Sync ollama docs from 9330bb91 on 2026-04-12
This commit is contained in:
The Librarian
@@ -41,13 +41,27 @@ ollama launch claude --model kimi-k2.5:cloud
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- `kimi-k2.5:cloud`
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- `glm-5:cloud`
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- `minimax-m2.5:cloud`
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- `minimax-m2.7:cloud`
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- `qwen3.5:cloud`
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- `glm-4.7-flash`
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- `qwen3.5`
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Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).
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## Non-interactive (headless) mode
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Run Claude Code without interaction for use in Docker, CI/CD, or scripts:
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```shell
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ollama launch claude --model kimi-k2.5:cloud --yes -- -p "how does this repository work?"
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```
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The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified. Arguments after `--` are passed directly to Claude Code.
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## Web search
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Claude Code can search the web through Ollama's web search API. See the [web search documentation](/capabilities/web-search) for setup and usage.
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## Scheduled Tasks with `/loop`
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The `/loop` command runs a prompt or slash command on a recurring schedule inside Claude Code. This is useful for automating repetitive tasks like checking PRs, running research, or setting reminders.
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@@ -82,6 +96,18 @@ The `/loop` command runs a prompt or slash command on a recurring schedule insid
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/loop 1h Remind me to review the deploy status
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```
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## Telegram
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Chat with Claude Code from Telegram by connecting a bot to your session. Install the [Telegram plugin](https://github.com/anthropics/claude-plugins-official), create a bot via [@BotFather](https://t.me/BotFather), then launch with the channel flag:
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```shell
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ollama launch claude -- --channels plugin:telegram@claude-plugins-official
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```
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Claude Code will prompt for permission on most actions. To allow the bot to work autonomously, configure [permission rules](https://code.claude.com/docs/en/permissions) or pass `--dangerously-skip-permissions` in isolated environments.
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See the [plugin README](https://github.com/anthropics/claude-plugins-official/tree/main/external_plugins/telegram) for full setup instructions including pairing and access control.
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## Manual setup
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Claude Code connects to Ollama using the Anthropic-compatible API.
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+20
-17
@@ -35,36 +35,39 @@ To use `codex` with Ollama, use the `--oss` flag:
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codex --oss
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```
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### Changing Models
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By default, codex will use the local `gpt-oss:20b` model. However, you can specify a different model with the `-m` flag:
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To use a specific model, pass the `-m` flag:
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```
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codex --oss -m gpt-oss:120b
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```
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### Cloud Models
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To use a cloud model:
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```
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codex --oss -m gpt-oss:120b-cloud
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```
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### Profile-based setup
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## Connecting to ollama.com
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Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
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To use ollama.com directly, edit your `~/.codex/config.toml` file to point to ollama.com.
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For a persistent configuration, add an Ollama provider and profiles to `~/.codex/config.toml`:
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```toml
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model = "gpt-oss:120b"
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model_provider = "ollama"
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[model_providers.ollama]
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[model_providers.ollama-launch]
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name = "Ollama"
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base_url = "https://ollama.com/v1"
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env_key = "OLLAMA_API_KEY"
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base_url = "http://localhost:11434/v1"
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[profiles.ollama-launch]
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model = "gpt-oss:120b"
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model_provider = "ollama-launch"
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[profiles.ollama-cloud]
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model = "gpt-oss:120b-cloud"
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model_provider = "ollama-launch"
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```
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Run `codex` in a new terminal to load the new settings.
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Then run:
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```
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codex --profile ollama-launch
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codex --profile ollama-cloud
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```
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@@ -0,0 +1,111 @@
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---
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title: Hermes Agent
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---
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Hermes Agent is a self-improving AI agent built by Nous Research. It features automatic skill creation, cross-session memory, and connects messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal, Email) to models through a unified gateway.
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## Quick start
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### Pull a model
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Before running the setup wizard, make sure you have a model available. Hermes will auto-detect models downloaded through Ollama.
