Lemur is an open-source language agent framework that enables developers to build and deploy AI agents for text reasoning, code generation, and natural language processing tasks. It benefits operations teams by automating workflows and integrating with Python-based systems. Lemur connects to Hugging Face models and supports Claude agents.
git clone https://github.com/OpenLemur/Lemur.githttps://arxiv.org/abs/2310.06830
Automate the generation of code snippets based on user-defined prompts.
Create intelligent chatbots that can engage in meaningful conversations and provide accurate information.
Develop interactive agents that can adapt to user feedback and perform tasks in real-world environments.
Implement natural language processing tasks such as summarization, translation, or question answering.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/OpenLemur/LemurCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a language agent using Lemur. I need to automate a workflow for [COMPANY] in the [INDUSTRY] sector. The workflow involves [DESCRIBE TASK]. Provide a step-by-step plan to automate this process, including any necessary code snippets or API integrations.
# Automated Workflow Plan for [COMPANY]
## Objective
Automate the monthly sales report generation process for [COMPANY] in the retail industry.
## Steps
1. **Data Collection**:
- Use Python to connect to the company's SQL database and extract sales data.
```python
import pyodbc
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=server;DATABASE=db;UID=user;PWD=password')
cursor = conn.cursor()
cursor.execute('SELECT * FROM sales WHERE month = \'[MONTH]\'')
data = cursor.fetchall()
```
2. **Data Processing**:
- Clean and process the data using Pandas.
```python
import pandas as pd
df = pd.DataFrame(data)
df_clean = df.dropna()
```
3. **Report Generation**:
- Generate a PDF report using ReportLab.
```python
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas("report.pdf", pagesize=letter)
c.drawString(100, 750, "Monthly Sales Report")
c.showPage()
c.save()
```
4. **Email Notification**:
- Send the report via email using smtplib.
```python
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email import encoders
```
## Tools and Integrations
- Python
- SQL Server
- Pandas
- ReportLab
- smtplib
## Expected Outcome
A fully automated monthly sales report generation process that saves time and reduces manual effort.Unlock data insights with interactive dashboards and collaborative analytics capabilities.
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