diff --git a/README.md b/README.md
index be4211f..9247554 100644
--- a/README.md
+++ b/README.md
@@ -1248,6 +1248,119 @@ console.log(result);
```
+## Agent wrappers
+
+The `agentevals` package also includes agent wrappers that help implement "in-the-loop" style evaluation, where the evaluator runs *as part* of your agent and shapes its trajectory and response.
+
+### Reflection
+
+The `wrap_agent_with_reflection` function wraps a LangGraph agent with a reflection loop. When the agent finishes running, it passes its result and trajectory to an evaluator. If the evaluator fails, it adds a user message detailing the failure and prompts the agent to fix its mistakes and try again. Otherwise, it returns as normal.
+
+You can pass your own evaluator into `wrap_agent_with_reflection`, but if you don't the wrapper will automatically generate criteria for success based on the input and use an LLM-as-judge evaluator to grade the agent's trajectory and output (as shown in the example below):
+
+```python
+import math
+
+from agentevals.wrappers.reflection import wrap_agent_with_reflection
+
+from langchain.chat_models import init_chat_model
+from langchain_core.tools import tool
+
+from langgraph.prebuilt import create_react_agent
+
+
+@tool
+def add(a: float, b: float) -> float:
+ """Add two numbers together."""
+ return a + b
+
+
+@tool
+def multiply(a: float, b: float) -> float:
+ """Multiply two numbers together."""
+ return a * b
+
+
+@tool
+def divide(a: float, b: float) -> float:
+ """Divide two numbers."""
+ return a / b
+
+
+@tool
+def subtract(a: float, b: float) -> float:
+ """Subtract two numbers."""
+ return a - b
+
+
+@tool
+def sin(a: float) -> float:
+ """Take the sine of a number."""
+ return math.sin(a)
+
+
+@tool
+def cos(a: float) -> float:
+ """Take the cosine of a number."""
+ return math.cos(a)
+
+
+@tool
+def radians(a: float) -> float:
+ """Convert degrees to radians."""
+ return math.radians(a)
+
+
+@tool
+def exponentiation(a: float, b: float) -> float:
+ """Raise one number to the power of another."""
+ return a**b
+
+
+@tool
+def sqrt(a: float) -> float:
+ """Take the square root of a number."""
+ return math.sqrt(a)
+
+
+@tool
+def ceil(a: float) -> float:
+ """Round a number up to the nearest integer."""
+ return math.ceil(a)
+
+# Initialize agent
+llm = init_chat_model("openai:gpt-4o", temperature=0.1)
+tools = [
+ sin,
+ cos,
+ radians,
+ ceil,
+ exponentiation,
+ sqrt,
+ add,
+ multiply,
+ divide,
+ subtract,
+]
+
+agent = wrap_agent_with_reflection(agent=create_react_agent(llm, tools))
+
+query = (
+ "A batter hits a baseball at 45.847 m/s at an angle of "
+ "23.474° above the horizontal. The outfielder, who starts facing the batter, picks up the baseball as it lands, "
+ "then throws it back towards the batter at 24.12 m/s at an angle of 39.12 degrees. "
+ "How far is the baseball from where the batter originally hit it? "
+ "Assume zero air resistance."
+)
+
+for step in agent.stream(
+ {"messages": query}, stream_mode="updates", config={"recursion_limit": 50}
+):
+ for _, update in step.items():
+ for message in update.get("messages", []):
+ message.pretty_print()
+```
+
## Python Async Support
All `agentevals` evaluators support Python [asyncio](https://docs.python.org/3/library/asyncio.html). As a convention, evaluators that use a factory function will have `async` put immediately after `create_` in the function name (for example, `create_async_trajectory_llm_as_judge`), and evaluators used directly will end in `async` (e.g. `trajectory_strict_match_async`).
diff --git a/python/agentevals/trajectory/llm.py b/python/agentevals/trajectory/llm.py
index a9c411f..19680be 100644
--- a/python/agentevals/trajectory/llm.py
+++ b/python/agentevals/trajectory/llm.py
@@ -1,5 +1,4 @@
from __future__ import annotations
-import json
from openevals.llm import (
_create_llm_as_judge_scorer,
@@ -36,13 +35,17 @@
- Is semantically equivalent to the provided reference trajectory
-Grade the following trajectory:
+Based on the following reference trajectory:
+
+
+{reference_outputs}
+
+
+Grade this actual trajectory:
{outputs}
-{inputs}
-{reference_outputs}
"""
TRAJECTORY_ACCURACY_PROMPT = """You are an expert data labeler.
