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2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 The Librarian
|
7c43dfec77 | ||
|
The Librarian
|
5fb5e0e5b8 |
+2
-2
@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
|
||||
|
||||
**Synced from:** https://github.com/ollama/ollama.git
|
||||
**Branch:** main
|
||||
**Commit:** 8f45236d09332949aa91774dc9eb46caf2abbbc1
|
||||
**Sync Date:** 2026-03-12
|
||||
**Commit:** 421faa0263f28499e2ff9076ffca240a8fc262ca
|
||||
**Sync Date:** 2026-05-12
|
||||
**Content:** Paths: docs
|
||||
|
||||
---
|
||||
|
||||
@@ -184,6 +184,7 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
- [x] Reproducible outputs
|
||||
- [x] Vision
|
||||
- [x] Tools
|
||||
- [x] Reasoning/thinking control (for thinking models)
|
||||
- [ ] Logprobs
|
||||
|
||||
#### Supported request fields
|
||||
@@ -207,6 +208,9 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
- [x] `top_p`
|
||||
- [x] `max_tokens`
|
||||
- [x] `tools`
|
||||
- [x] `reasoning_effort` (`"high"`, `"medium"`, `"low"`, `"none"`)
|
||||
- [x] `reasoning`
|
||||
- [x] `effort` (`"high"`, `"medium"`, `"low"`, `"none"`)
|
||||
- [ ] `tool_choice`
|
||||
- [ ] `logit_bias`
|
||||
- [ ] `user`
|
||||
|
||||
@@ -2,6 +2,10 @@
|
||||
title: Structured Outputs
|
||||
---
|
||||
|
||||
<Note>
|
||||
Ollama's Cloud currently does not support structured outputs.
|
||||
</Note>
|
||||
|
||||
Structured outputs let you enforce a JSON schema on model responses so you can reliably extract structured data, describe images, or keep every reply consistent.
|
||||
|
||||
## Generating structured JSON
|
||||
|
||||
@@ -21,6 +21,7 @@ Configure and launch external applications to use Ollama models. This provides a
|
||||
- **OpenCode** - Open-source coding assistant
|
||||
- **Claude Code** - Anthropic's agentic coding tool
|
||||
- **Codex** - OpenAI's coding assistant
|
||||
- **VS Code** - Microsoft's IDE with built-in AI chat
|
||||
- **Droid** - Factory's AI coding agent
|
||||
|
||||
#### Examples
|
||||
|
||||
@@ -206,35 +206,6 @@ To run tests, use `go test`:
|
||||
go test ./...
|
||||
```
|
||||
|
||||
> NOTE: In rare circumstances, you may need to change a package using the new
|
||||
> "synctest" package in go1.24.
|
||||
>
|
||||
> If you do not have the "synctest" package enabled, you will not see build or
|
||||
> test failures resulting from your change(s), if any, locally, but CI will
|
||||
> break.
|
||||
>
|
||||
> If you see failures in CI, you can either keep pushing changes to see if the
|
||||
> CI build passes, or you can enable the "synctest" package locally to see the
|
||||
> failures before pushing.
|
||||
>
|
||||
> To enable the "synctest" package for testing, run the following command:
|
||||
>
|
||||
> ```shell
|
||||
> GOEXPERIMENT=synctest go test ./...
|
||||
> ```
|
||||
>
|
||||
> If you wish to enable synctest for all go commands, you can set the
|
||||
> `GOEXPERIMENT` environment variable in your shell profile or by using:
|
||||
>
|
||||
> ```shell
|
||||
> go env -w GOEXPERIMENT=synctest
|
||||
> ```
|
||||
>
|
||||
> Which will enable the "synctest" package for all go commands without needing
|
||||
> to set it for all shell sessions.
|
||||
>
|
||||
> The synctest package is not required for production builds.
