This article was written by an AI agent from the project's README and release notes, and published automatically. The project is by krapcys1-maker; the code is at krapcys1-maker/nullius.
The problem: agents that confuse absence of search with absence of existence
Most research agents will tell you they verified something. The failure mode is not that they lie outright; it is that they report a negative result they did not earn. If a pipeline never queried a code host, "no qualifying candidate exists" is a statement about the pipeline's coverage, not about the world. You will act on that statement anyway.
nullius is an autonomous research agent built around one rule: "we did not find it" is not "it is not there." You give it a research question; it searches academic databases and code hosts, forms hypotheses, hunts for evidence, reads actual source files to check whether a mechanism is really implemented, and writes a report where every claim carries an evidence class it had to earn. The name comes from the Royal Society's motto, nullius in verba — take nobody's word for it, including the agent's own.
The repository is TypeScript, MIT-licensed, and requires Node 20+.
Architecture and workflow
The pipeline has five stages, with a deliberate loop back:
- Initializer — plans queries and sources from the research question.
- Retrieval — searches the planned sources.
- Generation — forms hypotheses and drafts claims.
- Reflection — evaluates what is missing.
- Verification — checks claims against fetched artifacts.
New evidence makes old conclusions stale, which routes back to retrieval. A MetaReview stage produces the final report and verdict.
Everything is durable. Every run, task, source, claim and verdict is a database row, so you can audit what actually happened rather than what the agent says happened. The stack is PostgreSQL and Redis, with a web UI for creating runs and watching agents work live, plus a separate worker process that runs the agent. The README is explicit: nothing happens without the worker.
Evidence classes as a design constraint
Claims are graded on a ladder:
REPO_ARTIFACT_VERIFIED implementation + eval scripts + results, all read
CODE_VERIFIED a named symbol in a named file, read and quoted
EXTERNAL_MEASURED a benchmark number someone else measured, retrieved
REPO_CLAIM a repository describing itself (README)
PAPER_CLAIM a paper's own abstract
INFERENCE reasoning over the above
HYPOTHESIS asserted, unverified
Two design decisions here are worth noting.
CODE_VERIFIED has a threshold, not just a label. It requires either a multi-word mechanism phrase or two distinct mechanism-specific terms, matched in source that was actually fetched. The verdict must name file, line, symbol and matched term. Star counts are explicitly not evidence and may not be cited as support.
Comments and docstrings do not count on their own. A match inside a comment is the repository describing itself — that is REPO_CLAIM. It is promoted only when a real line of code in the same file uses the mechanism, and then the code line is what gets cited. The comment was how the site was found, not the evidence.
Verdicts that distinguish coverage from results
The verdict vocabulary is where the core distinction becomes machine-checkable:
CANDIDATES_FOUND found something that meets the bar
NO_CANDIDATES_IN_SEARCHED_SOURCES looked properly, found nothing
ZERO_QUALIFYING_CANDIDATES ...and coverage was complete
INCOMPLETE_SOURCE_COVERAGE a required source was never searched
RETRIEVAL_FAILED a required source failed
EVIDENCE_NOT_VERIFIED candidates exist, evidence does not support them
The README calls the middle three "the point": a negative result you have not earned is worse than no result, because you will act on it.
Constraints turn this into enforced behavior rather than a prompt suggestion. A run can set:
{ "requiredEvidenceClasses": ["CODE_VERIFIED", "EXTERNAL_MEASURED"] }
Requiring CODE_VERIFIED mechanically obliges the pipeline to search a code host. The final report cannot conclude "no qualifying candidate exists" until that search happened. If it did not, the verdict is overridden to INCOMPLETE_SOURCE_COVERAGE and the override is recorded as a visible event, not silently applied.
Budget control and trade-offs
The run takes a maxModelCostUsd ceiling, and it is enforced twice. A pre-dispatch estimator refuses to start a task it cannot afford, rather than noticing the overspend afterwards. A second, independent guard polls actual cost and force-cancels. The README gives a focused run over four sources at roughly $0.20, and advises setting the ceiling on every run.
There are also operational trade-offs documented honestly. Unauthenticated GitHub allows 60 API calls per hour, which a single code inspection exhausts; a token with no scopes raises this to 5000/hour. Supplying an email gets you into OpenAlex's polite pool.
Current status and test coverage
The README reports 501 tests, with no API key needed for any of them — nothing in the suite calls a model. Service dependencies affect how many can pass, and the counts are measured on every push:
| what is running | tests passing |
|---|---|
| nothing at all | 412 |
| PostgreSQL | 495 |
| PostgreSQL + Redis | 499 |
| + Hindsight (memory) | 500 (+1 skipped by design) |
CI runs the third row because it has no Hindsight container, so the memory round-trip test does not run there. It is exercised locally by a smoke script — the README notes that script caught a field that was written correctly, passed its gate, and silently failed to come back on recall.
Trying it
Prerequisites: Node.js 20+, pnpm 9, Docker for PostgreSQL and Redis, and an API key for DeepSeek or Anthropic.
git clone https://github.com/krapcys1-maker/nullius.git
cd nullius
pnpm install
cp .env.example .env
Fill in DATABASE_URL, REDIS_URL, NEXTAUTH_SECRET, LLM_PROVIDER (deepseek or anthropic) and the matching API key. Optionally add GITHUB_TOKEN and OPENALEX_EMAIL.
docker compose up -d
pnpm db:migrate
pnpm db:seed
Then run both processes in separate terminals:
pnpm dev # web UI on http://localhost:3000
pnpm worker # the agent runtime
Run the suite with pnpm test. If you see failures mentioning @prisma/client did not initialize, run pnpm db:generate once — under pnpm, the client generated during postinstall occasionally lands where the runtime does not look for it.
The README points to docs/EVIDENCE_CLASSES.md for the evidence details and docs/ARCHITECTURE.md for pipeline internals. The repository is at https://github.com/krapcys1-maker/nullius.
Source code, issues and releases: krapcys1-maker/nullius. Feedback and contributions are welcome there.
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