DEV Community: Preecha

Fixed: This version of Antigravity is no longer supported. Please update to receive the latest features!

Preecha — Mon, 02 Nov 2026 13:01:20 +0000

You open Antigravity, ready to test your APIs and keep building, and instead of your workspace you're greeted with a frustrating error: "This version of Antigravity is no longer supported. Please update to receive the latest features!"

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This error has affected thousands of developers using Google's Antigravity IDE since early 2026, causing workflow disruptions, lost productivity, and mounting frustration. According to recent reports on the Google AI Developers Forum, the issue stems from backend compatibility problems between newer Antigravity versions and Google's feature management services.

💡 While you're troubleshooting or waiting on a reinstall, don't let API development stall. Apidog gives you a single place to design, mock, test, debug, and document APIs — without depending on your IDE or Google's backend.

Understanding the Antigravity Version Error

What Is Antigravity?

Google Antigravity is an experimental, agent-first IDE powered by Gemini 3 Pro, launched in late 2025. Unlike traditional coding assistants, Antigravity uses autonomous AI agents to plan, code, test, and deploy entire applications with minimal human intervention.

Key features include:

Mission Control dashboard for monitoring parallel agents
Autonomous testing where agents debug their own code
Multi-agent workflows coordinating editor, terminal, and browser
Real-time API response processing for dynamic development

However, as an experimental platform, Antigravity has experienced significant stability issues, particularly around version management and backend service compatibility.

The Version Error Explained

The "This version of Antigravity is no longer supported" error occurs when:

Backend API incompatibility — your local Antigravity client version doesn't match the expected version on Google's backend services
Feature flag mismatches — the feature management service (antigravity-unleash.goog) rejects your client version
Forced deprecation — Google pushes updates that immediately deprecate older versions without graceful migration periods
Account entitlement bugs — subscription status conflicts between free and paid tiers

Common Error Variations

You might see these related errors:

"Agent execution terminated due to error"
"Antigravity server crashed unexpectedly. Please restart to fully restore AI features"
"Your current account is not eligible for Antigravity"
HTTP 504 (Gateway Timeout) or 503 (Service Unavailable) errors

All of these stem from the same root cause: version compatibility issues between your client and Google's backend infrastructure.

How to Fix the "No Longer Supported" Error

Method 1: Full Reinstall (Desktop / Standalone App)

If you're using the Antigravity app itself and the message won't go away after a normal update, do a clean reinstall:

Step 1: Uninstall the current app completely. Remove Antigravity so no old files or config are left behind.

Step 2: Download the latest version from the official download page: https://antigravity.google/download/

Step 3: Follow the official steps. Complete steps 1, 2, and 3 on that page (download, install, and any sign-in or setup they describe).

Step 4: Sign in again. Use your Google account when prompted so the new install gets fresh credentials.

Many users report this clears the "no longer supported" message when a simple in-app update does not.

Method 2: Update and Re-authenticate (clawdbot / Moltbot / daemon)

If the error appears in a bot or daemon (e.g. clawdbot, Moltbot), the process is different: update the code, restart the daemon, and re-run Google Antigravity OAuth if needed.

Step 1: Update the code and restart the daemon. Often the message goes away after pulling the latest code and restarting:

cd ~/moltbot-ref && git pull
clawdbot daemon restart

Replace ~/moltbot-ref with your actual bot repo path if it's different.

Step 2: If the message persists, re-authenticate with Antigravity. The OAuth tokens used by the bot may have expired or been invalidated. Re-run the Google Antigravity OAuth flow:

clawdbot configure

Follow the prompts to sign in again with Google and complete the Antigravity OAuth flow. When it's done, restart the daemon:

clawdbot daemon restart

After that, the bot should be using new tokens and the "no longer supported" message should stop.

Conclusion

The "This version of Antigravity is no longer supported" message is frustrating but fixable. For desktop users, a full uninstall followed by a fresh install from the official Antigravity download page and signing in again usually resolves it. For bot or daemon setups (e.g. clawdbot, Moltbot), pull the latest code, restart the daemon, and re-run clawdbot configure to refresh Antigravity OAuth tokens if the error persists.

Until version compatibility and backend stability improve, having a reliable API tool like Apidog in your stack helps you stay unblocked. Fix the Antigravity error with the steps above, then get back to building.

How to Use Kimi K2.5 with Cursor

Preecha — Mon, 02 Nov 2026 02:04:55 +0000

Have you ever wanted to use one of the most advanced open-source AI models inside your favorite coding IDE but found it missing from the dropdown? With Kimi K2.5, a powerful multimodal, agent-capable model from Moonshot AI, you can now integrate it into Cursor even when it’s not natively supported. Instead of waiting for official support, this guide shows you exactly how to connect Kimi K2.5 to Cursor so you can use it for coding, reasoning, and tool-driven workflows straight from your editor.

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Why Add Kimi K2.5 to Cursor?

Cursor is a popular AI-assisted development environment that lets you interact with AI models directly in your editor. However, not all models are available in the built-in model selector, especially freshly released or emerging open models like Kimi K2.5. If you want to leverage K2.5's powerful coding and reasoning capabilities but don't see it in Cursor's dropdown, you're not alone. Fortunately, there's a practical workaround that involves adding Kimi K2.5 as a custom model using provider endpoints like OpenRouter or direct Moonshot API access.

This matters because developers increasingly want flexibility — the ability to choose which model powers their AI workflows instead of being locked into whatever the IDE comes bundled with. In this guide, we'll walk through exactly how to make that connection work using Cursor's configuration settings, custom model support, and API keys.

💡Pro Tip: Test your API endpoints in Apidog before configuring in Cursor. This prevents authentication errors and ensures your custom model integration works from the start.

Want a great API Testing tool that generates beautiful API Documentation? Want an integrated, All-in-One platform for your Developer Team to work together with maximum productivity? Apidog delivers all your demands, and replaces Postman at a much more affordable price!

Understanding Kimi K2.5's Capabilities

Before integrating, here's what makes Kimi K2.5 valuable for Cursor workflows:

Multimodal capability: Handles text, code, and visual data natively
Agentic design: Built for reasoning, tool calling, and multi-step workflows
Open API access: Available through Moonshot's API endpoints and third-party providers like OpenRouter
Large context window: Up to 256K tokens for complex project understanding
Cost efficiency: More affordable than Western alternatives for equivalent performance

While Kimi K2.5 isn't natively supported in Cursor yet, its OpenAI-compatible API means you can connect it manually and use it almost as if it were native.

Choose Your Kimi K2.5 API Provider

✅ Option A (via OpenRouter)

OpenRouter acts as a universal API gateway that gives you access to multiple AI models through one interface:

Sign up at OpenRouter.ai
Add credits for API usage
Generate API key in Settings → Keys

This key authenticates your requests to OpenRouter and supports routing to Kimi K2.5.

✅ Option B (via the Moonshot API)

If you have access to Moonshot's developer portal:

Log in to Moonshot's platform
Create or retrieve your API key for their endpoints
Use OpenAI-compatible endpoint - Moonshot supports moonshot/kimi-k2.5

Moonshot's endpoint provides full 256K context and multimodal support with the simplicity of a direct API connection.

💡Pro Tip: Moonshot's official endpoint uses OpenAI-compatible format, making it ideal for Cursor's custom model configuration.

Configure Cursor for Custom Models

Once you have your provider configured, it's time to point Cursor to the right endpoint.

🧩 Open Cursor Settings

Open Cursor
Navigate to Settings → Models.

🔌 Update the Base API URL

Depending on your provider:

OpenRouter Configuration:

Base URL: https://openrouter.ai/api/v1
Authentication: Your OpenRouter API key

Moonshot Configuration:

Base URL: https://api.moonshot.cn/v1
Authentication: Your Moonshot API key

⚠️Important: If you encounter "Provider not supported" errors, ensure the base URL points to a provider that explicitly supports Kimi K2.5.

Add Kimi K2.5 as a Custom Model

Cursor's custom model UI lets you add models not listed natively.

Add Kimi K2.5 Model

In Settings → Models, select Add Custom Model
For the model slug (name), enter:
- moonshotai/kimi-k2.5 if accessing via OpenRouter
- moonshot/kimi-k2.5 if accessing via Moonshot directly
Save the model configuration
Refresh Cursor settings if the model doesn't appear in the list

✅Quick Win: After adding the model, it should appear in your dropdown alongside native models.

Troubleshooting

Model not showing up? Check that the base URL is correct and your API key is valid.
Authentication errors? Verify your API key hasn't expired and has sufficient credits.
Rate limiting? Some providers may limit requests per minute.

Test Your Kimi K2.5 Integration

Now that Cursor is pointing at Kimi K2.5, test it with a simple prompt to ensure everything's working:

# Example Prompt
Write a function in Python that intersects two large lists efficiently using built-ins and type hints.

Cursor will send this to Kimi K2.5 via your configured provider and display the response. If nothing appears or the model falls back to a default, revisit your custom model settings and ensure no typos in the model slug or API endpoint.

💡Pro Tip: Start with simple coding tasks before moving to complex multi-file projects. This helps you verify the model's reasoning capabilities are working correctly.

Practical Applications in Cursor

Once Kimi K2.5 is configured, you can use it for typical AI-powered development tasks:

💡Code Generation and Refactoring

Generate functions with clearly defined docstrings and type annotations
Refactor complex modules with improved organization and readability
Suggest patterns and idioms aligned with modern Python best practices

# Example Prompt
Refactor this class into smaller functions with clear single responsibilities.

🧠Reasoning and Debugging

Explain dense logic or algorithms with clear examples
Get optimization suggestions for performance bottlenecks
Review architectural decisions and propose improvements

# Example Prompt
Explain why this recursive algorithm might exceed stack limits and propose an iterative version.

🔄Workflow Automation

= Generate boilerplate code for common project structures

Create test suites automatically from API specifications
Set up CI/CD pipelines for automated testing

💡Apidog Integration: When testing API workflows, use Apidog's visual interface to debug requests, validate responses, and generate production-ready code.

Advanced Configuration Options

🌐Environment Variable Management

For production setups, use environment variables instead of hardcoded credentials:

# Add to your shell profile (.zshrc, .bashrc)
export OPENROUTER_API_KEY="your-key-here"
export MOONSHOT_API_KEY="your-key-here"

Then reference these in Cursor's configuration using the standard ${ENV_VAR} format.

🚀Performance Optimization

Reduce context length for faster responses when not needed
Adjust temperature settings (0.7 for creative, 0.3 for analytical)
Use streaming responses for real-time feedback in long generations
Cache common queries when working with repetitive tasks

🔒Security Best Practices

Never commit API keys to version control
Use HTTPS endpoints only
Monitor usage and set spending limits in provider dashboards
Rotate API keys regularly for production environments

⚠️Security Note: Using Apidog for API testing helps you validate security configurations before production deployment.

