Features

Everything your team knows, in one place agents can reach.

trellismem is a memory layer: it ingests your team's knowledge, keeps it current, and answers questions from people and AI agents alike.

Memory

A knowledge graph, not a pile of chunks.

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.

Datasets

Group memory by project, team or customer. Each dataset is searched, shared and deleted on its own.

Categories

Tag uploads with a category and the graph keeps their entities together, so a question can be scoped to one topic.

Search that picks its own strategy

Ask a question and trellismem chooses how to look: across the graph, in the passages, by keyword, or by date.

Graph models

Describe the entities and relationships your domain cares about, and extraction fills your model instead of a generic one.

Knowledge graph view

Open the graph in the app and see what is connected to what.

Memory gap analysis

See the topics a dataset covers and the questions it answers worst, weakest first.

Sources

Connect the tools your team already uses.

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.

Slack

Channels and threads

GitHub

Repository code and docs

Linear

Issues, comments and projects

Notion

Pages and databases

Google Drive

Folders and shared drives

Gmail

The labels you select

Google Calendar

Events in the calendars you select

Confluence

Spaces, pages and comments

Jira

Issues and comments

HubSpot

Contacts, companies, deals and tickets

Salesforce

Accounts, contacts, opportunities and cases

Intercom

Conversations, contacts and help articles

Stripe

Customers, invoices and subscriptions

Agents

Every agent on the same memory.

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.

  • Claude Code
  • Codex
  • Claude Desktop
  • Cursor
  • VS Code
  • Gemini CLI
  • Cline
  • Hermes Agent
  • OpenClaw
  • n8n
  • Dify
  • REST API
  • MCP
Learning

Memory that gets better with use.

What people and agents ask is itself knowledge. trellismem keeps it.

Sessions

Conversations keep their own short-term memory, so a follow-up question knows what came before.

Self-improvement

Session answers, agent traces and feedback are folded back into the permanent graph.

Skills

Store your team's playbooks as skills. Agents find the right one for a task and load it on demand.

Activity and analytics

See what was remembered and recalled, and how memory is being used over time.

Access and security

Your team's memory stays your team's.

Permissions are enforced where the data is stored, not only in the interface.

Per-dataset permissions

Grant read, write, delete and share on each dataset to the people who need it.

Isolated storage

Each dataset has its own graph and its own vector space.

Passkeys

Sign in with a passkey. It verifies you on your device, so there is no password to steal.

Two-step sign-in

Add an authenticator app as a second step, with ten single-use recovery codes.

API keys

Agents and automations use their own keys. Create one per agent and revoke it without touching the others.

Members and invites

Close public registration and create accounts for teammates from the Members page.

Hosting

Runs where your data is allowed to live.

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.

Your LLM, your key

Use OpenAI, Anthropic, Gemini, Azure OpenAI, AWS Bedrock, Ollama or any OpenAI-compatible endpoint.

Local embeddings

Embeddings can run on a local model inside the API container, so document text is not sent to an embedding service.

Encrypted connector credentials

Tokens for connected sources are stored encrypted with a key that only your deployment holds.

Give your team and its agents one memory.

Connect a source, ask a question, and point your first agent at it.