@@ -65,7 +68,6 @@
{outputs}
-{inputs}
"""
if TYPE_CHECKING:
@@ -89,14 +91,11 @@ def _format_inputs(
formatted_inputs = f"\nThe agent generated the trajectory from the following input:\n\n{inputs}\n\n"
else:
formatted_inputs = ""
- if isinstance(outputs, dict):
- formatted_outputs = json.dumps(outputs)
- else:
- formatted_outputs = _chat_completion_messages_to_string(outputs)
+ formatted_outputs = _chat_completion_messages_to_string(outputs)
return (
+ formatted_inputs,
formatted_outputs,
formatted_reference_outputs,
- formatted_inputs,
)
@@ -163,32 +162,11 @@ def _wrapped_evaluator(
] = None,
**kwargs,
) -> EvaluatorResult:
- if prompt == TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE:
- if reference_outputs is None:
- raise ValueError(
- "TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE requires reference_outputs to compare against"
- )
- (
- formatted_outputs,
- formatted_reference_outputs,
- formatted_inputs,
- ) = _format_inputs(inputs, outputs, reference_outputs)
- elif prompt == TRAJECTORY_ACCURACY_PROMPT:
- if reference_outputs is not None:
- raise ValueError(
- "TRAJECTORY_ACCURACY_PROMPT requires reference_outputs to be None"
- )
- (
- formatted_outputs,
- formatted_reference_outputs,
- formatted_inputs,
- ) = _format_inputs(inputs, outputs, reference_outputs)
- else:
- formatted_outputs = _normalize_to_openai_messages_list(outputs)
- formatted_reference_outputs = _normalize_to_openai_messages_list(
- reference_outputs
- )
- formatted_inputs = inputs
+ (
+ formatted_inputs,
+ formatted_outputs,
+ formatted_reference_outputs,
+ ) = _format_inputs(inputs, outputs, reference_outputs)
return _run_evaluator(
run_name=f"llm_as_{feedback_key}_judge",
scorer=scorer,
@@ -265,32 +243,11 @@ async def _wrapped_evaluator(
] = None,
**kwargs,
) -> EvaluatorResult:
- if prompt == TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE:
- if reference_outputs is None:
- raise ValueError(
- "TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE requires reference_outputs to compare against"
- )
- (
- formatted_outputs,
- formatted_reference_outputs,
- formatted_inputs,
- ) = _format_inputs(inputs, outputs, reference_outputs)
- elif prompt == TRAJECTORY_ACCURACY_PROMPT:
- if reference_outputs is not None:
- raise ValueError(
- "TRAJECTORY_ACCURACY_PROMPT requires reference_outputs to be None"
- )
- (
- formatted_outputs,
- formatted_reference_outputs,
- formatted_inputs,
- ) = _format_inputs(inputs, outputs, reference_outputs)
- else:
- formatted_outputs = _normalize_to_openai_messages_list(outputs)
- formatted_reference_outputs = _normalize_to_openai_messages_list(
- reference_outputs
- )
- formatted_inputs = inputs
+ (
+ formatted_inputs,
+ formatted_outputs,
+ formatted_reference_outputs,
+ ) = _format_inputs(inputs, outputs, reference_outputs)
return await _arun_evaluator(
run_name=f"llm_as_{feedback_key}_judge",
scorer=scorer,
diff --git a/python/agentevals/wrappers/__init__.py b/python/agentevals/wrappers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/agentevals/wrappers/reflection.py b/python/agentevals/wrappers/reflection.py
new file mode 100644
index 0000000..0798573
--- /dev/null
+++ b/python/agentevals/wrappers/reflection.py
@@ -0,0 +1,228 @@
+from langgraph.graph import StateGraph
+from langgraph.graph.state import CompiledStateGraph
+from langgraph.graph.message import MessagesState
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages.utils import MessageLikeRepresentation
+from langchain_core.runnables import Runnable, RunnableLambda
+
+from langchain.chat_models import init_chat_model
+
+from agentevals.trajectory.llm import create_trajectory_llm_as_judge
+
+from openevals.utils import (
+ _chat_completion_messages_to_string,
+ _normalize_to_openai_messages_list,
+)
+from openevals.types import ChatCompletionMessage, EvaluatorResult, SimpleEvaluator
+
+from typing import Callable, Literal, Optional, Union
+
+
+def _generate_default_trajectory_prompt(criteria: str) -> str:
+ return f"""
+You are an expert data labeler.