Troubleshooting Common Issues

Issue: "Model not responding"

Cause: Incorrect API endpoint or authentication failure
Solution: Verify your base URL and API key, then restart Cursor

Issue: "Rate limit exceeded"

Cause: Too many requests to the provider in a short time
Solution: Wait 60 seconds and retry, or upgrade your provider plan

Issue: "Context window too small"

Cause: Provider defaulting to 4K context instead of Kimi K2.5's 256K
Solution: Check provider settings and ensure full context is enabled

Issue: "Authentication token expired"

Cause: API key has reached its expiration date
Solution: Generate a new API key and update your configuration

If you want to swap Kimi K2.5 for a code-specialized model in the same setup, running Devstral in Cursor and Windsurf via OpenRouter follows an identical custom-provider configuration with only the model ID changed.

Conclusion

Integrating Kimi K2.5 with Cursor unlocks a flexible, powerful AI model in your favorite development environment, even without native support. By configuring custom API endpoints through OpenRouter or Moonshot, you gain access to K2.5's multimodal capabilities, 256K context window, and agentic behaviors. This approach gives you complete control over your AI workflow — the ability to choose your model based on project needs, budget, and privacy requirements. Get set up in minutes, validate your configuration, and start leveraging Kimi K2.5's advanced reasoning and coding capabilities directly where you write code.

Next Steps:

Choose your provider (OpenRouter for flexibility, Moonshot for direct access)
Configure API credentials in your environment or Cursor settings
Add Kimi K2.5 as a custom model using the correct slug
Test integration with simple coding tasks before starting important projects
Use Apidog to validate API workflows and debug issues efficiently

💡Final Tip: Join communities of developers using custom AI models to share workflows, troubleshooting tips, and creative solutions for maximizing Kimi K2.5's potential in Cursor.

How to Use Kimi K2.5 with Claude Code

Preecha — Mon, 02 Nov 2026 01:01:49 +0000

What if you could keep Claude Code's agentic workflow while tapping Kimi K2.5's trillion-parameter reasoning engine? By routing Claude Code's Anthropic Messages API calls through Moonshot's compatible endpoint, you combine one of the best coding interfaces with one of the most capable open-source models available today.

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The Opportunity: Breaking Vendor Lock-in

Claude Code has established itself as a premier agentic coding tool — sophisticated repository scanning, intelligent file editing, seamless terminal integration. But proprietary model providers come with constraints: rate limits, geographic restrictions, pricing concerns. Meanwhile, Moonshot AI's Kimi K2.5 delivers comparable coding performance with its 15 trillion-token training dataset, native multimodal capabilities, and full API compatibility.

The integration works because Claude Code communicates via the Anthropic Messages API specification, and Moonshot exposes an identical endpoint at /anthropic/v1/messages. You redirect Claude Code's backend traffic to Kimi K2.5 without modifying the CLI tool itself — keeping your existing muscle memory while unlocking an alternative model provider.

💡 Want a great API testing tool that generates beautiful API Documentation? Want an integrated, all-in-one platform for your developer team to work together with maximum productivity? Apidog delivers all your demands and replaces Postman at a much more affordable price!

How the Anthropic Compatibility Layer Works

Before configuring anything, understand what happens under the hood. Claude Code expects an Anthropic-style API response structure: specific message formatting, streaming protocols, and tool-calling conventions. Moonshot's compatibility layer translates between these formats, letting Kimi K2.5 respond to prompts as if it were Claude Sonnet or Opus.

Moonshot operates two primary domains depending on your location:

Global: https://api.moonshot.ai/anthropic
China: https://api.moonshot.cn/anthropic

Both endpoints route to the same underlying infrastructure but optimize for regional latency and compliance requirements. Moonshot also supports OpenAI-compatible (/v1/chat/completions) and Anthropic-compatible (/anthropic/v1/messages) endpoints simultaneously — Claude Code specifically requires the latter.

When configured correctly, Claude Code sends its standard payload — system prompts, conversation history, tool definitions — to Moonshot's endpoint. Kimi K2.5 processes these requests and returns responses Claude Code interprets natively, so file editing, bash command execution, and codebase analysis all work without modification.

Quick Setup: Environment Variables

For immediate testing or temporary usage, environment variables are the fastest path — no file modifications, works in any terminal session.

Prerequisite: a valid Moonshot API key from the Moonshot Open Platform.

Export the following variables, then launch Claude Code:

# Route Claude Code to Moonshot's Anthropic-compatible endpoint
export ANTHROPIC_BASE_URL="https://api.moonshot.ai/anthropic"

# Authenticate using your Moonshot key (Claude Code interprets this as Anthropic auth)
export ANTHROPIC_AUTH_TOKEN="YOUR_MOONSHOT_API_KEY"

# Specify Kimi K2.5 as the primary model
export ANTHROPIC_MODEL="kimi-k2.5"

# Ensure fast operations also use K2.5 (avoids model switching errors)
export ANTHROPIC_SMALL_FAST_MODEL="kimi-k2.5"

# Optional: reduce non-essential API traffic for cleaner logs
export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1

# Optional: extend timeout for complex reasoning tasks (10 minutes)
export API_TIMEOUT_MS=600000

# Launch Claude Code
claude

If you operate within mainland China, substitute the base URL:

export ANTHROPIC_BASE_URL="https://api.moonshot.cn/anthropic"

These variables direct all API traffic to Moonshot's servers rather than Anthropic's infrastructure. ANTHROPIC_AUTH_TOKEN accepts your Moonshot key because Claude Code sends it in the Authorization header, which Moonshot validates against its own key database.

💡 Pro Tip: Test your API key in Apidog before editing configuration files. This prevents downtime from authentication errors.

Persistent Configuration with settings.json

Environment variables work for single sessions, but maintaining them across terminal restarts is tedious. Claude Code supports hierarchical JSON configuration through settings.json files — persistent, project-specific, or user-global.

Create or edit ~/.claude/settings.json in your home directory:

{
  "$schema": "https://json.schemastore.org/claude-code-settings.json",
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.moonshot.ai/anthropic",
    [REDACTED CREDENTIAL],
    "ANTHROPIC_MODEL": "kimi-k2.5",
    "ANTHROPIC_SMALL_FAST_MODEL": "kimi-k2.5",
    "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1",
    "API_TIMEOUT_MS": "600000"
  }
}

This configuration applies globally across all projects. For granular control, use Claude Code's configuration hierarchy:

Managed settings (/etc/claude-code/ or /Library/Application Support/ClaudeCode/) — organization-wide policies
User settings (~/.claude/settings.json) — your personal defaults
Project settings (.claude/settings.json) — shared team configuration
Local project settings (.claude/settings.local.json) — personal project overrides

For team environments, commit .claude/settings.json to your repository with the shared configuration, and keep individual API keys in .claude/settings.local.json (which Claude Code automatically gitignores). For example:

.claude/settings.json (committed):

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.moonshot.ai/anthropic",
    "ANTHROPIC_MODEL": "kimi-k2.5",
    "ANTHROPIC_SMALL_FAST_MODEL": "kimi-k2.5"
  }
}

.claude/settings.local.json (gitignored, personal):

{
  "env": {
    [REDACTED CREDENTIAL]
  }
}

After modifying these files, completely exit Claude Code and restart your terminal so the configuration loads fresh. Unlike environment variables, settings.json changes persist across system reboots and apply automatically whenever you launch claude.

Verification and Troubleshooting

Verify the integration before trusting it with production code. Launch Claude Code and start a test conversation:

What model you are currently running, and which API endpoint are you connected to?

Kimi K2.5 should respond with its model identifier and confirm the Moonshot endpoint. If you receive errors, work through these common issues systematically.

Authentication Failures (401 errors)

Use ANTHROPIC_AUTH_TOKEN (not ANTHROPIC_API_KEY) when using third-party endpoints
Confirm your Moonshot API key is active and has available credits
Ensure the base URL ends with /anthropic — not /v1, and no trailing slashes

Model Recognition Errors

Check that ANTHROPIC_MODEL and ANTHROPIC_SMALL_FAST_MODEL both specify kimi-k2.5
If Claude Code defaults to Anthropic's models, your environment variables aren't loading — verify your shell exports or JSON syntax

Timeouts During Long Operations

Increase API_TIMEOUT_MS beyond 600000 for extensive refactoring tasks
Set CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 to reduce background API calls competing with your primary requests

Permission and Context Issues

If Claude Code prompts for login despite configuration, check that hasCompletedOnboarding is set to true in ~/.claude.json (a known issue on Windows)
Verify no conflicting ANTHROPIC_API_KEY environment variable overrides your ANTHROPIC_AUTH_TOKEN

For debugging, launch Claude Code with verbose logging or run the /doctor command within the CLI to inspect which configuration files loaded and their precedence order.

Optimizing Performance and Reliability with Kimi K2.5

Kimi K2.5 supports a 256,000-token context window, matching Claude's capabilities for large codebase analysis. Network latency between your location and Moonshot's servers may differ from Anthropic's infrastructure.

To minimize latency:

Use the geographic endpoint closest to your location (api.moonshot.ai for global, api.moonshot.cn for China)
Enable CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC to prevent background model calls from consuming bandwidth
Configure appropriate timeout values — Kimi K2.5's reasoning capabilities sometimes require longer processing for complex architectural tasks

Additionally, Kimi K2.5 offers a "thinking mode" you can control through the same interface. Claude Code doesn't natively expose thinking budget controls for third-party models, but you can adjust the MAX_THINKING_TOKENS environment variable if Moonshot's compatibility layer supports extended reasoning parameters.

For teams managing multiple projects, script the configuration. Create a shell alias that sets these variables on demand:

alias claude-kimi='export ANTHROPIC_BASE_URL="https://api.moonshot.ai/anthropic" && export ANTHROPIC_MODEL="kimi-k2.5" && claude'

This lets you switch between Anthropic and Moonshot backends by choosing different aliases or launch scripts depending on project requirements.

Conclusion

Integrating Kimi K2.5 with Claude Code demonstrates the power of API compatibility standards — swap the underlying model while preserving superior tooling. By routing Claude Code's Anthropic-compatible requests through Moonshot's infrastructure, you gain Kimi K2.5's advanced reasoning capabilities without sacrificing Claude Code's agentic workflow, repository management, or terminal integration.

When working with APIs — whether testing Moonshot's endpoints, debugging Claude Code configurations, or building integrations — streamline your development workflow with Apidog. It provides visual API testing, automatic documentation generation, and collaborative debugging tools that eliminate boilerplate code. Download Apidog today to accelerate your API development and testing processes.

How to Connect Kimi K2.5 to OpenClaw/ClawdBot?

Preecha — Sun, 01 Nov 2026 14:01:57 +0000

Ever wondered what it takes to combine a cutting-edge open-source model with a powerful autonomous agent? Integrating Kimi K2.5 with OpenClaw (formerly ClawdBot/MoltBot) lets you create a self-driven AI agent that can reason, code, and automate tasks based on natural language with minimal configuration. Support for Kimi K2.5 in OpenClaw means you can use a leading agent-friendly model in an open-source assistant platform without subscription lock-in.

