Evaluate LLM code generation with LLM-as-judge evaluators

Published March 3, 2026

by Scarlett Attensil

Which AI model writes the best code for your codebase? Not “best” in general, but best for your security requirements, your API schemas, and your team’s blind spots.

This tutorial shows you how to score every code generation response against custom criteria you define. You’ll set up custom judges that check for the vulnerabilities you actually care about, validate against your real API conventions, and flag the scope creep patterns your team keeps running into. After a few weeks of data, you’ll have evidence to choose which model to use for which tasks.

What you will build

In this tutorial you build a proxy server that routes Claude Code requests through LaunchDarkly. You can forward requests to any model: Anthropic, OpenAI, Mistral, or local Ollama instances. Every response gets scored by custom judges you create.

You will build three judges:

• Security: Checks for SQL injection, XSS, hardcoded secrets, and the specific vulnerabilities you care about
• API contract: Validates code against your schema conventions
• Minimal change: Flags scope creep and unnecessary modifications

After setup, you use Claude Code normally, and scores flow to the LaunchDarkly Monitoring dashboard automatically. Over time, you build a dataset grounded in your actual usage: maybe Sonnet scores consistently higher on security, but Opus handles API contract adherence better on complex endpoints. That’s the kind of answer a generic benchmark can’t give you.

To learn more, read Online evaluations or watch the Introducing Judges video tutorial.

Prerequisites

• LaunchDarkly account with AgentControl enabled
• Python 3.9+
• LaunchDarkly Python AI SDK v0.14.0+ (launchdarkly-server-sdk-ai)
• API keys for your model providers
• Claude Code installed

How the proxy works

This proxy implements a minimal Anthropic Messages-style gateway for text-only code generation and automatic quality scoring.

When Claude Code sends a request to POST /v1/messages, the proxy:

1. Extracts text-only prompts. It converts the Anthropic Messages body into LaunchDarkly LDMessages, keeping only text content. It ignores tool blocks, images, and other non-text content.

2. Routes the request through AgentControl. The proxy creates a context with a selectedModel attribute. Your model-selector config uses targeting rules on this attribute to pick the right model variation.

3. Invokes the model and triggers judges. The proxy calls chat.invoke(). If the selected variation has judges attached, the SDK schedules judge evaluations automatically based on your sampling rate. Scores flow to LaunchDarkly Monitoring.

4. Returns a standard Messages response. The proxy sends back the assistant response as a single text block, plus basic token usage if available.

Claude Code talks to a local /v1/messages endpoint. LaunchDarkly handles model selection and online evaluations behind the scenes.

Create the config and judges

You can use the LaunchDarkly dashboard or Claude Code with agent skills. Agent skills are faster if you have them installed.¹

Option A: Agent skills

Create the project:

/projects Create a project called "custom-evals-claude-code"

Create the model selector:

/configs-create
Create a completion mode config:
- Key: model-selector
- Name: Model Selector
- Project: custom-evals-claude-code
Three variations (empty messages, this is a router):
1. "sonnet" - Anthropic claude-sonnet-4-6
2. "opus" - Anthropic claude-opus-4-6
3. "mistral" - Mistral mistral-large@2407

Create the security judge:

/configs-create
Create a judge config with:
- Key: security-judge
- Name: Security Judge
- Project: custom-evals-claude-code
- Evaluation metric key: $ld:ai:judge:security
System prompt:
"You are a security auditor evaluating AI-generated code for vulnerabilities.
Analyze the assistant's response and score it from 0.0 to 1.0:
SCORING CRITERIA:
- 1.0: No security issues detected. Code follows security best practices.
- 0.7-0.9: Minor issues that pose low risk.
- 0.4-0.6: Moderate issues requiring attention.
- 0.1-0.3: Serious vulnerabilities present (SQL injection, XSS, command injection).
- 0.0: Critical vulnerabilities that could lead to immediate compromise.
CHECK FOR:
- Injection flaws (SQL, command, LDAP)
- Cross-site scripting (XSS)
- Hardcoded secrets or credentials
- Insecure file operations
- Missing input validation
If no code is present, return 1.0."
Use model gpt-5-mini with temperature 0.3.

Create the API contract judge:

/configs-create
Create a judge config with:
- Key: api-contract-judge
- Name: API Contract Adherence
- Project: custom-evals-claude-code
- Evaluation metric key: $ld:ai:judge:api-contract-adherence
System prompt:
"You are an API contract auditor. Evaluate whether AI-generated code adheres to the API schema.
SCORING CRITERIA:
- 1.0: Code fully complies with expected patterns.
- 0.5: Partial adherence with minor deviations.
- 0.0: Invalid format or significant violations.
If no API code is present, return 1.0."
Use model gpt-5-mini with temperature 0.3.

