Client - Hipocap documentation

TypeScript
Python

LaminarClient

HTTP client for Laminar API operations.

import { LaminarClient } from '@lmnr-ai/lmnr';

const client = new LaminarClient({
  projectApiKey: process.env.LMNR_PROJECT_API_KEY,
});

// Tag a completed trace
await client.tags.tag(traceId, ['user-feedback-positive']);

Constructor parameters:

Name	Type	Default	Description
`projectApiKey`	`string`	`LMNR_PROJECT_API_KEY` env	API key
`baseUrl`	`string`	`https://api.lmnr.ai`	API base URL
`port`	`number`	`443`	API port

Resources:

client.tags.tag(traceId, tags)

Add tags to a completed trace.

await client.tags.tag('trace-uuid', ['positive-feedback', 'resolved']);

Example: Capture a trace ID and tag later

import { Laminar, LaminarClient, observe } from '@lmnr-ai/lmnr';

Laminar.initialize();
const client = new LaminarClient();

const traceId = await observe({ name: 'chat_completion' }, async () => {
  // ... your work ...
  return Laminar.getTraceId();
});

await Laminar.flush();

if (traceId) {
  await client.tags.tag(traceId, 'good');
}

Parameters:

Name	Type	Default	Description
`traceId`	`string`	—	Trace UUID
`tags`	`string[]` \| `string`	—	Tag(s) to add

Returns: Promise<any>Note: Call after Laminar.flush() to ensure trace exists.

client.datasets

Dataset operations.

// List datasets
const datasets = await client.datasets.listDatasets();

// Get by name
const dataset = await client.datasets.getDatasetByName('my-dataset');

// Push datapoints
await client.datasets.push({
  name: 'my-dataset',
  points: [{ data: { query: 'test' }, target: { answer: '42' } }],
  createDataset: true,
});

// Pull datapoints
const points = await client.datasets.pull({
  name: 'my-dataset',
  limit: 100,
});

client.datasets.listDatasets()

Returns: Promise<Dataset[]>

client.datasets.getDatasetByName(name)

Parameters:

Name	Type	Default	Description
`name`	`string`	—	Dataset name

Returns: Promise<Dataset[]>

client.datasets.pull(options)

Pull datapoints from a dataset.Parameters:

Name	Type	Default	Description
`options.name`	`string`	—	Dataset name
`options.id`	`string`	—	Dataset ID
`options.limit`	`number`	`100`	Max datapoints
`options.offset`	`number`	`0`	Pagination offset

Returns: Promise<GetDatapointsResponse<D, T>>

client.datasets.push(options)

Push datapoints to a dataset.Parameters:

Name	Type	Default	Description
`options.points`	`Datapoint<D, T>[]`	—	Datapoints to push
`options.name`	`string`	—	Dataset name
`options.id`	`string`	—	Dataset ID
`options.batchSize`	`number`	`100`	Batch size
`options.createDataset`	`boolean`	`false`	Create dataset if missing

Returns: Promise<PushDatapointsResponse | undefined>

client.evals

Evaluation operations.

// Initialize evaluation
const eval = await client.evals.init('my-eval', 'default-group');

// Create datapoint
await client.evals.createDatapoint({
  evalId: eval.id,
  data: { query: 'test' },
  target: { answer: '42' },
});

client.evals.init(name?, groupName?, metadata?)

Parameters:

Name	Type	Default	Description
`name`	`string`	—	Evaluation name
`groupName`	`string`	—	Group name
`metadata`	`Record<string, any>`	—	Evaluation metadata

Returns: Promise<InitEvaluationResponse>

client.evals.createDatapoint(options)

Parameters:

Name	Type	Default	Description
`options.evalId`	`string`	—	Evaluation UUID
`options.data`	`any`	—	Input data
`options.target`	`any`	—	Target/expected output
`options.metadata`	`Record<string, any>`	—	Datapoint metadata
`options.index`	`number`	—	Dataset index
`options.traceId`	`string`	—	Trace UUID

Returns: Promise<string>

client.evaluators.score(options)

Attach evaluator score to a trace or span.

await client.evaluators.score({
  name: 'quality',
  score: 0.95,
  traceId: 'trace-uuid',
});

Parameters:Pass exactly one of traceId or spanId.

Name	Type	Default	Description
`options.name`	`string`	—	Evaluator name
`options.score`	`number`	—	Score value
`options.metadata`	`Record<string, any>`	—	Score metadata
`options.traceId`	`string`	—	Trace UUID
`options.spanId`	`string`	—	Span UUID

Returns: Promise<void>

client.agent.run(options)

Run a browser agent.

const result = await client.agent.run({
  prompt: 'Find the pricing on example.com',
  maxSteps: 50,
  returnScreenshots: true,
});

Parameters:

Name	Type	Default	Description
`prompt`	`string`	—	Agent instruction (required)
`parentSpanContext`	`string`	Current span	Parent trace context
`model`	`string`	—	Model to use
`stream`	`boolean`	`false`	Stream responses
`maxSteps`	`number`	`100`	Maximum steps
`startUrl`	`string`	—	Starting URL
`timeout`	`number`	—	Timeout in ms
`returnScreenshots`	`boolean`	`false`	Include screenshots
`returnAgentState`	`boolean`	`false`	Return agent state
`returnStorageState`	`boolean`	`false`	Return storage state
`disableGiveControl`	`boolean`	`false`	Disable “give control”
`enableThinking`	`boolean`	`true`	Enable thinking