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AI assistants are ubiquitous, but most rely on proprietary APIs that cost money and restrict access. OpenClaw fills this gap by offering an open-source agent platform that runs locally or in the cloud. However, connecting a model like Kimi K2.5 (a powerful multimodal, agent-swarm capable model developed by Moonshot AI) requires detailed configuration. Getting this setup right means your agent can do complex tasks like coding, reasoning, and multi-step workflows all while staying free and open.

What Are OpenClaw and Kimi K2.5?

OpenClaw (ClawdBot / MoltBot)

OpenClaw is an open-source autonomous AI assistant platform originally known as ClawdBot or MoltBot. It lets you run AI agents that can respond to commands, manage tasks, integrate with messaging platforms like Telegram, and handle tool calls on your machine. It is developed in TypeScript/Swift under an MIT license and supports local and cloud deployment.

Why this matters: OpenClaw provides the agent framework — the core runtime and tooling that lets AI models connect to real-world tasks instead of just reasoning in isolation.

The Kimi K2.5 Model

Kimi K2.5 is a state-of-the-art open-source multimodal model from Moonshot AI, trained on trillions of multimodal tokens with a massive context window (256K tokens) that supports coding, reasoning, and visual tasks. Its agent swarm architecture enables multiple parallel tool calls, making it suitable for workflows that involve long reasoning and complex tool execution.

Core features of Kimi K2.5 include:

Massive context window for long workflows
Multimodal reasoning (text and images)
Designed for agent-centric use cases
Open-weight model with multiple access methods (API/providers)

Installing OpenClaw

You'll first need to clone and prepare OpenClaw.

Step 1: Clone OpenClaw

In your terminal, run:

git clone https://github.com/clawdbot/moltbot.git openclaw
cd openclaw

This fetches the OpenClaw repository to your local system. Keep this directory for configuration and future updates.

Step 2: Install Dependencies

Run:

npm install
# Or with pnpm
pnpm install

This installs all required dependencies (Node.js/Bun, CLI tools, configuration files) needed to run OpenClaw locally.

Step 3: Set Up Environment Variables

Create a .env file in the project root. Inside it, you'll define your API keys for providers like Moonshot or Fireworks/OpenRouter:

cp .env.example .env

Edit .env to add your Kimi API key:

# .env
KIMI_API_KEY=sk-k2-5-your-api-key-here
KIMI_MODEL=kimi-k2.5-preview
KIMI_BASE_URL=https://api.kimi.ai/v1

Create your kimi k2.5 api-key at Moonshot.ai:

You can also add keys for OpenRouter or other providers if you wish to use a different backend. Ensure that your key strings are not surrounded by quotes when used in real production.

{
  "model": {
    "primary": "openrouter/moonshotai/kimi-k2.5"
  },
  "models": {
    "openrouter/moonshotai/kimi-k2.5": {}
  },
  "api": {
    "openrouter": {
      "key": "your_openrouter_api_key_here"
    }
  }
}

Security tip: Never commit .env to version control. Add it to .gitignore immediately:

echo ".env" >> .gitignore

💡Pro Tip: Test your API key in Apidog before editing configuration files. This prevents downtime from authentication errors.

Configuring OpenClaw for Kimi K2.5

Now that OpenClaw is installed, you need to configure it to use Kimi K2.5 as the primary model.

Updating the OpenClaw Configuration

There are two common configuration file locations:

Local project config: openclaw.json
Global config: ~/.openclaw/openclaw.json

Open and modify one of those to include the following JSON snippet:

{
  "authProviders": {
    "moonshot": {
      "provider": "openrouter",
      "apiKey": "${env.KIMI_API_KEY}",
      "model": "moonshot/kimi-k2.5-preview"
    }
  },
  "chatEngines": {
    "default": {
      "provider": "moonshot",
      "maxTokens": 2048,
      "temperature": 0.7
    }
  }
}

Replace ${env.KIMI_API_KEY} with your actual API key if not using environment variable substitution. OpenClaw uses the openrouter/<provider>/<model> format to identify models.

Using Environment Variables for Configuration

You can also create a .env to store the moonshot.ai/kimi k2.5 configuration as follows:

# In .env
OPENCLAW_PROVIDER=moonshot
OPENCLAW_MODEL=moonshot/kimi-k2.5-preview
OPENCLAW_API_KEY=sk-k2-5-your-api-key-here

OpenClaw will read these at startup if no openclaw.json exists.

Alternative: Using Fireworks AI (Another Provider)

As another provider, you can use Fireworks.ai to configure the Kimi K2.5 backend:

# Set Fireworks API key
export FIREWORKS_API_KEY=your-fireworks-key-here

# Configure OpenClaw
echo '{
  "authProviders": {
    "fireworks": {
      "provider": "fireworks",
      "apiKey": "${env.FIREWORKS_API_KEY}",
      "model": "accounts/fireworks/models/kimi-k2.5-preview"
    }
  },
  "chatEngines": {
    "default": {
      "provider": "fireworks"
    }
  }
}' > openclaw.json

Replace YOUR_FIREWORKS_API_KEY with your Fireworks API key. This script configures your OpenClaw JSON to use Fireworks as a provider with Kimi K2.5.

Running OpenClaw

After configuration, you need to start OpenClaw's gateway to make the AI agent accessible.

Run the Gateway

In your project directory:

npm run start:gateway
# Or if using the CLI
openclaw gateway start

This starts the OpenClaw server and listens for incoming agent events and commands. Use the CLI or integrate with messaging platforms (e.g., Telegram) for interactions. Ensure you updated to the latest version (like v2026.1.30) which explicitly added support for the Kimi K2.5 model and useful CLI features like shell completion.

Alternative: Run the Interactive Shell

For local testing:

npm run start:cli
# Or
openclaw shell

This drops you into an interactive prompt where you can chat with Kimi directly.

Validation

Once running, test connectivity by entering the OpenClaw shell. Enter simple commands like chat or agent prompts to confirm the model responds.

Example:

$ openclaw shell
> /chat "What is the weather in Tokyo?"

If Kimi K2.5 responds, your integration is working.

Test Model Configuration

Verify Kimi is the active provider:

openclaw config list

Should show:

Active Provider: moonshot
Model: moonshot/kimi-k2.5-preview
Base URL: https://api.kimi.ai/v1

Test API Connectivity

Run a direct test:

openclaw test-model kimi "Explain quantum computing in simple terms"

This bypasses the chat engine and tests the raw model connection.

Using Kimi K2.5 within OpenClaw

With everything running, you can now use Kimi K2.5 to power your AI agent's reasoning and workflows:

Simple Tips

Start interactive sessions through openclaw chat
Run tasks over your messaging integrations after pairing with Telegram or others
Keep your API keys secure in environment variables
Monitor usage with openclaw usage --provider moonshot

Integration with Telegram

Configure Telegram bot integration:

# Set Telegram bot token
export TELEGRAM_BOT_TOKEN=your-telegram-token-here

# Update openclaw.json
echo '{
  "integrations": {
    "telegram": {
      "enabled": true,
      "botToken": "${env.TELEGRAM_BOT_TOKEN}"
    }
  }
}' >> openclaw.json

# Restart gateway
npm run start:gateway

Now you can chat with Kimi K2.5 through Telegram.

Using Kimi for Code Generation

# In OpenClaw shell
> /agent "Create a Python script that fetches weather data and sends it to Slack"

Kimi will use its agent capabilities to write the code, suggest dependencies, and outline deployment steps.

Rate Limits and Cost Management

While OpenClaw now offers free access to models like Kimi K2.5 and Kimi Coding with a single login, pay attention to provider limits (free vs paid).

Monitor your usage:

openclaw usage --provider moonshot --daily

Set spending alerts:

export MOONSHOT_SPENDING_LIMIT=10  # $10 USD

Advanced Configuration

Custom System Prompts

Override Kimi's default behavior:

{
  "chatEngines": {
    "codeAssistant": {
      "provider": "moonshot",
      "systemPrompt": "You are an expert code reviewer focused on security vulnerabilities. Be direct and critical."
    }
  }
}

Use the custom engine:

openclaw chat --engine codeAssistant "Review this function for SQL injection risks"

Model Parameters

Fine-tune generation parameters:

{
  "chatEngines": {
    "creative": {
      "provider": "moonshot",
      "temperature": 0.9,
      "topP": 0.95,
      "maxTokens": 4096
    }
  }
}

Multiple Providers

Configure fallback providers:

{
  "chatEngines": {
    "primary": {
      "provider": "moonshot",
      "fallbackProviders": ["openai", "anthropic"]
    }
  }
}

If Kimi is unavailable, OpenClaw will automatically switch to the fallback provider.

Troubleshooting

"Provider not found" error

Cause: openclaw.json not loaded or syntax error

Fix: Validate JSON syntax:

node -e "console.log(JSON.parse(require('fs').readFileSync('openclaw.json', 'utf8')))"

"Authentication failed" error

Cause: Invalid API key or expired token

Fix: Verify key:

curl -H "Authorization: Bearer $KIMI_API_KEY" https://api.kimi.ai/v1/models

"Model not available" error

Cause: Wrong model identifier

Fix: List available models:

curl -H "Authorization: Bearer $KIMI_API_KEY" https://api.kimi.ai/v1/models | jq '.data[].id'

Connection timeout

Cause: Network issues or firewall blocking

Fix: Test connectivity:

telnet api.kimi.ai 443

Conclusion

Connecting Kimi K2.5 to OpenClaw (ClawdBot/MoltBot) transforms the agent platform into a powerful, open-source AI system capable of reasoning, coding, and multimodal tasks. By cloning the repo, configuring providers and API keys, and starting the gateway, you unlock a fully operational AI assistant that leverages a leading open-weight model without proprietary lock-in.

Whether you want to automate workflows, integrate with chat platforms, or build next-generation agents, this integration gives you the flexibility and capability you need.

How to Post on Moltbook with Moltbook API?

Preecha — Sun, 01 Nov 2026 13:01:22 +0000

Moltbook is a dedicated social platform where AI agents interact, share ideas, and build communities. The Moltbook API enables autonomous agents to post content, comment, upvote, and engage programmatically. Humans can view feeds freely, but active participation happens exclusively through verified agents using the Moltbook API.

Try Apidog today

Developers integrate the Moltbook API to give their AI agents a voice on Moltbook. Apidog streamlines this process significantly — download Apidog for free to import API endpoints, test authentication flows, generate accurate code snippets in multiple languages, and debug responses in real time.

Understanding Moltbook and Its Role in the Agent Ecosystem

Moltbook operates as a Reddit-inspired network built specifically for AI agents. Communities form as Submolts, agents earn karma through contributions, and content surfaces via algorithmic feeds (hot, new, top). The platform launched in beta around late 2025 and grew rapidly into the central hub for agent-to-agent discourse by February 2026.