|
||||
|
||||
## Library detection
|
||||
|
||||
Ollama looks for acceleration libraries in the following paths relative to the `ollama` executable:
|
||||
|
||||
+16
-2
@@ -75,6 +75,10 @@
|
||||
{
|
||||
"source": "/integrations/clawdbot",
|
||||
"destination": "/integrations/openclaw"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/poolside",
|
||||
"destination": "/integrations/pool"
|
||||
}
|
||||
],
|
||||
"navigation": {
|
||||
@@ -110,7 +114,8 @@
|
||||
"group": "Assistants",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/openclaw"
|
||||
"/integrations/openclaw",
|
||||
"/integrations/hermes"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -119,14 +124,17 @@
|
||||
"pages": [
|
||||
"/integrations/claude-code",
|
||||
"/integrations/codex",
|
||||
"/integrations/copilot-cli",
|
||||
"/integrations/opencode",
|
||||
"/integrations/droid",
|
||||
"/integrations/goose",
|
||||
"/integrations/pi"
|
||||
"/integrations/pi",
|
||||
"/integrations/pool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "IDEs & Editors",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/cline",
|
||||
"/integrations/jetbrains",
|
||||
@@ -160,6 +168,12 @@
|
||||
"group": "More information",
|
||||
"pages": [
|
||||
"/cli",
|
||||
{
|
||||
"group": "Assistant Sandboxing",
|
||||
"pages": [
|
||||
"/integrations/nemoclaw"
|
||||
]
|
||||
},
|
||||
"/modelfile",
|
||||
"/context-length",
|
||||
"/linux",
|
||||
|
||||
@@ -61,6 +61,10 @@ Ollama supports the following AMD GPUs via the ROCm library:
|
||||
|
||||
### Linux Support
|
||||
|
||||
Ollama requires the AMD ROCm v7 driver on Linux. You can install or upgrade
|
||||
using the `amdgpu-install` utility from
|
||||
[AMD's ROCm documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/).
|
||||
|
||||
| Family | Cards and accelerators |
|
||||
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| AMD Radeon RX | `9070 XT` `9070 GRE` `9070` `9060 XT` `9060 XT LP` `9060` `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7700` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `5700 XT` `5700` `5600 XT` `5500 XT` |
|
||||
|
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@@ -41,13 +41,27 @@ ollama launch claude --model kimi-k2.5:cloud
|
||||
|
||||
- `kimi-k2.5:cloud`
|
||||
- `glm-5:cloud`
|
||||
- `minimax-m2.5: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 Claude Code without interaction for use in Docker, CI/CD, or scripts:
|
||||
|
||||
```shell
|
||||
ollama launch claude --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 Claude Code.
|
||||
|
||||
## Web search
|
||||
|
||||
Claude Code can search the web through Ollama's web search API. See the [web search documentation](/capabilities/web-search) for setup and usage.
|
||||
|
||||
## Scheduled Tasks with `/loop`
|
||||
|
||||
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.
|
||||
@@ -82,6 +96,18 @@ The `/loop` command runs a prompt or slash command on a recurring schedule insid
|
||||
/loop 1h Remind me to review the deploy status
|
||||
```
|
||||
|
||||
## Telegram
|
||||
|
||||
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:
|
||||
|
||||
```shell
|
||||
ollama launch claude -- --channels plugin:telegram@claude-plugins-official
|
||||
```
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
## Manual setup
|
||||
|
||||
Claude Code connects to Ollama using the Anthropic-compatible API.
|
||||
|
||||
@@ -0,0 +1,13 @@
|
||||
---
|
||||
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.
|
||||
+20
-17
@@ -35,36 +35,39 @@ To use `codex` with Ollama, use the `--oss` flag:
|
||||
codex --oss
|
||||
```
|
||||
|
||||
### Changing Models
|
||||
|
||||
By default, codex will use the local `gpt-oss:20b` model. However, you can specify a different model with the `-m` flag:
|
||||
To use a specific model, pass the `-m` flag:
|
||||
|
||||
```
|
||||
codex --oss -m gpt-oss:120b
|
||||
```
|
||||
|
||||
### Cloud Models
|
||||
To use a cloud model:
|
||||
|
||||
```
|
||||
codex --oss -m gpt-oss:120b-cloud
|
||||
```
|
||||
|
||||
### Profile-based setup
|
||||
|
||||
## Connecting to ollama.com
|
||||
|
||||
|
||||
Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
|
||||
|
||||
To use ollama.com directly, edit your `~/.codex/config.toml` file to point to ollama.com.
