langchain-ai/eval-writer

Eval Writer

Create new eval suites for the deepagentsjs monorepo. Each eval is an independent workspace package under evals/ that uses the @deepagents/evals harness, runs via vitest, and reports results to LangSmith.

Before you start

Read the existing eval infrastructure to understand current patterns:

internal/eval-harness/src/index.ts    # EvalRunner, RunAgentParams, matchers
internal/eval-harness/src/deepagent.ts # DeepAgentEvalRunner, extend()
internal/eval-harness/src/setup.ts     # Registered runners
evals/README.md                        # User-facing docs
internal/eval-harness/README.md        # Harness internals

Scan existing evals for conventions:

evals/basic/index.test.ts     # Simple: system prompt, reasoning
evals/files/index.test.ts     # File ops: read, write, edit, glob, grep
evals/subagents/index.test.ts # Delegation: task tool, named subagents

Workflow

1. Understand the eval requirements

Clarify with the user:

• What capability is being evaluated? (file ops, tool use, multi-turn reasoning, memory, code generation, etc.)
• Where do test cases come from? Options:
  • Inline — hardcoded in the test file (simple, good for <20 cases)
  • JSON/JSONL fixture — checked into the eval package (good for 20-200 cases)
  • External dataset — downloaded at setup time (good for published benchmarks)
  • LangSmith dataset — pulled from LangSmith API (good for collaborative curation)
• How should results be scored? Options:
  • Trajectory matchers — step count, tool calls, final text (built-in)
  • Exact/fuzzy match — compare output to reference (simple)
  • LLM-as-judge — use a model to grade the output (complex evals)
  • Code execution — run generated code and check results (SWE-bench style)
  • Custom evaluator — domain-specific scoring function
• Does the agent need special configuration? (custom tools, subagents, system prompt, initial files)

2. Create the eval package

Every eval is a workspace package under evals/<name>/.

Directory structure

evals/<name>/
├── package.json
├── vitest.config.ts
├── index.test.ts
├── README.md
└── (optional) fixtures/       # JSON/JSONL test data
└── (optional) vitest.setup.ts  # Dataset loading, custom setup
└── (optional) evaluators.ts   # Custom scoring functions

package.json

{
  "name": "@deepagents/eval-<name>",
  "private": true,
  "type": "module",
  "scripts": {
    "test:eval": "vitest run"
  },
  "dependencies": {
    "@deepagents/evals": "workspace:*",
    "deepagents": "workspace:*",
    "langsmith": "^0.5.4",
    "vitest": "^4.0.18"
  }
}

Add extra dependencies as needed (e.g. zod for tool schemas, langchain for tool() helper, dataset-specific packages).

vitest.config.ts

import { defineConfig } from "vitest/config";

export default defineConfig({
  test: {
    environment: "node",
    globals: false,
    testTimeout: 120_000,
    hookTimeout: 60_000,
    teardownTimeout: 60_000,
    include: ["**/*.test.ts"],
    setupFiles: ["@deepagents/evals/setup"],
    reporters: ["default", "langsmith/vitest/reporter"],
  },
});

Adjust testTimeout for long-running evals (multi-turn, code execution). Add "./vitest.setup.ts" to setupFiles if the eval needs custom setup (dataset loading, etc.).

README.md

# <name>

<One-line description of what this eval tests.>

Verify workspace registration

Check that pnpm-workspace.yaml includes "evals/*". It should already be there — if not, add it.

3. Design test cases

Pattern A: Inline test cases (simple evals)

Best for small, hand-crafted test suites. Each test is an ls.test() call with inputs and optional referenceOutputs.

ls.test(
  "descriptive test name",
  {
    inputs: { query: "What is 2+2?" },
    referenceOutputs: { expectedAnswer: "4" },
  },
  async ({ inputs, referenceOutputs }) => {
    const result = await runner.run({ query: inputs.query });
    // assertions...
  },
);

Pattern B: Data-driven with ls.test.each (medium evals)

Best for 10-200 cases from a fixture file. Load the data and iterate:

import testCases from "./fixtures/cases.json";

// testCases = [{ inputs: { query: "..." }, referenceOutputs: { answer: "..." } }, ...]

ls.test.each(testCases)(
  "case: ${inputs.query}",
  async ({ inputs, referenceOutputs }) => {
    const result = await runner.run({ query: inputs.query });
    // assertions using referenceOutputs...
  },
);

The fixture JSON must be an array of objects with at minimum { inputs: {...} }. Optional fields: referenceOutputs, id, metadata, split.

