用本地LLM跑AI编程智能体

想要数据不出本机的AI编程助手。

AI Coding Agent with Local LLM 2026: Run a Private Coder for Free

A fully local AI coding agent is possible in 2026: open-source agents plus local models. The catch is model quality. Here is the honest picture of what works, what does not, and the exact stack.

The Stack That Works

Setup in 5 Commands

# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 2. Pull a coding model
ollama pull qwen2.5-coder:7b

# 3. Install OpenCode
npm install -g opencode

# 4. Point OpenCode at the local model
opencode --model ollama/qwen2.5-coder:7b

# 5. Start working
cd your-project && opencode

The Honest Limits

• 7B models handle boilerplate, tests, and small fixes well; they struggle with complex architecture.
• 14B-32B models are noticeably better but need 32GB+ RAM.
• Autocomplete vs agent: local models are fine for chat/agent work; inline autocomplete quality still lags cloud models.
• Speed: on CPU-only, expect 5-15 tokens/s - usable but slow for big refactors.

When Local Makes Sense

• Your code is proprietary or regulated (medical, legal, finance)
• You want zero per-token cost for high-volume work
• You are offline or behind a firewall
• You are learning and want to iterate without budgets

FAQ

Is local coding quality good enough in 2026? For small-to-medium tasks, yes. For enterprise-scale refactors, cloud models still lead.

How much RAM do I need? 16GB for 7B models; 32GB for 14B+ with good context windows.

Can I use a hybrid approach? Yes - local for routine work, cloud for hard problems. OpenCode/Cline both support per-session model switching.