LLM Reference

What Veval does

Veval wraps your agent runs and LLM calls to record traces: inputs, outputs, step timing, token counts, and cost. Traces appear in the dashboard. You run scenarios and assertions against them in CI to catch regressions before they reach production. Three SDKs, same concept: C# (Veval.Sdk), Python (veval), Node (@veval/sdk).

Install

# C#
dotnet add package Veval.Sdk

# Python
pip install veval-sdk

# Node
npm install @veval/sdk

Initialize

using Veval.Sdk;

var veval = new VevalSdk(new VevalOptions { ApiKey = "YOUR_API_KEY" });

Python

from veval import VevalSdk, VevalOptions

veval = VevalSdk(VevalOptions(api_key="YOUR_API_KEY"))

Node

import { VevalSdk } from "@veval/sdk";

const veval = new VevalSdk({ apiKey: "YOUR_API_KEY" });

Core API

RunAsync — wrap a complete agent run

var result = await veval.RunAsync("agent-name", async ctx =>
{
    // your agent logic using ctx
    return output;
}, input: userMessage);  // input is optional

Python

result = await veval.run_async("agent-name", my_agent, user_message)
# my_agent is async def my_agent(ctx): ...
# input is optional (positional or keyword)

Node

const result = await veval.runAsync("agent-name", async (ctx) => {
  // your agent logic using ctx
  return output;
}, userMessage);  // input is optional

Sends a trace on both success and error. The ctx object is a VevalExecutionContext.

TrackStepAsync — record one LLM call or sub-operation

Simple overload (no metadata):

var output = await ctx.TrackStepAsync("step-name", input: text, async () =>
{
    return await llm.Call(text);
});

Python

async def step():
    return await llm.call(text)

output = await ctx.track_step_async("step-name", text, step)

Node

const output = await ctx.trackStepAsync("step-name", text, async () => {
  return await llm.call(text);
});

Handle overload (attach LLM metadata):

var output = await ctx.TrackStepAsync("step-name", input: text, async handle =>
{
    var response = await llm.Call(text);
    handle.SetMeta("model",      response.Model);
    handle.SetMeta("tokens_in",  response.Usage.InputTokens);
    handle.SetMeta("tokens_out", response.Usage.OutputTokens);
    handle.SetMeta("cost_usd",   0.0012m);
    return response.Content[0].Text;
});

Python

async def step(handle):
    response = await llm.call(text)
    handle.set_meta("model",      response.model)
    handle.set_meta("tokens_in",  response.usage.input_tokens)
    handle.set_meta("tokens_out", response.usage.output_tokens)
    handle.set_meta("cost_usd",   0.0012)
    return response.content[0].text

output = await ctx.track_step_async("step-name", text, step)

Node

const output = await ctx.trackStepAsync("step-name", text, async (handle) => {
  const response = await llm.call(text);
  handle.setMeta("model",      response.model);
  handle.setMeta("tokens_in",  response.usage.input_tokens);
  handle.setMeta("tokens_out", response.usage.output_tokens);
  handle.setMeta("cost_usd",   0.0012);
  return response.content[0].text;
});

StepHandle well-known keys (all SDKs use the same string keys):

Key	Type	Effect
`model`	string	Stored on `step.model`
`tokens_in`	int	Stored on `step.tokens_in`
`tokens_out`	int	Stored on `step.tokens_out`
`cost_usd`	float	Stored on `step.cost_usd`
`type`	string	Use `"tool"` for tool-call steps
(any other key)	any	Stored in `step.metadata` dict

Nested steps

Pass the parent handle’s context to nest steps in the dashboard tree:

var result = await ctx.TrackStepAsync("pipeline", query, async handle =>
{
    var a = await ctx.TrackStepAsync("classify", query, async () => await Classify(query));
    var b = await ctx.TrackStepAsync("answer",   a,     async () => await Answer(a));
    return b;
});

Python

async def pipeline(handle):
    a = await ctx.track_step_async("classify", query, lambda: classify(query))
    b = await ctx.track_step_async("answer",   a,     lambda: answer(a))
    return b

result = await ctx.track_step_async("pipeline", query, pipeline)

Node

const result = await ctx.trackStepAsync("pipeline", query, async (handle) => {
  const a = await ctx.trackStepAsync("classify", query, async () => await classify(query));
  const b = await ctx.trackStepAsync("answer",   a,     async () => await answer(a));
  return b;
});

Trace-level metadata

ctx.SetMetadata("user_id", userId);
ctx.SetMetadata("region",  "us-east-1");

Python

ctx.set_metadata("user_id", user_id)
ctx.set_metadata("region",  "us-east-1")

Node

ctx.setMetadata("user_id", userId);
ctx.setMetadata("region",  "us-east-1");