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```bash
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ollama pull kimi-k2.5:cloud
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```
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See [Recommended models](#recommended-models) for more options.
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### Install
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```bash
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curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
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```
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### Set up
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After installation, Hermes launches the setup wizard automatically. Choose **Quick setup**:
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```
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How would you like to set up Hermes?
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→ Quick setup — provider, model & messaging (recommended)
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Full setup — configure everything
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```
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### Connect to Ollama
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1. Select **More providers...**
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2. Select **Custom endpoint (enter URL manually)**
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3. Set the API base URL to the Ollama OpenAI-compatible endpoint:
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```
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API base URL [e.g. https://api.example.com/v1]: http://127.0.0.1:11434/v1
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```
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4. Leave the API key blank (not required for local Ollama):
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```
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API key [optional]:
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```
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5. Hermes auto-detects downloaded models, confirm the one you want:
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```
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Verified endpoint via http://127.0.0.1:11434/v1/models (1 model(s) visible)
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Detected model: kimi-k2.5:cloud
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Use this model? [Y/n]:
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```
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6. Leave context length blank to auto-detect:
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```
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Context length in tokens [leave blank for auto-detect]:
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```
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### Connect messaging
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Optionally connect a messaging platform during setup:
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```
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Connect a messaging platform? (Telegram, Discord, etc.)
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→ Set up messaging now (recommended)
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Skip — set up later with 'hermes setup gateway'
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```
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### Launch
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```
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Launch hermes chat now? [Y/n]: Y
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```
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## Recommended models
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**Cloud models**:
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- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
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- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
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- `glm-5.1:cloud` — Reasoning and code generation
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- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
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**Local models:**
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- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
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- `qwen3.5` — Reasoning, coding, and visual understanding locally (~11 GB VRAM)
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More models at [ollama.com/search](https://ollama.com/models).
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## Configure later
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Re-run the setup wizard at any time:
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```bash
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hermes setup
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```
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To configure just messaging:
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```bash
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hermes setup gateway
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```
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@@ -20,6 +20,7 @@ Coding assistants that can read, modify, and execute code in your projects.
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AI assistants that help with everyday tasks.
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- [OpenClaw](/integrations/openclaw)
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- [Hermes Agent](/integrations/hermes)
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## IDEs & Editors
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@@ -0,0 +1,67 @@
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---
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title: NemoClaw
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---
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NemoClaw is NVIDIA's open source security stack for [OpenClaw](/integrations/openclaw). It wraps OpenClaw with the NVIDIA OpenShell runtime to provide kernel-level sandboxing, network policy controls, and audit trails for AI agents.
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## Quick start
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Pull a model:
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```bash
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ollama pull nemotron-3-nano:30b
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```
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Run the installer:
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```bash
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curl -fsSL https://www.nvidia.com/nemoclaw.sh | \
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NEMOCLAW_NON_INTERACTIVE=1 \
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NEMOCLAW_PROVIDER=ollama \
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NEMOCLAW_MODEL=nemotron-3-nano:30b \
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bash
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```
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Connect to your sandbox:
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```bash
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nemoclaw my-assistant connect
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```
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Open the TUI:
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```bash
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openclaw tui
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```
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<Note>Ollama support in NemoClaw is still experimental.</Note>
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## Platform support
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| Platform | Runtime | Status |
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|----------|---------|--------|
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| Linux (Ubuntu 22.04+) | Docker | Primary |
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| macOS (Apple Silicon) | Colima or Docker Desktop | Supported |
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| Windows | WSL2 with Docker Desktop | Supported |
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CMD and PowerShell are not supported on Windows — WSL2 is required.
