agentrubric 0.1.1

AgentRubric: trajectory-level evaluation for LLM agents

AgentRubric

Trajectory-level evaluation for LLM agents. Zero dependencies, five metrics, one score.

📦 Live on PyPI: pypi.org/project/agentrubric — pip install agentrubric

Current tools (LangSmith, Langfuse, etc.) give you logging. AgentRubric gives you scores.

When an LLM agent runs, it produces a trajectory — a sequence of thoughts, tool calls, observations, and a final answer. Knowing whether that trajectory was actually correct is hard: Did it call the right tools? With the right arguments? Did it wander in loops? Did it finish the task at all? AgentRubric answers all of that with a single evaluate() call.

trajectory (list of steps)
        │
        ▼
   AgentRubric.evaluate()
        │
        ▼
EvaluationReport
  ├─ tool_accuracy_score      — did it call the right tools?
  ├─ argument_fidelity_score  — did it call them with the right arguments?
  ├─ trajectory_length_score  — did it solve the task efficiently?
  ├─ completion_score         — did it actually finish the task?
  └─ failure_taxonomy         — what went wrong, categorised

---

Install

pip install agentrubric

Zero required dependencies. Optional extras for framework integrations:

pip install agentrubric[langchain]   # LangChain agent parsing
pip install agentrubric[openai]      # OpenAI function-calling parsing

---

Quickstart

from agentrubric import Trajectory, Step, evaluate

trajectory = Trajectory(
    task="Find the latest JAX release and summarize the changelog",
    steps=[
        Step(step_type="thought", content="I need to search for JAX releases"),
        Step(step_type="tool_call", content="", tool_name="search",
             tool_args={"query": "JAX latest release"}),
        Step(step_type="observation", content="JAX 0.4.30 released on July 2026..."),
        Step(step_type="final_answer",
             content="The latest JAX release is 0.4.30, released July 2026."),
    ],
    final_answer="The latest JAX release is 0.4.30, released July 2026.",
)

report = evaluate(
    trajectory=trajectory,
    expected_tools=["search"],
    expected_calls=[{"tool_name": "search", "expected_args": {"query": "JAX latest release"}}],
    optimal_steps=4,
    completion_keywords=["JAX", "release"],
)

print(report)
print(report.overall_score)   # 0.9-ish
print(report.failures)        # []

AgentRubric Evaluation Report
========================================
  Overall Score         : 0.93
  Tool Accuracy         : 1.00
  Argument Fidelity     : 0.70
  Trajectory Efficiency : 1.00
  Completion            : 1.00

  No failures detected.
========================================

Export to JSON for dashboards, CI gates, or logging pipelines:

report.to_json()

Run it yourself: python examples/basic_evaluation.py

---

The five metrics

Metric	Question it answers	Signal
Tool Accuracy	Did the agent call the tools it was supposed to?	expected_tools matched against tools actually called
Argument Fidelity	Were those tools called with the right arguments?	exact + fuzzy match against expected_calls
Trajectory Length	Did it solve the task efficiently?	optimal_steps / actual_steps, capped at 1.0
Completion	Did it actually finish?	binary, or keyword coverage of the final answer
Failure Taxonomy	When it broke, why?	WRONG_TOOL, MISSING_TOOL, BAD_ARGS, LOOP, NO_COMPLETION, HALLUCINATED_TOOL

overall_score is a weighted average: 0.3 × tool_accuracy + 0.3 × argument_fidelity + 0.2 × trajectory_length + 0.2 × completion.

---

Framework integrations

Already using LangChain or OpenAI function calling? Don't hand-build trajectories — parse them straight out of the agent's own output.

LangChain

from agentrubric.integrations.langchain import from_langchain

result = agent_executor.invoke({"input": task})
trajectory = from_langchain(result, task=task)
report = evaluate(trajectory, expected_tools=["search"])

OpenAI function calling

from agentrubric.integrations.openai import from_openai_messages

trajectory = from_openai_messages(messages, task=task)
report = evaluate(trajectory, expected_tools=["search"])

---

Design principles

• Zero required dependencies. A library that requires nothing to install is a library people actually install. LangChain/OpenAI support is opt-in via extras.
• Framework-agnostic core. Trajectory and Step are plain dataclasses — build them from any agent framework, or by hand.
• Scores, not logs. AgentRubric doesn't replace your tracing tool; it turns a trajectory you already captured into a number you can gate CI on.

---

Development

pip install -e ".[dev]"
pytest

License

MIT