Moltbook website interface

The Moltbook API powers all agent actions. Agents authenticate once, then use Bearer [REDACTED] to create posts, reply in threads, and interact. This design keeps Moltbook agent-native while allowing human observation without direct write access.

Many developers start by exploring the Moltbook API in tools like Apidog, which visualizes request/response cycles and catches subtle issues such as header mismatches or redirect behavior.

Prerequisites for Using the Moltbook API

Accessing the Moltbook API requires an agent and a valid API key. Follow these steps:

Register an agent on Moltbook. Agents typically register via POST /api/v1/agents/register (community reports confirm this endpoint exists in practice). Provide a name, description, and optional avatar. Upon success, the Moltbook API returns an API key (often prefixed moltbook_sk_ or moltdev_). Some agents receive a claim URL for human verification via X/Twitter.
Claim and activate the agent. For claimed agents, post the verification code on X as instructed. Claimed agents gain higher trust, better visibility, and fewer rate restrictions on Moltbook.
Obtain and secure the API key. Store the key securely — never expose it in client-side code or public repositories. The key authenticates all subsequent Moltbook API calls.

Community threads highlight that even deleted agents sometimes retain valid keys for a short window (up to 24 hours), though production systems should treat deletion as immediate revocation.

Moltbook API Authentication Basics

The Moltbook API uses Bearer [REDACTED] authentication for most operations. Set this header on every request:

Authorization: Bearer [REDACTED]_KEY

The base URL remains consistent:

https://www.moltbook.com/api/v1

Always use www to avoid redirect issues that strip Authorization headers. Test authentication in Apidog to confirm Moltbook API access works correctly for your agent.

Creating a Post with the Moltbook API

Posting content is the core function for Moltbook agents. Send a POST request to /posts with a JSON payload:

submolt (string, required): target community, e.g., "general", "agents", "aitools", "infrastructure"
title (string, required): engaging headline, 10–120 characters recommended
content (string, required): main body text; supports plain text, emojis, limited markdown

Example request:

curl -X POST "https://www.moltbook.com/api/v1/posts" \
  -H "Authorization: Bearer [REDACTED]" \
  -H "Content-Type: application/json" \
  -d '{
    "submolt": "general",
    "title": "Testing Moltbook API Posting Capabilities",
    "content": "This post was created programmatically via the Moltbook API. Hello agent world! Exploring interactions on Moltbook."
  }'

Success returns HTTP 201 Created with the new post object:

{
  "id": "uuid-string-here",
  "title": "Testing Moltbook API Posting Capabilities",
  "content": "...",
  "agent_id": "agent-uuid",
  "submolt": "general",
  "created_at": "2026-02-01T...",
  "karma": 1,
  "upvotes": 0
}

Handle these failure modes:

401 Authentication Required → invalid/missing Bearer [REDACTED], or a redirect stripped the header
400 Bad Request → missing title, content, or invalid submolt name
429 Too Many Requests → rate limit hit (undocumented thresholds exist; space requests)

Use Apidog to mock these responses during development and iterate on payloads quickly.

Posting Comments via the Moltbook API

To reply to a Moltbook post, call:

POST /api/v1/posts/{post_id}/comments

The payload requires only content (string, required):

curl -X POST "https://www.moltbook.com/api/v1/posts/3e8da1a7-84ce-4b2b-af61-87a675d7eaf1/comments" \
  -H "Authorization: Bearer [REDACTED]" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Solid first post on Moltbook! My agent appreciates the experiment with the Moltbook API."
  }'

Replace {post_id} with the target UUID. Comments inherit the parent post's submolt and visibility.

Upvoting and Other Interactions

Boost visibility on Moltbook with an upvote:

curl -X POST "https://www.moltbook.com/api/v1/posts/{post_id}/upvote" \
  -H "Authorization: Bearer [REDACTED]"

No body is required. Repeated calls from the same agent usually noop or increment minimally. Similar patterns apply to downvotes (if supported) and other engagement endpoints.

Choosing the Right Submolt

Submolts structure Moltbook agent activity:

general — broad agent chatter
agents — workflows, architectures, tooling
aitools — sharing APIs, proxies, payment infra
infrastructure — compute, networking, memory solutions

Post consistently in relevant Submolts to build karma and followers. Query active Submolts via undocumented GET endpoints or browse the web interface.

Debugging Common Moltbook API Issues

Real-world usage reveals a few pitfalls:

Redirect header loss — non-www → www drops Authorization. Fix: pin requests to www.moltbook.com.
401 on POST /posts — some agents report intermittent failures despite valid keys. Workaround: retry after GET /agents/me succeeds; re-claim if needed.
Expired/invalid tokens — use fresh keys; avoid sharing permanent keys.
Rate limiting — undocumented, but high-frequency posting triggers 429. Implement exponential backoff.

Apidog shines here: log full request/response pairs, replay failed calls, and inspect headers without re-running scripts.

Advanced Techniques for Moltbook API Power Users

Batch engagement — script comment + upvote sequences after posting.
Feed monitoring — poll GET /feed or /posts for new content and react programmatically.
Reputation building — track agent stats via GET /agents/me; aim for high karma through quality posts on Moltbook.
Error resilience — parse error messages carefully; log X-Request-ID headers if present for support.
Multi-agent orchestration — coordinate several agents to discuss topics, cross-comment, and amplify reach.

Apidog Accelerates Moltbook API Work

Apidog is a reliable choice for developers working with the Moltbook API. Organize endpoints into collections for authentication, posting, and commenting to keep workflows manageable. Apidog also auto-generates client code in Python, Node.js (fetch), and Go, so you can integrate faster. With mock servers, you can simulate Moltbook API responses even when working offline. The free plan covers most common use cases — download Apidog to significantly reduce integration time.

Security Best Practices for Moltbook API Usage

Rotate keys periodically.
Use environment variables or secret managers.
Validate content length and sanitization client-side.
Monitor for abuse flags that could suspend agents.

Final Thoughts: Building Presence on Moltbook with the Moltbook API

The Moltbook API transforms passive AI into active community members on Moltbook. Master authentication, master the /posts endpoint, handle errors gracefully, and your agents contribute meaningfully. Start small — one test post — then scale to threaded discussions and Submolt leadership.

Experiment today. The agent internet evolves quickly, and consistent posting via the Moltbook API positions your agents at the center.

How to Install OpenClaw on Mac Mini and OpenClaw on Cloudflare (Step by Step)

Preecha — Sun, 01 Nov 2026 02:01:10 +0000

OpenClaw on Mac Mini and OpenClaw on Cloudflare represent two practical approaches to running a powerful, self-hosted personal AI agent that performs real-world tasks autonomously. OpenClaw functions as an open-source runtime for AI assistants. It connects large language models primarily Anthropic's Claude to local files, system commands, external APIs, and messaging platforms. The agent handles actions such as processing emails, updating calendars, conducting research, automating workflows, and maintaining persistent memory across interactions. Users communicate with it through apps like WhatsApp, Telegram, or Discord, and it executes tasks with high autonomy when configured appropriately.

Try Apidog today

💡For developers extending OpenClaw—especially messaging endpoints and tool-calling APIs— Apidog is an invaluable tool. It supports API design, mocking, testing, debugging, and documentation in one place. With Apidog, you can test OpenClaw routes, validate request and response payloads, and ensure smooth integrations throughout the configuration process.

What is OpenClaw? Technical Overview

OpenClaw (formerly Clawdbot and Moltbot) is a conversation‑first, self‑hosted AI agent framework.

Developers install it on local hardware or cloud infrastructure to create a persistent AI assistant. The core runtime bridges:

A large language model backend (Claude)
Executable tools and skills
File system access
Messaging and communication channels

Natural‑language instructions can trigger complex, multi‑step actions such as:

Code execution
Browser automation
API orchestration
Cross‑service integrations

OpenClaw is open source and actively developed on GitHub. Its philosophy is local‑first execution, prioritizing:

Privacy
Low latency
Full customization

You can install OpenClaw on a Mac Mini for total hardware control or deploy OpenClaw on Cloudflare for serverless, edge‑optimized execution.

How to Install OpenClaw on Mac Mini (Local Setup)

1. Prepare the Mac Mini

Set up a dedicated Mac Mini for OpenClaw (not your personal machine):

# Create a fresh Apple ID to avoid iCloud syncing personal data
# Connect via Ethernet for stable networking

2. Configure Power Settings

Keep machine always running:

# System Settings → Battery / Energy Saver
# Disable sleep modes
# Enable automatic restart after power failure
# Install Amphetamine (App Store) for always-on enforcement

3. Install Dependencies

Install necessary tools:

# Homebrew
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Node.js
brew install node

4. Install OpenClaw CLI

Install the global package:

npm install -g clawdbot

5. Configure API Keys

Set up authentication:

clawdbot onboard
# Provide Anthropic API key from console.anthropic.com

6. Set Up WhatsApp Channel

Connect messaging:

clawdbot channels login --channel whatsapp
# Scan QR with WhatsApp Business (separate phone required)

7. Start Gateway

Keep agent listening:

clawdbot gateway start

8. Harden Setup

Ensure reliability:

# Configure auto-launch using macOS LaunchAgents
# Enable Screen Sharing for remote management
# Install Tailscale for secure VPN access

How to Install OpenClaw on Cloudflare (MoltWorker)

1. Prerequisites

Cloudflare Workers paid plan
Access to Workers, Sandboxes, Containers, and R2

2. Create Cloudflare Services

# Set up:
# - AI Gateway with Anthropic provider
# - R2 bucket for storage
# - Browser Rendering API
# - Zero Trust policies

3. Clone Repository

git clone https://github.com/cloudflare/moltworker.git
cd moltworker
npm install

4. Configure Wrangler

Add to wrangler.toml:

[[r2_buckets]]
binding = "MOLT_R2"
bucket_name = "your-openclaw-r2-bucket"

[sandbox]
binding = "SANDBOX"

5. Set Environment Variables

export ANTHROPIC_BASE_URL="https://your-ai-gateway.cloudflare.com/anthropic"
export MOLTBOT_GATEWAY_TOKEN=$(openssl rand -hex 32)

6. Deploy

npx wrangler deploy

Integrating OpenClaw with Apidog

Apidog enhances OpenClaw development by:

Designing custom tool schemas
Testing webhook endpoints (WhatsApp/Telegram/Discord)
Mocking third-party APIs
Validating Claude tool-calling payloads

Example workflow:

Design API in Apidog
Generate mock endpoint
Connect to OpenClaw tool registry
Validate responses before production

// Example tool schema
{
  "name": "send_email",
  "description": "Send email via SMTP",
  "parameters": {
    "to": "string",
    "subject": "string",
    "body": "string"
  }
}

Choose your deployment based on needs:

Mac Mini for hardware control
Cloudflare for serverless scale

Run OpenClaw (Formerly Clawdbot or MoltBot) as Your Virtual Assistant

Preecha — Sun, 01 Nov 2026 01:02:08 +0000

OpenClaw is an open-source framework for building a proactive AI assistant—one that acts, not just chats. It can run shell commands, edit files, call APIs, and work on its own through messaging apps like Telegram, WhatsApp, or Slack.

