Sync ollama docs from 8f45236d on 2026-03-12
This commit is contained in:
The Librarian
+2
-2
@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
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**Synced from:** https://github.com/ollama/ollama.git
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**Branch:** main
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**Commit:** f8dc7c9f54a753d2c6d3410936e73486f9bf463d
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**Sync Date:** 2026-02-12
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**Commit:** 8f45236d09332949aa91774dc9eb46caf2abbbc1
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**Sync Date:** 2026-03-12
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**Content:** Paths: docs
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---
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@@ -12,7 +12,6 @@ To use Ollama with tools that expect the Anthropic API (like Claude Code), set t
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```shell
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export ANTHROPIC_AUTH_TOKEN=ollama # required but ignored
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export ANTHROPIC_API_KEY="" # required but ignored
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export ANTHROPIC_BASE_URL=http://localhost:11434
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```
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@@ -269,7 +268,7 @@ ollama launch claude --config
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Set the environment variables and run Claude Code:
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```shell
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ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY="" claude --model qwen3-coder
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ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 claude --model qwen3-coder
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```
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Or set the environment variables in your shell profile:
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@@ -277,7 +276,6 @@ Or set the environment variables in your shell profile:
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```shell
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export ANTHROPIC_AUTH_TOKEN=ollama
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export ANTHROPIC_BASE_URL=http://localhost:11434
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export ANTHROPIC_API_KEY=""
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```
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Then run Claude Code with any Ollama model:
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@@ -6,7 +6,7 @@ Ollama provides compatibility with parts of the [OpenAI API](https://platform.op
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## Usage
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### Simple `v1/chat/completions` example
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### Simple `/v1/chat/completions` example
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<CodeGroup dropdown>
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@@ -57,7 +57,7 @@ curl -X POST http://localhost:11434/v1/chat/completions \
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</CodeGroup>
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### Simple `v1/responses` example
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### Simple `/v1/responses` example
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<CodeGroup dropdown>
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@@ -103,7 +103,7 @@ curl -X POST http://localhost:11434/v1/responses \
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</CodeGroup>
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### v1/chat/completions with vision example
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### `/v1/chat/completions` with vision example
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<CodeGroup dropdown>
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+1
-1
@@ -40,7 +40,7 @@ ollama launch claude
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Launch with a specific model:
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```
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ollama launch claude --model qwen3-coder
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ollama launch claude --model qwen3.5
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```
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Configure without launching:
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@@ -226,3 +226,7 @@ curl https://ollama.com/api/chat \
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</Tab>
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</Tabs>
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## Local only
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Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features.
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@@ -51,6 +51,9 @@ Install prerequisites:
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- [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
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- (Optional) VULKAN GPU support
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- [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
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- (Optional) MLX engine support
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- [CUDA 13+ SDK](https://developer.nvidia.com/cuda-downloads)
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- [cuDNN 9+](https://developer.nvidia.com/cudnn)
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Then, configure and build the project:
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@@ -101,6 +104,10 @@ Install prerequisites:
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- (Optional) VULKAN GPU support
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- [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
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- Or install via package manager: `sudo apt install vulkan-sdk` (Ubuntu/Debian) or `sudo dnf install vulkan-sdk` (Fedora/CentOS)
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- (Optional) MLX engine support
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- [CUDA 13+ SDK](https://developer.nvidia.com/cuda-downloads)
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- [cuDNN 9+](https://developer.nvidia.com/cudnn)
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- OpenBLAS/LAPACK: `sudo apt install libopenblas-dev liblapack-dev liblapacke-dev` (Ubuntu/Debian)
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> [!IMPORTANT]
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> Ensure prerequisites are in `PATH` before running CMake.
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@@ -118,6 +125,67 @@ Lastly, run Ollama:
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go run . serve
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```
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## MLX Engine (Optional)
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The MLX engine enables running safetensor based models. It requires building the [MLX](https://github.com/ml-explore/mlx) and [MLX-C](https://github.com/ml-explore/mlx-c) shared libraries separately via CMake. On MacOS, MLX leverages the Metal library to run on the GPU, and on Windows and Linux, runs on NVIDIA GPUs via CUDA v13.
