uselemma-tracing 7.0.0

HTTP tracing SDK for Lemma

uselemma-tracing

HTTP tracing SDK for AI agents. The primary API sends trace payloads directly to Lemma over HTTP.

Installation

pip install uselemma-tracing

Quick Start

from uselemma_tracing import Lemma

lemma = Lemma()

def run(trace):
    docs = search_docs(user_message)
    trace.record_tool(
        name="search_docs",
        input={"query": user_message},
        output=docs,
        tool_parameters={"query": "string"},
    )

    response = call_model(user_message, docs)
    trace.record_generation(
        name="draft-reply",
        input=response.messages,
        output=response.text,
        model="gpt-4o",
        llm_input_messages=[{"role": "user", "content": user_message}],
        llm_invocation_parameters={"temperature": 0.2},
    )

    return response.text

answer = lemma.trace(
    "support-agent",
    run,
    input=user_message,
    thread_id=conversation_id,
    user_id=user.id,
)

lemma.trace() measures the trace from callback start to completion. Use async_trace() for async callbacks.

Live Spans

def run(trace):
    span = trace.start_span(name="retrieve-context", input=query)
    try:
        docs = retrieve(query)
        span.end(output={"count": len(docs)})
        return docs
    except Exception as error:
        span.end(status="ERROR", error=error)
        raise

Live handles know their start time when created and their end time when .end() is called, so you usually do not pass duration_ms. Pass duration_ms only when replaying historical work or overriding the measured duration with a value from another timer.

For one-off records where you already measured the work, pass duration_ms on the record call:

trace.record_generation(
    name="answer",
    output=text,
    model="gpt-4o",
    duration_ms=measured_model_ms,
)

The same handle pattern is available for tool calls and generations:

tool = trace.start_tool(name="search_docs", input={"query": query})
docs = search_docs(query)
tool.end(output=docs)

generation = trace.start_generation(name="answer", input=messages)
response = call_model(messages)
generation.end(output=response.text)

OpenAI Agents SDK

Install the OpenAI Agents extra and register the Lemma processor:

pip install "uselemma-tracing[openai-agents]" openai-agents

from agents import Agent, Runner
from uselemma_tracing import instrument_openai_agents

instrument_openai_agents()

agent = Agent(
    name="support-agent",
    instructions="Answer customer questions clearly and concisely.",
)

async def call_agent(user_message: str):
    result = await Runner.run(agent, user_message)
    return result.final_output

The processor creates one Lemma trace for each OpenAI Agents trace. Generation spans become Lemma generations, function spans become Lemma tool spans, and parent IDs are preserved so tools stay nested under the generation or agent span that called them.

Enable debug mode to validate live span shape while developing:

from uselemma_tracing import enable_debug_mode

enable_debug_mode()

Use openai_agents(record_inputs=False, record_outputs=False) when you need a processor that avoids sending prompts, tool inputs, tool outputs, and generated text.

LangChain and LangGraph

Install the optional integration dependency and pass langchain() as a callback handler:

pip install "uselemma-tracing[langchain]" langchain-openai

from langchain_openai import ChatOpenAI
from uselemma_tracing import langchain

model = ChatOpenAI(
    model="gpt-4o",
    callbacks=[langchain(agent_name="support-agent")],
)

response = model.invoke(user_message)

LangGraph uses LangChain callbacks too:

pip install "uselemma-tracing[langgraph]"

from uselemma_tracing import langgraph

result = graph.invoke(
    {"input": user_message},
    {"callbacks": [langgraph(agent_name="support-graph")]},
)

The handler creates one Lemma trace for the root chain/graph run, records LLM calls as generations, tools as tool spans, retrievers as spans, and nested chains or graph nodes as child spans.

Use langchain(record_inputs=False, record_outputs=False) or langgraph(record_inputs=False, record_outputs=False) to avoid sending prompts, tool inputs, tool outputs, or generated text.

Supported Contract Fields

Use native SDK keyword arguments for OpenInference-style fields:

• LLM: llm_model_name, llm_provider, llm_system, llm_invocation_parameters, llm_input_messages, llm_output_messages, llm_tools, token counts, and prompt template fields
• tools: tool_description, tool_parameters
• embeddings and rerankers: embedding_model_name, embedding_invocation_parameters, embedding_embeddings, reranker_model_name, reranker_input_documents, reranker_output_documents

Use attributes for raw attributes that do not yet have a native SDK keyword.

Configuration

Option	Environment variable	Default
api_key	LEMMA_API_KEY	Required
project_id	LEMMA_PROJECT_ID	Required
base_url	none	https://api.uselemma.ai

The SDK sends to {base_url}/traces/ingest.

You can pass configuration directly to the constructor instead of using environment variables:

lemma = Lemma(
    api_key="sk_...",
    project_id="proj_...",
    base_url="https://api.uselemma.ai",
)

Debug Mode

Debug mode logs trace starts, span starts, span completions, send attempts, and send results as they happen:

from uselemma_tracing import enable_debug_mode

enable_debug_mode()

You can also set LEMMA_DEBUG=true. Use this when validating that spans are created in the expected order and the SDK is sending to the intended URL.

License

MIT