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<Note>Ollama must be installed and running before the installer runs. When running inside WSL2 or a container, ensure Ollama is reachable from the sandbox (e.g. `OLLAMA_HOST=0.0.0.0`).</Note>
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## System requirements
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- CPU: 4 vCPU minimum
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- RAM: 8 GB minimum (16 GB recommended)
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- Disk: 20 GB free (40 GB recommended for local models)
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- Node.js 20+ and npm 10+
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- Container runtime (Docker preferred)
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## Recommended models
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- `nemotron-3-super:cloud` — Strong reasoning and coding
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- `qwen3.5:cloud` — 397B; reasoning and code generation
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- `nemotron-3-nano:30b` — Recommended local model; fits in 24 GB VRAM
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- `qwen3.5:27b` — Fast local reasoning (~18 GB VRAM)
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- `glm-4.7-flash` — Reasoning and code generation (~25 GB VRAM)
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More models at [ollama.com/search](https://ollama.com/search).
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@@ -15,13 +15,29 @@ Ollama handles everything automatically:
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1. **Install** — If OpenClaw isn't installed, Ollama prompts to install it via npm
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2. **Security** — On the first launch, a security notice explains the risks of tool access
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3. **Model** — Pick a model from the selector (local or cloud)
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4. **Onboarding** — Ollama configures the provider, installs the gateway daemon, and sets your model as the primary
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4. **Onboarding** — Ollama configures the provider, installs the gateway daemon, sets your model as the primary, and installs the web search and fetch plugin
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5. **Gateway** — Starts in the background and opens the OpenClaw TUI
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<Note>OpenClaw requires a larger context window. It is recommended to use a context window of at least 64k tokens if using local models. See [Context length](/context-length) for more information.</Note>
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<Note>Previously known as Clawdbot. `ollama launch clawdbot` still works as an alias.</Note>
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## Web search and fetch
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OpenClaw ships with a web search and fetch plugin that gives local or cloud models the ability to search the web and extract readable page content.
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```bash
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ollama launch openclaw
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```
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Web search and fetch is enabled automatically when launching OpenClaw through Ollama. To install the plugin directly:
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```bash
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openclaw plugins install @ollama/openclaw-web-search
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```
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<Note>Web search for local models requires `ollama signin`.</Note>
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## Configure without launching
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To change the model without starting the gateway and TUI:
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@@ -43,15 +59,27 @@ If the gateway is already running, it restarts automatically to pick up the new
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**Cloud models**:
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- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
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- `minimax-m2.5:cloud` — Fast, efficient coding and real-world productivity
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- `glm-5:cloud` — Reasoning and code generation
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- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
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- `glm-5.1:cloud` — Reasoning and code generation
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- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
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**Local models:**
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- `glm-4.7-flash` — Reasoning and code generation locally (~25 GB VRAM)
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- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
|
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- `qwen3.5` — Reasoning, coding, and visual understanding locally (~11 GB VRAM)
|
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|
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More models at [ollama.com/search](https://ollama.com/search?c=cloud).
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## Non-interactive (headless) mode
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Run OpenClaw without interaction for use in Docker, CI/CD, or scripts:
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```bash
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ollama launch openclaw --model kimi-k2.5:cloud --yes
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```
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The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified.
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## Connect messaging apps
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```bash
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@@ -2,7 +2,7 @@
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title: Pi
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---
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Pi is a minimal AI agent toolkit with plugin support.
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Pi is a minimal and extensible coding agent.
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## Install
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@@ -20,13 +20,65 @@ npm install -g @mariozechner/pi-coding-agent
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ollama launch pi
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```
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This installs Pi, configures Ollama as a provider including web tools, and drops you into an interactive session.
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To configure without launching:
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```shell
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ollama launch pi --config
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```
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### Manual setup
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### Run directly with a model
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```shell
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ollama launch pi --model qwen3.5:cloud
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```
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Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
|
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## Extensions
|
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Pi ships with four core tools: `read`, `write`, `edit`, and `bash`. All other capabilities are added through its extension system.
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On-demand capability packages invoked via `/skill:name` commands.
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Install from npm or git:
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```bash
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pi install npm:@foo/some-tools
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pi install git:github.com/user/repo@v1
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```
|
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|
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See all packages at [pi.dev](https://pi.dev/packages)
|
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### Web search
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Pi can use web search and fetch tools via the `@ollama/pi-web-search` package.
