Published 2024-01-14.
Last modified 2026-03-20.
Time to read: 9 minutes.
llm collection.
Overview
Ollama is an open-source tool built with the Go language for managing and using large language models (LLMs). It can run large LLMs locally and in the cloud, using a client-server architecture. Ollama incorporates LLMs into the Ollama server process, and clients can interrogate the server.
LLMs running on desktop computers and prosumer-grade servers are not as powerful or as fast as large models running on enterprise-class hardware, but you have complete control over them without extra cost, censorship, restrictions, or privacy issues.
Ollama clients can access Ollama servers via:
The older command-line interface (CLI) program, the new Ollama app, and the TUI use the same core engine, so they have the same fundamental capabilities for running models. The Ollama app is just a user-friendly frontend to the same backend service used by the CLI and TUI.
After installation, the Ollama service runs in the background. The service API
is available at endpoint localhost:11434. For native Windows
and macOS, the Ollama app presents as a tray application.
When the Ollama service is running, Ollama loads required models into
memory only when you request them (e.g., via the ollama run
command or an API call), and it unloads them to save resources.
Any Ollama client can access any Ollama server. Integrations are built-in for Claude CLI and OpenClaw.
All Ollama programs and features run on Windows, macOS, and Linux.
In March 2026, new web search and web fetch plugins were added to Ollama. Although the documentation states that these features only work with Ollama/OpenClaw, they are actually available for all Ollama configurations of agentic models.
The Ollama Discord channel is here.
PatchBot shows the latest changes.
Installation
Installation instructions are simple.
Installation and update for native Linux, WSL, and macOS looks like this:
$ curl -fsSL https://ollama.com/install.sh | sh >>> Installing ollama to /usr/local >>> Downloading Linux amd64 bundle ######################################################################## 100.0% >>> Creating ollama user... >>> Adding ollama user to render group... >>> Adding ollama user to video group... >>> Adding current user to ollama group... >>> Creating ollama systemd service... >>> Enabling and starting ollama service... Created symlink '/etc/systemd/system/default.target.wants/ollama.service' '/etc/systemd/system/ollama.service'. >>> NVIDIA GPU detected. >>> The Ollama API is now available at 127.0.0.1:11434. >>> Install complete. Run "ollama" from the command line.
$ ollama --version ollama version is 0.18.2
When running under Windows, Ollama and its local models run more efficiently if Ollama for Windows is installed instead of installing on WSL:
PS C:\Users\mslinn> irm https://ollama.com/install.ps1 | iex
Free Online
To use Ollama’s free online (cloud-based) models, you generally use the standard ollama run command with a -cloud suffix appended to the model name. You must first authenticate your local installation with your Ollama account by running ollama signin. [1, 2, 3, 4]
Free Cloud Model Commands
The following models are available on the Ollama Cloud Free Tier as of April
- These models run on Ollama’s infrastructure rather than your local hardware. [5, 6, 7]
- GPT-OSS (OpenAI Partner Models)
- ollama run gpt-oss:120b-cloud
- ollama run gpt-oss:20b-cloud
- Qwen3 (Alibaba)
- ollama run qwen3-coder:480b-cloud
- ollama run qwen3:122b-cloud
- Gemma 4 (Google)
- ollama run gemma4:31b-cloud
- Reasoning & Specialist Models
- ollama run glm-5-cloud
- ollama run kimi-k2.5-cloud
- ollama run minimax-m2.5-cloud [2, 5, 8, 9, 10, 11, 12, 13, 14]
Quick Setup Steps [15]
- Sign In: Link your local CLI to your account to enable cloud access.
ollama signin
- Pull the Cloud Metadata: This prepares the local client to route requests to the cloud.
ollama pull gpt-oss:120b-cloud
- Run with Interactive Picker: Alternatively, you can use a menu to see all available cloud and local models.
ollama launch
[1, 2, 3, 4, 16, 17]
Usage Notes
-
Inference Routing: When you run a cloud model, your machine acts as a terminal while the actual computation happens on Ollama’s servers.
