Causal Crew

Is the number real? If yes, every lead gets its own investigator — and the evidence decides.

When a business metric moves ("revenue dropped 14% in two weeks — why?"), two very different things can be true:

1. The number is wrong. A day loaded twice, a feed stopped, cents became dollars. The business is fine; the dashboard is lying.
2. The number is right. Something real happened, and finding it means following several leads — a region, a channel, a pricing change — each needing its own deep drill-down. Done by hand, that takes days, and whoever investigates anchors on the first plausible story.

Causal Crew checks the number first and stops if it can't be trusted. If it can, every lead gets an independent investigator with its own isolated database, and a deterministic judge compares the evidence on equal footing. Verified findings become memory for the next question.

It does attribution, not causal proof: which segments account for the change, by how much, and what context lines up with it.

How it works

flowchart TD
    Q[Question + windows] --> H{1. Health check<br/>deterministic}
    H -- FAIL --> S[Stop: don't trust this number yet<br/>names the check and the day]
    H -- PASS --> P[2-3. Planner<br/>data-driven leads from SQL<br/>+ context leads from Gemini via RocketRide]
    P --> I1[4. Investigator<br/>own workspace]
    P --> I2[4. Investigator<br/>own workspace]
    P --> I3[4. Investigator<br/>own workspace]
    I1 & I2 & I3 --> J[5. Judge<br/>noise, seasonality, consistency, timing<br/>overlap merge, verdicts]
    J --> R[6. Report<br/>SQL behind every number]
    J --> M[7. Memory<br/>verified findings for next time]

Stage	Module	What it does
0. Question	causal_crew/question.py	Relative periods ("last two weeks") are computed in code. Other phrasings are read by Gemini via RocketRide and validated against the data. Users can also set the dates.
1. Health check	causal_crew/health.py	Freshness, row counts (robust z + relative floor), duplicate order ids, days copied under new ids, null spikes, unit/scale breaks. Any failure stops the run.
2-3. Planner	causal_crew/planner.py	The largest single-dimension deltas always become leads (SQL). Changelog notes whose title names a segment always become leads too. Gemini, run as a RocketRide pipeline, fills the remaining slots using nearby notes and prior findings.
4. Investigators	causal_crew/investigator.py, workspace.py	One per lead, in parallel, each in its own database: size the lead, drill down two levels, find the change point, split volume from order value, match context. At each level the investigator's own RocketRide pipeline task chooses the path among sub-segments that pass a statistical threshold. Every query is logged.
5. Judge	causal_crew/judge.py	Deterministic evidence checks, overlap merge, ranking, verdicts. The LLM never writes a verdict.
6. Report	causal_crew/run.py	Markdown + JSON, with the SQL behind every number.
7. Memory	causal_crew/memory.py	Supported findings are appended to memory/findings.jsonl and fed to the planner next run; optionally pushed to Cognee (--remember).
Web app	causal_crew/web/	FastAPI and a single-page dashboard: ask a question, watch the pipeline and the investigator crew, read a plain-English answer built only from the judge's output, then drill into findings, data health, leads, memory, and SQL. Past runs are kept in history.
LLM layer	causal_crew/llm.py, rocketride_llm.py	RocketRide first, Gemini direct as backup, per agent. Each call is its own RocketRide task with a unique project id.

Every threshold lives in causal_crew/config.py.

The demo

The data is synthetic, with a planted cause — see PLANTED_CAUSE.md. The West region's flat shipping fee rises from $4.99 to $8.99 on 2026-08-27; West orders fall ~34%, concentrated in new customers. A decoy (the annual summer email campaign ending two days earlier) dips email orders in every region — and did the same last year.

make setup     # venv + dependencies
make data      # generate 637k orders (+ a broken copy) and verify the planted story
make test      # unit tests + lint, no network
make app       # the dashboard at http://localhost:8000
make demo-a    # Run A: broken data
make demo-b    # Run B: clean data

Run A — a day loaded twice. The health check fails on row counts (+73%, |z| = 28.7) and duplicate order ids, both naming 2026-09-03, and the run stops.

