> For the complete documentation index, see [llms.txt](https://docs.convai.com/api-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.convai.com/api-docs/plugins-and-integrations/convai-unreal-engine-plugin/features/long-term-memory.md). # Long-term memory Find guides for enabling long-term memory, identifying players, resuming sessions, and troubleshooting recall in Unreal Engine. Long-term memory (LTM) lets a Convai character retain facts about a specific player across separate sessions. In the Convai Unreal Engine plugin, memory works when the character has LTM enabled and the chatbot sends a stable `EndUserID` at connect time.


How long-term memory works Understand what LTM stores, how player identity scopes memory, and what your project must persist between sessions.	How long-term memory works
Long-term memory quick start Enable memory for one character, assign a stable player identity, and verify cross-session recall in Play In Editor.	Long-term memory quick start
Configure memory for a character Enable or verify the character LTM setting and reset the local conversation link when needed.	Configure memory for a character
End-user identity Choose and assign a stable `EndUserID` before `StartSession` so memory stays scoped to the correct player.	End-user identity
Speaker ID management Create, list, and delete Speaker ID records through Blueprint nodes when your project uses the Speaker ID workflow.	Speaker ID management
LTM Blueprint reference Reference for runtime `Convai\|LTM` Blueprint nodes, `FConvaiSpeakerInfo`, and the memory properties on both Convai components.	LTM Blueprint reference
Long-term memory usage examples Blueprint-first patterns for returning learners, shared devices, account-backed identity, and fresh-start resets.	Long-term memory usage examples
Troubleshoot long-term memory Diagnose disabled memory, identity mismatches, Speaker ID failures, and sessions that start fresh.	Troubleshoot long-term memory

### Next steps Start with [How long-term memory works](/api-docs/plugins-and-integrations/convai-unreal-engine-plugin/features/long-term-memory/how-long-term-memory-works.md) for the mental model, then follow [Long-term memory quick start](/api-docs/plugins-and-integrations/convai-unreal-engine-plugin/features/long-term-memory/long-term-memory-quick-start.md) to get memory running in your level. {% content-ref url="/pages/Bc8OjnguS3F6H3oW7CJi" %} [How long-term memory works](/api-docs/plugins-and-integrations/convai-unreal-engine-plugin/features/long-term-memory/how-long-term-memory-works.md) {% endcontent-ref %} {% content-ref url="/pages/qCWhjxGBv6qEO4Rkl0Ph" %} [Long-term memory quick start](/api-docs/plugins-and-integrations/convai-unreal-engine-plugin/features/long-term-memory/long-term-memory-quick-start.md) {% endcontent-ref %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.convai.com/api-docs/plugins-and-integrations/convai-unreal-engine-plugin/features/long-term-memory.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.