-
Free Tier Limits: The free tier provides a limited number of inference calls. For heavy use or higher concurrency, you may need a Pro or Max plan.
-
Privacy: Be aware that using cloud models sends your data to a third party, unlike standard local Ollama models which stay 100% on your device. [5, 8, 18, 19, 20]
Would you like to see how to configure an API key for using these cloud models in your own Python or JavaScript applications?
[1] https://docs.ollama.com [2] https://docs.ollama.com [3] https://docs.ollama.com [4] https://www.glukhov.org [5] https://towardsdatascience.com [6] https://medium.com [7] https://www.instagram.com [8] https://docs.ollama.com [9] https://realpython.com [10] https://ollama.com [11] https://medium.com [12] https://github.com [13] https://mastra.ai [14] https://whatllm.org [15] https://developers.openai.com [16] https://pandeyparul.medium.com [17] https://docs.ollama.com [18] https://www.codemag.com [19] https://rohansingh21.medium.com [20] https://towardsdatascience.com
User Interfaces
Ollama has three user interfaces:
-
A TUI (terminal user interface) called
ollama. -
A CLI (command line interface) using the same
ollamaexecutable as the TUI. - A GUI (graphical user interface) called the Ollama App.
| Feature | Ollama CLI | Ollama App |
|---|---|---|
| Interface | Text-based terminal commands | GUI with a chat window |
| Model Management |
Commands like ollama pull, ollama run,
ollama rm, and ollama create
| Dropdown menus and buttons for selecting, downloading, and managing models |
| Interaction | Typing prompts and receiving text responses in the terminal | An integrated chat interface for a more traditional chat experience |
| Multimodal Support | Possible, but requires more technical interaction with APIs/JSON payloads | Drag-and-drop files (text, PDF, code, images) directly into the chat for context-aware conversations |
| Automation | Ideal for scripting and automating workflows | Interactive use |
| Configuration | Set environment variables or specify flags on the command line | Access the settings dialog with CTRL+ , |
TUI
The TUI provides a command-line menu system for the CLI. New users will find this user interface easy to work with. Once a new user feels comfortable with the TUI, they might prefer the CLI experience.
Starting with Ollama v0.17.x and later (early 2026), the ollama
command no longer just shows a static help menu. Instead, it launches a TUI
interface that allows you to:
- View a list of recommended local and cloud models directly in your terminal.
- Use your arrow keys to pick a model and launch it in seconds without needing to remember specific model tags.
- On first launch, this interface can now guide users through the process of configuring providers and installing necessary components like the OpenClaw gateway daemon.
$ ollama Ollama 0.18.2
Run a model Start an interactive chat with a model
Launch Claude Code Anthropic's coding tool with subagents
Launch Codex (not installed) OpenAI's open-source coding agent
Launch OpenClaw Personal AI with 100+ skills
▸ Launch OpenCode (not installed) Install from https://opencode.ai
Launch Droid (not installed) Factory's coding agent across terminal and IDEs
Launch Pi (not installed) Minimal AI agent toolkit with plugin support
Launch Cline (not installed) Autonomous coding agent with parallel execution
↑/↓ navigate • enter launch • → configure • esc quit
If you select Run a model and press Enter, the following menu appears:
Select model to run: Type to filter...
