Just-in-Time Memory Framework Open-Sourced: Resolving Memory Drift & Context Bloat in LLM Agents

Core Background & Industry Pain Points Autonomous agents deployed for complex codebases suffer from memory trade-offs: retaining complete execution histories quickly saturates context windows and inflates token bills, while standard vector RAG frequently surfaces stale, irrelevant code snippets that pollute model attention. ### Architecture Highlights & Internals JIT-Memory decouples working state from episodic memories using a bi-level utility router. Historical learnings are distilled into modular heuristic rules, which are injected just-in-time when an agent encounters high-stakes decision points, pruning operational context while maintaining temporal coherence. ### Authoritative Benchmarks & Measured Scores On the AgentMemoryBench multi-day evaluation suite, JIT-Memory reduces total token overhead by 68.3% and speeds up inference by 41%. Multi-turn cross-session recall achieves 93.6% accuracy, while memory-induced hallucination loops decrease by 79%. ### Developer Hands-on Guide The framework is available via pip install jit-memory with plug-and-play drivers for PostgreSQL, vLLM, and LiteLLM, accompanied by reproduction guides on GitHub and Hugging Face.