Run B — clean data (revenue −14.4%):

#	Lead	Final segment	Share of change	Verdict
1	West	West, new customers	58.9%	SUPPORTED + CONTEXT_LINKED — change point 2026-08-27, same day as the fee note; volume explains ~100%
2	Email	Email	16.4%	REJECTED (seasonality) — the same windows last year dropped 87% as much
3	New customers	West, new customers	—	MERGED into West — the same lost orders
4	Web in West	West, web	—	MERGED into West

Rote Plays: from memory to muscle memory

The first answer to "why did revenue drop?" is expensive: the planner and the investigator agents reason through RocketRide, and on the demo data that took 65 seconds and 8 LLM path decisions to land on West → new customers. The next time the same question comes up, nothing needs to be re-reasoned. Causal Crew saves the drill-down path of every SUPPORTED finding as a recipe, and two Rote Plays turn that into deterministic, shareable code.

Play	What it does	Steps
data-health-check	Is this number real? Six deterministic checks on any orders CSV or parquet file. Says OK or DON'T TRUST THIS NUMBER YET, with the evidence.	check_health
metric-drop-replay	Replays the learned drill-down with 0 LLM calls, re-measures every number in SQL, re-runs the judge, and says whether today's data still supports the saved path.	check_health → replay_drilldown (a failed health check blocks the replay)

make plays                                                          # build and install into ~/.rote/flows
rote play run ~/.rote/flows/data-health-check/main.ts orders=demo-broken
rote play run ~/.rote/flows/metric-drop-replay/main.ts orders=demo
rote play run ~/.rote/flows/data-health-check/main.ts orders=/abs/path/orders.csv days=7

orders=demo generates the seeded demo data inside the run; demo-broken is the same data with one day loaded twice. Results on the demo data:

Run	Result
Learn (python -m causal_crew.plays learn)	LLM crew via RocketRide: 65 s, 8 path decisions → recipe region-west.json
metric-drop-replay orders=demo	0 LLM calls, replay step 1.3 s: West new customers, 58.9% of the change, change point 2026-08-27, SUPPORTED + CONTEXT_LINKED
metric-drop-replay orders=demo-broken	check_health fails on 2026-09-03 (+73% rows, 1,433 duplicate order ids); replay_drilldown is blocked
data-health-check orders=demo-broken	FAIL: row_counts and duplicates, both naming 2026-09-03

How they were made: each Play was recorded with rote proc run in a clean Rote workspace, exported with rote workspace export, then generalized: recorded literals became $orders/$days parameters, host paths became packaged @resource{…} files, dependencies are pinned through uv, and a presentation renders the JSON the steps print. The packaged Python is the same deterministic code as the app (causal_crew/plays.py, entry point plays/crew.py), rebuilt into each package's resources/ by scripts/build_play_resources.py. A replayed recipe that the data no longer supports is flagged, not silently trusted.

Design decisions

• Deterministic core, LLM at the edges. Health checks, drill-downs, statistics, and verdicts are plain Python over SQL, with unit tests. The LLM proposes leads and writes hypotheses; it never produces a number or a verdict. Its output is validated against real dimension values before use.
• Context can't decide which leads exist. The largest data deltas are always leads; Gemini only adds to them. Otherwise a misleading changelog note could steer the whole investigation.
• Isolation per investigator. Each lead gets its own database with a private copy of the data and its own scratch tables, and sees only its own lead. Findings can't bias each other and each one is auditable on its own. The Workspace class is the seam: today it's a local DuckDB file per lead; Hotdata instant databases with one fork per investigator are the drop-in upgrade (signup required a credit card at the event).
• The health gate must not flag real business moves. Robust z alone flags the real West drop (|z| = 6.2 on a −16% day). A day is flagged only when it is both statistically unusual and more than 25% off the trailing median — a doubled or missing day always is.
• The LLM proposes the path; statistics dispose. A sub-segment qualifies for a drill-down only if its share of the change is ≥1.25× its share of baseline revenue (otherwise the investigator would always descend into the biggest bucket). The investigator's LLM chooses among qualifying sub-segments and explains why; an unsupported choice is overridden by the rule, an outage falls back to it, and a rationale that cites a number not in the evidence is withheld. The report records who decided each level.
• Statistics that fit revenue. The bootstrap resamples days, not orders (resampling a fixed number of orders can't see volume changes). Seasonality compares percent changes, so year-over-year growth doesn't shrink last year's effect. Consistency requires sub-segments holding ≥80% of baseline revenue to move with the aggregate — the guard against Simpson's paradox. Overlap is measured on lost orders.
• Graceful degradation. The planner tries Gemini via RocketRide, then Gemini directly, then a keyword match on changelog titles; the report names which one answered. Cognee is opt-in. The demo never depends on an LLM being up.
• Dates are arithmetic, not language. Relative periods are computed in code. Asked for "the last two weeks", an LLM once returned 15-day windows, which silently shifted every number downstream. The LLM only reads phrasings the rule can't, and its dates are validated against the data.
• No secrets in the repo. pipelines/planner.pipe holds a ${ROCKETRIDE_GEMINI_KEY} placeholder that RocketRide fills at run time; keys live only in .