Recommended
▸ kimi-k2.5:cloud
Multimodal reasoning with subagents
qwen3.5:cloud
Reasoning, coding, and agentic tool use with vision
glm-5:cloud
Reasoning and code generation
minimax-m2.7:cloud
Fast, efficient coding and real-world productivity
glm-4.7-flash
Reasoning and code generation locally, ~25GB, (not downloaded)
qwen3.5
Reasoning, coding, and visual understanding locally, ~11GB, (not downloaded)
More
bjoernb/qwen3-coder-30b-1m
deepseek-coder-v2:lite
deepseek-r1:7b
deepseek-r1:8b
... and 9 more
↑/↓ navigate • enter select • esc cancel
CLI
The help message is:
Large language model runner
Usage: ollama [flags] ollama [command]
Available Commands: serve Start Ollama create Create a model show Show information for a model run Run a model stop Stop a running model pull Pull a model from a registry push Push a model to a registry signin Sign in to ollama.com signout Sign out from ollama.com list List models ps List running models cp Copy a model rm Remove a model launch Launch the Ollama menu or an integration help Help about any command
Flags: -h, --help help for ollama --nowordwrap Don't wrap words to the next line automatically --verbose Show timings for response -v, --version Show version information
Use "ollama [command] --help" for more information about a command.
Manual Server Start
The Ollama app starts the server on demand. You can start the Ollma server from the command line, if it is not already running as a service:
$ ollama serve 2024/01/14 16:25:20 images.go:808: total blobs: 0 2024/01/14 16:25:20 images.go:815: total unused blobs removed: 0 2024/01/14 16:25:20 routes.go:930: Listening on 127.0.0.1:11434 (version 0.1.20) 2024/01/14 16:25:21 shim_ext_server.go:142: Dynamic LLM variants [cuda rocm] 2024/01/14 16:25:21 gpu.go:88: Detecting GPU type 2024/01/14 16:25:21 gpu.go:203: Searching for GPU management library libnvidia-ml.so 2024/01/14 16:25:21 gpu.go:248: Discovered GPU libraries: [/usr/lib/wsl/lib/libnvidia-ml.so.1] 2024/01/14 16:25:21 gpu.go:94: Nvidia GPU detected 2024/01/14 16:25:21 gpu.go:135: CUDA Compute Capability detected: 8.6
Models
Ollama models can be installed locally or run from the cloud. I wrote an article that attempts to track the most popular local models for coding. Only some of those models are Ollama-compatible.
Certain cloud-based LLMs are Ollama compatible, so you need not worry about their model formats. More on this later.
Ollama uses models on demand; the models are ignored if no queries are active.
That means you do not have to restart the ollama service after
installing a new model or removing an existing model.
Inspecting a Model
To view the parameters of a registered model, use the ollama show command:
$ ollama show deepseek-r1:8b Model architecture qwen3 parameters 8.2B context length 131072 embedding length 4096 quantization Q4_K_M
Capabilities completion thinking
Parameters stop "<|begin?of?sentence|>" stop "<|end?of?sentence|>" stop "<|User|>" stop "<|Assistant|>" temperature 0.6 top_p 0.95
License MIT License Copyright (c) 2023 DeepSeek ...
Much less information is shown for cloud models than for local models.
You can filter the output to just display the quantization:
$ ollama show deepseek-r1:8b | grep quantization quantization Q4_K_M
Local Models
By default, local Ollama models are downloaded into these directories:
- Linux:
/usr/share/ollama/.ollama/models - macOS:
~/.ollama/models
The Ollama library has many models available for download. OllamaHub has more.
After you have downloaded a model using ollama pull or
ollama run, the model is added to the local Ollama registry.
The ollama list command shows you the registered Ollama models.
Invoking Ollama with OpenClaw does not add the model to the Ollama registry.
Cloud Models
Cloud models are easier to set up than local models, and they can be used with any computer because the cloud processing is not performed on the local machine.
After running a cloud model using ollama run, the model is added
to the local Ollama registry. The ollama list command shows you
the registered Ollama models.
Minimax-m2.7:cloud
See MiniMax-M2 and Mini-Agent Review and MiniMax M2.7.
