entropy-ai 0.1.3

Behavioral evaluation framework for non-deterministic AI agents

EntroPy

Behavioral evaluation framework for non-deterministic AI agents.

Measures what other frameworks miss: uncertainty, drift, stochastic behavior, and emergent failures — via Monte Carlo evaluation over repeated runs and entropy-based metrics. Framework-agnostic, local-first, zero vendor lock-in.

Install

pip install entropy-ai                 # from PyPI — core (pandas, matplotlib, jinja2, rich, pyyaml)
pip install "entropy-ai[langchain]"    # optional: a framework adapter
pip install "entropy-ai[pdf,jupyter]"  # optional: PDF / Jupyter reports

Developing EntroPy itself? Clone the repo and use an editable install instead:

pip install -e .                       # editable install from the repo
pip install -e ".[pdf,jupyter]"        # + report extras (what CI installs)

Quick start

from entropy import evaluate

def my_agent(inp): ...
    # just return the output — no boilerplate needed

# One line. Pass raw inputs; success = "didn't error" by default.
results = evaluate(my_agent, ["q1", "q2", "q3"], trials=100)
print(results)   # success_rate, behavioral_entropy, drift_score, ...

Need more control? Everything above is just sugar over Suite:

from entropy import Suite, Dataset, Case

dataset = Dataset([Case(input="q1", expected="correct answer")])
results = Suite(seed=42).run(my_agent, dataset, trials=100)

evaluate / Suite.run accept a list of raw inputs, a {input: expected} dict, or a Dataset — and a shared check= callable as the success criterion.

The canonical output (spec §9 / §21) includes: success_rate, reliability, variance, robustness, entropy, confidence_interval, behavioral_entropy, behavioral_variance, trajectory_entropy, trajectory_diversity, tool_reliability, drift_score, goal_stability, recovery_score, emergent_behavior, and more. 56 metrics total; entropy.default_metrics() lists the canonical set.

Tracing

from entropy import trace, event, instrument
with trace() as t:
    event("action", "tool:search")
# or auto-wrap any agent:
wrapped = instrument(my_agent)          # returns an AgentRun per call

Custom metrics (plugin system)

from entropy import metric
@metric("my_metric")
class MyMetric:
    def compute(self, batch): ...       # batch has outputs/events/successes/costs/...

Simulation, Chaos, Observability

from entropy import AdversarialSimulator, ChaosRunner, Watcher
adv = AdversarialSimulator(seed=0)
for v in adv.iterate("book a flight"):
    print(v)                            # typo / injection / distraction / leak variants

ChaosRunner(seed=0).run(agent, dataset, ["api_fail", "timeout"])   # fault injection
Watcher(agent, dataset, trials=20).watch(rounds=10)                # live drift + anomalies

Framework adapters (lazy)

from entropy import from_langchain, from_langgraph, from_openai, from_custom
runnable = from_langchain(my_langchain_agent)   # maps framework runs -> AgentRun

All 9 adapters (LangChain, LangGraph, OpenAI Agents, CrewAI, PydanticAI, AutoGen, Google ADK, MCP, custom) import their framework lazily, so import entropy works with none of them installed.

CLI

entropy init                      # scaffold a project
entropy run    --agent m:a --dataset d.json --trials 100
entropy test   --agent m:a --dataset d.json --baseline base.json [--update]
entropy benchmark --agents m:a m:b --dataset d.json
entropy chaos   --agent m:a --dataset d.json --faults api_fail,timeout
entropy simulate --kind adversarial --text "book a flight"
entropy ui      --agent m:a --dataset d.json --serve
entropy watch   --agent m:a --dataset d.json --rounds 10
entropy report  --results r.json --format html
entropy dataset --path d.json
entropy doctor                      # check optional deps

Reports

from entropy import export_report
export_report(results, "html", "report.html")      # also: json, markdown, pdf, jupyter

Layout

core/ (models, tracing, exporters, suite) · metrics/ (registry + all categories) · datasets/ · simulation/ · chaos/ · regression/ · benchmarks/ · observability/ · reports/ · dashboard/ · integrations/ (adapters) · cli/ · plugins/.

Tests: python -m pytest tests/.

Documentation

• Live site: https://satyamsingh8306.github.io/entropy-ai/

• Source: docs/ — plain HTML/CSS/JS with no build step, deployed to GitHub Pages.

• Source: docs/index.html, docs/guide/*.html, docs/api.html, docs/examples.html, plus docs/assets/ (CSS, JS, logo, search index).

• Local preview: serve the folder, e.g. python -m http.server -d docs 8000 then open http://localhost:8000.

• Deploy: push to main; the .github/workflows/pages.yml workflow uploads docs/ and publishes it via GitHub Pages. Alternatively, set Settings → Pages → Build and deployment → Source to Deploy from a branch and choose the main branch /docs folder.