Verdict rules (judge.verdict)

Condition	Verdict
Noise, seasonality, or consistency fails	REJECTED (<failed checks>)
All pass and share of change ≥ 30%	SUPPORTED, plus CONTEXT_LINKED if a note lands within ±3 days of the change point
All pass, share < 30%	INSUFFICIENT_EVIDENCE
> 50% of its lost orders sit inside a stronger finding	MERGED into <lead>

Architecture & Tools

Tool / Layer	Role here
RocketRide	Runs every agent's LLM step on the staging server, each as its own task with a unique project id: the planner (pipelines/planner.pipe) and each investigator's drill-down decisions (pipelines/investigator.pipe). The fan-out itself runs in Python threads.
DuckDB workspaces	One isolated database per investigator (workspaces/{lead_id}.duckdb), each with its own copy of the data and its own scratch tables, so agents never share state.
Hotdata	Designed in behind the Workspace interface (one forked instant database per investigator); not live because signup needed a credit card at the event.
Cognee	Changelog notes loaded into a knowledge graph (scripts/ingest_context.py); verified findings can be pushed back with --remember.
Snyk	Dependency scanning (make scan): the 34 core dependencies have no known vulnerabilities. Static analysis (snyk code test): 8 medium DOM-XSS warnings, all on the dashboard's innerHTML rendering, where every value is escaped first. See Security.

Security

• Dependencies (Snyk). make scan tests the 34 core dependencies: no known vulnerabilities (2026-09-11).
• Cognee is isolated as an optional extra (requirements-cognee.txt, make setup-cognee). Snyk reports 5 issues in cognee 1.5.4 and its dependencies, none with a fixed version yet: arbitrary code injection in cognee (critical), deserialization of untrusted data in diskcache (high), and session and authentication issues in litellm's proxy server (high, medium). The demo and the dashboard never import cognee. It loads only in scripts/ingest_context.py and run --remember, both opt-in and run on our own changelog notes, and the litellm proxy server is never started.
• Secrets live only in .env. Pipeline files hold ${ROCKETRIDE_GEMINI_KEY} placeholders, and commits were scanned for key material before every push.
• LLM output is untrusted input. Replies are parsed as JSON or as Python literals (ast.literal_eval), never executed. Planner leads are validated against real dimension values, investigator path choices against the statistical threshold, rationales that cite a number not in the evidence are withheld, and the dashboard HTML-escapes all LLM text.
• SQL. Dimension names are checked against config; values are always bound as parameters.
• Static analysis. ruff runs in CI. snyk code test reports 8 medium DOM-XSS warnings, all where the dashboard builds HTML with innerHTML. Each is a value that passes through the page's escaping helper first, which Snyk's taint analysis can't follow; the two error paths that carried a message were rewritten to use textContent so nothing from the network reaches innerHTML there. They are tracked, not ignored.

Limitations

• Revenue is the only metric, and the data is one synthetic orders table. Connecting a warehouse or uploading a CSV is the next step.
• Attribution, not causation. A SUPPORTED finding means the evidence is consistent and large; it does not rule out an unobserved driver.
• Investigators' SQL runs locally in isolated DuckDB workspaces; only their LLM steps run on RocketRide.
• LLM path choices are limited to sub-segments that pass the concentration threshold, by design: the LLM can't steer an investigation down a statistically unsupported path.
• Gemini's free tier allows 20 requests per model per day, which bounds live runs and keeps Cognee off the live path.

Layout

causal_crew/     pipeline modules; config.py holds every threshold
data/            demo data generator + verify_orders.py (checks the planted story holds)
context/         ten dated changelog / incident notes
pipelines/       RocketRide pipeline definitions
scripts/         environment smoke test, Cognee ingest
tests/           unit tests on deterministic fixtures (CI: .github/workflows/tests.yml)

Setup details

Copy .env.example to .env and fill in ROCKETRIDE_APIKEY and LLM_API_KEY (Gemini). make check smoke-tests every service. make setup-cognee adds the optional Cognee memory integration; make app serves the dashboard at http://localhost:8000.