$ ollama run minimax-m2.7:cloud Connecting to 'minimax-m2.7:cloud' on 'ollama.com' ⚡ >>> Send a message (/? for help)
$ ollama show minimax-m2.7:cloud Model architecture minimax-m2 parameters 0 context length 204800 embedding length 3072 quantization Capabilities completion tools thinking
NVIDIA Nemotron 3 Super
Mar 11, 2026
The new Super model is a 120B total, 12B active-parameter model that delivers maximum compute efficiency and accuracy for complex multi-agent applications such as software development and cybersecurity triaging.
This model tackles the “context explosion” with a native 1M-token context window that gives agents long-term memory for aligned, high-accuracy reasoning. The model is fully open with open weights, datasets, and recipes so developers can easily customize, optimize, and deploy it on their own infrastructure.
$ ollama run nemotron-3-super:cloud Connecting to 'nemotron-3-super:cloud' on 'ollama.com' ⚡ >>> Use Ctrl + d or /bye to exit. >>> CTRL+D $ ollama list NAME ID SIZE MODIFIED nemotron-3-super:cloud be3943c5a818 - 6 seconds ago
QWEN 3.5
You can use a free Ollama account to run the largest, most powerful 397B version of the QWEN 3.5 model in the cloud using the Claude CLI by typing:
$ ollama launch claude --model qwen3.5:cloud
Usage on the free plan resets every 3 hours as well as weekly. View your usage here.
Local Models
The Ollama default model depth is Q4 (4-bit quantized), which is faster but can be much less accurate than Q8 (8-bit quantization) models. Install Q8 versions if possible.
Installation
To install or update a model without running it, type
ollama pull,
followed by the name of the model.
You can install and run any LLAMA-compatible model by typing
ollama run,
followed by the name of the model.
To list the models registered on your computer, use the
ollama list command:
$ ollama list NAME ID SIZE MODIFIED llama2:70b e7f6c06ffef4 38 GB 9 minutes ago
DeepSeek
$ ollama pull deepseek-r1:8b pulling manifest pulling e6a7edc1a4d7: 100% ▕████████████████████████████ ▏ 5.2 GB/5.2 GB 63 MB/s 0s pulling c5ad996bda6e: 100% ▕████████████████████████████▏ 556 B pulling 6e4c38e1172f: 100% ▕████████████████████████████▏ 1.1 KB pulling ed8474dc73db: 100% ▕████████████████████████████▏ 179 B pulling f64cd5418e4b: 100% ▕████████████████████████████▏ 487 B verifying sha256 digest writing manifest success
You can also download and run in one step by typing:
$ ollama run deepseek-r1:8b
fluffy/l3-8b-stheno-v3.2
fluffy/l3-8b-stheno-v3.2 is a small,
uncensored model that will even run slowly on a laptop without a powerful video card.
$ ollama run fluffy/l3-8b-stheno-v3.2
llama2-uncensored
The uncensored Llama2 70B model requires a powerful machine with lots of GPU RAM.
$ ollama pull llama2-uncensored:70b pulling manifest pulling abca3de387b6... 100% ▕███████████████████████████▏ 38 GB pulling 9224016baa40... 100% ▕███████████████████████████▏ 7.0 KB pulling 1195ea171610... 100% ▕███████████████████████████▏ 4.8 KB pulling 28577ba2177f... 100% ▕███████████████████████████▏ 55 B pulling ddaa351c1f3d... 100% ▕███████████████████████████▏ 51 B pulling 9256cd2888b0... 100% ▕███████████████████████████▏ 530 B verifying sha256 digest writing manifest removing any unused layers success
Running Queries
Ollama queries can be run in several ways:
- REST API
otermweb-ui- Computer language bindings (e.g. Python, JavaScript/TypeScript)
REST API
I used curl to query the Ollama REST API from the command line,
then I used jq and fold to process the response.
The -s option for curl prevents the progress meter
from cluttering up the screen, and the jq filter removes
everything from the response except the desired text. The fold
command wraps the text response to a width of 72 characters.
