Ollama — run AI locally with Screenpipe

Run local AI models with Ollama and Screenpipe. Configure tool calling, verify access to recordings, and troubleshoot model, endpoint, and API authentication errors.

Ollama can run AI models on your computer without a cloud inference key. Screenpipe’s app license is separate. Keep transcription, sync, connected apps, and any cloud model fallback in mind when choosing a local data flow.

Setup

1. Install Ollama and download a model

Install Ollama, then download a model with tool-calling support, for example:

ollama pull llama3.2
ollama list
curl --fail-with-body http://localhost:11434/api/tags

If the server is not running, start the Ollama app or run ollama serve. Downloading a model needs a network connection and enough disk space. Runtime memory needs depend on the model and context length.

Screenpipe’s agent needs to call tools to retrieve recordings. A model that answers plain text may still fail that workflow. Check the exact model’s tool support, then verify retrieval below. Model capability alone is not a guarantee of reliable answers.

2. Select Ollama in Screenpipe

Open Screenpipe’s AI provider settings, expand Advanced, and choose Ollama. Pick a downloaded model, save the preset, and select it for the chat or task you want to run. The built-in Ollama connection lists models from http://localhost:11434/api/tags.

3. Verify one useful answer

Display a harmless phrase in a text editor while Screenpipe records. Ask the selected model to find that phrase in the last five minutes and return its source app and timestamp. Compare the result with the timeline and inspect the tool result. A successful model-list request only proves server connectivity.

Once this works, try a short meeting summary before a whole-day recap. If the model cannot call the required tools or follow their results, choose another tool-capable model. A reviewed transcript pasted into Ollama directly can test text summarization separately from Screenpipe access.

Custom OpenAI-compatible endpoints

For another local or hosted server, choose the custom API provider and enter its base URL before /models and /chat/completions, for example http://localhost:11434/v1 for Ollama’s OpenAI-compatible interface. Set the API key only if that server requires one.

curl --fail-with-body http://localhost:11434/v1/models

The custom provider requests the configured base URL’s /models route. It does not automatically fall back to Ollama’s /api/tags. Run Run diagnostics in the AI preset to check endpoint, authentication, model listing, and chat separately. A passing basic chat check still needs the retrieval check above.

Troubleshooting

Symptom	Check
Ollama not detected	Start Ollama and test http://localhost:11434/api/tags
Model missing	Run ollama list, download the exact model tag, then refresh the model list
Slow or out-of-memory responses	Shorten the source window or context, close other memory-heavy apps, or try a smaller tool-capable model
Fluent answer with no recording evidence	Check tool execution, recorder health, capture permissions, and local API authentication
Tool-use error	Check the exact model and endpoint’s tool-calling support; a different prompt does not add protocol support

Troubleshooting Azure & custom OpenAI endpoints

Error: “unsupported tool use” or “does not support more than one tool call”

Screenpipe sends multiple tool calls to the LLM for agentic features. Some models (especially older Azure-hosted models like Phi-4, older Llama versions) don’t support this.

Fixes:

• Use a model that supports tool use — most current frontier and mid-size open models do; check the model’s documentation for tool/function-calling support
• If you only need text summarization, test a reviewed excerpt directly in the provider outside the Screenpipe agent. Removing tool instructions from a prompt does not disable the tools sent by Screenpipe.
• On Azure, try switching to the latest model version available

Error: “max tokens is not supported”

Your endpoint doesn’t recognize the max_tokens parameter that Screenpipe sends.

Fixes:

1. Verify your endpoint supports OpenAI-compatible API: curl -H "Authorization: Bearer YOUR_KEY" https://your-endpoint/v1/models
2. If using Azure, ensure you’re using the OpenAI-compatible endpoint format (not the old REST API format)
3. Try a custom endpoint URL wrapper if your server needs parameter translation

API key not being passed to Screenpipe API

If Screenpipe says “unauthorized” when accessing the local API, but your custom LLM endpoint is configured:

The model provider key and the Screenpipe recorder token are different credentials. A working AI endpoint does not prove access to the recorder.

Fix: configure your scheduled task or app to use the API key explicitly:

curl "http://localhost:3030/search?limit=5" \
  -H "Authorization: Bearer YOUR_SCREENPIPE_API_KEY"

Retrieve the recorder token locally with npx -y screenpipe@latest auth token, then configure it for the client making the request. See API authentication.

Custom endpoint not responding or models not detected

Check the configured base URL and its /models route, then run the AI preset diagnostics. For the built-in Ollama provider, test /api/tags instead. Verify the server’s own authentication and tool-calling requirements. Share the provider name, model tag, app version, and redacted error when asking for help; do not share keys or private transcripts.

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