Metadata-Version: 2.4
Name: memra
Version: 0.2.3
Summary: Declarative framework for enterprise workflows with MCP integration - Client SDK
Home-page: https://github.com/memra/memra-sdk
Author: Memra
Author-email: Memra <support@memra.com>
License: MIT
Project-URL: Homepage, https://memra.co
Project-URL: Repository, https://github.com/memra-platform/memra-sdk
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
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Requires-Dist: httpx>=0.24.0
Requires-Dist: typing-extensions>=4.0.0
Requires-Dist: aiohttp>=3.8.0
Requires-Dist: aiohttp-cors>=0.7.0
Provides-Extra: dev
Requires-Dist: pytest>=6.0; extra == "dev"
Requires-Dist: pytest-asyncio; extra == "dev"
Requires-Dist: black; extra == "dev"
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Provides-Extra: mcp
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Dynamic: author
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# Memra SDK

The core Memra framework for building AI-powered business workflows.

## Installation

```bash
pip install memra
```

## Quick Start

```python
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 Components

### Agent
An AI worker that performs specific tasks using LLMs and tools.

### Department
A team of agents working together to accomplish a mission.

### ExecutionEngine
Orchestrates the execution of departments and their workflows.

### LLM
Configuration for language models used by agents.

## Examples

See the `examples/` directory for basic usage examples:
- `simple_text_to_sql.py` - Basic text-to-SQL conversion
- `ask_questions.py` - Simple question answering

## Documentation

For detailed documentation, visit [docs.memra.co](https://docs.memra.co)

## License

MIT License - see LICENSE file for details. 