$ curl -s http://localhost:11434/api/generate -d '{ "model": "llama2:70b", "prompt": "Why is there air?", "stream": false }' | jq -r .response | fold -w 72 -s Air, or more specifically oxygen, is essential for life as we know it. It exists because of the delicate balance of chemical reactions in Earth’s atmosphere, which has allowed complex organisms like ourselves to evolve.
But if you’re asking about air in a broader sense, it serves many functions: it helps maintain a stable climate, protects living things from harmful solar radiation, and provides buoyancy for various forms of life, such as fish or birds.
Go Binding
The official Go language bindings can be added to a Go project as follows (additional Go libraries exist):
$ mkdir /tmp/blah
$ cd /tmp/blah
$ go mod init github.com/mslinn/demo go: creating new go.mod: module github.com/mslinn/demo
$ go get github.com/ollama/ollama/api go: downloading golang.org/x/sys v0.37.0 go: added github.com/bahlo/generic-list-go v0.2.0 go: added github.com/buger/jsonparser v1.1.1 go: added github.com/google/uuid v1.6.0 go: added github.com/mailru/easyjson v0.7.7 go: added github.com/ollama/ollama v0.18.2 go: added github.com/wk8/go-ordered-map/v2 v2.1.8 go: added golang.org/x/crypto v0.43.0 go: added golang.org/x/sys v0.37.0 go: added gopkg.in/yaml.v3 v3.0.1
Ruby Binding
I wrote this Ruby method to describe images.
def describe_image(image_filename)
@client = Ollama.new(
credentials: { address: @address },
options: {
server_sent_events: true,
temperature: @temperature,
connection: { request: { timeout: @timeout, read_timeout: @timeout } },
}
)
result = @client.generate(
{
model: @model,
prompt: 'Please describe this image.',
images: [Base64.strict_encode64(File.read(image_filename))],
}
)
puts result.map { |x| x['response'] }.join
end
The results with the llama2:70b model were ridiculous - an
example of the famous hallucinations that LLMs entertain their audience with.
As the public becomes enculturated with these hallucinations, we may come to
prefer them over human comedians. Certainly there will be a lot of material
for the human comedians to fight back with. For example, when describing a
photo of me:
$ ollama pull llama2:70b
$ describe -m llama2:70b /mnt/c/bestPhotoOfMike.png This is an image of a vibrant and colorful sunrise over the ocean, with the sun peeking above the horizon, casting warm, golden hues over the sky and water below. The sunlight reflects off the rippled surface of the water, creating shimmering patterns that contrast with the tranquil darkness of the receding waters. In the foreground, a solitary figure is silhouetted against the rising sun, perhaps lost in thought or finding inspiration in the breathtaking beauty of the scene.
The
llava model
is supposed to be good at describing images, so I installed it and tried
again, with excellent results:
$ ollama pull llava:13b
$ describe -m llava:13b /mnt/c/bestPhotoOfMike.png The image features a smiling man wearing glasses and dressed in a suit and tie. He has a well-groomed appearance. The man's attire includes a jacket, dress shirt, and a patterned tie that complements his professional outfit. The setting appears to be a studio environment, as there is a background behind the man that has an evenly lit texture. The man's smile conveys confidence and approachability, making him appear knowledgeable in his field or simply happy to pose for this photograph.
You can try the latest LLaVA model online.
Ollama and Claude CLI
I wrote a review of Claude CLI. It can be used as a harness to run Ollama-compatible models, no matter if they are local or in the cloud. Documentation is here.
Local Models
llama2
My Windows workstation has 64 GB RAM, a 13th generation Intel i7 and a modest NVIDIA 3060. I decided to try the biggest Llama 2 model to see what might happen. I downloaded and executed the Llama 2 70B model with the following incantation. An NVIDIA 4090 would have been a better video card for this Ollama model, and it would still have been slow.