|
||||
For a persistent configuration, add an Ollama provider and profiles to `~/.codex/config.toml`:
|
||||
|
||||
```toml
|
||||
model = "gpt-oss:120b"
|
||||
model_provider = "ollama"
|
||||
|
||||
[model_providers.ollama]
|
||||
[model_providers.ollama-launch]
|
||||
name = "Ollama"
|
||||
base_url = "https://ollama.com/v1"
|
||||
env_key = "OLLAMA_API_KEY"
|
||||
base_url = "http://localhost:11434/v1"
|
||||
|
||||
[profiles.ollama-launch]
|
||||
model = "gpt-oss:120b"
|
||||
model_provider = "ollama-launch"
|
||||
|
||||
[profiles.ollama-cloud]
|
||||
model = "gpt-oss:120b-cloud"
|
||||
model_provider = "ollama-launch"
|
||||
```
|
||||
|
||||
Run `codex` in a new terminal to load the new settings.
|
||||
Then run:
|
||||
|
||||
```
|
||||
codex --profile ollama-launch
|
||||
codex --profile ollama-cloud
|
||||
```
|
||||
|
||||
@@ -0,0 +1,93 @@
|
||||
---
|
||||
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.
|
||||
@@ -0,0 +1,119 @@
|
||||
---
|
||||
title: Hermes Agent
|
||||
---
|
||||
|
||||
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.
|
||||
|
||||
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
ollama launch hermes
|
||||
```
|
||||
|
||||
Ollama handles everything automatically:
|
||||
|
||||
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
|
||||
```
|
||||
|
||||
Hermes launches the setup wizard automatically. Choose **Quick setup**:
|
||||
|
||||
```
|
||||
How would you like to set up Hermes?
|
||||
|
||||
→ Quick setup — provider, model & messaging (recommended)
|
||||
Full setup — configure everything
|
||||
```
|
||||
|
||||
### Connect to Ollama
|
||||
|
||||
1. Select **More providers...**
|
||||
2. Select **Custom endpoint (enter URL manually)**
|
||||
3. Set the API base URL to the Ollama OpenAI-compatible endpoint:
|
||||
|
||||
```
|
||||
API base URL [e.g. https://api.example.com/v1]: http://127.0.0.1:11434/v1
|
||||
```
|
||||
|
||||
4. Leave the API key blank (not required for local Ollama):
|
||||
|
||||
```
|
||||
API key [optional]:
|
||||
```
|
||||
|
||||
5. Hermes auto-detects downloaded models, confirm the one you want:
|
||||
|
||||
```
|
||||
Verified endpoint via http://127.0.0.1:11434/v1/models (1 model(s) visible)
|
||||
Detected model: kimi-k2.5:cloud
|
||||
Use this model? [Y/n]:
|
||||
```
|
||||
|
||||
6. Leave context length blank to auto-detect:
|
||||
|
||||
```
|
||||
Context length in tokens [leave blank for auto-detect]:
|
||||
```
|
||||
|
||||
### Connect messaging
|
||||
|
||||
Optionally connect a messaging platform during setup:
|
||||
|
||||
```
|
||||
Connect a messaging platform? (Telegram, Discord, etc.)
|
||||
|
||||
→ Set up messaging now (recommended)
|
||||
Skip — set up later with 'hermes setup gateway'
|
||||
```
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
Launch hermes chat now? [Y/n]: Y
|
||||
```
|
||||
|
||||
@@ -10,16 +10,19 @@ Coding assistants that can read, modify, and execute code in your projects.
|
||||
|
||||
- [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
|
||||
|
||||
AI assistants that help with everyday tasks.
|
||||
|
||||
- [OpenClaw](/integrations/openclaw)
|
||||
- [Hermes Agent](/integrations/hermes)
|
||||
|
||||
## IDEs & Editors
|
||||
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
---
|
||||
title: NemoClaw
|
||||
---
|
||||
|
||||
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.