Pattern C: External dataset (published benchmarks)

For published benchmarks (oolong, AgentBench, SWE-bench, etc.), download and cache the dataset in a setup file.

Create vitest.setup.ts:

import { existsSync, mkdirSync, writeFileSync, readFileSync } from "fs";
import { join } from "path";

const CACHE_DIR = join(import.meta.dirname, ".cache");
const DATA_PATH = join(CACHE_DIR, "dataset.json");

export async function loadDataset(): Promise<TestCase[]> {
  if (existsSync(DATA_PATH)) {
    return JSON.parse(readFileSync(DATA_PATH, "utf-8"));
  }

  mkdirSync(CACHE_DIR, { recursive: true });

  // Download from source — adapt to the specific benchmark
  const response = await fetch("https://example.com/dataset.json");
  const data = await response.json();

  // Transform into eval format
  const cases = data.map((item: any) => ({
    inputs: { query: item.question },
    referenceOutputs: { answer: item.gold_answer },
    metadata: { source: item.id, category: item.category },
  }));

  writeFileSync(DATA_PATH, JSON.stringify(cases, null, 2));
  return cases;
}

Add .cache/ to .gitignore in the eval package.

Then register it as a vitest setup file in vitest.config.ts:

setupFiles: ["@deepagents/evals/setup", "./vitest.setup.ts"],

And in the test file:

import { loadDataset } from "./vitest.setup.js";

const dataset = await loadDataset();

ls.describe(runner.name, () => {
  ls.test.each(dataset)(
    "${metadata.source}: ${inputs.query}",
    async ({ inputs, referenceOutputs }) => {
      // ...
    },
  );
}, { projectName: "deepagents-js-<name>", upsert: true });

Pattern D: LangSmith dataset

Pull test cases from a LangSmith dataset. Useful for collaborative curation where non-engineers add examples via the LangSmith UI.

import { Client } from "langsmith";

const client = new Client();

export async function loadDataset(): Promise<TestCase[]> {
  const examples = [];
  for await (const example of client.listExamples({
    datasetName: "my-dataset-name",
  })) {
    examples.push({
      id: example.id,
      inputs: example.inputs,
      referenceOutputs: example.outputs ?? {},
    });
  }
  return examples;
}

4. Write scoring logic

Built-in trajectory matchers

The harness provides vitest matchers that also log LangSmith feedback scores. Use these as the primary building blocks:

// Exact step count
expect(result).toHaveAgentSteps(3);

// Exact tool-call count across all steps
expect(result).toHaveToolCallRequests(2);

// Check a specific tool call in step N (1-indexed)
expect(result).toHaveToolCallInStep(1, {
  name: "write_file",
  argsContains: { file_path: "/out.txt" },  // partial match
  argsEquals: { file_path: "/out.txt" },     // exact match
});

// Final response text
expect(result).toHaveFinalTextContaining("hello", true /* caseInsensitive */);

// Extract final text for custom assertions
import { getFinalText } from "@deepagents/evals";
const text = getFinalText(result);
expect(text.trim()).toBe("4");

// File system assertions
expect(result.files["/output.md"]).toContain("expected content");
expect(Object.keys(result.files)).toHaveLength(3);

Custom feedback logging

Log additional LangSmith feedback scores beyond what matchers provide:

import * as ls from "langsmith/vitest";

// Numeric score
ls.logFeedback({ key: "accuracy", score: 0.95 });

// Boolean score
ls.logFeedback({ key: "correct", score: 1 });

// With comment
ls.logFeedback({ key: "quality", score: 0.8, comment: "Minor formatting issue" });

