Send OpenTelemetry Traces to Weave

Overview

Weave supports ingestion of OpenTelemetry compatible trace data through a dedicated endpoint. This endpoint allows you to send OTLP (OpenTelemetry Protocol) formatted trace data directly to your Weave project.

Endpoint details

Path: /otel/v1/traces Method: POST Content-Type: application/x-protobuf Base URL: The base URL for the OTEL trace endpoint depends on your W&B deployment type:

Multi-tenant Cloud:
https://trace.wandb.ai/otel/v1/traces
Dedicated Cloud and Self-Managed instances:
https://<your-subdomain>.wandb.io/traces/otel/v1/traces

Replace <your-subdomain> with your organization’s unique W&B domain, e.g., acme.wandb.io.

Authentication

Standard W&B authentication is used. You must have write permissions to the project where you’re sending trace data.

Required Headers

project_id: <your_entity>/<your_project_name>
Authorization=Basic <Base64 Encoding of api:$WANDB_API_KEY>

Examples:

You must modify the following fields before you can run the code samples below:

WANDB_API_KEY: You can get this from https://wandb.ai/authorize.
Entity: You can only log traces to the project under an entity that you have access to. You can find your entity name by visiting your W&N dashboard at [https://wandb.ai/home], and checking the Teams field in the left sidebar.
Project Name: Choose a fun name!
OPENAI_API_KEY: You can obtain this from the OpenAI dashboard.

OpenInference Instrumentation:

This example shows how to use the OpenAI instrumentation. There are many more available which you can find in the official repository: https://github.com/Arize-ai/openinference First, install the required dependencies:

pip install openai openinference-instrumentation-openai opentelemetry-exporter-otlp-proto-http

Performance Recommendation: Always use BatchSpanProcessor instead of SimpleSpanProcessor when sending traces to Weave. SimpleSpanProcessor exports spans synchronously, potentially impacting the performance of other workloads. These examples illustrate BatchSpanProcessor, which is recommended in production because it batches spans asynchronously and efficiently.

Next, paste the following code into a python file such as openinference_example.py

import base64
import openai
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, BatchSpanProcessor
from openinference.instrumentation.openai import OpenAIInstrumentor

OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
WANDB_BASE_URL = "https://trace.wandb.ai"
PROJECT_ID = "<your-entity>/<your-project>"

OTEL_EXPORTER_OTLP_ENDPOINT = f"{WANDB_BASE_URL}/otel/v1/traces"

# Can be found at https://wandb.ai/authorize
WANDB_API_KEY = "<your-wandb-api-key>"
AUTH = base64.b64encode(f"api:{WANDB_API_KEY}".encode()).decode()

OTEL_EXPORTER_OTLP_HEADERS = {
    "Authorization": f"Basic {AUTH}",
    "project_id": PROJECT_ID,
}

tracer_provider = trace_sdk.TracerProvider()

# Configure the OTLP exporter
exporter = OTLPSpanExporter(
    endpoint=OTEL_EXPORTER_OTLP_ENDPOINT,
    headers=OTEL_EXPORTER_OTLP_HEADERS,
)

# Add the exporter to the tracer provider
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))

# Optionally, print the spans to the console.
tracer_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

def main():
    client = openai.OpenAI(api_key=OPENAI_API_KEY)
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Describe OTEL in a single sentence."}],
        max_tokens=20,
        stream=True,
        stream_options={"include_usage": True},
    )
    for chunk in response:
        if chunk.choices and (content := chunk.choices[0].delta.content):
            print(content, end="")

if __name__ == "__main__":
    main()

Finally, once you have set the fields specified above to their correct values, run the code:

python openinference_example.py

OpenLLMetry Instrumentation:

The following example shows how to use the OpenAI instrumentation. Additional examples are available at https://github.com/traceloop/openllmetry/tree/main/packages. First install the required dependencies:

pip install openai opentelemetry-instrumentation-openai opentelemetry-exporter-otlp-proto-http