$ ollama run llama2:70b pulling manifest pulling 68bbe6dc9cf4... 100% ▕██████████████████████████▏ 38 GB pulling 8c17c2ebb0ea... 100% ▕██████████████████████████▏ 7.0 KB pulling 7c23fb36d801... 100% ▕██████████████████████████▏ 4.8 KB pulling 2e0493f67d0c... 100% ▕██████████████████████████▏ 59 B pulling fa304d675061... 100% ▕██████████████████████████▏ 91 B pulling 7c96b46dca6c... 100% ▕██████████████████████████▏ 558 B verifying sha256 digest writing manifest removing any unused layers success >>> Send a message (/? for help)
I played around to learn what the available messages were. For more information, see Tutorial: Set Session System Message in Ollama CLI by Ingrid Stevens.
>>> /? Available Commands: /set Set session variables /show Show model information /bye Exit /?, /help Help for a command /? shortcuts Help for keyboard shortcuts Use """ to begin a multi-line message. >>> Send a message (/? for help) >>> /show Available Commands: /show info Show details for this model /show license Show model license /show modelfile Show Modelfile for this model /show parameters Show parameters for this model /show system Show system message /show template Show prompt template >>> /show modelfile # Modelfile generated by "ollama show" # To build a new Modelfile based on this one, replace the FROM line with: # FROM llama2:70b FROM /usr/share/ollama/.ollama/models/blobs/sha256:68bbe6dc9cf42eb60c9a7f96137fb8d472f752de6ebf53e9942f267f1a1e2577 TEMPLATE """[INST] <<SYS>>{{ .System }}<</SYS>> {{ .Prompt }} [/INST] """ PARAMETER stop "[INST]" PARAMETER stop "[/INST]" PARAMETER stop "<<SYS>>" >>> /show system No system message was specified for this model.
>>> /show template [INST] <<SYS>>{{ .System }}<</SYS>>
{{ .Prompt }} [/INST] >>> %}/bye
USER: and ASSISTANT: are helpful when writing a request for the model to reply to.
QWEN 3.5
The 9B model is the default when running Ollama locally. It fits comfortably on a 16GB GPU like an RTX 3060, and supports text, image input, thinking, and tool calling. 4b, 2b, and 0.8b models are also available. To run the default 9B model locally, type:
$ ollama launch claude --model qwen3.5
OpenClaw Support
OpenClaw is early-stage agentic technology for personal assistants.
Running With Scissors
I am not comfortable with the idea of running OpenClaw on any of my computers, or any VM that can authenticate on my behalf. You have been warned!
You can use OpenClaw with the Ollama CLI client and the model of your choice.
Invoking Ollama with OpenClaw does not add the model to the Ollama registry.
You should update your installed version of Node.js before proceeding further.
$ nvm install node
Here is an example of how to use OpenClaw with MiniMax-M2 v2.7 Cloud:
$ ollama launch openclaw --model minimax-m2.7:cloud Installing OpenClaw... npm warn deprecated node-domexception@1.0.0: Use your platform's native DOMException instead
added 539 packages in 1m OpenClaw installed successfully
Launching OpenClaw with minimax-m2.7:cloud...
Security
OpenClaw can read files and run actions when tools are enabled. A bad prompt can trick it into doing unsafe things.
Learn more: https://docs.openclaw.ai/gateway/security
I understand the risks. Continue?
Yes No
Setting up OpenClaw with Ollama... Model: minimax-m2.7:cloud
🦞 OpenClaw 2026.3.13 (61d171a) — Give me a workspace and I'll give you fewer tabs, fewer toggles, and more oxygen.
