Custom Scorers

judgeval provides abstractions to implement custom scorers arbitrarily in code, enabling full flexibility in your scoring logic and use cases. You can use any combination of code, custom LLMs as a judge, or library dependencies.

Currently, Python is the only supported language for creating Custom Scorers.

Implement a Custom Scorer

There are two types of custom scorers you can create:

ExampleScorer: For scoring individual examples
TraceScorer: For scoring traces (agent execution flows)

Create your scorer file

Create a new Python file for your scorer. Inherit from ExampleScorer and implement the score method.

my_scorer.py

from judgeval.v1.data import Example
from judgeval.v1.scorers import ExampleScorer, CustomScorerResult

class ResolutionScorer(ExampleScorer):
    def score(self, example: Example) -> CustomScorerResult:
        actual_output = example.get_property("actual_output")
        
        if "package" in actual_output:
            return CustomScorerResult(
                score=1.0,
                reason="The response contains package information."
            )
        
        return CustomScorerResult(
            score=0.0,
            reason="The response does not contain package information."
        )

The score method:

Takes an Example as input
Returns a CustomScorerResult with a score (float between 0 and 1) and a reason (string explanation)

Create a requirements file

Create a requirements.txt file with any dependencies your scorer needs.

requirements.txt

# Add any dependencies your scorer needs
# openai>=1.0.0
# numpy>=1.24.0

Create your scorer file

Create a new Python file for your scorer. Inherit from TraceScorer and implement the score method.

my_trace_scorer.py

from judgeval.v1.data import Trace
from judgeval.v1.scorers import TraceScorer, CustomScorerResult

class ToolCallScorer(TraceScorer):
    def score(self, trace: Trace) -> CustomScorerResult:
        tool_calls = [span for span in trace if span["span_kind"] == "tool"]
        
        if len(tool_calls) > 0:
            return CustomScorerResult(
                score=1,
                reason=f"Agent made {len(tool_calls)} tool call(s)."
            )
        
        return CustomScorerResult(
            score=0,
            reason="Agent did not make any tool calls."
        )

The score method:

Takes a Trace (list of TraceSpan objects) as input
Returns a CustomScorerResult with a score (float between 0 and 1) and a reason (string explanation)

See Trace for all available TraceSpan properties.

Create a requirements file

Create a requirements.txt file with any dependencies your scorer needs.

requirements.txt

# Add any dependencies your scorer needs
# openai>=1.0.0
# numpy>=1.24.0

If you are accessing environmental variables in your custom scorer, you can set these on the Judgment platform once you have uploaded your scorer. Refer to this section for more information.

Upload Your Scorer

Once you've implemented your custom scorer, upload it to Judgment using the CLI.

Install the CLI

The CLI is included with the judgeval package:

pip install judgeval

Set your credentials

Set your Judgment API key and organization ID as environment variables:

export JUDGMENT_API_KEY="your-api-key"
export JUDGMENT_ORG_ID="your-org-id"

Upload the scorer

judgeval upload-scorer my_scorer.py -r requirements.txt -p my_project

You can also provide a custom name for the scorer:

judgeval upload-scorer my_scorer.py -r requirements.txt -n "Resolution Scorer" -p my_project

To overwrite an existing scorer with the same name:

judgeval upload-scorer my_scorer.py -r requirements.txt -o -p my_project

CLI Options

scorer_file_pathrequired

:str

Path to the Python file containing your scorer class.

--requirement, -r

:str

Path to the requirements.txt file with dependencies.

--name, -n

:str

Custom name for the scorer. Auto-detected from the class name if not provided.

--project, -p

:str

The project to upload the scorer to.

--overwrite, -o

:flag

Overwrite the scorer if it already exists.

Set Environmental Variables

You can also set environmental variables for your custom scorer on the Judgment platform.

Navigate to the Scorers page within your project and click on the Custom Scorers tab

Click on the custom scorer you would like to add the environmental variables for

Click on the "Environment variables" button on the top right of the page

Enter in the environmental variable and click the "Add" button. Do this for all environmental variables needed for the custom scorer.

Use Your Uploaded Scorer

After uploading, you can use your custom scorer in evaluations by retrieving it through the client factory and running it within a traced function.

Use async_evaluate to run an ExampleScorer on a specific example:

from judgeval import Judgeval
from judgeval.v1.data import Example

judgeval = Judgeval(project_name="my_project")

# Retrieve your uploaded scorer by name
scorer = judgeval.scorers.custom_scorer.get(name="Resolution Scorer")

# Create a tracer
tracer = judgeval.tracer.create()

@tracer.observe(span_type="function")
def my_agent(question: str) -> str:
    # Your agent logic here
    response = f"Your package will arrive tomorrow at 10:00 AM."
    
