How to use Continue CLI mode for local AI coding control

1. What “Continue CLI mode” actually means

Strictly speaking, Continue is not primarily a standalone CLI tool. Instead, “CLI mode” usually refers to one of these patterns:

A. Terminal-driven usage via Continue API/runtime

You run Continue’s model engine locally (or via a server) and interact with it from:

• shell scripts
• custom CLI wrappers
• tools like curl
• Node/Python scripts calling Continue backend

B. CLI-like workflow inside terminal tools

You combine Continue with:

npx continue (experimental / community wrappers)

• custom commands that send context to Continue server
• local LLM runtimes (Ollama, LM Studio) controlled via CLI

C. IDE + terminal hybrid usage (most common)

You run Continue in VS Code or JetBrains, but use terminal for:

• git workflows
• file context gathering
• piping code into Continue chat

2. Core architecture (important to understand)

Continue works like this:

CLI / IDE / Script
        ↓
Continue Core (prompt orchestration)
        ↓
LLM Provider (local or cloud)
        ↓
Response back to terminal/editor

For local AI coding control, the key is:

You control the LLM backend + context feeding layer.

3. Setup for local AI coding (recommended stack)

Step 1 — Install Continue

If using Node-based CLI wrappers or IDE integration:

npm install -g @continuedev/cli

Or install IDE extension and enable local server mode.

Step 2 — Set up a local model (critical)

Most users pair Continue with one of:

Option A: Ollama (most popular)

curl -fsSL https://ollama.com/install.sh | sh

Run a model:

ollama run codellama

or better coding models:

ollama run deepseek-coder
ollama run qwen2.5-coder

Option B: LM Studio

• GUI + local OpenAI-compatible server
• Exposes endpoint like:

http://localhost:1234/v1

Step 3 — Configure Continue to use local model

Create or edit:

~/.continue/config.json

Example configuration:

{
  "models": [
    {
      "title": "Local Ollama",
      "provider": "ollama",
      "model": "codellama"
    }
  ],
  "tabAutocompleteModel": {
    "provider": "ollama",
    "model": "codellama"
  }
}

Or OpenAI-compatible local server:

{
  "models": [
    {
      "title": "Local LM Studio",
      "provider": "openai",
      "model": "local-model",
      "apiBase": "http://localhost:1234/v1",
      "apiKey": "not-needed"
    }
  ]
}

4. Using Continue in CLI-like mode

Option A: Pipe code into Continue (simple workflow)

Example concept:

cat main.py | continue "refactor this into clean modular design"

Or:

git diff | continue "explain bugs and suggest fixes"

Option B: Using curl (direct API-style control)

If Continue server is running:

curl http://localhost:port/chat \
  -d '{
    "message": "Write a Python function to parse logs",
    "context": "You are a senior backend engineer"
  }'

Option C: Scripted automation (power user mode)

Python example:

import requests

response = requests.post("http://localhost:port/chat", json={
    "message": "Optimize this SQL query",
    "context": open("query.sql").read()
})

print(response.json()["text"])

Option D: Git-aware CLI workflow

Common pattern:

git diff | continue "review this code for security issues"

or

find . -name "*.js" | xargs cat | continue "find architectural issues"

5. Typical use cases for CLI-style Continue

1. Code review automation

git diff main | continue "perform senior code review"

2. Debugging assistance

cat error.log | continue "diagnose root cause"

3. Refactoring pipelines

cat legacy.py | continue "convert to modern Python with type hints"

4. Architecture planning

continue "design a microservices architecture for this monolith"

5. Test generation

cat service.ts | continue "generate unit tests with edge cases"

6. Advanced local AI control patterns

A. Multi-model routing (recommended)

You can configure:

• fast model → autocomplete
• strong model → refactor/review

Example:

"tabAutocompleteModel": {
  "provider": "ollama",
  "model": "qwen2.5-coder:7b"
},
"models": [
  {
    "title": "Reasoning Model",
    "provider": "ollama",
    "model": "deepseek-coder"
  }
]

B. Context injection (important CLI concept)

You can feed structured context:

continue "fix bug" < context.txt

Or:

continue "explain dependency graph" <<< "$(tree -L 3)"

C. System prompt control

You can simulate “agent modes”:

"systemMessage": "You are a strict senior software architect. Be precise and concise."

8. When Continue CLI mode is worth using

Use it if you want:

• local-first AI coding (privacy-sensitive work)
• offline coding assistance
• automated refactoring pipelines
• git-driven AI workflows
• scriptable LLM integration

Avoid it if:

• you only want chat-style coding help (IDE extension is simpler)
• you don’t want to manage models locally

9. If you want a more “true CLI AI coder”

If your goal is pure terminal-native AI coding agents, you may also want to look at:

• Aider (git-based coding agent)
• Codex-style CLI wrappers
• Open Interpreter-style tools

Continue sits between:

IDE assistant ↔ programmable AI coding backend