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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.

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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.

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  • 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
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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 (github.com/moltbot/moltbot) for your exact setup.

  1. 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.

  2. Install Apidog for testing. It handles authentication headers, JSON bodies, and response streaming effectively.

  3. 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]
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(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
}
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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
          }
        ]
      }
    }
  }
}
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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
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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
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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
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--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
          }
        ]
      }
    }
  }
}
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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?
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Kimi K2.5 typically responds identifying Moonshot AI.

  1. Check the logs: 
moltbot logs
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Look for requests routed to api.moonshot.ai or localhost:8080.

  1. 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](http://apidog.com/blog/run-kimi-k2-5-locally/)

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.

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