ollama37/README.md

<div align="center">
  <a href="https://ollama.com">
    <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
  </a>
</div>

# Ollama

## Installation Guide: CUDA 11.4 on Rocky Linux 8

**Prerequisites:**

*   A Rocky Linux 8 system or a container based on Rocky Linux 8.
*   Root privileges.
*   Internet connectivity.

**Steps:**

1.  **Update the system:**  Start by updating the operating system packages.

    ```bash
    dnf -y update
    ```

2.  **Install EPEL Repository:**  The Extra Packages for Enterprise Linux (EPEL) repository is required for some dependencies.

    ```bash
    dnf -y install epel-release
    ```

3.  **Add NVIDIA CUDA Repository:** Add the NVIDIA CUDA repository for RHEL 8. This allows `dnf` to find and install the necessary CUDA packages.

    ```bash
    dnf -y config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
    ```

4.  **Install NVIDIA Driver (Version 470):**  Install the NVIDIA driver version 470 using the DKMS (Dynamic Kernel Module Support) system.  This ensures the driver is automatically rebuilt when the kernel is updated.

    ```bash
    dnf -y module install nvidia-driver:470-dkms
    ```

5.  **Install CUDA Toolkit 11.4:** Install the CUDA Toolkit version 11.4.

    ```bash
    dnf -y install cuda-11-4
    ```

6.  **Set up CUDA Environment Variables (Optional but Recommended):**  Copy a script (`cuda-11.4.sh`) to `/etc/profile.d/` to set environment variables. If performing this installation manually, you need to ensure that the `PATH` and `LD_LIBRARY_PATH` environment variables are correctly configured.  The contents of `cuda-11.4.sh` would typically include something like:

    ```bash
    # Create /etc/profile.d/cuda-11.4.sh
    echo "export PATH=/usr/local/cuda-11.4/bin:${PATH}" > /etc/profile.d/cuda-11.4.sh
    echo "export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64:${LD_LIBRARY_PATH}" >> /etc/profile.d/cuda-11.4.sh
    ```

    To apply these changes, you can either:

    *   Source the script: `source /etc/profile.d/cuda-11.4.sh`
    *   Log out and log back in to refresh your shell environment.
    *   Add the lines above to your `.bashrc` or equivalent shell configuration file.

**Verification:**

After installation, verify that CUDA is properly installed by running:

```bash
nvcc --version
```

This command should display the CUDA compiler version.  You can also check the installed driver version with:

```bash
nvidia-smi
```
## GCC 10 Installation Guide

This guide details the steps to install GCC 10.

**Steps:**

1. **Update and Install Prerequisites:**
   - Installs `wget`, `unzip`, and `lbzip2` to download and extract the GCC source code.
   - Installs the "Development Tools" group, which includes necessary build tools.
   ```bash
   dnf -y install wget unzip lbzip2 \
       && dnf -y groupinstall "Development Tools"
   ```

2. **Download GCC 10 Source Code:**
   - Downloads the GCC 10 source code from the gcc-mirror GitHub repository.
   ```bash
   cd /usr/local/src \
       && wget https://github.com/gcc-mirror/gcc/archive/refs/heads/releases/gcc-10.zip
   ```

3. **Extract Source Code:**
   - Unzips the downloaded GCC 10 archive.
   ```bash
   unzip gcc-10.zip
   ```

4. **Prepare Build Environment:**
   - Navigates into the extracted GCC 10 directory.
   ```bash
   cd gcc-releases-gcc-10
   ```

5. **Download Prerequisites:**
   - Downloads build prerequisites using the `contrib/download_prerequisites` script.
    ```bash
    contrib/download_prerequisites
    ```

6. **Create Installation Directory:**
    - Creates a directory `/usr/local/gcc-10` where GCC 10 will be installed.
    ```bash
    mkdir /usr/local/gcc-10
    ```

7. **Configure GCC Build:**
   - Configures the GCC build process using the `./configure` script.  The `--disable-multilib` flag disables the build of multilib support, which can simplify the build.
   ```bash
   cd /usr/local/gcc-10 \
   && /usr/local/src/gcc-releases-gcc-10/configure --disable-multilib
   ```

8. **Compile GCC:**
   - Compiles GCC using `make`. The `-j ${nproc}` flag utilizes all available CPU cores for parallel compilation, speeding up the process.
   ```bash
   make -j ${nproc}
   ```

