Chatbot with Profile Schema Guide
Chatbot with Profile Schema Review We introduced the LangGraph Memory Store as a way to save and retrieve long-term memories. We built a simple chatbot that uses both short-term (within-thread) and long-term (across-thread) memory. It saved long-term semantic memory (facts about the user) "in the hot path", as the user is chatting with it. Goals Our chatbot saved memories as a string. In practice, we often want memories to have a structure. For example, memories can be a single, continuously updated schema. In our case, we want this to be a single user profile. We'll extend our chatbot to save semantic memories to a single user profile. We'll also introduce a library, Trustcall, to update this schema with new information.
When to use Chatbot with Profile Schema
Chatbot with Profile Schema Review We introduced the LangGraph Memory Store as a way to save and retrieve long-term memories. We built a simple chatbot that uses both short-term (within-thread) and long-term (across-thread) memory. It saved long-term semantic memory (facts about the user) "in the hot path", as the user is chatting with it. Goals Our chatbot saved memories as a string. In practice, we often want memories to have a structure. For example, memories can be a single, continuously updated schema. In our case, we want this to be a single user profile. We'll extend our chatbot to save semantic memories to a single user profile. We'll also introduce a library, Trustcall, to update this schema with new information.
How to use Chatbot with Profile Schema
Chatbot with Profile Schema is a single agent agent built on the LangGraph framework. Set it up from the source repository, configure your model credentials, and invoke it for tasks that match its description. Review the safety profile below before running it against production data or systems.
Safety profile
Autonomy
Semi-autonomous
Sandbox-aware
No declared sandbox guidance
Network access
Unspecified
Filesystem access
Unspecified