+Your task is to grade the accuracy of an AI agent's internal trajectory and its final result when responding to a given input.
+
+
+ {criteria}
+
+
+First, try to understand the goal of the trajectory by looking at the input
+(if the input is not present try to infer it from the content of the first message),
+as well as the output of the final message. Once you understand the goal, grade the trajectory
+as it relates to achieving that goal.
+
+Grade the following trajectory:
+
+
+{{outputs}}
+
+"""
+
+
+def _default_reflection_response_formatter(
+ eval_result: EvaluatorResult,
+) -> ChatCompletionMessage:
+ return HumanMessage(
+ content=f"""
+In your last attempt to solve the given task, you made some errors, and an evaluator measuring "{eval_result["key"]}"
+gave your response a score of:
+
+
+{eval_result["score"]}
+
+
+This did not meet the required threshold.
+
+
+{eval_result["comment"]}
+
+
+Stop and reflect on the original task again, and think of a revised plan to fix these errors and improve your score.
+"""
+ )
+
+
+def _default_criteria_generator(messages: list[MessageLikeRepresentation]) -> str:
+ llm = init_chat_model("openai:o3-mini")
+ task = _chat_completion_messages_to_string(
+ _normalize_to_openai_messages_list(messages)
+ )
+ res = llm.invoke(
+ [
+ {
+ "role": "system",
+ "content": """
+You are a expert data labeler that generates evaluation criteria that measures whether a task is completed correctly.
+The criteria you choose:
+
+- Should be specific, measurable, and achievable.
+- Should be concise and easy to understand.
+- May take intermediate steps into account, but should prioritize the correctness of the overall end result over specific intermediate steps.
+""",
+ },
+ {
+ "role": "user",
+ "content": f"""Generate criteria that would measure whether the following task has been solved correctly.
+Respond with only the criteria and nothing else.
+
+
+{task}
+
+""",
+ },
+ ]
+ )
+ return res.content
+
+
+def wrap_agent_with_reflection(
+ *,
+ agent: Union[CompiledStateGraph, Callable[[dict], dict]],
+ evaluators: list[Optional[SimpleEvaluator]] = None,
+ evaluator_type: Union[
+ Literal["trajectory", "final_output"],
+ list[Literal["trajectory", "final_output"]],
+ ] = "trajectory",
+ criteria_generator: Optional[
+ Callable[[list[MessageLikeRepresentation]], str]
+ ] = None,
+ reflection_response_formatter: Optional[
+ Callable[[EvaluatorResult], ChatCompletionMessage]
+ ] = None,
+ max_reflections: int = 5,
+ max_reflections_strategy: Literal["raise", "return"] = "raise",
+ evaluator_score_threshold: float = 0.5,
+) -> Runnable:
+ if criteria_generator is not None and evaluators:
+ raise ValueError("Cannot provide both a criteria generator and an evaluator")
+
+ class ReflectionAgentState(
+ agent.builder.schema if isinstance(agent, CompiledStateGraph) else MessagesState
+ ):
+ agentevals_evaluation_criteria: Optional[str]
+ reflection_attempts: Optional[int]
+ original_input_messages: Optional[list]
+ nested_agent_call_params: Optional[dict]
+
+ def generate_evaluation_criteria(
+ state: ReflectionAgentState,
+ ) -> ReflectionAgentState:
+ nonlocal evaluators
+ if criteria_generator is None:
+ criteria = _default_criteria_generator(state["original_input_messages"])
+ else:
+ criteria = criteria_generator(state["original_input_messages"])
