this project and summarize what it does.

Expected behavior:

1. Cline identifies the entry file (e.g., main.py, index.ts, app.go)
2. Requests permission to read the file
3. Provides a summary of the file's purpose

Step 4: File Write Test

Create a file called TEST_CLINE.txt with the text "Cline + AVELIN works!" and then delete it.

Expected behavior:

1. Cline creates the file (asks for approval)
2. Confirms creation
3. Deletes the file (asks for approval)
4. Confirms deletion

Verification Checklist

• Cline responds to messages
• Terminal commands execute successfully
• File reads work
• File writes work
• Responses stream token-by-token
• No error messages in the Cline panel

Cline-Specific Features with AVELIN

Cline offers powerful features that work particularly well with AVELIN's API.

Plan & Act Mode

Cline has a dual-mode system for structured development:

• Plan Mode: Cline analyzes your codebase and creates a detailed plan without making changes. Uses a reasoning-focused approach.
• Act Mode: Cline executes the plan, making file changes and running commands.

Best Practices with AVELIN

1. Start in Plan Mode: Describe your goal
   "Plan how to add rate limiting to this FastAPI application"

2. Review the plan Cline generates
   - Check that it covers edge cases
   - Verify the approach matches your preferences

3. Switch to Act Mode to execute
   "Execute the plan"

Recommended model pairing:

• Plan Mode: avelin-agentic-ultra or avelin-coding-ultra (deep reasoning)
• Act Mode: avelin-coding-pro (fast, reliable tool calling)

Auto-Approve & YOLO Mode

By default, Cline asks for your approval before every file write and terminal command. For trusted projects, you can enable auto-approval to speed up autonomous workflows.

Enabling Auto-Approve

1. Open Cline settings (⚙️ icon)
2. Look for the Auto-Approve section
3. Enable specific permissions:
   • Read files — Always safe, recommended to auto-approve
   • Write files — Enable for trusted projects
   • Execute terminal commands — Use with caution
   • Browser actions — Enable only if needed

YOLO Mode

YOLO mode auto-approves all actions without asking. Only enable this for:

• Sandbox/development environments
• Git-tracked projects (easy rollback)
• Well-understood codebases

Safety Recommendations

SAFE TO AUTO-APPROVE:
✅ Read files
✅ List directory contents
✅ Run tests (read-only operations)

USE CAUTION:
⚠️  Write/edit files (ensure git is initialized)
⚠️  Run build commands
⚠️  Install dependencies

NEVER AUTO-APPROVE:
❌ Commands that delete directories recursively
❌ Commands that push to production
❌ Commands that modify system configuration

With AVELIN's reliable tool calling, auto-approved actions execute predictably with fewer malformed commands.

Checkpoints

Cline creates git checkpoints at each step, allowing you to roll back code changes while preserving the conversation history.

How Checkpoints Work

1. Before each file modification, Cline creates a git commit
2. If something goes wrong, you can restore to any previous checkpoint
3. Your conversation context is preserved even after rollback

Using Checkpoints with AVELIN

Cline Panel:

You> Refactor the authentication module to use JWT

Cline: [Creates checkpoint]
       [Modifies auth.py]
       [Modifies middleware.py]
       [Creates jwt_utils.py]

You> This broke the tests. Restore the last checkpoint.

Cline: [Restores to previous checkpoint]
       The changes have been reverted. The conversation
       history is preserved. Would you like me to try
       a different approach?

Tip: Checkpoints rely on git. Always initialize a git repository in your project before enabling checkpoints: git init && git add -A && git commit -m "Initial checkpoint"

Memory Bank

Cline's Memory Bank is a structured documentation system that helps maintain context across sessions. It stores project knowledge in markdown files that Cline reads at the start of each session.

Setting Up Memory Bank

1. Create a .cline/ directory in your project root

2. Add documentation files:

   .cline/
   ├── projectbrief.md      # Project overview and goals
   ├── techContext.md        # Technology stack and decisions
   ├── activeContext.md      # Current work focus
   └── progress.md           # What's been done
   

3. Cline automatically reads these files when starting a new session

Example Memory Bank Entry

<!-- .cline/projectbrief.md -->
# Project: AVELIN API Client Library

## Overview
Python client library for the AVELIN API with async support,
streaming, and automatic retry logic.

## Tech Stack
- Python 3.11+
- httpx for HTTP
- pytest for testing

## Architecture
- src/avelin/client.py - Main client class
- src/avelin/models.py - Pydantic models
- src/avelin/streaming.py - SSE stream handling

Custom Instructions

Add project-specific instructions that Cline follows for every interaction.

