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Ollama lets you run AI models locally on your machine. screenpipe integrates natively with Ollama — no API keys, no cloud, completely private.

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setup

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1. install Ollama & pull a model

# install from https://ollama.com then:
ollama run ministral-3
this downloads the model and starts Ollama. you can use any model — ministral-3 is a good starting point (fast, works on most machines).

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2. select Ollama in screenpipe

  1. open the screenpipe app
  2. click the AI preset selector (top of the chat/timeline)
  3. click Ollama
  4. pick your model from the dropdown (screenpipe auto-detects pulled models)
  5. start chatting
that’s it. screenpipe talks to Ollama on localhost:11434 automatically.
modelsizebest for
ministral-3~2 GBfast, general use, recommended starting point
gemma3:4b~3 GBstrong quality for size, good for summaries
qwen3:4b~3 GBmultilingual, good reasoning
deepseek-r1:8b~5 GBstrong reasoning, needs 16 GB+ RAM
pull any model with: 
ollama pull <model-name>

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requirements

  • Ollama installed and running
  • at least one model pulled
  • screenpipe running

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custom OpenAI-compatible endpoints

if you’re running a custom LLM server (Qwen, vLLM, Text Generation WebUI, etc.), screenpipe auto-detects the endpoint format:
  1. first tries OpenAI-compatible format: GET {endpoint}/v1/models
  2. falls back to Ollama format: GET {endpoint}/api/tags
if your endpoint uses neither format, you may need to:
  • check what path your server uses for model listing (/models, /v1/list, etc.)
  • if unsure, test with curl first: curl {your-endpoint}/path-to-models
  • join our Discord — we can help troubleshoot custom setups
example: a Qwen server on http://localhost:5000 with OpenAI-compatible API should work automatically. if screenpipe can’t find models, verify the server responds to: curl http://localhost:5000/v1/models

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troubleshooting

“ollama not detected”
  • make sure Ollama is running: ollama serve
  • check it’s responding: curl http://localhost:11434/api/tags
model not showing in dropdown?
  • pull it first: ollama pull ministral-3
  • you can also type the model name manually in the input field
slow responses?
  • try a smaller model (ministral-3)
  • close other GPU-heavy apps
  • ensure you have enough free RAM (model size + ~2 GB overhead)

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troubleshooting Azure & custom OpenAI endpoints

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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: gpt-4, gpt-4-turbo, claude-3-5-sonnet, gpt-oss-120b
  • or disable agentic features in your pipe prompts (remove tool calls, just ask for text summaries)
  • on Azure, try switching to the latest model version available

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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

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API key not being passed to screenpipe API

if screenpipe says “unauthorized” when accessing the local API, but your custom LLM endpoint is configured: cause: screenpipe CLI doesn’t automatically share API credentials with the local REST API server. fix: configure your pipe or app to use the API key explicitly: 
curl "http://localhost:3030/search?limit=5" \
  -H "Authorization: Bearer YOUR_SCREENPIPE_API_KEY"
or set the API key in screenpipe settings → API security → enable API key auth, then provide that key in your requests.

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Custom endpoint not responding / models not detected

screenpipe tries both OpenAI and Ollama formats. if neither works:
  1. test your endpoint manually: 
    curl https://your-endpoint/v1/models
curl https://your-endpoint/api/tags
    (one should return a model list; if neither does, your server may use a different path)
  2. check authorization: 
    curl -H "Authorization: Bearer YOUR_KEY" https://your-endpoint/v1/models
  3. verify TLS/SSL: if using https, ensure your certificate is valid (self-signed certs need special config)
  4. common endpoint paths:
    • OpenAI-compatible: /v1/models, /v1/chat/completions
    • Ollama-compatible: /api/tags, /api/generate
    • vLLM: /v1/models (OpenAI-compatible)
    • Text Generation WebUI: /api/v1/models (may vary)
if stuck, join our Discord — share your endpoint URL structure and error logs. need help? join our discord — get recommendations on models and configs from the community.