Code Graders

Code graders (also accepts code-judge for backward compatibility) are scripts that evaluate agent responses deterministically. Write them in any language — Python, TypeScript, Node, or any executable.

Contract

Code graders communicate via stdin/stdout JSON:

Input (stdin):

{
  "input_text": "What is 15 + 27?",
  "criteria": "Correctly calculates 15 + 27 = 42",
  "output_text": "The answer is 42.",
  "expected_output_text": "42"
}

**Output (stdout):**
```json
{
  "score": 1.0,
  "assertions": [
    { "text": "Answer contains correct value (42)", "passed": true }
  ]
}

Output Field	Type	Description
score	number	0.0 to 1.0
assertions	Array<{ text, passed, evidence? }>	Per-aspect results with verdict and optional evidence

Python Example

validators/check_answer.py

import json, sys
data = json.load(sys.stdin)
output_text = data.get("output_text", "")

assertions = []

if "42" in output_text:
    assertions.append({"text": "Output contains correct value (42)", "passed": True})
else:
    assertions.append({"text": "Output does not contain expected value (42)", "passed": False})

passed = sum(1 for a in assertions if a["passed"])
score = passed / len(assertions) if assertions else 0.0

print(json.dumps({
    "score": score,
    "assertions": assertions,
}))

TypeScript Example

validators/check_answer.ts

import { readFileSync } from "fs";

const data = JSON.parse(readFileSync("/dev/stdin", "utf-8"));
const outputText: string = data.output_text ?? "";

const assertions: Array<{ text: string; passed: boolean }> = [];

if (outputText.includes("42")) {
  assertions.push({ text: "Output contains correct value (42)", passed: true });
} else {
  assertions.push({ text: "Output does not contain expected value (42)", passed: false });
}

const passed = assertions.filter(a => a.passed).length;

console.log(JSON.stringify({
  score: passed > 0 ? 1.0 : 0.0,
  assertions,
  reasoning: `Passed ${passed} check(s)`,
}));

Referencing in Eval Files

assertions:
  - name: my_validator
    type: code-grader
    command: [./validators/check_answer.py]

@agentv/eval SDK

The @agentv/eval package provides a declarative API with automatic stdin/stdout handling. Use defineCodeGrader (formerly defineCodeJudge) to skip boilerplate:

#!/usr/bin/env bun
import { defineCodeGrader } from '@agentv/eval';

export default defineCodeGrader(({ outputText, criteria }) => {
  const assertions: Array<{ text: string; passed: boolean }> = [];

  if (outputText.includes(criteria)) {
    assertions.push({ text: 'Output matches expected outcome', passed: true });
  } else {
    assertions.push({ text: 'Output does not match expected outcome', passed: false });
  }

  const passed = assertions.filter(a => a.passed).length;
  return {
    score: assertions.length === 0 ? 0 : passed / assertions.length,
    assertions,
  };
});

SDK exports: defineCodeGrader, Message, ToolCall, TraceSummary, CodeGraderInput, CodeGraderResult

Target Access

Code graders can call an LLM through a target proxy for metrics that require multiple LLM calls (contextual precision, semantic similarity, etc.).

Configuration

Add a target block to the evaluator config:

assertions:
  - name: contextual-precision
    type: code-grader
    command: [bun, scripts/contextual-precision.ts]
    target:
      max_calls: 10  # Default: 50

Usage

Use createTargetClient from the SDK:

#!/usr/bin/env bun
import { createTargetClient, defineCodeGrader } from '@agentv/eval';

export default defineCodeGrader(async ({ inputText, outputText }) => {
  const target = createTargetClient();
  if (!target) return { score: 0, assertions: [{ text: 'Target not configured', passed: false }] };

  const response = await target.invoke({
    question: `Is this relevant to: ${inputText}? Response: ${outputText}`,
    systemPrompt: 'Respond with JSON: { "relevant": true/false }'
  });

  const result = JSON.parse(response.rawText ?? '{}');
  return { score: result.relevant ? 1.0 : 0.0 };
});

Use target.invokeBatch(requests) for multiple calls in parallel.

Environment variables (set automatically when target is configured):

Variable	Description
AGENTV_TARGET_PROXY_URL	Local proxy URL
AGENTV_TARGET_PROXY_TOKEN	Bearer token for authentication

Advanced Input Fields

Beyond the basic text fields (input_text, output_text, expected_output_text, criteria), code graders receive additional structured context:

Field	Type	Description
