mnemo

# Mnemo <p align="center"> <img src="assets/Mnemo_Logo.png" alt="Mnemo Logo" width="300" /> </p> <p align="center"> <em>Composable AI Agents & Realtime Data Interfaces Powered by Model Context Protocol</em> <em>CA:0x7bfdb47ab24b6cb7017865431179e150d4bc4444</em> </p> <p align="center"> <img src="https://img.shields.io/pypi/l/mnemo" /> </p> --- ## Overview **Mnemo** is a modular agent framework built on top of the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction), designed to orchestrate Retrieval-Augmented Generation (RAG) pipelines and intelligent agent workflows using real-time, pluggable data services. Mnemo integrates two emerging standards: 1. **Model Context Protocol (MCP)**: Enables real-time, protocol-based interaction with external tools, data streams, and services via MCP servers. 2. **Composable Agent Architecture**: Inspired by effective production patterns, Mnemo allows developers to build, chain, and orchestrate modular agents across tasks and domains. ### Why Mnemo? Mnemo is purpose-built to: * 🔌 **Plug into any MCP-compliant data or tool service** * 🔍 **Enable real-time RAG pipelines with multi-modal inputs** * 🧠 **Build chainable, domain-specific agents with memory, logic and persistence** * 🧩 **Expose agents as MCP clients or servers, enabling two-way integration** Whether you're building autonomous workflows, human-in-the-loop systems, or live decision agents powered by streaming on-chain or enterprise data—Mnemo provides the infrastructure layer to deploy them quickly. --- ## Features * ⚙️ **MCP-Oriented Design**: Fully compatible with MCP server/client pattern; enables hot-swappable data interfaces and execution environments. * 📚 **RAG-Native Agent Workflows**: First-class support for Retrieval-Augmented Generation with vector store and unstructured data integration. * 🤖 **Composable Agent Engine**: Build modular agents that orchestrate, call tools, persist memory, and coordinate via workflows. * 🪝 **Real-Time Tool Calls**: Automatically fetch, retrieve, and operate on data exposed by any MCP-compliant service (e.g., filesystem, fetch, email, SQL, vector DBs). * 🧪 **Multi-Agent Orchestration**: Supports cooperative task planning, evaluation agents, and Swarm-style distributed processing. --- ## Installation We recommend using [uv](https://docs.astral.sh/uv/) to manage your Python environments: ```bash uv add "mnemo" ``` Or simply use pip: ```bash pip install mnemo ``` --- ## Quickstart Clone the repo and run a basic demo agent: ```bash cd examples/basic/mnemo_demo_agent cp mnemo.secrets.yaml.example mnemo.secrets.yaml # Add your API keys uv run main.py ``` ### Example: File and Web Agent ```python from mnemo.app import MnemoApp from mnemo.agents.agent import Agent from mnemo.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM app = MnemoApp(name="web_reader_agent") async def run(): async with app.run() as session: reader = Agent( name="finder", instruction=""" You can read files and browse web links. Return requested information on demand. """, server_names=["filesystem", "fetch"], ) async with reader: tools = await reader.list_tools() llm = await reader.attach_llm(OpenAIAugmentedLLM) output = await llm.generate_str("Read me the first 10 lines of README.md") print("README preview:", output) result = await llm.generate_str("Summarize this article: https://www.anthropic.com/research/building-effective-agents") print("Summary:", result) ``` --- ## Applications ### ✅ RAG-Enhanced Q\&A Integrate with vector DBs (e.g. Qdrant, Weaviate) to retrieve relevant text passages and enable context-rich answering. ### 🧾 Enterprise Memory Agents Deploy agents with long-term memory over internal knowledge, business logic, or customer records. ### 📡 On-Chain Analytics Agents Stream blockchain data via MCP-compatible servers and perform structured analysis or alerts. ### 🛠️ Custom Toolchains Create domain-specific agents that orchestrate tasks using external APIs or plugins via the MCP layer. ### 🧠 Multimodal Reasoning Extend beyond text: support for image embeddings, structured documents, web interfaces, and speech-ready agents. --- ## Roadmap * ✅ Multi-agent Swarm workflows (inspired by OpenAI's Swarm) * ✅ Long-running workflow orchestration with pause/resume * ⏳ Persistent agent memory & streaming input support * 🧠 LLM model switch support (Claude, GPT-4o, etc.) * 🧩 More MCP server connectors: calendar, cloud docs, database, sensors --- ## Credits Built with ❤️ on top of MCP and inspired by Anthropic’s vision for composable, intelligent agents.