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### macOS (Apple Silicon)
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Requires the Metal toolchain. Install [Xcode](https://developer.apple.com/xcode/) first, then:
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```shell
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xcodebuild -downloadComponent MetalToolchain
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```
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Verify it's installed correctly (should print "no input files"):
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```shell
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xcrun metal
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```
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Then build:
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```shell
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cmake -B build --preset MLX
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cmake --build build --preset MLX --parallel
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cmake --install build --component MLX
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```
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> [!NOTE]
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> Without the Metal toolchain, cmake will silently complete with Metal disabled. Check the cmake output for `Setting MLX_BUILD_METAL=OFF` which indicates the toolchain is missing.
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### Windows / Linux (CUDA)
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Requires CUDA 13+ and [cuDNN](https://developer.nvidia.com/cudnn) 9+.
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```shell
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cmake -B build --preset "MLX CUDA 13"
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cmake --build build --target mlx --target mlxc --config Release --parallel
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cmake --install build --component MLX --strip
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```
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### Local MLX source overrides
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To build against a local checkout of MLX and/or MLX-C (useful for development), set environment variables before running CMake:
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```shell
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export OLLAMA_MLX_SOURCE=/path/to/mlx
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export OLLAMA_MLX_C_SOURCE=/path/to/mlx-c
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```
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For example, using the helper scripts with local mlx and mlx-c repos:
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```shell
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OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c ./scripts/build_linux.sh
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OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c ./scripts/build_darwin.sh
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```
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```powershell
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$env:OLLAMA_MLX_SOURCE="../mlx"
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$env:OLLAMA_MLX_C_SOURCE="../mlx-c"
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./scripts/build_darwin.ps1
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```
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## Docker
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```shell
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+10
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@@ -106,20 +106,23 @@
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"group": "Integrations",
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"pages": [
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"/integrations/index",
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{
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"group": "Assistants",
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"expanded": true,
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"pages": [
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"/integrations/openclaw"
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]
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},
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{
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"group": "Coding",
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"expanded": true,
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"pages": [
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"/integrations/claude-code",
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"/integrations/codex",
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"/integrations/opencode",
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"/integrations/droid",
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"/integrations/goose"
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]
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},
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{
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"group": "Assistants",
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"pages": [
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"/integrations/openclaw"
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"/integrations/goose",
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"/integrations/pi"
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]
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},
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{
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@@ -160,6 +160,26 @@ docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-
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Ollama runs locally. We don't see your prompts or data when you run locally. When using cloud-hosted models, we process your prompts and responses to provide the service but do not store or log that content and never train on it. We collect basic account info and limited usage metadata to provide the service that does not include prompt or response content. We don't sell your data. You can delete your account anytime.
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## How do I disable Ollama's cloud features?
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Ollama can run in local only mode by disabling Ollama's cloud features. By turning off Ollama's cloud features, you will lose the ability to use Ollama's cloud models and web search.
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Set `disable_ollama_cloud` in `~/.ollama/server.json`:
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```json
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{
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"disable_ollama_cloud": true
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}
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```
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You can also set the environment variable:
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```shell
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OLLAMA_NO_CLOUD=1
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```
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Restart Ollama after changing configuration. Once disabled, Ollama's logs will show `Ollama cloud disabled: true`.
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## How can I expose Ollama on my network?
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Ollama binds 127.0.0.1 port 11434 by default. Change the bind address with the `OLLAMA_HOST` environment variable.
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+17
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@@ -61,11 +61,13 @@ Ollama supports the following AMD GPUs via the ROCm library:
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### Linux Support
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| Family | Cards and accelerators |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
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| AMD Radeon RX | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `Vega 64` |
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| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` `V420` `V340` `V320` `Vega II Duo` `Vega II` `SSG` |
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| AMD Instinct | `MI300X` `MI300A` `MI300` `MI250X` `MI250` `MI210` `MI200` `MI100` `MI60` |
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| Family | Cards and accelerators |
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| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| 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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| AMD Radeon AI PRO | `R9700` `R9600D` |
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| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` |
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| AMD Ryzen AI | `Ryzen AI Max+ 395` `Ryzen AI Max 390` `Ryzen AI Max 385` `Ryzen AI 9 HX 475` `Ryzen AI 9 HX 470` `Ryzen AI 9 465` `Ryzen AI 9 HX 375` `Ryzen AI 9 HX 370` `Ryzen AI 9 365` |
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| AMD Instinct | `MI350X` `MI300X` `MI300A` `MI250X` `MI250` `MI210` `MI100` |
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### Windows Support
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@@ -97,17 +99,20 @@ This table shows some example GPUs that map to these LLVM targets:
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| **LLVM Target** | **An Example GPU** |
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|-----------------|---------------------|
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| gfx908 | Radeon Instinct MI100 |
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| gfx90a | Radeon Instinct MI210 |
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| gfx940 | Radeon Instinct MI300 |
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| gfx941 | |
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| gfx942 | |
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| gfx90a | Radeon Instinct MI210/MI250 |
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| gfx942 | Radeon Instinct MI300X/MI300A |
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| gfx950 | Radeon Instinct MI350X |
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| gfx1010 | Radeon RX 5700 XT |
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| gfx1012 | Radeon RX 5500 XT |
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| gfx1030 | Radeon PRO V620 |
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| gfx1100 | Radeon PRO W7900 |
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| gfx1101 | Radeon PRO W7700 |
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| gfx1102 | Radeon RX 7600 |
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AMD is working on enhancing ROCm v6 to broaden support for families of GPUs in a
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future release which should increase support for more GPUs.