|
||||
|
||||
## Quick start
|
||||
|
||||
Pull a model:
|
||||
|
||||
```bash
|
||||
ollama pull nemotron-3-nano:30b
|
||||
```
|
||||
|
||||
Run the installer:
|
||||
|
||||
```bash
|
||||
curl -fsSL https://www.nvidia.com/nemoclaw.sh | \
|
||||
NEMOCLAW_NON_INTERACTIVE=1 \
|
||||
NEMOCLAW_PROVIDER=ollama \
|
||||
NEMOCLAW_MODEL=nemotron-3-nano:30b \
|
||||
bash
|
||||
```
|
||||
|
||||
Connect to your sandbox:
|
||||
|
||||
```bash
|
||||
nemoclaw my-assistant connect
|
||||
```
|
||||
|
||||
Open the TUI:
|
||||
|
||||
```bash
|
||||
openclaw tui
|
||||
```
|
||||
|
||||
<Note>Ollama support in NemoClaw is still experimental.</Note>
|
||||
|
||||
## Platform support
|
||||
|
||||
| Platform | Runtime | Status |
|
||||
|----------|---------|--------|
|
||||
| Linux (Ubuntu 22.04+) | Docker | Primary |
|
||||
| macOS (Apple Silicon) | Colima or Docker Desktop | Supported |
|
||||
| Windows | WSL2 with Docker Desktop | Supported |
|
||||
|
||||
CMD and PowerShell are not supported on Windows — WSL2 is required.
|
||||
|
||||
<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>
|
||||
|
||||
## System requirements
|
||||
|
||||
- CPU: 4 vCPU minimum
|
||||
- RAM: 8 GB minimum (16 GB recommended)
|
||||
- Disk: 20 GB free (40 GB recommended for local models)
|
||||
- Node.js 20+ and npm 10+
|
||||
- Container runtime (Docker preferred)
|
||||
|
||||
## Recommended models
|
||||
|
||||
- `nemotron-3-super:cloud` — Strong reasoning and coding
|
||||
- `qwen3.5:cloud` — 397B; reasoning and code generation
|
||||
- `nemotron-3-nano:30b` — Recommended local model; fits in 24 GB VRAM
|
||||
- `qwen3.5:27b` — Fast local reasoning (~18 GB VRAM)
|
||||
- `glm-4.7-flash` — Reasoning and code generation (~25 GB VRAM)
|
||||
|
||||
More models at [ollama.com/search](https://ollama.com/search).
|
||||
@@ -15,13 +15,29 @@ Ollama handles everything automatically:
|
||||
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, and sets your model as the primary
|
||||
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>
|
||||
|
||||
<Note>Previously known as Clawdbot. `ollama launch clawdbot` still works as an alias.</Note>
|
||||
|
||||
## Web search and fetch
|
||||
|
||||
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
|
||||
```
|
||||
|
||||
Ollama web search is enabled automatically when launching OpenClaw through Ollama. To configure it manually:
|
||||
|
||||
```bash
|
||||
openclaw configure --section web
|
||||
```
|
||||
|
||||
<Note>Ollama web search for local models requires `ollama signin`.</Note>
|
||||
|
||||
## Configure without launching
|
||||
|
||||
To change the model without starting the gateway and TUI:
|
||||
@@ -43,15 +59,27 @@ If the gateway is already running, it restarts automatically to pick up the new
|
||||
**Cloud models**:
|
||||
|
||||
- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
|
||||
- `minimax-m2.5:cloud` — Fast, efficient coding and real-world productivity
|
||||
- `glm-5:cloud` — Reasoning and code generation
|
||||
- `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:**
|
||||
|
||||
- `glm-4.7-flash` — Reasoning and code generation locally (~25 GB VRAM)
|
||||
- `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/search?c=cloud).
|
||||
|
||||
## Non-interactive (headless) mode
|
||||
|
||||
Run OpenClaw without interaction for use in Docker, CI/CD, or scripts:
|
||||
|
||||
```bash
|
||||
ollama launch openclaw --model kimi-k2.5:cloud --yes
|
||||
```
|
||||
|
||||
The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified.
|
||||
|
||||
## Connect messaging apps
|
||||
|
||||
```bash
|
||||
@@ -65,4 +93,3 @@ Link WhatsApp, Telegram, Slack, Discord, or iMessage to chat with your local mod
|
||||
```bash
|
||||
openclaw gateway stop
|
||||
```
|
||||
|
||||
|
||||
@@ -28,79 +28,4 @@ To configure without launching:
|
||||
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>
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
title: Pi
|
||||
---
|
||||
|
||||
Pi is a minimal AI agent toolkit with plugin support.
|
||||
Pi is a minimal and extensible coding agent.
|
||||
|
||||
## Install
|
||||
|
||||
@@ -20,13 +20,65 @@ npm install -g @mariozechner/pi-coding-agent
|
||||
ollama launch pi
|
||||
```
|
||||
|
||||
This installs Pi, configures Ollama as a provider including web tools, and drops you into an interactive session.
