How to Use Kimi K-2.5 with OpenClaw quickly?

Kimi K-2.5 is MoonshotAI’s latest native multimodal, agentic model series (an evolution of the Kimi K2 line). It’s engineered for visual + language reasoning, strong coding capabilities, and advanced “agent” features including an Agent-Swarm paradigm (parallelized sub-agents for complex workflows). Kimi K-2.5 is available as open-source weights and via managed APIs (CometAPI publishs API endpoints for it). If you’re building automation that needs vision + stepwise tool calls (e.g., screenshots → code changes → system calls), Kimi K-2.5 is designed for that class of tasks.

OpenClaw is an open-source personal AI assistant/gateway you run locally or on a server. It acts as a bridge between chat channels (WhatsApp, Telegram, Slack, Discord, web UI, etc.) and a model backend — and it adds workflows, skill plugins, tool execution, and connectors. OpenClaw is model-agnostic: you can point it at cloud model APIs (OpenAI, Anthropic, CometAPI) or at local inference endpoints. The project has seen active releases and community docs in early 2026.

Why should you connect Kimi K-2.5 to OpenClaw?

Connecting Kimi K-2.5 to OpenClaw combines two complementary strengths:

• Multimodal execution: Kimi K-2.5 natively handles text, images, and code — ideal for tasks that mix document analysis, UI/prototype generation, and automated reporting. OpenClaw supplies the agent runtime and channels to act on those outputs (post to Slack, update docs, run scripts).
• Scale and orchestration: Kimi’s “agent swarm” design (multiple cooperating agents or specialized reasoning modes) pairs with OpenClaw’s orchestration hooks to coordinate multi-step jobs (data collection → analysis → publish). This is especially useful for research, batch content generation, and automated ops tasks.
• Flexibility: You can run Kimi K-2.5 locally (self-host inference) or via an API aggregator (CometAPI, Moonshot’s own platform). OpenClaw supports both models-as-a-provider and local providers, so you pick the tradeoffs you want—latency, cost, control, or data privacy.

Why this pairing matters: Kimi K-2.5 brings multimodal, agentic model capabilities (visual understanding, code generation, long-context reasoning), while OpenClaw provides the agent orchestration, connectors, and runtime to deploy those capabilities into practical workflows. Put simply, Kimi is the brain; OpenClaw is the body and nervous system that lets that brain act across chat channels, local files, and other services.

How to Use Kimi K-2.5 with OpenClaw quickly?

Below is a concise, production-aware quick path. Follow these steps in sequence: prepare your environment, obtain an API key (CometAPI example), install OpenClaw (Feb 2026 notes), set up Kimi (cloud or local), and wire them together. After the steps I summarize practical API vs local tradeoffs and best practices.

Note: this guide shows the fastest reliable route in 2026: use Moonshot’s official API or a routing provider (OpenRouter / CometAPI) and configure OpenClaw to use that provider. If you prefer local-only, skip the API key steps and follow the local deployment notes below.

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Prerequisites: Proper setup for Windows / WSL2 in 2026

If you’re on Windows (Windows 10/11), WSL2 is the recommended developer environment for Linux-native tooling, containers, and GPU acceleration workflows.

• Install WSL via the single-line method in an elevated PowerShell: wsl --install — this installs the WSL framework and Ubuntu by default. You can set WSL2 as the default and use wsl --set-default-version 2 where appropriate. Microsoft’s docs walk through wsl --install, distro selection, and troubleshooting.
• Hardware: For API use — any modern laptop/desktop with internet. For local inference of Kimi K-2.5 (if you later choose local), expect multi-GPU servers (A100/H100 class or specialized inference infra) or optimized runtimes (vLLM/vCUDA + multi-GPU distribution). Kimi K-2.5 is large and agentic; running it locally is nontrivial.
• Node.js / npm: OpenClaw installers and scripts expect Node.js 22+ (or as listed in the OpenClaw install docs). Install Node 22+ in WSL or Windows.
• A CometAPI account (or another supported aggregator): this guide uses CometAPI because it exposes Kimi K-2.5 and provides an OpenAI-compatible endpoint, letting OpenClaw use it with minimal configuration. Create an API key in CometAPI’s console.

Quick WSL2 install (one-liner)

Open PowerShell as Administrator and run:

wsl --install
# Restart when prompted
# After restart, open a WSL terminal and optionally:
wsl --update
wsl -l -v

(If you need to install specific distro: wsl --install -d ubuntu.) Microsoft’s WSL docs show wsl --install as the recommended, supported command in 2026.

