Evaluate agentic systems with reproducible, evidence-first grading.
Project description
agentic-evalkit
agentic-evalkit is a standalone evaluation toolkit for agentic systems. It combines dynamic dataset discovery, typed evaluation contracts, benchmark-valid grading, calibrated judges, statistical reporting, and a developer-friendly Python API and CLI.
agentic-evalkit separates datasets, grading, and reporting from the system under test through callable/subprocess/HTTP targets, and objective checks gate before judges. Existing evaluation frameworks couple dataset access, grading, and reporting to specific agent platforms or model-provider SDKs; this package's neutral ExecutionTarget protocol makes any callable, subprocess, or HTTP system evaluable without framework lock-in.
Coexistence note: legacy evaluation code may remain in host repositories. This package neither imports nor migrates it — integration happens only through the public ExecutionTarget protocol described above.
See the architecture specification for the full design, or jump straight to the quickstart guide.
Identity
- Distribution and repository:
agentic-evalkit - Python package:
agentic_evalkit - CLI:
agentic-evalkit
Quickstart
pip install agentic-evalkit
agentic-evalkit doctor
agentic-evalkit init --preset gsm8k --output eval.yaml
agentic-evalkit run eval.yaml --limit 5 --yes
This resolves the curated GSM8K preset from Hugging Face, runs five samples
through the packaged smoke target, grades them with a normalized exact-match
grader, and writes a canonical JSON report. No importer code, manual dataset
download, datasets, pyarrow, or Docker is required. See
docs/guides/quickstart.md for the full walkthrough,
including the standalone report command that regenerates a self-contained
HTML report from that JSON.
Optional extras
The parquet, judges, and swebench extras (e.g.
pip install agentic-evalkit[swebench]) are currently reserved placeholders
per ADR-0009: installing
them adds nothing today. They reserve the capability names that
agentic-evalkit doctor reports on, ahead of their own future
implementation.
Documentation
- Quickstart — install to first report
- Providers — local formats, Hugging Face auth, cache/offline, plugins
- Graders — objective-first order, hard gates, calibrated judges
- Targets — callable, subprocess, and HTTP execution targets
- SWE-bench — preview/prediction workflow and the harness boundary
- HTTP agent example — evaluating a real HTTP agent endpoint
Repository boundary
This project does not modify or import Agentic Runtime Platform or ExecutionKit internals. Those systems may be evaluated through stable callable, subprocess, or HTTP target adapters — see ADR-0001 and ADR-0006.
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