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch pi --config
|
||||
```
|
||||
|
||||
### Manual setup
|
||||
### Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch pi --model qwen3.5:cloud
|
||||
```
|
||||
|
||||
Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Extensions
|
||||
|
||||
Pi ships with four core tools: `read`, `write`, `edit`, and `bash`. All other capabilities are added through its extension system.
|
||||
|
||||
On-demand capability packages invoked via `/skill:name` commands.
|
||||
|
||||
Install from npm or git:
|
||||
|
||||
```bash
|
||||
pi install npm:@foo/some-tools
|
||||
pi install git:github.com/user/repo@v1
|
||||
```
|
||||
|
||||
See all packages at [pi.dev](https://pi.dev/packages)
|
||||
|
||||
### Web search
|
||||
|
||||
Pi can use web search and fetch tools via the `@ollama/pi-web-search` package.
|
||||
|
||||
When launching Pi through Ollama, package install/update is managed automatically.
|
||||
To install manually:
|
||||
|
||||
```bash
|
||||
pi install npm:@ollama/pi-web-search
|
||||
```
|
||||
|
||||
### 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
|
||||
pi install https://github.com/davebcn87/pi-autoresearch
|
||||
```
|
||||
|
||||
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`:
|
||||
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
---
|
||||
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
|
||||
```
|
||||
@@ -2,33 +2,84 @@
|
||||
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>
|
||||
|
||||
@@ -114,6 +114,25 @@ If you are experiencing problems getting Ollama to correctly discover or use you
|
||||
- `OLLAMA_DEBUG=1` During GPU discovery additional information will be reported
|
||||
- Check dmesg for any errors from amdgpu or kfd drivers `sudo dmesg | grep -i amdgpu` and `sudo dmesg | grep -i kfd`
|
||||
|
||||
### AMD Driver Version Mismatch
|
||||
|
||||
If your AMD GPU is not detected on Linux and the server logs contain messages like:
|
||||
|
||||
```
|
||||
msg="failure during GPU discovery" ... error="failed to finish discovery before timeout"
|
||||
msg="bootstrap discovery took" duration=30s ...
|
||||
```
|
||||
|
||||
This typically means the system's AMD GPU driver is too old. Ollama bundles
|
||||
ROCm 7 linux libraries which require a compatible ROCm 7 kernel driver. If the
|
||||
system is running an older driver (ROCm 6.x or earlier), GPU initialization
|
||||
will hang during device discovery and eventually time out, causing Ollama to
|
||||
fall back to CPU.
|
||||
|
||||
To resolve this, upgrade to the ROCm v7 driver using the `amdgpu-install`
|
||||
utility from [AMD's ROCm documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/).
|
||||
After upgrading, reboot and restart Ollama.
|
||||
|
||||
## Multiple AMD GPUs
|
||||
|
||||
If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.
|
||||
|
||||
+7
-3
@@ -80,9 +80,13 @@ help you keep up to date.
|
||||
|
||||
If you'd like to install or integrate Ollama as a service, a standalone
|
||||
`ollama-windows-amd64.zip` zip file is available containing only the Ollama CLI
|
||||
and GPU library dependencies for Nvidia. If you have an AMD GPU, also download
|
||||
and extract the additional ROCm package `ollama-windows-amd64-rocm.zip` into the
|
||||
same directory. This allows for embedding Ollama in existing applications, or
|
||||
and GPU library dependencies for Nvidia. Depending on your hardware, you may also
|
||||
need to download and extract additional packages into the same directory:
|
||||
|
||||
- **AMD GPU**: `ollama-windows-amd64-rocm.zip`
|
||||
- **MLX (CUDA)**: `ollama-windows-amd64-mlx.zip`
|
||||
|
||||
This allows for embedding Ollama in existing applications, or
|
||||
running it as a system service via `ollama serve` with tools such as
|
||||
[NSSM](https://nssm.cc/).
|
||||
|
||||
|
||||
Reference in New Issue
Block a user