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Step 1 — Create an API key via CometAPI (fast example)

If you want to call Kimi through a 3rd-party API gateway such as CometAPI (handy when you don’t want to wire a direct provider), the CometAPI quick-start flow is simple:

1. Top-up / create an account on CometAPI.
2. Create a token on the dashboard → that becomes your API key. CometAPI’s quick-start says: create a new token to get your API Key.
3. Replace your base URL in clients from OpenAI to CometAPI: and replace your key in the Authorization header.\

Example: set the key as an environment variable in WSL:

export COMETAPI_KEY="sk-xxxxxxxxxxxxxxxx"
# optionally add to ~/.bashrc or ~/.zshrc
echo 'export COMETAPI_KEY="sk-xxxxxxxxxxxxxxxx"' >> ~/.bashrc

Why use CometAPI? It’s a quick bridge when you don’t want to manage Moonshot platform quotas, or when you use tooling already wired to CometAPI’s base URL. Always verify that the provider offers the Kimi model with the appropriate slug and pricing.

Step 2 — Install OpenClaw (February 2026 recommended installs)

OpenClaw provides a quick installer and an npm package. Two common methods:

Method A — One-liner (recommended on macOS/Linux; works in WSL):

curl -fsSL https://openclaw.ai/install.sh | bash
# or clone the repo and run setup per the repo README

Method B — npm install (if you already manage Node):

npm install -g openclaw@latest
openclaw --version

Use the onboard wizard:

# example quoted from OpenRouter docs (OpenClaw onboarding)$ openclaw onboard

The wizard steps you through provider selection, API key entry, and example channel configuration.

Manual config (if you prefer): edit ~/.openclaw/openclaw.json and drop env keys (or use OpenClaw auth profiles to keep keys in the system keychain). CometAPI docs show how to set OPENROUTER_API_KEY or create an auth profile; the same pattern applies to other providers when supported.

Important security step: set OpenClaw to run in a restricted environment. Run it under a dedicated user, and enable auth profiles rather than plaintext keys in config. OpenClaw supports openclaw auth set openrouter:default --key "$KEY" to store keys in a system keychain.

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Step 3 — Configure OpenClaw to use CometAPI (Kimi K-2.5)

OpenClaw stores config at ~/.openclaw/openclaw.json (or the UI config). You’ll define an environment variable for the API key and set the primary model to the CometAPI Kimi model slug.

Minimal ~/.openclaw/openclaw.json snippet (example):

{
  "env": {
    "COMETAPI_KEY": "${COMETAPI_KEY}"
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "cometapi/moonshotai/kimi-k2-5"
      },
      "models": {
        "cometapi/moonshotai/kimi-k2-5": {}
      }
    }
  },
  "models": {
    "providers": {
      "cometapi": {
        "type": "openai-completions",
        "base_url": "https://api.cometapi.com",
        "auth_env": "COMETAPI_KEY"
      }
    }
  }
}

Notes & tips:

• The providers block lets you add custom OpenAI-compatible endpoints (CometAPI is OpenAI-compatible). OpenClaw’s docs show that built-in providers exist but you can add models.providers for custom backends. After editing the file, restart OpenClaw.
• Replace the model slug with the kimi-k2.5 shown on CometAPI’s model page (the page for Kimi K-2.5 on the CometAPI catalog).

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Step 4 — Sanity check: test CometAPI from your machine (curl)

Before starting OpenClaw, test that your key and the model work:

curl -s -X POST "https://api.cometapi.com/v1/chat/completions" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k2-5",
    "messages": [
      {"role":"system","content":"You are a concise assistant."},
      {"role":"user","content":"Say hello and give your model name and mode."}
    ],
    "max_tokens": 200,
    "temperature": 0.2
  }' | jq

If successful you’ll see a JSON response with model output. CometAPI supports the OpenAI-style /v1/chat/completions endpoint, so most existing OpenAI-style clients will work with just a base URL/key swap.

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Step 5 — Start OpenClaw and select the model

1. Start OpenClaw (CLI or Docker as you prefer).
2. In the OpenClaw web UI: Settings → Config → Agents (or edit openclaw.json raw). Set the default agent model to cometapi/moonshotai/kimi-k2-5. Save and restart the gateway. OpenClaw will then route agent calls to CometAPI, which will call the Kimi K-2.5 backend. OpenClaw docs and community guides show how to add API keys and pick provider model slugs.

openclaw.json — fuller example (drop into ~/.openclaw/openclaw.json)

{
  "env": {
    "COMETAPI_KEY": "sk-REPLACE_WITH_YOURS"
  },
  "models": {
    "providers": {
      "cometapi": {
        "type": "openai-completions",
        "base_url": "https://api.cometapi.com",
        "auth_env": "COMETAPI_KEY"
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "cometapi/moonshotai/kimi-k2-5"
      },
      "models": {
        "cometapi/moonshotai/kimi-k2-5": {
          "context_size": 131072,
          "max_tokens": 4096
        }
      }
    }
  }
}

Restart OpenClaw after editing. If OpenClaw won’t start, check logs for invalid JSON or missing env strings.

