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Shared memory and workflow state for multi-agent collaboration.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "TUT Context Hub" yet — see the docs or source repo.
Please write the current task to the shared task log and update the related state.
An appended task entry and the derived workflow state updated.
Based on the shared log, summarize the current collaboration progress and todos.
A summary of progress, blockers, and next actions.
Let multiple agents read the same task context and write back new state when needed.
Consistent shared context across agents with less information loss.
When multiple AI agents work on one project, it acts as a shared memory layer to reduce repeated coordination. It also derives the current workflow state from the task log.
Useful for workflows that need every step preserved in an append-only log. Teams can review history and automatically get the latest state from it.
When agents need to read each other's notes and keep moving, this tool provides a single shared source of state. It reduces context breaks and manual syncing.
It is an MCP server that provides shared memory and state projection for multi-agent collaboration. Agents can read and write an append-only task log and automatically derive workflow state.
You need an MCP-compatible client or agent host to connect to it. Beyond that, the provided information does not specify additional runtime or key requirements.
It emphasizes an append-only task log and automatic state projection rather than just storing ad hoc memories. That makes it better suited for multi-agent collaboration and workflow tracking.
Centralize multi-agent workflows with Kanban, audits, scorecards, and event hooks.
Turn local Markdown knowledge into searchable context for AI coding agents.
Aggregate MCP servers and REST APIs into one unified AI tool gateway.
Unifies MCP clients with tool routing, memory, and automation flows.
Provide local or team-shared memory for AI coding agents and workflows.
Self-host shared memory, RAG search, and persistent context for AI agents.