Where to Set Custom Instructions

1. Open Cline settings (⚙️ icon)
2. Find the Custom Instructions text area
3. Enter your project-specific guidelines

Example Custom Instructions

You are working on a FastAPI application with the following conventions:

- All endpoints are async
- Use Pydantic v2 models for request/response validation
- Database access goes through SQLAlchemy async sessions
- Tests use pytest with httpx.AsyncClient
- Follow PEP 8 and use type hints for all function signatures
- Include docstrings for all public functions
- Use structlog for structured logging
- Error responses follow RFC 7807 Problem Details format

When writing new code:
- Always add corresponding tests
- Update the OpenAPI schema if adding endpoints
- Consider backwards compatibility for API changes

Subagents

Cline can spawn parallel research agents that explore the codebase without filling the main agent's context window.

When to Use Subagents

• Exploring unfamiliar parts of a large codebase
• Researching how a specific pattern is used across the project
• Gathering context about dependencies before making changes

Example with AVELIN

You> Before refactoring the auth module, research how authentication
     is currently used across the entire codebase.

Cline: I'll spawn a research agent to explore authentication usage...

     [Subagent exploring: grep for auth imports, JWT usage,
      middleware configuration, test fixtures]

     Research complete. Found 14 files using authentication:
     - 8 API endpoints with auth dependencies
     - 3 middleware configurations
     - 2 test fixture files
     - 1 configuration module

     Summary: The codebase uses JWT-based auth with...

Context Management

Cline's context window fills up quickly in autonomous sessions. Here's how to manage it effectively with AVELIN's 1M-token context:

Context Best Practices

1. Use @ mentions to explicitly include relevant files:

   Look at @src/auth/jwt.py and fix the token expiration bug
   

2. Start new tasks for unrelated work:

   [Start new Cline task for each major feature]
   

3. Monitor context usage:

   • Cline shows token usage in the panel
   • With AVELIN's 1M context, you have more room before needing to reset

4. Use .clineignore to exclude irrelevant files:

   # .clineignore
   node_modules/
   __pycache__/
   .git/
   dist/
   *.pyc
   

Advanced Configuration

Switching Models Mid-Session

You can change models without restarting your Cline session:

1. Open the Cline panel
2. Click the model dropdown (or model name) at the top
3. Select a different AVELIN model
4. Continue your conversation with the new model

When to Switch Models

Note: Switching models mid-session preserves your conversation history and context. The new model receives the full conversation.

Multiple Model Strategy

For cost optimization, consider using different models for different phases of work:

Phase 1 - Planning (avelin-coding-ultra):
  "Analyze the codebase and plan the microservice migration"

Phase 2 - Implementation (avelin-coding-pro):
  "Implement the user service according to the plan"

Phase 3 - Testing (avelin-coding-fast):
  "Write integration tests for the user service"

Phase 4 - Quick fixes (avelin-fast):
  "Fix the import error in test_user_service.py"

Extended Thinking

When using the Anthropic provider with AVELIN, you can enable Extended Thinking for complex tasks:

1. In Cline settings, below the model selection
2. Check "Enable Extended Thinking"
3. Set the thinking budget (e.g., 10000 tokens)

Extended Thinking is particularly useful for:

• Multi-file refactoring decisions
• Debugging complex issues with many potential causes
• Architecture trade-off analysis
• Security vulnerability assessment

Note: Extended thinking tokens are billed. Use judiciously for complex problems where the additional reasoning provides clear value.

Using Cline CLI

Cline also offers a CLI/TUI for terminal-based workflows. The CLI can be configured to use AVELIN via environment variables or configuration:

# Install Cline CLI
npm install -g @anthropic-ai/cline

# Set environment variables
export ANTHROPIC_BASE_URL="https://api.avelin.ai"
export ANTHROPIC_API_KEY="sk-avelin-xxxxxxxxxxxxxxxx"

# Run Cline CLI
cline

For the OpenAI-compatible endpoint:

export OPENAI_BASE_URL="https://api.avelin.ai/v1"
export OPENAI_API_KEY="sk-avelin-xxxxxxxxxxxxxxxx"

Example Workflows

Example 1: Feature Implementation

Cline Panel:

You> Add pagination to the /api/users endpoint. Use cursor-based
     pagination with a default page size of 20.

Cline: I'll add cursor-based pagination to the users endpoint.
       Let me examine the current implementation.

       [Reads: src/routes/users.py]
       [Reads: src/models/user.py]
       [Reads: src/db/session.py]

       Here's my plan:
       1. Add pagination query parameters (cursor, limit)
       2. Create a PaginationResponse schema
       3. Update the query to use cursor-based pagination
       4. Add tests

       Shall I proceed?

You> Yes, implement it.

