Adaptive Recall: Adding Long-Term M

Adaptive Recall is essentially a patch for the "goldfish memory" problem inherent in current LLMs. Even with massive context windows, most models suffer from "lost in the middle" syndrome or simply forget specific user preferences and historical data once the token limit is hit. This project implements a dynamic memory layer that acts like a long-term hippocampus for your AI, allowing it to store, retrieve, and—most importantly—update information based on the relevance of the conversation.

The core problem it solves is the inefficiency of standard RAG (Retrieval-Augmented Generation). Traditional RAG is static; it pulls chunks of text based on vector similarity, which often leads to the AI retrieving outdated information if you've changed your mind or updated a project requirement halfway through a chat. Adaptive Recall uses a weighted decay and importance-scoring mechanism. It doesn't just find "similar" text; it finds "significant" and "current" text.

Getting this running is straightforward if you're comfortable with Python and have an API key (OpenAI or Anthropic). You'll need to set up a vector store—it supports ChromaDB and Pinecone out of the box—to handle the embedding storage.

git clone https://github.com/adaptive-recall/adaptive-recall.git
cd adaptive-recall
pip install -r requirements.txt

After installing, you configure your memory threshold in the .env file. A lower threshold makes the AI "clingy" (remembering every tiny detail), while a higher threshold keeps it focused on high-level concepts.

# example config snippet
MEMORY_DECAY_RATE: 0.1
IMPORTANCE_THRESHOLD: 0.7
VECTOR_STORE: "chromadb"

Is it actually worth the overhead? If you're just using an AI for one-off queries, no. But if you're building a personalized agent, a coding assistant that needs to remember your specific architectural preferences across ten different files, or a roleplay bot, it's a game-changer. The "adaptive" part is the real winner here. I noticed that when I corrected the AI on a specific naming convention in my code, the memory updated the weight of that preference. Subsequent retrievals prioritized the correction over the initial (wrong) prompt.

The main trade-off is latency. Because there's an extra step of "scoring" the retrieved memories before they hit the prompt, you'll see a slight bump in Time to First Token (TTFT). However, the increase in coherence and the reduction in "hallucinated contradictions" far outweigh a few extra milliseconds of lag. It transforms the interaction from a series of isolated sessions into a continuous relationship with the model.