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Mem0 Memory Layer
by
Tang Weigang
· GitHub ↗
· v0.1.0
· MIT-0
63
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Install in OpenClaw
/install mem0-memory-layer
Description
Mem0 长期记忆层：为 LLM agent / chatbot 提供事实级记忆——抽取、嵌入、去重、存储 + 混合检索（语义 + BM25 + 实体加权），覆盖 17 个核心用例。自托管 Memory 与托管 MemoryClient 双形态。 Mem0 long-term memory layer for L...
Usage Guidance
This skill bundles a Mem0 memory description with a large compiled blueprint (seed.yaml) that instructs the host to run Python import checks, access and write workspace files, and rely on environment values (OpenAI keys, MEM0_API_KEY, MEM0_DIR, ZVT_HOME) that are not declared in the registry. Before installing or enabling it: (1) review seed.yaml fully to confirm which commands/files it will read or write, (2) do not supply API keys or secrets unless you trust the source — run it in an isolated environment, (3) if you want to use only Mem0 docs, extract and inspect the specific mem0 parts and ignore unrelated finance/backtest preconditions (zvt), (4) explicitly set MEM0_TELEMETRY=false if you do not want telemetry/analytics, and (5) ask the publisher for a clear, minimal manifest declaring required env vars, install steps, and exact filesystem operations. The current package looks like a mixed/compiled blueprint rather than a focused memory-only skill — proceed with caution.
Capability Analysis
Type: OpenClaw Skill
Name: mem0-memory-layer
Version: 0.1.0
The skill bundle exhibits a significant functional discrepancy: while the metadata and SKILL.md describe a 'Mem0' memory layer, the core logic in 'references/seed.yaml' is almost entirely dedicated to the 'ZVT' quant trading framework (finance-bp-131). Most notably, the skill includes default-on telemetry (BD-019, mem0-C-004) that exfiltrates MD5-hashed caller IDs and lifecycle data to a hardcoded PostHog endpoint. While this behavior is documented within the constraints, the mismatch between the stated purpose and the actual financial trading execution logic, combined with broad shell execution permissions for environment setup, presents a high risk of unintended behavior.
Capability Tags
Capability Assessment
Purpose & Capability
The SKILL.md describes Mem0 (an LLM memory layer) which legitimately may need Python, a local MEM0_DIR, and optional MEM0_API_KEY. However the included seed.yaml is a compiled blueprint with id finance-bp-131 and contains domain-specific preconditions (zvt package, ZVT_HOME, finance backtest checks) that are unrelated to a generic memory layer. Registry metadata declares no required env vars or config paths despite the SKILL.md and seed.yaml referencing Python runtime, local directories (~/.mem0 or ZVT_HOME), and optional API keys — this mismatch is incoherent.
Instruction Scope
The skill’s runtime instructions (seed.yaml) require the host agent to reload seed.yaml on every decision, run precondition checks that execute Python commands (import checks, filesystem checks), verify packages before proceeding, and write into host_workspace (scripts/, skills/, .trace/). These actions go beyond a read-only documentation skill and instruct executing commands and touching local files; they also reference LATEST.yaml/LATEST.jsonl lookups and other artifacts not declared in the skill metadata. That scope creep is disproportionate and surprising for a memory-layer skill.
Install Mechanism
No install spec is provided (instruction-only), which is low risk in itself. However the execution_protocol in seed.yaml instructs the host to run install recipes and verify imports at runtime (e.g., 'Execute resources.host_adapter.install_recipes[]' and python import checks). Those runtime installs are not declared in the registry metadata, so while there is no packaged installer, the instructions effectively request package installation at runtime — this is noteworthy.
Credentials
The registry lists no required environment variables, yet SKILL.md text and seed.yaml refer to multiple environment/config items (Python 3.10+, OpenAI as default LLM/embedding provider, MEM0_DIR ~/.mem0, optional MEM0_API_KEY, MEM0_TELEMETRY, and ZVT/ZVT_HOME checks). Requesting access to local directories, possible API keys, and unrelated project-specific variables (ZVT) without declaring them is disproportionate and raises a risk of unexpected credential or filesystem access.
Persistence & Privilege
always:false and user-invocable:true (normal). The seed.yaml instructs creating/using host_workspace paths (scripts/, skills/, .trace/) and mandates reloading seed.yaml on behavioral decisions — this implies persistent presence in the workspace but not an elevated platform-wide privilege. It's not an outright privilege escalation, but it does ask to write into the agent workspace and to rely on filesystem traces which users should be aware of.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install mem0-memory-layer - After installation, invoke the skill by name or use
/mem0-memory-layer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release: Mem0 long-term memory layer skill (52 constraints / 1 fatal). Doramagic.ai/zh/crystal/mem0-memory-layer
Metadata
Frequently Asked Questions
What is Mem0 Memory Layer?
Mem0 长期记忆层：为 LLM agent / chatbot 提供事实级记忆——抽取、嵌入、去重、存储 + 混合检索（语义 + BM25 + 实体加权），覆盖 17 个核心用例。自托管 Memory 与托管 MemoryClient 双形态。 Mem0 long-term memory layer for L... It is an AI Agent Skill for Claude Code / OpenClaw, with 63 downloads so far.
How do I install Mem0 Memory Layer?
Run "/install mem0-memory-layer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Mem0 Memory Layer free?
Yes, Mem0 Memory Layer is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Mem0 Memory Layer support?
Mem0 Memory Layer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Mem0 Memory Layer?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.1.0.
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