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Fetches trace logs and returns them as a paginated response.

Get Trace Logs

Answer Matches Target

Will be automatically populated if this score was from a deployed evaluation metric.

Evaluation Metric Id

Name

Reason

Score

EvaluationScoreSchema

FilterOperator

Inputs for LLM inference such as messages, functions and additional parameters.

Controls how the model responds to function calls. â€œautoâ€ means the model can pick between an end-user or calling a function. To specifying a particular function name use a dictionary with key=â€œnameâ€ and value=â€œmy_functionâ€. This will force the model to call that function.

Function Call

A list of functions the model may generate JSON inputs for. Assumes every item in list has key 'name', 'description' and 'parameters'

Functions

Messages

Model

Provider

InputsSchema

Content

MessageSchema

Additional parameters to run inference of LLMs, such as temperature, top_p, etc.
Following the OpenAI API format.

Frequency Penalty

Max Length

Presence Penalty

Response format. See OpenAI docs for definition

Response Format

Safe Prompt

Temp

Top P

ParamsSchema

Role

TimeRange

Annotation Name

Created At

Defined Annotation Id

Trace Id

User Email Address

User Id

Value

TraceLogAnnotationSchema

Comment

TraceLogCommentSchema

Caption

TraceLogImage

Any annotations on log which were collected on Parea frontend. It maps annoation criterion ID to a dictionary mapping user_id (Parea user ID) to annotation.

Annotations

If specified, evals given with `evaluation_metric_names` will be applied to this log with this fraction.

Apply Eval Frac

Cache Hit

Children

IDs of any children. Will be automatically populated.

Children Ids

Children Logs

Any comments on log which were collected on Parea frontend.

Comments

If this log was a LLM call, this will contain the configuration used for the call.

If this was a LLM call, this will contain the cost of the call.

Cost

Optionally, provide the ID of the used deployed prompt in this log.

Deployment Id

Depth/level of nestedness of span in overall trace. Root-level trace is 0 and it always increments by 1.

Depth

End Timestamp

End User Identifier

If `status=error`, this should contain any additional information such as the stacktrace

Error

Evaluation Metric Ids

Names of evaluation metrics deployed on Parea which should be applied to this log.

Evaluation Metric Names

The execution number of span in trace. It starts with 0 and increments by 1 with every span.

Execution Order

If given, will be used to associate this log with an experiment.

Experiment Uuid

Feedback Score

Fill Children

Images

If this was a LLM call, this will contain the number of tokens in the input.

Input Tokens

Key-value pair inputs of this trace. Note, there is a special field to capture messages in LLM calls. You can still use it in the case of LLM calls to track the key-value pairs for prompt templates.

Inputs

Latency

If specified, this log and its entire associated trace will logged with this probability. Must be between 0 and 1 (incl.). Defaults to 1.0 (i.e., keeping all logs)

Log Sample Rate

Organization ID associated with Parea API key. Will be automatically determined from API key

Organization Id

Response of this step/log/function. If response isnâ€™t a string, it needs to be serialized to a string.

Output

If provided, will be used as output for any specified evaluation metric.

Output For Eval Metrics

If this was a LLM call, this will contain the number of tokens in the output.

Output Tokens

If given, current trace will be a child of this trace. If current child is not a child, `parent_trace_id` should be equal to `trace_id`

Parent Trace Id

Name of the project with which the trace/log should be associated with. Must be provided if `project_uuid` is not provided

Project Name

UUID of project with which this log is associated. Will be automatically filled-in by SDKs

Project Uuid

This is the UUID of the root trace/span of this trace. If current trace is the root trace, `root_trace_id` must be equal to `trace_id`

Root Trace Id

Any scores/eval results associated with this log.

Scores

Unique identifier for a session. Can you be used to associated multiple logs in e.g. chat applications.

Session Id

Start Timestamp

Status

The target or â€œgold standardâ€ response for the inputs of this log.

Target

If this was a LLM call, this will contain the time taken to generate the first token.

Time To First Token

If this was a LLM call, this will contain the total number of tokens in the input and output.

Total Tokens

UUID of the trace log. Ex: e3267953-a16f-47f5-b37e-622dbb29d730

Trace Name

TraceLogTreeSchema

Location

Message

Error Type

ValidationError

QueryParamsPaginationPublic

APIKeyHeader

PaginatedResponse[TraceLogTreeSchema]

HTTPValidationError

Get Trace Logs Api

What is Parea AI?

Evaluation Quickstart

Monitor your LLM requests and application functions.

Monitoring Quickstart

Evaluation

Overview

Record discrete events or related events in your application from LLM requests/chains to functions.

Logging and Tracing

Enrich events with context and details to facilitate analysis and debugging.

Metadata

Record feedback from users on the quality LLM results.

Feedback

Use details from a trace log to build datasets.

Dataset from Trace

From Trace to Playground

Organize logs, traces & experiments into projects.

Projects

Attach evaluations to a trace to identify failure cases

Evaluations in Trace

OpenAI auto-logging also works with the new OpenAI Assistants Python API.

OpenAI Assistant

Using templated messages in auto-traced OpenAI & Anthropic calls

Templated LLM Calls

Incorporate human review into your AI software evaluation process

Criterion

Logs View

Annotation Queue

Automatically create LLM evals aligned with manual annotations

Self-improving LLM Eval

Compare

Add functions to prompts in the playground.

Function calling

Datasets

Add evaluation metrics to the playground.

Evaluation metrics

Evaluate your prompts on a dataset with evaluation metrics.

Trigger experiments

Deploy versioned prompts and use them via the SDK.

Deployed Prompts

Instrumenting LLM frameworks & provider SDKs with Parea AI

Instrumenting your Langchain applications with Parea AI

LangChain

Instrument & test `instructor` code with Parea AI

Instructor

Instrumenting your DSPy application with Parea AI

DSPy

Instrumenting your LiteLLM proxy with Parea AI

LiteLLM Proxy

Instrumenting your SGLang application with Parea AI

SGLang

Instrumenting your OpenAI calls made through Trigger.dev with Parea

Trigger.dev

On-premise deployment of Parea in your environment

On-premise deployment of Parea in your environment via Docker

Docker

Tutorial on improving a Langchain RAG application using Parea's Evals, Tracing, and Playground.

Optimize a LangChain RAG App

How many samples are necessary to achieve good performance with DSPy?

Optimize a RAG DSPy Application

REST API Walkthrough

Leverage user feedback to run A/B tests of prompts, models & other approaches

A/B Testing of LLM Apps

You will need an API Key and Organization to use the API/SDK.

Authentication

Create a project or get the project if it already exists. Is used to find out UUID of project.

Create or get Project

Health Check

Log a (LLM) span to visualize inference results, or chains.

General

Tracing

Experiments

LLM Proxy

Datasets

SDK

Get Trace Logs

Authorizations

Body

Response