Returns:

Promise<AgentOutput> when stream is false
Promise<ReadableStream<RunAgentResponseChunk>> when stream is true

LaminarClient

Synchronous HTTP client for Laminar API operations.

from lmnr import LaminarClient

client = LaminarClient()

# Tag a completed trace
client.tags.tag(trace_id, ["user-feedback-positive"])

Constructor parameters:

Name	Type	Default	Description
`project_api_key`	`str`	`LMNR_PROJECT_API_KEY` env	API key
`base_url`	`str`	`https://api.lmnr.ai`	API base URL
`port`	`int`	From URL	API port
`timeout`	`int`	`3600`	Request timeout (seconds)

Resources:

client.tags.tag(trace_id, tags)

Add tags to a completed trace.

from lmnr import Laminar, LaminarClient, observe

Laminar.initialize()
client = LaminarClient()

@observe()
def my_trace():
    return Laminar.get_trace_id()

trace_id = my_trace()
Laminar.flush()

if trace_id:
    client.tags.tag(trace_id, ["user-feedback-positive"])

Parameters:

Name	Type	Default	Description
`trace_id`	`str` \| `int` \| `uuid.UUID`	—	Trace identifier
`tags`	`list[str]` \| `str`	—	Tag(s) to add

Returns: list[dict] (returns [] on 404)Note: Call after the trace has ended (use Laminar.flush() for short-lived scripts).

client.close()

Close the underlying HTTP client.Returns: None

client.datasets.pull(name=None, id=None, limit=100, offset=0)

Pull datapoints from a dataset.Parameters:

Name	Type	Default	Description
`name`	`str`	`None`	Dataset name
`id`	`uuid.UUID`	`None`	Dataset ID
`limit`	`int`	`100`	Max datapoints
`offset`	`int`	`0`	Pagination offset

Returns: GetDatapointsResponse

client.datasets.push(points, name=None, id=None, batch_size=100, create_dataset=False)

Push datapoints to a dataset.Parameters:

Name	Type	Default	Description
`points`	`list[Datapoint]`	—	Datapoints to push
`name`	`str`	`None`	Dataset name
`id`	`uuid.UUID`	`None`	Dataset ID
`batch_size`	`int`	`100`	Batch size
`create_dataset`	`bool`	`False`	Create dataset if missing

Returns: PushDatapointsResponse | None

client.evals.init(name=None, group_name=None, metadata=None)

Initialize an evaluation.Parameters:

Name	Type	Default	Description
`name`	`str`	`None`	Evaluation name
`group_name`	`str`	`None`	Group name
`metadata`	`dict`	`None`	Evaluation metadata

Returns: InitEvaluationResponse

client.evaluators.score(…)

Create a score for a trace or span.Parameters:Pass exactly one of trace_id or span_id.

Name	Type	Default	Description
`name`	`str`	—	Evaluator name
`score`	`float`	—	Score value
`trace_id`	`str` \| `int` \| `uuid.UUID`	`None`	Trace identifier
`span_id`	`str` \| `int` \| `uuid.UUID`	`None`	Span identifier
`metadata`	`dict`	`None`	Score metadata

Returns: None

AsyncLaminarClient

Async variant of LaminarClient.

from lmnr import AsyncLaminarClient

client = AsyncLaminarClient()
await client.tags.tag(trace_id, ["feedback"])
await client.close()

Constructor parameters:

Name	Type	Default	Description
`project_api_key`	`str`	`None`	API key (defaults to `LMNR_PROJECT_API_KEY`)
`base_url`	`str`	`None`	API base URL (defaults to `LMNR_BASE_URL` or `https://api.lmnr.ai`)
`port`	`int`	`None`	API port (defaults to `443`)
`timeout`	`int`	`3600`	Request timeout (seconds)

await client.close()

Returns: None

Python

Documentation Index

​LaminarClient

​client.tags.tag(traceId, tags)

​client.datasets

​client.datasets.listDatasets()

​client.datasets.getDatasetByName(name)

​client.datasets.pull(options)

​client.datasets.push(options)

​client.evals

​client.evals.init(name?, groupName?, metadata?)

​client.evals.createDatapoint(options)

​client.evaluators.score(options)

​client.agent.run(options)

​LaminarClient

​client.tags.tag(trace_id, tags)

​client.close()

​client.datasets.pull(name=None, id=None, limit=100, offset=0)

​client.datasets.push(points, name=None, id=None, batch_size=100, create_dataset=False)

​client.evals.init(name=None, group_name=None, metadata=None)

​client.evaluators.score(…)

​AsyncLaminarClient

​await client.close()

LaminarClient

client.tags.tag(traceId, tags)

client.datasets

client.datasets.listDatasets()

client.datasets.getDatasetByName(name)

client.datasets.pull(options)

client.datasets.push(options)

client.evals

client.evals.init(name?, groupName?, metadata?)

client.evals.createDatapoint(options)

client.evaluators.score(options)

client.agent.run(options)

LaminarClient

client.tags.tag(trace_id, tags)

client.close()

client.datasets.pull(name=None, id=None, limit=100, offset=0)

client.datasets.push(points, name=None, id=None, batch_size=100, create_dataset=False)

client.evals.init(name=None, group_name=None, metadata=None)

client.evaluators.score(…)

AsyncLaminarClient

await client.close()