9. **Install GCC:**
   - Installs the compiled GCC binaries.
   ```bash
   make install
   ```

10. **Post-Install Configuration:**
    - Configures the system environment for GCC 10 compatibility. This involves creating a file (`/etc/profile.d/gcc-10.sh`) to automatically set `LD_LIBRARY_PATH="/usr/local/lib64"` and adding a configuration file (`/etc/ld.so.conf.d/gcc-10.conf`) to update the dynamic linker's cache.
    ```bash
    # Create /etc/profile.d/gcc-10.sh
    echo "export LD_LIBRARY_PATH=/usr/local/lib64:\$LD_LIBRARY_PATH" > /etc/profile.d/gcc-10.sh

    # Create /etc/ld.so.conf.d/gcc-10.conf
    echo "/usr/local/lib64" > /etc/ld.so.conf.d/gcc-10.conf

    # Update dynamic linker cache
    ldconfig
    ```
## CMake Installation Guide

1.  **Update Package Manager (Optional but Recommended):**

    While not explicitly in the Dockerfile, it's good practice to start by updating your package lists to ensure you're getting the latest available software. This isn't shown in the provided Dockerfile.

2.  **Install OpenSSL Development Libraries:**

    ```bash
    dnf -y install openssl-devel
    ```

    *   **Purpose:** CMake often relies on OpenSSL for secure build configurations.  The `-devel` package provides the header files and libraries necessary for building software that uses OpenSSL.
    *   **`dnf`:**  This is the package manager for Fedora and related distributions (like CentOS, Rocky Linux, etc.). If you're using a different distribution, use the appropriate package manager (e.g., `apt` for Debian/Ubuntu, `yum` for older CentOS versions).
    *   **`-y`:**  This flag automatically answers "yes" to any prompts during the installation, making the process non-interactive.

3.  **Download CMake Source Code:**

    ```bash
    cd /usr/local/src
    wget https://github.com/Kitware/CMake/releases/download/v4.0.0/cmake-4.0.0.tar.gz
    ```

    *   **`cd /usr/local/src`:** Changes the current directory to `/usr/local/src`.  This is a common location for temporary source files.
    *   **`wget`:** Downloads the CMake source code archive from the specified URL.  `wget` is a command-line utility for retrieving files from the web.  Make sure you have `wget` installed.

4.  **Extract the Archive:**

    ```bash
    tar xvf cmake-4.0.0.tar.gz
    ```

    *   **`tar`:**  This is the GNU Tape Archiver, a common utility for creating and extracting archive files.
    *   **`xvf`:** These are the `tar` options:
        *   `x`: Extract files.
        *   `v`: Verbose mode (lists the files being extracted).
        *   `f` : Specifies the archive file.

5.  **Create a CMake Installation Directory:**

    ```bash
    mkdir /usr/local/cmake-4
    ```

    *   **`mkdir`:** Creates a new directory. This directory is where CMake will be installed.

6.  **Configure CMake:**

    ```bash
    cd /usr/local/cmake-4
    /usr/local/src/cmake-4.0.0/configure
    ```

    *   **`cd /usr/local/cmake-4`:** Changes the directory to the newly created installation directory.
    *   **`/usr/local/src/cmake-4.0.0/configure`:**  This script prepares the CMake source code for compilation based on your system's configuration. It checks for dependencies and creates Makefiles.

7.  **Compile CMake:**

    ```bash
    make -j ${nproc}
    ```

    *   **`make`:**  This command compiles the CMake source code.
    *   **`-j ${nproc}`:** This option tells `make` to use multiple processor cores to speed up the compilation. `${nproc}` is an environment variable that contains the number of available processors.

8.  **Install CMake:**

    ```bash
    make install
    ```

    *   **`make install`:** This command installs the compiled CMake binaries and related files to the system directories. This step usually requires root privileges (e.g., using `sudo`).

## Go Installation Guide

This guide installs Go version 1.24.2, as specified in the Dockerfile.

1.  **Download Go Distribution:**

    ```bash
    cd /usr/local
    wget https://go.dev/dl/go1.24.2.linux-amd64.tar.gz
    ```

    *   **`cd /usr/local`:** Changes the current directory to `/usr/local`.  This is a common location for installing software.
    *   **`wget`:** Downloads the Go distribution archive from the specified URL. Ensure that `wget` is installed on your system.