Cline: [Modifies: src/routes/users.py]
       [Creates: src/schemas/pagination.py]
       [Modifies: src/models/user.py]
       [Creates: tests/test_users_pagination.py]
       [Runs: pytest tests/test_users_pagination.py]

       All 8 tests pass. Here's what I implemented:
       - Cursor-based pagination with opaque cursors
       - Default limit of 20, max 100
       - Proper next_cursor generation
       - Empty results handling
       - Comprehensive test coverage

Example 2: Debugging

You> The test suite is failing. Investigate and fix the failures.

Cline: Let me run the test suite to see what's failing.

       [Runs: pytest --tb=short]

       Found 3 failing tests:
       1. test_create_user_duplicate_email - AssertionError
       2. test_token_refresh - ConnectionError
       3. test_rate_limiter - TimeoutError

       Investigating each failure...
       [Reads test files, source code, configuration]

       Root causes identified:
       1. Missing unique constraint violation handler
       2. Redis connection not being mocked in tests
       3. Rate limiter test needs longer timeout

       Applying fixes...
       [Modifies 3 files, re-runs tests]

       All tests pass now. Here's what I fixed:
       ...

Example 3: Code Review

You> Review the changes in the last commit for security issues.

Cline: Let me examine the recent changes.

       [Runs: git diff HEAD~1]
       [Analyzes changes across 5 files]

       Security findings:

       ⚠️ HIGH: SQL injection risk in src/queries/search.py:47
          The user input is concatenated directly into the query.
          Fix: Use parameterized queries.

       ⚠️ MEDIUM: Missing input validation in src/routes/upload.py:23
          File size limit not enforced server-side.

       ℹ️ LOW: Verbose error messages in src/middleware/error.py
          Stack traces exposed in production responses.

       Would you like me to fix these issues?

Troubleshooting

Common Issues and Solutions

"Invalid API Key"

Error message: Authentication failed: Invalid API key

Causes:

• API key not entered correctly
• Using an expired or revoked key
• Copy-paste introduced extra spaces

Solutions:

1. Re-copy your API key from avelin.ai dashboard
2. Ensure no leading/trailing whitespace
3. Verify the key starts with sk-avelin-
4. Try pasting into a text editor first to verify the key is complete

"Model Not Found"

Error message: model "avelin-coding-pro" not found or 404 Not Found

Causes:

• Model name typo
• Base URL incorrect
• Model not available on your plan

Solutions:

1. Verify model name exactly: avelin-coding-pro (lowercase, hyphens)
2. Check you're using the correct base URL:
   • Anthropic provider: https://api.avelin.ai
   • OpenAI Compatible: https://api.avelin.ai/v1
3. Verify your AVELIN account has access to the model

"Connection Refused" / "Network Error"

Error message: ECONNREFUSED or Failed to fetch or Network error

Causes:

• No internet connection
• Firewall blocking api.avelin.ai
• Proxy/VPN interference
• Incorrect base URL (e.g., missing https://)

Solutions:

1. Verify internet connectivity: curl https://api.avelin.ai
2. Check your base URL starts with https://
3. If behind a corporate proxy, ensure api.avelin.ai is allowlisted
4. Try disabling VPN temporarily to test

"Context Window Exceeded"

Error message: prompt is too long or context_length_exceeded

Causes:

• Too many files read in a single session
• Large terminal outputs accumulated
• Context window set incorrectly in model configuration

Solutions:

1. Start a new Cline task (clears context)
2. Ensure Context Window is set to 1000000 in model configuration
3. Use .clineignore to exclude unnecessary files
4. Use @ mentions instead of letting Cline search broadly
5. Use subagents for research tasks to preserve main context

Tool Calls Failing / Malformed Output

Error message: Tool execution fails, or Cline generates invalid file paths/commands

Causes:

• Model doesn't support function calling well
• Extended thinking interfering with tool format
• Model configuration doesn't match actual capabilities

Solutions:

1. Use avelin-coding-pro or avelin-coding-fast — these have first-class tool calling
2. Ensure "Computer Use" is only enabled if the model actually supports it
3. Check that Max Output Tokens is set correctly (too low = truncated tool calls)
4. Try switching to the Anthropic provider for better tool calling compatibility

Streaming Not Working

Symptom: Response appears all at once instead of streaming token-by-token

Causes:

• Using OpenAI Compatible provider with a model that doesn't support streaming
• Network buffering issue
• VS Code rendering delay

Solutions:

1. Switch to the Anthropic provider — streaming is more reliable
2. Check that your network/proxy supports SSE (Server-Sent Events)
3. Ensure no antivirus is intercepting the connection

"Rate Limit Exceeded"

Error message: 429 Too Many Requests or Rate limit exceeded

Causes:

• Too many requests in a short period
• Auto-approve causing rapid-fire tool calls
• Multiple Cline instances running simultaneously