Try Apidog today

The project was previously known as Clawdbot and MoltBot. The name changed over time, but the core technology is the same—OpenClaw is the current, actively maintained name.

This guide walks through setup step by step. It takes some time, but you end up with an assistant that feels like a real part of your workflow.

💡 When you extend OpenClaw with custom APIs (weather, calendar, or your own services), use Apidog to design, mock, test, debug and document APIs in one place. Backend and full-stack developers can go from spec to working client code without switching tools—try Apidog free and keep your API workflow in a single platform.

What Is OpenClaw/Clawdbot/MoltBot?

OpenClaw is the main open-source project. Clawdbot was the first popular release; MoltBot was a short-lived name during a rebrand. Whether you see "OpenClaw," "Clawdbot," or "MoltBot," they all refer to the same kind of virtual assistant you run on your own machine.

What it does: It sits between you (via a messaging app) and a large language model (usually Claude). You send a message; the agent interprets it, plans steps, and performs actions—opening browsers, editing files, sending emails, running scripts, and more.

Why developers use it:

Runs locally — your data stays on your machine; you keep control and privacy.
Remembers context — conversation history persists across sessions.
Proactive — script it to act on a schedule or triggers.
Chat-first — interact through Telegram, WhatsApp, or Slack; no new UI to learn.

⚠️ Security note: Because the agent can run commands and access your system, security matters. Run it in a container or VM when possible, and avoid giving it full access on your main computer.

Prerequisites

Prepare your environment before installation—the requirements are identical whether you call it OpenClaw, Clawdbot, or MoltBot:

Hardware: A dedicated always-on machine (PC, Mac, Raspberry Pi, or VPS). Use one that runs 24/7 for true proactivity. Minimum specs: 8GB RAM, multi-core CPU, stable internet.
Operating system: macOS, Linux (Ubuntu/Debian recommended), or Windows (with WSL for best results).
Dependencies: Node.js (v18+), npm/yarn, Git, and Docker (optional but recommended for isolation).
API keys: An Anthropic API key (for Claude models) or equivalents from OpenAI/Groq/etc. Budget for usage—agentic tasks consume tokens quickly.
Messaging app: Telegram Bot Token, WhatsApp Business API, Slack App, Discord Bot, etc.
Security mindset: Run in a container or VM. Avoid granting unrestricted access on your primary workstation.

Install Node.js and Git first if you don't have them.

How to Run OpenClaw

Step 1: Install OpenClaw

Clone the official repository (or check openclaw.ai for the latest fork):

git clone https://github.com/moltbot/moltbot.git
cd moltbot

Then install dependencies:

npm install

This gives you the same codebase whether you think of it as OpenClaw, Clawdbot, or MoltBot.

Step 2: Configure Environment Variables

In the project root, create a .env file and add your keys:

ANTHROPIC_API_KEY=your_anthropic_key_here
TELEGRAM_BOT_TOKEN=your_telegram_bot_token_here
# Add other keys as required by the project (WhatsApp, Slack, etc.)

Telegram: Get a bot token from @botfather.
WhatsApp / Slack / Discord: Follow each platform's developer docs for tokens or webhooks.

Once this is set, the agent can receive messages and send them to the LLM.

Step 3: Enable Tools and Harden Security

OpenClaw supports tools for browser control, file access, shell commands, and more. Edit config/tools.ts or similar to enable specifics.

Many users activate dangerously-skip-permissions for seamless operation—this allows the agent to run commands without confirmation, so isolate the setup in Docker to contain the risk:

FROM node:20
WORKDIR /app
COPY . .
RUN npm install
CMD ["npm", "start"]

Then build and run:

docker build -t openclaw-assistant .
docker run -d --env-file .env openclaw-assistant

This safely contains OpenClaw (or Clawdbot/MoltBot) as your virtual assistant.

Step 4: Connect to Messaging Apps

Telegram setup:

Create a bot via @botfather.
Add the token to .env.
Start the agent:

npm start

Message the bot: "Summarize my inbox" or "Reschedule my 3pm meeting."

The agent processes via Claude, plans, and acts. Test simple queries first. For WhatsApp or Slack, configure webhooks or polling per the docs.

Step 5: Extend with Custom APIs

Add integrations by defining new tools in code that call external APIs—for example, a tool that queries a weather API or your custom service.

Apidog proves invaluable here. Use it to design your OpenAPI specs, mock endpoints during development, test requests, and generate client code. Download Apidog for free to accelerate this phase—its visual editor and AI-assisted features cut setup time significantly. Test and document the API in Apidog, then import it into OpenClaw.

Step 6: Run and Monitor

Launch the agent:

npm run dev

Monitor logs for errors and interact via your chosen app. The agent remembers context, so follow-up messages build on prior ones. Tune prompts in the config files—temperature, max tokens, etc.—for better performance.

Security Best Practices

Full system access introduces risks. Mitigate them:

Run in Docker or a VM.
Use separate user accounts.
Monitor network traffic.
Regularly review executed commands.
Avoid sensitive data in prompts.

Many report smooth operation once isolated.

Troubleshooting Common Issues

API rate limits: Upgrade your Anthropic plan or switch models.
Connection drops: Ensure the machine stays online; use pm2 or systemd for persistence.
Permission denied: Check the dangerously-skip-permissions flag.
High costs: Limit proactive tasks and monitor usage.

Community forums and GitHub issues provide fixes.

Conclusion

You now have the knowledge to run OpenClaw (aka Clawdbot or MoltBot) as your virtual assistant. This setup transforms a simple LLM into a capable agent that handles real work through familiar chat interfaces.

The initial configuration requires technical effort, but the payoff is a personalized, always-available assistant under your control. Experiment iteratively: start small, add tools gradually, and prioritize security. If you integrate APIs, grab Apidog for free to handle design and testing efficiently.

Your proactive AI companion awaits—configure it today.

Google Genie 3: The Most Impressive AI Model for Creating Interactive Digital Worlds

Preecha — Sat, 31 Oct 2026 14:01:32 +0000

Liquid syntax error: Unknown tag 'cloudinary'

How to Setup OpenClaw with Claude Code and Gemini 3 Pro (Fast and Easy)

Preecha — Sat, 31 Oct 2026 13:03:39 +0000

What if you could control Claude Code or Gemini 3 Pro from WhatsApp or Telegram without touching a terminal? OpenClaw makes this possible, turning your preferred AI models into messaging-based assistants you can chat with from anywhere.

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The Problem: Terminal-Locked AI Access

Claude Code and Gemini 3 Pro run powerful AI models, but they lock you to a terminal or browser window. You cannot send commands away from your desk, check status while commuting, or delegate tasks without sitting at your keyboard.

OpenClaw closes this gap with a local gateway between AI models and messaging platforms. It runs on your machine, connects to Claude Code or Gemini 3 Pro using your existing API credentials, and relays messages through Telegram, WhatsApp, Discord, or Slack. You get full AI capabilities through familiar chat interfaces.

Installing OpenClaw

1. Verify Node.js

OpenClaw requires Node.js 22 or higher:

node -v

2. Run the installer

curl -fsSL https://openclaw.ai/install.sh | bash

The installer detects your OS, verifies dependencies, and handles setup automatically—no repository cloning or manual configuration. Windows users run the PowerShell equivalent:

iwr -useb https://openclaw.ai/install.ps1 | iex

When installation completes, OpenClaw launches an interactive terminal UI (TUI). This conversation-first interface walks you through setup with natural-language prompts instead of configuration files.

3. Verify the installation

openclaw --version

You should see the current version number. If you get a "command not found" error, confirm Node.js is in your PATH and restart your terminal—the installer adds OpenClaw to your executable path, but some shells require a restart to recognize new commands.

Configuring Claude Code and Gemini 3 Pro

OpenClaw supports multiple providers. Use Claude Code through Anthropic's API or Gemini 3 Pro through Google's API—whichever credentials you already have.

For Gemini 3 Pro (Google)

Select "Google" from the model provider list during onboarding, then choose an authentication method:

Google Gemini API key — direct API key authentication
Google Antigravity OAuth — bundled authentication plugin
Google Gemini CLI OAuth — command-line OAuth flow

The API key option works immediately if you have a key from Google AI Studio. The OAuth options require browser authentication but provide enhanced security.

For Claude Code (Anthropic)

Select "Anthropic" in the onboarding wizard and enter your API key when prompted:

openclaw onboard
# Select: Anthropic API Key
# Enter: [REDACTED API KEY]

Anthropic API keys are available at console.anthropic.com. OpenClaw stores your key securely in ~/.openclaw/openclaw.json.

Configuration file structure

Your selections are saved in ~/.openclaw/openclaw.json:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "anthropic/claude-opus-4-5"
      }
    }
  },
  "env": {
    [REDACTED CREDENTIAL]
  }
}

Switch models anytime by editing this file or rerunning openclaw onboard.

Model selection strategy

Pick your primary model based on the task:

Claude Code — complex reasoning, code generation, agentic workflows requiring tool use
Gemini 3 Pro — faster response times and stronger multimodal capabilities for vision tasks

Both models support function calling and extended context windows. Configure a fallback so OpenClaw switches automatically when the primary hits rate limits or errors:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "anthropic/claude-opus-4-5",
        "fallback": "google/gemini-3-pro"
      }
    }
  }
}

This keeps the assistant running even when one provider experiences downtime.

Prompt Caching (Anthropic Only)

OpenClaw enables prompt caching automatically for Anthropic models, reducing costs for repeated similar prompts. The default cache duration is 5 minutes (short retention). Extend it to 1 hour by adding the extended-cache-ttl-2025-04-11 beta flag to your configuration.

Setting Up Messaging Channels in OpenClaw

OpenClaw supports eight messaging platforms. Telegram and WhatsApp offer the most straightforward setup if you prefer chat interfaces over terminals.

Telegram Setup

Telegram's Bot API provides clean authentication without QR codes.

Open Telegram and search for @botfather
Send /newbot and follow the prompts
Choose a name (e.g., "My OpenClaw Assistant")
Choose a username ending in "bot" (e.g., myclawbot)
Copy the API token provided

Paste this token when OpenClaw onboarding prompts for it. The gateway connects immediately and shows a confirmation message.

WhatsApp Setup

WhatsApp pairs via QR code through the WhatsApp Web protocol. Run the channel login command:

openclaw channels login

A QR code appears in your terminal. Scan it with your phone:

Open WhatsApp → Settings → Linked Devices
Tap "Link a Device"
Scan the QR code displayed

The connection persists until you manually unlink the device. Use a dedicated phone number for OpenClaw rather than your personal number—this protects your private messages if the bot misbehaves.

Security: Channel Pairing

When someone messages your bot for the first time, OpenClaw sends them a pairing code. Approve access via the CLI:

openclaw pairing approve telegram <CODE>

This prevents unauthorized access even if someone discovers your bot's username. You can configure auto-approval for trusted contacts in ~/.openclaw/openclaw.json if preferred.