2.  **Extract the Archive:**

    ```bash
    tar xvf go1.24.2.linux-amd64.tar.gz
    ```

    *   **`tar`:** The GNU Tape Archiver.
    *   **`xvf`:**  As described in the CMake installation guide, these options extract the archive and list the extracted files.

3. **Post Install Configuration:**

    After copying the binary to `/usr/local`, you should add `/usr/local/go/bin` to the PATH environment variable. To do this, you can create a file in `/etc/profile.d/`.

    ```bash
    echo 'export PATH=$PATH:/usr/local/go/bin' | sudo tee /etc/profile.d/go.sh
    ```

    *   **`echo`:** Prints the string to standard output.
    *   **`sudo tee`:** Writes the string to a file with superuser privileges. The `-a` option can be used to append to the file instead of overwriting it.

## Compilation Guide: Ollama37

**Prerequisites:**

*   Rocky Linux 8.
*   `git`:  For cloning the repository.
*   `cmake`:  For managing the C++ build process.
*   `go`: The Go compiler and toolchain.
*   `gcc`: version 10 (GNU Compiler Collection) and G++: For C++ compilation (although the Dockerfile explicitly sets these via `CC` and `CXX`)
*   CUDA Toolkit 11.4

**Steps:**

1.  **Navigate to the Build Directory:**

    ```bash
    cd /usr/local/src
    ```

2.  **Clone the Repository:**

    ```bash
    git clone https://github.com/dogkeeper886/ollama37
    ```

3.  **Change Directory:**

    ```bash
    cd ollama37
    ```

4.  **CMake Configuration:**

    This step configures the build system.  The `CC` and `CXX` variables are explicitly set to `/usr/local/bin/gcc` and `/usr/local/bin/g++`, respectively.  This is critical if the system's default compilers are incompatible or need to be overridden.

    ```bash
    CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ cmake -B build
    ```

5.  **CMake Build:**

    This step actually compiles the C++ code using the configuration generated in the previous step.

    ```bash
    CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ cmake --build build
    ```

6.  **Go Build:**

    Finally, this step compiles the Go code, creating the `ollama` executable.

    ```bash
    go build -o ollama .
    ```

### Docker

The official [Ollama Docker image](https://hub.docker.com/r/dogkeeper886/ollama37) `dogkeeper886/ollama37` is available on Docker Hub.

This Docker image provides a ready-to-use environment for running Ollama, a local Large Language Model (LLM) runner, specifically optimized to leverage the capabilities of an NVIDIA K80 GPU. This setup is ideal for AI researchers and developers looking to experiment with models in a controlled home lab setting.

To pull the image from Docker Hub, use:

```bash
docker pull dogkeeper886/ollama37
```

### Libraries

- [ollama-python](https://github.com/ollama/ollama-python)
- [ollama-js](https://github.com/ollama/ollama-js)

### Community

- [Discord](https://discord.gg/ollama)
- [Reddit](https://reddit.com/r/ollama)

## Quickstart

To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):

```shell
ollama run llama3.2
```

## Model library

Ollama supports a list of models available on [ollama.com/library](https://ollama.com/library 'ollama model library')

Here are some example models that can be downloaded:

| Model              | Parameters | Size  | Download                         |
| ------------------ | ---------- | ----- | -------------------------------- |
| Gemma 3            | 1B         | 815MB | `ollama run gemma3:1b`           |
| Gemma 3            | 4B         | 3.3GB | `ollama run gemma3`              |
| Gemma 3            | 12B        | 8.1GB | `ollama run gemma3:12b`          |
| Gemma 3            | 27B        | 17GB  | `ollama run gemma3:27b`          |
| QwQ                | 32B        | 20GB  | `ollama run qwq`                 |
| DeepSeek-R1        | 7B         | 4.7GB | `ollama run deepseek-r1`         |
| DeepSeek-R1        | 671B       | 404GB | `ollama run deepseek-r1:671b`    |
| Llama 3.3          | 70B        | 43GB  | `ollama run llama3.3`            |
| Llama 3.2          | 3B         | 2.0GB | `ollama run llama3.2`            |
| Llama 3.2          | 1B         | 1.3GB | `ollama run llama3.2:1b`         |
| Llama 3.2 Vision   | 11B        | 7.9GB | `ollama run llama3.2-vision`     |
| Llama 3.2 Vision   | 90B        | 55GB  | `ollama run llama3.2-vision:90b` |
| Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`            |
| Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`       |
| Phi 4              | 14B        | 9.1GB | `ollama run phi4`                |
| Phi 4 Mini         | 3.8B       | 2.5GB | `ollama run phi4-mini`           |
| Mistral            | 7B         | 4.1GB | `ollama run mistral`             |
| Moondream 2        | 1.4B       | 829MB | `ollama run moondream`           |
| Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`         |
| Starling           | 7B         | 4.1GB | `ollama run starling-lm`         |
| Code Llama         | 7B         | 3.8GB | `ollama run codellama`           |
| Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored`   |
| LLaVA              | 7B         | 4.5GB | `ollama run llava`               |
| Granite-3.2         | 8B         | 4.9GB | `ollama run granite3.2`          |

