Spin up 24/7 Digital Employees

Connect data → define outcome → pip install memra → live agents in under an hour

⏱ Fast local demo 🔒 SOC 2 & HIPAA 📊 Full audit trail

# Quick start in a clean conda environment
pip install memra

The API in Action

# create_employee.py from memra import Agent employee = Agent( role="Support Specialist", tools=["TicketClassifier", "KnowledgeSearch"] ) employee.run({"ticket_id": 123}) _{▶ View more examples}

Production Queues

Redis Streams, backpressure handling, dead letter queues built-in

Hard Budget Limits

Set $0.05 per job, kills at $0.051. No surprise bills.

Live Traces & Cost Graphs

Every decision logged, Grafana dashboards out-of-box

Pick Your Digital Employee Type

Data Analyst

SQL from questions

▼

from memra import Agent

analyst = Agent(
    role="Data Analyst",
    tools=["TextToSQL", "ChartGen"]
)

result = analyst.run({
    "question": "Revenue by product last 90 days"
})
                        

Doc Processor

Extract from PDFs

▼

from memra import Agent

processor = Agent(
    role="Invoice Parser",
    tools=["PDFExtractor", "Validator"]
)

data = processor.run({
    "file": "invoice.pdf"
})
                        

Support Agent

Auto-resolve tickets

▼

from memra import Agent

support = Agent(
    role="Support Specialist",
    tools=["TicketClassifier", "KnowledgeBase"]
)

response = support.run({
    "ticket": "Cannot login to dashboard"
})
                        

Revenue Ops

Score & route leads

▼

from memra import Agent

revenue = Agent(
    role="Lead Scorer",
    tools=["DataEnricher", "CRMRouter"]
)

score = revenue.run({
    "lead_email": "cto@startup.com"
})
                        

Run the demo in 60 sec

$ memra demo 🚀 Starting Memra ETL Demo... ================================================== 📦 Setting up demo environment... ✅ Demo files extracted to: ~/.memra/demo 🔧 Configuring environment... ✅ Set MEMRA_API_KEY ✅ Set DATABASE_URL 📦 Installing demo dependencies... ✅ Dependencies installed 🐳 Starting Docker services... ✅ Docker services started successfully ⏳ Waiting for PostgreSQL to be ready... ✅ PostgreSQL ready! 🏢 Starting ETL Invoice Processing Department 📋 Mission: Complete end-to-end ETL process 👥 Team: Invoice Parser, Data Validator, Database Engineer 🔄 Processing 15 invoices... ✅ Department work complete 📊 Results: 15 invoices processed in 47 seconds

LangChain vs Memra

Feature	LangChain	Memra
Production queues	✗ DIY with Celery	✓ Redis Streams built-in
Cost controls	✗ Manual token counting	✓ Hard limits per job
Monitoring	✗ Console logs	✓ Grafana dashboards
Multi-agent coordination	✗ Manual callbacks	✓ Department primitives
Deployment	✗ Docker + K8s YAML	✓ memra deploy

1000+

Digital employees deployed

<2min

Average setup time

$0.001-0.05

Per task (varies by complexity)

24/7

Availability

Ready to ship your first agent?

Book ROI Session

Example Workflows ✨

See how easy it is to build and deploy AI agents for common business tasks

Invoice Processing

# Extract data from PDFs automatically
agent = Agent(
    role="Invoice Parser",
    tools=["PDFProcessor", "DatabaseWriter"]
)

result = agent.run({"file": "invoice.pdf"})
# Output: Extracted invoice #INV-001
                    

Multi-Agent Workflow

# Chain agents together for complex tasks
department = Department(
    name="ETL Pipeline",
    agents=[parser, validator, writer]
)

engine = ExecutionEngine()
engine.execute_department(department, data)
# Agents work in sequence automatically
                    