TypingMind for Teams

I'd like to request a feature that allows AI agents to trigger and communicate with other AI agents directly from within their system prompts. Currently, the platform allows users to tag an agent in the chat interface, but there's no native capability for agents to autonomously initiate communication with other specialized agents when needed. This would enable creating hierarchical or collaborative agent networks where: A primary agent could recognize when it needs specialized knowledge The primary agent could trigger a secondary agent with specific expertise The secondary agent would provide its specialized input The primary agent would incorporate this input into its final response Similar to how users can use "@" in the chat interface, agents should be able to reference other agents within their system prompts with directives like: If you need code information, request data from @CodeAgent This would extend the current multi-agent workflows to be more autonomous and embedded within the agent's decision-making process.

The UX of having lots of SSO users join the platform, but experiencing a login error until we manually bump the seat limit, is not great. On the other side, provisioning a buffer ahead of time just to avoid this possibility feels quite wasteful. Consider having a floating usage-based seat limit for teams configured with SSO, and billing at the end of the month based on how many users were actually active, a la Slack or other similar usage-based tools.

There are sometimes very expensive models O3 or gpt-image, etc. I'd like to deploy agents that use a different key to better monitor usage. Option 1. In the admin environment, we store another key. Option 2. The user brings his key as with the gpt-image plugin.

Objective : Enhance TypingMind by adding long-term memory capabilities to AI agents, allowing them to retain and recall information over extended interactions, thereby improving user experience and personalization. Solution Overview : Integrate long-term memory using one of the following options: mem0.ai : A specialized memory platform for AI agents that offers seamless memory storage and recall, optimized for conversational AI. Integrating mem0.ai would allow TypingMind agents to retain knowledge across sessions without complex infrastructure. LangChain Memory : If TypingMind's tech stack is more aligned with Python-based frameworks, LangChain provides a variety of memory modules such as ConversationBufferMemory and EntityMemory , which can be customized to store and retrieve information dynamically. Implementation : Data Storage : Use vector embeddings to store conversation context or important information. Retrieval : Retrieve relevant memory chunks based on context to ensure seamless interactions. Tech Stack Compatibility : Evaluate whether mem0.ai or LangChain aligns better with TypingMind’s existing infrastructure (cloud, database, language). Recommendation : Depending on TypingMind's existing stack, either implement a pre-built memory system (e.g., mem0.ai ) or use LangChain's modular memory capabilities. Alternatively, the development team can research and propose a custom solution that fits their stack.

Not sure if is already possible, but just like GPT image generator could we get the Veo 2 video generator plugin? The videos are pretty good from Google.

Extract text from PDFs while inserting markers for actual PDF viewer page numbers (e.g., [Page 5] at the start of each page). Pass these markers into the prompt text, so the AI can accurately reference the same page numbers seen in a PDF viewer.

For the purpose of Digital Security, Legal, and internal KPIs, I would like the TypingMind API to be updated to allow admins access to pull chat conversations from TM storage databases.

Redesign the chat logs interface in the admin panel to display a comprehensive table view with additional columns Token consumption in/out, Training datasets used