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.

Live leaderboard → — score any model in your browser, no keys.

Use it (any model, no API keys)

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, and corrections are the most valuable contribution possible.
• Scores measure stack-routing competence, not general Swahili fluency.
• Dataset: CC BY 4.0 (usable by everyone, including commercial labs — that's the point). Harness: MIT.

IP & Collaboration

MIT-licensed harness, CC BY 4.0 data. Feedback via GitHub Issues only — pull requests are not accepted; task corrections and additions via Issues are actively wanted. Full policy: docs/architecture/IP_POLICY.md. Security: see SECURITY.md.

Part of the East Africa coordination stack

• Install & run: pip install reli-cli && reli list — 33 MCP servers on the official MCP Registry under io.github.gabrielmahia
• Evaluate any model on Swahili agent tasks: kipimo · dataset · leaderboard
• Coordinate across servers: africa-coord-bus — offline-first event bus with a built-in Kenya routing table
• Datasets: huggingface.co/gmahia · Docs hub: nairobi-stack

Model-agnostic by design: closed APIs, open-weight models, and small distilled models are all first-class citizens.

Running a scorecard (kipimo run)

Fan out across targets in parallel, converge to one ranked scorecard. kipimo still never calls a model API — you supply a generator command per target (any process that reads tasks JSONL on stdin and writes predictions JSONL on stdout; see examples/generate_predictions.py).

cat > generators.json <<'JSON'
{
  "gemma-3-12b":    "python generate_predictions.py --model gemma-3-12b",
  "inkubalm-0.4b":  "python generate_predictions.py --model inkubalm-0.4b",
  "kimi-k3":        ""
}
JSON

kipimo run generators.json --timeout 600 --workers 4

An untested target is UNKNOWN, never zero. If a generator crashes, times out, or is unconfigured, that target appears under untested with a reason — it is not ranked at 0.0. Conflating "the model failed the task" with "we never tested the model" is how an evaluation starts measuring its own assumptions instead of reality. Every scorecard also reports coverage, so a partial run cannot be misread as a ranking of the field.