How to run Kimi K2.5 with ClawdBot (MoltBot) ?
MoltBot (formerly ClawdBot) is a self-hosted agent that plugs directly into Telegram, WhatsApp, Discord, and Slack. It executes real tasks on your own machine, so you keep privacy and latency under your control.
{% cta apidog.com %} Try Apidog today {% endcta %}
Connecting Kimi K2.5 to MoltBot gives you a versatile, cost-efficient assistant: strong performance on general tasks, creative work, and agentic behavior at a fraction of the cost of Claude 3.5 Sonnet or GPT-4o. For privacy-focused setups, local deployment with quantized GGUF weights eliminates external data transmission entirely.
💡 Small configuration details usually decide success. Before editing any files, verify connectivity. Download Apidog for free—it provides an intuitive interface to test Moonshot API endpoints or local servers, inspect JSON payloads, monitor latency, and generate integration code snippets. This step prevents most downstream errors when linking the model to MoltBot.
This guide covers both the API and local methods, with configuration examples, verification steps, and fixes for frequent problems.
Why Pair MoltBot with Kimi K2.5?
MoltBot is the execution layer; the LLM supplies the intelligence. Kimi K2.5 brings several concrete advantages to that role:
- MoE efficiency — relevant experts activate on demand, delivering high capacity without proportional cost.
- Native multimodal input — MoltBot can process screenshots, UI designs, or short videos, for example to generate code from visuals.
- 256K token context (most deployments) — retain extensive project codebases, documentation, and conversation histories, essential for persistent assistants.
!Image
- Low API costs — substantially cheaper than Western alternatives; heavy users save the most over time.
- Local inference option — quantized weights run on consumer hardware for zero ongoing expense and maximum control.
- Strong agentic behavior — self-directed swarms of up to 100 sub-agents executing tools in parallel. Routed through MoltBot's skill system, these automate complex workflows directly from chat messages.
- Provider portability — MoltBot accepts any OpenAI-compatible endpoint, so switching providers is just a configuration update.
Prerequisites
Complete this checklist before configuration:
1. Install MoltBot. Run the installation script if you haven't already:
curl -fsSL https://molt.bot/install.sh | bash
> The project rebranded from ClawdBot to MoltBot on January 27, 2026, following a trademark request from Anthropic. Older installations may retain the ~/.clawdbot directory; recent versions use moltbot commands and ~/.moltbot or similar paths. Check the docs at molt.bot or the GitHub repository (moltbot/moltbot) for your exact setup.
2. Obtain Kimi K2.5 access — pick one route:
- API: create an account at platform.moonshot.ai, generate an API key, and note any project budget limits.
- Local: download quantized weights (e.g., Hugging Face moonshotai/Kimi-K2.5 or community repos like unsloth/Kimi-K2.5-GGUF), install llama.cpp, and start a server.
3. Install Apidog for testing. It handles authentication headers, JSON bodies, and response streaming effectively.
4. Ensure Node.js runs for MoltBot. Basic terminal familiarity helps when editing JSON files.
Method 1: Connecting via Moonshot API (Recommended for Most Users)
This route requires minimal hardware and provides full 256K context plus multimodal support.
Step 1: Validate the API Connection Using Apidog
Launch Apidog and create a new POST request set to https://api.moonshot.ai/v1/chat/completions.
Add the header:
Authorization: Bearer [REDACTED API KEY]
(Replace with your actual key.)
Use this body for a basic test:
{
"model": "kimi-k2.5",
"messages": [
{
"role": "user",
"content": "Confirm you are Kimi K2.5 and describe your capabilities briefly."
}
],
"temperature": 0.7,
"max_tokens": 256
}
Send the request. A successful 200 response with coherent output confirms the key works. Surface any rate-limit or budget errors here—before they hide inside MoltBot.
Step 2: Locate and Edit the Configuration File
MoltBot stores settings in a JSON file, typically:
- ~/.moltbot/moltbot.json
- Legacy: ~/.clawdbot/moltbot.json or ~/.clawdbot/agents/default/config.json
Open it and add or modify the providers section:
{
"agent": {
"model": {
"primary": "moonshot/kimi-k2.5"
}
},
"models": {
"providers": {
"moonshot": {
"baseUrl": "https://api.moonshot.ai/v1",
[REDACTED CREDENTIAL],
"api": "openai-completions",
"models": [
{
"id": "kimi-k2.5",
"name": "Kimi K2.5 (API)",
"contextWindow": 262144,
"maxTokens": 8192
}
]
}
}
}
}
> Security note: avoid hardcoding keys in production. Set an environment variable instead (e.g., export MOONSHOT_API_KEY=sk-...) and reference it if your MoltBot version supports expansion.