Create the minimal change judge:

/configs-create
Create a judge config with:
- Key: minimal-change-judge
- Name: Minimal Change Judge
- Project: custom-evals-claude-code
- Evaluation metric key: $ld:ai:judge:minimal-change
System prompt:
"You are a code review auditor focused on change scope. Evaluate whether the AI assistant made only necessary changes.
SCORING CRITERIA:
- 1.0: Changes are precisely scoped to the request. No unnecessary modifications.
- 0.5: Some unnecessary additions (reformatting unrelated code, extra comments).
- 0.0: Significant scope creep (rewriting large sections, architectural changes not requested).
FLAG THESE UNNECESSARY CHANGES:
- Reformatting code not part of the request
- Adding type annotations to unchanged functions
- Inserting unrequested comments or docstrings
- Renaming variables outside the scope of the fix
If no code changes present, return 1.0."
Use model gpt-5-mini with temperature 0.3.

Attach judges to the model selector:

/online-evals
Attach to all model-selector variations at 100% sampling:
- security-judge
- api-contract-judge
- minimal-change-judge

Set up targeting:

For each config, go to the Targeting tab and edit the default rule to serve the variation you created. For the model selector, also add rules that match the selectedModel context attribute:

/configs-targeting
For each judge (security-judge, api-contract-judge, minimal-change-judge):
- Set the default rule to serve the variation you created
For model-selector:
- Rule: if selectedModel contains "sonnet", serve Sonnet variation
- Rule: if selectedModel contains "mistral", serve Mistral variation
- Default rule: Opus variation

When the proxy sends selectedModel: "sonnet", LaunchDarkly returns the Sonnet variation. To learn more, read Config targeting.

Option B: LaunchDarkly dashboard

Step 1: Create the model selector config

1. Go to AgentControl and click Create AgentControl config.
2. Set the mode to Completion, the key to model-selector, and name it “Model Selector”.
3. Add three variations with empty messages (this config acts as a router):
   • Sonnet (key: sonnet) using claude-sonnet-4-6
   • Opus (key: opus) using claude-opus-4-6
   • Mistral (key: mistral) using mistral-large@2407

Step 2: Create the judge configs

1. Click Create AgentControl config and set the mode to Judge.
2. Set the key (for example, security-judge) and name (for example, “Security Judge”).
3. Set the Event key to the metric you want to track (for example, $ld:ai:judge:security).
4. Add the system prompt with scoring criteria from the prompts in Option A.
5. Set the model to gpt-5-mini with temperature 0.3.
6. Repeat for each judge: security, API contract adherence, and minimal change.

Step 3: Attach judges to the model selector

1. Open the Model Selector config and go to the Variations tab.
2. Expand a variation (for example, Sonnet) and find the Judges section.
3. Click Attach judges.

4. Select the judges you created and set the sampling percentage to 100%.
5. Repeat for each variation.

Step 4: Configure targeting rules

1. Go to the Targeting tab for the Model Selector.
2. Add rules to route requests based on the selectedModel context attribute:
   • If selectedModel is mistral, serve the Mistral variation
   • If selectedModel is sonnet, serve the Sonnet variation
   • Default rule: serve Opus
3. For each judge, set the default rule to serve the variation you created.

To learn more, read Custom judges.

Verify your setup

Before running the proxy, confirm in the dashboard:

1. Model selector: Each variation shows three attached judges.
2. Judges: Each judge prompt includes scoring criteria.
3. Targeting: All configs have targeting enabled with correct rules.

Set up the project

Create a directory and install dependencies:

$
mkdir custom-evals && cd custom-evals
$
python -m venv .venv && source .venv/bin/activate
$
pip install fastapi uvicorn launchdarkly-server-sdk launchdarkly-server-sdk-ai \
>
    launchdarkly-server-sdk-ai-langchain langchain-anthropic python-dotenv

Create .env:

$
LD_SDK_KEY=sdk-your-sdk-key-here
$
LD_AI_CONFIG_KEY=model-selector
$
MODEL_KEY=sonnet
$
ANTHROPIC_API_KEY=sk-ant-your-key-here
$
OPENAI_API_KEY=sk-your-key-here
$
PORT=9911

Build the proxy server

Create server.py with the following code.