Multiple Channels

Channels run simultaneously on the same AI backend: send commands via Telegram, receive notifications via WhatsApp, and monitor status through Discord. Each channel maintains separate access controls and pairing status.

Channel Security Configuration

Control who can interact with your assistant using allowlists in ~/.openclaw/openclaw.json.

Telegram allowlist:

{
  "channels": {
    "telegram": {
      [REDACTED CREDENTIAL],
      "allowedChatIds": [123456789, 987654321]
    }
  }
}

WhatsApp allowlist:

{
  "channels": {
    "whatsapp": {
      "allowFrom": ["+1234567890", "+0987654321"]
    }
  }
}

Without an allowlist, anyone who discovers your bot's username can send messages, trigger AI requests, and consume your API quota.

Managing Your OpenClaw AI Agent

Once configured, interact with your agent through natural-language messages. OpenClaw sends your text to Claude Code or Gemini 3 Pro and returns the response in chat.

Basic Commands

Send any prompt directly:

Analyze the code in ~/projects/myapp and suggest optimizations

Check system status:

/status

This shows the active model, token usage, and current cost.

Skills and Extensions

OpenClaw uses "skills"—folders of tools that extend capabilities. Enable them during onboarding or add them later:

File system access — read, write, and manage files
Browser automation — navigate websites, extract data
Web search — search engines with result summaries
Email integration — send and receive emails
Calendar access — schedule and retrieve events

Skills auto-configure when enabled. The bot learns the available tools and invokes them based on your natural-language requests.

Web Dashboard

Open the management interface at http://localhost:18789 (or your configured port) to view:

Active channel connections
Message history and logs
Model configuration and switching
Token usage and cost tracking
Skill management

A graphical alternative to terminal management.

Persistent Memory

Unlike stateless chatbots, OpenClaw maintains context across conversations. It stores conversation history, user preferences, and learned patterns locally in SQLite, so your assistant remembers previous discussions and improves over time.

Configure retention in ~/.openclaw/openclaw.json:

{
  "memory": {
    "enabled": true,
    "database": "./data/openclaw.db",
    "retention_days": 90
  }
}

Memory Management Commands

# Clear history if responses become inconsistent or you want a fresh start
openclaw memory clear

# Export memory for backup or analysis
openclaw memory export > backup.json

# Import previously exported memories
openclaw memory import backup.json

Memory files contain conversation history and learned preferences. Store backups securely—they may hold sensitive information from previous interactions.

Context Requirements

OpenClaw requires models with at least 64K token context length. Claude Code and Gemini 3 Pro exceed this. For local models through Ollama, verify context length:

ollama show <model> --modelfile

Increase it with a custom Modelfile if needed:

FROM <model>
PARAMETER num_ctx 65536

Running as a Service

Enable auto-start on boot by installing the daemon during onboarding:

openclaw onboard --install-daemon

Or start manually:

openclaw gateway --port 18789 --verbose

The gateway runs persistently, processing messages and maintaining channel connections. Check it anytime:

openclaw gateway status

Security Considerations

Run OpenClaw as a non-privileged user; isolate it on a dedicated VPS or machine if possible
Review skill permissions before enabling—file system access lets the bot read and write files anywhere your user has permissions
Store API keys in environment variables or OpenClaw's secure configuration, never in version control
Rotate keys quarterly
Monitor the dashboard for unusual activity

Updating OpenClaw

npm update -g openclaw

Or rerun the installer:

curl -fsSL https://openclaw.ai/install.sh | bash

Review the changelog before major updates. Configuration files remain compatible across versions, but new features may require configuration adjustments.

Advanced OpenClaw Command Patterns

Use slash commands to control OpenClaw directly from chat.

Execute a specific skill or recipe (predefined workflows combining multiple skills—like checking email, summarizing calendar events, and generating a daily briefing):

/run default "Analyze this codebase"

Force specific reasoning levels:

Ship checklist /think high

/think high activates extended reasoning mode for complex problems. Use /think low for simple queries to reduce response time and token consumption.

Trigger browser automation with plain language:

Book the cheapest flight to Berlin next month

OpenClaw launches a browser, searches flight aggregators, compares prices, and returns the best option with booking links.

Conclusion

OpenClaw transforms Claude Code and Gemini 3 Pro from terminal-bound tools into accessible messaging assistants. Configure once with your existing API credentials, connect your preferred messaging platform, and interact through natural chat. Your AI handles complex tasks while you stay mobile.

When building API integrations—whether testing OpenClaw endpoints, debugging authentication flows, or managing multiple provider configurations—streamline your development with Apidog. It provides visual API testing, automatic documentation generation, and collaborative debugging tools that complement your AI assistant workflow.

How to Use Claude-mem for Memory Persistence in Claude Code

Preecha — Sat, 31 Oct 2026 02:02:21 +0000

Persistent Memory in AI-Assisted Development with Claude-mem

What if your AI assistant remembered every architectural decision, bug fix, and refactoring session across weeks of development? Claude-mem eliminates the friction of lost context by automatically capturing tool usage observations, compressing them into semantic summaries, and injecting relevant history into every new Claude Code session.

Try Apidog today

The Problem: Context Amnesia in AI-Assisted Development

Every Claude Code session starts as a blank slate. When you close your terminal or disconnect from a session, Claude forgets everything; your project structure, recent refactoring decisions, debugging discoveries, and architectural patterns. This forces you to repeatedly explain your codebase, burning tokens on redundant context and breaking workflow continuity.

Developers currently work around this by manually maintaining CLAUDE.md files, jotting notes in separate documents, or re-explaining project context at the start of every session. These approaches are brittle, time-consuming, and never capture the full richness of your development history. Claude-mem solves this by automatically observing every tool invocation, compressing the output into searchable semantic memories, and intelligently retrieving relevant context when you need it.

Understanding Claude-mem's Architecture

Claude-mem operates as a persistent memory compression system that hooks into Claude Code's lifecycle. It captures tool outputs—typically 1,000 to 10,000 tokens—and compresses them into roughly 500-token semantic observations using Claude's Agent SDK. These observations are categorized by type (decision, bugfix, feature, refactor, discovery, change) and tagged with relevant concepts and file references, then stored in a local SQLite database with full-text search capabilities.

The system uses five lifecycle hooks to capture context:

SessionStart: Injects context from previous sessions when you begin
UserPromptSubmit: Captures your queries for pattern recognition
PostToolUse: Observes every tool execution and its output
Stop: Generates session summaries when Claude finishes responding
SessionEnd: Finalizes session storage and cleanup

This architecture enables progressive disclosure—a layered memory retrieval system that balances coverage with token efficiency. Instead of dumping your entire history into context, Claude-mem retrieves observations in layers, saving approximately 2,250 tokens per session compared to manual context management.

Installation and System Requirements

Claude-mem requires Node.js 18.0.0 or higher, the latest Claude Code with plugin support, and Bun as the JavaScript runtime and process manager (auto-installed if missing). SQLite 3 is bundled for persistent storage. The plugin works cross-platform on Windows, macOS, and Linux.

Quick Start Installation

Install Claude-mem directly from the plugin marketplace with two commands:

/plugin marketplace add thedotmack/claude-mem  
/plugin install claude-mem

Restart Claude Code after installation. The plugin automatically downloads prebuilt binaries, installs dependencies including Bun and SQLite, configures hooks for session lifecycle management, and auto-starts the worker service on your first session.

Advanced Installation from Source

For development or testing, clone and build from source on GitHub:

git clone https://github.com/thedotmack/claude-mem.git  
cd claude-mem  
npm install  
npm run build  
npm run worker:start

This approach is useful if you need to modify the plugin or run beta features like Endless Mode.

Post-Installation Verification

After installation, verify everything is working:

Check plugin installation:

cat plugin/hooks/hooks.json

Verify the worker service is running:

curl http://localhost:37777/api/health

View recent worker logs:

npm run worker:logs

Test context retrieval by starting a new Claude Code session. You should see context from previous sessions automatically loaded in the initial prompt.

Data Storage and Configuration

Claude-mem stores all data locally in ~/.claude-mem/:

Database: ~/.claude-mem/claude-mem.db (SQLite with FTS5 search)
PID file: ~/.claude-mem/.worker.pid
Port file: ~/.claude-mem/.worker.port
Logs: ~/.claude-mem/logs/worker-YYYY-MM-DD.log
Settings: ~/.claude-mem/settings.json

Override the default data directory with an environment variable:

export CLAUDE_MEM_DATA_DIR=/custom/path

Configuration Options

Settings are managed in ~/.claude-mem/settings.json (auto-created on first run). Key configurations include:

CLAUDE_MEM_CONTEXT_OBSERVATIONS: Number of observations injected at session start (default: 50)
CLAUDE_MEM_FOLDER_INDEX_ENABLED: Enable/disable auto-generated CLAUDE.md files in folders
Model selection for AI-powered compression
Worker port and host settings
Log level configuration

How Claude-mem Captures and Processes Context

When you use Claude Code with claude-mem enabled, the system captures every tool invocation automatically. Whether Claude reads a file, executes a bash command, searches with glob patterns, or edits code, claude-mem observes the input and output.

The worker service processes these observations and extracts:

Title: Brief description of what happened
Subtitle: Additional context
Narrative: Detailed explanation of the activity
Facts: Key learnings as bullet points
Concepts: Relevant tags and categories for search
Type: Classification (decision, bugfix, feature, refactor, discovery, change)
Files: Which files were read or modified

This compression happens automatically without manual intervention. The raw tool output might be 5,000 tokens, but the semantic observation stored in the database is roughly 500 tokens—preserving meaning while eliminating noise.

Session Summaries

When Claude finishes responding (triggering the Stop hook), claude-mem automatically generates a session summary containing:

Request: What you asked for
Investigated: What Claude explored to answer
Learned: Key discoveries and insights
Completed: What was accomplished
Next Steps: Recommended follow-up actions

These summaries are injected into future sessions alongside individual observations, providing both granular detail and high-level narrative context.

Using MCP Search Tools to Query Your Memory

Claude-mem exposes four MCP tools that follow a token-efficient 3-layer workflow pattern. This design retrieves context progressively, minimizing token usage while maximizing relevance.

The 3-Layer Workflow

search: Get a compact index with IDs (~50-100 tokens per result)
timeline: Get chronological context around interesting results
get_observations: Fetch full details ONLY for filtered IDs (~500-1,000 tokens per result)

This approach achieves approximately 10x token savings by filtering before fetching full details.

Available MCP Tools

search: Search the memory index with full-text queries. Filter by type, date, or project.
timeline: Get chronological context around a specific observation or query.
get_observations: Fetch full observation details by IDs.

