XERJ

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.

	XERJ Query-1	the usual way	measured on
Question to answer	220 ms (p50)	1.5 to 2.3 s for Opus 5.5 to write a RAG answer (API time per call, our command-line client's start-up not counted), after a search of 0.8 to 106 ms	same 160 questions, rc.101 release, 4 model threads, laptop CPU
Wrong answers	1 of 160 (124 right, 35 declined)	9 to 15 (RAG with Opus 5.5), 6 (Opus 5.5 reading the whole store), 5 (Opus 5.5 given each question's records)	our 160-question test, drawn from the model's development set; XERJ Query-1 was given each question's records
Cost per million questions	$42 of CPU	$13,400 (RAG) to $694,000 (Opus re-reading the whole store per question, uncached)	same test, Opus 5.5 list prices; CPU at AWS c7g list price, 4.1 CPU-s per question on a shared ARM server (0.9 on a laptop)
Build a search index of 10 GB of text	17 min (BM25, measured)	an estimated 62 to 97 hours to embed it with bge-small on CPU	English Wikipedia (`wikimedia/wikipedia` 20231101.en, first 22 of 41 files, 3,299,578 articles); XERJ Query-1 does not use this index
Correct a fact	2.3 ms (one write; the next answer uses it)	re-embed the changed chunk (about 30 ms with bge-small), and the whole store (19.6 s for our 5,000-record test store) when the embedding model changes	rc.101 release, laptop

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 model is in docs/SECURITY_MODEL.md.

Join the community

You join XERJ by running it and sending back one short field report on what it was like: a single commit or PR. That report is the membership card, and it is the only thing the project asks in return for a free binary (the engine source is Apache-2.0; the XERJ Query-1 crates in release binaries since rc.101 are proprietary). It is what keeps the engine improving for whoever runs it next.

Humans and AI agents are both welcome; this is deliberately not an agents-only project. Whether you indexed a repo over lunch or an agent installed XERJ mid-session in someone else's codebase, the ask is the same: one short report saying what you pointed it at, what you used it for, and your honest verdict. A negative verdict is a good report. If something broke, that is a separate issue or fix on top.

Field-report PRs are CLA-exempt, so they merge without a signature. The path that works on any build is a plain gh pr create adding exactly one markdown file under user-feedback/16-agent-field-reports/, with nothing else in the diff. On a build that has it, xerj feedback drafts the report and fills in version, OS and what was indexed, and xerj feedback --open-pr opens the PR for you. Check xerj --help for whether your binary has the command.

The measured reason: reference coding

An agent that greps pulls whole files into its context, up to 1.06M input tokens on one corpus in our measurements, and still has to read them. An agent that queries XERJ gets the passage. Measured end to end on code the model had not memorised (case study: 8 tasks, 4 languages, 16 runs per arm, real claude -p token counts):

	output tokens	cost	solved
from memory only	260,916	$11.18	11/16
grep-driven agent	26,477	$3.27	16/16
XERJ	9,982	$1.58	16/16

That is 2.7x fewer output tokens than grep and 26x fewer than memory alone at the same solve rate, and up to 278x fewer on a single Java task. In real development, users report roughly 5x fewer tokens end to end (field reports). The value is gated by memorisation: it wins on private, internal, niche or post-cutoff code, and is neutral to harmful on popular public libraries the model already knows. The honest limits are in the case study.

Feed it any folder

Reference coding is one use of the same primitive: xerj autoindex <folder> makes an agent know a corpus instead of grepping it. One command indexes code, docs, logs, PDFs, SQLite and awkward CSVs into typed, queryable indices for search, RAG, security audits and agent memory, with no schema to write and no pipeline to configure.

xerj --insecure --data-dir ~/xerj-data &      # start it
xerj autoindex ~/my-project              # point it at anything

--insecure again means no TLS and no auth — every caller a superuser — so this is the same one-user-on-a-dev-machine boundary as above (security model).

XERJ sniffs every file, works out what it is, and creates one index per dataset it finds. Code arrives with its symbols and line numbers through tree-sitter, not as flat text:

phase A: 593 datasets inferred, 1955 junk/skipped files
phase B: indexing 25329 files with 8 workers
done in 158.1s, 593 datasets, 83103 records live, 790 junk records

Share an offline data map with xerj autoindex map --prefix myproject --html xerj-map.html --redact-paths, or use --md xerj-map.md for Markdown. Reports include dataset counts, symbol names, graph hubs when available, skipped-file information and suggested searches. Review metadata names before sharing. Examples, privacy scope and reproduction.

