docs(ollama): align onboarding guidance with code

docs/concepts/model-providers.md

@@ -357,7 +357,7 @@ Ollama is a local LLM runtime that provides an OpenAI-compatible API:
 - Provider: `ollama`
 - Auth: None required (local server)
 - Example model: `ollama/llama3.3`
-- Installation: [https://ollama.ai](https://ollama.ai)
+- Installation: [https://ollama.com/download](https://ollama.com/download)
 
 ```bash
 # Install Ollama, then pull a model:
@@ -372,7 +372,7 @@ ollama pull llama3.3
 }
 ```
 
-Ollama is automatically detected when running locally at `http://127.0.0.1:11434/v1`. See [/providers/ollama](/providers/ollama) for model recommendations and custom configuration.
+Ollama is detected locally at `http://127.0.0.1:11434` when you opt in with `OLLAMA_API_KEY`, and `openclaw onboard` can configure it directly as a first-class provider. See [/providers/ollama](/providers/ollama) for onboarding, cloud/local mode, and custom configuration.
 
 ### vLLM
 

docs/gateway/local-models.md

@@ -11,6 +11,8 @@ title: "Local Models"
 
 Local is doable, but OpenClaw expects large context + strong defenses against prompt injection. Small cards truncate context and leak safety. Aim high: **≥2 maxed-out Mac Studios or equivalent GPU rig (~$30k+)**. A single **24 GB** GPU works only for lighter prompts with higher latency. Use the **largest / full-size model variant you can run**; aggressively quantized or “small” checkpoints raise prompt-injection risk (see [Security](/gateway/security)).
 
+If you want the lowest-friction local setup, start with [Ollama](/providers/ollama) and `openclaw onboard`. This page is the opinionated guide for higher-end local stacks and custom OpenAI-compatible local servers.
+
 ## Recommended: LM Studio + MiniMax M2.5 (Responses API, full-size)
 
 Best current local stack. Load MiniMax M2.5 in LM Studio, enable the local server (default `http://127.0.0.1:1234`), and use Responses API to keep reasoning separate from final text.

docs/help/faq.md

@@ -2084,8 +2084,21 @@ More context: [Models](/concepts/models).
 
 ### Can I use selfhosted models llamacpp vLLM Ollama
 
-Yes. If your local server exposes an OpenAI-compatible API, you can point a
-custom provider at it. Ollama is supported directly and is the easiest path.
+Yes. Ollama is the easiest path for local models.
+
+Quickest setup:
+
+1. Install Ollama from `https://ollama.com/download`
+2. Pull a local model such as `ollama pull glm-4.7-flash`
+3. If you want Ollama Cloud too, run `ollama signin`
+4. Run `openclaw onboard` and choose `Ollama`
+5. Pick `Local` or `Cloud + Local`
+
+Notes:
+
+- `Cloud + Local` gives you Ollama Cloud models plus your local Ollama models
+- cloud models such as `kimi-k2.5:cloud` do not need a local pull
+- for manual switching, use `openclaw models list` and `openclaw models set ollama/<model>`
 
 Security note: smaller or heavily quantized models are more vulnerable to prompt
 injection. We strongly recommend **large models** for any bot that can use tools.

docs/providers/ollama.md

@@ -8,7 +8,7 @@ title: "Ollama"
 
 # Ollama
 
-Ollama is a local LLM runtime that makes it easy to run open-source models on your machine. OpenClaw integrates with Ollama's native API (`/api/chat`), supporting streaming and tool calling, and can **auto-discover tool-capable models** when you opt in with `OLLAMA_API_KEY` (or an auth profile) and do not define an explicit `models.providers.ollama` entry.
+Ollama is a local LLM runtime that makes it easy to run open-source models on your machine. OpenClaw integrates with Ollama's native API (`/api/chat`), supports streaming and tool calling, and can auto-discover local Ollama models when you opt in with `OLLAMA_API_KEY` (or an auth profile) and do not define an explicit `models.providers.ollama` entry.
 
 <Warning>
 **Remote Ollama users**: Do not use the `/v1` OpenAI-compatible URL (`http://host:11434/v1`) with OpenClaw. This breaks tool calling and models may output raw tool JSON as plain text. Use the native Ollama API URL instead: `baseUrl: "http://host:11434"` (no `/v1`).
@@ -16,21 +16,40 @@ Ollama is a local LLM runtime that makes it easy to run open-source models on yo
 
 ## Quick start
 
-1. Install Ollama: [https://ollama.ai](https://ollama.ai)
+1. Install Ollama: [https://ollama.com/download](https://ollama.com/download)
 
