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docs/integrations/claude-desktop.mdx
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---
title: Claude Desktop
---
Claude Desktop is no longer supported by `ollama launch`.
Existing installations can be restored to the usual Claude profile:
```shell
ollama launch claude-desktop --restore
```
Use [Claude Code](/integrations/claude-code) for Anthropic-compatible coding workflows with Ollama.
docs/integrations/copilot-cli.mdx
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---
title: Copilot CLI
---
GitHub Copilot CLI is GitHub's AI coding agent for the terminal. It can understand your codebase, make edits, run commands, and help you build software faster.
Open models can be used with Copilot CLI through Ollama, enabling you to use models such as `qwen3.5`, `glm-5.1:cloud`, `kimi-k2.5:cloud`.
## Install
Install [Copilot CLI](https://github.com/features/copilot/cli/):
<CodeGroup>
```shell macOS / Linux (Homebrew)
brew install copilot-cli
```
```shell npm (all platforms)
npm install -g @github/copilot
```
```shell macOS / Linux (script)
curl -fsSL https://gh.io/copilot-install | bash
```
```powershell Windows (WinGet)
winget install GitHub.Copilot
```
</CodeGroup>
## Usage with Ollama
### Quick setup
```shell
ollama launch copilot
```
### Run directly with a model
```shell
ollama launch copilot --model kimi-k2.5:cloud
```
## Recommended Models
- `kimi-k2.5:cloud`
- `glm-5:cloud`
- `minimax-m2.7:cloud`
- `qwen3.5:cloud`
- `glm-4.7-flash`
- `qwen3.5`
Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).
## Non-interactive (headless) mode
Run Copilot CLI without interaction for use in Docker, CI/CD, or scripts:
```shell
ollama launch copilot --model kimi-k2.5:cloud --yes -- -p "how does this repository work?"
```
The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified. Arguments after `--` are passed directly to Copilot CLI.
## Manual setup
Copilot CLI connects to Ollama using the OpenAI-compatible API via environment variables.
1. Set the environment variables:
```shell
export COPILOT_PROVIDER_BASE_URL=http://localhost:11434/v1
export COPILOT_PROVIDER_API_KEY=
export COPILOT_PROVIDER_WIRE_API=responses
export COPILOT_MODEL=qwen3.5
```
1. Run Copilot CLI:
```shell
copilot
```
Or run with environment variables inline:
```shell
COPILOT_PROVIDER_BASE_URL=http://localhost:11434/v1 COPILOT_PROVIDER_API_KEY= COPILOT_PROVIDER_WIRE_API=responses COPILOT_MODEL=glm-5:cloud copilot
```
**Note:** Copilot requires a large context window. We recommend at least 64k tokens. See the [context length documentation](/context-length) for how to adjust context length in Ollama.
docs/integrations/hermes.mdx
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title: Hermes Agent
---
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.
Hermes Agent is a self-improving AI agent built by Nous Research. It features automatic skill creation, cross-session memory, and 70+ skills that it ships with by default.
![Hermes Agent with Ollama](/images/hermes.png)
## Quick start
### Pull a model
Before running the setup wizard, make sure you have a model available. Hermes will auto-detect models downloaded through Ollama.
```bash
ollama pull kimi-k2.5:cloud
ollama launch hermes
```
See [Recommended models](#recommended-models) for more options.
Ollama handles everything automatically:
### Install
1. **Install** — If Hermes isn't installed, Ollama prompts to install it via the Nous Research install script
2. **Model** — Pick a model from the selector (local or cloud)
3. **Onboarding** — Ollama configures the Ollama provider, points Hermes at `http://127.0.0.1:11434/v1`, and sets your model as the primary
4. **Gateway** — Optionally connects a messaging platform (Telegram, Discord, Slack, WhatsApp, Signal, Email) and launches the Hermes chat
<Note>Hermes on Windows requires WSL2. Install it with `wsl --install` and re-run from inside the WSL shell.</Note>
## Recommended models
**Cloud models**:
- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
- `glm-5.1:cloud` — Reasoning and code generation
- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
**Local models:**
- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
- `qwen3.6` — Reasoning, coding, and visual understanding locally (~24 GB VRAM)
More models at [ollama.com/search](https://ollama.com/search?c=cloud).
## Connect messaging apps
Link Telegram, Discord, Slack, WhatsApp, Signal, or Email to chat with your models from anywhere:
```bash
hermes gateway setup
```
## Reconfigure
Re-run the full setup wizard at any time:
```bash
hermes setup
```
## Manual setup
If you'd rather drive Hermes's own wizard instead of `ollama launch hermes`, install it directly:
```bash
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
```
### Set up
After installation, Hermes launches the setup wizard automatically. Choose **Quick setup**:
Hermes launches the setup wizard automatically. Choose **Quick setup**:
```
How would you like to set up Hermes?
@@ -80,32 +117,3 @@ Connect a messaging platform? (Telegram, Discord, etc.)
