feat: add new Opik Tracer (#6928)

* feat: add new Opik Tracer

This PR adds a new Tracer to sends traces and spans to Opik.

* Fix typo in docs

* Optimize Opik integration to reduce number of HTTP calls

* Add session_id and user_id to the Opik tracer

* Check Opik configuration on the client side before making a remote check

* Tackle documentation reviews

* Apply suggestions from code review

Co-authored-by: Jacques Verré <jverre@gmail.com>

* Fix one of the doc link

* Explicitely check for Opik environment variables before enabling the Opik tracer

* test: add opik tracing unit tests

---------

Co-authored-by: Nadir J <31660040+NadirJ@users.noreply.github.com>
Co-authored-by: Jacques Verré <jverre@gmail.com>
Co-authored-by: Ítalo Johnny <italojohnnydosanjos@gmail.com>
This commit is contained in:
Boris Feld 2025-03-21 19:18:19 +01:00 committed by GitHub
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8 changed files with 567 additions and 5 deletions

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@ -61,11 +61,11 @@ Replace `SECRET_KEY`, `PUBLIC_KEY`, and `HOST_URL` with the API key information
1. Start Langflow in the same terminal or environment where you set the environment variables:
```bash
python -m langflow run
```
```bash
uv run langflow run
```
2. In Langflow, open and existing project, and then run a flow.
2. In Langflow, open an existing project, and then run a flow.
## View tracing data in Langfuse

View file

@ -0,0 +1,52 @@
---
title: Opik
slug: /integrations-opik
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Integrate Opik with Langflow
[Opik](https://www.comet.com/site/products/opik/) is an open-source platform designed for evaluating, testing, and monitoring large language model (LLM) applications. Developed by Comet, it aims to facilitate more intuitive collaboration, testing, and monitoring of LLM-based applications.
This guide walks you through how to configure Langflow to collect [tracing](https://www.comet.com/docs/opik/tracing/log_traces) data about your flow executions and automatically send the data to Opik.
## Prerequisites
- A project in Langflow with a runnable flow
- An Open-Source Opik server or an Opik Cloud account - You can learn more about the differences [here](https://www.comet.com/docs/opik/faq#what-is-the-difference-between-opik-cloud-and-the-open-source-opik-platform-)
- If you are using Opik Cloud, you will also need your [Opik API key](https://www.comet.com/docs/opik/faq#where-can-i-find-my-opik-api-key-)
## Set your Opik configuration as environment variables
Call the CLI `opik configure` to save your Opik configuration in the same environment where you run Langflow.
```bash
opik configure
```
For self-hosted Opik, you can also configure it using the Opik CLI by running the following command:
```bash
opik configure --use_local
```
See the [Opik documentation](https://www.comet.com/docs/opik/tracing/sdk_configuration) for more ways to configure Opik SDKs.
## Start Langflow and run a flow
1. Start Langflow in the same terminal or environment where you set the environment variables:
```bash
uv run langflow run
```
2. In Langflow, open an existing project, and then run a flow.
3. Navigate to your Opik project dashboard and view the collected tracing data.
## Disable the Opik integration
To disable the Opik integration, remove the environment variables you set in the previous steps and restart Langflow.

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@ -147,6 +147,7 @@ module.exports = {
"Integrations/integrations-langfuse",
"Integrations/integrations-langsmith",
"Integrations/integrations-langwatch",
"Integrations/integrations-opik",
{
type: "doc",
id: "Integrations/integrations-mcp",

View file

@ -119,6 +119,7 @@ dependencies = [
"ruff>=0.9.7",
"langchain-graph-retriever==0.6.1",
"graph-retriever==0.6.1",
"opik>=1.6.3",
]
[dependency-groups]

