entropy-agent-eval 0.1.3

Entropy-based evaluation metrics for AI agent behavior, tools, trajectories, uncertainty reduction, and robustness.

Entropy-Based Evaluation of AI Agents

entropy-agent-eval implements EEA, a toolkit for measuring agent behavior with entropy metrics:

• action entropy for action-selection uncertainty
• trajectory entropy for strategy diversity
• tool entropy for tool-use specialization
• information gain for uncertainty reduction
• entropy curves for temporal behavior
• robustness summaries across repeated runs
• a configurable Entropic Agent Score

Any agent library can integrate by converting its trace events into AgentRun records.

Who This Is For

Use EEA when you want to compare agent behavior beyond success rate:

• framework authors who want behavioral diagnostics
• application teams evaluating agent changes before deployment
• researchers comparing ReAct, planner, tool-using, or multi-agent systems
• observability teams turning traces into evaluation metrics

Install

Requires Python 3.12 or newer.

From GitHub:

pip install git+https://github.com/olahsymbo/entropy-agent-eval.git

For local development:

poetry install --with dev

Optional plotting support:

pip install "entropy-agent-eval[plots]"

Build source and wheel distributions:

poetry build

Install a local wheel:

pip install dist/entropy_agent_eval-v0.1.1-py3-none-any.whl

Release

Package builds are handled by Poetry. To cut a release:

poetry version patch
git tag v0.1.1
git push origin main --tags

Quick Start

from entropy_agent_eval import AgentRun, EntropyEvaluator

runs = [
    AgentRun.from_mapping(
        {
            "task": "Write sorting algorithm",
            "success": True,
            "cost": 0.12,
            "trajectory": ["search", "python", "test", "answer"],
            "before": {"A": 0.4, "B": 0.3, "C": 0.2, "D": 0.1},
            "after": {"A": 0.9, "B": 0.05, "C": 0.03, "D": 0.02},
        }
    )
]

report = EntropyEvaluator().evaluate(runs)
print(report.as_dict())

CLI

eea examples/runs.json
eea examples/runs.json --per-run

The CLI accepts JSON objects with a top-level runs list, raw JSON lists, or JSONL files.

Integration Model

You do not have to export JSON logs. JSON is only one supported path.

EEA needs one thing: normalized traces as AgentRun objects. Those traces can come from live callbacks, custom wrappers, databases, observability systems, JSON/JSONL files, or benchmark harnesses.

LangChain / Google ADK / custom agent / stored trace
        ↓
AgentRun
        ↓
EntropyEvaluator
        ↓
entropy metrics + Entropic Agent Score

Data Contract

The central integration type is AgentRun:

{
  "task": "qa-001",
  "success": true,
  "cost": 0.08,
  "trajectory": ["search", "read", "answer"],
  "before": {"correct": 0.45, "distractor": 0.55},
  "after": {"correct": 0.92, "distractor": 0.08}
}

For richer logs, use explicit events:

{
  "task_id": "coding-42",
  "events": [
    {"kind": "tool", "name": "search"},
    {"kind": "tool", "name": "python"},
    {"kind": "action", "name": "answer"}
  ],
  "success": true
}

Cost

cost is user or framework supplied. It can mean USD, total tokens, token-normalized cost, tool-call cost, compute cost, or any other numeric penalty you want to apply consistently across compared runs.

The evaluator reports it as mean_cost and subtracts it inside EntropicAgentScore. If cost is unknown or irrelevant, omit it or leave it as 0.0.

Full guide: docs/concepts/cost.md

Custom Agent Integration

from entropy_agent_eval import EntropyEvaluator
from entropy_agent_eval.adapters import EventRecorder

recorder = EventRecorder(task_id="task-123")
recorder.tool("search")
recorder.tool("python")
recorder.action("answer")

run = recorder.to_run(success=True, cost=0.04)
print(EntropyEvaluator().evaluate([run]).as_dict())

Full guide: docs/integrations/custom-agents.md

LangChain Integration

from entropy_agent_eval.adapters.langchain import EntropyCallbackHandler

handler = EntropyCallbackHandler(task_id="lc-001")

# Pass `handler` in your LangChain config/callbacks.
# result = chain.invoke(inputs, config={"callbacks": [handler]})

run = handler.to_run(success=True, cost=0.10)

Full guide: docs/integrations/langchain.md

Google ADK-Style Event Integration

from entropy_agent_eval.adapters.google_adk import runs_from_adk_events

run = runs_from_adk_events(
    "adk-001",
    [
        {"event_type": "tool", "tool_name": "Search"},
        {"event_type": "model", "model": "gemini"},
    ],
    success=True,
)

Full guide: docs/integrations/google-adk.md

Stored Trace Integration

If your traces are already in a database, warehouse, or observability platform, export or query them into AgentRun-compatible dictionaries and evaluate them offline.

Full guide: docs/integrations/observability.md

Metric Notes

High entropy is not automatically good. EEA treats entropy as a behavioral signature:

• low action entropy can mean focus or brittle determinism
• medium entropy can indicate adaptive branching
• high entropy can indicate exploration or chaos
• successful agents should often reduce state entropy over time
• robust agents can have moderate trajectory entropy with low outcome entropy

EntropicAgentScore is configurable:

from entropy_agent_eval import EntropicAgentScore, EntropyEvaluator

evaluator = EntropyEvaluator(
    EntropicAgentScore(
        success_weight=2.0,
        information_gain_weight=1.0,
        exploration_efficiency_weight=0.5,
        cost_weight=1.5,
    )
)

Benchmark

Any callable that accepts a BenchmarkTask and returns an AgentRun or compatible dictionary can be benchmarked:

from entropy_agent_eval.benchmarks import QA_TASKS, run_benchmark

def agent(task):
    return {
        "task_id": task.id,
        "trajectory": ["think", "answer"],
        "success": True,
    }

runs = run_benchmark(QA_TASKS, agent)

Controlled Benchmark

The experiments directory contains a controlled benchmark that compares reference agent patterns across factual QA, multi-hop, and coding tasks.

poetry run python scripts/run_experiment.py

The script writes normalized runs and per-agent summaries to experiments/results/.

Learning Roadmap Agent Experiment

The project also includes a framework-backed experiment for a Learning Roadmap Agent. It can run with LangChain, Google ADK, or both when the optional dependencies and API keys are installed.

pip install "entropy-agent-eval[langchain]"
export OPENAI_API_KEY="..."
poetry run python scripts/run_learning_roadmap_experiment.py --provider langchain

pip install "entropy-agent-eval[google-adk]"
export GOOGLE_API_KEY="..."
poetry run python scripts/run_learning_roadmap_experiment.py --provider google-adk

The roadmap experiment runner also reads .env automatically. For Google ADK, set GOOGLE_API_KEY or GEMINI_API_KEY.

Full guide: docs/experiments/learning-roadmap-agent.md

Contributing

See CONTRIBUTING.md. New adapters are welcome, especially for frameworks that can expose tool calls, model calls, actions, costs, outcomes, and uncertainty states.

License

MIT. See LICENSE.