+ if not evaluators:
+ evaluators = [
+ create_trajectory_llm_as_judge(
+ model="openai:o3-mini",
+ prompt=_generate_default_trajectory_prompt(criteria),
+ )
+ ]
+ return ReflectionAgentState(agentevals_evaluation_criteria=criteria)
+
+ def reflect(state: ReflectionAgentState) -> ReflectionAgentState:
+ inputs = state["original_input_messages"]
+ for i, evaluator in enumerate(evaluators):
+ current_evaluator_type = (
+ evaluator_type[i]
+ if isinstance(evaluator_type, list)
+ else evaluator_type
+ )
+ outputs = (
+ state["messages"]
+ if current_evaluator_type == "trajectory"
+ else [state["messages"][-1]]
+ )
+ eval_result = evaluator(
+ inputs=inputs,
+ outputs=outputs,
+ criteria=state.get("agentevals_evaluation_criteria", ""),
+ )
+ if eval_result["score"] < evaluator_score_threshold:
+ if state.get("reflection_attempts", 0) > max_reflections:
+ if max_reflections_strategy == "raise":
+ raise ValueError(
+ f"Could not generate a suitable response in {max_reflections} reflections."
+ )
+ else:
+ return ReflectionAgentState(
+ messages=[],
+ reflection_attempts=state.get("reflection_attempts", 0) + 1,
+ )
+ if reflection_response_formatter is None:
+ message = _default_reflection_response_formatter(eval_result)
+ else:
+ message = reflection_response_formatter(eval_result)
+ return ReflectionAgentState(
+ messages=[message],
+ reflection_attempts=state.get("reflection_attempts", 0) + 1,
+ )
+ return ReflectionAgentState(
+ messages=[],
+ reflection_attempts=state.get("reflection_attempts", 0) + 1,
+ )
+
+ def restart_or_end(state: ReflectionAgentState) -> str:
+ return "agent" if state["messages"][-1].type == "human" else "__end__"
+
+ reflection_graph = StateGraph(ReflectionAgentState)
+ reflection_graph.add_node(
+ "store_original_messages",
+ lambda state: ReflectionAgentState(original_input_messages=state["messages"]),
+ )
+ reflection_graph.add_node(
+ "agent",
+ agent
+ if isinstance(agent, CompiledStateGraph)
+ else lambda state: agent(
+ {"messages": state["messages"], **state["nested_agent_call_params"]}
+ ),
+ )
+ reflection_graph.add_edge("__start__", "store_original_messages")
+ reflection_graph.add_node("reflect", reflect)
+ reflection_graph.add_edge("agent", "reflect")
+ reflection_graph.add_conditional_edges(
+ "reflect", restart_or_end, ["agent", "__end__"]
+ )
+
+ if not evaluators or criteria_generator is not None:
+ reflection_graph.add_node(
+ "generate_evaluation_criteria", generate_evaluation_criteria
+ )
+ reflection_graph.add_edge(
+ "store_original_messages", "generate_evaluation_criteria"
+ )
+ reflection_graph.add_edge("generate_evaluation_criteria", "agent")
+ else:
+ reflection_graph.add_edge("store_original_messages", "agent")
+ wrapped_agent = reflection_graph.compile()
+ if isinstance(agent, CompiledStateGraph):
+ return wrapped_agent
+ else:
+ return (
+ RunnableLambda(lambda params: {**params, "nested_agent_call_params": params})
+ | wrapped_agent
+ )
diff --git a/python/pyproject.toml b/python/pyproject.toml
index 2fbf5c2..4ea2667 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -1,17 +1,17 @@
[project]
name = "agentevals"
-version = "0.0.2"
+version = "0.0.3rc4"
license = {text = "MIT"}
description = "Open-source evaluators for LLM agents"
readme = "README.md"
-requires-python = ">=3.11"
+requires-python = ">=3.9"
dependencies = [
- "openevals==0.0.3"
+ "langgraph>=0.3.0,<0.4.0",
+ "openevals>=0.0.4",
]
[dependency-groups]
dev = [
- "langgraph>=0.2.73",
"mypy>=1.15.0",
"openai>=1.61.1",
"pytest>=8.3.4",
diff --git a/python/tests/wrappers/test_reflection.py b/python/tests/wrappers/test_reflection.py
new file mode 100644
index 0000000..f6182ac
--- /dev/null
+++ b/python/tests/wrappers/test_reflection.py
@@ -0,0 +1,168 @@
+import math
+import pytest
+
+from agentevals.wrappers.reflection import wrap_agent_with_reflection
+from langgraph.graph import StateGraph
+from langgraph.graph.message import MessagesState
+from openevals.prompts import CONCISENESS_PROMPT
+from openevals.llm import create_llm_as_judge
+
+from langchain.chat_models import init_chat_model
+from langchain_core.tools import tool
+from langsmith import testing as t
+from langgraph.prebuilt import create_react_agent
+
+
+@pytest.mark.langsmith
+@pytest.mark.skip(reason="Long running and expensive")
+def test_trajectory_match():
+ @tool
+ def add(a: float, b: float) -> float:
+ """Add two numbers together."""
+ return a + b
+
+ @tool
+ def multiply(a: float, b: float) -> float:
+ """Multiply two numbers together."""
+ return a * b
+
+ @tool
+ def divide(a: float, b: float) -> float:
+ """Divide two numbers."""
+ return a / b
+
+ @tool
+ def subtract(a: float, b: float) -> float:
+ """Subtract two numbers."""
+ return a - b
+
+ @tool
+ def sin(a: float) -> float:
+ """Take the sine of a number."""
+ return math.sin(a)
+
+ @tool
+ def cos(a: float) -> float:
+ """Take the cosine of a number."""
+ return math.cos(a)
+
+ @tool
+ def radians(a: float) -> float:
+ """Convert degrees to radians."""
+ return math.radians(a)
+
+ @tool
+ def exponentiation(a: float, b: float) -> float:
+ """Raise one number to the power of another."""
+ return a**b
+
+ @tool
+ def sqrt(a: float) -> float:
+ """Take the square root of a number."""
+ return math.sqrt(a)
+
+ @tool
+ def ceil(a: float) -> float:
+ """Round a number up to the nearest integer."""
+ return math.ceil(a)
+
+ # Initialize agent
+ llm = init_chat_model("openai:gpt-4o", temperature=0.1)
+ tools = [
+ sin,
+ cos,
+ radians,
+ ceil,
+ exponentiation,
+ sqrt,
+ add,
+ multiply,
+ divide,
+ subtract,
+ ]
+
+ agent = wrap_agent_with_reflection(agent=create_react_agent(llm, tools))
+
+ query = {
+ "role": "human",
+ "content": (
+ "A batter hits a baseball at 45.847 m/s at an angle of "
+ "23.474° above the horizontal. The outfielder, who starts facing the batter, picks up the baseball as it lands, "
+ "then throws it back towards the batter at 24.12 m/s at an angle of 39.12 degrees. "
+ "How far is the baseball from where the batter originally hit it? "
+ "Assume zero air resistance."
+ ),
+ }
+
+ t.log_inputs({"messages": [query]})
+
+ last_message = None
+ for step in agent.stream(
+ {"messages": [query]}, stream_mode="updates", config={"recursion_limit": 50}
+ ):
+ for _, update in step.items():
+ for message in update.get("messages", []):
+ message.pretty_print()
+ last_message = message
+
+ assert "98" in last_message.content
+
+
+@pytest.mark.langsmith()
+@pytest.mark.skip(reason="Long running and expensive")
+@pytest.mark.parametrize(
+ "question",
+ [
+ (
+ "What was the approximate ratio of Soviet to German military deaths on the Eastern Front?"
