Swahili agent-task evaluation suite — model-agnostic seed benchmark for the East Africa coordination stack (46 tasks; golds machine-derived from authoritative sources).
# kipimo
Over one hundred million people coordinate their lives in Swahili, yet no benchmark measures whether an AI agent can route their requests correctly — send money, check drought status, find a clinic, verify a worker's credentials. Agents targeting East Africa are evaluated on English tasks and deployed on faith.
`kipimo` (Swahili: *a measure*) is a model-agnostic seed benchmark for exactly that gap: **46 tasks** across three types, with golds machine-derived from authoritative sources — the coordination-stack registry and the live `africa-coord-bus` routing table — never from memory.
| Type | n | What it measures | Metric |
|---|---|---|---|
| `server_routing` | 25 | Swahili request → correct stack server (payments, tax, health, land, labour…) | exact |
| `term_grounding` | 14 | Swahili domain term → English meaning | exact (case-insensitive) |
| `cascade_routing` | 7 | Coordination event → which sectors must be notified | set F1 |
**Live leaderboard →** — score any model in your browser, no keys.
## Use it (any model, no API keys)
```bash
pip install kipimo
kipimo tasks > tasks.jsonl # feed to your agent however you like
kipimo template > preds.jsonl # fill "prediction": [...] per id
kipimo score preds.jsonl # per-type + overall report
```
The harness never calls a model — you generate predictions with whatever system you're evaluating; kipimo only scores. Any lab can publish comparable numbers.
## Data sovereignty
The models deployable under African data-sovereignty constraints are open-weight and self-hostable. kipimo scores that tier on equal terms with frontier APIs so builders can test whether the model they are *allowed* to run is good enough — see docs/DATA_SOVEREIGNTY.md and `examples/generate_predictions.py`.
## Honesty box
- **v0.1 is a seed set.** 46 tasks establish the format and scoring; breadth comes from contributions.
- Swahili phrasing is simple-register and **pending native-speaker review** — that is issue #1, …