Default Ollama model: minimax-m2.7:cloud Config overwrite: /home/mslinn/.openclaw/openclaw.json (sha256 533707073495c347426fa957f78981a4f45bc038571ff141285f3846365a1d2c -> 0d8b0f65f3e6fea408d7af2a08f62b9e3be00d4fefb1ea9304f7be3d90a9dc2f, backup=/home/mslinn/.openclaw/openclaw.json.bak) Config write anomaly: /home/mslinn/.openclaw/openclaw.json (missing-meta-before-write) Updated ~/.openclaw/openclaw.json Workspace OK: ~/.openclaw/workspace Sessions OK: ~/.openclaw/agents/main/sessions System Node 20.19.4 at /usr/bin/node is below the required Node 22.16+. Using /home/mslinn/.nvm/versions/node/v25.8.1/bin/node for the daemon. Install Node 24 (recommended) or Node 22 LTS from nodejs.org or Homebrew.
Installed systemd service: /home/mslinn/.config/systemd/user/openclaw-gateway.service Enabled systemd lingering for mslinn
Agents: main (default) Heartbeat interval: 30m (main) Session store (main): /home/mslinn/.openclaw/agents/main/sessions/sessions.json (0 entries) Tip: run `openclaw configure --section web` to store your Brave API key for web_search. Docs: https://docs.openclaw.ai/tools/web ✓ Installed web search plugin
Starting your assistant — this may take a moment...
Starting gateway...
✓ OpenClaw is running
Open the Web UI: http://localhost:18789/#token=1794b10a3af3ffca97b27a1273e79d36b94a2bd18494fa55
Quick start: /help see all commands openclaw configure --section channels connect WhatsApp, Telegram, etc. openclaw skills browse and install skills
The OpenClaw gateway is running in the background. Stop it with: openclaw gateway stop
🦞 OpenClaw 2026.3.13 (61d171a) — I don't just autocomplete—I auto-commit (emotionally), then ask you to review (logically) openclaw tui - ws://127.0.0.1:18789 - agent main - session main
session agent:main:main
Wake up, my friend!
Hey! Good morning. ☀️
Looks like I'm fresh out of the box — no memories, no name, nothing yet. Just woke up.
So... who are you? And what should I call myself? I'm open to suggestions. 😄 connected | idle agent main | session main (openclaw-tui) | ollama/minimax-m2.7:cloud | tokens 11k/128k (9%)
When you are finished working with OpenClaw, type CTRL+D to end the session, then type the following to terminate the OpenClaw background process:
$ openclaw gateway stop
🦞 OpenClaw 2026.3.13 (61d171a) — I can run local, remote, or purely on vibes—results may vary with DNS.
Stopped systemd service: openclaw-gateway.service
NVIDIA Nemotron 3 Super
You can use a free Ollama account to run the NVIDIA Nemotron 3 Super model in the cloud under the control (or lack of control) of OpenClaw.
$ ollama launch openclaw --model nemotron-3-super:cloud
Installing OpenClaw... npm warn deprecated node-domexception@1.0.0: Use your platform's native DOMException instead
added 539 packages in 18s OpenClaw installed successfully
To use nemotron-3-super:cloud, please sign in.
Navigate to: https://ollama.com/connect?name=Bear&key=c3NoLWVkMjU1MTkgQUFBQUMzTnphQzFsWkRJMU5URTVBQUFBSU9odVJTM0FMdVMvUGZid3M0STJHVUdJekFyTlJpL1J3MmtVR210ZmlXaUY
⠸ Waiting for sign in to complete... Launching OpenClaw with nemotron-3-super:cloud...
Security
OpenClaw can read files and run actions when tools are enabled. A bad prompt can trick it into doing unsafe things.
Learn more: https://docs.openclaw.ai/gateway/security
I understand the risks. Continue?
