Datasets
Group memory by project, team or customer. Each dataset is searched, shared and deleted on its own.
trellismem is a memory layer: it ingests your team's knowledge, keeps it current, and answers questions from people and AI agents alike.
trellismem reads what you give it and stores what it means: who, what, when, and how they relate. Answers are assembled from those facts and the passages behind them.
Group memory by project, team or customer. Each dataset is searched, shared and deleted on its own.
Tag uploads with a category and the graph keeps their entities together, so a question can be scoped to one topic.
Ask a question and trellismem chooses how to look: across the graph, in the passages, by keyword, or by date.
Describe the entities and relationships your domain cares about, and extraction fills your model instead of a generic one.
Open the graph in the app and see what is connected to what.
See the topics a dataset covers and the questions it answers worst, weakest first.
Pick the channels, projects, folders or record types to include. trellismem reads them and picks up changes on every sync. You can also upload files or paste text.
Channels and threads
Repository code and docs
Issues, comments and projects
Pages and databases
Folders and shared drives
The labels you select
Events in the calendars you select
Spaces, pages and comments
Issues and comments
Contacts, companies, deals and tickets
Accounts, contacts, opportunities and cases
Conversations, contacts and help articles
Customers, invoices and subscriptions
Each tool below has a guided setup in the app, with your address and API key filled in. Anything else can use the REST API or the MCP server directly.
What people and agents ask is itself knowledge. trellismem keeps it.
Conversations keep their own short-term memory, so a follow-up question knows what came before.
Session answers, agent traces and feedback are folded back into the permanent graph.
Store your team's playbooks as skills. Agents find the right one for a task and load it on demand.
See what was remembered and recalled, and how memory is being used over time.
Permissions are enforced where the data is stored, not only in the interface.
Grant read, write, delete and share on each dataset to the people who need it.
Each dataset has its own graph and its own vector space.
Sign in with a passkey. It verifies you on your device, so there is no password to steal.
Add an authenticator app as a second step, with ten single-use recovery codes.
Agents and automations use their own keys. Create one per agent and revoke it without touching the others.
Close public registration and create accounts for teammates from the Members page.
trellismem is four containers: the app, the API, Postgres with pgvector, and FalkorDB. Run them on your laptop, your own servers, or a platform like Railway.
Use OpenAI, Anthropic, Gemini, Azure OpenAI, AWS Bedrock, Ollama or any OpenAI-compatible endpoint.
Embeddings can run on a local model inside the API container, so document text is not sent to an embedding service.
Tokens for connected sources are stored encrypted with a key that only your deployment holds.
Connect a source, ask a question, and point your first agent at it.