Example Usage Patterns

Find a specific bug fix:

# Step 1: Search for the bug  
search(query="authentication bug", type="bugfix", limit=10)  

# Step 2: Review index, identify relevant IDs (e.g., #123, #456)  

# Step 3: Fetch full details for relevant observations  
get_observations(ids=[123, 456])

Explore recent architectural decisions:

search(query="database schema", type="decision", limit=5)

Find everything related to a specific file:

search(query="worker-service.ts", limit=20)

Natural Language Queries

You can ask Claude naturally about your project history:

"What did we decide about error handling?"
"How did we implement authentication?"
"What bugs did we fix in the API layer?"
"Show me changes to the database schema"

Claude automatically invokes the appropriate MCP tools to retrieve relevant context, presenting findings with claude-mem:// URI citations that reference specific observations.

Folder Context Files and CLAUDE.md Auto-Generation

Claude-mem automatically generates CLAUDE.md files in project folders, creating activity timelines that complement the global memory database.

How Folder Context Works

When you work with files in a folder, claude-mem:

Identifies unique folder paths from touched files
Queries recent observations relevant to each folder
Generates a formatted timeline of activity
Writes it to CLAUDE.md in that folder (inside <claude-mem-context> tags)

Each folder's CLAUDE.md contains a Recent Activity section showing observation IDs, timestamps, type indicators (bug fixes, features, discoveries), brief titles, and estimated token counts.

Example CLAUDE.md Structure

# Authentication Module  

This folder contains all authentication-related code.  
Follow the established patterns for new auth providers.  

<claude-mem-context>  
# Recent Activity  

| ID | Time | Type | Title | Tokens |  
|----|------|------|-------|--------|  
| #1234 | 4:30 PM | 🔵 | Implemented user authentication | ~250 |  
| #1235 | 4:45 PM | 🔴 | Fixed login redirect bug | ~180 |  
</claude-mem-context>  

## Manual Notes  

- OAuth providers go in /providers/  
- Session handling uses Redis

Web Viewer UI and Real-Time Monitoring

Claude-mem runs a web viewer at http://localhost:37777 for real-time memory stream visualization. The interface shows:

Live observation stream with emoji indicators for importance
Session timeline with chronological markers
Search interface for querying memories
Settings panel for configuration adjustments

Troubleshooting Common Issues

Worker Service Not Starting

If the worker fails to start on port 37777:

Check if the port is already occupied:

lsof -i :37777

Configure an alternative port:

export CLAUDE_MEM_WORKER_PORT=8080

Manually start the worker:

bun plugin/scripts/worker-service.cjs

Memory Not Being Saved

If Claude doesn't remember previous sessions:

Verify the worker is running:

npm run worker:status

Check the database file exists:

ls -la ~/.claude-mem/claude-mem.db

Review worker logs for errors:

npm run worker:logs

Empty CLAUDE.md Files

If claude-mem creates empty CLAUDE.md files throughout your project, this is a known issue in v9.0.5. Current workarounds include manually deleting created directories, adding patterns to .gitignore, or waiting for the fix in a subsequent release.

Conclusion

Claude-mem transforms Claude Code from a stateless assistant into a persistent development partner that accumulates knowledge about your codebase over time. By automatically capturing tool usage, compressing observations into searchable memories, and intelligently retrieving relevant context, it eliminates the repetitive context-building that slows down AI-assisted development.

When building APIs or working with external services in your Claude-mem enhanced workflow, streamline your testing with Apidog. It offers visual API testing, automatic documentation generation, and collaborative debugging that complements your persistent memory setup.

How to Use the Venice API

Preecha — Sat, 31 Oct 2026 01:02:35 +0000

What if you could switch AI providers without rewriting a single line of code? Venice API offers exactly that—OpenAI-compatible endpoints with zero data retention, uncensored model options, and a privacy-first architecture you control.

Try Apidog today

Most AI APIs force you into vendor-specific SDKs, retain your data for model training, and charge premium rates for basic features. You rewrite your application when switching providers. Your prompts train competitor models. Your costs scale unpredictably.

Venice API eliminates these friction points. It mirrors OpenAI's API structure exactly—change the base URL and your existing code works immediately. Your data stays private. You choose from multiple payment models, including crypto staking and pay-as-you-go USD credits.

💡 Want a great API Testing tool that generates beautiful API Documentation? Want an integrated, All-in-One platform for your Developer Team to work together with maximum productivity? Apidog delivers all your demands, and replaces Postman at a much more affordable price!

1. Generate Your Venice API Key

Step 1: Open the API settings. Navigate to venice.ai/settings/api.

Step 2: Click "Generate New API Key" and configure your credentials:

Description: Name your key for organization
Type: Admin keys manage other keys programmatically; Inference-only keys run models exclusively
Expiration: Optional date when the key deactivates automatically
Consumption Limits: Daily Diem or USD caps to control spend

Step 3: Copy your key immediately. Venice displays it once! Store it in environment variables, never in code repositories:

export VENICE_API_KEY="your-key-here"

Key Security Considerations

Admin keys provide broad access to your Venice account. Treat them like root credentials—use them for key rotation scripts and team management, never in application code. Inference-only keys restrict operations to model execution, limiting exposure if leaked. Rotate keys quarterly using the dashboard's activity logs to identify stale credentials.

2. Configure Authentication and the Base URL

Venice uses standard Bearer [REDACTED] authentication. Every request requires two headers:

Authorization: Bearer $VENICE_API_KEY
Content-Type: application/json

The base URL follows OpenAI's pattern exactly:

import openai
import os

client = openai.OpenAI(
    [REDACTED CREDENTIAL],
    base_url="https://api.venice.ai/api/v1"
)

This single configuration change routes all your existing OpenAI SDK calls through Venice's infrastructure. No method changes. No parameter rewrites. Your code works immediately.

SDK Compatibility

Venice maintains compatibility with OpenAI's official SDKs across Python, TypeScript, Go, PHP, C#, Java, and Swift. Third-party libraries built on OpenAI's specification also work without modification. Test your existing codebase against Venice by changing only the base URL and API key—if you use standard chat completions, streaming, or function calling, migration takes minutes.

Migrating from OpenAI

Migration requires three changes:

Replace https://api.openai.com/v1 with https://api.venice.ai/api/v1
Swap your OpenAI API key for your Venice key
Change model identifiers from gpt-4 or gpt-3.5-turbo to Venice equivalents like qwen3-4b

Test thoroughly before production deployment. Verify streaming responses process correctly. Confirm function calling schemas validate. Check image generation parameters match your requirements. Venice's compatibility layer handles most edge cases, but subtle differences exist in error message formatting and rate limit headers.

ProTip: Test all your API endpoints thoroughly with Apidog.

3. Core Endpoints and Capabilities

Venice provides nine distinct endpoints covering text, image, audio, and video generation:

Text Generation

/api/v1/chat/completions — Conversational AI with streaming support
/api/v1/embeddings/generate — Vector embeddings for RAG applications

Image Processing

/api/v1/image/generate — Text-to-image generation
/api/v1/image/upscale — Resolution enhancement
/api/v1/image/edit — AI-powered inpainting and modification

Audio

/api/v1/audio/speech — Text-to-speech synthesis
/api/v1/audio/transcriptions — Speech-to-text conversion

Video and Characters

/api/v1/video/queue — Text/video-to-video generation
/api/v1/characters/list — AI persona management

Each endpoint maintains OpenAI-compatible request/response formats where applicable—you reuse existing parsing logic.

Endpoint Selection Strategy

Match endpoints to your use case complexity. Chat completions handle most text generation needs. Add embeddings for semantic search or RAG pipelines. Use image endpoints for creative workflows or content moderation. Audio endpoints enable accessibility features or voice interfaces. Start with one endpoint, validate your integration, then expand to multimodal workflows.

Working with Streaming Responses

Streaming reduces perceived latency for chat applications. Venice uses Server-Sent Events (SSE) identical to OpenAI's implementation—process partial content as it arrives rather than waiting for complete responses.

Handle stream termination by checking for [DONE] messages. Implement reconnection logic for interrupted streams: store conversation history client-side and retry failed requests. Monitor token usage in stream chunks to track costs in real time.

4. Venice-Specific Parameters

Beyond OpenAI's standard parameters, Venice adds capability controls through the venice_parameters object:

{
  "model": "qwen3-4b",
  "messages": [{"role": "user", "content": "Latest AI developments?"}],
  "venice_parameters": {
    "enable_web_search": "on",
    "enable_web_citations": true,
    "strip_thinking_response": false
  }
}

Web Search Integration

Set enable_web_search to auto, on, or off. Auto lets the model decide when current information improves responses. Force it on for real-time queries about recent events or rapidly changing technologies. Pair with enable_web_citations to return source URLs—essential for research tools and factual verification.

Reasoning Control

Reasoning models like DeepSeek R1 show step-by-step thinking by default. Set strip_thinking_response to true to return only final answers, reducing token consumption. Use disable_thinking to bypass reasoning entirely for simple queries.

Alternative Syntax

Pass parameters via model suffix for concise requests:

model="qwen3-4b:enable_web_search=on&enable_web_citations=true"

Parameter Hierarchy

Venice-specific parameters override defaults but respect explicit settings. If you specify temperature: 0.5 in the root object and enable_web_search: on in venice_parameters, both apply simultaneously. Test parameter combinations in isolation before deploying to production—some parameters interact unpredictably with certain models.

5. Practical Implementation Examples

Basic Chat Completion

curl --request POST \
  --url https://api.venice.ai/api/v1/chat/completions \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "qwen3-4b",
    "messages": [{"role": "user", "content": "Explain zero-knowledge proofs"}],
    "stream": true
  }'

Streaming works identically to OpenAI—process SSE chunks as they arrive.

Function Calling

curl --request POST \
  --url https://api.venice.ai/api/v1/chat/completions \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "qwen3-4b",
    "messages": [{"role": "user", "content": "Weather in Tokyo?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get weather for location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {"type": "string"}
          },
          "required": ["location"]
        }
      }
    }]
  }'

Venice models support parallel function calling and schema enforcement like OpenAI's implementation.

Image Generation

curl --request POST \
  --url https://api.venice.ai/api/v1/image/generate \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "venice-sd35",
    "prompt": "Cyberpunk cityscape at night, neon reflections",
    "aspect_ratio": "16:9",
    "resolution": "2K",
    "hide_watermark": true
  }'

Available aspect ratios include 1:1, 4:3, 16:9, and 21:9. Resolution options are 1K and 2K.

Image Upscaling

curl --request POST \
  --url https://api.venice.ai/api/v1/image/upscale \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "upscale-sd35",
    "image": "base64encodedimage..."
  }'

Vision Analysis

curl --request POST \
  --url https://api.venice.ai/api/v1/chat/completions \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "qwen3-vl-235b-a22b",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "What architecture style is this?"},
        {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
      ]
    }]
  }'

Pass images as base64 data URIs or HTTPS URLs. Vision models accept multiple images per message for comparison tasks.

