memra 0.2.5

Declarative framework for enterprise workflows with MCP integration - Client SDK

Memra SDK

The declarative framework for building enterprise-grade AI workflows with MCP integration.

🎯 Why Memra?

Building AI-powered business workflows is hard. You need to:

• Orchestrate multiple AI agents with different roles and responsibilities
• Integrate with external tools (databases, APIs, file systems)
• Handle complex business logic with validation and error recovery
• Scale from prototypes to production without rewriting everything
• Maintain consistency across different workflows and teams

Memra solves these challenges by providing a declarative framework that lets you focus on what you want to accomplish, not how to wire everything together.

🚀 Quick Start (5 minutes!)

Want to see Memra in action immediately? Check out our Quick Start Guide to run the ETL demo in minutes!

Installation

pip install memra

📖 New to Memra? For a complete beginner-friendly setup with step-by-step instructions, see our Detailed Installation Guide or run our automated setup script:

# Automated setup for new users
bash scripts/setup_newbie.sh

🔧 Repository Structure: This repo uses git submodules with sparse checkout to provide minimal access to infrastructure and workflow templates while keeping private content secure.

Basic Example

from memra import Agent, Department, LLM, ExecutionEngine

# Define an agent
agent = Agent(
    role="Data Analyst",
    job="Analyze customer data",
    llm=LLM(model="llama-3.2-11b-vision-preview"),
    sops=["Load data", "Perform analysis", "Generate report"],
    output_key="analysis_result"
)

# Create a department
department = Department(
    name="Analytics",
    mission="Provide data insights",
    agents=[agent],
    workflow_order=["Data Analyst"]
)

# Execute the workflow
engine = ExecutionEngine()
result = engine.execute_department(department, {"data": "customer_data.csv"})

🏗️ Core Architecture

Agent

An AI worker that performs specific tasks using LLMs and tools. Agents have:

• Role: What they do (e.g., "Data Analyst", "Invoice Processor")
• Job: Specific task description
• LLM: Language model configuration
• Tools: External tools they can use
• SOPs: Standard operating procedures

Department

A team of agents working together to accomplish a mission. Departments:

• Coordinate multiple agents in a workflow
• Handle dependencies between agents
• Provide execution policies (retries, timeouts, error handling)
• Manage context and data flow

ExecutionEngine

Orchestrates the execution of departments and their workflows with:

• Automatic agent coordination
• Tool integration via MCP (Model Context Protocol)
• Error handling and retries
• Execution tracing and monitoring

LLM

Configuration for language models used by agents, supporting:

• Multiple model providers (OpenAI, Anthropic, local models)
• Custom parameters (temperature, max tokens, etc.)
• Model-specific configurations

🔥 Real-World Examples

ETL Invoice Processing Demo

Complete end-to-end workflow that processes PDF invoices using vision models and stores data in PostgreSQL:

# Run the ETL demo
python demos/etl_invoice_processing/etl_invoice_demo.py

This demo showcases:

• Vision model integration for PDF processing
• Multi-agent workflow (Extractor, Validator, Database Engineer)
• MCP tool integration (PostgreSQL, SQL execution)
• Data validation and error handling
• Production-ready patterns

Smart File Discovery

Automatically discover and process files with intelligent routing:

from memra import Agent

# Smart agent that discovers and processes files automatically
smart_parser = Agent(
    role="Smart Invoice Parser",
    job="Discover and process invoice files intelligently",
    tools=[
        {"name": "FileDiscovery", "hosted_by": "memra"},
        {"name": "FileCopy", "hosted_by": "memra"},
        {"name": "InvoiceExtractionWorkflow", "hosted_by": "memra"}
    ]
)

# Three modes of operation:
# 1. Auto-discovery: Scan invoices/ directory
# 2. External file: Copy from Downloads to invoices/
# 3. Specific file: Process exact file path

Accounts Payable Workflow

Complete accounts payable processing with validation and database integration:

# See examples/accounts_payable_smart.py for full implementation
from memra import Department, Agent

ap_department = Department(
    name="Accounts Payable",
    mission="Process and validate vendor invoices",
    agents=[
        Agent(role="Invoice Extractor", ...),
        Agent(role="Data Validator", ...),
        Agent(role="Database Engineer", ...)
    ],
    workflow_order=["Invoice Extractor", "Data Validator", "Database Engineer"]
)

🛠️ Key Features

🔌 MCP Integration

Built-in support for Model Context Protocol (MCP) tools:

• Database operations (PostgreSQL, MySQL, etc.)
• File system operations (discovery, copying, processing)
• API integrations (REST, GraphQL, custom APIs)
• Custom tool development with simple Python functions

🎯 Declarative Workflows

Define workflows in terms of what you want to accomplish:

# Instead of writing procedural code, declare your workflow
department = Department(
    name="Invoice Processing",
    mission="Extract, validate, and store invoice data",
    agents=[
        Agent(role="Extractor", job="Extract data from PDFs"),
        Agent(role="Validator", job="Validate extracted data"),
        Agent(role="Database Engineer", job="Store data in database")
    ],
    workflow_order=["Extractor", "Validator", "Database Engineer"]
)

🔄 Error Handling & Recovery

Built-in resilience with:

• Automatic retries with configurable policies
• Fallback agents for critical workflows
• Validation at each step
• Comprehensive error reporting

📊 Monitoring & Observability

Track workflow execution with:

• Execution traces showing agent and tool usage
• Performance metrics (timing, costs)
• Error logs with context
• Audit trails for compliance

🚀 Production Ready

Scale from prototype to production:

• Async execution for high throughput
• Resource management and connection pooling
• Configuration management for different environments
• Security best practices for API keys and credentials

📚 Documentation

• Quick Start Guide - Get up and running in 5 minutes
• Detailed Installation Guide - Complete beginner-friendly setup instructions
• System Architecture - Deep dive into Memra's design
• Text-to-SQL Guide - Building database query workflows
• Examples Directory - Complete working examples
• Demos Directory - Advanced workflow demonstrations

🏢 Use Cases

Financial Services

• Invoice processing and accounts payable automation
• Document classification and routing
• Compliance monitoring and reporting
• Risk assessment and fraud detection

Healthcare

• Medical record processing and data extraction
• Claims processing and validation
• Patient data analysis and insights
• Regulatory compliance workflows

Manufacturing

• Quality control and inspection workflows
• Supply chain optimization and monitoring
• Equipment maintenance scheduling
• Production planning and optimization

Retail & E-commerce

• Order processing and fulfillment
• Customer service automation
• Inventory management and forecasting
• Market analysis and trend detection

🤝 Contributing

We welcome contributions! Please see our contributing guide for details.

Development Setup

# Clone the repository
git clone https://github.com/memra-platform/memra-sdk.git
cd memra-sdk

# Install in development mode
pip install -e .

# Run tests
pytest tests/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License Summary

• ✅ Commercial use - Use in proprietary software
• ✅ Modification - Modify and distribute
• ✅ Distribution - Distribute copies
• ✅ Private use - Use privately
• ❌ Liability - No warranty provided
• ❌ Warranty - No warranty provided

🆘 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

🔗 Related Projects

• memra-workflows - Production workflow templates
• memra-ops - Operations and deployment tools

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Built with ❤️ by the memra team