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@ -0,0 +1,235 @@
from __future__ import annotations
import os
import types
from typing import TYPE_CHECKING, Any
from langchain_core.documents import Document
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
from loguru import logger
from typing_extensions import override
from langflow.schema.data import Data
from langflow.schema.message import Message
from langflow.services.tracing.base import BaseTracer
if TYPE_CHECKING:
from collections.abc import Sequence
from uuid import UUID
from langchain.callbacks.base import BaseCallbackHandler
from langflow.graph.vertex.base import Vertex
from langflow.services.tracing.schema import Log
def get_distributed_trace_headers(trace_id, span_id):
"""Returns headers dictionary to be passed into tracked function on remote node."""
return {"opik_parent_span_id": span_id, "opik_trace_id": trace_id}
class OpikTracer(BaseTracer):
flow_id: str
def __init__(
self,
trace_name: str,
trace_type: str,
project_name: str,
trace_id: UUID,
user_id: str | None = None,
session_id: str | None = None,
):
self._project_name = project_name
self.trace_name = trace_name
self.trace_type = trace_type
self.opik_trace_id = None
self.user_id = user_id
self.session_id = session_id
self.flow_id = trace_name.split(" - ")[-1]
self.spans: dict = {}
config = self._get_config()
self._ready: bool = self._setup_opik(config, trace_id) if config else False
self._distributed_headers = None
@property
def ready(self):
return self._ready
def _setup_opik(self, config: dict, trace_id: UUID) -> bool:
try:
from opik import Opik
from opik.api_objects.trace import TraceData
self._client = Opik(project_name=self._project_name, _show_misconfiguration_message=False, **config)
missing_configuration, _ = self._client._config.get_misconfiguration_detection_results()
if missing_configuration:
return False
if not self._check_opik_auth(self._client):
return False
# Langflow Trace ID seems to always be random
metadata = {
"langflow_trace_id": trace_id,
"langflow_trace_name": self.trace_name,
"user_id": self.user_id,
"created_from": "langflow",
}
self.trace = TraceData(
name=self.flow_id,
metadata=metadata,
thread_id=self.session_id,
)
self.opik_trace_id = self.trace.id
except ImportError:
logger.exception("Could not import opik. Please install it with `pip install opik`.")
return False
except Exception as e: # noqa: BLE001
logger.exception(f"Error setting up opik tracer: {e}")
return False
return True
def _check_opik_auth(self, opik_client) -> bool:
try:
opik_client.auth_check()
except Exception as e: # noqa: BLE001
logger.error(f"Opik auth check failed, OpikTracer will be disabled: {e}")
return False
else:
return True
@override
def add_trace(
self,
trace_id: str,
trace_name: str,
trace_type: str,
inputs: dict[str, Any],
metadata: dict[str, Any] | None = None,
vertex: Vertex | None = None,
) -> None:
if not self._ready:
return
from opik.api_objects.span import SpanData
name = trace_name.removesuffix(f" ({trace_id})")
processed_inputs = self._convert_to_opik_types(inputs) if inputs else {}
processed_metadata = self._convert_to_opik_types(metadata) if metadata else {}
span = SpanData(
trace_id=self.opik_trace_id,
name=name,
input=processed_inputs,
metadata=processed_metadata,
type="general", # The LLM span will comes from the langchain callback
)
self.spans[trace_id] = span
self._distributed_headers = get_distributed_trace_headers(self.opik_trace_id, span.id)
@override
def end_trace(
self,
trace_id: str,
trace_name: str,
outputs: dict[str, Any] | None = None,
error: Exception | None = None,
logs: Sequence[Log | dict] = (),
) -> None:
if not self._ready:
return
from opik.decorator.error_info_collector import collect
span = self.spans.get(trace_id, None)
if span:
output: dict = {}
output |= outputs or {}
output |= {"logs": list(logs)} if logs else {}
content = {"output": output, "error_info": collect(error) if error else None}
span.init_end_time().update(**content)
self._client.span(**span.__dict__)
else:
logger.warning("No corresponding span found")
@override
def end(
self,
inputs: dict[str, Any],
outputs: dict[str, Any],
error: Exception | None = None,
metadata: dict[str, Any] | None = None,
) -> None:
if not self._ready:
return
from opik.decorator.error_info_collector import collect
self.trace.init_end_time().update(
input=inputs, output=outputs, error_info=collect(error) if error else None, metadata=metadata
)
self._client.trace(**self.trace.__dict__)
self._client.flush()
def get_langchain_callback(self) -> BaseCallbackHandler | None:
if not self._ready:
return None
from opik.integrations.langchain import OpikTracer as LangchainOpikTracer
# Set project name for the langchain integration
os.environ["OPIK_PROJECT_NAME"] = self._project_name
return LangchainOpikTracer(distributed_headers=self._distributed_headers)
def _convert_to_opik_types(self, io_dict: dict[str | Any, Any]) -> dict[str, Any]:
"""Converts data types to Opik compatible formats."""
return {str(key): self._convert_to_opik_type(value) for key, value in io_dict.items() if key is not None}
def _convert_to_opik_type(self, value):
"""Recursively converts a value to a Opik compatible type."""
if isinstance(value, dict):
value = {key: self._convert_to_opik_type(val) for key, val in value.items()}
elif isinstance(value, list):
value = [self._convert_to_opik_type(v) for v in value]
elif isinstance(value, Message):
value = value.text
elif isinstance(value, Data):
value = value.get_text()
elif isinstance(value, (BaseMessage | HumanMessage | SystemMessage)):
value = value.content
elif isinstance(value, Document):
value = value.page_content
elif isinstance(value, (types.GeneratorType | types.NoneType)):
value = str(value)
return value
@staticmethod
def _get_config() -> dict:
host = os.getenv("OPIK_URL_OVERRIDE", None)
api_key = os.getenv("OPIK_API_KEY", None)
workspace = os.getenv("OPIK_WORKSPACE", None)
# API Key is mandatory for Opik Cloud and URL is mandatory for Open-Source Opik Server
if host or api_key:
return {"host": host, "api_key": api_key, "workspace": workspace}
return {}

