Agent memory and loop tracking tool
A tool that helps developers monitor and debug AI agents by tracking their actions, preventing loops, and managing memory. It allows agents to share memory across different functions (e.g., customer support agent talking to pricing agent). Built for daily agent users who need visibility into agent behavior.
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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.
Baton: Monitor AI coding agents and get notified when they need human input
Baton is a tool that monitors AI coding agents and alerts developers when an agent requires human intervention. It uses LLMs to analyze agent outputs and determine when a human needs to step in, solving the problem of developers having to constantly check on multiple agents.
Replay coding-agent sessions on a 3D codebase map
Mindwalk replays coding-agent sessions on a 3D map of your codebase, letting developers visualize and debug agent actions spatially. It uses LLMs to power the coding agent whose sessions are replayed, helping developers understand agent behavior and codebase structure.
odek: AI agent with long-term semantic memory across sessions
odek is an AI agent that maintains a structured, semantic long-term memory across sessions, remembering user preferences, codebases, and goals without requiring re-explanation. It uses LLMs to process and store information in a three-tier memory system, solving the problem of session-only memory for users who need continuity in their interactions.
Safety layer for AI agents with alerts, kill switch, and dashboard
AgentKavach is a safety layer for AI agents that monitors usage, spending, and behavior. It provides real-time alerts, an automatic kill switch to stop misbehaving agents, and a dashboard with insights. It solves the problem of runaway costs and unintended behavior for teams deploying autonomous AI agents.