Next, paste the following code into a python file such as openllmetry_example.py. Note that this is the same code as above, except the OpenAIInstrumentor is imported from opentelemetry.instrumentation.openai instead of openinference.instrumentation.openai

import base64
import openai
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, BatchSpanProcessor
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
WANDB_BASE_URL = "https://trace.wandb.ai"
PROJECT_ID = "<your-entity>/<your-project>"

OTEL_EXPORTER_OTLP_ENDPOINT = f"{WANDB_BASE_URL}/otel/v1/traces"

# Can be found at https://wandb.ai/authorize
WANDB_API_KEY = "<your-wandb-api-key>"
AUTH = base64.b64encode(f"api:{WANDB_API_KEY}".encode()).decode()

OTEL_EXPORTER_OTLP_HEADERS = {
    "Authorization": f"Basic {AUTH}",
    "project_id": PROJECT_ID,
}

tracer_provider = trace_sdk.TracerProvider()

# Configure the OTLP exporter
exporter = OTLPSpanExporter(
    endpoint=OTEL_EXPORTER_OTLP_ENDPOINT,
    headers=OTEL_EXPORTER_OTLP_HEADERS,
)

# Add the exporter to the tracer provider
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))

# Optionally, print the spans to the console.
tracer_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

def main():
    client = openai.OpenAI(api_key=OPENAI_API_KEY)
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Describe OTEL in a single sentence."}],
        max_tokens=20,
        stream=True,
        stream_options={"include_usage": True},
    )
    for chunk in response:
        if chunk.choices and (content := chunk.choices[0].delta.content):
            print(content, end="")

if __name__ == "__main__":
    main()

Finally, once you have set the fields specified above to their correct values, run the code:

python openllmetry_example.py

Without Instrumentation

If you would prefer to use OTEL directly instead of an instrumentation package, you may do so. Span attributes will be parsed according to the OpenTelemetry semantic conventions described at https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/. First, install the required dependencies:

pip install openai opentelemetry-sdk opentelemetry-api opentelemetry-exporter-otlp-proto-http

Next, paste the following code into a python file such as opentelemetry_example.py

import json
import base64
import openai
from opentelemetry import trace
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, BatchSpanProcessor

OPENAI_API_KEY = "YOUR_OPENAI_API_KEY"
WANDB_BASE_URL = "https://trace.wandb.ai"
PROJECT_ID = "<your-entity>/<your-project>"

OTEL_EXPORTER_OTLP_ENDPOINT = f"{WANDB_BASE_URL}/otel/v1/traces"

# Can be found at https://wandb.ai/authorize
WANDB_API_KEY = "<your-wandb-api-key>"
AUTH = base64.b64encode(f"api:{WANDB_API_KEY}".encode()).decode()

OTEL_EXPORTER_OTLP_HEADERS = {
    "Authorization": f"Basic {AUTH}",
    "project_id": PROJECT_ID,
}

tracer_provider = trace_sdk.TracerProvider()

# Configure the OTLP exporter
exporter = OTLPSpanExporter(
    endpoint=OTEL_EXPORTER_OTLP_ENDPOINT,
    headers=OTEL_EXPORTER_OTLP_HEADERS,
)

# Add the exporter to the tracer provider
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))

# Optionally, print the spans to the console.
tracer_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))

trace.set_tracer_provider(tracer_provider)
# Creates a tracer from the global tracer provider
tracer = trace.get_tracer(__name__)
tracer.start_span('name=standard-span')

def my_function():
    with tracer.start_as_current_span("outer_span") as outer_span:
        client = openai.OpenAI()
        input_messages=[{"role": "user", "content": "Describe OTEL in a single sentence."}]
        # This will only appear in the side panel
        outer_span.set_attribute("input.value", json.dumps(input_messages))
        # This follows conventions and will appear in the dashboard
        outer_span.set_attribute("gen_ai.system", 'openai')
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=input_messages,
            max_tokens=20,
            stream=True,
            stream_options={"include_usage": True},
        )
        out = ""
        for chunk in response:
            if chunk.choices and (content := chunk.choices[0].delta.content):
                out += content
        # This will only appear in the side panel
        outer_span.set_attribute("output.value", json.dumps({"content": out}))

if __name__ == "__main__":
    my_function()

Finally, once you have set the fields specified above to their correct values, run the code:

python opentelemetry_example.py

The span attribute prefixes gen_ai and openinference are used to determine which convention to use, if any, when interpreting the trace. If neither key is detected, then all span attributes are visible in the trace view. The full span is available in the side panel when you select a trace.

