TeleLogsAgent
A Benchmark for LLM Tool-Use in 5G Network Root Cause Analysis
Developed by the NetOp Team, Huawei Paris Research Center
Mohamed Sana ยท Nicola Piovesan ยท Antonio De Domenico ยท Fadhel Ayed
> [!NOTE]
> IMPORTANT: Please help us protect the integrity of this benchmark by not publicly sharing, re-uploading, or distributing the dataset.
## Dataset Description
- **Repository (Dataset & Evaluation Code):** https://huggingface.co/datasets/netop/TeleLogsAgent
- **Paper:** https://arxiv.org/abs/2506.10674
TeleLogsAgent is a benchmark and evaluation framework designed to measure the ability of Large Language Model (LLM) agents to perform **structured tool-use** in the telecommunications domain.
It simulates the workflow of a 5G network engineer diagnosing performance degradation during drive testing, requiring agents to:
- inspect configuration data,
- analyze time-series KPIs,
- reason across multiple tools,
- identify the most plausible root cause.
## Overview
The benchmark consists of **two main components**:
1. **FastAPI Server (`fastapi_server.py`)**
Exposes realistic analytical tools (HTTP endpoints) to access 5G drive-test scenarios.
Agents interact with this environment using OpenAI-style function calls.
2. **LLM Evaluation Agent (`benchmark.py`)**
Connects to either the FastAPI server and evaluates LLMs on their ability to plan, call tools, and reason over multiple steps.
In addition, we conveniently provide a **FastMCP Server (`fastmcp_server.py`)** as an alternative implementation of the FastAPI server using **FastMCP**. This version is especially convenient for MCP-native LLM agents.
## Project Structure
```text
TeleLogsAgent/
โโโ fastapi_server.py # FastAPI benchmark server (HTTP tools)
โโโ fastmcp_server.py # FastMCP benchmark server (MCP tools)
โโโ benchmark.py # LLM evaluation / benchmarking script
โโโ TS1/test.json # Scenario 1: root cause identification based on high-level network configuration and user-plane data.
โโโ TS2/test.json # Scenario 2: root cause identification based on high-level and low-level network configuration, signaling-plane and user-plane data.
โโโ TS3/test.json # Scenario 3: root cause remediation based on high-level and low-level network configuration, signaling-plane and user-plane data.
โโโ requirements.txt # Dependencies
โโโ README.md # This file
````
Main dependencies include:
* fastapi
* uvicorn
* fastmcp
* pandas
* requests
* openai
* numpy
* tqdm
## Running the Benchmark Environment
### Option A โ FastAPI Server (HTTP Tools)
```bash
export TELELOGS_AGENT_CONFIG="TS1"; python fastapi_server.py
```
Server address:
```
http://localhost:7861
```
Scenario context is managed using the HTTP header:
```
X-Scenario-Id: