memra 0.2.17

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 (2 minutes!)

Want to see Memra in action immediately?

# Install Memra
pip install memra

# Run the ETL demo
memra demo

That's it! The demo will automatically:

• ✅ Set up PostgreSQL database
• ✅ Start MCP bridge server
• ✅ Process PDF invoices with AI vision
• ✅ Store data in the database
• ✅ Show you the results

📖 Need more details? Check out our Quick Start Guide for step-by-step instructions.

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
memra demo

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
• Installation Guide - Detailed setup instructions
• Text-to-SQL Guide - Build SQL queries with natural language
• Contributing - How to contribute to Memra
• System Architecture - Deep dive into Memra's design

🔧 Development

Prerequisites

• Python 3.8+
• Docker (for database and services)
• Git

Local 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 the demo
memra demo

Running Tests

# Run all tests
python -m pytest tests/

# Run specific test file
python -m pytest tests/test_execution.py

📄 License

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

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details on how to submit pull requests, report issues, and contribute to the project.

📞 Support

• Documentation: Check our guides above
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Ready to build AI workflows that scale? Start with pip install memra and memra demo! 🚀