Langfuse gives any drone video analytics framework the same level of introspection, traceability, and performance tuning that modern LLM‑powered systems rely on. It becomes the “black box opener” for every agentic step in your pipeline—retrieval, detection, summarization, geospatial reasoning, and cost/performance optimization—so you can debug, benchmark, and continuously improve your drone‑vision workflows with production‑grade rigor.
Failures can occur at many layers such as frame ingestion & compression, object detection & tracking, geospatial fusion, LLM‑based summarization or anomaly explanation, agentic retrieval (ReAct, tool calls, SQL queries, vector search) and cost and latency across edge ↔ cloud. Langfuse provides the missing “flight recorder” for all of this.
Langfuse captures full traces of LLM and agentic interactions, including nested calls, retrieval steps, and tool invocations. For drone analytics, this means we can trace how a single drone frame flows through detection → captioning → geolocation → anomaly scoring, inspect why a ReAct agent chose a particular tool (SQL, vector search, geospatial lookup), debug failures in temporal reasoning (e.g., tracking drift, inconsistent object IDs), build datasets of problematic cases for evaluation. This is invaluable for your ezbenchmark framework, where reproducibility and cross‑pipeline comparability matter.
Langfuse provides analytics for prompts, outputs, token usage, and tool calls. For your drone system, we can compare prompt templates for summarizing flight paths or describing anomalies, iddentify which retrieval strategies (vector search vs. SQL vs. geospatial index) produce the most accurate situational awareness, track model drift when switching between vision‑LLMs (LLaVA, PaliGemma, GeoChat, RemoteCLIP) and quantify latency hotspots—e.g., slow object detection vs. slow LLM reasoning.
Langfuse gives clear visibility into token consumption and associated costs. This allows us to track cost per flight, mission, or frame batch, compare cost of pure vision‑LLM vs. agentic retrieval vs. hybrid pipelines and optimize for your goal of maximizing insight per token and minimizing energy per inference. This directly supports your cost‑efficiency research and TCO modeling.
Langfuse supports scoring, human feedback, dataset versioning, and experiment comparison. This helps to build eval datasets from real drone missions (e.g., anomaly frames, occlusion cases, low‑light failures), score outputs from ReAct, agentic, and vision‑LLM pipelines side‑by‑side, version datasets for DOTA, VisDrone, UAVDT, and your own ezbenchmark scenarios, and run multi‑score comparisons (accuracy, latency, cost, geospatial consistency).
Langfuse is built on OpenTelemetry and integrates with Python, JS/TS, LangChain, LangGraph, LlamaIndex, CrewAI, and more. We could Instrument edge inference nodes (e.g., YOLOv8, RT-DETR, SAM2), instrument cloud‑side LLM reasoning (OpenAI, Bedrock, Vertex), correlate edge timestamps with cloud agentic traces and build a unified timeline of the entire mission.
Sample invocation for observability:
import os
from langfuse.openai import openai
from langfuse.openai import AzureOpenAI
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
import httpx
auth = "https://some-iam-provider.com/oauth2/token"
scope = "https://some-iam-provider.com/.default"
grant_type = "client_credentials"
# Use an asynchronous client to make a POST request to the auth URL.
async with httpx.AsyncClient() as client:
body = {
"grant_type": grant_type,
"scope": scope,
"client_id": os.environ["PROJECT_CLIENT_ID"],
"client_secret": os.environ["PROJECT_CIENT_SECRET"],
}
headers = {"Content-Type": "application/x-www-form-urlencoded"}
resp = await client.post(auth, headers=headers, data=body, timeout=60)
access_token = resp.json()["access_token"]
print(resp.json())
# Define the deployment name and project ID.
#deployment_name = "gpt-4o-mini_2024-07-18"
deployment_name = "gpt-4o_2024-11-20"
# Define the Azure OpenAI endpoint and API version.
shared_quota_endpoint = os.environ["HTTPS_API_GATEWAY_URL"]
azure_openai_api_version="2025-01-01-preview"
# Initialize the OpenAI client.
oai_client = AzureOpenAI(
azure_endpoint=shared_quota_endpoint,
api_version=azure_openai_api_version,
azure_deployment=deployment_name,
azure_ad_token=access_token,
default_headers={
"projectId": os.environ["PROJECT_GUID"]
}
)
# Define the messages to be processed by the model.
from langfuse import get_client
langfuse = get_client()
messages = [{"role": "user", "content": "Tell me all about custom metrics with Langfuse."}]
#prompt = langfuse.get_prompt("original")
# Request the model to process the messages.
response = oai_client.chat.completions.create(
model="o1-mini",
messages=messages,
metadata={"someMetadataKey": "someValue"},
)
# Print the response from the model.
print(response.model_dump_json(indent=2))