Metis

Metis is a local-first equity research app with financial calculations and optional AI. Its name comes from the ancient Greek Titan goddess associated with wisdom and planning; see the brand guide. Saved reports expose number provenance and missing evidence, model-derived review conditions, scenario display weights, sensitivity conclusions, and eligible peer comparisons with exclusions. The evaluation CLI freezes snapshots and compares eligible outcomes; it does not establish forecasting accuracy. Settings → Appearance switches between Metis Ink and Metis Ivory, saved in this browser. Both designs offer paginated price/relative-strength tables.

Informational only — not investment advice. Grades and price targets are model outputs from disclosed data and assumptions, not human recommendations. Market data and AI output can be delayed, incomplete, or wrong.

What it does

• Fetches typed data from Financial Modeling Prep, SEC EDGAR, Yahoo Finance, FINRA, FRED, Finnhub and the U.S. Treasury (risk-free fallback), and validates freshness, balance-sheet identities and selected vendor figures against EDGAR XBRL.
• Computes growth, returns, capital structure, valuation, scenarios, technicals, grades and forensic indicators in deterministic TypeScript, on a sector route drawn from the industry label and SIC code and checked against filed facts, which can refine broad financial-industry labels when corroborated.
• Optionally runs separate bull and bear analyses and a judge pass, traces cited numbers against the registry without another model call, and turns missing inputs into disclosed gaps rather than fabricated values.

Quick start

Node.js 24 or newer and npm (package.json); CI tests Node 24, the version the test suite is known to pass on.

git clone https://github.com/eligorelick/Metis.git
cd Metis
npm ci
Copy-Item .env.example .env
npm run dev

On macOS/Linux, use cp .env.example .env. Production: npm run build, then npm start. Keep .env* private; only the placeholder .env.example belongs in Git. Settings → Provider keys and SEC contact offers Save, Test, and Remove saved for each value; a green check confirms usable provider test data. Saved values use protected local storage, override environment keys, and apply without restarting. Removing an override restores any environment value and keeps account connections intact. Existing saved keys stay hidden; tests do not generate paid Claude responses.

Open http://127.0.0.1:3000; development and production both bind to 127.0.0.1. Every provider key is optional: with no FMP_API_KEY, set EDGAR_CONTACT to a truthful "Name email" identity and real US-listed tickers come from SEC EDGAR and Yahoo. With no key at all, /report/sample renders a fictional report, and /company/DEMO and /company/DBNK are reserved strings served from fixtures/fmp whatever keys are set. None of the three reaches a market-data provider: the company slices disclose fixtures and the sample is synthetic. Other symbols use live/cache data; generating fixture reports can still call selected AI.

AI connections

In Settings → AI connections, connect ChatGPT through official plan OAuth or Google through the separately installed Gemini CLI 0.36.x, then save. ChatGPT supports account model refresh, GPT-6.1 Sol with an access notice, reasoning choices and opt-in Fast speed. Reports retain actual execution details. Sign-in runs no inference; failures never switch to paid API usage. Claude API remains separately billed. Plan allowances and credit settings apply; API dollar caps do not measure them. See setup, controls and limits. Connections have offline test coverage, and complete reports have run live through a ChatGPT connection; inference through a live Gemini account has not been verified. Stop all Metis servers before changing credential-lock versions; see upgrade/downgrade guidance.

Configuration

Every key is optional. This table is generated from .env.example, which carries the long form of each one. METIS_* keys accept legacy THESIS_* aliases; explicit canonical values win, including blank/invalid values. See compatibility.

Claude model/effort settings beat environment/defaults; reset with npm run settings:reset -- --yes. Operations covers AI selection, jobs, exports and API contracts.

Where the numbers come from

With an FMP key, FMP is primary unless the statement policy selects EDGAR. Metis reads history limits from FMP's rejection and retries within them; restricted endpoints and symbols use available fallbacks or disclosed gaps. Some plans return five periods; that depth can support annual calculations, subject to each module's input gates. The own-history multiple rank needs eight compatible quarterly observations and waits for them.

Configure EDGAR_CONTACT even with a free FMP key. Metis learns explicit access restrictions and skips repeat refusals for 15 minutes; see fallback limits.

With no FMP key, set EDGAR_CONTACT for real US filers' research: supported statements, share counts and public float from SEC EDGAR XBRL company facts, prices from Yahoo's unofficial chart endpoint, and the profile and enterprise values derived from the two. METIS_STATEMENT_SOURCE chooses between the vendor and EDGAR; no statement row mixes source fields, and the manifest names how many each served.

Where a filer uses an extension tag the field is null rather than a guess, and each stand-in is named in the manifest with the periods it served. Analyst estimates, price targets, peers, insider trades (SEC Form 4), institutional ownership, news, transcripts, executive compensation and segment revenue have no implemented keyless source; IFRS-only facts also remain disclosed gaps. See License and data rights for what each allows.

What the numbers mean

Methodology defines the conventions and sources. Sector routing weighs XBRL evidence, SIC and industry labels, disclosing contradictions. Banks, insurers and mortgage REITs use excess returns to equity instead of inapplicable industrial-company valuation and forensic models. Growth shows supported methods and their range; free cash flow shows before/after stock-based compensation. Historical multiple ranks show sample size. Project-specific choices are labelled house conventions beside the affected figures.

docs/RESEARCH.md documents the forensic scores' papers, coefficients, estimation populations and limits; the source cites it by section.

With AI off or unavailable, reports retain deterministic results and disclose missing analysis. Failed AI runs may retain charges and produce such a report: it has no analyst assessment, and any letter grade is a deterministic score band.

AI analysis

Two analysts build the bull and bear cases independently: neither sees the other's output, and the bear prompt forbids assuming a bull case exists. A judge pass then reads both and writes the report. Which case it reads first is drawn from the job id rather than fixed, so first position is not a standing advantage, and the order is printed in the report header. Both cases share one character target; actual lengths and protected-content excess are disclosed. Each analyst scores its own side 1-5 against a stated rubric, and the judge may discount a side that scored itself low. METIS_JUDGE_ORDER=both adds a second judge pass with the cases swapped and compares every section grade; a retry that keeps earlier sections skips it.

The verification pass makes no model call. It measures citation coverage — can each figure be traced to the record it cites — and separately checks the prose around figures it can locate: a direction word must match the sign of its change, a period naming a year must match the cited year (and a comparison base the period the figure is measured against), a unit word must fit the registered unit, and a claim naming a person must rest on a filing, a transcript, a registry figure or attributed executive/insider rows; any other source, or none, fails it. Checked is printed beside cited, never merged into it.

Claude model prices and limits come from config/models.json; unknown IDs degrade to data-only reports because their spend cannot be bounded. auto prefers Opus 5.5; Haiku's judge uses Sonnet 5.5 with a disclosed model floor. See Claude model, effort and usage guidance.

Registry snapshot 2026-10-03, generated by npm run docs:pricing from config/models.json and the reservation code. Each request is admitted against spend caps using up to ten input contexts per search-enabled model, at five-minute cache-write rates, plus output and configured fallback exposure. Analyst requests also reserve eight $0.01 searches; the judge never searches.

The worst case is every request one pass could make (36: six transport attempts, each able to pause and resume five times); in the default request mode it is reported, not reserved; a job cap covers settled spend plus in-flight reservations, while METIS_RESERVATION_MODE=pass reserves it whole. Neither reservation column varies with effort — both bound a request at the model's full context and output ceiling.

The primary-model output ceiling varies with effort; thinking bills as output. Below high, passes use analyst 64K/judge 96K; at high and above, the model's registry ceiling applies. Fallback output is additional and covered by request reservations. A cut-off request is presumed at its worst case until reconciled.

The estimated run is a calculation, not a measurement: the fixture shape at effort high and registry rates, Haiku's synthesize figures those of Claude Sonnet 5.5 because that pass is raised to it. It does NOT scale with effort — usage at other levels has not been measured. Higher effort can consume more tokens; these examples are neither minimum nor maximum prices. New model quality and end-to-end costs have not been measured in live Metis runs for this update.

Running it safely

Metis has no telemetry, analytics or update check; its own outbound traffic goes only to the providers you configure. The Next.js CLI has separate anonymous telemetry, off when the shipped NEXT_TELEMETRY_DISABLED=1 is in your .env (or npx next telemetry disable). Privacy and safety names what each receives, where the local database lives, and how to delete it.

The application is single-user and has no authentication; keep it on loopback unless you add a security layer of your own. Mutating routes reject a request carrying neither browser Fetch Metadata nor a matching Origin; browsers send those automatically; scripts send X-Metis-Token (legacy X-Thesis-Token accepted) with the contents of the csrf-token file the server writes at every start and names on stdout. That is a browser-CSRF boundary, not local access control: any process on the machine can present the same headers. Report security problems through the repository's private advisory form, not a public issue.

Commands

The product suite makes no network request whatever your .env contains; one live SEC check is opt-in with EDGAR_LIVE_SMOKE=1 and a real EDGAR_CONTACT. Protect main by requiring the full and windows-smoke checks in GitHub. Contributions should preserve source tracing, add regressions and pass npm run verify.

Limitations

• Metis is a research tool for one local user, not a broker or a trading system. A traced number can still come from incorrect source data, and AI narrative can be wrong even when its citations resolve.
• Verification checks numeric content against dated registry records and retained passages; citation presence alone does not validate a claim or qualitative truth. Oversized analyst cases disclose removed material and any remaining excess.

License and data rights

Metis is source-available under the Metis No-Sale Attribution License 1.0, which permits internal business use with attribution but prohibits charging for copies, downloads or access without separate written permission. Distribution/download pages and software interfaces require visible credit: Metis by Eli Gorelick — https://eligorelick.com; see the license for CLI/headless equivalents. This is not an OSI-approved open-source license. Earlier MIT-licensed copies retain their prior rights. Preserve third-party notices; separate data rights apply to retrieved data and model output, including shared reports.