-2. Pull a model:
+2. Pull a local model if you want local inference:
 
 ```bash
+ollama pull glm-4.7-flash
+# or
 ollama pull gpt-oss:20b
 # or
 ollama pull llama3.3
-# or
-ollama pull qwen2.5-coder:32b
-# or
-ollama pull deepseek-r1:32b
 ```
 
-3. Enable Ollama for OpenClaw (any value works; Ollama doesn't require a real key):
+3. If you want Ollama Cloud models too, sign in:
+
+```bash
+ollama signin
+```
+
+4. Run onboarding and choose `Ollama`:
+
+```bash
+openclaw onboard
+```
+
+- `Local`: local models only
+- `Cloud + Local`: local models plus Ollama Cloud models
+- Cloud models such as `kimi-k2.5:cloud`, `minimax-m2.5:cloud`, and `glm-5:cloud` do **not** require a local `ollama pull`
+
+OpenClaw currently suggests:
+
+- local default: `glm-4.7-flash`
+- cloud defaults: `kimi-k2.5:cloud`, `minimax-m2.5:cloud`, `glm-5:cloud`
+
+5. If you prefer manual setup, enable Ollama for OpenClaw directly (any value works; Ollama doesn't require a real key):
 
 ```bash
 # Set environment variable
@@ -40,13 +59,20 @@ export OLLAMA_API_KEY="ollama-local"
 openclaw config set models.providers.ollama.apiKey "ollama-local"
 ```
 
-4. Use Ollama models:
+6. Inspect or switch models:
+
+```bash
+openclaw models list
+openclaw models set ollama/glm-4.7-flash
+```
+
+7. Or set the default in config:
 
 ```json5
 {
   agents: {
     defaults: {
-      model: { primary: "ollama/gpt-oss:20b" },
+      model: { primary: "ollama/glm-4.7-flash" },
     },
   },
 }
@@ -56,14 +82,13 @@ openclaw config set models.providers.ollama.apiKey "ollama-local"
 
 When you set `OLLAMA_API_KEY` (or an auth profile) and **do not** define `models.providers.ollama`, OpenClaw discovers models from the local Ollama instance at `http://127.0.0.1:11434`:
 
-- Queries `/api/tags` and `/api/show`
-- Keeps only models that report `tools` capability
-- Marks `reasoning` when the model reports `thinking`
-- Reads `contextWindow` from `model_info["<arch>.context_length"]` when available
-- Sets `maxTokens` to 10× the context window
+- Queries `/api/tags`
+- Uses best-effort `/api/show` lookups to read `contextWindow` when available
+- Marks `reasoning` with a model-name heuristic (`r1`, `reasoning`, `think`)
+- Sets `maxTokens` to the default Ollama max-token cap used by OpenClaw
 - Sets all costs to `0`
 
-This avoids manual model entries while keeping the catalog aligned with Ollama's capabilities.
+This avoids manual model entries while keeping the catalog aligned with the local Ollama instance.
 
 To see what models are available:
 
@@ -98,7 +123,7 @@ Use explicit config when:
 
 - Ollama runs on another host/port.
 - You want to force specific context windows or model lists.
-- You want to include models that do not report tool support.
+- You want fully manual model definitions.
 
 ```json5
 {
@@ -170,7 +195,7 @@ Once configured, all your Ollama models are available:
 
 ### Reasoning models
 
-OpenClaw marks models as reasoning-capable when Ollama reports `thinking` in `/api/show`:
+OpenClaw treats models with names such as `deepseek-r1`, `reasoning`, or `think` as reasoning-capable by default:
 
 ```bash
 ollama pull deepseek-r1:32b
@@ -230,7 +255,7 @@ When `api: "openai-completions"` is used with Ollama, OpenClaw injects `options.
 
 ### Context windows
 
-For auto-discovered models, OpenClaw uses the context window reported by Ollama when available, otherwise it defaults to `8192`. You can override `contextWindow` and `maxTokens` in explicit provider config.
+For auto-discovered models, OpenClaw uses the context window reported by Ollama when available, otherwise it falls back to the default Ollama context window used by OpenClaw. You can override `contextWindow` and `maxTokens` in explicit provider config.
 
 ## Troubleshooting
 
@@ -250,16 +275,17 @@ curl http://localhost:11434/api/tags
 
 ### No models available
 
-OpenClaw only auto-discovers models that report tool support. If your model isn't listed, either:
+If your model is not listed, either:
 
-- Pull a tool-capable model, or
+- Pull the model locally, or
 - Define the model explicitly in `models.providers.ollama`.
 
 To add models:
 
 ```bash
 ollama list  # See what's installed
-ollama pull gpt-oss:20b  # Pull a tool-capable model
+ollama pull glm-4.7-flash
+ollama pull gpt-oss:20b
 ollama pull llama3.3     # Or another model
 ```