> [!NOTE]
> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.

## Customize a model

### Import from GGUF

Ollama supports importing GGUF models in the Modelfile:

1. Create a file named `Modelfile`, with a `FROM` instruction with the local filepath to the model you want to import.

   ```
   FROM ./vicuna-33b.Q4_0.gguf
   ```

2. Create the model in Ollama

   ```shell
   ollama create example -f Modelfile
   ```

3. Run the model

   ```shell
   ollama run example
   ```

### Import from Safetensors

See the [guide](docs/import.md) on importing models for more information.

### Customize a prompt

Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.2` model:

```shell
ollama pull llama3.2
```

Create a `Modelfile`:

```
FROM llama3.2

# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1

# set the system message
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""
```

Next, create and run the model:

```
ollama create mario -f ./Modelfile
ollama run mario
>>> hi
Hello! It's your friend Mario.
```

For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.

## CLI Reference

### Create a model

`ollama create` is used to create a model from a Modelfile.

```shell
ollama create mymodel -f ./Modelfile
```

### Pull a model

```shell
ollama pull llama3.2
```

> This command can also be used to update a local model. Only the diff will be pulled.

### Remove a model

```shell
ollama rm llama3.2
```

### Copy a model

```shell
ollama cp llama3.2 my-model
```

### Multiline input

For multiline input, you can wrap text with `"""`:

```
>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.
```

### Multimodal models

```
ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
```

> **Output**: The image features a yellow smiley face, which is likely the central focus of the picture.

### Pass the prompt as an argument

```shell
ollama run llama3.2 "Summarize this file: $(cat README.md)"
```

> **Output**: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.

### Show model information

```shell
ollama show llama3.2
```

### List models on your computer

```shell
ollama list
```

### List which models are currently loaded

```shell
ollama ps
```

### Stop a model which is currently running

```shell
ollama stop llama3.2
```

### Start Ollama

`ollama serve` is used when you want to start ollama without running the desktop application.

## Building

See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)

### Running local builds

Next, start the server:

```shell
./ollama serve
```

Finally, in a separate shell, run a model:

```shell
./ollama run llama3.2
```

## REST API

Ollama has a REST API for running and managing models.

### Generate a response

```shell
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.2",
  "prompt":"Why is the sky blue?"
}'
```

### Chat with a model

```shell
curl http://localhost:11434/api/chat -d '{
  "model": "llama3.2",
  "messages": [
    { "role": "user", "content": "why is the sky blue?" }
  ]
}'
```

See the [API documentation](./docs/api.md) for all endpoints.