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| gfx1103 | Radeon 780M |
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| gfx1150 | Ryzen AI 9 HX 375 |
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| gfx1151 | Ryzen AI Max+ 395 |
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| gfx1200 | Radeon RX 9070 |
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| gfx1201 | Radeon RX 9070 XT |
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Reach out on [Discord](https://discord.gg/ollama) or file an
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[issue](https://github.com/ollama/ollama/issues) for additional help.
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@@ -4,7 +4,7 @@ title: Claude Code
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Claude Code is Anthropic's agentic coding tool that can read, modify, and execute code in your working directory.
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Open models can be used with Claude Code through Ollama's Anthropic-compatible API, enabling you to use models such as `glm-4.7`, `qwen3-coder`, `gpt-oss`.
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Open models can be used with Claude Code through Ollama's Anthropic-compatible API, enabling you to use models such as `qwen3.5`, `glm-5:cloud`, `kimi-k2.5:cloud`.
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@@ -32,13 +32,57 @@ irm https://claude.ai/install.ps1 | iex
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ollama launch claude
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```
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To configure without launching:
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### Run directly with a model
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```shell
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ollama launch claude --config
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ollama launch claude --model kimi-k2.5:cloud
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```
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### Manual setup
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## Recommended Models
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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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- `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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## 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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```
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/loop <interval> <prompt or /command>
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||||
```
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### Examples
|
||||
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||||
**Check in on your PRs**
|
||||
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||||
```
|
||||
/loop 30m Check my open PRs and summarize their status
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||||
```
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||||
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||||
**Automate research tasks**
|
||||
|
||||
```
|
||||
/loop 1h Research the latest AI news and summarize key developments
|
||||
```
|
||||
|
||||
**Automate bug reporting and triaging**
|
||||
|
||||
```
|
||||
/loop 15m Check for new GitHub issues and triage by priority
|
||||
```
|
||||
|
||||
**Set reminders**
|
||||
|
||||
```
|
||||
/loop 1h Remind me to review the deploy status
|
||||
```
|
||||
|
||||
## Manual setup
|
||||
|
||||
Claude Code connects to Ollama using the Anthropic-compatible API.
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@@ -53,23 +97,14 @@ export ANTHROPIC_BASE_URL=http://localhost:11434
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2. Run Claude Code with an Ollama model:
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||||
|
||||
```shell
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claude --model gpt-oss:20b
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claude --model qwen3.5
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||||
```
|
||||
|
||||
Or run with environment variables inline:
|
||||
|
||||
```shell
|
||||
ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY="" claude --model qwen3-coder
|
||||
ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY="" claude --model glm-5:cloud
|
||||
```
|
||||
|
||||
**Note:** Claude Code 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.
|
||||
|
||||
## Recommended Models
|
||||
|
||||
- `qwen3-coder`
|
||||
- `glm-4.7`
|
||||
- `gpt-oss:20b`
|
||||
- `gpt-oss:120b`
|
||||
|
||||
Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).
|
||||
|
||||
|
||||
@@ -13,6 +13,7 @@ Coding assistants that can read, modify, and execute code in your projects.
|
||||
- [OpenCode](/integrations/opencode)
|
||||
- [Droid](/integrations/droid)
|
||||
- [Goose](/integrations/goose)
|
||||
- [Pi](/integrations/pi)
|
||||
|
||||
## Assistants
|
||||
|
||||
|
||||
@@ -4,47 +4,65 @@ title: OpenClaw
|
||||
|
||||
OpenClaw is a personal AI assistant that runs on your own devices. It bridges messaging services (WhatsApp, Telegram, Slack, Discord, iMessage, and more) to AI coding agents through a centralized gateway.
