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chatsorter memory layer for ai chatbots chatsorter features how it works compare try it login get api key your ai remembers what actually matters a drop in memory layer for ai chatbots chatsorter scores every message extracts structured facts and returns only the context that s relevant right now not everything ever stored start free live demo 3 memory layers free open beta 2 api endpoints integration py 1 store every message requests post api chatsorter com process json chat_id user_id message user_message 2 retrieve relevant context r requests post api chatsorter com search json chat_id user_id query user_message what comes back summary user is allergic to peanuts source structured score 0 94 not just storage intelligent filtering most memory tools store everything and dump it all into context chatsorter scores filters and returns only what s relevant right now importance scoring every message scored 1 10 i love pizza scores 4 i m allergic to peanuts scores 10 only high signal messages get extracted permanently fact extraction structured key value facts with confidence scores my dog is named max becomes pet max confidence 0 95 not a paragraph of text narrative summaries every n messages compressed into a third person summary your model sees 20 summaries instead of 400 raw messages token costs drop fast relevance retrieval query returns only what matters for the current message semantic similarity importance weighting recency decay all combined into one score contradiction handling when someone says i moved to sf the system updates location not appends it volatile facts like health conditions are tracked separately with staleness detection drop in integration two api calls post process to store a message post search to retrieve relevant context works with any python backend any llm any stack three layers one brain most tools have one layer a database chatsorter has three each doing a specific job l1 short term buffer last n messages in a rolling window no llm involved zero latency keeps the model aware of recent context without any processing overhead l2 narrative summaries every batch of n messages compressed into a single third person summary via local llm inference stored with importance scores and timestamps decays over time l3 structured facts high signal messages parsed into typed key value facts name job allergies pets preferences confidence scored volatile facts health lawsuits tracked separately with staleness detection pick your tool chatsorter is built specifically for chatbot memory not general purpose vector storage feature mem0 supermemory chatsorter confidence scores on facts importance gated extraction volatile fact tracking bring your own vector db local inference option free tier limited 1m tokens mo full beta access target use case enterprise full platform chatbot memory engine get your api key in 60 seconds free beta access no credit card two endpoints away from working memory for your chatbot start building 2026 chatsorter github contact privacy proudly listed on launchpadly startup directory