LLM-as-judge evaluators

For subjective quality, use ls.wrapEvaluator() to create a traced evaluator that logs feedback automatically:

import * as ls from "langsmith/vitest";
import { ChatAnthropic } from "@langchain/anthropic";

const judge = new ChatAnthropic({ model: "claude-sonnet-4-5-20250929" });

const evaluateHelpfulness = ls.wrapEvaluator(
  async ({ inputs, outputs, referenceOutputs }) => {
    const response = await judge.invoke([
      {
        role: "system",
        content: `Rate the helpfulness of the assistant's response on a scale of 0-1.
          Respond with JSON: { "score": <number>, "reasoning": "<explanation>" }`,
      },
      {
        role: "user",
        content: `Question: ${inputs.query}\nExpected: ${referenceOutputs.answer}\nActual: ${outputs.response}`,
      },
    ]);

    const parsed = JSON.parse(response.content as string);
    return {
      key: "helpfulness",
      score: parsed.score,
      comment: parsed.reasoning,
    };
  },
);

// In a test:
const result = await runner.run({ query: inputs.query });
const text = getFinalText(result);
await evaluateHelpfulness({
  inputs: { query: inputs.query },
  outputs: { response: text },
  referenceOutputs: referenceOutputs ?? {},
});

5. Wire up the test file

Minimal template

import * as ls from "langsmith/vitest";
import { expect } from "vitest";
import { getDefaultRunner, getFinalText } from "@deepagents/evals";

const runner = getDefaultRunner();

ls.describe(
  runner.name,
  () => {
    ls.test(
      "test name",
      {
        inputs: { query: "..." },
        referenceOutputs: { answer: "..." },
      },
      async ({ inputs, referenceOutputs }) => {
        const result = await runner.run({ query: inputs.query });

        expect(result).toHaveAgentSteps(1);
        expect(result).toHaveFinalTextContaining(referenceOutputs.answer);
      },
    );
  },
  { projectName: "deepagents-js-<name>", upsert: true },
);

Key conventions

• getDefaultRunner() — reads EVAL_RUNNER env var. Throws if not set.
• runner.name — used as ls.describe name → becomes the LangSmith dataset name.
• runner.run({ query, initialFiles? }) — pure invocation. Returns AgentTrajectory.
• runner.extend({ systemPrompt?, tools?, subagents?, ... }) — returns a new runner with agent config overrides. Use for tests that need custom agent setup.
• projectName in ls.describe config — sets the LangSmith project for tracing. Convention: "deepagents-js-<eval-name>".
• upsert: true — reuse existing dataset/project instead of creating new ones each run.
• Always import expect from vitest — the harness extends it with custom matchers at import time.
• ls.logOutputs() is called inside the runner — do NOT call it in test code.

Using extend() for custom agent config

// Custom system prompt
const result = await runner
  .extend({ systemPrompt: "You are a code reviewer." })
  .run({ query: inputs.query });

// Custom tools
const result = await runner
  .extend({ tools: [myCustomTool] })
  .run({ query: inputs.query });

// Custom subagents
const result = await runner
  .extend({
    subagents: [{
      name: "researcher",
      description: "Research assistant",
      systemPrompt: "You help with research.",
      tools: [searchTool],
    }],
  })
  .run({ query: inputs.query });

Using initialFiles for seeded state

const result = await runner.run({
  query: "Read /data.csv and count the rows.",
  initialFiles: {
    "/data.csv": "name,age\nAlice,30\nBob,25\n",
  },
});

Sandbox-backed evals (containerized execution)

The default eval runners use the in-memory StateBackend — the agent can read/write files but cannot execute shell commands, install packages, or interact with a real OS. This is fine for testing tool selection, reasoning, and file operations.

For evals that need real execution (SWE-bench, code generation, agentic benchmarks), the agent must run against a sandbox backend. Available sandbox providers:

| Provider | Package | Use case | | --- | --- | --- | | Modal | @deepagents/modal | Remote containers, GPU support | | Daytona | @deepagents/daytona | Cloud dev environments | | Deno | @deepagents/deno | Lightweight local sandboxes | | Node VFS | @deepagents/node-vfs | In-process virtual filesystem + shell |