Organize OTEL traces into threads

Add specific span attributes to organize your OpenTelemetry traces into Weave threads, then use Weave’s Thread UI to analyze related operations like multi-turn conversations or user sessions in Weave’s thread UI. Add the following attributes to your OTEL spans to enable thread grouping:

wandb.thread_id: Groups spans into a specific thread
wandb.is_turn: Marks a span as a conversation turn (appears as a row in the thread view)

The following code shows several examples of organizing OTEL traces into Weave threads. They use wandb.thread_id to group related operations, and use wandb.is_turn to view high level operations as rows in the thread view. Each example performs the followingmark high-level operations that appear as rows in the thread view).

Initial set up

Use this configuration to run these examples:

import base64
import json
import os
from opentelemetry import trace
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, BatchSpanProcessor

# Configuration
ENTITY = "YOUR_ENTITY"
PROJECT = "YOUR_PROJECT"
PROJECT_ID = f"{ENTITY}/{PROJECT}"
WANDB_API_KEY = os.environ["WANDB_API_KEY"]

# Set up OTLP endpoint and headers
OTEL_EXPORTER_OTLP_ENDPOINT="https://trace.wandb.ai/otel/v1/traces"
AUTH = base64.b64encode(f"api:{WANDB_API_KEY}".encode()).decode()
OTEL_EXPORTER_OTLP_HEADERS = {
    "Authorization": f"Basic {AUTH}",
    "project_id": PROJECT_ID,
}

# Initialize tracer provider
tracer_provider = trace_sdk.TracerProvider()

# Configure the OTLP exporter
exporter = OTLPSpanExporter(
    endpoint=OTEL_EXPORTER_OTLP_ENDPOINT,
    headers=OTEL_EXPORTER_OTLP_HEADERS,
)

# Add the exporter to the tracer provider
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))

# Optionally, print the spans to the console
tracer_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))

# Set the tracer provider
trace.set_tracer_provider(tracer_provider)

# Create a tracer from the global tracer provider
tracer = trace.get_tracer(__name__)

Trace a basic single-turn thread

def example_1_basic_thread_and_turn():
    """Example 1: Basic thread with a single turn"""
    print("\n=== Example 1: Basic Thread and Turn ===")

    # Create a thread context
    thread_id = "thread_example_1"

    # This span represents a turn (direct child of thread)
    with tracer.start_as_current_span("process_user_message") as turn_span:
        # Set thread attributes
        turn_span.set_attribute("wandb.thread_id", thread_id)
        turn_span.set_attribute("wandb.is_turn", True)

        # Add some example attributes
        turn_span.set_attribute("input.value", "Hello, help me with setup")

        # Simulate some work with nested spans
        with tracer.start_as_current_span("generate_response") as nested_span:
            # This is a nested call within the turn, so is_turn should be false or unset
            nested_span.set_attribute("wandb.thread_id", thread_id)
            # wandb.is_turn is not set or set to False for nested calls

            response = "I'll help you get started with the setup process."
            nested_span.set_attribute("output.value", response)

        turn_span.set_attribute("output.value", response)
        print(f"Turn completed in thread: {thread_id}")

def main():
    example_1_basic_thread_and_turn<A()
if __name__ == "__main__":
    main()

Trace a multi-turn conversation sharing one thread ID

def example_2_multiple_turns():
    """Example 2: Multiple turns in a single thread"""
    print("\n=== Example 2: Multiple Turns in Thread ===")

    thread_id = "thread_conversation_123"

    # Turn 1
    with tracer.start_as_current_span("process_message_turn1") as turn1_span:
        turn1_span.set_attribute("wandb.thread_id", thread_id)
        turn1_span.set_attribute("wandb.is_turn", True)
        turn1_span.set_attribute("input.value", "What programming languages do you recommend?")

