MemPalace is the leading open-source memory layer for AI agents, with 53,800+ GitHub stars and a sustained growth velocity of +228 stars/day. It provides persistent memory that survives across sessions, enabling agents to recall context, preferences, and learned information with benchmark-leading accuracy. The system supports pluggable storage backends and works with multiple LLM providers, making it a drop-in memory upgrade for existing agent architectures.
MemPalace addresses the most persistent limitation of AI agents: forgetting everything between sessions. The system provides a structured memory layer that agents can write to and read from across conversations, enabling genuine continuity. Rather than dumping entire conversation histories into context windows, MemPalace indexes memories by type and relevance, retrieving only what matters for the current interaction.
The project has gained rapid adoption — 53,800+ stars with a +228/day growth velocity — because it solves a problem every agent developer hits. Whether building a coding assistant, a research agent, or a personal AI, the pattern is the same: the agent needs to remember what it learned yesterday. MemPalace provides that with minimal integration overhead and strong retrieval benchmarks.
Structured memory taxonomy: Memories are categorized into types — facts, user preferences, events, entity relationships — enabling precise retrieval rather than brute-force similarity search. The system knows the difference between “the user prefers dark mode” and “the user deployed to production on Tuesday.”
Pluggable storage: MemPalace supports SQLite for single-user setups, PostgreSQL for team deployments, and vector store backends for semantic search at scale. Switching backends requires a configuration change, not a code rewrite.
Provider-agnostic design: The memory layer sits between the application and the LLM provider. It works with Claude, GPT, Gemini, open-source models, and any provider that accepts standard message formats.
Agent developers who need persistent memory without building it from scratch. Particularly valuable for personal AI assistants, long-running coding agents, and any application where user context accumulates over time. Teams already running agents on any LLM provider can add MemPalace as a memory layer without changing their model or framework.
Builder.io's open-source framework for building agent-native applications — shared actions, SQL-backed state, identity, tools, skills, jobs, observability, and UI surfaces that all work together.
Persistent memory layer for AI coding agents — benchmark-backed (95.2% on LongMemEval-S), 92% fewer tokens per session vs full-context pasting, zero manual memory.add() calls.
Open-source AI pair programming tool that works in your terminal to edit code across your entire repository.