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SideSeat

Other Frameworks

SideSeat works with any framework that emits OpenTelemetry traces. This page covers frameworks without dedicated integration pages.

LangChain

Section titled “LangChain”
Terminal window
pip install langchain langchain-openai "sideseat[langchain]"
 
from sideseat import SideSeat, Frameworks
from langchain_openai import ChatOpenAI


SideSeat(framework=Frameworks.LangChain)


llm = ChatOpenAI(model="gpt-5-mini")
response = llm.invoke("Hello!")
print(response.content)

PydanticAI

Section titled “PydanticAI”
Terminal window
pip install pydantic-ai "sideseat[pydantic-ai]"
 
from sideseat import SideSeat, Frameworks
from pydantic_ai import Agent


SideSeat(framework=Frameworks.PydanticAI)


agent = Agent('openai:gpt-5-mini')
result = agent.run_sync('What is the capital of France?')
print(result.output)

LlamaIndex

Section titled “LlamaIndex”

LlamaIndex does not emit OpenTelemetry itself — it has its own instrumentation dispatcher and no opentelemetry dependency. Bridging it needs the OpenInference instrumentor, which SideSeat does not bundle as an extra, so install it alongside:

Terminal window
pip install llama-index sideseat openinference-instrumentation-llama-index
 
from sideseat import SideSeat
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


# auto_instrument=False: without a LlamaIndex branch the SDK would otherwise instrument
# whichever framework it happens to auto-detect from your installed packages.
client = SideSeat(auto_instrument=False)
LlamaIndexInstrumentor().instrument(tracer_provider=client.telemetry.tracer_provider)


documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()


response = query_engine.query("What is this about?")
print(response)

There is no Frameworks.LlamaIndex constant. Passing framework="llama-index" would not do what it looks like: an unrecognised value is recorded as a provider, and framework still falls back to auto-detection. Hence auto_instrument=False above. The resulting spans are classified as OpenInference, which is what the instrumentor emits.

AutoGen

Section titled “AutoGen”
Terminal window
pip install autogen-agentchat "autogen-ext[openai]" "sideseat[autogen]"
 
import asyncio


from sideseat import SideSeat, Frameworks
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient


SideSeat(framework=Frameworks.AutoGen)




async def main():
    model_client = OpenAIChatCompletionClient(model="gpt-5-mini", api_key="sk-xxx")
    assistant = AssistantAgent("assistant", model_client=model_client)
    result = await assistant.run(task="Hello!")
    print(result.messages[-1].content)




asyncio.run(main())

Agno

Section titled “Agno”
Terminal window
pip install agno openai "sideseat[agno]"
from sideseat import SideSeat, Frameworks
from agno.agent import Agent
from agno.models.openai import OpenAIChat


SideSeat(framework=Frameworks.Agno)


agent = Agent(model=OpenAIChat(id="gpt-5-mini"), tools=[])
agent.print_response("Hello!")

Agno is instrumented through OpenInference. Its spans carry agno.* attributes alongside openinference.*, and SideSeat’s detection prefers the specific agno. prefix so runs are labelled Agno rather than the generic OpenInference.

Smolagents

Section titled “Smolagents”
Terminal window
pip install smolagents "sideseat[smolagents]"
from sideseat import SideSeat, Frameworks
from smolagents import CodeAgent, InferenceClientModel


SideSeat(framework=Frameworks.Smolagents)


agent = CodeAgent(tools=[], model=InferenceClientModel())
agent.run("What is 2+2?")

Only the agent run span carries smolagents.*; step, model and tool spans inside the run are attributed by their own attributes.

AG2

Section titled “AG2”
Terminal window
pip install "ag2[openai]<1.0" "sideseat[ag2]"
from sideseat import SideSeat, Frameworks
from autogen import ConversableAgent


SideSeat(framework=Frameworks.AG2)


assistant = ConversableAgent(
    name="assistant",
    llm_config={"model": "gpt-5-mini"},
)
print(assistant.generate_reply(messages=[{"role": "user", "content": "Hello!"}]))

AG2 is the community fork of AutoGen and keeps the autogen import path, so its OpenInference instrumentor is openinference-instrumentation-autogen. Pin below 1.0: ag2 1.0 renamed its top-level module from autogen to ag2 and removed ConversableAgent, and the instrumentor patches autogen. SideSeat distinguishes the two by the ag2.* attribute prefix, so AG2 runs are not mislabelled AutoGen.

AgentScope

Section titled “AgentScope”
Terminal window
pip install agentscope sideseat
from sideseat import SideSeat, Frameworks


SideSeat(framework=Frameworks.AgentScope)


# AgentScope emits OpenTelemetry itself and only needs the provider SideSeat installs.

No extra is needed: AgentScope exports spans through the global tracer provider. Its spans carry agentscope.* attributes and gen_ai.conversation.id, which SideSeat reads as the session id.

Langflow

Section titled “Langflow”
Terminal window
pip install langflow sideseat

Langflow emits OpenTelemetry itself. Either run it in a process where SideSeat has installed the provider, or point Langflow’s own OTLP exporter at SideSeat:

Terminal window
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:5388/otel/default

Flow spans carry langflow.flow_id, langflow.flow_name and langflow.session_id.

Haystack

Section titled “Haystack”
Terminal window
pip install haystack-ai "sideseat[haystack]"
from sideseat import SideSeat, Frameworks
from haystack import Pipeline
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage


SideSeat(framework=Frameworks.Haystack)


pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-5-mini"))
result = pipeline.run({"llm": {"messages": [ChatMessage.from_user("Hello")]}})
print(result["llm"]["replies"][0].text)

Component spans carry haystack.component.name and haystack.component.type, which SideSeat uses to label the run Haystack rather than generic OpenInference.

browser-use

Section titled “browser-use”
Terminal window
pip install browser-use sideseat
import asyncio


from sideseat import SideSeat, Frameworks
from browser_use import Agent, ChatOpenAI


SideSeat(framework=Frameworks.BrowserUse)




async def main():
    agent = Agent(task="Find the docs", llm=ChatOpenAI(model="gpt-5-mini"))
    print(await agent.run())




asyncio.run(main())

No extra is needed: browser-use exports OpenTelemetry through the global provider. Every span it emits sets gen_ai.provider.name = "browser_use", which is how SideSeat detects it.

Generic OpenTelemetry

Section titled “Generic OpenTelemetry”

For frameworks not listed above, send traces directly via the OpenTelemetry SDK:

  1. Start SideSeat

    Terminal window
    npx sideseat

  2. Set the endpoint

    Terminal window
    export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:5388/otel/default

  3. Install and configure

    Terminal window
    pip install opentelemetry-sdk opentelemetry-exporter-otlp
     
    from opentelemetry import trace
    from opentelemetry.sdk.trace import TracerProvider
    from opentelemetry.sdk.trace.export import BatchSpanProcessor
    from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
    

    provider = TracerProvider()
    provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
    trace.set_tracer_provider(provider)

SideSeat auto-detects framework spans and normalizes them into the workbench timeline.

Next Steps

Section titled “Next Steps”