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Basic Information

Mnemo is a modular agent framework designed to orchestrate Retrieval-Augmented Generation pipelines and real-time data-driven agent workflows using the Model Context Protocol. It provides an infrastructure layer for building chainable, domain-specific agents that can persist memory, call tools, and coordinate multi-step tasks. The project emphasizes MCP compatibility so agents can act as MCP clients or servers and hot-swap data interfaces and execution environments. Typical uses shown in the README include autonomous workflows, human-in-the-loop systems, live decision agents that consume streaming enterprise or on-chain data, and agents that integrate vector stores, unstructured documents, and multimodal inputs. The repository includes examples, an installable package via pip or uv, and a quickstart demo illustrating file and web reader agents. Mnemo is aimed at developers and teams who need a composable, protocol-oriented foundation for deploying agents with real-time tool integration and long-term memory.

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App Details

Features
Mnemo highlights an MCP-oriented architecture that enables protocol-based interaction with external tools, data streams, and services. It includes first-class support for RAG workflows with vector store and unstructured data integration, and a composable agent engine for building modular agents that orchestrate, call tools, and persist memory. The framework supports real-time tool calls to MCP-compliant services such as filesystem, fetch, email, SQL, and vector databases. Multi-agent orchestration features enable cooperative task planning, evaluation agents, and swarm-style distributed processing. The codebase provides connectors and patterns for multimodal reasoning, plug-in toolchains, and long-running workflows with pause and resume semantics. The roadmap notes model switching and additional MCP connectors for calendars, cloud docs, and sensors, reflecting emphasis on extensibility and interoperability.
Use Cases
Mnemo helps engineers and teams rapidly construct intelligent, production-ready agent systems that integrate external data and live services via MCP. It simplifies building RAG-enhanced Q&A systems by connecting to vector databases, supports enterprise memory agents for long-term knowledge and business logic, and enables on-chain analytics by streaming blockchain data through MCP servers. Developers can compose domain-specific toolchains, attach LLMs for augmented generation, and run demo agents using provided examples. The framework"s modular design reduces integration work for new data sources and toolkits, and its real-time tool call capability makes it easier to implement alerting, streaming analytics, or human-in-the-loop processes. Installation is available via pip or uv and a quickstart demonstrates attaching LLMs and reading files or web content to produce summaries and answers.

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