nanobot is a small, self-hosted personal AI agent framework written in Python, providing an agent runtime that can operate through a WebUI, terminal, or chat applications. Users use it to connect language models with tools, memory, MCP integrations, scheduled automation, and multi-agent workflows. The catalogue add-ons provide skills and instructions for working with nanobot.
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 skills add HKUDS/nanobot --skill memorygit clone --depth 1 https://github.com/HKUDS/nanobotWrote 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/hkuds/nanobot/memory)<a href="https://agentmods.dev/skills/hkuds/nanobot/memory"><img src="https://agentmods.dev/badge/skills/hkuds/nanobot/memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/nanobot/memory"><img src="https://agentmods.dev/badge/skills/hkuds/nanobot/memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00012 | $0.00100 |
| Opus 5.5 | $0.00005 | $0.00040 |
| Sonnet 5.5 | $0.00002 | $0.00020 |
| Haiku 4.5 | $0.00001 | $0.00010 |
Grade A, and why
memory 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 20d 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
Memory
Search Past Events
Search the exact History log path from the system prompt using an available text-search
tool. This path belongs to the agent workspace, which can differ from the current project.
The append-only JSONL log stores cursor, timestamp, and content per entry. Retrieve
entries on demand by topic or date, and inspect neighboring entries when context matters.
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.
- 20d ago Changed · -6 lines 19c562a9a8aa
- 1mo ago Changed · -18 lines · -2 tokens per session 15a3157baa2e
- 1mo ago First seen · 39 lines · 14 tokens per session scan A 0946a34d4519
memory is a skill published in the GitHub repository HKUDS/nanobot (49,152 stars, last pushed yesterday), licensed MIT. It adds 12 tokens to every session and 100 once invoked, about $0.0000 per session on Opus 5.5. 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
agenticx-memory-architect
Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents. Use when the user wants to add memory to agents, persist conversation history, build memory-aware workflows, or integrate with Mem0 for long-term recall.
company-agent-infrastructure
Use when designing or building internal/company AI agents that need shared context across company systems, durable organizational memory across sessions or agents, user-scoped permissions, approvals, audit, or governed actions. Also use when deciding whether ordinary MCP tools, RAG, or local agent memory are enough…
lobu
Use Lobu MCP for shared, permission-aware company context, durable organizational memory, and governed actions. Trigger when the user asks what the organization knows, needs context from connected company systems, wants to preserve a durable fact or decision, or needs to discover and use Lobu SDK capabilities.
swarmvault
Use when working with a SwarmVault knowledge vault (raw/, wiki/, swarmvault.schema.md). Establishes schema-first conventions and prefers graph queries over broad search.
swarmclaw
AI agent runtime and multi-agent orchestration platform. Teaches agents how to use SwarmClaw's 6 primitive tools, persistent memory, dreaming, delegation, connectors, credentials, and the skill system. Use when an agent is running on SwarmClaw and needs to understand the platform's capabilities.
citedy-content-writer
From topic to published blog post in one conversation — generate SEO- and GEO-optimized articles with AI illustrations and voice-over in 55 languages, create social media adaptations for 9 platforms, set up automated content sessions, and manage product knowledge base. End-to-end blog autopilot. Powered by Citedy.