Your AI agent finally gets a hippocampus that forgets on schedule

What it does

PowerMem is a memory backend for AI agents and applications. It stores, retrieves, and distills agent interactions using a hybrid of vector, full-text, and graph retrieval, with LLM-driven extraction and Ebbinghaus-style time decay. The system supports multi-agent isolation, user profiles, and multimodal inputs, and can run fully offline using an embedded database and a local embedding model.

The interesting bit

The “two-layer Experience + Skill distillation” is the core mechanic: raw interactions are compressed into reusable skills, which shrinks token usage dramatically. The README claims this cuts token counts by over 96% on the LOCOMO benchmark and drops p95 latency from 17 seconds to 1.44 seconds compared to the baseline.

Key highlights

• Ships with first-party plugins for Claude Code, Cursor, VS Code, GitHub Copilot, Windsurf, and MCP-compatible clients, all pointing at the same backend.
• Benchmarked on LOCOMO and AppWorld; reported metrics include 87.79% accuracy (vs. 52.9% baseline) and 39% pass rate on AppWorld (vs. 24%).
• Supports Python, Go, Java, and TypeScript SDKs, plus LangChain and LangGraph integrations.
• Defaults to a fully offline stack: embedded seekdb storage and a local all-MiniLM-L6-v2 model, with no API keys required for basic operation.
• Apache 2.0 licensed.

Caveats

• Most benchmark comparisons lack explicit baseline descriptions—the tables simply list “Baseline” without naming the specific system or method, so treat the improvement percentages as project-reported figures.

Verdict

Worth evaluating if you’re building multi-turn agents that drown in their own context windows. Skip it if you just need a simple vector store for RAG; the value is in the memory lifecycle and agent-specific integrations.