    # Evaluate the response asynchronously
    tracer.async_evaluate(
        scorer=scorer,
        example=Example.create(
            input=question,
            actual_output=response
        )
    )
    
    return response

# Run your agent
result = my_agent("Where is my package?")

Use async_trace_evaluate to run a TraceScorer on the current trace:

from judgeval import Judgeval

judgeval = Judgeval(project_name="my_project")

# Retrieve your uploaded trace scorer by name
scorer = judgeval.scorers.custom_scorer.get(name="ToolCallScorer")

# Create a tracer
tracer = judgeval.tracer.create()

@tracer.observe(span_type="tool")
def search_database(query: str) -> str:
    # Tool logic here
    return f"Results for: {query}"

@tracer.observe(span_type="function")
def my_agent(question: str) -> str:
    # Your agent logic with tool calls
    search_result = search_database(question)
    response = f"Based on my search: {search_result}"
    
    # Evaluate the entire trace asynchronously
    tracer.async_trace_evaluate(scorer=scorer)
    
    return response

# Run your agent
result = my_agent("What products are available?")

Full Example

Here's a complete example of creating, uploading, and using a custom scorer:

Create the scorer

sentiment_scorer.py

from judgeval.v1.data import Example
from judgeval.v1.scorers import ExampleScorer, CustomScorerResult

class SentimentScorer(ExampleScorer):
    def score(self, example: Example) -> CustomScorerResult:
        actual_output = example.get_property("actual_output")
        
        positive_words = ["great", "excellent", "happy", "pleased", "thank"]
        negative_words = ["sorry", "unfortunately", "cannot", "unable", "problem"]
        
        positive_count = sum(1 for word in positive_words if word in actual_output.lower())
        negative_count = sum(1 for word in negative_words if word in actual_output.lower())
        
        if positive_count > negative_count:
            return CustomScorerResult(
                score=1.0,
                reason=f"Response has positive sentiment ({positive_count} positive, {negative_count} negative words)."
            )
        elif negative_count > positive_count:
            return CustomScorerResult(
                score=0.0,
                reason=f"Response has negative sentiment ({positive_count} positive, {negative_count} negative words)."
            )
        
        return CustomScorerResult(
            score=0.5,
            reason="Response has neutral sentiment."
        )

Create requirements.txt

requirements.txt

# No external dependencies needed for this scorer

Upload the scorer

judgeval upload-scorer sentiment_scorer.py -r requirements.txt -n "Sentiment Scorer" -p sentiment_analysis

Use the scorer in an evaluation

from judgeval import Judgeval
from judgeval.v1.data import Example

judgeval = Judgeval(project_name="sentiment_analysis")

# Retrieve the uploaded scorer
scorer = judgeval.scorers.custom_scorer.get(name="Sentiment Scorer")

# Create a tracer
tracer = judgeval.tracer.create()

@tracer.observe(span_type="function")
def customer_support_agent(question: str) -> str:
    # Your agent logic here
    response = "I had a great experience! The service was excellent."
    
    # Evaluate the response
    tracer.async_evaluate(
        scorer=scorer,
        example=Example.create(
            input=question,
            actual_output=response
        )
    )
    
    return response

# Run your agent
result = customer_support_agent("How was your experience?")

Create the scorer

tool_call_scorer.py

from judgeval.v1.data import Trace
from judgeval.v1.scorers import TraceScorer, CustomScorerResult

class ToolCallScorer(TraceScorer):
    def score(self, trace: Trace) -> CustomScorerResult:
        tool_calls = [span for span in trace if span["span_kind"] == "tool"]
        
        if len(tool_calls) > 0:
            return CustomScorerResult(
                score=1.0,
                reason=f"Agent made {len(tool_calls)} tool call(s)."
            )
        
        return CustomScorerResult(
            score=0.0,
            reason="Agent did not make any tool calls."
        )

Create requirements.txt

requirements.txt

# No external dependencies needed for this scorer

Upload the scorer

judgeval upload-scorer tool_call_scorer.py -r requirements.txt -n "Tool Call Scorer" -p agent_monitoring

Use the scorer in an evaluation

from judgeval import Judgeval

judgeval = Judgeval(project_name="agent_monitoring")

# Retrieve the uploaded trace scorer
scorer = judgeval.scorers.custom_scorer.get(name="Tool Call Scorer")

# Create a tracer
tracer = judgeval.tracer.create()

@tracer.observe(span_type="tool")
def search_products(query: str) -> str:
    return f"Found 5 products matching: {query}"

@tracer.observe(span_type="function")
def shopping_agent(question: str) -> str:
    # Agent makes tool calls
    products = search_products(question)
    response = f"Here are the results: {products}"
    
    # Evaluate the entire trace
    tracer.async_trace_evaluate(scorer=scorer)
    
    return response

# Run your agent
result = shopping_agent("Show me laptops under $1000")

Custom Scorers

scorer_file_pathrequired

--requirement, -r

--name, -n

--project, -p

--overwrite, -o

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