|
||||
|
||||
## Install
|
||||
|
||||
Install [OpenClaw](https://openclaw.ai/)
|
||||
|
||||
```bash
|
||||
npm install -g openclaw@latest
|
||||
```
|
||||
|
||||
Then run the onboarding wizard:
|
||||
|
||||
```bash
|
||||
openclaw onboard --install-daemon
|
||||
```
|
||||
|
||||
<Note>OpenClaw requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
ollama launch openclaw
|
||||
```
|
||||
|
||||
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
|
||||
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>
|
||||
|
||||
This configures OpenClaw to use Ollama and starts the gateway.
|
||||
If the gateway is already running, no changes need to be made as the gateway will auto-reload the changes.
|
||||
## Configure without launching
|
||||
|
||||
To change the model without starting the gateway and TUI:
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
```bash
|
||||
ollama launch openclaw --config
|
||||
```
|
||||
|
||||
## Recommended Models
|
||||
To use a specific model directly:
|
||||
|
||||
- `qwen3-coder`
|
||||
- `glm-4.7`
|
||||
- `gpt-oss:20b`
|
||||
- `gpt-oss:120b`
|
||||
```bash
|
||||
ollama launch openclaw --model kimi-k2.5:cloud
|
||||
```
|
||||
|
||||
If the gateway is already running, it restarts automatically to pick up the new model.
|
||||
|
||||
## Recommended models
|
||||
|
||||
**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
|
||||
|
||||
**Local models:**
|
||||
|
||||
- `glm-4.7-flash` — Reasoning and code generation locally (~25 GB VRAM)
|
||||
|
||||
More models at [ollama.com/search](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Connect messaging apps
|
||||
|
||||
```bash
|
||||
openclaw configure --section channels
|
||||
```
|
||||
|
||||
Link WhatsApp, Telegram, Slack, Discord, or iMessage to chat with your local models from anywhere.
|
||||
|
||||
## Stopping the gateway
|
||||
|
||||
```bash
|
||||
openclaw gateway stop
|
||||
```
|
||||
|
||||
Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).
|
||||
|
||||
@@ -0,0 +1,57 @@
|
||||
---
|
||||
title: Pi
|
||||
---
|
||||
|
||||
Pi is a minimal AI agent toolkit with plugin support.
|
||||
|
||||
## Install
|
||||
|
||||
Install [Pi](https://github.com/badlogic/pi-mono):
|
||||
|
||||
```bash
|
||||
npm install -g @mariozechner/pi-coding-agent
|
||||
```
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch pi
|
||||
```
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch pi --config
|
||||
```
|
||||
|
||||
### Manual setup
|
||||
|
||||
Add a configuration block to `~/.pi/agent/models.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"providers": {
|
||||
"ollama": {
|
||||
"baseUrl": "http://localhost:11434/v1",
|
||||
"api": "openai-completions",
|
||||
"apiKey": "ollama",
|
||||
"models": [
|
||||
{
|
||||
"id": "qwen3-coder"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Update `~/.pi/agent/settings.json` to set the default provider:
|
||||
|
||||
```json
|
||||
{
|
||||
"defaultProvider": "ollama",
|
||||
"defaultModel": "qwen3-coder"
|
||||
}
|
||||
```
|
||||
+1
-1
@@ -101,7 +101,7 @@ nvidia-smi
|
||||
|
||||
### Install AMD ROCm drivers (optional)
|
||||
|
||||
[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v6.
|
||||
[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v7.
|
||||
|
||||
### Start Ollama
|
||||
|
||||
|
||||
+40
-115
@@ -2,7 +2,7 @@
|
||||
title: Quickstart
|
||||
---
|
||||
|
||||
This quickstart will walk your through running your first model with Ollama. To get started, download Ollama on macOS, Windows or Linux.
|
||||
Ollama is available on macOS, Windows, and Linux.
|
||||
|
||||
<a
|
||||
href="https://ollama.com/download"
|
||||
@@ -12,131 +12,56 @@ This quickstart will walk your through running your first model with Ollama. To
|
||||
Download Ollama
|
||||
</a>
|
||||
|
||||
## Run a model
|
||||
## Get Started
|
||||
|
||||
<Tabs>
|
||||
<Tab title="CLI">
|
||||
Open a terminal and run the command:
|
||||
|
||||
```sh
|
||||
ollama run gemma3
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="cURL">
|
||||
```sh
|
||||
ollama pull gemma3
|
||||
```
|
||||
|
||||
Lastly, chat with the model:
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"messages": [{
|
||||
"role": "user",
|
||||
"content": "Hello there!"