        # Nested operations
        with tracer.start_as_current_span("analyze_query") as analyze_span:
            analyze_span.set_attribute("wandb.thread_id", thread_id)
            # No is_turn attribute or set to False for nested spans

        response1 = "I recommend Python for beginners and JavaScript for web development."
        turn1_span.set_attribute("output.value", response1)
        print(f"Turn 1 completed in thread: {thread_id}")

    # Turn 2
    with tracer.start_as_current_span("process_message_turn2") as turn2_span:
        turn2_span.set_attribute("wandb.thread_id", thread_id)
        turn2_span.set_attribute("wandb.is_turn", True)
        turn2_span.set_attribute("input.value", "Can you explain Python vs JavaScript?")

        # Nested operations
        with tracer.start_as_current_span("comparison_analysis") as compare_span:
            compare_span.set_attribute("wandb.thread_id", thread_id)
            compare_span.set_attribute("wandb.is_turn", False)  # Explicitly false for nested

        response2 = "Python excels at data science while JavaScript dominates web development."
        turn2_span.set_attribute("output.value", response2)
        print(f"Turn 2 completed in thread: {thread_id}")

def main():
    example_2_multiple_turns()
if __name__ == "__main__":
    main()

Trace deeply nested operations and mark only the outermost span as a turn

def example_3_complex_nested_structure():
    """Example 3: Complex nested structure with multiple levels"""
    print("\n=== Example 3: Complex Nested Structure ===")

    thread_id = "thread_complex_456"

    # Turn with multiple levels of nesting
    with tracer.start_as_current_span("handle_complex_request") as turn_span:
        turn_span.set_attribute("wandb.thread_id", thread_id)
        turn_span.set_attribute("wandb.is_turn", True)
        turn_span.set_attribute("input.value", "Analyze this code and suggest improvements")

        # Level 1 nested operation
        with tracer.start_as_current_span("code_analysis") as analysis_span:
            analysis_span.set_attribute("wandb.thread_id", thread_id)
            # No is_turn for nested operations

            # Level 2 nested operation
            with tracer.start_as_current_span("syntax_check") as syntax_span:
                syntax_span.set_attribute("wandb.thread_id", thread_id)
                syntax_span.set_attribute("result", "No syntax errors found")

            # Another Level 2 nested operation
            with tracer.start_as_current_span("performance_check") as perf_span:
                perf_span.set_attribute("wandb.thread_id", thread_id)
                perf_span.set_attribute("result", "Found 2 optimization opportunities")

        # Another Level 1 nested operation
        with tracer.start_as_current_span("generate_suggestions") as suggest_span:
            suggest_span.set_attribute("wandb.thread_id", thread_id)
            suggestions = ["Use list comprehension", "Consider caching results"]
            suggest_span.set_attribute("suggestions", json.dumps(suggestions))

        turn_span.set_attribute("output.value", "Analysis complete with 2 improvement suggestions")
        print(f"Complex turn completed in thread: {thread_id}")

def main():
    example_3_complex_nested_structure()
if __name__ == "__main__":
    main()

Trace background operations that belong to a thread but aren't turns

def example_4_non_turn_operations():
    """Example 4: Operations that are part of a thread but not turns"""
    print("\n=== Example 4: Non-Turn Thread Operations ===")

    thread_id = "thread_background_789"

    # Background operation that's part of thread but not a turn
    with tracer.start_as_current_span("background_indexing") as bg_span:
        bg_span.set_attribute("wandb.thread_id", thread_id)
        # wandb.is_turn is unset or false - this is not a turn
        bg_span.set_attribute("wandb.is_turn", False)
        bg_span.set_attribute("operation", "Indexing conversation history")
        print(f"Background operation in thread: {thread_id}")

    # Actual turn in the same thread
    with tracer.start_as_current_span("user_query") as turn_span:
        turn_span.set_attribute("wandb.thread_id", thread_id)
        turn_span.set_attribute("wandb.is_turn", True)
        turn_span.set_attribute("input.value", "Search my previous conversations")
        turn_span.set_attribute("output.value", "Found 5 relevant conversations")
        print(f"Turn completed in thread: {thread_id}")

def main():
    example_4_non_turn_operations()
if __name__ == "__main__":
    main()

After sending these traces, you can view them in the Weave UI under the Threads tab, where they’ll be grouped by thread_id and each turn will appear as a separate row.