Pass the sandbox via extend({ backend }). Manage its lifecycle with beforeAll / afterAll (suite-level) or beforeEach / afterEach (per-test isolation):

import * as ls from "langsmith/vitest";
import { expect, beforeAll, afterAll } from "vitest";
import { getDefaultRunner, getFinalText } from "@deepagents/evals";
import { ModalSandbox } from "@deepagents/modal";

const runner = getDefaultRunner();
let sandbox: ModalSandbox;

beforeAll(async () => {
  sandbox = await ModalSandbox.create({
    image: "python:3.12-slim",
    timeout: 600,
  });
});

afterAll(async () => {
  await sandbox?.terminate();
});

ls.describe(
  runner.name,
  () => {
    ls.test(
      "agent can run python",
      { inputs: { query: "Write a Python script that prints 'hello' and run it." } },
      async ({ inputs }) => {
        const result = await runner
          .extend({ backend: sandbox })
          .run({ query: inputs.query });

        expect(result).toHaveFinalTextContaining("hello");
      },
    );
  },
  { projectName: "deepagents-js-sandbox-eval", upsert: true },
);

For per-test isolation (each test gets a fresh sandbox):

import { beforeEach, afterEach } from "vitest";

let sandbox: ModalSandbox;

beforeEach(async () => {
  sandbox = await ModalSandbox.create({ image: "python:3.12-slim" });
});

afterEach(async () => {
  await sandbox?.terminate();
});

When to containerize:

• The eval requires execute() (shell commands)
• The eval involves installing packages or modifying system state
• The eval runs untrusted or generated code
• Tests need filesystem isolation from each other

When in-memory is fine:

• Testing tool selection, reasoning, or response quality
• File read/write/edit operations (handled by StateBackend + initialFiles)
• System prompt adherence, subagent routing

Add the sandbox provider to package.json dependencies:

{
  "dependencies": {
    "@deepagents/modal": "workspace:*"
  }
}

And increase testTimeout in vitest.config.ts — sandbox creation adds overhead:

testTimeout: 300_000,  // 5 minutes for sandbox evals
hookTimeout: 120_000,  // sandbox setup/teardown

6. Install and verify

# From repo root
pnpm install

# Build the harness (if you changed it)
cd internal/eval-harness && pnpm build && cd ../..

# Run the new eval
EVAL_RUNNER=sonnet-4-5 pnpm --filter @deepagents/eval-<name> test:eval

7. Update documentation

Add the new eval to evals/README.md in the "Available eval suites" table:

| [`<name>/`](./<name>/) | <one-line description> |

Parity with Python deepagents evals

The Python deepagents package has eval suites in libs/deepagents/tests/evals/. The JS evals should maintain parity. When creating a new eval, check the Python source at https://github.com/langchain-ai/deepagents/blob/v0.5/libs/deepagents/tests/evals/ for the reference implementation.

Current parity status

| Python eval | JS eval | Status | | --- | --- | --- | | test_system_prompt.py | evals/basic/ | ✅ Covered | | test_file_operations.py | evals/files/ | ✅ Covered | | test_subagents.py | evals/subagents/ | ✅ Covered | | test_memory.py | evals/memory/ | ✅ Covered | | test_hitl.py | evals/hitl/ | ✅ Covered | | test_skills.py | evals/skills/ | ✅ Covered | | test_summarization.py | evals/summarization/ | ❌ Missing |

Notes on HITL evals

HITL evals require multi-step invocation (invoke → check interrupts → resume with Command). The eval runner's run() does a single invocation, so HITL tests construct agents directly via createDeepAgent() with a checkpointer and interruptOn config. See evals/hitl/index.test.ts for the pattern.

Notes on summarization evals

Summarization evals need SummarizationMiddleware with low token thresholds, a checkpointer, a real/virtual filesystem backend, and multi-turn invocations. These tests would bypass the standard EvalRunner and construct agents directly, similar to HITL.

Reference: ls.test.each API

ls.test.each is the most powerful pattern for data-driven evals. The table must be an array of objects with at least { inputs }:

ls.test.each([
  { inputs: { query: "Q1" }, referenceOutputs: { answer: "A1" } },
  { inputs: { query: "Q2" }, referenceOutputs: { answer: "A2" } },
  // Optional additional fields: id, metadata, split
  { id: "custom-id", inputs: { query: "Q3" }, referenceOutputs: { answer: "A3" }, split: "hard" },
])(
  "case: ${inputs.query}",  // Name template — interpolates from row
  async ({ inputs, referenceOutputs, testMetadata }) => {
    // testMetadata.exampleId, testMetadata.datasetId, etc.
    const result = await runner.run({ query: inputs.query });
    // ...
  },
);