Step 3: Apply Changes and Restart
Save the file, then restart:
moltbot restart
Or stop and start the gateway/service as needed.
Method 2: Connecting via Local Kimi K2.5 Deployment
Local execution prioritizes privacy and eliminates recurring costs, though it demands substantial VRAM/RAM.
Step 1: Launch the Local Inference Server
Use llama.cpp for compatibility. Build it with GPU support if available:
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make LLAMA_CUDA=1 # or the appropriate flags for your platform
Download a quantized GGUF variant (e.g., UD-TQ1_0 for balance) via huggingface-cli or a direct download, then start the OpenAI-compatible server:
./llama-server \
-m /path/to/Kimi-K2.5-UD-TQ1_0.gguf \
--port 8080 \
--ctx-size 32768 \
--n-gpu-layers 99 \
--host 0.0.0.0
--ctx-size 32768 is a practical starting point—adjust upward to your hardware limit. Full 256K context requires extreme resources.
Verify by browsing http://localhost:8080/v1/models; the model should be listed.
Step 2: Update MoltBot Configuration for the Local Endpoint
Edit the JSON file:
{
"agent": {
"model": {
"primary": "local-kimi/kimi-k2.5"
}
},
"models": {
"providers": {
"local-kimi": {
"baseUrl": "http://127.0.0.1:8080/v1",
[REDACTED CREDENTIAL],
"api": "openai-completions",
"models": [
{
"id": "kimi-k2.5-local",
"name": "Kimi K2.5 Local",
"contextWindow": 32768,
"maxTokens": 4096
}
]
}
}
}
}
contextWindow must match the server's --ctx-size.
> Docker note: if MoltBot runs containerized, replace 127.0.0.1 with host.docker.internal.
Step 3: Restart and Monitor Resource Usage
Restart MoltBot and watch system monitors. Local inference consumes significant memory; offload fewer layers or reduce context if the machine struggles.
Testing and Verification
Confirm the integration works:
1. Send a message to your MoltBot instance from any connected app:
Who are you powered by right now?
Kimi K2.5 typically responds identifying Moonshot AI.
2. Check the logs:
moltbot logs
Look for requests routed to api.moonshot.ai or localhost:8080.
3. Test multimodal (API route): upload an image via chat and ask for a description or code generation from it.
[How to Run Kimi K2.5 Locally?Learn how to run the massive 1T parameter Kimi K2.5 model locally using llama.cpp and Unsloth GGUF. Detailed hardware requirements, installation steps, and Apidog integration guide
!Image
Apidog BlogAshley Innocent
!Image](apidog.com)
Troubleshooting Common Issues
| Symptom | Fix |
| --- | --- |
| Provider verification fails | Re-test the exact baseUrl + key in Apidog. Network proxies or firewalls often interfere. |
| Context overflow errors | Align contextWindow in the JSON with the server's --ctx-size. MoltBot truncates or summarizes when limits hit; mismatched values cause crashes. |
| Slow responses locally | Reduce --n-gpu-layers, use lower quantization, or enable flash attention in llama.cpp. |
| Unexpected formatting/hallucinations | Experiment with temperature (0.6–1.0) or add custom system prompts in MoltBot agent config for Kimi-specific tuning. |
| API budget exhaustion | Monitor usage at platform.moonshot.ai and set daily limits. |
Conclusion
Integrating Kimi K2.5 with MoltBot delivers a high-performance, economical, and optionally fully private personal AI agent. The API method offers convenience and maximum capabilities; the local setup ensures complete data sovereignty.
Experiment with both approaches, and use Apidog throughout to isolate issues quickly. As Moonshot continues updating Kimi models and MoltBot evolves, this combination keeps you at the forefront of accessible agentic AI.
Start configuring now—your enhanced assistant awaits.
!Image
Run OpenClaw (Formerly Clawdbot or MoltBot) as Your Virtual Assistant
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.
{% cta apidog.com %} Try Apidog today {% endcta %}
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.
