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Source channel @githubtrending · Post #15299 · Nov 12

#python#agent#ai#aiagent#awesome#chatgpt#hacktoberfest#hacktoberfest2025#llm#long_short_term_memory#memori_ai#memory#memory_management#python#rag#state_management Memori is an open-source memory engine that gives AI language models human-like memory using standard SQL databases like PostgreSQL, MySQL, or SQLite.[1][2] With just one line of code, you can enable any LLM to remember conversations, learn from interactions, and maintain context across sessions.[1] The key benefits are significant cost savings of 80-90% compared to expensive vector databases, complete data ownership and transparency since memories are stored in SQL databases you control, and zero vendor lock-in allowing you to export and move your data anywhere.[1][3] Memori works with popular frameworks like OpenAI, Anthropic, and LangChain, making it easy to integrate into existing projects without complex setup.[1] https://github.com/GibsonAI/Memori

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djangoproject

@djangoproject · Post #77 · 07/05/2016, 07:46 AM

https://docs.python.org/2/library/logging.html This module defines functions and classes which implement a flexible event logging system for applications and libraries. The key benefit of having the #logging_API provided by a standard library module is that all Python modules can participate in logging, so your application log can include your own messages integrated with messages from third-party modules. The module provides a lot of functionality and flexibility. If you are unfamiliar with #logging, the best way to get to grips with it is to see the tutorials (see the links on the right). The basic classes defined by the module, together with their functions, are listed below. #Loggers expose the interface that application code directly uses. Handlers send the log records (created by loggers) to the appropriate destination. Filters provide a finer grained facility for determining which log records to output. Formatters specify the layout of log records in the final output.