Replay / Test SDK

VevalTestSdk is a drop-in test double. It mocks LLM step outputs from a recorded trace so no real API calls are made. Throws if a step name isn’t found in the trace (strict mode).

var trace   = await veval.GetTraceAsync("tr_...");
var testSdk = new VevalTestSdk(new VevalOptions { ApiKey = "..." }).WithReplay(trace);
var service = new MyAgentService(testSdk, mockLlm);

var result = await testSdk.RunAsync("agent-name", service.ExecuteAsync);

Assert.Equal("success", testSdk.LastStatus);
Assert.Null(testSdk.LastError);

Python

trace    = await veval.get_trace_async("tr_...")
test_sdk = VevalTestSdk(VevalOptions(api_key="...")).with_replay(trace)
service  = MyAgentService(test_sdk, mock_llm)

result = await test_sdk.run_async("agent-name", service.execute_async)

assert test_sdk.last_status == "success"
assert test_sdk.last_error is None

Node

const trace   = await veval.getTraceAsync("tr_...");
const testSdk = new VevalTestSdk({ apiKey: "..." }).withReplay(trace);
const service = new MyAgentService(testSdk, mockLlm);

const result = await testSdk.runAsync("agent-name", (ctx) => service.execute(ctx));

expect(testSdk.lastStatus).toBe("success");
expect(testSdk.lastError).toBeNull();

ReplayAsync (lower-level)

var r = await testSdk.ReplayAsync(trace, service.ExecuteAsync,
    new ReplayOptions { MockLlmResponses = true, Assertions = [TraceAssert.NoErrors()] });
// r.Failures, r.ReplayedContext, r.Output, r.Status, r.Error

Python

r = await test_sdk.replay_async(trace, service.execute_async,
    ReplayOptions(mock_llm_responses=True, assertions=[TraceAssert.no_errors()]))
# r.failures, r.replayed_context, r.output, r.status, r.error

Node

const r = await testSdk.replayAsync(trace, (ctx) => service.execute(ctx),
  { mock_llm_responses: true, assertions: [TraceAssert.noErrors()] });
// r.failures, r.replayed_context, r.output, r.status, r.error

Assertions

All assertions implement ITraceAssertion. They return null on pass or a failure string. Built-in factory — all three languages:

C#	Python	Node	Description
`TraceAssert.NoErrors()`	`TraceAssert.no_errors()`	`TraceAssert.noErrors()`	Fail if any step has error status
`TraceAssert.MaxSteps(n)`	`TraceAssert.max_steps(n)`	`TraceAssert.maxSteps(n)`	Fail if total step count > n
`TraceAssert.MaxCost(n)`	`TraceAssert.max_cost(n)`	`TraceAssert.maxCost(n)`	Fail if total cost_usd > n
`TraceAssert.MaxDuration(ms)`	`TraceAssert.max_duration(ms)`	`TraceAssert.maxDuration(ms)`	Fail if total duration > ms
`TraceAssert.StepExists("name")`	`TraceAssert.step_exists("name")`	`TraceAssert.stepExists("name")`	Fail if named step not found
`TraceAssert.OutputContains("s")`	`TraceAssert.output_contains("s")`	`TraceAssert.outputContains("s")`	Fail if no step output contains s
`TraceAssert.ToolCalled("name")`	`TraceAssert.tool_called("name")`	`TraceAssert.toolCalled("name")`	Fail if no tool step with that name

Custom assertion:

public class MyAssertion : ITraceAssertion
{
    public string? Evaluate(VevalExecutionContext ctx)
    {
        // inspect ctx.Steps, return null to pass or a message to fail
        return null;
    }
}

Python

from veval import ITraceAssertion

class MyAssertion(ITraceAssertion):
    def evaluate(self, ctx) -> str | None:
        # inspect ctx.steps, return None to pass or a message to fail
        return None

Node

class MyAssertion {
  evaluate(ctx) {
    // inspect ctx.steps, return null to pass or a message to fail
    return null;
  }
}

Scenarios

Run your agent against multiple inputs and post pass/fail to the dashboard.

var result = await veval.RunScenarioAsync(
    scenarioName:       "my-scenario",
    agent:              service.ExecuteAsync,
    scenarioAssertions: [TraceAssert.NoErrors(), TraceAssert.MaxCost(0.10m)],
    items: [
        new ScenarioItem { Name = "q1", Input = "What is prompt caching?" },
        new ScenarioItem { Name = "replay", TraceId = "tr_...", Assertions = [TraceAssert.MaxCost(0.01m)] },
    ]
);
// result.Passed, result.PassCount, result.FailCount, result.Results