+ )
+ ],
+)
+def test_reflection(question):
+ conciseness_evaluator = create_llm_as_judge(
+ prompt=CONCISENESS_PROMPT, feedback_key="conciseness", model="openai:o3-mini"
+ )
+ llm = init_chat_model("openai:gpt-4o-mini")
+ workflow = StateGraph(MessagesState)
+ workflow.add_node(
+ "agent", lambda state: {"messages": [llm.invoke(state["messages"])]}
+ )
+ workflow.add_edge("__start__", "agent")
+ agent = workflow.compile()
+ graph_with_reflection = wrap_agent_with_reflection(
+ agent=agent, evaluators=[conciseness_evaluator], evaluator_type="final_output"
+ )
+ normal_answer = llm.invoke(question).content
+ answer_with_reflection = graph_with_reflection.invoke({"messages": [question]})[
+ "messages"
+ ][-1].content
+
+ assert len(answer_with_reflection) <= len(normal_answer)
+
+
+@pytest.mark.langsmith()
+@pytest.mark.skip(reason="Long running and expensive")
+@pytest.mark.parametrize(
+ "question",
+ [
+ (
+ "What was the approximate ratio of Soviet to German military deaths on the Eastern Front?"
+ )
+ ],
+)
+def test_reflection_non_langgraph(question):
+ conciseness_evaluator = create_llm_as_judge(
+ prompt=CONCISENESS_PROMPT, feedback_key="conciseness", model="openai:o3-mini"
+ )
+ llm = init_chat_model("openai:gpt-4o-mini")
+ graph_with_reflection = wrap_agent_with_reflection(
+ agent=lambda state: {"messages": [llm.invoke(state["messages"])]},
+ evaluators=[conciseness_evaluator],
+ evaluator_type="final_output",
+ )
+ normal_answer = llm.invoke(question).content
+ answer_with_reflection = graph_with_reflection.invoke({"messages": [question]})[
+ "messages"
+ ][-1].content
+
+ assert len(answer_with_reflection) <= len(normal_answer)
diff --git a/python/uv.lock b/python/uv.lock
index c052719..9d5a5bf 100644
--- a/python/uv.lock
+++ b/python/uv.lock
@@ -1,5 +1,5 @@
version = 1
-requires-python = ">=3.11"
+requires-python = ">=3.9"
resolution-markers = [
"python_full_version >= '3.12.4'",
"python_full_version >= '3.12' and python_full_version < '3.12.4'",
@@ -8,15 +8,15 @@ resolution-markers = [
[[package]]
name = "agentevals"
-version = "0.0.2"
+version = "0.0.3rc4"
source = { virtual = "." }
dependencies = [
+ { name = "langgraph" },
{ name = "openevals" },
]
[package.dev-dependencies]
dev = [
- { name = "langgraph" },
{ name = "mypy" },
{ name = "openai" },
{ name = "pytest" },
@@ -25,11 +25,13 @@ dev = [
]
[package.metadata]
-requires-dist = [{ name = "openevals", specifier = "==0.0.3" }]
+requires-dist = [
+ { name = "langgraph", specifier = ">=0.3.0,<0.4.0" },
+ { name = "openevals", specifier = ">=0.0.4" },
+]
[package.metadata.requires-dev]
dev = [
- { name = "langgraph", specifier = ">=0.2.73" },
{ name = "mypy", specifier = ">=1.15.0" },
{ name = "openai", specifier = ">=1.61.1" },
{ name = "pytest", specifier = ">=8.3.4" },
@@ -53,6 +55,7 @@ source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohappyeyeballs" },
{ name = "aiosignal" },
+ { name = "async-timeout", marker = "python_full_version < '3.11'" },
{ name = "attrs" },
{ name = "frozenlist" },
{ name = "multidict" },
@@ -61,6 +64,22 @@ dependencies = [
]
sdist = { url = "https://files.pythonhosted.org/packages/37/4b/952d49c73084fb790cb5c6ead50848c8e96b4980ad806cf4d2ad341eaa03/aiohttp-3.11.12.tar.gz", hash = "sha256:7603ca26d75b1b86160ce1bbe2787a0b706e592af5b2504e12caa88a217767b0", size = 7673175 }
wheels = [
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