PowerMem

Persistent, self-evolving memory for AI agents and applications.

PowerMem combines vector, full-text, and graph retrieval with LLM-driven memory extraction and Ebbinghaus-style time decay. It ships two-layer Experience + Skill distillation for self-evolving memory, multi-agent isolation, user profiles, and multimodal signals (text, image, audio).

Benchmarks

LOCOMO

Metric	PowerMem	Baseline	Improvement
Accuracy	87.79%	52.9%	+65.9%
Search p95 latency	1.44 s	17.12 s	-91.6%
Tokens	~0.9 k	26 k	-96.5%

AppWorld

Metric	PowerMem	Baseline	Improvement
Pass	39%	24%	+62.5%
Avg steps	6.2	9.5	-34.7%
Total tokens	1.74 M	2.56 M	-32.0%

Reproduce: benchmark/. Under the hood: two-layer Experience + Skill distillation + 4-way hybrid retrieval + LLM auto-merge (API: memory.distill_all() / add_skill / add_experience / search_*, demo examples/experience_skill_usage.py).

Integrations — pick your client, copy one line

PowerMem ships first-party plugins and setup guides for the most common AI clients. All of them point at the same backend (HTTP server, MCP server, or local pmem CLI) — no per-client schema rewrites. All agents share the same memory server.