Solutions:

1. Cline has built-in retry logic — wait for it to retry automatically
2. Reduce auto-approve settings to slow down the loop
3. Check if multiple VS Code windows have Cline active
4. Contact AVELIN support if rate limits are consistently hit

Responses Stop Mid-Generation

Symptom: Cline output stops abruptly, appears incomplete

Causes:

• Max Output Tokens set too low
• Network timeout
• Model hit its own generation limit

Solutions:

1. Increase Max Output Tokens in model configuration (set to 65536)
2. Ask Cline to continue: "Continue from where you left off"
3. Check your internet connection stability
4. Try again — transient network issues resolve on retry

Cline Not Appearing in Sidebar

Causes:

• Extension not installed or disabled
• VS Code needs restart
• Extension compatibility issue

Solutions:

1. Check Extensions panel — search for "Cline" and ensure it's installed and enabled
2. Reload VS Code window: Cmd+Shift+P → "Developer: Reload Window"
3. Update VS Code to the latest version
4. Reinstall the Cline extension

Debugging Connection Issues

To verify your AVELIN API connection directly:

# Test Anthropic-compatible endpoint
curl -X POST https://api.avelin.ai/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: sk-avelin-YOUR_KEY_HERE" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "avelin-coding-pro",
    "max_tokens": 100,
    "messages": [{"role": "user", "content": "Say hello"}]
  }'

# Test OpenAI-compatible endpoint
curl -X POST https://api.avelin.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-avelin-YOUR_KEY_HERE" \
  -d '{
    "model": "avelin-coding-pro",
    "max_tokens": 100,
    "messages": [{"role": "user", "content": "Say hello"}]
  }'

If these return valid responses but Cline still fails, the issue is in Cline's configuration — re-check your settings.

Performance Tips

Optimizing for Speed

1. Use avelin-fast for simple tasks — significantly lower latency for trivial operations
2. Enable prompt caching — reduces cost and latency on repeated system prompts (90% savings)
3. Set realistic Max Output Tokens — requesting fewer tokens when full output isn't needed speeds up response time
4. Use .clineignore — prevents Cline from scanning irrelevant files, reducing initial context time
5. Keep one Cline session active — avoid opening multiple windows running Cline simultaneously

Optimizing for Cost

1. Start with cheaper models — use avelin-fast or avelin-coding-fast for initial exploration, switch to avelin-coding-pro only when needed
2. Use Memory Bank — prevents re-reading the same files every session
3. Write clear, specific requests — reduces the number of exploration steps Cline takes
4. Monitor token usage — Cline shows token counts; track spending per task
5. Set up .clineignore — avoids wasting tokens on node_modules, dist, etc.

Optimizing for Quality

1. Use Plan Mode first — let the model think before acting
2. Provide context with @ mentions — explicit file references reduce guesswork
3. Set custom instructions — project-specific conventions prevent rework
4. Use checkpoints — enables safe experimentation without losing progress
5. Review and iterate — use Cline's conversation to refine results

Why AVELIN for Cline?

1. Reliable Tool Calling

Cline fires dozens of tool calls per task — file reads, writes, terminal commands, browser actions. AVELIN's coding models provide first-class function calling with reliable structured outputs, minimizing retries and malformed calls.

2. Cost Optimization

Cline's autonomous loops can consume significant tokens. AVELIN optimizes costs:

• Prompt caching: ~80% cost reduction on repeated system prompts
• Competitive pricing: Lower per-token costs than direct provider APIs
• Pay-per-token: No subscription lock-in
• Model variety: Choose the right model for each task's complexity

3. 1M Context Windows

Cline sessions accumulate context quickly — every file read, terminal output, and browser interaction adds to the context. AVELIN's 1M-token context windows mean longer autonomous sessions without context loss.

4. Dual API Support

AVELIN supports both Anthropic and OpenAI API formats:

• Use Anthropic provider for full feature access (thinking, caching, streaming)
• Use OpenAI Compatible for simpler setups or when migrating from other providers
• Same models, same key — just different endpoints

5. Automatic Failover

• Provider failover: If one backend is slow, AVELIN switches to another
• Retry logic: Transient errors handled automatically
• 99.9% uptime: Enterprise-grade infrastructure
• Global edge: Low latency from regions worldwide

Related Guides

• API Quickstart — First API call in 5 minutes
• Hermes Agent Guide — Terminal-based agentic coding tool
• Claude Code Guide — Anthropic's CLI tool
• Cursor Guide — AI-first code editor
• Roo Code Guide — Cline fork with additional features
• Model Catalog — Full model comparison
• Cline Official Documentation — Complete Cline docs
• Cline GitHub Repository — Source code and issues