Attribute Mappings

Weave automatically maps OpenTelemetry span attributes from various instrumentation frameworks to its internal data model. When multiple attribute names map to the same field, Weave applies them in priority order, allowing frameworks to coexist in the same traces.

Supported Frameworks

Weave supports attribute conventions from the following observability frameworks and SDKs:

OpenTelemetry GenAI: Standard semantic conventions for generative AI (gen_ai.*)
OpenInference: Arize AI’s instrumentation library (input.value, output.value, llm.*, openinference.*)
Vercel AI SDK: Vercel’s AI SDK attributes (ai.prompt, ai.response, ai.model.*, ai.usage.*)
MLflow: MLflow tracking attributes (mlflow.spanInputs, mlflow.spanOutputs)
Traceloop: OpenLLMetry instrumentation (traceloop.entity.*, traceloop.span.kind)
Google Vertex AI: Vertex AI agent attributes (gcp.vertex.agent.*)
OpenLit: OpenLit observability attributes (gen_ai.content.completion)
Langfuse: Langfuse tracing attributes (langfuse.startTime, langfuse.endTime)

Attribute Reference

Attribute Field Name	W&B Mapping	Description	Type	Example
`ai.prompt`	`inputs`	User prompt text or messages.	String, list, dict	`"Write a short haiku about summer."`
`gen_ai.prompt`	`inputs`	AI model prompt or message array.	List, dict, string	`[{"role":"user","content":"abc"}]`
`input.value`	`inputs`	Input value for model invocation.	String, list, dict	`{"text":"Tell a joke"}`
`mlflow.spanInputs`	`inputs`	Span input data.	String, list, dict	`["prompt text"]`
`traceloop.entity.input`	`inputs`	Entity input data.	String, list, dict	`"Translate this to French"`
`gcp.vertex.agent.tool_call_args`	`inputs`	Tool call arguments.	Dict	`{"args":{"query":"weather in SF"}}`
`gcp.vertex.agent.llm_request`	`inputs`	LLM request payload.	Dict	`{"contents":[{"role":"user","parts":[...]}]}`
`input`	`inputs`	Generic input value.	String, list, dict	`"Summarize this text"`
`inputs`	`inputs`	Generic input array.	List, dict, string	`["Summarize this text"]`
`ai.response`	`outputs`	Model response text or data.	String, list, dict	`"Here is a haiku..."`
`gen_ai.completion`	`outputs`	AI completion result.	String, list, dict	`"Completion text"`
`output.value`	`outputs`	Output value from model.	String, list, dict	`{"text":"Answer text"}`
`mlflow.spanOutputs`	`outputs`	Span output data.	String, list, dict	`["answer"]`
`gen_ai.content.completion`	`outputs`	Content completion result.	String	`"Answer text"`
`traceloop.entity.output`	`outputs`	Entity output data.	String, list, dict	`"Answer text"`
`gcp.vertex.agent.tool_response`	`outputs`	Tool execution response.	Dict, string	`{"toolResponse":"ok"}`
`gcp.vertex.agent.llm_response`	`outputs`	LLM response payload.	Dict, string	`{"candidates":[...]}`
`output`	`outputs`	Generic output value.	String, list, dict	`"Answer text"`
`outputs`	`outputs`	Generic output array.	List, dict, string	`["Answer text"]`
`gen_ai.usage.input_tokens`	`usage.input_tokens`	Number of input tokens consumed.	Int	`42`
`gen_ai.usage.prompt_tokens`	`usage.prompt_tokens`	Number of prompt tokens consumed.	Int	`30`
`llm.token_count.prompt`	`usage.prompt_tokens`	Prompt token count.	Int	`30`
`ai.usage.promptTokens`	`usage.prompt_tokens`	Prompt tokens consumed.	Int	`30`
`gen_ai.usage.completion_tokens`	`usage.completion_tokens`	Number of completion tokens generated.	Int	`40`