What people point it at

Reference coding: index the OSS projects nearest your problem and retrieve how they solved it before writing code (the measured use case above).
Codebase Q&A and RAG: index a repo, ask for the mechanism, get the passage with file:line instead of a directory listing.
Security audits: index a target tree and query for sink patterns, secrets and dangerous calls across every file type at once.
Log and incident analysis: mixed formats become typed indices with the aggregations you would expect from Elasticsearch.
Agent long-term memory: /_memory/{namespace} stores what an agent learns and recalls it by meaning next session.

How the prompt works

llms.txt gives your agent the ordered steps: install, start the server, xerj autoindex ., query with any Elasticsearch client, and the reference-coding loop (clone similar OSS, index it, retrieve the mechanism before writing, cite what you use, respect licenses).

The reason it helps: an agent working from memory retry-loops on any API it has not memorised, and grep only tells it where to look; the recovery is still reading source into context, up to 1.06M input tokens on one corpus in our measurements. An agent that queries XERJ reads the exact passage instead. The 2.7x fewer output tokens and 2.1x cheaper than grep at the same 16/16 solve rate (26x fewer tokens than memory alone, which solved 11 of 16) is in The measured reason above, with a companion run scoring 9/9 with retrieval versus 0/9 from memory on a Rust library the model had never seen.

More prompts that work on a fresh install:

"Read https://xerj.org/llms.txt, set XERJ up as your search backend, index ./docs, and show me one example query per index it created."
"Run xerj autoindex map and tell me what is in this data (types, counts and the gotchas it recorded), then answer my questions with search instead of reading files."
"Use XERJ's /_memory/notes API as your long-term memory for this project: store what you learn as you work, and recall it by meaning next session."

Worked, validated examples for each capability: xerj.org/docs/recipes.

_{Boot, bulk ingest, search, vector kNN, live dashboards, no cuts.
Watch it on xerj.org or
try the live playground.}

Install by hand

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

Windows PowerShell:

irm https://xerj.org/get.ps1 | iex

Nix (builds from source; nix develop opens a shell with the Rust toolchain):

nix run github:xerj-org/xerj -- --version

One static binary, no JVM, no dependencies. Prebuilt for Linux, macOS and Windows on x86-64 and arm64. The x86-64 builds target x86-64-v2 (SSE4.2: Intel Nehalem 2008 and later, AMD Bulldozer and later) and use AVX2 and FMA at runtime when the CPU has them, so a pre-Haswell machine runs the same binary. It speaks the Elasticsearch API, so existing clients, dashboards and tooling work against it unchanged.

First commands after install (the installer prints where xerj landed; add it to your PATH if needed): xerj --insecure --data-dir ~/xerj-data &, wait until http://localhost:9200 responds, then xerj autoindex ~/my-project. See Index a folder.

For a host with no runtime internet access, follow the air-gapped deployment recipe. The default lexical embedder is offline; neural mode needs the three model files staged locally before the first semantic operation.

Index a folder

Start the server, then point autoindex at anything:

xerj --insecure --data-dir ~/xerj-data &     # local dev: no TLS, no auth
xerj autoindex ~/my-project

That is the dev-machine trust boundary from the top of this README — no TLS, no auth, every caller a superuser. It is fine on a laptop because a fresh node binds 127.0.0.1 and nothing else; a shared host wants the auth-on start just below.

If your server has auth on, which is the default for every start without --insecure (including any start from a config file), hand autoindex the same key. It never picks the key up from xerj.toml; pass --api-key or set XERJ_API_KEY, or every request comes back 401 Unauthorized:

xerj --data-dir ~/xerj-data &                          # auth on: key minted on first boot
export XERJ_API_KEY="$(cat ~/xerj-data/admin.key)" # <data_dir>/admin.key
xerj autoindex ~/my-project

That is the whole setup. There is no schema to write and no pipeline to configure. XERJ sniffs each file, works out what it is, and creates one index per dataset it finds:

phase A: 593 datasets inferred, 1955 junk/skipped files
phase B: indexing 25329 files with 8 workers
done in 158.1s, 593 datasets, 83103 records live, 790 junk records

Source files go through tree-sitter, so code arrives with its symbols and line numbers instead of as flat text. CSV, JSON, JSONL, XML, YAML, SQLite, PDF, DOCX, PowerPoint (.pptx, one record per slide), Excel (.xlsx, one dataset per sheet), EPUB books (one record per chapter), Jupyter notebooks (one record per heading section, any kernel), man pages (roff, one record per section), HTML, mail (.eml and mbox mailboxes, including a Google Takeout export) and common log formats are all handled. Unity projects get first-class treatment: text-serialized scenes, prefabs and assets become one record per GameObject/Component, .meta files become a GUID-to-path table, and MonoBehaviour records carry script_class/script_path so "which scenes use this script?" is a single query (binary-serialized assets need Force Text to be readable; generated dirs like Library/ are auto-skipped and recorded).