|
||||
}],
|
||||
"stream": false
|
||||
}'
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="Python">
|
||||
Start by downloading a model:
|
||||
|
||||
```sh
|
||||
ollama pull gemma3
|
||||
```
|
||||
|
||||
Then install Ollama's Python library:
|
||||
|
||||
```sh
|
||||
pip install ollama
|
||||
```
|
||||
|
||||
Lastly, chat with the model:
|
||||
|
||||
```python
|
||||
from ollama import chat
|
||||
from ollama import ChatResponse
|
||||
|
||||
response: ChatResponse = chat(model='gemma3', messages=[
|
||||
{
|
||||
'role': 'user',
|
||||
'content': 'Why is the sky blue?',
|
||||
},
|
||||
])
|
||||
print(response['message']['content'])
|
||||
# or access fields directly from the response object
|
||||
print(response.message.content)
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
Start by downloading a model:
|
||||
|
||||
```
|
||||
ollama pull gemma3
|
||||
```
|
||||
|
||||
Then install the Ollama JavaScript library:
|
||||
```
|
||||
npm i ollama
|
||||
```
|
||||
|
||||
Lastly, chat with the model:
|
||||
|
||||
```shell
|
||||
import ollama from 'ollama'
|
||||
|
||||
const response = await ollama.chat({
|
||||
model: 'gemma3',
|
||||
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
|
||||
})
|
||||
console.log(response.message.content)
|
||||
```
|
||||
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
See a full list of available models [here](https://ollama.com/models).
|
||||
|
||||
## Coding
|
||||
|
||||
For coding use cases, we recommend using the `glm-4.7-flash` model.
|
||||
|
||||
Note: this model requires 23 GB of VRAM with 64000 tokens context length.
|
||||
```sh
|
||||
ollama pull glm-4.7-flash
|
||||
```
|
||||
|
||||
Alternatively, you can use a more powerful cloud model (with full context length):
|
||||
```sh
|
||||
ollama pull glm-4.7:cloud
|
||||
```
|
||||
|
||||
Use `ollama launch` to quickly set up a coding tool with Ollama models:
|
||||
Run `ollama` in your terminal to open the interactive menu:
|
||||
|
||||
```sh
|
||||
ollama launch
|
||||
ollama
|
||||
```
|
||||
|
||||
### Supported integrations
|
||||
Navigate with `↑/↓`, press `enter` to launch, `→` to change model, and `esc` to quit.
|
||||
|
||||
- [OpenCode](/integrations/opencode) - Open-source coding assistant
|
||||
- [Claude Code](/integrations/claude-code) - Anthropic's agentic coding tool
|
||||
- [Codex](/integrations/codex) - OpenAI's coding assistant
|
||||
- [Droid](/integrations/droid) - Factory's AI coding agent
|
||||
The menu provides quick access to:
|
||||
- **Run a model** - Start an interactive chat
|
||||
- **Launch tools** - Claude Code, Codex, OpenClaw, and more
|
||||
- **Additional integrations** - Available under "More..."
|
||||
|
||||
### Launch with a specific model
|
||||
## Assistants
|
||||
|
||||
Launch [OpenClaw](/integrations/openclaw), a personal AI with 100+ skills:
|
||||
|
||||
```sh
|
||||
ollama launch claude --model glm-4.7-flash
|
||||
ollama launch openclaw
|
||||
```
|
||||
|
||||
### Configure without launching
|
||||
## Coding
|
||||
|
||||
Launch [Claude Code](/integrations/claude-code) and other coding tools with Ollama models:
|
||||
|
||||
```sh
|
||||
ollama launch claude --config
|
||||
ollama launch claude
|
||||
```
|
||||
|
||||
```sh
|
||||
ollama launch codex
|
||||
```
|
||||
|
||||
```sh
|
||||
ollama launch opencode
|
||||
```
|
||||
|
||||
See [integrations](/integrations) for all supported tools.
|
||||
|
||||
## API
|
||||
|
||||
Use the [API](/api) to integrate Ollama into your applications:
|
||||
|
||||
```sh
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"messages": [{ "role": "user", "content": "Hello!" }]
|
||||
}'
|
||||
```
|
||||
|
||||
See the [API documentation](/api) for Python, JavaScript, and other integrations.
|
||||
|
||||
Reference in New Issue
Block a user