Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/pinkpixel-dev/mem0-mcp/mem0-apinpx skills add pinkpixel-dev/mem0-mcp --skill mem0-apigit clone --depth 1 https://github.com/pinkpixel-dev/mem0-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pinkpixel-dev/mem0-mcp/mem0-api)<a href="https://agentmods.dev/skills/pinkpixel-dev/mem0-mcp/mem0-api"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/mem0-mcp/mem0-api.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00186 | $0.02808 |
| Opus 5 | $0.00093 | $0.01404 |
| Sonnet 5 | $0.00037 | $0.00562 |
| Haiku 4.5 | $0.00019 | $0.00281 |
Grade A, and why
mem0-api scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mem0 API & SDK
Mem0 is a managed memory layer for LLM agents. It extracts facts from conversations, deduplicates, and exposes them via semantic search — giving agents persistent, cross-session context without you managing a vector DB.
Two products, one mental model:
- Mem0 Platform (managed, recommended) — hosted API + dashboard, sub-50ms retrieval
- Mem0 OSS (self-hosted) — run your own vector store, LLM, and embedder stack
Quick Product Routing
User imports `from mem0 import MemoryClient` → Platform
User imports `from mem0 import Memory` → OSS (Python)
User imports `import { Memory } from "mem0ai/oss"` → OSS (Node)
User imports `import MemoryClient from "mem0ai"` → Platform (TS/JS)
No package installed yet → Recommend Platform first
Install
# Python
pip install mem0ai
# Node / TypeScript
npm install mem0ai
# CLI
pip install mem0-cli # Python CLI
npm install -g @mem0/cli # Node CLI
API Key: Get from app.mem0.ai/dashboard/api-keys
Auth header: Authorization: Token <YOUR_API_KEY>
Platform SDK — Core CRUD (TypeScript / Python)
TypeScript (primary for this project)
import MemoryClient from "mem0ai";
const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
// ADD — async, returns event_id
const result = await client.add(
[{ role: "user", content: "I moved to Austin last month." }],
{ user_id: "alice" }
);
// result.event_id → poll GET /v1/event/{id}/ to confirm SUCCEEDED
// SEARCH — V3 hybrid (semantic + BM25 + entity). Filters REQUIRED in V3.
const hits = await client.search("Where does Alice live?", {
filters: { user_id: "alice" },
top_k: 10,
});
// GET ALL
const all = await client.getAll({ user_id: "alice" });
// GET ONE
const mem = await client.get("<memory_id>");
// UPDATE
await client.update("<memory_id>", { text: "Alice lives in Austin, TX" });
// DELETE ONE
await client.delete("<memory_id>");
// DELETE ALL for a scope
await client.deleteAll({ user_id: "alice" });
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 326 lines · 186 tokens per session scan A 4452223bf058
mem0-api is a skill published in the GitHub repository pinkpixel-dev/mem0-mcp (98 stars, last pushed 1mo ago), licensed MIT. It adds 186 tokens to every session and 2,808 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
braindb
Memory recall and save. Use at every conversation start and whenever the user shares personal information, expertise, project context, preferences, or decisions worth remembering long-term.
braindb-agent
Persistent memory across sessions via the BrainDB agent. Use at conversation start and whenever you need to recall what you know about the user or save new information to long-term memory.
braindb-custom-profile
How to author a BrainDB custom profile — prompt add/replace fragments and an optional keyless ingestor — that shapes wiki naming/structure and feeds a custom ingestion source, with zero effect on defaults when inactive.
memory-add-patterns
Patterns for implementing memoryadd tool in Akashic Context — LLM extraction pipeline, deduplication by merge, file naming, prompts. Preload into agents implementing Sprint 1.
akashic-codebase-map
Complete map of the Akashic Context codebase — key files, types, conventions, and current implementation state. Preload into agents working on this project.
per-user-isolation
Patterns for implementing per-user isolation in Akashic Context — directory structure, DB path conventions, workspace isolation, working memory (context.json). Preload into agents implementing Sprint 0.