## Community Integrations

### Web & Desktop

- [Open WebUI](https://github.com/open-webui/open-webui)
- [SwiftChat (macOS with ReactNative)](https://github.com/aws-samples/swift-chat)
- [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
- [Hollama](https://github.com/fmaclen/hollama)
- [Lollms-Webui](https://github.com/ParisNeo/lollms-webui)
- [LibreChat](https://github.com/danny-avila/LibreChat)
- [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
- [HTML UI](https://github.com/rtcfirefly/ollama-ui)
- [Saddle](https://github.com/jikkuatwork/saddle)
- [TagSpaces](https://www.tagspaces.org) (A platform for file based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
- [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
- [Chatbot UI v2](https://github.com/mckaywrigley/chatbot-ui)
- [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
- [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
- [Ollamac](https://github.com/kevinhermawan/Ollamac)
- [big-AGI](https://github.com/enricoros/big-AGI/blob/main/docs/config-local-ollama.md)
- [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
- [Amica](https://github.com/semperai/amica)
- [chatd](https://github.com/BruceMacD/chatd)
- [Ollama-SwiftUI](https://github.com/kghandour/Ollama-SwiftUI)
- [Dify.AI](https://github.com/langgenius/dify)
- [MindMac](https://mindmac.app)
- [NextJS Web Interface for Ollama](https://github.com/jakobhoeg/nextjs-ollama-llm-ui)
- [Msty](https://msty.app)
- [Chatbox](https://github.com/Bin-Huang/Chatbox)
- [WinForm Ollama Copilot](https://github.com/tgraupmann/WinForm_Ollama_Copilot)
- [NextChat](https://github.com/ChatGPTNextWeb/ChatGPT-Next-Web) with [Get Started Doc](https://docs.nextchat.dev/models/ollama)
- [Alpaca WebUI](https://github.com/mmo80/alpaca-webui)
- [OllamaGUI](https://github.com/enoch1118/ollamaGUI)
- [OpenAOE](https://github.com/InternLM/OpenAOE)
- [Odin Runes](https://github.com/leonid20000/OdinRunes)
- [LLM-X](https://github.com/mrdjohnson/llm-x) (Progressive Web App)
- [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
- [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
- [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
- [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
- [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
- [RAGFlow](https://github.com/infiniflow/ragflow) (Open-source Retrieval-Augmented Generation engine based on deep document understanding)
- [StreamDeploy](https://github.com/StreamDeploy-DevRel/streamdeploy-llm-app-scaffold) (LLM Application Scaffold)
- [chat](https://github.com/swuecho/chat) (chat web app for teams)
- [Lobe Chat](https://github.com/lobehub/lobe-chat) with [Integrating Doc](https://lobehub.com/docs/self-hosting/examples/ollama)
- [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG)
- [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation)
- [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
- [RWKV-Runner](https://github.com/josStorer/RWKV-Runner) (RWKV offline LLM deployment tool, also usable as a client for ChatGPT and Ollama)
- [Ollama Grid Search](https://github.com/dezoito/ollama-grid-search) (app to evaluate and compare models)
- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support and multiple large language models.)
- [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
- [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
- [Shinkai Desktop](https://github.com/dcSpark/shinkai-apps) (Two click install Local AI using Ollama + Files + RAG)
- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in discord )
- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
- [R2R](https://github.com/SciPhi-AI/R2R) (Open-source RAG engine)
- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy to use GUI with sample custom LLM for Drivers Education)
- [OpenGPA](https://opengpa.org) (Open-source offline-first Enterprise Agentic Application)
- [Painting Droid](https://github.com/mateuszmigas/painting-droid) (Painting app with AI integrations)
- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
- [AI Studio](https://github.com/MindWorkAI/AI-Studio)
- [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client)
- [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
- [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
- [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for linux and macos made with GTK4 and Adwaita)
- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
- [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
- [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
- [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
- [Claude Dev](https://github.com/saoudrizwan/claude-dev) - VSCode extension for multi-file/whole-repo coding
- [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
- [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
- [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
- [Tkinter-based client](https://github.com/chyok/ollama-gui) (Python tkinter-based Client for Ollama)
- [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
- [Local Multimodal AI Chat](https://github.com/Leon-Sander/Local-Multimodal-AI-Chat) (Ollama-based LLM Chat with support for multiple features, including PDF RAG, voice chat, image-based interactions, and integration with OpenAI.)
- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG on Mac/Windows/Linux)
- [OrionChat](https://github.com/EliasPereirah/OrionChat) - OrionChat is a web interface for chatting with different AI providers
- [G1](https://github.com/bklieger-groq/g1) (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
- [Web management](https://github.com/lemonit-eric-mao/ollama-web-management) (Web management page)
- [Promptery](https://github.com/promptery/promptery) (desktop client for Ollama.)
- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
- [chat-ollama](https://github.com/annilq/chat-ollama) (a React Native client for Ollama)
- [SpaceLlama](https://github.com/tcsenpai/spacellama) (Firefox and Chrome extension to quickly summarize web pages with ollama in a sidebar)
- [YouLama](https://github.com/tcsenpai/youlama) (Webapp to quickly summarize any YouTube video, supporting Invidious as well)
- [DualMind](https://github.com/tcsenpai/dualmind) (Experimental app allowing two models to talk to each other in the terminal or in a web interface)
- [ollamarama-matrix](https://github.com/h1ddenpr0cess20/ollamarama-matrix) (Ollama chatbot for the Matrix chat protocol)
- [ollama-chat-app](https://github.com/anan1213095357/ollama-chat-app) (Flutter-based chat app)
- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard and said in the meetings)
- [Hexabot](https://github.com/hexastack/hexabot) (A conversational AI builder)
- [Reddit Rate](https://github.com/rapidarchitect/reddit_analyzer) (Search and Rate Reddit topics with a weighted summation)
- [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
- [VT](https://github.com/vinhnx/vt.ai) (A minimal multimodal AI chat app, with dynamic conversation routing. Supports local models via Ollama)
- [Nosia](https://github.com/nosia-ai/nosia) (Easy to install and use RAG platform based on Ollama)
- [Witsy](https://github.com/nbonamy/witsy) (An AI Desktop application available for Mac/Windows/Linux)
- [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
- [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
- [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
- [Ollama Chat WebUI for Docker ](https://github.com/oslook/ollama-webui) (Support for local docker deployment, lightweight ollama webui)
- [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
- [MinimalNextOllamaChat](https://github.com/anilkay/MinimalNextOllamaChat) (Minimal Web UI for Chat and Model Control)
- [Chipper](https://github.com/TilmanGriesel/chipper) AI interface for tinkerers (Ollama, Haystack RAG, Python)
- [ChibiChat](https://github.com/CosmicEventHorizon/ChibiChat) (Kotlin-based Android app to chat with Ollama and Koboldcpp API endpoints)
- [LocalLLM](https://github.com/qusaismael/localllm) (Minimal Web-App to run ollama models on it with a GUI)
- [Ollamazing](https://github.com/buiducnhat/ollamazing) (Web extension to run Ollama models)
- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivent endpoint with Ollama support for running locally)
- [AntSK](https://github.com/AIDotNet/AntSK) (Out-of-the-box & Adaptable RAG Chatbot)
- [MaxKB](https://github.com/1Panel-dev/MaxKB/) (Ready-to-use & flexible RAG Chatbot)
- [yla](https://github.com/danielekp/yla) (Web interface to freely interact with your customized models)
- [LangBot](https://github.com/RockChinQ/LangBot) (LLM-based instant messaging bots platform, with Agents, RAG features, supports multiple platforms)
- [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
- [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
- [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
- [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
- [Ollamb](https://github.com/hengkysteen/ollamb) (Simple yet rich in features, cross-platform built with Flutter and designed for Ollama. Try the [web demo](https://hengkysteen.github.io/demo/ollamb/).)
- [Writeopia](https://github.com/Writeopia/Writeopia) (Text editor with integration with Ollama)

