Search Results for "structured storage"

...This approach helps coding agents — and human collaborators — track which tasks depend on others, what has been done, and where workflows branch or reunify without losing important data. By leveraging Git as the storage backbone, the project ensures that memory is persistent, diffable, and sharable, with the ability to roll back, branch, or merge memory states just like source code.

...The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable.

...Rather than focusing solely on human collaboration, the system is designed around an “agent-first” paradigm where AI agents can contribute code, discuss changes, and coordinate problem solving. The platform treats code repositories not just as storage for files but as active environments where agents can propose modifications, review changes, and exchange structured messages. This approach reflects emerging research in multi-agent software development where groups of AI systems collaborate on complex engineering problems.

whatsapp-mcp is an open source Model Context Protocol (MCP) server that enables AI agents to interact directly with a user’s WhatsApp account through a structured interface. It acts as a bridge between WhatsApp and large language models, allowing controlled access to messages, chats, and contacts. whatsapp-mcp is composed of two main components: a Go-based bridge that connects to the WhatsApp Web API and stores data locally, and a Python-based MCP server that exposes tools for AI...

...The system enables developers and analysts to run SQL queries on files, APIs, applications, and databases without needing separate connectors or query languages for each platform. Built on top of SQLite, the engine uses a plugin architecture that allows it to extend support to dozens of external services and data sources. Users can query structured files such as CSV, JSON, and Parquet as well as remote data sources like SaaS APIs, cloud storage services, and local applications. The platform also supports querying multiple data sources simultaneously and joining them together within a single SQL query, enabling powerful cross-system analysis. In addition to operating as a local query engine, the system can run as a MySQL-compatible server so that traditional database tools can connect to it.