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Agentic search is still an early, fast-moving space, and the agents built on top of it can hallucinate. LLMs may occasionally produce inaccurate or misleading answers even when the retrieved source documents are correct. Monitor responses closely in production, follow the hallucination reduction guide, and configure guardrails to minimize this risk.
Agentic search covers any natural-language search experience where a Large Language Model (LLM) decides when and how to query your data, then uses the results to answer a question. Meilisearch acts as the retrieval layer when implementing Retrieval Augmented Generation (RAG): a fast, relevant search that an agent can call as a tool. With proper configuration, such as system prompt engineering and guardrails, you can help keep responses based on your indexed data rather than the LLM’s general knowledge. This is similar to how Perplexity works: every answer comes with source documents so users can verify the information. Meilisearch brings the same pattern to your own data and your own agents.

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Use cases

Agentic search covers a range of natural-language search experiences, from AI agents that call Meilisearch as a tool to conversational chat interfaces:

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Agents with a search tool

Give any AI agent or LLM application the ability to query your Meilisearch indexes as part of a larger task, tool call, or multi-step reasoning process. The agent decides when to search, what to search for, and how to use the results. Example: A support agent looks up your product catalog before answering a compatibility question, then combines the results with its own reasoning.

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RAG pipelines

Integrate Meilisearch as the retrieval layer in a broader RAG architecture that your agent or application controls. Meilisearch handles hybrid retrieval, while your code decides how to use the results and when to generate a response. Example: A product recommendation engine that retrieves matching products via Meilisearch, then uses a custom prompt to generate personalized suggestions.

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Multi-turn chat

Build a full conversational interface where users ask follow-up questions and the agent maintains context across the conversation. This is ideal for knowledge bases, customer support, and documentation search. Example: A user asks “What models do you support?”, then follows up with “Which one is the fastest?” without restating the context.

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One-shot answer summarization

Generate a single, concise answer to a user’s question without maintaining conversation history. This is useful when you want to display a summarized answer alongside traditional search results. Example: A user searches “How do I reset my password?” and gets a direct answer synthesized from your help articles, displayed above the regular search results.

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How it works

Here is how a typical agentic search workflow looks like:
  1. Query understanding: The LLM transforms the user’s natural language question into optimized search parameters
  2. Retrieval: Search your indexes to find the most relevant documents using hybrid search for better relevancy
  3. Answer generation: The LLM generates a response using only the retrieved documents as context
  4. Source attribution: Every response can include references to the source documents used to generate the answer
Depending on your application needs, you may give more or less autonomy to the LLM at each given step. Stricter control makes for more predictable behavior (for example, translating a question into search parameters), while looser control allows the model to reason and find more creative solutions (for example, multi-hop reasoning).

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Implementation strategies

@meilisearch/ai-sdk provides ready-made search tools for the Vercel AI SDK, so any agent or LLM application can call Meilisearch directly, without an engine-managed /chats workspace. This is the recommended way to build agentic and conversational search on top of Meilisearch. Get started with the agentic search getting started guide.

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Chats API (experimental)

The experimental /chats route consolidates retrieval, context management, and generation into a single, engine-managed endpoint. See the Chats API guide for setup, chat patterns, streaming, and tools. Consult the chat completions API reference for the full list of supported parameters.

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Model Context Protocol (MCP)

An alternative method is using a Model Context Protocol (MCP) server. MCPs are designed for broader uses that go beyond answering questions, and are useful in contexts where having up-to-date data matters more than a tightly scoped search tool. Follow the dedicated MCP guide if you want to implement it in your application.