How to Run Free, Private AI Agents Locally with Sahara AI + Ollama
Running an AI agent all day can get expensive. Every scheduled task, file summary, classification, or routine automation can mean another model call and another API charge.
But most of that work doesn't need a frontier model.
With Sahara AI's ClawApp and Ollama, you can run AI agents using models directly on your own computer with no per-token inference fees and no prompts sent to an external model provider.
ClawApp now automatically detects Ollama and any local models you have installed. Choose one as your agent's model and it can run inference locally on your machine instead of calling a hosted AI API. ClawApp supports both hosted models and fully local inference through Ollama.
That means:
No model API fees
No per-token inference costs
Prompts and model responses stay on your machine
Local inference can work without an internet connection
Your agents can still use ClawApp's Skills, tools, permissions, MCP servers, and automations where supported
You decide which work needs a frontier cloud model and which can run locally for free.
What is a local AI agent?
A local AI agent uses a model running on your own computer instead of sending each prompt to a hosted model provider.
Normally, an agent might send its instructions and context to an API from OpenAI, Anthropic, Google, or another provider and receive the model's response over the internet.
With a local model, inference happens on your own hardware.
ClawApp provides the agent layer: instructions, Skills, tools, permissions, MCP servers, schedules, and automations. Ollama runs the AI model locally. ClawApp can use Ollama models alongside its other supported model options.
Can I run Sahara AI agents for free?
Yes, you can run ClawApp agents with local Ollama models without paying per-token or API inference fees.
Once a compatible model has been downloaded to your computer, your own hardware performs the inference.
This can be particularly useful for agents doing frequent or repetitive work such as:
Renaming or organizing files
Summarizing documents
Classifying information
Triaging messages
Scanning logs
Extracting structured information
Running recurring internal analysis
Processing local data
Performing the same scheduled task every day
These workflows can make hundreds or thousands of model calls over time. Even when each individual call is inexpensive, always-on agents can accumulate significant inference costs.
Running suitable workloads locally removes the model API cost entirely.
Why use a local model for an AI agent?
Agents change the economics of AI.
When you're chatting with an AI manually, you decide when to send each prompt. An autonomous agent might run every morning, every hour, or whenever an event occurs.
That means routine work can consume far more inference than a normal chatbot.
At the same time, not every step requires the most powerful model available.
You may want a frontier model for complex reasoning, difficult coding, or high-stakes analysis. But a smaller local model may be more than capable of sorting files, extracting fields, summarizing a thread, formatting data, or completing a predictable recurring workflow.
ClawApp lets you choose the model for each agent independently.
Instead of paying frontier-model prices for everything your agents do, you can reserve expensive models for work that actually benefits from them and run routine inference locally.
Are local AI agents private?
Local model inference can keep your prompts, context, and model responses on your own computer rather than sending them to an external model provider.
When a ClawApp agent uses an Ollama model locally, there is no hosted model API that needs to receive the prompt to generate a response. Sahara AI explicitly supports running ClawApp fully locally through Ollama.
This can be useful when agents work with:
Private documents
Internal notes
Local files
Source code
Business information
Personal knowledge bases
Other information you prefer not to send to a hosted model
There is one important distinction: local inference does not automatically make every external tool an agent uses local.
If you give an agent access to a web service, external API, cloud-based MCP server, messaging platform, or another online tool, information sent to that service is still subject to that service's infrastructure and policies.
If your agent uses only local models and local resources, its model inference can remain entirely on your machine.
Can ClawApp and Ollama AI agents work offline?
The model itself can run offline once it has been downloaded.
Ollama runs supported models locally on your machine, so generating a response does not require sending a request to a hosted model API. Ollama provides local versions of model families including Llama, Qwen, Gemma, Mistral, gpt-oss, and many others.
Whether the entire agent workflow can run offline depends on what you ask it to do.
An agent working with local files can operate without internet access.
An agent asked to search the web, check an online account, call a remote API, or send a message obviously needs connectivity for those specific actions.
How do I connect Ollama to ClawApp?
ClawApp automatically detects Ollama, so there is no API key or manual endpoint configuration required.
Step 1: Install Ollama
Download and install Ollama:
Ollama is available for macOS, Windows, and Linux.
Once Ollama is running, ClawApp can detect the local Ollama installation.
You don't need to create a model-provider account or enter an API key.