View file

@ -47,6 +47,12 @@ def _get_arize_phoenix_tracer():
return ArizePhoenixTracer
def _get_opik_tracer():
from langflow.services.tracing.opik import OpikTracer
return OpikTracer
trace_context_var: ContextVar[TraceContext | None] = ContextVar("trace_context", default=None)
component_context_var: ContextVar[ComponentTraceContext | None] = ContextVar("component_trace_context", default=None)
@ -176,6 +182,17 @@ class TracingService(Service):
trace_id=trace_context.run_id,
)
def _initialize_opik_tracer(self, trace_context: TraceContext) -> None:
opik_tracer = _get_opik_tracer()
trace_context.tracers["opik"] = opik_tracer(
trace_name=trace_context.run_name,
trace_type="chain",
project_name=trace_context.project_name,
trace_id=trace_context.run_id,
user_id=trace_context.user_id,
session_id=trace_context.session_id,
)
async def start_tracers(
self,
run_id: UUID,
@ -201,6 +218,7 @@ class TracingService(Service):
self._initialize_langwatch_tracer(trace_context)
self._initialize_langfuse_tracer(trace_context)
self._initialize_arize_phoenix_tracer(trace_context)
self._initialize_opik_tracer(trace_context)
except Exception as e: # noqa: BLE001
logger.debug(f"Error initializing tracers: {e}")

View file

@ -135,6 +135,10 @@ def mock_tracers():
"langflow.services.tracing.service._get_arize_phoenix_tracer",
return_value=MockTracer,
),
patch(
"langflow.services.tracing.service._get_opik_tracer",
return_value=MockTracer,
),
):
yield
@ -294,7 +298,7 @@ async def test_get_langchain_callbacks(tracing_service):
assert tracer.get_langchain_callback_called
# Verify returned callbacks list length
assert len(callbacks) == 4 # Four tracers
assert len(callbacks) == 5 # Five tracers
# Cleanup
await tracing_service.end_tracers({})

251
uv.lock generated
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@ -688,6 +688,25 @@ wheels = [
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[package.optional-dependencies]
bedrock-runtime = [
{ name = "mypy-boto3-bedrock-runtime" },
]
[[package]]
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@ -4479,6 +4510,7 @@ dependencies = [
{ name = "numexpr" },
{ name = "openinference-instrumentation-langchain" },
{ name = "opensearch-py" },
{ name = "opik" },
{ name = "pgvector" },
{ name = "pyarrow" },
{ name = "pydantic-ai" },
@ -4668,6 +4700,7 @@ requires-dist = [
{ name = "nv-ingest-client", marker = "extra == 'nv-ingest'", specifier = "==2025.2.7.dev0" },
{ name = "openinference-instrumentation-langchain", specifier = ">=0.1.29" },
{ name = "opensearch-py", specifier = "==2.8.0" },
{ name = "opik", specifier = ">=1.6.3" },
{ name = "pgvector", specifier = "==0.3.6" },
{ name = "pyarrow", specifier = "==19.0.0" },
{ name = "pydantic-ai", specifier = ">=0.0.19" },
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