### Cloud

- [Google Cloud](https://cloud.google.com/run/docs/tutorials/gpu-gemma2-with-ollama)
- [Fly.io](https://fly.io/docs/python/do-more/add-ollama/)
- [Koyeb](https://www.koyeb.com/deploy/ollama)

### Terminal

- [oterm](https://github.com/ggozad/oterm)
- [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
- [Emacs client](https://github.com/zweifisch/ollama)
- [neollama](https://github.com/paradoxical-dev/neollama) UI client for interacting with models from within Neovim
- [gen.nvim](https://github.com/David-Kunz/gen.nvim)
- [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
- [ollero.nvim](https://github.com/marco-souza/ollero.nvim)
- [ollama-chat.nvim](https://github.com/gerazov/ollama-chat.nvim)
- [ogpt.nvim](https://github.com/huynle/ogpt.nvim)
- [gptel Emacs client](https://github.com/karthink/gptel)
- [Oatmeal](https://github.com/dustinblackman/oatmeal)
- [cmdh](https://github.com/pgibler/cmdh)
- [ooo](https://github.com/npahlfer/ooo)
- [shell-pilot](https://github.com/reid41/shell-pilot)(Interact with models via pure shell scripts on Linux or macOS)
- [tenere](https://github.com/pythops/tenere)
- [llm-ollama](https://github.com/taketwo/llm-ollama) for [Datasette's LLM CLI](https://llm.datasette.io/en/stable/).
- [typechat-cli](https://github.com/anaisbetts/typechat-cli)
- [ShellOracle](https://github.com/djcopley/ShellOracle)
- [tlm](https://github.com/yusufcanb/tlm)
- [podman-ollama](https://github.com/ericcurtin/podman-ollama)
- [gollama](https://github.com/sammcj/gollama)
- [ParLlama](https://github.com/paulrobello/parllama)
- [Ollama eBook Summary](https://github.com/cognitivetech/ollama-ebook-summary/)
- [Ollama Mixture of Experts (MOE) in 50 lines of code](https://github.com/rapidarchitect/ollama_moe)
- [vim-intelligence-bridge](https://github.com/pepo-ec/vim-intelligence-bridge) Simple interaction of "Ollama" with the Vim editor
- [x-cmd ollama](https://x-cmd.com/mod/ollama)
- [bb7](https://github.com/drunkwcodes/bb7)
- [SwollamaCLI](https://github.com/marcusziade/Swollama) bundled with the Swollama Swift package. [Demo](https://github.com/marcusziade/Swollama?tab=readme-ov-file#cli-usage)
- [aichat](https://github.com/sigoden/aichat) All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI tools & agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
- [PowershAI](https://github.com/rrg92/powershai) PowerShell module that brings AI to terminal on Windows, including support for Ollama
- [DeepShell](https://github.com/Abyss-c0re/deepshell) Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
- [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
- [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull and download models from Ollama Registry in your terminal.

