​

Kilo CLI

• Edit code across multiple files without leaving the terminal
• Generate, explain, and refactor code with AI assistance
• Automate repetitive tasks such as writing unit tests, fixing bugs, and reviewing changes
• Switch between models based on task complexity

​

Prerequisites

• An Openference account and API key (create one at openference.com)
• Node.js 18.0 or later

​

Install Kilo CLI

npm install -g @kilocode/cli

kilo --version

​

Get your API key

​

Configure the connection

mkdir -p ~/.config/kilo

{
  "$schema": "https://app.kilo.ai/config.json",
  "provider": {
    "openference": {
      "name": "Openference",
      "options": {
        "baseURL": "https://api.openference.com/v1",
        "apiKey": "{env:OPENFERENCE_API_KEY}"
      },
      "models": {
        "GLM-5.2": {
          "name": "GLM-5.2 (via Openference)",
          "limit": { "context": 850000, "output": 128000 }
        },
        "Kimi K2.6": {
          "name": "Kimi K2.6 (via Openference)",
          "limit": { "context": 254000, "output": 98304 }
        },
        "DeepSeek-V4-Pro": {
          "name": "DeepSeek-V4-Pro (via Openference)",
          "limit": { "context": 850000, "output": 131072 }
        },
        "Qwen3.7 Plus": {
          "name": "Qwen3.7 Plus (via Openference)",
          "limit": { "context": 915000, "output": 65536 }
        },
        "DeepSeek-V4-Flash": {
          "name": "DeepSeek-V4-Flash (via Openference)",
          "limit": { "context": 850000, "output": 131072 }
        }
      }
    }
  }
}

Tip: the limit block tells Kilo each model’s real context window and max output. Without it, coding agents assume a small default (~128k) and start compacting the session around ~100k tokens. Current values are advertised as context_length and max_output_tokens in GET /v1/models.

export OPENFERENCE_API_KEY="YOUR_API_KEY"

​

Select a model and start coding

1. Run /models in Kilo CLI and select the Openference provider.
2. Pick a model (e.g. openference/GLM-5.2).

what does this project do?
find and fix any bugs in the main module
write unit tests for the auth module

​

Recommended models

1. GLM-5.2 — top tier (most capable for complex reasoning, agents, and architecture)
2. Kimi K2.6 — all rounder (strong general-purpose choice for most tasks)
3. DeepSeek-V4-Pro — medium (balanced performance and value)
4. Qwen3.7 Plus — Vision (use when you need image / multimodal understanding)
5. DeepSeek-V4-Flash — instant (fast and lightweight responses)

curl https://api.openference.com/v1/models \
  -H "Authorization: Bearer $OPENFERENCE_API_KEY"

​

FAQ

​

I get an error or no response after connecting

• Verify that OPENFERENCE_API_KEY is exported in the current shell.
• Confirm the model name exactly matches an ID from GET /v1/models (including spaces and casing).
• Check JSON syntax in config.json (missing commas are a common cause).

​

How do I switch models?

​

Can I use the same key for other tools?

​

Next steps

• CLI & IDE Compatibility Matrix
• Chinese LLM Models
• Base URL & Authentication