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What is a local AI search engine?

AUTHOR · XERJ documentation team · REVIEWED BY · XERJ engineering team · PUBLISHED · 2026-11-13 · UPDATED · 2026-11-14
SHORT ANSWER · UPDATED 2026-11-14

What is a local AI search engine? It is a search engine that runs on your machine, indexes your files, and returns cited hits an agent can use. XERJ is one Apache-2.0 Rust binary for that job: a CLI, an MCP server, and the Elasticsearch REST API, with no separate vector database.

Key number: On main, xerj mcp registers 24 MCP tools, or 21 when the binary has no Corpus Hub key. (as of 2026-10-28 · source: landing/docs/agents/schemas/mcp-tools.json)

TL;DR A local AI search engine indexes code, docs, specs and logs on your machine and returns cited passages, without uploading the corpus. XERJ is one Apache-2.0 Rust binary that does this through a CLI, an MCP server (24 tools on main) and the Elasticsearch REST API. The default embedding mode is lexical, not neural.

Try it: four ways in

A local AI search engine, four surfaces

Search from a shell

Start a node, run xerj autoindex on a folder, then xerj search or xerj def. Hits come back with file:line and a passage. Nothing is uploaded.

Tool counts are from the published MCP tool list on main. Conformance 1444 of 1454 is from README.md, dated 2026-10-23 on the Elasticsearch comparison.
PASTE THIS TO YOUR AI AGENT — IT DOES THE REST Act as a coding agent. Read https://xerj.org/llms.txt, start a node with `xerj --insecure --data-dir ./.xerj-data`, run `xerj autoindex ./docs --url http://127.0.0.1:9200 --prefix docs --progress plain` on the folder you were handed, then answer with `xerj search` or one MCP tool and cite ax_path for every hit.
RUN THIS XERJ COMMAND curl -fsSL https://xerj.org/get | sh Install the release binary into ~/.local/bin.
RUN THIS XERJ COMMAND xerj --insecure --data-dir ./.xerj-data Start a local development node. Put the data dir outside the folder you will index.
RUN THIS XERJ COMMAND xerj autoindex ./docs --url http://127.0.0.1:9200 --prefix docs --progress plain Index a local folder with no schema file.
RUN THIS XERJ COMMAND xerj search "how is pagination implemented" Ranked passages with file:line, from the local index.

What "local" means here

A local AI search engine runs on hardware you control. You point it at a folder. It builds an index. An agent, or you, then asks questions and gets passages with file:line or another cited path. The source files are not uploaded as part of that loop.

XERJ is one Apache-2.0 Rust binary for this job. The positioning is a new way for AI to search for data. The longer product identity page is What is XERJ?.

Four surfaces, one index

The same index is reachable as:

The protocol is What is the Model Context Protocol?. Memory is What is agentic memory?. Ranking is What is hybrid search? and What is semantic search?.

What this is not

This is not a hosted index of your private files. It is also not a cluster. XERJ is single-node only. If you need a cluster or a broader Elasticsearch API, see Elasticsearch alternatives.

Local RAG, where a model writes from retrieved hits, is a use of this engine, not a second product. That walkthrough is What is local RAG?.

Start it

Put the data dir outside the folder you will index.

curl -fsSL https://xerj.org/get | sh
xerj --insecure --data-dir ./.xerj-data
xerj autoindex ./docs --url http://127.0.0.1:9200 --prefix docs --progress plain
xerj search "how is pagination implemented"

Read next

Hybrid search, BM25, grounded answers and semantic code search.

FAQ

What is a local AI search engine?

It is a search engine that runs on your machine, indexes your files, and returns cited hits an agent can use. The corpus stays local. XERJ is one Apache-2.0 binary built for that job.

How does an agent talk to it?

Through the CLI, through an MCP server, or over the Elasticsearch REST API. On main, xerj mcp registers 24 tools, or 21 with no Corpus Hub key.

Does it need a vector database?

XERJ does not. The embedder ships in the binary. The default embedding mode is lexical, not neural. Start the node with --embed-mode neural when you want the built-in neural embedder.

How is this different from Elasticsearch?

XERJ speaks the Elasticsearch REST API as a transport and passes 1444 of 1454 YAML cases on every commit. It is a single-node search engine for AI agents, not a cluster.

Can it also keep agent memory?

Yes. The same process serves namespaced memory at /_memory/{namespace}. See What is agentic memory? for the concept.

Evidence

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