### Apple Vision Pro

- [SwiftChat](https://github.com/aws-samples/swift-chat) (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
- [Enchanted](https://github.com/AugustDev/enchanted)

### Database

- [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
   - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
- [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
- [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)

### Package managers

- [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)
- [Gentoo](https://github.com/gentoo/guru/tree/master/app-misc/ollama)
- [Homebrew](https://formulae.brew.sh/formula/ollama)
- [Helm Chart](https://artifacthub.io/packages/helm/ollama-helm/ollama)
- [Guix channel](https://codeberg.org/tusharhero/ollama-guix)
- [Nix package](https://search.nixos.org/packages?show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
- [Flox](https://flox.dev/blog/ollama-part-one)

### Libraries

- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
- [crewAI](https://github.com/crewAIInc/crewAI)
- [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
- [Spring AI](https://github.com/spring-projects/spring-ai) with [reference](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html) and [example](https://github.com/tzolov/ollama-tools)
- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
- [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
- [LangChain for .NET](https://github.com/tryAGI/LangChain) with [example](https://github.com/tryAGI/LangChain/blob/main/examples/LangChain.Samples.OpenAI/Program.cs)
- [LLPhant](https://github.com/theodo-group/LLPhant?tab=readme-ov-file#ollama)
- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
- [LiteLLM](https://github.com/BerriAI/litellm)
- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
- [Ollama for Ruby](https://github.com/gbaptista/ollama-ai)
- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
- [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp)
- [Ollama4j for Java](https://github.com/ollama4j/ollama4j)
- [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
- [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
- [Ollama for Dart](https://github.com/breitburg/dart-ollama)
- [Ollama for Laravel](https://github.com/cloudstudio/ollama-laravel)
- [LangChainDart](https://github.com/davidmigloz/langchain_dart)
- [Semantic Kernel - Python](https://github.com/microsoft/semantic-kernel/tree/main/python/semantic_kernel/connectors/ai/ollama)
- [Haystack](https://github.com/deepset-ai/haystack-integrations/blob/main/integrations/ollama.md)
- [Elixir LangChain](https://github.com/brainlid/langchain)
- [Ollama for R - rollama](https://github.com/JBGruber/rollama)
- [Ollama for R - ollama-r](https://github.com/hauselin/ollama-r)
- [Ollama-ex for Elixir](https://github.com/lebrunel/ollama-ex)
- [Ollama Connector for SAP ABAP](https://github.com/b-tocs/abap_btocs_ollama)
- [Testcontainers](https://testcontainers.com/modules/ollama/)
- [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama)
- [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
- [LlamaScript](https://github.com/Project-Llama/llamascript)
- [llm-axe](https://github.com/emirsahin1/llm-axe) (Python Toolkit for Building LLM Powered Apps)
- [Gollm](https://docs.gollm.co/examples/ollama-example)
- [Gollama for Golang](https://github.com/jonathanhecl/gollama)
- [Ollamaclient for Golang](https://github.com/xyproto/ollamaclient)
- [High-level function abstraction in Go](https://gitlab.com/tozd/go/fun)
- [Ollama PHP](https://github.com/ArdaGnsrn/ollama-php)
- [Agents-Flex for Java](https://github.com/agents-flex/agents-flex) with [example](https://github.com/agents-flex/agents-flex/tree/main/agents-flex-llm/agents-flex-llm-ollama/src/test/java/com/agentsflex/llm/ollama)
- [Parakeet](https://github.com/parakeet-nest/parakeet) is a GoLang library, made to simplify the development of small generative AI applications with Ollama.
- [Haverscript](https://github.com/andygill/haverscript) with [examples](https://github.com/andygill/haverscript/tree/main/examples)
- [Ollama for Swift](https://github.com/mattt/ollama-swift)
- [Swollama for Swift](https://github.com/marcusziade/Swollama) with [DocC](https://marcusziade.github.io/Swollama/documentation/swollama/)
- [GoLamify](https://github.com/prasad89/golamify)
- [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
- [Ollama for Zig](https://github.com/dravenk/ollama-zig)
- [Abso](https://github.com/lunary-ai/abso) (OpenAI-compatible TypeScript SDK for any LLM provider)
- [Nichey](https://github.com/goodreasonai/nichey) is a Python package for generating custom wikis for your research topic
- [Ollama for D](https://github.com/kassane/ollama-d)

