# How GreatArrow.ai compares

> Against built-in AI memory, memory libraries and enterprise search, including where each alternative is the better choice.

Canonical page: https://www.greatarrow.ai/compare
Site map for agents: https://www.greatarrow.ai/llms.txt (full text: https://www.greatarrow.ai/llms-full.txt)

Drawn from public documentation as of September 2026; not independently benchmarked. Each alternative column describes a category of approach, not one vendor.

| Capability | GreatArrow.ai | Built-in AI memory (ChatGPT, Claude) | A memory library (Mem0, Zep, Letta) | Enterprise search (Glean and similar) |
| --- | --- | --- | --- | --- |
| Works across your tools | 21 AI clients, plus web, terminal and mobile | One vendor only | Whatever you build | A search box, not your assistant |
| Reads your company | 44 providers, synced and kept current | Your chat history | You supply the corpus | Broad connectors |
| Knows what is still true | Facts retire with a date and a replacement, and stay queryable | No — old facts persist | Rarely, and only one does it well | Indexes what exists now |
| Tells you when it is broken | A failed search is reported as failed, never shown as empty | Returns nothing, looks empty | Up to your implementation | Returns nothing, looks empty |
| Your agents can write to it | 639+ tools your agents already reach | Implicitly, and invisibly | Yes, once you build it | No write path |
| Team permissions | Enforced server-side on every request, and fails closed | Personal to each account | You build it | Strong — this is their core strength |
| Effort to run it | About a minute to connect | None | An engineering project | A rollout |

## Where an alternative is the better choice

### If you only use one assistant, use its memory

Built-in memory is free, needs no setup, and for a single person inside a single tool it is the right answer. We are worth it when the context has to outlive the tool — a second assistant, a teammate, or a vendor you might leave.

### If you are building your own product, use a library

Memory libraries are good infrastructure and some are excellent on the hard parts — one models time properly, which most do not. If you want to own the stack and build the product yourself, start there. We are the finished system, not a component.

### If you need company-wide search, that is its own category

Enterprise search has years of work in permission-aware indexing across large estates, and does it better than we do. It answers questions; it has no write path an agent can use and no model of a fact ceasing to be true. Teams often run both.