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When launching Pi through Ollama, package install/update is managed automatically.
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To install manually:
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||||
|
||||
```bash
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pi install npm:@ollama/pi-web-search
|
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```
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||||
|
||||
### Autoresearch with `pi-autoresearch`
|
||||
|
||||
[pi-autoresearch](https://github.com/davebcn87/pi-autoresearch) brings autonomous experiment loops to Pi. Inspired by Karpathy's autoresearch, it turns any measurable metric into an optimization target: test speed, bundle size, build time, model training loss, Lighthouse scores.
|
||||
|
||||
```bash
|
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pi install https://github.com/davebcn87/pi-autoresearch
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||||
```
|
||||
|
||||
Tell Pi what to optimize. It runs experiments, benchmarks each one, keeps improvements, reverts regressions, and repeats — all autonomously. A built-in dashboard tracks every run with confidence scoring to distinguish real gains from benchmark noise.
|
||||
|
||||
```bash
|
||||
/autoresearch optimize unit test runtime
|
||||
```
|
||||
|
||||
Each kept experiment is automatically committed. Each failed one is reverted. When you're done, Pi can group improvements into independent branches for clean review and merge.
|
||||
|
||||
## Manual setup
|
||||
|
||||
Add a configuration block to `~/.pi/agent/models.json`:
|
||||
|
||||
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||||
@@ -2,33 +2,84 @@
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||||
title: VS Code
|
||||
---
|
||||
|
||||
## Install
|
||||
VS Code includes built-in AI chat through GitHub Copilot Chat. Ollama models can be used directly in the Copilot Chat model picker.
|
||||
|
||||
Install [VS Code](https://code.visualstudio.com/download).
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
|
||||
1. Open Copilot side bar found in top right window
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Ollama v0.18.3+
|
||||
- [VS Code 1.113+](https://code.visualstudio.com/download)
|
||||
- [GitHub Copilot Chat extension 0.41.0+](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-chat)
|
||||
|
||||
<Note> VS Code requires you to be logged in to use its model selector, even for custom models. This doesn't require a paid GitHub Copilot account; GitHub Copilot Free will enable model selection for custom models.</Note>
|
||||
|
||||
## Quick setup
|
||||
|
||||
```shell
|
||||
ollama launch vscode
|
||||
```
|
||||
|
||||
Recommended models will be shown after running the command. See the latest models at [ollama.com](https://ollama.com/search?c=tools).
|
||||
|
||||
Make sure **Local** is selected at the bottom of the Copilot Chat panel to use your Ollama models.
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/local.png"
|
||||
alt="Ollama Local Models"
|
||||
width="60%"
|
||||
style={{ borderRadius: "4px", marginTop: "10px", marginBottom: "10px" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
## Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch vscode --model qwen3.5:cloud
|
||||
```
|
||||
Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Manual setup
|
||||
|
||||
To configure Ollama manually without `ollama launch`:
|
||||
|
||||
1. Open the **Copilot Chat** side bar from the top right corner
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-sidebar.png"
|
||||
alt="VS Code chat Sidebar"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
2. Select the model dropdown > **Manage models**
|
||||
2. Click the **settings gear icon** (<Icon icon="gear" />) to bring up the Language Models window
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-models.png"
|
||||
src="/images/vscode-other-models.png"
|
||||
alt="VS Code model picker"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
3. Enter **Ollama** under **Provider Dropdown** and select desired models (e.g `qwen3, qwen3-coder:480b-cloud`)
|
||||
3. Click **Add Models** and select **Ollama** to load all your Ollama models into VS Code
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-model-options.png"
|
||||
alt="VS Code model options dropdown"
|
||||
src="/images/vscode-add-ollama.png"
|
||||
alt="VS Code model options dropdown to add ollama models"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
4. Click the **Unhide** button in the model picker to show your Ollama models
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-unhide.png"
|
||||
alt="VS Code unhide models button"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user