input_files	string[]	Paths to input files referenced in the eval
input	Message[]	Full resolved input message array
expected_output	Message[]	Expected agent behavior including tool calls
output	Message[]	Actual agent execution trace with tool calls
trace	TraceSummary	Lightweight execution metrics (tool calls, errors)
token_usage	{input, output}	Token consumption
cost_usd	number	Estimated cost in USD
duration_ms	number	Total execution duration
start_time	string	ISO timestamp of first event
end_time	string	ISO timestamp of last event
file_changes	string | null	Unified diff of workspace file changes (when workspace_template is configured)
workspace_path	string | null	Absolute path to the workspace directory (when workspace_template is configured)

trace structure

{
  "event_count": 5,
  "tool_names": ["fetch", "search"],
  "tool_calls_by_name": { "search": 2, "fetch": 1 },
  "error_count": 0,
  "llm_call_count": 2
}

Field	Type	Description
event_count	number	Total tool invocations
tool_names	string[]	Unique tool names used
tool_calls_by_name	Record<string, number>	Count per tool
error_count	number	Failed tool calls
llm_call_count	number	Number of LLM calls (assistant messages)

Use expected_output for retrieval context in RAG evals (tool calls with outputs) and output for the actual agent execution trace from live runs.

Workspace Access

When workspace_template is configured on a target, code graders receive the workspace path in two ways:

1. JSON payload: workspace_path field in the stdin input
2. Environment variable: AGENTV_WORKSPACE_PATH

This enables functional grading — running commands like npm test, pytest, or cargo test directly in the agent’s workspace.

Example: Deploy-and-Test Pattern

#!/usr/bin/env bun
import { readFileSync } from "fs";
import { execFileSync } from "child_process";

const input = JSON.parse(readFileSync("/dev/stdin", "utf-8"));
const cwd = input.workspace_path;

const assertions: Array<{ text: string; passed: boolean }> = [];

// Stage 1: Install dependencies
try {
  execFileSync("npm", ["install"], { cwd, stdio: "pipe" });
  assertions.push({ text: "npm install passed", passed: true });
} catch { assertions.push({ text: "npm install failed", passed: false }); }

// Stage 2: Typecheck
try {
  execFileSync("npx", ["tsc", "--noEmit"], { cwd, stdio: "pipe" });
  assertions.push({ text: "typecheck passed", passed: true });
} catch { assertions.push({ text: "typecheck failed", passed: false }); }

// Stage 3: Run tests
try {
  execFileSync("npm", ["test"], { cwd, stdio: "pipe" });
  assertions.push({ text: "tests passed", passed: true });
} catch { assertions.push({ text: "tests failed", passed: false }); }

const passed = assertions.filter(a => a.passed).length;
console.log(JSON.stringify({
  score: assertions.length > 0 ? passed / assertions.length : 0,
  assertions,
}));

targets.yaml

targets:
  - name: my_agent
    provider: cli
    command: "my-agent --task {INPUT_FILE} --output {OUTPUT_FILE}"
    workspace_template: ./workspace-template

# dataset.eval.yaml
tests:
  - id: implement-feature
    criteria: Agent implements the feature correctly
    input: "Implement the TODO functions in src/index.ts"
    assertions:
      - name: functional-check
        type: code-grader
        command: [bun, scripts/functional-check.ts]

See examples/features/functional-grading/ for a complete working example.

Testing Locally

With agentv eval assert

Run a grader from .agentv/graders/ by name — no manual JSON piping required:

# Pass agent output and input directly
agentv eval assert rouge-score --agent-output "The fox jumps over the dog" --agent-input "Summarise this"

# Or pass a JSON file with { output, input } fields
agentv eval assert rouge-score --file result.json

The command:

1. Discovers the grader script by walking up directories looking for .agentv/graders/<name>.{ts,js,mts,mjs}
2. Passes { output_text, output, input, input_text } to the script via stdin
3. Prints the grader’s JSON result to stdout
4. Exits 0 if score >= 0.5, exit 1 otherwise

This is the same interface that agent-orchestrated evals use — the EVAL.yaml transpiler emits agentv eval assert instructions for code graders so external grading agents can run them directly.

With stdin pipe

Pipe JSON directly to the grader script for full control:

echo '{"input_text":"What is 2+2?","criteria":"4","output_text":"4","expected_output_text":"4"}' | python validators/check_answer.py