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lycheemem

@lycheemem⭐ 1.1k stars

Forceful operating rules for using LycheeMem as the primary structured long-term memory path inside OpenClaw.

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// RATINGS

⭐GitHub Stars

⭐⭐⭐⭐⭐ 1.1k on GitHubGitHub ↗

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// README

LycheeMemory is a compact memory framework for LLM agents. It starts from efficient conversational memory—through structured organization, lightweight consolidation, and adaptive retrieval—and gradually extends toward action-aware, usage-aware memory for more capable agentic systems.

🔥 News

[07/07/2026] OpenAI-compatible Chat Completions endpoints are now available, with request-level consolidation control via consolidate or store.
[05/08/2026] Transformer memory reranker v0 improves evidence selection in semantic memory search, with positive hit@10 gains on LoCoMo and zero-shot LongMemEval-S / MSC-MemFuse / HotpotQA fixtures. See Transformer Reranker v0.
[04/29/2026] Hermes and Claude Code plugin integrations are now available, bringing LycheeMemory's automatic recall, turn mirroring, and consolidation workflow to more agent runtimes. Setup guides: Hermes · Claude Code
[04/26/2026] Visual (Multimodal) Memory module added! See Visual Memory.
[04/13/2026] LycheeMem is now LycheeMemory.
[04/03/2026] The project now supports installation via pip install lycheemem. You can easily start the service from anywhere using lycheemem-cli!
[03/30/2026] We evaluated LycheeMemory on PinchBench with the OpenClaw plugin: compared to OpenClaw's native memory, it achieved an ~6% score improvement, while reducing token consumption by ~71% and cost by ~55%!
[03/28/2026] Semantic memory has been upgraded to Compact Semantic Memory (SQLite + LanceDB), no Neo4j required. See /quick-start for details.
[03/27/2026] OpenClaw Plugin is now available at /openclaw-plugin ! Setup guide →
[03/26/2026] MCP support is available at /mcp !
[03/23/2026] LycheeMemory is now open source: GitHub Repository →

🔗 Related Projects

LycheeMemory is part of the 3rd-generation Lychee (立知) large model series, which focuses on memory intelligence, continual learning, and long-context reasoning.

We welcome you to explore our related works:

LycheeMemory (ACL 2026, CCF-A): a unified framework for implicit long-term memory and explicit working memory collaboration in large language models
LycheeMem (this project): long-term memory infrastructure for LLM-based agents
LycheeDecode (ICLR 2026, CCF-A): selective recall from massive KV-cache context memory
LycheeCluster (ACL 2026, CCF-A): structured organization and hierarchical indexing for context memory

⚡ Quick Start

Prerequisites

Python 3.9+
An LLM API key (OpenAI, Gemini, or any litellm-compatible provider)

Installation

Install the core package:

pip install lycheemem

Recommended install with the default transformer memory reranker:

pip install "lycheemem[rerank]"

The rerank extra adds PyTorch / Transformers runtime dependencies. With it installed, LycheeMemory enables the hosted LycheeMem/reranker checkpoint by default. Without the extra, the core memory system still works and reranki

// HOW IT'S BUILT

KEY FILES

openclaw-plugin/skills/lycheemem/SKILL.mdREADME.md

// REPO STATS

1.1k stars

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