Overview
Memories are the heart of MemoryLake: structured, durable facts distilled from your conversations and documents. Unlike a chat log (noisy, unbounded) or a document chunk (raw text), a memory is an extracted statement worth keeping — deduplicated, searchable, traceable to its source, and checked for consistency.Where Memories Come From
Conversations
Playground chats and agent sessions are analyzed automatically — durable facts are extracted, merged with what’s already known, and stored. Transcripts stay transcripts; facts become memories.
Documents
Uploaded files are parsed and indexed; key content becomes retrievable memory alongside conversational facts.
Explicit capture
Store a memory deliberately — via the API (
add memory), agent tools (memory_store), or by telling the assistant “remember this”.Agent auto-capture
Connected agents (OpenClaw and friends) report conversation turns in the background; the server decides what to store, update, or merge.
What Makes a Memory Trustworthy
Source Traces
Every memory keeps a trace back to its origin — the conversation turn or document it was extracted from. Open any memory in the console and check its Source Trace tab, or fetch it via the trace API. When an assistant asserts something, you can always answer “how do you know that?”Conflict Detection
When a new memory contradicts an existing one — a moved deadline, a changed policy, a corrected fact — MemoryLake doesn’t silently keep both or overwrite blindly. It raises a conflict:- The conflicting pair appears in the project’s Conflicts tab
- A reviewer (anyone with the resolve permission) picks what’s true
- Memory stays current, and the resolution is deliberate rather than accidental
Deduplication & Merging
Repeating yourself doesn’t create ten copies of the same fact — extraction merges new information into existing memories where they overlap, and updates them when details change.Retrieving Memories
- Console: browse and search in the Memories page (workspace-wide) or a project’s Memories tab — including a word-frequency visualization for a quick sense of what a project knows
- Semantic search: hybrid keyword + embedding retrieval, scoped to a project or workspace — the same engine behind the Playground, agent tools, and the search API
- Automatic recall: connected assistants retrieve relevant memories before answering — you don’t query anything by hand
Managing Memories
Browse & Inspect
The Memories views support search, filtering, pagination, and bulk selection. Click any memory for details: full content, metadata, and source trace.Edit
Open a memory and edit its content when a fact needs refinement. Edits take effect immediately in future retrieval.Forget (Delete)
Delete from the memory’s action menu (or the forget API). This is also your “right to be forgotten” mechanism when building user-facing products.Individual and Organizational Memory
The same machinery serves two different kinds of knowledge:
Both flow through extraction → trace → conflict-checking → retrieval; the difference is scope and governance. See the Personal AI Memory and Team Knowledge Base scenarios.
Working with Memories Programmatically
https://app.memorylake.ai/openapi/memorylake. Start with Core Memory Operations for the end-to-end flow.
Earlier project-scoped
api/v1 and api/v2 memory endpoints remain available for existing integrations and are documented under Deprecated in the API reference. New integrations should use the v3 paths above.Next Steps
Best Practices
Get better memories out of conversations, documents, and agents
Integrations
Bring memory recall into your agents and apps
Memories API
Manage memories programmatically