Chatbot with message summarization Guide
Chatbot with message summarization Review We've covered how to customize graph state schema and reducer. We've also shown a number of ways to trim or filter messages in graph state. Goals Now, let's take it one step further! Rather than just trimming or filtering messages, we'll show how to use LLMs to produce a running summary of the conversation. This allows us to retain a compressed representation of the full conversation, rather than just removing it with trimming or filtering. We'll incorporate this summarization into a simple Chatbot. And we'll equip that Chatbot with memory, supporting long-running conversations without incurring high token cost / latency.
When to use Chatbot with message summarization
Chatbot with message summarization Review We've covered how to customize graph state schema and reducer. We've also shown a number of ways to trim or filter messages in graph state. Goals Now, let's take it one step further! Rather than just trimming or filtering messages, we'll show how to use LLMs to produce a running summary of the conversation. This allows us to retain a compressed representation of the full conversation, rather than just removing it with trimming or filtering. We'll incorporate this summarization into a simple Chatbot. And we'll equip that Chatbot with memory, supporting long-running conversations without incurring high token cost / latency.
How to use Chatbot with message summarization
Chatbot with message summarization 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