`llm.token_count.completion`	`usage.completion_tokens`	Completion token count.	Int	`40`
`ai.usage.completionTokens`	`usage.completion_tokens`	Completion tokens generated.	Int	`40`
`llm.usage.total_tokens`	`usage.total_tokens`	Total tokens used in request.	Int	`70`
`llm.token_count.total`	`usage.total_tokens`	Total token count.	Int	`70`
`gen_ai.system`	`attributes.system`	System prompt or instructions.	String	`"You are a helpful assistant."`
`llm.system`	`attributes.system`	System prompt or instructions.	String	`"You are a helpful assistant."`
`weave.span.kind`	`attributes.kind`	Span type or category.	String	`"llm"`
`traceloop.span.kind`	`attributes.kind`	Span type or category.	String	`"llm"`
`openinference.span.kind`	`attributes.kind`	Span type or category.	String	`"llm"`
`gen_ai.response.model`	`attributes.model`	Model identifier.	String	`"gpt-4o"`
`llm.model_name`	`attributes.model`	Model identifier.	String	`"gpt-4o-mini"`
`ai.model.id`	`attributes.model`	Model identifier.	String	`"gpt-4o"`
`llm.provider`	`attributes.provider`	Model provider name.	String	`"openai"`
`ai.model.provider`	`attributes.provider`	Model provider name.	String	`"openai"`
`gen_ai.request`	`attributes.model_parameters`	Model generation parameters.	Dict	`{"temperature":0.7,"max_tokens":256}`
`llm.invocation_parameters`	`attributes.model_parameters`	Model invocation parameters.	Dict	`{"temperature":0.2}`
`wandb.display_name`	`display_name`	Custom display name for UI.	String	`"User Message"`
`gcp.vertex.agent.session_id`	`thread_id`	Session or thread identifier.	String	`"thread_123"`
`wandb.thread_id`	`thread_id`	Thread identifier for conversations.	String	`"thread_123"`
`wb_run_id`	`wb_run_id`	Associated W&B run identifier.	String	`"abc123"`
`wandb.wb_run_id`	`wb_run_id`	Associated W&B run identifier.	String	`"abc123"`
`gcp.vertex.agent.session_id`	`is_turn`	Marks span as conversation turn.	Boolean	`true`
`wandb.is_turn`	`is_turn`	Marks span as conversation turn.	Boolean	`true`
`langfuse.startTime`	`start_time` (override)	Override span start timestamp.	Timestamp (ISO8601/unix ns)	`"2024-01-01T12:00:00Z"`
`langfuse.endTime`	`end_time` (override)	Override span end timestamp.	Timestamp (ISO8601/unix ns)	`"2024-01-01T12:00:01Z"`

Limitations

The Weave UI does not support rendering OTEL trace tool calls the Chat view. They appear as raw JSON, instead.

Guides

Cookbooks

Reference

Open Source

Community

Overview

Endpoint details

Authentication

Required Headers

Examples:

OpenInference Instrumentation:

OpenLLMetry Instrumentation:

Without Instrumentation

Organize OTEL traces into threads

Attribute Mappings

Supported Frameworks

Attribute Reference

Limitations

Guides

Cookbooks

Reference

Open Source

Community

​Overview

​Endpoint details

​Authentication

​Required Headers

​Examples:

​OpenInference Instrumentation:

​OpenLLMetry Instrumentation:

​Without Instrumentation

​Organize OTEL traces into threads

​Attribute Mappings

​Supported Frameworks

​Attribute Reference

​Limitations

Overview

Endpoint details

Authentication

Required Headers

Examples:

OpenInference Instrumentation:

OpenLLMetry Instrumentation:

Without Instrumentation

Organize OTEL traces into threads

Attribute Mappings

Supported Frameworks

Attribute Reference

Limitations