!OpenClaw website interface
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:
1. Create a bot via @BotFather.
2. Add the token to .env.
3. Start the agent:
npm start
4. 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.
Mentions
How to run Kimi K2.5 with ClawdBot (MoltBot) ? MoltBot (formerly ClawdBot) is a self-hosted agent that plugs directly into Telegram, WhatsApp, Discord, and Slack. It executes real tasks on your own machine, so you keep privacy and latency under your control. {% cta apidog.com %} Try Apidog today {% endcta %} Connecting Kimi K2.5 to MoltBot gives you a versatile, cost-efficient assistant: strong performance on general tasks, creative work, and agentic behavior at a fraction of the cost of Claude 3.5 Sonnet or GPT-4o. For privacy-focused setups, local deployment with quantized GGUF weights eliminates external data transmission entirely. 💡 Small configuration details usually decide success. Before editing any files, verify connectivity. Download Apidog for free—it provides an intuitive interface to test Moonshot API endpoints or local servers, inspect JSON payloads, monitor latency, and generate integration code snippets. This step prevents most downstream errors when linking the model to MoltBot. This guide covers both the API and local methods, with configuration examples, verification steps, and fixes for frequent problems. Why Pair MoltBot with Kimi K2.5? MoltBot is the execution layer; the LLM supplies the intelligence. Kimi K2.5 brings several concrete advantages to that role: - MoE efficiency — relevant experts activate on demand, delivering high capacity without proportional cost. - Native multimodal input — MoltBot can process screenshots, UI designs, or short videos, for example to generate code from visuals. - 256K token context (most deployments) — retain extensive project codebases, documentation, and conversation histories, essential for persistent assistants. !Image - Low API costs — substantially cheaper than Western alternatives; heavy users save the most over time. - Local inference option — quantized weights run on consumer hardware for zero ongoing expense and maximum control. - Strong agentic behavior — self-directed swarms of up to 100 sub-agents executing tools in parallel. Routed through MoltBot's skill system, these automate complex workflows directly from chat messages. - Provider portability — MoltBot accepts any OpenAI-compatible endpoint, so switching providers is just a configuration update. Prerequisites Complete this checklist before configuration: 1. Install MoltBot. Run the installation script if you haven't already: curl -fsSL https://molt.bot/install.sh | bash > The project rebranded from ClawdBot to MoltBot on January 27, 2026, following a trademark request from Anthropic. Older installations may retain the ~/.clawdbot directory; recent versions use moltbot commands and ~/.moltbot or similar paths. Check the docs at molt.bot or the GitHub repository (moltbot/moltbot) for your exact setup. 2. Obtain Kimi K2.5 access — pick one route: - API: create an account at platform.moonshot.ai, generate an API key, and note any project budget limits. - Local: download quantized weights (e.g., Hugging Face moonshotai/Kimi-K2.5 or community repos like unsloth/Kimi-K2.5-GGUF), install llama.cpp, and start a server. 3. Install Apidog for testing. It handles authentication headers, JSON bodies, and response streaming effectively. 4. Ensure Node.js runs for MoltBot. Basic terminal familiarity helps when editing JSON files. Method 1: Connecting via Moonshot API (Recommended for Most Users) This route requires minimal hardware and provides full 256K context plus multimodal support. Step 1: Validate the API Connection Using Apidog Launch Apidog and create a new POST request set to https://api.moonshot.ai/v1/chat/completions. Add the header: Authorization: Bearer [REDACTED API KEY] (Replace with your actual key.) Use this body for a basic test: { "model": "kimi-k2.5", "messages": [ { "role": "user", "content": "Confirm you are Kimi K2.5 and describe your capabilities briefly." } ], "temperature": 0.7, "max_tokens": 256 } Send the request. A successful 200 response with coherent output confirms the key works. Surface any rate-limit or budget errors here—before they hide inside MoltBot. Step 2: Locate and Edit the Configuration File MoltBot stores settings in a JSON file, typically: - ~/.moltbot/moltbot.json - Legacy: ~/.clawdbot/moltbot.json or ~/.clawdbot/agents/default/config.json Open it and add or modify the providers section: { "agent": { "model": { "primary": "moonshot/kimi-k2.5" } }, "models": { "providers": { "moonshot": { "baseUrl": "https://api.moonshot.ai/v1", [REDACTED CREDENTIAL], "api": "openai-completions", "models": [ { "id": "kimi-k2.5", "name": "Kimi K2.5 (API)", "contextWindow": 262144, "maxTokens": 8192 } ] } } } } > Security note: avoid hardcoding keys in production. Set an environment variable instead (e.g., export MOONSHOT_API_KEY=sk-...) and reference it if your MoltBot version supports expansion. Step 3: Apply Changes and Restart Save the file, then restart: moltbot restart Or stop and start the gateway/service as needed. Method 2: Connecting via Local Kimi K2.5 Deployment Local execution prioritizes privacy and eliminates recurring costs, though it demands substantial VRAM/RAM. Step 1: Launch the Local Inference Server Use llama.cpp for compatibility. Build it with GPU support if available: git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make LLAMA_CUDA=1 # or the appropriate flags for your platform Download a quantized GGUF variant (e.g., UD-TQ1_0 for balance) via huggingface-cli or a direct download, then start the OpenAI-compatible server: ./llama-server \ -m /path/to/Kimi-K2.5-UD-TQ1_0.gguf \ --port 8080 \ --ctx-size 32768 \ --n-gpu-layers 99 \ --host 0.0.0.0 --ctx-size 32768 is a practical starting point—adjust upward to your hardware limit. Full 256K context requires extreme resources. Verify by browsing http://localhost:8080/v1/models; the model should be listed. Step 2: Update MoltBot Configuration for the Local Endpoint Edit the JSON file: { "agent": { "model": { "primary": "local-kimi/kimi-k2.5" } }, "models": { "providers": { "local-kimi": { "baseUrl": "http://127.0.0.1:8080/v1", [REDACTED CREDENTIAL], "api": "openai-completions", "models": [ { "id": "kimi-k2.5-local", "name": "Kimi K2.5 Local", "contextWindow": 32768, "maxTokens": 4096 } ] } } } } contextWindow must match the server's --ctx-size. > Docker note: if MoltBot runs containerized, replace 127.0.0.1 with host.docker.internal. Step 3: Restart and Monitor Resource Usage Restart MoltBot and watch system monitors. Local inference consumes significant memory; offload fewer layers or reduce context if the machine struggles. Testing and Verification Confirm the integration works: 1. Send a message to your MoltBot instance from any connected app: Who are you powered by right now? Kimi K2.5 typically responds identifying Moonshot AI. 2. Check the logs: moltbot logs Look for requests routed to api.moonshot.ai or localhost:8080. 3. Test multimodal (API route): upload an image via chat and ask for a description or code generation from it. [How to Run Kimi K2.5 Locally?Learn how to run the massive 1T parameter Kimi K2.5 model locally using llama.cpp and Unsloth GGUF. Detailed hardware requirements, installation steps, and Apidog integration guide !Image Apidog BlogAshley Innocent !Image](apidog.com) Troubleshooting Common Issues | Symptom | Fix | | --- | --- | | Provider verification fails | Re-test the exact baseUrl + key in Apidog. Network proxies or firewalls often interfere. | | Context overflow errors | Align contextWindow in the JSON with the server's --ctx-size. MoltBot truncates or summarizes when limits hit; mismatched values cause crashes. | | Slow responses locally | Reduce --n-gpu-layers, use lower quantization, or enable flash attention in llama.cpp. | | Unexpected formatting/hallucinations | Experiment with temperature (0.6–1.0) or add custom system prompts in MoltBot agent config for Kimi-specific tuning. | | API budget exhaustion | Monitor usage at platform.moonshot.ai and set daily limits. | Conclusion Integrating Kimi K2.5 with MoltBot delivers a high-performance, economical, and optionally fully private personal AI agent. The API method offers convenience and maximum capabilities; the local setup ensures complete data sovereignty. Experiment with both approaches, and use Apidog throughout to isolate issues quickly. As Moonshot continues updating Kimi models and MoltBot evolves, this combination keeps you at the forefront of accessible agentic AI. Start configuring now—your enhanced assistant awaits. !Image
Run OpenClaw (Formerly Clawdbot or MoltBot) as Your Virtual Assistant 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. {% cta apidog.com %} Try Apidog today {% endcta %} 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. !OpenClaw website interface 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: 1. Create a bot via @BotFather. 2. Add the token to .env. 3. Start the agent: npm start 4. 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.