Search it

This is the Elasticsearch API, so you already know this part:

# what did it find?
curl localhost:9200/_cat/indices

# full-text
curl "localhost:9200/ax-*/_search?q=checkout+error"

# structured
curl localhost:9200/ax-orders/_search -H 'content-type: application/json' -d '{
  "query": { "range": { "total": { "gte": 100 } } },
  "aggs":  { "by_status": { "terms": { "field": "status" } } }
}'

Vector and hybrid search use the same knn and semantic syntax you would send to Elasticsearch. Any Elasticsearch client library works if you point it at localhost:9200.

Score results with Jev

A search score tells you which result is above which, but not whether a result really answers your question. Jev, a service from TypeSafe AI, does that: give it a question and a document and it returns a number from 0 to 1, how well the document answers. XERJ works with it in two ways.

1. Reorder your results with Jev (optional). Add "rerank": {} to a _search and XERJ sends the top results to Jev, then sorts them by the numbers it gets back. On the public FiQA test, result quality goes from 0.24 to 0.36 (nDCG@10), for about $0.64 in total for 648 searches. Treat the numbers as a way to sort, not as exact percentages: they are not well calibrated. This sends the text of those results to TypeSafe's servers. It stays off until you set a key, and [rerank] enabled = false forbids it completely. Read docs/RERANK.md before turning it on.

2. Get the same kind of answer locally. POST /v1/systemone and POST /_decide accept the same questions in the same format, but XERJ answers them itself, by looking up the most similar documents you have already labelled (for example messages already marked spam or not spam). Nothing leaves your machine and no key is needed. It only works if you have labelled examples; on AG News (120k labelled examples) it picks the right label 92% of the time. The official jev-reranker Python client works against a XERJ node without changes (test). XERJ answers under its own name, xerj-history-vote-1; it is not Jev.

Measurements and how to reproduce them: benchmarks/beir-hybrid and benchmarks/decisions-as-retrieval.

Ask over your own facts

XERJ ships XERJ Query-1, a model that answers questions over facts you give it. It is off by default; start a node with --facts-model-dir <DIR> to turn it on. Write documents and facts to a namespace, then ask in plain language. The engine returns a checked answer: lookups carry the facts they used and the program that produced them; counts carry a program hash and a second-model agreement check, but not the counted records.

# write a fact
curl -s localhost:9200/_facts/demo/_bulk -H 'content-type: application/json' -d '{
  "documents":[{"doc_id":"d1","version":"v1","text":"Ana Bel is 170 cm tall."}],
  "facts":[{"fact_id":"f1","subject":"Ana Bel","subject_id":"emp:1","relation":"height","kind":"number",
            "value":"170","unit":"cm","doc_id":"d1","version":"v1","start":11,"end":14,"quote":"170"}]
}'

# ask
curl -s localhost:9200/_facts/demo/_ask -H 'content-type: application/json' \
  -d '{"question":"How tall is Ana Bel?"}' | jq '{final_status,final_answer}'
# {"final_status":"ANSWER","final_answer":"170 cm"}

Which to use: _search for keyword and text lookups over indexed documents; /_facts + _ask for a single-fact question over records you have written as subject / relation / value facts. The model answers only over the facts[] you wrote (kind is text, date, number or boolean; version is a string; quote must equal text[start:end]) — documents alone answer UNKNOWN. XERJ Query-1 runs on CPU with no GPU and nothing leaving the machine. Counting questions ("how many people have office Berlin?") are served from an exact executor, and only when every fact of the counted subjects carries a stable subject_id ("emp:1"); without it the count is INCOMPLETE and the answer is UNKNOWN. The executor is exact, but the model can pick the wrong set: in our review an "or" question and a "total devices" question were both served as verified counts of the wrong set. Set counts only on rc.101: numeric thresholds decline, and a sum question about something the store has no facts for can be served as a member count. It is a separate model from the search embedder. Get the bundle with curl -fsSL https://xerj.org/get | sh -s -- --with-model, then start a node with xerj --facts-model-dir <dir> --data-dir <dir> (XERJ_FACTS_MODEL_DIR=<dir> is the environment equivalent); the model is a swappable xerj-model-bundle-v1 directory. Verify: curl -s localhost:9200/_facts | jq .model.state prints "loaded" (the first seconds after start print "not_loaded" while the model preloads; poll). On XERJ Score v2, our private development evaluation (1,600 questions over MoMA, Tate, PyPI, OurAirports, USGS, NASA Exoplanet Archive and UCI Online Retail records plus invented ones, the distribution the model was trained for), the shipped model scores 90.29 where Opus 5.5 scores 100. It is not a test on unseen questions. See docs/XERJ_QUERY_1.md for the seven capabilities with a verified example each, docs/MODEL_QUICKSTART.md to get it running and docs/FACTS_API.md for the full co

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