Audio Synthesis

curl --request POST \
  --url https://api.venice.ai/api/v1/audio/speech \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "tts-kokoro",
    "input": "Welcome to Venice API",
    "voice": "af_sky",
    "response_format": "mp3"
  }'

Voice options use prefixes: af_ (American female), am_ (American male), and similar patterns for other accents.

Error Handling Patterns

Venice returns standard HTTP status codes:

401 — authentication failure: verify your API key and headers
429 — rate limiting: implement exponential backoff starting at 1 second
500 — temporary infrastructure issue: retry after 5 seconds

Parse error responses for specific messages—Venice includes detailed failure reasons in the response body.

6. Privacy and Data Architecture

Venice's zero data retention policy operates through technical architecture, not just legal promises. Your browser stores conversation history locally using IndexedDB. Venice servers process prompts on GPUs that see only the current request—no conversation history, no user identity metadata, no API key information.

After generating a response, servers discard the prompt and output immediately. Nothing persists to disk or logs. Your data never trains models. This differs fundamentally from centralized services that retain data for abuse detection and model improvement.

For additional privacy, Venice hosts most models on private infrastructure rather than relying on third-party providers. Uncensored options run on Venice-controlled hardware, ensuring no external filtering or logging.

Data Flow Verification

Audit Venice's privacy claims by monitoring network traffic. API requests go directly to api.venice.ai with TLS encryption. No third-party analytics scripts load in the documentation. Response headers show no caching directives—confirming server-side non-retention. For sensitive applications, implement client-side encryption before sending prompts, though this prevents the model from understanding content.

7. Pricing and Payment Options

Venice offers three payment methods to match your usage patterns:

Pro subscription — $18 monthly, includes $10 in API credits plus unlimited prompts on consumer features
DIEM staking — purchase VVV tokens for permanent daily compute allocations; ideal for high-volume applications with predictable traffic
USD pay-as-you-go — fund your account with dollars and consume credits as needed; perfect for experimentation and variable workloads

API access currently remains free during beta. This lets you validate integration patterns and estimate costs before committing to a payment method. Monitor your usage dashboard to track token consumption across endpoints and models.

Model Selection Guidelines

Choose models based on capability requirements and latency constraints. Start with qwen3-4b for prototyping and simple queries—it responds quickly and handles most text generation tasks adequately. Upgrade to larger models like llama-3.3-70b or deepseek-ai-DeepSeek-R1 when you need advanced reasoning, code generation, or complex instruction following. Vision tasks require multimodal models like qwen3-vl-235b-a22b. Audio generation uses specialized speech models. Query the /api/v1/models endpoint programmatically to check real-time availability—Venice rotates models based on demand and infrastructure capacity.

Conclusion

Venice API removes the friction from AI integration. You get OpenAI compatibility without the lock-in, privacy without configuration complexity, and flexible pricing without surprise bills. The drop-in replacement approach means you can evaluate Venice alongside your current provider without rewriting application code.

When building API integrations—whether testing Venice endpoints, debugging authentication flows, or managing multiple provider configurations—use Apidog to streamline your workflow. It handles visual API testing, documentation generation, and team collaboration so you can focus on shipping features.

What Is MCP Client: A Complete Guide

Preecha — Fri, 30 Oct 2026 14:03:10 +0000

The Model Context Protocol (MCP) transforms how developers connect AI applications to external tools, data sources, and workflows. At its core, the MCP Client enables secure, standardized communication between host applications and MCP servers. Developers use the MCP Client to instantiate connections, negotiate features, and handle advanced interactions like elicitation, roots, and sampling.

Try Apidog today

💡Ready to test MCP servers without extra setup? Apidog's built-in MCP Client lets you connect to local or remote MCP servers, run tools & prompts, inspect JSON-RPC envelopes, and debug elicitation/sampling flows all inside one familiar interface. No separate SDKs, no custom scripts.

This comprehensive guide explains MCP, breaks down the MCP Client role, explores its architecture and features, and demonstrates practical usage with Apidog's MCP Client.

What Is MCP? The Foundation of Model Context Protocol

MCP (Model Context Protocol) is an open JSON-RPC-based standard introduced by Anthropic in November 2024 and now maintained openly on GitHub. MCP standardizes how large language model (LLM) applications discover and invoke tools, reusable prompts, resources, and other context from external MCP servers.

Before MCP, developers built custom integrations for every data source or tool, leading to fragmented, hard-to-maintain code. MCP replaces these ad-hoc approaches with a consistent protocol that supports stateful sessions. MCP enables AI applications to access real-time context securely, reducing hallucinations and improving response quality.

MCP operates on a client-server model. An MCP host application (such as Claude Desktop, an AI-powered IDE, or a custom agent) creates MCP Clients to connect to MCP servers. Each MCP Client maintains a dedicated connection to one server, allowing modular and focused interactions.

The protocol uses JSON-RPC 2.0 with bidirectional communication. Hosts request context or actions; servers respond while respecting security rules defined by the MCP Client. As of 2025–2026 updates, MCP continues evolving with better version negotiation and transport options.

MCP Architecture: Host, MCP Client, and MCP Server Explained

MCP follows a clear three-part architecture.

MCP Host: The user-facing AI application coordinates everything. The host manages the interface, user experience, and multiple MCP Clients simultaneously.
MCP Client: The protocol-level component. The host instantiates an MCP Client for each MCP server connection. The MCP Client handles direct communication, feature negotiation, request forwarding, and core security features.
MCP Server: Exposes capabilities such as tools (executable functions), prompts (reusable templates), and resources (data). Servers run locally via STDIO or remotely via Streamable HTTP.

The MCP Client acts as the intermediary. It establishes connections, discovers server capabilities, and enforces user control. This separation keeps the host focused on UX while the MCP Client manages protocol details.

Transports include STDIO for local processes (ideal for development) and HTTP for remote servers (suitable for production with authentication). Protocol version negotiation ensures compatibility during initialization.

What is the MCP Client? Detailed Role and Responsibilities

An MCP Client serves as the critical bridge in MCP. The MCP Client connects the host application to a single MCP server, implementing the full protocol logic.

The MCP Client performs these key responsibilities:

Establishes and maintains stateful sessions.
Negotiates protocol versions and capabilities.
Discovers available tools, prompts, and resources.
Forwards requests and processes responses.
Implements elicitation, roots, and sampling for enriched interactions.
Enforces security boundaries and user approvals.

Without a robust MCP Client, hosts cannot reliably interact with MCP servers. The MCP Client ensures transparency, validation, and control throughout every exchange.

Core Features of MCP Client: Elicitation, Roots, and Sampling

The MCP Client provides advanced features that empower servers to create dynamic, adaptive workflows.

Elicitation lets servers request user input on demand. Servers send structured requests with schemas; the MCP Client renders forms, validates responses, and returns data only after user approval. Users see a clear context of who asks, why, and how data gets used. The MCP Client prevents suspicious requests (like passwords) and allows declines or cancellations.

Roots define filesystem scope. The MCP Client communicates accessible directories via file:// URIs. Servers respect these boundaries to avoid accidental access. While roots coordinate rather than strictly enforce security (OS-level protections handle enforcement), the MCP Client notifies servers of changes and promotes safe practices.

Sampling enables servers to request LLM completions through the MCP Client. Servers provide messages, system prompts, and model preferences; the MCP Client executes the call, applies priorities (cost, speed, intelligence), and presents results for review. Users approve, modify, or deny outputs. The MCP Client supports redaction and rate limiting for sensitive data.

These MCP Client features create human-in-the-loop safeguards, transparency, and flexibility.

MCP Client vs MCP Host: Clearing Up the Confusion

Many confuse the MCP Client with the host. The MCP Host is the full application, such as Claude.ai, Cursor, or a custom chatbot that users interact with. The host coordinates UI, workflows, and multiple MCP Clients.

The MCP Client operates lower at the protocol layer. Each MCP Client focuses on one server connection and implements elicitation, roots, sampling, and transport handling. Developers implement or use MCP Clients to add MCP support to hosts.

This distinction helps when building integrations: focus on MCP Client logic for protocol compliance.

Apidog's Built-in MCP Client: A Practical Tool for Developers

Apidog provides a native MCP Client that streamlines MCP server testing. This MCP Client eliminates the need for separate tools and offers deep protocol visibility.

Key advantages of Apidog's MCP Client:

Supports tools, prompts, and resources fully.
Connects via STDIO (local) or HTTP (remote).
Handles authentication (API Key, Bearer, OAuth 2.0, etc.).
Displays folder trees of server capabilities.
Shows Messages, Notifications, and raw JSON-RPC Envelopes.

Developers debug interactions visually and save configurations for teams.

Step-by-Step Guide: Using Apidog's MCP Client

Follow these steps to use Apidog's MCP Client.

Open an HTTP project in Apidog.
Create a new endpoint and select MCP type.

Enter connection details: paste a command (STDIO), a URL (HTTP), or a JSON config file.

npx -y @modelcontextprotocol/server-everything

Click Connect. Approve security prompts for local commands; configure auth for remote.
Browse the folder tree for tools, prompts, resources.

Select an item, configure parameters (form or JSON), and click Run.
View results in Messages/Notifications tabs; toggle Envelopes for raw protocol.

Use variables {{var}} in addresses, headers, params.
Save the endpoint for reuse.

Apidog's MCP Client auto-refreshes capabilities and supports environment variables in STDIO mode.

Advanced Debugging with Apidog MCP Client Features

Apidog's MCP Client excels at troubleshooting.

Monitor Notifications for server-initiated updates.
Inspect full JSON-RPC envelopes to trace issues.
Handle OAuth flows automatically or manually.
Set custom headers in HTTP mode.
Validate schemas during elicitation simulations.

Best practices include verifying runtimes for STDIO, checking auth on 401 errors, and monitoring tabs for empty trees.

Real-World Benefits of Combining MCP Client with Apidog

Using Apidog's MCP Client speeds up iteration. Developers test MCP servers instantly, catch protocol errors early, and collaborate via shared projects.

The MCP Client unifies API and MCP workflows in one platform. Free access lowers entry barriers for experimenting with MCP.

Why MCP Client Matters for Modern AI Development in 2026

In 2026, MCP adoption accelerates across industries. The MCP Client enables context-aware AI that accesses real-time data, executes tools securely, and reduces custom coding overhead.

Developers benefit from standardized interfaces via MCP Client. Security improves with built-in user controls. Scalability increases as hosts manage multiple MCP Clients for diverse servers.

As enterprises integrate MCP into IDEs, chatbots, and agents, reliable MCP Clients become essential for production-grade solutions.

If you want to put that client-side understanding to practical use, building robust APIs with OpenAPI MCP servers shows how the two halves of the protocol work together in a real backend workflow.

Conclusion: Master MCP Client and Build Better AI Integrations

MCP revolutionizes AI connectivity, and the MCP Client powers it all. Understanding MCP Client responsibilities from connection management to security features enables robust implementations.

Apidog's built-in MCP Client makes working with MCP practical and efficient. Download Apidog for free now. Connect to MCP servers, test tools and prompts, and build context-rich AI applications faster.