Python

result = await veval.run_scenario_async(
    scenario_name="my-scenario",
    agent=service.execute_async,
    scenario_assertions=[TraceAssert.no_errors(), TraceAssert.max_cost(0.10)],
    items=[
        ScenarioItem(name="q1", input="What is prompt caching?"),
        ScenarioItem(name="replay", trace_id="tr_...", assertions=[TraceAssert.max_cost(0.01)]),
    ],
)
# result.passed, result.pass_count, result.fail_count, result.results

Node

const result = await veval.runScenarioAsync(
  "my-scenario",
  (ctx) => service.execute(ctx),
  [TraceAssert.noErrors(), TraceAssert.maxCost(0.10)],
  [
    { name: "q1", input: "What is prompt caching?", assertions: [] },
    { name: "replay", trace_id: "tr_...", assertions: [TraceAssert.maxCost(0.01)] },
  ]
);
// result.passed, result.pass_count, result.fail_count, result.results

ScenarioItem fields: name (string), input (any), trace_id (string) — provide either input (live LLM) or trace_id (mocked replay), not both. assertions is always an array (can be empty). Pass items: null / omit items to fetch them from the dashboard by scenarioName.

Snapshots

Detect structural regressions by comparing step shape against a pinned golden trace.

// load golden once at startup
var golden = await veval.LoadSnapshotAsync("tr_golden...");

// inside RunAsync callback, after your agent runs
var diff = await veval.CompareSnapshotAsync("snapshot-name", golden!, ctx);
if (diff.HasChanges) { /* alert */ }

Python

golden = await veval.load_snapshot_async("tr_golden...")

# inside run_async callback, after your agent runs
diff = await veval.compare_snapshot_async("snapshot-name", golden, ctx)
if diff.has_changes:
    pass  # alert

Node

const golden = await veval.loadSnapshotAsync("tr_golden...");

// inside runAsync callback, after your agent runs
const diff = await veval.compareSnapshotAsync("snapshot-name", golden, ctx);
if (diff.has_changes) { /* alert */ }

SnapshotDiff fields (same names in all SDKs): has_changes (bool), added_steps (string[]), removed_steps (string[]), order_changes (string[]).

Naming convention cheat sheet

Concept	C#	Python	Node
SDK class	`VevalSdk`	`VevalSdk`	`VevalSdk`
Test SDK	`VevalTestSdk`	`VevalTestSdk`	`VevalTestSdk`
Options	`VevalOptions { ApiKey }`	`VevalOptions(api_key=)`	`{ apiKey: }`
Run agent	`RunAsync`	`run_async`	`runAsync`
Record step	`TrackStepAsync`	`track_step_async`	`trackStepAsync`
Trace metadata	`SetMetadata`	`set_metadata`	`setMetadata`
Step metadata	`handle.SetMeta`	`handle.set_meta`	`handle.setMeta`
Load replay	`WithReplay`	`with_replay`	`withReplay`
Run replay	`ReplayAsync`	`replay_async`	`replayAsync`
Run scenario	`RunScenarioAsync`	`run_scenario_async`	`runScenarioAsync`
Load snapshot	`LoadSnapshotAsync`	`load_snapshot_async`	`loadSnapshotAsync`
Compare snapshot	`CompareSnapshotAsync`	`compare_snapshot_async`	`compareSnapshotAsync`
Assertion factory	`TraceAssert.NoErrors()`	`TraceAssert.no_errors()`	`TraceAssert.noErrors()`
Context input	`ctx.Input`	`ctx.input`	`ctx.input`
Context trace ID	`ctx.TraceId`	`ctx.trace_id`	`ctx.traceId`
Last run status	`testSdk.LastStatus`	`test_sdk.last_status`	`testSdk.lastStatus`

Getting Started

Guides

Reference

LLM Reference

What Veval does

Install

Initialize

Core API

RunAsync — wrap a complete agent run

TrackStepAsync — record one LLM call or sub-operation

Nested steps

Trace-level metadata

Replay / Test SDK

ReplayAsync (lower-level)

Assertions

Scenarios

Snapshots

Naming convention cheat sheet

Getting Started

Guides

Reference

​What Veval does

​Install

​Initialize

​Core API

​RunAsync — wrap a complete agent run

​TrackStepAsync — record one LLM call or sub-operation

​Nested steps

​Trace-level metadata

​Replay / Test SDK

​ReplayAsync (lower-level)

​Assertions

​Scenarios

​Snapshots

​Naming convention cheat sheet

What Veval does

Install

Initialize

Core API

RunAsync — wrap a complete agent run

TrackStepAsync — record one LLM call or sub-operation

Nested steps

Trace-level metadata

Replay / Test SDK

ReplayAsync (lower-level)

Assertions

Scenarios

Snapshots

Naming convention cheat sheet