Bento Documentation

Both SDKs are built on OpenTelemetry, so most of your code maps over directly.

Migrate with an AI coding tool

Install Bento’s Agent Skills so your AI coding tool knows how to do the migration. Copy this prompt into Claude Code, Cursor, Codex, OpenCode, or Windsurf:

Migrate this project from Langfuse to Bento.

```bash
npx skills add BentoLabs-ai/skills
```

Use the `bentolabs-migrate` skill. Walk me through it step by step and confirm before editing.

The skill carries the full playbook: it finds your Langfuse call sites, installs Bento alongside, translates each construct, verifies traces land, then removes Langfuse. The rest of this page is the same translation guide, by hand.

The three paths

Pick the smoothest path that applies and fall through to the next. Most migrations land on Path B for the bulk and Path C for a handful of bespoke decorators. If your app runs Google ADK agents, take Path A: three lines at startup. If your call sites use langfuse.openai, langfuse.langchain, or Anthropic through an instrumentor, take Path B: drop one import and register one instrumentor. For bespoke @observe, start_as_current_observation, or custom spans, Path C renames them call site by call site.

Path A: Google ADK integration

If your app runs Google ADK agents, this captures every model call, tool call, and agent step automatically.

pip install "bentolabs-sdk[adk]"

import bentolabs_sdk as bento

bento.init(
    user_id=lambda: get_current_user_id(),
    session_id=lambda: get_current_session_id(),
)
bento.instrument()

That’s the whole change. See Integrations for the full reference.

Path B: Auto-capture with OpenInference

Replace Langfuse’s from langfuse.openai import OpenAI (and the langfuse.langchain.CallbackHandler) with an OpenInference instrumentor registered against a BentoLabsSpanProcessor. Your call sites stay untouched, since Bento captures every LLM call at the SDK level.

pip install openinference-instrumentation-openai
# Other libs as needed: -anthropic, -langchain, -bedrock, -llama-index

from openinference.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry import trace

from bentolabs_sdk import BentoLabsSpanProcessor

provider = TracerProvider()
provider.add_span_processor(BentoLabsSpanProcessor())
trace.set_tracer_provider(provider)

OpenAIInstrumentor().instrument(tracer_provider=provider)

Now swap from langfuse.openai import OpenAI for the stock import. The instrumentor wraps the client for you:

from openai import OpenAI

client = OpenAI()
resp = client.chat.completions.create(model="gpt-4o", messages=[...])
# Bento captures the call. Nothing else to do.

For LangChain, drop the CallbackHandler and register LangChainInstrumentor() the same way. For Anthropic or Bedrock, install the matching openinference-instrumentation-* and register it. See OTel transport for the full reference.

Path C: Manual translation

For bespoke @observe() decorators, manual context managers, and identity helpers, translate per the tables.

Setup

Langfuse	Bento
`pip install langfuse`	`pip install bentolabs-sdk`
`LANGFUSE_PUBLIC_KEY`, `LANGFUSE_SECRET_KEY`, `LANGFUSE_HOST`	`BENTOLABS_API_KEY`
`Langfuse(...)` / `get_client()`	`bento.init(api_key=...)`
`langfuse.flush()`	`bento.flush()`

Decorators

Langfuse	Bento
`@observe()` on a handler	`@bento.interaction`
`@observe()` on a tool	`@bento.tool`
`@observe(as_type="generation", ...)`	Replace body with `bento.track_ai(...)`

Multi-step / context managers

Langfuse	Bento
`start_as_current_observation(as_type="span", ...)`	`with bento.begin(...) as t:`
Nested `as_type="span"` (tool)	`t.tool_span(...)`

Identity

Langfuse	Bento
`update_current_trace(...)`	`bento.update_current_trace(...)`
`update_current_observation(...)`	`bento.update_current_span(...)`
`propagate_attributes(...)`	`bento.propagate_attributes(...)`

What’s gone

Langfuse	Where it goes
`langfuse.score(...)`	`bento.update_current_trace(properties={"score_<name>": value})`
`langfuse.get_prompt(...)`	Move prompts to code or config; stash version in `properties`
Datasets / dataset runs	No primitive yet

Watch out for

A few renames don’t map one to one. Bento uses session_id everywhere except bento.track_ai, which uses convo_id; it’s the same attribute under the hood. Langfuse metadata becomes properties, a direct rename, but the magic keys like langfuse_user_id don’t exist, so pass the real kwargs instead. And there’s no OpenAI drop-in: reach for Path B (OpenInference) rather than hunting for a from bento.openai import OpenAI equivalent.

Side-by-side

Langfuse

from langfuse import observe, propagate_attributes
from langfuse.openai import OpenAI

client = OpenAI()

@observe()
def answer(user_id, session_id, question):
    with propagate_attributes(user_id=user_id, session_id=session_id, tags=["prod"]):
        resp = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": question}],
            name="answer-llm",
        )
        return resp.choices[0].message.content

Bento

import bentolabs_sdk as bento
from openai import OpenAI

bento.init(api_key="bl_pk_...")
client = OpenAI()

@bento.interaction
def answer(user_id, session_id, question):
    with bento.propagate_attributes(user_id=user_id, session_id=session_id, tags=["prod"]):
        resp = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": question}],
        )
        reply = resp.choices[0].message.content
        bento.track_ai(
            event="answer-llm",
            model="gpt-4o-mini",
            provider="openai",
            input=question,
            output=reply,
        )
        return reply

Configuration

init(), identity getters, env vars.

Tracking events

The bento.track_ai reference.

Overview

Migrations

Concepts

From Langfuse

Migrate with an AI coding tool

The three paths

Path A: Google ADK integration

Path B: Auto-capture with OpenInference

Path C: Manual translation

Setup

Decorators

Multi-step / context managers

Identity

What’s gone

Watch out for

Side-by-side

See also

Configuration

Tracking events

​Migrate with an AI coding tool

​The three paths

​Path A: Google ADK integration

​Path B: Auto-capture with OpenInference

​Path C: Manual translation

​Setup

​Decorators

​Multi-step / context managers

​Identity

​What’s gone

​Watch out for

​Side-by-side

​See also

Configuration

Tracking events

Migrate with an AI coding tool

The three paths

Path A: Google ADK integration

Path B: Auto-capture with OpenInference

Path C: Manual translation

Setup

Decorators

Multi-step / context managers

Identity

What’s gone

Watch out for

Side-by-side

See also