XERJ

XERJ

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The search engine for AI, now with XERJ Query-1: a model that turns your question into a small program over facts you have written.

XERJ Query-1 · Blog post · Release rc.101 · llms.txt · Docs · Donate

XERJ Query-1, new in rc.101

Most AI search finds text in milliseconds and then asks a language model to write the answer. The model is what makes a RAG answer good, and it is also where the seconds and the per-question cost come from.

XERJ Query-1 works the other way. It reads your question and writes a short program (look this up, compare these, count those), and the engine runs that program over facts you stored. Lookup and comparison steps point at a stored fact and its quoted span; counts return a program hash, not the records. If the data cannot support an answer you get UNKNOWN; if two facts disagree you get CONFLICT. It runs on four CPU cores, offline, with no embeddings to build and no GPU.

Retrieval quality (R@k, nDCG@k) on BEIR, on SearchTome and on our 160-question test, the latency split and every data set: benchmarks/query-1-2026-11.

The model is off by default; without the flag there is no /_facts route and the MCP tool list is unchanged. rc.101 upgrades the data directory to format 4 on first write, and rc.100 cannot open it again. What it does not do yet, measured: sums and totals (a sum question can come back as a member count), and large fact sets (on a 95,000-fact namespace asks took 0.82 s at the median and all 20 test questions were declined). It declines more often than an LLM would guess. The numbers and their caveats: blog post, capabilities.

Try it with the model

curl -fsSL https://xerj.org/get | sh -s -- --with-model

Verify: the installer ends by printing the start line with the model directory it unpacked.

xerj --facts-model-dir ~/.local/share/xerj/model --insecure --data-dir ~/xerj-data &

curl -s localhost:9200/_facts | grep -o '"state":"loaded"'

Verify: prints `"state":"loaded"`. In the first few seconds after start it prints nothing, because the model is still loading (`"not_loaded"`); run it again.

Write a fact and ask about it: docs/MODEL_QUICKSTART.md. From an agent with no shell, the same calls work over HTTP and the MCP tool xerj_facts_ask; see llms.txt.

XERJ is research in the open. The model was trained on donated GPU time; if it is useful to you, xerj.org/donate funds the next models. The engine is open source; the model ships as a binary bundle, and its training and evaluation data are not public yet.

The engine underneath

Local-first search engine and MCP server for AI agents. One Rust binary indexes code, docs, logs, JSONL/CSV and PDFs for BM25 full-text, vector and hybrid search, code search and agent memory, behind an Elasticsearch-compatible API. Works with Claude Code, Cursor and any MCP client.

XERJ is a local AI search engine that indexes any folder automatically, so your coding agent stops burning tokens reading files one by one and pulls the exact code it needs instead. Reference coding is its main use case and the clearest win: point an agent at a task and it downloads the open-source repos closest to it, indexes them, and reuses how they solved the problem before writing its own code. In a controlled study that cut a coding agent's output tokens by 2.7x at the same 16/16 solve rate (case study), and people report roughly 5x in everyday work (field reports). It is enough for a smaller, cheaper model to out-code a pricier one working from memory, while you spend less time in fix loops. Try it now with a one-prompt install:

Install XERJ (docs: https://xerj.org/llms.txt), index this project's sources, and set up

reference coding: clone and index the open-source repos closest to what we're building, and search how they solved a problem before writing code.

Or run it yourself:

curl -fsSL https://xerj.org/get | sh

xerj --insecure --data-dir ~/xerj-data & xerj init # wire XERJ into Claude Code/Cursor (MCP + skill), one command xerj autoindex ~/my-project xerj search "how do we rotate the WAL segment" # ranked passages, file:line xerj def "euler_to_rotationmatrix" # go-to-definition: file:line + signature xerj gain # what it did for you — counted, not estimated

For several IDs or questions at once, use xerj batch-search --input queries.json or MCP xerj_batch_search: ordered results, citations and per-item errors.

Keep that data directory outside any folder you will autoindex. data/ is not in the built-in ignore list, so --data-dir ./data inside a tree you then index makes the node index its own storage.

At startup, Data dir shows the resolved absolute path, Data source identifies the flag, selected config file or default, and Data status says whether this launch created the directory. A new directory can make an older node's indices or owner account appear missing: restart with the previous --data-dir or --config. XERJ does not discover or migrate other data directories automatically.

Windows and a hand-verified install are under Install by hand.

That --insecure flag is a trust boundary, not a formality. It turns off TLS and API-key auth, so every caller that can reach the port is a superuser who can read and delete every index, including every /_memory namespace. It is meant for one user on a dev machine: a fresh node binds 127.0.0.1 only, and a cleartext bind on any other interface refuses to start unless you explicitly allow it. The full mod