### Mobile

- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS and iPad)
- [Enchanted](https://github.com/AugustDev/enchanted)
- [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
- [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) (No need for Termux, start the Ollama service with one click on an Android device)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)

### Extensions & Plugins

- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama)
- [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel)
- [Continue](https://github.com/continuedev/continue)
- [Vibe](https://github.com/thewh1teagle/vibe) (Transcribe and analyze meetings with Ollama)
- [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama)
- [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq)
- [NotesOllama](https://github.com/andersrex/notesollama) (Apple Notes Ollama plugin)
- [Dagger Chatbot](https://github.com/samalba/dagger-chatbot)
- [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot)
- [Ollama Telegram Bot](https://github.com/ruecat/ollama-telegram)
- [Hass Ollama Conversation](https://github.com/ej52/hass-ollama-conversation)
- [Rivet plugin](https://github.com/abrenneke/rivet-plugin-ollama)
- [Obsidian BMO Chatbot plugin](https://github.com/longy2k/obsidian-bmo-chatbot)
- [Cliobot](https://github.com/herval/cliobot) (Telegram bot with Ollama support)
- [Copilot for Obsidian plugin](https://github.com/logancyang/obsidian-copilot)
- [Obsidian Local GPT plugin](https://github.com/pfrankov/obsidian-local-gpt)
- [Open Interpreter](https://docs.openinterpreter.com/language-model-setup/local-models/ollama)
- [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use ollama as a copilot like Github copilot)
- [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
- [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
- [Plasmoid Ollama Control](https://github.com/imoize/plasmoid-ollamacontrol) (KDE Plasma extension that allows you to quickly manage/control Ollama model)
- [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
- [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
- [ChatGPTBox: All in one browser extension](https://github.com/josStorer/chatGPTBox) with [Integrating Tutorial](https://github.com/josStorer/chatGPTBox/issues/616#issuecomment-1975186467)
- [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.)
- [node-red-contrib-ollama](https://github.com/jakubburkiewicz/node-red-contrib-ollama)
- [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
- [vnc-lm](https://github.com/jake83741/vnc-lm) (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)
- [LSP-AI](https://github.com/SilasMarvin/lsp-ai) (Open-source language server for AI-powered functionality)
- [QodeAssist](https://github.com/Palm1r/QodeAssist) (AI-powered coding assistant plugin for Qt Creator)
- [Obsidian Quiz Generator plugin](https://github.com/ECuiDev/obsidian-quiz-generator)
- [AI Summmary Helper plugin](https://github.com/philffm/ai-summary-helper)
- [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
- [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
- [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language
- [Simple-Discord-AI](https://github.com/zyphixor/simple-discord-ai)
- [LLM Telegram Bot](https://github.com/innightwolfsleep/llm_telegram_bot) (telegram bot, primary for RP. Oobabooga-like buttons, [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API integration e.t.c)
- [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs)

### Supported backends

- [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.

### Observability
- [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.
- [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
- [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
- [Langfuse](https://langfuse.com/docs/integrations/ollama) is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
- [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.