Today, we're announcing the Nimble integration for LangChain. Developers can now equip LangChain agents with live web search and research tools that deliver more accurate context at a lower cost.

What You Can Do with Web Search Agents + LangChain

The langchain-nimble toolkit gives LangChain agents access to Nimble's full suite of web tools, including Search, Extract, Crawl, Map, Extract Templates, and Web Search Agents, from a single integration. Developers enable the tools they need, pass them to a LangChain agent, and let the model select the right capability for each task.

For quick facts, the agent can use Nimble Search. For complex questions requiring multiple sources, extraction, and reasoning, it can launch a cited Web Search Agent research run. Developers simply start sending tasks through the API, and the Web Search Agent automatically adapts search and crawling to the use case.

Run over run, Web Search Agents learn which sources and retrieval paths produce the best results for your domain. This helps them go deeper into relevant sources, avoid unnecessary searches, and retrieve better web context more efficiently over time.

With Web Search Agents and LangChain, you can:

• Improve accuracy. Adapt search and crawling to the agent's specific objective instead of relying on shallow, broadly relevant results from generic search.
• Reduce costs. Reuse learned retrieval paths, avoid unnecessary searches, and return cleaner context that requires fewer tokens to process.
• Automate research workflows. Handle multi-step search, extraction, and synthesis without manually orchestrating each tool call.
• Access more of the web. Render JavaScript, crawl multiple pages, and retrieve content from dynamic websites that many generic search tools cannot reliably access.

Web Search Agent runs are resumable, so LangChain can start a research task, check its status, and retrieve the structured result when it is complete. Each result can include claim-level citations and confidence data, giving agents better context while making their outputs easier to verify.

Cookbook: Enrich Business Data with LangChain

This cookbook demonstrates how to build a LangChain agent that fills missing business information in a CSV. The agent evaluates each empty cell, selects the most efficient Nimble tool for the task, and returns an enriched file with provenance and confidence data attached to every confirmed value.

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Here's how it works:

• Start with an incomplete dataset. Provide a CSV containing company names and empty fields such as website, headquarters, employee count, funding stage, or total funding.
• Preserve existing data. The agent skips cells that already contain values, avoiding unnecessary searches and spending nothing to retrieve information the file already has.
• Select the right retrieval method. LangChain uses Nimble Search for facts that can be confirmed with one lookup, such as a company website or headquarters location.
• Launch deeper research when needed. Fields requiring multi-source verification, such as employee count, funding stage, or total capital raised, are routed to a Web Search Agent.
• Run research asynchronously. Multiple research tasks can be launched and collected without blocking the entire enrichment workflow. The input CSV's column names become the Web Search Agent's output schema, making results easier to map back into the file.
• Attach trust data to every value. Each confirmed field includes its source URL, confidence grade, and the Nimble tool used to retrieve it.
• Leave uncertain fields empty. When a value cannot be confirmed from a retrieved source, the agent leaves the cell blank rather than guessing.

The repository includes a six-company sample containing 23 empty cells and a cached result from a real run. In the example, 20 cells are filled and three remain empty because they could not be reliably confirmed.

Give LangChain Agents Better Web Context with Nimble

LangChain provides the agent framework and tool-calling loop. Nimble gives those agents the web infrastructure needed to search, crawl, extract, and research live information at production scale.

By adapting retrieval to the agent's use case, learning the most effective paths to relevant information, and selecting the right tool for each task, Nimble helps LangChain agents retrieve more accurate context with fewer searches and lower processing costs.

Together, Nimble and LangChain make it easier to build agents that do more than search the web. They retrieve the exact information they need, verify it, and turn it into structured outputs ready for action.