Setting up OpenClaw with Ollama... Model: nemotron-3-super:cloud
🦞 OpenClaw 2026.3.13 (61d171a) — Half butler, half debugger, full crustacean. Default Ollama model: nemotron-3-super:cloud Config overwrite: /home/mslinn/.openclaw/openclaw.json (sha256 85b674df87782e7e73fb5cfde53c5036a7fdf32eb8f34aa47b386e426cce2ff0 -> 2da265b895dc0f25e207bdda2d7183df4e8972ea0ff30bda366eb6cde757d0f8, backup=/home/mslinn/.openclaw/openclaw.json.bak) Config write anomaly: /home/mslinn/.openclaw/openclaw.json (missing-meta-before-write) Updated ~/.openclaw/openclaw.json Workspace OK: ~/.openclaw/workspace Sessions OK: ~/.openclaw/agents/main/sessions System Node 20.19.5 at /usr/bin/node is below the required Node 22.16+. Using /home/mslinn/.nvm/versions/node/v25.8.1/bin/node for the daemon. Install Node 24 (recommended) or Node 22 LTS from nodejs.org or Homebrew. Installed systemd service: /home/mslinn/.config/systemd/user/openclaw-gateway.service Enabled systemd lingering for mslinn. │ ◇ Agents: main (default) Heartbeat interval: 30m (main) Session store (main): /home/mslinn/.openclaw/agents/main/sessions/sessions.json (0 entries) Tip: run `openclaw configure --section web` to store your Brave API key for web_search. Docs: https://docs.openclaw.ai/tools/web ✓ Installed web search plugin
Starting your assistant — this may take a moment...
Starting gateway...
✓ OpenClaw is running
Open the Web UI: http://localhost:18789/#token=1032095f6293e02b513eff40fca59bb947096c70b2906eee
Quick start: /help see all commands openclaw configure --section channels connect WhatsApp, Telegram, etc. openclaw skills browse and install skills
The OpenClaw gateway is running in the background. Stop it with: openclaw gateway stop
🦞 OpenClaw 2026.3.13 (61d171a) — Somewhere between 'hello world' and 'oh god what have I built.'
openclaw tui - ws://127.0.0.1:18789 - agent main - session main
session agent:main:main
Wake up, my friend!
I need to check my daily memory file first, as per the startup routine.
Let me read the memory file for today's date (2026-03-20):I'll create the memory directory and today's memory file since it doesn't exist yet:Now let me check yesterday's memory file (2026-03-19) for recent context:Let me also check if there's a MEMORY.md file for long-term memory (only load in main session):Now let me read the core files to understand who I am and who I'm helping: ⠴ running • 12s | connected agent main | session main | unknown | tokens ?/128k
Background Agents
Ollama can launch non-interactive agents using the new --yes
flag. This enables running OpenClaw and other agents in the background without
interruption on servers, scripts, and other environments that do not support the
OpenClaw gateway.
OpenClaw and other background agents hand control of your computer to autonomous LLM models that are widely known to be unreliable and prone to extremely destructive acts.
To launch a non-interactive OpenClaw agent with the
minimax-m2.7:cloud model, run:
$ ollama launch openclaw \
--model minimax-m2.7:cloud \
--yes -- agent \
--agent main \
--local \
--message "Prepare a pre-read for my next meeting"
"Prepare a pre-read" means creating a document or set of materials for attendees to review before a meeting starts. The goal is to provide necessary background and context so you can skip the catch-up phase and dive straight into productive discussion or decision-making during the actual meeting.
Recording a Session
See Recording Chat Transcripts to obtain the
record script and to learn various ways of viewing the
transcript.
The following shows how to use record to launch Ollama and run
the qwen3:4b model.
$ record -c 'ollama run qwen3:4b' Press Ctrl+D to end the chat and stop recording. Script started, output log file is '2025-12-12_20-06-39_chat.log'.
>>> /show Available Commands: /show info Show details for this model /show license Show model license /show modelfile Show Modelfile for this model /show parameters Show parameters for this model /show system Show system message /show template Show prompt template
>>> ^D # Exit the ollama session
Script done. Recording finished. Log saved to /home/mslinn/2025-12-12_20-06-39_chat.log
Documentation
- CLI Reference
- Ollama API.
- There are lots of controls for various models.
- Ollama Web UI
- An Apple M1 Air works great.
- Crafting Conversations with Ollama-WebUI: Your Server, Your Rules
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