agentft 0.2.0

Agent Flow Test (AgentFT) - A simple evaluation harness for AI agents.

AgentFT

Agent Flow Test - Pytest for AI agents

AgentFT (Agent Flow Test) is a pytest-style evaluation framework for AI agents. It provides composable primitives for tasks, scenarios, agents, and judges, with production-ready infrastructure for concurrency, retries, observability, and regression control.

What's New

• Secure declarative config mode: --config-json --strict-config
• Distributed local orchestration: aft orchestrate for sharded multi-process runs + merge
• Retry policy by error type with richer error taxonomy
• Task-level vs judge-level aggregation with configurable task pass rules
• Ranking support: aft rank (Elo)
• Artifact migration tooling: aft migrate
• Columnar export API (DuckDB/Parquet)
• Environment/episode scenario toolkit and additional preset benchmark suites
• Structured external event sinks for run lifecycle telemetry
• Interactive report drilldown with filtering

Features

• Pytest-like simplicity: Minimal boilerplate, clear abstractions
• Production-ready execution: Async runner, retries, rate limiting, timeouts, fail-fast modes
• Flexible parallelism: Independent controls for agents, tasks, and judges
• Deterministic reproducibility: Seeded shuffle + deterministic sharding
• Strong artifact model: JSONL or SQLite backends with schema versioning and checkpoints
• Regression workflows: Compare, gate, rejudge, analyze, rank, merge
• Extensibility: Plugin registry for agents, scenarios, and judges
• Scenario breadth: List/CSV/JSONL/HuggingFace + rollout-based episode scenarios
• Governance support: API stability policy and governance/compliance guidance

Quick Start

Install from PyPI:

pip install agentft

Run an example:

git clone https://github.com/Geddydukes/agentflowtest
cd agentflowtest
aft run --config examples/config_example.py

Or use in code:

from agentft import RunConfig, run
from agentft.presets import build_math_basic_scenario, ExactMatchJudge

class MyAgent:
    name = "my_agent"
    version = "0.1.0"
    provider_key = None

    async def setup(self):
        pass

    async def reset(self):
        pass

    async def teardown(self):
        pass

    async def run_task(self, task, context=None):
        return {"response": "42"}

config = RunConfig(
    name="quick_test",
    agents=[MyAgent()],
    scenarios=[build_math_basic_scenario()],
    judges=[ExactMatchJudge()],
)

results = run(config)
print(f"Passed: {sum(r.passed for r in results)}/{len(results)}")

Core CLI Commands

aft run

aft run --config path/to/config.py

Strict non-executable mode:

aft run --config-json path/to/config.json --strict-config

Useful overrides:

aft run --config examples/config_example.py \
  --max-tasks-parallel 8 \
  --max-agents-parallel 2 \
  --max-judges-parallel 4 \
  --artifact-backend sqlite \
  --shard-count 4 --shard-index 1

aft summary

aft summary --run-dir runs/my_run/

Task-level rollup mode:

aft summary --run-dir runs/my_run/ --aggregation-level task --task-pass-rule majority

aft compare

aft compare --run-a runs/base/ --run-b runs/candidate/

aft gate

aft gate --run-a runs/base/ --run-b runs/candidate/ --max-regressions 0

aft analyze

aft analyze --run-dir runs/my_run/ --output-json runs/my_run/analytics.json

aft rejudge

aft rejudge --run-dir runs/my_run/ --config examples/config_example.py

aft merge

aft merge --run-dirs runs/shard0/ runs/shard1/ --output-dir runs/merged/

aft orchestrate

aft orchestrate --config examples/config_example.py --shards 4 --output-dir runs/merged_orchestrated/

aft rank

aft rank --run-dir runs/my_run/

aft migrate

aft migrate --run-dir runs/legacy_run/ --target-schema 1.1.0

Run Artifacts

Each run creates runs/<run_id>/ containing:

• results.jsonl or artifacts.db
• traces.jsonl
• run_metadata.json
• run_checkpoint.json
• agent_outputs.jsonl (JSONL backend cache, optional)
• cached_outputs table in artifacts.db (SQLite backend cache, optional)
• report.html

Smoke Test Visualization (2026-02-11)

Recent 5-run local validation matrix:

Run	Mode	Path	Result	Notes
R1	Baseline	runs/smoke_r1_baseline-5a5f7d3c	4/4 (100%)	Control run
R2	Parallel + Shuffle	runs/smoke_r2_parallel-0e22f470	4/4 (100%)	max_agents_parallel=2, max_tasks_parallel=4, seed=123
R3	SQLite + Cache	runs/smoke_r3_sqlite_cache-5890a078	4/4 (100%)	SQLite artifacts and cached outputs
R4	Resume/Checkpoint	runs/smoke_r4_resume-e3bb2645	1/1 -> 4/4 (100%)	Early stop then resumed to completion
R5	Orchestrated Shards	runs/smoke_r5_orchestrated	4/4 (100%)	2 shards + merged output

Validation checks:

• summary on R1: pass rate 100%
• compare R1 vs R2: delta 0.00%, regressions 0
• gate R1 vs R2: passed
• analyze on R3: pass rate 100%, macro scenario rate 100%
• rejudge on cached outputs: 4/4 passed

Pass-rate bars:

• R1: ██████████ 100%
• R2: ██████████ 100%
• R3: ██████████ 100%
• R4: ██████████ 100% (after resume)
• R5: ██████████ 100%

Extended Functionality Guide

A comprehensive guide for all updated functionality is available at:

• docs/UPDATED_FUNCTIONALITY.md

Testing

AgentFT includes 90+ automated tests across runner behavior, storage backends, reporting, CLI, presets, plugins, and resilience checks.

pip install -e ".[dev]"
PYTHONPATH=src pytest -q

Optional columnar export dependency:

pip install -e ".[columnar]"

Project Status

AgentFT is in active development.

Current version: 0.1.0

Links

• PyPI: https://pypi.org/project/agentft/
• GitHub: https://github.com/Geddydukes/agentflowtest
• API Stability: docs/API_STABILITY.md
• Governance: docs/GOVERNANCE.md
• Docs Map: docs/DOCS_UNIFICATION.md