WHYdr/llm-memory-agent

A learning-oriented project implementing a long-term memory AI agent with persistent semantic memory and retrieval-augmented context management. Intended for developers and researchers exploring memory systems, RAG, agent workflows and long-context interaction. Key features: - Chat interface that embeds user messages, retrieves relevant memories, injects context into prompts, and stores important information. - Modular design: Chat, LLM (Ollama), Memory, Embedding, Retrieval. - Uses local LLMs (Llama/Qwen), LangChain, ChromaDB, sentence-transformers and HuggingFace tools. Current state: basic chat implemented; long-term memory, embedding, ChromaDB retrieval and prompt assembly planned. Run: python app/main.py

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