Mgeeeek
a2516269cc
CLI now consumes the unified mem0_notice surface that the platform side
emits for unclaimed Agent Mode keys. The notice is a directive to the
LLM agent reading the output, with a verbatim sentence to relay to the
human owner. Two presentation paths:
- Human/text output: yellow stderr banner after the primary output,
once per command. Skipped in agent mode (the JSON envelope carries
it instead, so no duplication).
- JSON/agent output (--json/--agent): folded into the envelope as
"mem0_notice" so an agent parsing the output sees it without
inspecting HTTP headers.
CLI changes (Python + Node, kept in lockstep):
- state.{ts,py}: captureNotice / takeNotice helpers — last-write-wins
stash so multi-request commands fire the notice exactly once.
- backend/platform.{ts,py}: _request extracts notice from response
bodies (top-level dict or list[0]) with header fallback, strips
from downstream payload, captures for end-of-command surfacing.
- output.{ts,py}: JSON envelope formatters fold in any pending notice.
- index.ts / app.py: entrypoint surfaces notice on exit when not in
agent mode.
- commands/agent-mode.{ts,py}: init success path prints the platform's
notice verbatim (fallback to dim claim-command line if a stale
backend doesn't return it).
Init-flag handling fix: the Python argv preprocessor was stripping
--agent from sys.argv unconditionally as the global JSON-output alias.
That swallowed `mem0 init --agent` (where --agent is a subcommand flag
for unattended bootstrap). Now preserved when "init" is in argv.
Parity tests: cli/python/tests/test_agent_mode.py and
cli/node/tests/agent-mode.test.ts — 7 tests each, kept in sync.
cli-spec.json updated: init now lists --agent and --source.
Docs:
- README.md: Agent Mode promo at top of Quickstart.
- docs/llms.txt: fast-path block for AI agents reading the docs.
- skills/mem0/SKILL.md, skills/mem0-cli/SKILL.md,
skills/mem0-integrate/SKILL.md, mem0-plugin/skills/mem0/SKILL.md:
autonomous-setup section + fallback hints.
- mem0-plugin/README.md, openclaw/README.md: "Quick path for agents"
blocks above the human Quick Start.
Mem0 Skill for Claude
Add persistent memory to any AI application in minutes using Mem0 Platform.
What This Skill Does
When installed, Claude can:
- Set up Mem0 in your Python or TypeScript project
- Integrate memory into your existing AI app (LangChain, CrewAI, Vercel AI, OpenAI Agents, LangGraph, LlamaIndex, etc.)
- Generate working code using real API references and tested patterns
- Search live docs on demand for the latest Mem0 documentation
Installation
This skill is included automatically when you install the Mem0 plugin:
/plugin marketplace add mem0ai/mem0
/plugin install mem0@mem0-plugins
See the plugin README for full setup instructions.
Prerequisites
-
A Mem0 Platform API key (Get one here)
-
Python 3.10+ or Node.js 18+
-
Set the environment variable:
export MEM0_API_KEY="m0-your-api-key"
Quick Start
After installing, just ask Claude:
- "Set up mem0 in my project"
- "Add memory to my chatbot"
- "Help me search user memories with filters"
- "Integrate mem0 with my LangChain app"
- "Add graph memory to track entity relationships"
What's Inside
skills/mem0/
├── SKILL.md # Skill definition and instructions
├── README.md # This file
├── LICENSE # Apache-2.0
├── scripts/
│ └── mem0_doc_search.py # Search live Mem0 docs on demand
└── references/ # Documentation (loaded on demand)
├── quickstart.md # Full quickstart (Python, TS, cURL)
├── sdk-guide.md # All SDK methods (Python + TypeScript)
├── api-reference.md # REST endpoints, filters, memory object
├── architecture.md # Processing pipeline, lifecycle, scoping, performance
├── features.md # Retrieval, graph, categories, MCP, webhooks, multimodal
├── integration-patterns.md # LangChain, CrewAI, Vercel AI, LangGraph, LlamaIndex, etc.
└── use-cases.md # 7 real-world patterns with Python + TypeScript code
Links
License
Apache-2.0