Collaborative context-sharing memory platform for AI agents and teams
A platform that enables AI agents and human teams to share and persist context across sessions. It uses LLMs to manage memory, allowing agents to recall past interactions and collaborate on tasks. Solves the problem of fragmented context in multi-agent systems and team workflows.
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Euclis: shared project memory that captures decisions and blockers from chat and AI tools
Euclis is a free tool that automatically captures decisions, blockers, milestones, and insights from WhatsApp, Claude, Cursor, and the web, turning scattered chat into structured project memory. It helps teams and AI agents avoid starting from zero by providing persistent context. The tool uses LLMs to extract and organize information from conversations.
Persistent memory for AI assistants via MCP
Adaptive Recall provides persistent memory for AI assistants using the Model Context Protocol (MCP). It solves the problem of AI assistants forgetting context between sessions, enabling long-term personalized interactions.
Inkfold – workspace across multiple AI providers with shared memory
Inkfold is a workspace that lets users interact with multiple AI providers (e.g., OpenAI, Anthropic) through a unified interface, with a shared memory system that persists context across sessions. It solves the problem of managing separate chat histories and contexts when using different AI models, providing a seamless experience for power users and developers.
Hermes Agent: personal AI with memory, tools, and daily workflow integration
Hermes Agent is a personal AI agent that goes beyond chatbots by incorporating memory, tools, and daily workflow integration. It uses LLMs to remember user context, execute actions via tools, and become a persistent part of the user's routine, solving the problem of AI tools being ephemeral and disconnected from daily tasks.
Client context project system for consultants using LLM
A consultant shares a system using Claude projects to maintain per-client context, including standing info, decision logs, and meeting notes. The LLM is used to quickly retrieve relevant context before meetings, reducing cognitive load from context switching. Solves the problem of forgetting client details for consultants managing multiple clients.