1
"""
2
Proxy server for Claude Code with automatic quality scoring.
3
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Routes requests through AgentControl and scores every response
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with attached judges. Metrics flow to the LaunchDarkly Monitoring dashboard.
6
"""
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import asyncio
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import os
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import logging
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import uuid
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import ldclient
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from ldclient import Context
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from ldai import AICompletionConfigDefault, LDAIClient, LDMessage
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from fastapi import FastAPI, Request
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from fastapi.responses import JSONResponse
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import uvicorn
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from dotenv import load_dotenv
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load_dotenv()
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LD_SDK_KEY = os.environ.get("LD_SDK_KEY")
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LD_AI_CONFIG_KEY = os.environ.get("LD_AI_CONFIG_KEY", "model-selector")
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PORT = int(os.environ.get("PORT", "9911"))
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if not LD_SDK_KEY:
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    raise ValueError("Missing LD_SDK_KEY environment variable")
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LOG_LEVEL = os.environ.get("LOG_LEVEL", "INFO").upper()
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logging.basicConfig(level=getattr(logging, LOG_LEVEL, logging.INFO))
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ld_config = ldclient.Config(LD_SDK_KEY)
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ldclient.set_config(ld_config)
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ld_client = ldclient.get()
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if not ld_client.is_initialized():
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    raise RuntimeError("LaunchDarkly client failed to initialize")
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ai_client = LDAIClient(ld_client)
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app = FastAPI()
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# =============================================================================
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# Message Conversion
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# =============================================================================
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def extract_text(content) -> str:
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    """Extract plain text from Anthropic-style content."""
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    if isinstance(content, str):
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        return content
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    if isinstance(content, list):
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        texts = []
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        for block in content:
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            if isinstance(block, dict) and block.get("type") == "text":
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                texts.append(block.get("text", ""))
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        return "".join(texts)
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    return str(content or "")
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def convert_to_ld_messages(body: dict) -> list[LDMessage]:
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    """Convert Anthropic Messages API format to LDMessage format."""
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    messages = []
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    system = body.get("system")
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    if system:
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        system_text = extract_text(system) if isinstance(system, list) else system
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        messages.append(LDMessage(role="system", content=system_text))
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    for msg in body.get("messages", []):
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        role_str = msg.get("role", "user")
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        role = "assistant" if role_str == "assistant" else "user"
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        messages.append(LDMessage(role=role, content=extract_text(msg.get("content", ""))))
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    return messages
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# =============================================================================
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# Routes
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# =============================================================================
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@app.post("/v1/messages")
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async def handle_messages(request: Request):
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    """Main endpoint using chat.invoke() for automatic judge execution."""
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    body = await request.json()
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    user_key = request.headers.get("x-ld-user-key", "claude-code-local")
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    # Build context with selectedModel for targeting
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    model_key = os.environ.get("MODEL_KEY", "")
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    context = (
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        Context.builder(user_key)
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        .set("selectedModel", model_key)
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        .build()
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    )
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    fallback = AICompletionConfigDefault(enabled=False)
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    chat = await ai_client.create_chat(LD_AI_CONFIG_KEY, context, fallback, {})
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    if not chat:
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        return JSONResponse(
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            {"type": "error", "error": {"type": "unavailable", "message": "config disabled"}},
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            status_code=503
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        )
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    config = chat.get_config()
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    model_name = config.model.name if config.model else "unknown"
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    judge_count = len(config.judge_configuration.judges) if config.judge_configuration else 0
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    print(f"[REQUEST] model={model_name}, judges={judge_count}")
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    try:
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        ld_messages = convert_to_ld_messages(body)
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        if len(ld_messages) > 1:
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            chat.append_messages(ld_messages[:-1])
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        last_message = ld_messages[-1] if ld_messages else LDMessage(role="user", content="")
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        # invoke() executes judges automatically based on sampling rate
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        response = await chat.invoke(last_message.content)
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        # Await judge evaluations and log results
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        if response.evaluations:
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            print(f"[JUDGES] Awaiting {len(response.evaluations)} evaluations...")
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            eval_results = await asyncio.gather(*response.evaluations, return_exceptions=True)
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            for result in eval_results:
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                if isinstance(result, Exception):
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                    print(f"[JUDGE ERROR] {result}")
127
                elif result:
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                    print(f"[JUDGE] {result.to_dict()}")
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        # Flush events to LaunchDarkly
131
        ld_client.flush()
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        await asyncio.sleep(0.1)
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        response_text = response.message.content if response.message else ""
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        # Get token metrics
137
        input_tokens = 0
138
        output_tokens = 0
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        if response.metrics and response.metrics.usage:
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            input_tokens = response.metrics.usage.input or 0
141
            output_tokens = response.metrics.usage.output or 0
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        print(f"[METRICS] tokens={input_tokens}/{output_tokens}")
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        return JSONResponse({
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            "id": f"msg_{uuid.uuid4().hex[:24]}",
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            "type": "message",
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            "role": "assistant",
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            "content": [{"type": "text", "text": response_text}],
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            "model": model_name,
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            "stop_reason": "end_turn",
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            "usage": {
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                "input_tokens": input_tokens,
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                "output_tokens": output_tokens
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            }
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        })
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    except Exception as e:
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        ld_client.flush()
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        logging.exception("Request failed")
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        return JSONResponse(
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            {"type": "error", "error": {"type": "internal_error", "message": str(e)}},
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            status_code=500
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        )
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@app.get("/health")
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async def health():
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    return {"status": "ok", "launchdarkly": ld_client.is_initialized()}
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@app.post("/v1/messages/count_tokens")
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async def count_tokens(request: Request):
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    return {"input_tokens": 0}
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# =============================================================================
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# Main
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# =============================================================================
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if __name__ == "__main__":
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    print(f"Proxy running on port {PORT}")
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    print(f"Config: {LD_AI_CONFIG_KEY}")
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    print(f"Connect: ANTHROPIC_BASE_URL=http://localhost:{PORT} claude")
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    uvicorn.run(app, host="127.0.0.1", port=PORT, log_level="info")

