Most people who wanted their local LLM to access current information went down the same rabbit hole: vector databases, embedding pipelines, chunking strategies, SearXNG configuration, Open WebUI toggles. Hours of setup for something that's been standard in cloud AI for years.
Ollama 0.18.1, released March 17, 2026, cuts most of that down to a single terminal command.
The release ships a web search and web fetch plugin baked into OpenClaw — Ollama's personal agent framework. Your local model can now search the live web, pull readable content from pages, and answer with current information. No RAG infrastructure. No Chroma or Qdrant. No embedding model running alongside your main model eating VRAM.
Just ollama launch openclaw.
What Actually Shipped
The headline feature is the @ollama/openclaw-web-search plugin, which gets installed automatically when you launch OpenClaw through Ollama. It gives any model — local or cloud — two new tools:
- Web search: query the live web for recent content and news
- Web fetch: pull a specific URL and extract readable text for processing
One thing to know upfront: this feature does not execute JavaScript. Pages that are client-rendered SPAs won't give you much. That's not a dealbreaker for most searches — news articles, documentation, product pages, Wikipedia — but if you were hoping to scrape a React-powered SaaS dashboard, that won't work here.
The second thing Ollama shipped in this release is a non-interactive headless mode for ollama launch. More on that in a minute.
Setup: Three Commands
Getting web search working with a local model takes three steps.
Step 1: Update Ollama
You need Ollama 0.18.1 or later. Check your version:
ollama --versionIf you're behind, grab the latest from ollama.com or run the install script again — it handles upgrades.
Step 2: Sign in
This is the step that trips people up. When using local models with the Ollama web search plugin, you need to be authenticated with your Ollama account. The web search calls route through Ollama's API, which requires an account (free tier available).
ollama signinIf you skip this, the web search tool will be installed but fail silently when your local model tries to use it. Worth noting this explicitly because the release notes bury it.
Step 3: Launch OpenClaw
ollama launch openclawOllama handles everything from here — detects whether OpenClaw (formerly Clawdbot, before that Moltbot) is installed, prompts you through npm installation if not, installs the gateway daemon, connects to your model, and installs the web search and fetch plugin automatically.
If you already have OpenClaw running and just want to add web search:
openclaw plugins install @ollama/openclaw-web-searchTip
Use the native Ollama API URL with OpenClaw — http://host:11434 — not the /v1 OpenAI-compatible path. The /v1 endpoint breaks tool calling and your model may start outputting raw tool JSON as plain text instead of executing the search.
The Local Model Constraint That Matters
Cloud models (Ollama offers Kimi-K2.5 cloud, among others) get web search automatically and work out of the box with any hardware. Local models have a harder requirement: at least 64k tokens of context window.
This isn't arbitrary. Web search results need to fit in context alongside your conversation and the tool response. Most 7B and 8B models max out at 8k-16k tokens by default, which is too tight.
For reliable local web search with OpenClaw, the community consensus in early 2026 points to Qwen3-coder:32b as the top pick. It has extremely stable tool calling, rarely hallucinates function calls, and handles the agent loop well. Hardware requirement: 24-32GB VRAM.
If you're on 16GB VRAM, you're in harder territory. GPT-OSS 20B fits and runs reasonably well — 139 tokens/second on an RTX 4080 — but tool calling reliability drops compared to the 32B class models. The honest answer is that sub-32B models work but you'll hit more loops and missed tool calls.
Warning
Models under 14B parameters are generally unreliable for OpenClaw tool calling tasks. They tend to forget parameters, hallucinate tool names, or get stuck repeating the same search query. Start at 14B minimum, 32B if you can swing the VRAM.
For most people who want web search on modest hardware, using a cloud model via Ollama and paying nothing (free tier) is the practical path until they upgrade.
Headless Mode Is Quietly Useful
The second feature in 0.18.1 gets less attention but it's handy: ollama launch now supports non-interactive mode.
ollama launch claude --model kimi-k2.5:cloud --yes -- -p "how does this repository work?"The --yes flag auto-pulls the model and skips interactive prompts. The -- separator passes arguments directly to the launched app.
The official use cases are Docker, CI/CD pipelines, and automation scripts. Spinning up an agent as a pipeline step to do a code review or security scan, running evals, then tearing it down — all scriptable now without someone sitting at a keyboard.
This is the kind of thing that used to require awkward expect scripts or separate tooling. Now it's one line.
[!INFO] Ollama now has 166K GitHub stars as of this release. OpenClaw (the underlying agent framework) is at approximately 195K. The ecosystem around local agents has grown considerably faster than most predicted when Ollama launched.
RAG Still Has a Job — Just Not This One
It's worth being clear about what this replaces and what it doesn't.
RAG (retrieval-augmented generation) is built for querying your own documents — contracts, codebases, internal wikis, PDFs that never touch the public web. It excels at giving your model access to private, structured knowledge bases. The embedding pipeline complexity is worth it when the data is yours and it doesn't change frequently.
Web search is for live, public information. Current prices. Breaking news. Documentation that got updated last week. Package changelogs. Research papers that dropped yesterday.
These solve different problems. The Ollama 0.18.1 update doesn't make RAG obsolete — it fills the gap that RAG was never meant to fill, which is "what's happening on the internet right now."
If you've been duct-taping SearXNG into Open WebUI to answer questions about current events, this is a cleaner path. If you need your LLM to reason over 47 internal PDFs, you still want a local RAG setup. For setting up a full local inference stack, the vLLM single-GPU consumer setup guide covers the infrastructure side.
The One-Command Reality Check
ollama launch openclaw does handle a lot. But "one command" glosses over a few requirements:
- Node.js and npm must be installed (OpenClaw uses npm under the hood)
- You need an Ollama account for local model web search
- First launch includes a security notice about tool access you have to read through
- A model with 64k+ context and solid tool calling is what makes it actually work
On Windows, WSL is required — OpenClaw doesn't run natively on Windows yet.
None of these are blockers. But the pitch of "zero config" slightly undersells what you're actually setting up: a gateway daemon, a messaging-app-to-agent bridge, and a plugin system. It's not complex, but it's not nothing.
Verdict
This is a genuinely useful update. Ollama has been the easiest way to run local models for a while, and the web search capability fills the most common gap people complained about. The OpenClaw architecture gives you something closer to a real agent setup — one that can reach out to the web, pull content, and reason over it — without requiring you to maintain your own retrieval infrastructure.
The 64k context requirement and tool calling quality dependency mean your GPU matters here. For anyone with a 3090, 4090, or RX 7900 XTX who's been waiting for a reason to pull a capable local model and run it against live data — this is it.
For everyone else: the cloud model option is there, it's free at reasonable usage, and it works today.