Reference: LangSmith integration

How datasets map

| Concept | LangSmith entity | | --- | --- | | ls.describe(name, ...) | Dataset (name = dataset name) | | ls.test(name, { inputs, referenceOutputs }, fn) | Example in dataset | | Running the test suite | Experiment on the dataset | | ls.logFeedback(...) | Feedback on the experiment run | | ls.logOutputs(...) | Experiment output (called by runner) |

Environment variables

| Variable | Purpose | | --- | --- | | EVAL_RUNNER | Which model runner to use (e.g. sonnet-4-5) | | LANGSMITH_API_KEY | LangSmith auth | | LANGSMITH_PROJECT | Override tracing project (normally set via projectName) | | LANGSMITH_TEST_TRACKING | Set to "false" to disable LangSmith reporting | | ANTHROPIC_API_KEY | For Anthropic model runners | | OPENAI_API_KEY | For OpenAI model runners |

Available runners

Defined in internal/eval-harness/src/setup.ts:

| Runner name | Model | | --- | --- | | sonnet-4-5 | Claude Sonnet 4.5 | | sonnet-4-5-thinking | Claude Sonnet 4.5 with extended thinking | | opus-4-6 | Claude Opus 4.6 | | gpt-4.1 | GPT-4.1 | | gpt-4.1-mini | GPT-4.1 Mini | | o3-mini | o3-mini |

Reference: Implementing published benchmarks

When implementing an existing benchmark, follow these attribution and methodology guidelines.

Attribution

Always credit the original benchmark authors. In the eval's README.md:

# <benchmark-name>

Implementation of [<Benchmark Name>](<paper-or-repo-url>) by <authors> (<year>).

> <brief description from the paper abstract>

## Citation

\`\`\`bibtex
@article{...}
\`\`\`

## Adaptations

<Describe any differences from the original benchmark methodology:>
- <e.g. "Subset of N cases selected for cost efficiency">
- <e.g. "Adapted for agentic tool-use evaluation rather than direct QA">
- <e.g. "Uses LLM-as-judge instead of human annotation">

Common benchmark patterns

QA / Factual benchmarks (e.g. MMLU, TrivialQA)

• Test cases: question + gold answer
• Scoring: exact match or fuzzy match on final text
• Pattern: ls.test.each with fixture JSON

Multi-turn / Conversational (e.g. MT-Bench)

• Test cases: conversation turns that build on each other
• Scoring: LLM-as-judge on each turn
• Pattern: sequential runner.run() calls sharing state, or multi-message queries

Tool-use benchmarks (e.g. ToolBench, API-Bank)

• Test cases: task requiring specific tool calls
• Scoring: trajectory matchers (correct tools called, correct args)
• Pattern: runner.extend({ tools: [...] }) with custom tools that return canned responses

Code generation (e.g. HumanEval, SWE-bench)

• Test cases: problem description + test suite
• Scoring: execute generated code, check test results
• Requires sandbox: yes — agent must run code and observe output
• Pattern: runner.extend({ backend: sandbox }), write code to file, execute tests via sandbox

Memory / Long-context (e.g. oolong, needle-in-haystack)

• Test cases: large context + retrieval question
• Scoring: whether the agent finds the target information
• Requires sandbox: no — seed files via initialFiles, check final text
• Pattern: in-memory StateBackend is sufficient

Agentic benchmarks (e.g. AgentBench, WebArena)

• Test cases: multi-step tasks requiring planning
• Scoring: combination of trajectory analysis + final state checking
• Requires sandbox: yes — agent needs shell access, package installs, environment interaction
• Pattern: runner.extend({ backend: sandbox }) with per-test sandbox isolation

Handling large datasets

For benchmarks with thousands of cases:

1. Subset selection — pick a representative subset (e.g. 100 cases per category). Document the selection criteria.
2. Split support — use split field in test cases to categorise (e.g. "easy", "hard"). Run subsets via vitest filtering.
3. Caching — download once, cache in .cache/ (gitignored).
4. Cost awareness — estimate API cost before running. Log it in the README. Consider a --dry-run that validates fixtures without calling the LLM.