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/mem0-remembernpx skills add mem0ai/mem0 --skill mem0-remembergit 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/mem0-remember)<a href="https://agentmods.dev/skills/mem0ai/mem0/mem0-remember"><img src="https://agentmods.dev/badge/skills/mem0ai/mem0/mem0-remember.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 | $0.00559 |
| Opus 5 | $0.00026 | $0.00280 |
| Sonnet 5 | $0.00010 | $0.00112 |
| Haiku 4.5 | $0.00005 | $0.00056 |
Grade A, and why
mem0-remember 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 Remember
Store a fact or learning directly into mem0.
Execution
Step 1: Extract the content
The user provides the content as an argument: /mem0-remember <text>
If no text was provided, ask: "What should I remember?"
Step 2: Classify the memory
Based on the content, pick the best metadata.type:
| Content signal | Type |
|---|---|
| "we decided...", "always use...", "never..." | decision |
| "X doesn't work because...", "don't try..." | anti_pattern |
| "I prefer...", "use X instead of Y" | user_preference |
| "the convention is...", "we always..." | convention |
| "learned that...", "figured out..." | task_learning |
| setup, env, tooling, config | environmental |
| anything else | task_learning |
Step 3: Store
Call add_memory with:
text="<the user's text>"user_id=<active_user_id>app_id=<active_project_id>metadata={"type": "<classified_type>", "branch": "<active_branch>", "confidence": 1.0, "source": "remember_command"}infer=False
infer=False because the user stated the fact explicitly — no extraction needed.
confidence=1.0 because the user explicitly asked to store this.
Step 4: Confirm
The add_memory response returns event_id (not memory_id) because writes are async.
Call get_event_status(event_id=<event_id>) once.
- If status is
SUCCEEDED: print the memory ID from the result. - If status is
PENDINGorprocessing: print with the event ID as fallback.
Remembered as <type>: "<content, first 80 chars>"
Memory ID: <id from event status>
Append ... only if content was truncated (longer than 80 chars).
Output formatting
IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like bold, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
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 · 62 lines · 52 tokens per session scan A 899a91086e1a
mem0-remember 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 559 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.
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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.
add-memory-kind
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browserwing-executor
Control browser automation through HTTP API. Supports page navigation, element interaction (click, type, select), data extraction, accessibility snapshot analysis, screenshot, JavaScript execution, and batch operations.
release
Cut a versioned release and publish everos to PyPI via the tag-triggered workflow.