mem0 is memory infrastructure that lets AI agents and applications store and retrieve information across interactions. It supports agents and developers who need persistent context for AI systems.
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/mem0ai/mem0/peeknpx skills add mem0ai/mem0 --skill peekgit clone --depth 1 https://github.com/mem0ai/mem0Wrote 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/mem0ai/mem0/peek)<a href="https://agentmods.dev/skills/mem0ai/mem0/peek"><img src="https://agentmods.dev/badge/skills/mem0ai/mem0/peek.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 | $0.00052 | $0.00571 |
| Opus 5 | $0.00026 | $0.00285 |
| Sonnet 5 | $0.00010 | $0.00114 |
| Haiku 4.5 | $0.00005 | $0.00057 |
Grade A, and why
peek scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Mem0 Peek
Quick search with compact output. Lighter than /mem0:tour.
Execution
Step 1: Parse query
The user provides a search query: /mem0:peek auth middleware
If no query provided, ask: "What should I search for?"
Memory ID detection: If the query matches any of these patterns, treat it as a direct memory ID lookup instead of a search:
- Bare hex:
^[a-f0-9]{8}$(short ID) or^[a-f0-9]{8}-[a-f0-9-]+$(full UUID) - Citation ref:
[mem0:<hex>]— extract the hex portion
When an ID is detected:
- Call
get_memory(<id>)directly (if short ID, try as prefix of full UUID) - If found, skip to Step 3 and display the single result
- If not found, fall through to search using the ID as query text
Step 2: Search
Run 2 parallel search_memories calls:
- Broad:
query=<user's query>,filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]},top_k=10,rerank=true - Targeted:
query=<user's query>,filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "decision"}}]},top_k=5,rerank=true
Step 3: Display
Deduplicate by ID, then show compact results:
## mem0 peek: "<query>" (<N> results)
1. [decision] Auth module uses JWT with RS256 keys (2025-05-15) [mem0:a3f8b2c1]
2. [anti_pattern] Don't use symmetric HS256 — leaked in env (2025-05-10) [mem0:7e2d9f4a]
3. [convention] All middleware in src/middleware/ (2025-05-08) [mem0:c4d5e6f7]
Format: <number>. [<type>] <content, 80 chars> (<date>) [mem0:<short_id>]
If no results:
No memories matching "<query>" for project <project_id>.
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.
- 5d ago First seen · 53 lines · 52 tokens per session scan A 1c0796029f80
peek is a skill published in the GitHub repository mem0ai/mem0 (64,662 stars, last pushed yesterday), licensed Apache-2.0. It adds 52 tokens to every session and 571 once invoked, about $0.0003 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
honcho
Configure and troubleshoot Honcho memory for Hermes.
openclaw-migration
Import an OpenClaw setup (memories, skills) into Hermes.
pinecone-research
Agent RAG and long-term memory with Pinecone.
agent-carnet
Use this skill when the user asks to save, recall, find, or organize notes. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check the notebook', 'find in carnet'. Also use proactively when discovering findings worth preserving across sessions.
llm-wiki
Karpathy's LLM Wiki: build/query interlinked markdown KB.
qmd
Hybrid local search over notes, docs, and transcripts.