> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nimbleway.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Omnigent
> Nimble is a built-in web_search provider in Omnigent: grounded, live web search for any model, including Databricks-hosted Claude and Llama.
### Overview
[Omnigent](https://github.com/omnigent-ai/omnigent) is an open-source AI agent framework and meta-harness from Databricks. It gives you one orchestration layer over Claude Code, Codex, Cursor, Pi, and agents you write yourself, with shared sessions, policies, and sandboxing.
Nimble ships as a first-class `web_search` provider in Omnigent. Set `search_provider: nimble` in any agent spec and the agent gets grounded, live web results from Nimble's [Search API](/nimble-sdk/web-tools/search). No fork, no plugin.
### Web search for any model
Omnigent lets you run any model and swap harnesses with a one-line change. Nimble brings that same flexibility to web search.
The `web_search` builtin pairs each model with a search backend. Set `search_provider: nimble` and any model gets grounded web search through Nimble: Claude, Llama, and the Databricks-hosted models you run in Omnigent. One model-agnostic backend, with anti-bot handling, JS rendering, and geo-targeting underneath.
Databricks-hosted models get grounded, live web search through Nimble out of the box.
### Quick Start
#### 1. Get a Nimble API key
Get your API key from [Nimble's dashboard](https://online.nimbleway.com/account-settings/api-keys) (free trial available) and export it:
```bash theme={"system"}
export NIMBLE_API_KEY="your-api-key"
```
#### 2. Enable the provider in your agent spec
A custom agent lives in its own directory with a `config.yaml` at the root. Create `research-agent/config.yaml`, add the `web_search` builtin, and set `search_provider: nimble`:
```yaml theme={"system"}
spec_version: 1
name: research-agent
prompt: |
You are a research assistant. Use web_search to ground your
answers in current information from the web.
executor:
type: omnigent
model: databricks-claude-sonnet-4-6 # run on any model
tools:
builtins:
- name: web_search
search_provider: nimble
api_key: ${NIMBLE_API_KEY}
```
#### 3. Run the agent
Reference the agent by its directory path:
```bash theme={"system"}
omni run ./research-agent -p "What were the biggest AI model releases this month?"
```
The agent now calls Nimble whenever it searches the web.
### Configuration
The `web_search` builtin accepts these keys under the Nimble provider:
**Required.** Set to `nimble` to route web search through Nimble.
**Required.** Your Nimble API key. Use `${NIMBLE_API_KEY}` to read it from the environment instead of hardcoding it in the spec.
**Optional.** Number of results to return, from `1` to `100`. Defaults to `5`. Values outside the range are clamped.
**Optional.** Controls the speed-vs-richness tradeoff. Defaults to `lite`.
* `lite`: titles, URLs, and snippets. Lowest latency, best for broad discovery.
* `deep`: full real-time page extraction for each result. Higher latency, richer content.
See [Search Depth](/nimble-sdk/web-tools/search-depth) for details.
A fully configured provider looks like this:
```yaml theme={"system"}
tools:
builtins:
- name: web_search
search_provider: nimble
api_key: ${NIMBLE_API_KEY}
max_results: 10
search_depth: deep
```
### Additional Resources
The open-source agent framework and meta-harness.
Define and run a custom agent: directory layout, `config.yaml`, and the run command.
The Search endpoint behind the provider: parameters and response format.
The full Databricks integration: Marketplace MCP, Genie, and SQL-native enrichment.