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Give a local AI agent web search by connecting it to one explicit search tool, then make that tool return a predictable result format the agent can inspect and cite. You can run the search layer yourself with SearXNG or call a managed service such as Brave Search API; the model can remain local either way. Reliability depends on the whole integration—endpoint availability, response handling, timeouts, and usable source links—not simply on the size of an index or where the software runs.
Choose where the search layer will run
A local language model does not have to fetch web pages itself. Its agent can call a separate search endpoint, receive results, and use those results as context for an answer. The two options documented here make different trade-offs: SearXNG gives you control over a self-hosted metasearch service, while Brave’s managed API provides an agent-oriented endpoint that returns extracted content and source metadata.
| Decision point | Self-hosted SearXNG | Managed Brave Search API |
|---|---|---|
| Operating model | You run and configure the metasearch service. | Your application calls Brave with an API key. |
| Returned material | The HTTP API can return formats such as JSON, CSV, or RSS, depending on instance configuration. | The LLM Context endpoint returns extracted page chunks and source metadata, with controls for context limits and relevance or source ranking. |
| Cost evidence | The project documentation describes the software and interface but does not establish deployment or operating costs. | Brave listed Search API pricing at $5 per 1,000 requests, with $5 in monthly credits, on a page checked October 7, 2026. This is vendor pricing and may change; confirm the applicable plan. |
| Key integration check | Confirm the instance enables the output format your agent expects; public instances may disable JSON. | Keep the API token secure and validate the endpoint parameters and response schema. |
Neither choice guarantees correct answers or uninterrupted search. SearXNG’s documentation does not promise that upstream search engines will always respond, and a managed provider’s scale or freshness claims are not independent reliability measurements.
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SearXNG is a self-hostable metasearch service with a documented HTTP interface. Its API accepts GET and POST requests at / and /search. The project describes its aim as “Search without being tracked”; treat that as the project’s positioning, not an independently verified privacy guarantee. See the SearXNG documentation for current configuration and API details.
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- Run and configure the instance. Choose which output formats it will expose. SearXNG can provide JSON, CSV, or RSS, but JSON must be enabled in the instance configuration.
- Point your agent’s search tool at the instance. Have the tool make an HTTP GET or POST request to
/search(or the documented/interface), passing the query and requesting the configured format. - Normalize the response. Convert each result into the fields your agent uses: query, title, URL, useful text or snippet, and source metadata when available. Preserve the original URL so the agent can cite it.
- Handle unavailable or malformed results. Set a timeout, make only sensible retries, and return an explicit tool error if the instance or an upstream source fails. Do not let the model present an empty or failed search as evidence that nothing exists.
A public SearXNG instance may disable JSON, and the existence of an HTTP API does not establish that any particular public instance is dependable for production use. For a service you rely on, verify the instance configuration and monitor its actual behavior.
Connect Brave’s agent-oriented search endpoint
Brave documents its LLM Context endpoint for agents and chatbots. It returns extracted web-content chunks with source metadata and includes controls for context limits and relevance or source ranking. Brave also documents freshness filters and optional source-metadata enrichment; check the current endpoint documentation for supported parameters and response details. Brave describes the product as “Web search for AI agents and chatbots,” which is product positioning rather than a performance result. The Brave Search API documentation was checked October 7, 2026.
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- Use the LLM Context endpoint for an agent. Configure your server-side search tool to call that endpoint, rather than assuming a general search response already contains the context format your model needs.
- Authenticate on the server. Send the API key in the
X-Subscription-Tokenheader. Keep it in server-side configuration or a secret store—not in a browser bundle, public repository, or prompt, and never in user-visible output. - Set limits deliberately. Use the endpoint’s documented token and URL limits and relevance controls to keep retrieved material appropriate for the task and available context window.
- Validate responses before passing them to the model. Check that content and source URLs are present, handle API errors explicitly, and preserve enough metadata for the agent to identify where each passage came from.
Brave reported more than 40 billion indexed pages and more than 100 million page updates every day on its vendor page, checked October 7, 2026. Those are vendor-reported scale figures; they do not independently measure relevance, geographic coverage, uptime, or success on a particular query.
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A search tool is useful only if the agent can call it, interpret what it returns, and report when it fails. Treat the tool boundary as a small, explicit contract rather than feeding arbitrary provider responses directly into a prompt.
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- Use a stable result shape. Return the query, result title, URL, useful text, and source metadata in consistent fields, regardless of the backend.
- Preserve provenance. Keep URLs attached to the excerpts they support so the agent can cite sources rather than making unsupported claims.
- Bound requests. Configure timeouts and restrained retries. Surface a clear failure state when search is unavailable instead of silently substituting stale or empty context.
- Inspect real responses. Verify formats, source URLs, freshness options, and failure behavior against the endpoint and instance you actually use.
- Keep retrieval distinct from inference. A local model can use a local SearXNG service or a remote search API; using a remote search provider does not, by itself, mean the model’s inference is no longer local.
These are practical integration recommendations, not results from a comparative uptime or relevance test. The available documentation does not establish that either option is universally more reliable.
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