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Step 6 — Try an agentic task: screenshot → code suggestion (example)

OpenClaw supports tool calls and file attachments. A simple agentic test:

• From your UI, ask the assistant: Analyze this screenshot and produce a minimal React component that reproduces the UI.
• Attach a screenshot (OpenClaw supports attachments in the chat flow); OpenClaw forwards the multimodal input through CometAPI → Kimi K-2.5, which is designed to accept image + text inputs. If you need to tune costs or latency, reduce image size or test with smaller payloads first.

API vs. Local Kimi K-2.5: what are the tradeoffs?

When deciding whether to use Kimi K-2.5 via an API provider (CometAPI, Moonshot’s hosted service) or self-host locally, consider the following dimensions.

Latency and performance

• Local (self-host): If you run inference on local GPU(s) (NVIDIA/AMD with supported runtimes), latency for interactive tasks can be lower and you have complete control over batch sizes, quantization, and memory usage. However, you need sufficient GPU RAM (often 24 GB+ for large model variants or careful quantization for smaller hardware). Self-hosting also requires maintenance: updating weights, model wrappers, and inference stacks.
• API: Hosted providers abstract inference hardware. You pay for compute and benefit from scalable endpoints, managed updates, and lower operations overhead. Latency depends on network roundtrips and provider load. For many teams, API access is the fastest route to production integration.

Cost and operational overhead

• Local: Capital and operational costs (GPU hardware, power, cooling) can be high. But predictable once hardware is owned; per-call cost is essentially zero beyond infrastructure amortization. You also shoulder model updates and bugfixes.
• API: Pay-as-you-go model reduces upfront investment and maintenance work, but costs scale with usage. CometAPI offer more competitive pricing than an official hosted model endpoint.

Privacy and data control

• Local: Best for sensitive data and compliance because data never leaves your environment (assuming no external connectors). Ideal for on-premise deployments.
• API: Easier to set up, but you must evaluate the provider’s data retention, logging, and compliance policy. Use end-to-end encryption (TLS), minimal payloads, and redact secrets before sending prompts.

Feature velocity and updates

• API: Providers push model updates and optimizations (better performance, bug fixes). This is convenient but can change model behavior unexpectedly.
• Local: You control when and how to update model weights; useful when reproducibility is a priority.

Bottom line: If your priority is speed of integration and low ops burden, CometAPI is the fastest route. If you must keep data fully private or need extreme low-latency multimodal workloads on specialty hardware, self-hosting is the preferred option.

API vs Local Kimi K-2.5 — Advantages & Disadvantages

Aspect	Kimi K-2.5 via API (e.g., CometAPI)	Local Kimi K-2.5 Deployment
Setup Speed	✅ Fast — ready in minutes	❌ Slow — requires hardware & configuration
Cost	✅ Low — no infrastructure purchase, Usage-based (tokens / requests); predictable but accumulative	✅ Very high — GPU servers, infrastructure, Fixed hardware cost; potentially cheaper at high, sustained usage
Hardware Requirements	✅ None (besides client machine)	❌ Requires multi-GPU servers
Scalability	✅ Elastic, provider-managed	⚠️ Manual scaling required
Maintenance	✅ Minimal — provider handles	❌ High — updates, infra, monitoring
Model Updates	✅ Auto updates from provider	❌ Manual updates required
Performance Consistency	⚠️ May vary with traffic	✅ Consistent (local hardware)
Integration with OpenClaw	✅ Simple OpenAI-compatible	⚠️ Requires custom endpoint
Best For	Rapid prototyping, startups, low ops teams	Enterprise, strict data control, high volume

Troubleshooting — quick fixes for common problems

• 401 / 403 errors: check that your API key is set, valid and has credits.
• Model not responding / wrong model slug: verify the provider’s model list.
• OpenClaw fails to start: run openclaw gateway run from the home config and consult the logs in ~/.openclaw/logs. Use the onboarding wizard if manual config fails.
• Slow responses: ensure network connectivity; for heavy multimodal tasks prefer a direct Moonshot endpoint to reduce extra hops (CometAPI→ Moonshot adds a routing step but usually minimal latency). Consider local deployment for latency-sensitive loops.

Final note — be pragmatic but cautious

Kimi K-2.5 brings real multimodal, agentic power to workflows; OpenClaw turns that into always-on, multi-channel automation. Combined they can dramatically accelerate tasks — from generating polished slides and structured spreadsheets to running multi-agent research flows. But the same capabilities dramatically broaden the attack surface: in early February 2026 security researchers and governments flagged misconfigurations and malware risks in OpenClaw skill registries, and providers are actively patching and introducing guardrails. Balance speed with operational hygiene: prototype on the cloud (Moonshot/CometAPI) and harden before moving to unattended, production agent automation.