Launch hermes chat now? [Y/n]: Y
```
## Recommended models
**Cloud models**:
- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
- `glm-5.1:cloud` — Reasoning and code generation
- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
**Local models:**
- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
- `qwen3.5` — Reasoning, coding, and visual understanding locally (~11 GB VRAM)
More models at [ollama.com/search](https://ollama.com/models).
## Configure later
Re-run the setup wizard at any time:
```bash
hermes setup
```
To configure just messaging:
```bash
hermes setup gateway
```
docs/integrations/index.mdx
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- [Claude Code](/integrations/claude-code)
- [Codex](/integrations/codex)
- [Copilot CLI](/integrations/copilot-cli)
- [OpenCode](/integrations/opencode)
- [Droid](/integrations/droid)
- [Goose](/integrations/goose)
- [Pi](/integrations/pi)
- [Pool](/integrations/pool)
## Assistants
docs/integrations/openclaw.mdx
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1. **Install** — If OpenClaw isn't installed, Ollama prompts to install it via npm
2. **Security** — On the first launch, a security notice explains the risks of tool access
3. **Model** — Pick a model from the selector (local or cloud)
4. **Onboarding** — Ollama configures the provider, installs the gateway daemon, sets your model as the primary, and installs the web search and fetch plugin
4. **Onboarding** — Ollama configures the provider, installs the gateway daemon, sets your model as the primary, and enables OpenClaw's bundled Ollama web search
5. **Gateway** — Starts in the background and opens the OpenClaw TUI
<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>
@@ -24,19 +24,19 @@ Ollama handles everything automatically:
## Web search and fetch
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.
OpenClaw ships with a bundled Ollama `web_search` provider that lets local or cloud-backed Ollama setups search the web through the configured Ollama host.
```bash
ollama launch openclaw
```
Web search and fetch is enabled automatically when launching OpenClaw through Ollama. To install the plugin directly:
Ollama web search is enabled automatically when launching OpenClaw through Ollama. To configure it manually:
```bash
openclaw plugins install @ollama/openclaw-web-search
openclaw configure --section web
```
<Note>Web search for local models requires `ollama signin`.</Note>
<Note>Ollama web search for local models requires `ollama signin`.</Note>
## Configure without launching
@@ -93,4 +93,3 @@ Link WhatsApp, Telegram, Slack, Discord, or iMessage to chat with your local mod
```bash
openclaw gateway stop
```
docs/integrations/opencode.mdx
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ollama launch opencode --config
```
### Manual setup
Add a configuration block to `~/.config/opencode/opencode.json`:
```json
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"ollama": {
"npm": "@ai-sdk/openai-compatible",
"name": "Ollama",
"options": {
"baseURL": "http://localhost:11434/v1"
},
"models": {
"qwen3-coder": {
"name": "qwen3-coder"
}
}
}
}
}
```
## Cloud Models
`glm-4.7:cloud` is the recommended model for use with OpenCode.
Add the cloud configuration to `~/.config/opencode/opencode.json`:
```json
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"ollama": {
"npm": "@ai-sdk/openai-compatible",
"name": "Ollama",
"options": {
"baseURL": "http://localhost:11434/v1"
},
"models": {
"glm-4.7:cloud": {
"name": "glm-4.7:cloud"
}
}
}
}
}
```
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
2. Update `~/.config/opencode/opencode.json` to point to ollama.com:
```json
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"ollama": {
"npm": "@ai-sdk/openai-compatible",
"name": "Ollama Cloud",
"options": {
"baseURL": "https://ollama.com/v1"
},
"models": {
"glm-4.7:cloud": {
"name": "glm-4.7:cloud"
}
}
}
}
}
```
Run `opencode` in a new terminal to load the new settings.
<Note>`ollama launch opencode` passes its configuration to OpenCode inline via the `OPENCODE_CONFIG_CONTENT` environment variable. OpenCode deep-merges its config sources on startup, so anything you declare in `~/.config/opencode/opencode.json` is still respected and available inside OpenCode. Models declared only in `opencode.json` won't appear in `ollama launch`'s model-selection menu.</Note>
docs/integrations/pool.mdx
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---
title: Pool
---
Pool is Poolside's software agent for the terminal, built for enterprise development workflows.
## Install
Install [Pool](https://github.com/poolsideai/pool):
## Usage with Ollama
### Quick setup
```shell
ollama launch pool
```
### Run directly with a model
```shell
ollama launch pool --model kimi-k2.6:cloud
```
### Pass arguments through to Pool
Arguments after `--` are passed directly to Pool:
```shell
ollama launch pool -- --help
```
## Manual setup
Pool connects to Ollama using the OpenAI-compatible API via environment variables.
1. Set the environment variables:
```shell
export POOLSIDE_STANDALONE_BASE_URL=http://localhost:11434/v1
export POOLSIDE_API_KEY=ollama
```
2. Run Pool with an Ollama model:
```shell
pool -m kimi-k2.6:cloud
```
Or run with environment variables inline:
```shell
POOLSIDE_STANDALONE_BASE_URL=http://localhost:11434/v1 POOLSIDE_API_KEY=ollama pool -m kimi-k2.6:cloud
```