Step 2: Download a local AI model
Choose a model from the Ollama library and pull it onto your machine:
Browse the Ollama model library
Popular options include:
Qwen3 from Alibaba's Qwen team
gpt-oss from OpenAI
Llama 3.1 from Meta
Gemma from Google
Mistral from Mistral AI
Ollama offers these models in different sizes, allowing you to choose based on your hardware and workload. For example, Qwen3 ranges from very small local variants through much larger models, while Meta's Llama 3.1 is available in 8B, 70B, and 405B variants.
OpenAI's gpt-oss is also available through Ollama in 20B and 120B versions and is specifically designed to support reasoning and agentic tasks.
Step 3: Open ClawApp
Once Ollama is running and you've downloaded your models, open ClawApp.
ClawApp automatically adds your local Ollama models to the Models page.
There is no need to manually create a provider configuration for each model.
Step 4: Give the local model to an agent
Create a new ClawApp agent or open an existing one and select the Ollama model you want it to use.
Your agent can now use that model for local inference rather than making paid model API calls.
That's it.
Which Ollama model should I use for an AI agent?
Choose the model based on the job, not simply the largest model your computer can run.
Smaller models can be enough for many predictable agent tasks and generally require fewer system resources.
A lightweight local model may be suitable for:
Classification
Summarization
Extraction
File organization
Simple recurring workflows
A larger or more capable model may make more sense for:
Complex reasoning
Coding
Multi-step planning
Difficult tool use
Tasks requiring more nuanced instruction following
For example, Ollama offers Qwen3 in sizes ranging from 0.6B to much larger variants and marks the family as supporting tools and agent capabilities.
If your ClawApp agent needs to call tools, interact with files, execute commands, or use MCP servers, check that the specific model you choose has the tool-use capabilities required for that workflow.
The Ollama library identifies model capabilities so you can choose accordingly.
Can I use OpenAI gpt-oss locally with ClawApp?
Yes.
OpenAI's open-weight gpt-oss models are available through Ollama and can be used for local inference.
Ollama offers:
gpt-oss:20b
gpt-oss:120b
The 20B version is intended for lower-latency and local use cases, while both models include capabilities designed for agentic workflows such as function calling and structured outputs.
Once you pull a local gpt-oss model through Ollama, ClawApp can detect it along with your other Ollama models.
Can I use Qwen, Llama, Gemma, and Mistral with ClawApp?
Yes.
Ollama's model library includes local models from many of the leading open-model ecosystems.
Examples include:
Model family | Organization |
Qwen3 | Alibaba Qwen |
gpt-oss | OpenAI |
Llama 3.1 | Meta |
Gemma | |
Mistral | Mistral AI |
Ollama also supports many additional models and variants in its model library.
Once installed locally through Ollama, supported models automatically appear as model options in ClawApp.
Can one ClawApp agent use a local model while another uses Claude or GPT?
Yes.
You do not need to make your entire setup local.
ClawApp lets you configure models on an agent-by-agent basis. It supports local models through Ollama alongside hosted access to GPT, Claude, Gemini, Grok, and compatible external providers.
That makes hybrid setups possible.
For example:
A local Qwen agent handles repetitive file processing.
A local Llama agent runs a recurring internal summary.
A frontier model handles difficult research.
Another agent uses a specialized model for coding.
This is often more useful than forcing every workload through the same model.
Use local inference where privacy and cost matter most, and use frontier models when the task actually needs them.
Do local AI agents have usage limits?
There is no provider token bill or API quota for inference running entirely on your own machine.
Your practical limits instead come from your hardware: memory, compute, model size, context length, and how many workloads your computer can comfortably run.
Smaller models typically require fewer resources and can make sense for agents expected to run frequently.
That makes local inference particularly useful for scheduled and always-on agents, where small recurring costs would otherwise accumulate every time the agent runs.
Why is Sahara AI supporting local AI models?
Our goal with ClawApp is to give users control over how their agents run.
Some tasks need the latest frontier models. Others don't.
Some users want the convenience of a hosted model. Others need their inference to remain local.
Some agents make only a few calls. Others may work throughout the day.
ClawApp supports both instead of forcing every agent through a single provider.
Your agent. Your model. Your data. Your choice of where the inference runs.
With Ollama support, that can mean an always-on agent running directly on hardware you own, with no model API bill and no external model provider receiving its prompts.
How do I start running a free, private AI agent with Sahara AI?
The setup is simple:
Choose and download a model from the Ollama library
Open ClawApp
Find your automatically detected Ollama models under Models
Select a local model for your agent
Add the Skills, tools, permissions, and schedule it needs
Let it run locally
You can switch models later or use different models for different agents.
Download ClawApp and start building local AI agents →
No per-token inference fees. No model API key. And when the workflow stays local, no need to send your prompts to an external model provider.
