llm-observatory 0.3.0

Context0 SDK — one import, all calls logged

llm-observatory

Drop-in LLM observability for Python. One import change, all calls logged automatically.

Install

pip install llm-observatory

Usage

from llm_observatory import configure, OpenAI

configure(api_key="your-api-key")

client = OpenAI(api_key="sk-...")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
)

Supports OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI, Anthropic, and AsyncAnthropic.

You can also set the LLM_OBSERVATORY_API_KEY environment variable instead of passing api_key to configure() — the SDK picks it up automatically.

Langfuse integration

Already using Langfuse? wrap_langfuse() captures all Langfuse traces, generations, and spans — including metadata like user_id, session_id, and tags — and forwards them to Context0 automatically. Zero code changes to your existing Langfuse setup.

Requires Langfuse Python SDK v3.0.0+ (OTel-native).

Setup

from llm_observatory import configure, wrap_langfuse

configure(api_key="your-api-key")

# Call once after Langfuse is initialized — adds Context0 processor
wrap_langfuse()

What gets captured

After calling wrap_langfuse(), every Langfuse observation is automatically forwarded to Context0:

from langfuse.openai import OpenAI as LangfuseOpenAI
from langfuse import propagate_attributes

client = LangfuseOpenAI(api_key="sk-...")

# Per-request metadata (user_id, session_id, tags) — captured automatically
with propagate_attributes(user_id="alice", session_id="sess-1", tags=["prod"]):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "What is observability?"}],
    )

# @observe decorator — captured automatically
from langfuse.decorators import observe

@observe(as_type="generation")
def my_llm_call(prompt):
    return client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
    )

# Manual traces with start_as_current_observation — captured automatically
from langfuse import Langfuse

langfuse = Langfuse()
with langfuse.start_as_current_observation(as_type="span", name="rag-pipeline"):
    # Nested spans create parent-child relationships in Context0
    with langfuse.start_as_current_observation(as_type="span", name="retrieval"):
        docs = vector_db.search(query)

    with langfuse.start_as_current_observation(as_type="generation", name="completion"):
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": f"{docs}\n\n{query}"}],
        )

Works with all Langfuse v3+ patterns: @observe, LangfuseOpenAI, start_as_current_observation(), propagate_attributes(), and framework integrations (LangChain, LlamaIndex).

wrap() — per-client alternative

If you only want to observe a specific client instance (rather than all Langfuse observations globally), use wrap() instead:

from langfuse.openai import OpenAI as LangfuseOpenAI
from llm_observatory import configure, wrap

configure(api_key="your-api-key")

client = wrap(LangfuseOpenAI(api_key="sk-..."))

# Both Langfuse AND Context0 capture this call
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
)

wrap() works with any client that has a .chat (OpenAI-shaped) or .messages (Anthropic-shaped) interface. If the client is already a Context0 wrapper, it's returned as-is — no double-wrapping.

When to use which: Use wrap_langfuse() to capture everything Langfuse sees globally (recommended). Use wrap() if you only want Context0 on specific client instances.

Tracing

Group related LLM calls into traces to see your full pipeline as a tree.

@observe decorator

Automatically creates a trace (top-level) or span (nested). Captures function args as input and return value as output.

import llm_observatory as obs

client = obs.OpenAI()

@obs.observe
def retrieve_docs(query: str):
    return vector_db.search(query)

@obs.observe
def rag_pipeline(question: str):
    docs = retrieve_docs(question)       # child span
    response = client.chat.completions.create(  # auto-captured as generation
        model="gpt-4o",
        messages=[{"role": "user", "content": f"{docs}\n\n{question}"}],
    )
    return response.choices[0].message.content

Options:

• @obs.observe(name="custom-name") — override the span name (default: function name)
• @obs.observe(capture_input=False) — don't log args (for sensitive data)
• @obs.observe(capture_output=False) — don't log return value

Works with both sync and async functions.

Context managers

Use trace() and span() for inline code blocks that aren't standalone functions:

with obs.trace(name="rag-pipeline", input={"query": q}) as t:
    with obs.span(name="vector-search") as s:
        results = search(q)
        s.set_output(results)

    response = client.chat.completions.create(model="gpt-4o", messages=[...])
    t.set_output(response.choices[0].message.content)

Mix freely — @observe and with span() compose within the same trace.

Cross-service propagation

Pass trace context across services (e.g., API to SQS worker) via W3C traceparent:

# Producer — get traceparent inside a trace
with obs.trace(name="api-request"):
    traceparent = obs.get_current_traceparent()
    sqs.send_message(Body=json.dumps({"traceparent": traceparent, ...}))

# Consumer — restore context
msg = json.loads(sqs_message["Body"])
with obs.trace(name="worker", traceparent=msg["traceparent"]):
    client.chat.completions.create(...)  # appears as child in same trace

Use obs.emit_completed_span() to backfill spans with explicit start/end times (e.g., queue wait duration).

Serverless / short-lived environments

In AWS Lambda, Convex, or any environment where the process freezes or terminates after your handler returns, buffered events may be lost before the background flush fires. Call flush() before returning:

from llm_observatory import configure, OpenAI, flush

configure(api_key="your-api-key")
client = OpenAI(api_key="sk-...")

def handler(event, context):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": event["prompt"]}],
    )

    flush()  # blocks until all buffered events are sent — call before the process freezes

    return {"statusCode": 200, "body": response.choices[0].message.content}

The SDK also registers an atexit handler that flushes on normal process shutdown, but Lambda freezes bypass atexit — so an explicit flush() is required.

Using with Langfuse in serverless

If you're using wrap_langfuse(), Langfuse also buffers spans and needs flushing. The OTel TracerProvider's shutdown() flushes all registered span processors — both Langfuse and Context0 — in one call:

from opentelemetry import trace

def handler(event, context):
    # ... your LLM calls ...

    trace.get_tracer_provider().shutdown()  # flushes both Langfuse and Context0

    return {"statusCode": 200, "body": "..."}