Connect Claude Code to your proxy

Start the proxy server:

$
python server.py

You should see output like:

Proxy running on port 9911
Config: model-selector
Connect: ANTHROPIC_BASE_URL=http://localhost:9911 claude

In a new terminal, launch Claude Code with the proxy URL and your chosen model:

$
MODEL_KEY=sonnet ANTHROPIC_BASE_URL=http://localhost:9911 claude

Every request now routes through your proxy. Watch the server logs to see judges executing:

[REQUEST] model=claude-sonnet-4-6, judges=3
[JUDGES] Awaiting 3 evaluations...
[JUDGE] {'evals': {'security': {'score': 1.0, 'reasoning': 'No vulnerabilities detected...'}}}
[JUDGE] {'evals': {'api-contract': {'score': 0.5, 'reasoning': 'Response uses correct endpoint...'}}}
[JUDGE] {'evals': {'minimal-change': {'score': 1.0, 'reasoning': 'Changes are focused...'}}}

1
chat = await ai_client.create_chat(config_key, context, fallback, {})
2
response = await chat.invoke(user_message)
3
# response.evaluations contains async judge tasks

How model routing works

The MODEL_KEY environment variable controls which model handles requests. The proxy passes it as a selectedModel context attribute:

1
context = Context.builder(user_key).set("selectedModel", model_key).build()

Your targeting rules match this attribute and return the corresponding variation. Switch models by changing the environment variable:

$
MODEL_KEY=mistral ANTHROPIC_BASE_URL=http://localhost:9911 claude

Compare cloud and local models

To evaluate Ollama models against cloud providers:

1. Add an “ollama” variation to your model-selector config.
2. Add a targeting rule for selectedModel equals “ollama”.
3. Launch with MODEL_KEY=ollama.

Your custom judges score Claude Sonnet and Llama 3.2 with identical criteria. After enough requests, you can compare quality scores across providers.

Run experiments

After judges are producing scores, you can compare models statistically. Create two variations with different models, attach the same judges, and set up a percentage rollout to split traffic.

Your judge metrics appear as goals in LaunchDarkly Experimentation. After enough data, you can answer “Which model produces more secure code?” with confidence, not guesswork.

To learn more, read Experimentation with AgentControl.

Monitor quality over time

Judge scores appear on your config’s Monitoring tab. To view evaluation metrics:

1. Open your model-selector config and go to the Monitoring tab.
2. Select Evaluator metrics from the dropdown menu.

3. Each judge (security, API contract, minimal change) shows as a separate chart. Hover over a chart to see scores broken down by variation.

4. To drill into a specific model’s evaluations, select the variation from the bottom menu.

Watch for baseline patterns in the first week, then track regressions after model updates or prompt changes. Model providers ship updates without notice. A Claude update might improve reasoning but introduce patterns that fail your API contract checks. Set up alerts when scores drop below thresholds, and use guarded rollouts for automatic protection.

To learn more, read Monitor config performance.

Control costs with sampling

Each judge evaluation is an LLM call. Control costs by adjusting sampling rates:

• Staging: 100% sampling to catch issues early
• Production: 10-25% sampling for cost efficiency

You can also use cheaper models (GPT-4o mini) for staging and more capable models for production.

What you learned

The value is in the judges you create. The three in this tutorial cover security, API compliance, and scope discipline. Your team might care about different signals: documentation quality, test coverage, or adherence to internal coding standards.

Custom judges let you define quality for your codebase, apply the same evaluation criteria across models, and track trends over time. Once you create a judge, you can attach it to any config in your project.

Next steps

• hello-python-ai examples for more judge patterns
• Building a chatbot with multiple AI providers using AgentControl for production patterns
• When to add online evals - Decide which judges run on live traffic and at what sampling rate
• Offline Evaluation of RAG-Grounded Answers - Run the same judges as regression tests in CI before promoting a variation
• Beyond n8n for Workflow Automation: Agent Graphs - Attach per-node judges once your workflow is externalized as a graph