--- title: "Ollama Integration" description: "Run open-source models like Llama, Qwen, DeepSeek, and Gemma on your own hardware and call them directly from flows. Generation, embeddings, and model management with no data leaving your infrastructure." url: https://flowrunner.ai/integrations/ollama date_modified: 2026-08-07T17:20:23-07:00 --- # Ollama [AI](https://flowrunner.ai/integrations/category/ai-llms) Run open-source models like Llama, Qwen, DeepSeek, and Gemma on your own hardware and call them directly from flows. Generation, embeddings, and model management with no data leaving your infrastructure. [Verified](https://flowrunner.ai/integrations/verified "What does verified mean?") · 10 actions · API key · available [Ollama website](https://ollama.com/) · [Platform Documentation](https://github.com/ollama/ollama/blob/main/docs/api.md) · Capability data verified 2026-08-12 1. Sensitive internal document queued for classification 2. Agent sends the document to a local Ollama model 3. Agent reads the classification and extracted fields 4. Agent routes by classification without the data leaving the network 5. Agent files the document with its classification 6. The owning team gets a summary of the batch 7. A document the model is unsure about routes to a person to label ## What This Integration Enables Ollama runs open models locally and exposes them to a flow: generation for text tasks, embeddings for retrieval, and model management to pull and organize the models you run. Because the model runs on your own hardware, the data being processed never leaves your network, and there is no per-call inference bill. For regulated or air-gapped environments, local inference is not a preference but a requirement. The value is being able to build the same kind of AI workflow you would with a cloud model, entirely on-prem. An orchestration layer runs that workflow and inserts a person where judgment is needed, and FlowRunner is built for that layer, including self-hosted deployment so the whole flow stays inside your network. ### Without FlowRunner **Data leaves the network**: Sensitive text sent to an external model provider **Per-call inference cost**: Every model call metered by a vendor **No local model option**: On-prem requirements rule out cloud-only AI ### With FlowRunner **Inference on your hardware**: Models run where the data already lives **No per-call fees**: Inference cost is your own compute, not a vendor bill **On-prem AI**: Open models run inside your own network ## Use Case Scenarios ### On-Prem Document Classification Sensitive documents cannot leave the network but still need classifying. The agent sends each to a local Ollama model, reads the classification, and routes accordingly, all inside your own infrastructure. Confident classifications flow through; anything the model is unsure about routes to a person. The data never touches an external provider. ### Private Knowledge Retrieval An internal knowledge base holds information that cannot go to a cloud model. The agent uses local Ollama embeddings to index it and retrieve answers, keeping the entire retrieval pipeline on-prem. Employees get grounded answers, and questions touching the most sensitive material still route to the owning team. ### Cost-Controlled Bulk Processing A high volume of routine text tasks would run up a large per-call bill on a cloud provider. The agent runs them against a local Ollama model instead, trading vendor fees for your own compute. The work gets done at a predictable cost, and exceptions the model flags are routed to a person. ## Human-in-Loop Highlight Running a model on your own hardware changes where the data goes, not whether its output needs judgment. When a local Ollama model returns a low-confidence classification, or produces output that drives a real decision, FlowRunner routes it through a [human-in-the-loop](https://flowrunner.ai/concepts/human-in-the-loop) step: the agent pauses, presents the output and its context, and sends it to the right person via Slack. They decide. Local inference keeps the data in your network; a person still owns the calls that matter, and with self-hosted FlowRunner the entire flow stays on-prem. Agent processes routinely Detects exception requiring judgment Clear match Continues automatically Ambiguous Routes to human via Slack Human decides Agent resumes with decision ## Agent Capabilities 10 actions ### Generation 2 - **Generate Completion** Generates a single completion for a prompt using a local Ollama model. Supports JSON mode and structured outputs (JSON schema), thinking control for reasoning models (e.g. deepseek-r1, qwen3), and raw model options like temperature and context size. - **Chat** Sends a multi-turn conversation to a local Ollama model and returns the next assistant message. ### Embeddings 1 - **Create Embeddings** Generates embedding vectors for one or more input texts using a local embedding model (e.g. 'nomic-embed-text', 'mxbai-embed-large', 'all-minilm'). ### Model Management 6 - **List Local Models** Lists all models available locally on the Ollama server, including name, size in bytes, digest, last modified date and details such as family, parameter size and quantization level. - **Show Model Info** Retrieves detailed information about a local model: its Modelfile, default parameters, prompt template, details (family, parameter size, quantization) and capabilities (e.g. 'completion', 'vision', 'tools', 'thinking'). - **Pull Model** Downloads a model from the Ollama library (https://ollama. com/library) to the server, e.g. 'llama3. - **Copy Model** Copies an existing local model to a new name, e.g. to tag a custom variant before editing. - **Delete Model** Permanently deletes a model and its data from the Ollama server's local storage. Fails if the model does not exist. - **List Running Models** Lists the models currently loaded into memory on the Ollama server, including total size, VRAM usage (size\_vram) and when each model is scheduled to be unloaded (expires\_at). ### Server 1 - **Get Version** Retrieves the version of the Ollama server. Also useful as a connectivity check for the configured server URL. ## Frequently Asked Questions ### What can FlowRunner do with Ollama? FlowRunner agents can run Generate Completion, Chat, and Create Embeddings in Ollama, plus 7 more actions. ### Does connecting Ollama to FlowRunner require OAuth? No. Ollama connects to FlowRunner with an API key, no OAuth flow required. ### Can Ollama trigger a FlowRunner workflow automatically? Ollama doesn't currently expose triggers in FlowRunner. It connects as an action step inside workflows started by another trigger. **Work at Ollama?** This integration exposes Ollama to AI agents on every FlowRunner plan, including through MCP, at no cost to you. [See what FlowRunner offers integration partners](https://flowrunner.ai/integrations/partners), including how to keep this page current. --- Markdown version of https://flowrunner.ai/integrations/ollama. Site index: https://flowrunner.ai/llms.txt