setup-ollama-local

name	setup-ollama-local
description	Interactively guide setup for connecting the Claude Code CLI to a local Ollama LLM on Mac. NOTE — This is for the standalone Claude Code CLI itself, NOT MulmoClaude (MulmoClaude does not currently support Ollama as a backend). Covers Ollama install, model pull, env switching, and verification. Respond in the user's language.
allowed-tools	Read, Bash, Glob, Grep

Setup Claude Code × Ollama (local LLM)

Scope / 適用範囲

This skill sets up the standalone claude CLI to talk to a local Ollama server. It is independent of MulmoClaude; MulmoClaude itself does not currently support Ollama (see plans/feat-mulmoclaude-ollama-support.md for a tentative plan).

このスキルは claude CLI 単体をローカルの Ollama サーバに接続するセットアップです。MulmoClaude とは独立しており、MulmoClaude 本体は現在 Ollama 接続をサポートしていません（実装案は plans/feat-mulmoclaude-ollama-support.md を参照）。

For detailed findings and pitfalls, see docs/tips/claude-code-ollama.md (Japanese) / docs/tips/claude-code-ollama.en.md (English).

Prerequisites / 前提知識

• Ollama v0.14.0 or later is required (Anthropic Messages API compatibility was added in that version).
• Claude Code sends roughly 50,000–57,000 tokens per request, so the model needs at least a 64k context window.
• 3B-class small models effectively cannot drive Claude Code (no tool calling, broken templates).
• Even on supported models, the first turn often takes 10+ minutes on a MacBook Air; subsequent turns benefit from KV cache and drop to 1–3 minutes.

Step 1: Verify / install Ollama

1-1. Check existing install

which ollama && ollama --version

• Installed and v0.14.0+: proceed to 1-2.
• Older version: brew upgrade ollama and then brew services restart ollama (Homebrew installs).
• Not installed: suggest one of:
  • Official installer (recommended): https://ollama.com/download/mac
  • Homebrew: brew install ollama

1-2. Verify the server is running

curl -s http://localhost:11434/api/tags | head -c 200

• Got JSON back: server is up, go to Step 2.
• Empty / connection refused: start it.
  • Official app: click the Ollama menu-bar icon.
  • Homebrew: brew services start ollama or ollama serve.

Step 2: Verify Claude Code

which claude && claude --version

• Installed: continue to Step 3.
• Not installed: install via npm (recommended), the official script, or Homebrew:
  • npm install -g @anthropic-ai/claude-code
  • curl -fsSL https://claude.ai/install.sh | sh
  • brew install anthropic/tap/claude-code

Step 3: Choose and pull a model

Confirm the user's RAM and use case before recommending. Verified working models on a MacBook Air M4 32GB are summarized in docs/tips/claude-code-ollama.md. Quick picks:

RAM	Recommended	Size	Notes
8–16GB	(Claude Code × Ollama is impractical here)	—	Cold start exceeds 10 min timeout
32GB	qwen3.5:9b	6.6GB	Most practical, lightest fit
32GB	qwen3.6:35b-a3b	23GB	MoE (3B active), heavier but works
16–32GB	gemma4:e4b	3GB on disk (~10.9 GiB resident)	Verified on 32GB; thinking blocks render correctly
24GB+ (NVIDIA)	glm-4.7-flash	19GB	198k context, untested on Mac

Avoid: qwen3:14b (40k training limit), qwen2.5-coder:14b (older runner ignores OLLAMA_CONTEXT_LENGTH), gemma4:26b (Content block parse errors — note: gemma4:e4b is fine), gpt-oss:20b (Ollama template bug). See findings doc for details.

ollama pull <model>
ollama list

Step 4: Start Ollama with the right context window

Claude Code requires ≥64k context. The default is 32k, so always extend it when launching Ollama for Claude Code:

brew services stop ollama   # if running under brew services
OLLAMA_CONTEXT_LENGTH=65536 ollama serve

This terminal must stay open for the duration of the session. For longer sessions add OLLAMA_KEEP_ALIVE=30m so the KV cache survives idle gaps.

Step 5: Warm up the model

In a second terminal, load the model into memory and confirm it responds at all:

ollama run <model> "hello"

Expect a response within a few seconds. If this hangs, the model is unsuitable for Claude Code.

Step 6: Run Claude Code against Ollama

In a third terminal, set the env vars and launch:

export ANTHROPIC_AUTH_TOKEN="ollama"
export ANTHROPIC_API_KEY=""
export ANTHROPIC_BASE_URL="http://localhost:11434"
claude --verbose --model <model>

Role of each variable:

Variable	Value	Purpose
ANTHROPIC_AUTH_TOKEN	"ollama"	Enables Ollama mode
ANTHROPIC_API_KEY	"" (empty)	Disables the cloud API key (prevents collision)
ANTHROPIC_BASE_URL	http://localhost:11434	Routes API calls to the local server

Send a simple message (e.g. "Hello, what model are you?") to confirm. The first turn can take 10+ minutes; subsequent turns drop to 1–3 minutes once the KV cache is warm.

While waiting, watch the Ollama log in another terminal to see what's happening:

tail -f /opt/homebrew/var/log/ollama.log    # Homebrew install
# or just watch the terminal where `ollama serve` is running

Key log signals:

• KvSize:65536 ✓ — context is correctly extended
• truncating input prompt limit=XXXXX ✗ — model/runner ignores the env var; switch model
• POST /v1/messages 200 ✓ — successful turn
• POST /v1/messages 500 ✗ — template incompatibility; switch model

Step 7: Switching back to cloud Claude

The local mode is scoped to the terminal where the env vars were set:

1. Easiest: close that terminal and open a fresh one — back to cloud.
2. Or unset explicitly:

   unset ANTHROPIC_AUTH_TOKEN ANTHROPIC_API_KEY ANTHROPIC_BASE_URL
   

Step 8 (optional): Convenience alias

If the user wants a one-liner, suggest an alias in ~/.zshrc. Do not put bare export ANTHROPIC_BASE_URL=... lines in a startup file — that breaks normal cloud usage everywhere.

alias claude-local='ANTHROPIC_AUTH_TOKEN="ollama" ANTHROPIC_API_KEY="" ANTHROPIC_BASE_URL="http://localhost:11434" claude'

After source ~/.zshrc, usage is:

claude-local --model qwen3.5:9b   # local
claude                            # cloud, unchanged

Key pitfalls to highlight

• Ollama < v0.14.0 has no Anthropic API compatibility — always check the version first.
• ANTHROPIC_API_KEY must be explicitly empty; otherwise an existing cloud key may collide.
• 3B-class models cannot reliably emit Claude's tool-use JSON, so file edits and shell commands fail.
• Even tool-capable open models (Gemma 4, gpt-oss) are not fully aligned with Anthropic's response format — complex skill chains misbehave.
• Large models (20B+) eat memory; watch with vm_stat or Activity Monitor.
• Permanent export ANTHROPIC_BASE_URL=... in .zshrc / .bashrc will silently break normal cloud Claude usage. Use an alias instead.
• The first-turn 10-minute Claude Code timeout is unavoidable, but Ollama keeps processing in the background, so a retry usually succeeds via cache reuse.

Reference links

• Ollama Claude Code integration: https://docs.ollama.com/integrations/claude-code
• Ollama Anthropic compatibility blog: https://ollama.com/blog/claude
• Claude Code official docs: https://code.claude.com/docs/en/overview
• Local findings: docs/tips/claude-code-ollama.md (ja) / .en.md (en)