vibo

ViBo — memory for AI agents: persistent memory, web-search savings, thread memory, usage stats

Package Information

Released: 2 mo ago
Downloads: 0 weekly / 0 monthly
Latest Version: 0.1.4

Available Nodes

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ViBo Memory icon
ViBo Memory
Memory for AI agents: search facts, save facts, thread memory

Documentation

n8n-nodes-vibo

ViBo — Memory for AI agents. Persistent memory, web-search savings
and thread memory right inside your n8n workflows.

One node, three capabilities — and a savings counter:

Capability What it does
🧠 Memory Save facts, search them semantically across sessions
🌐 Web savings Compressed context — fewer tokens to the LLM
💬 Thread Long conversations compressed (-72%), details restored on demand
📊 Usage See your real token savings (today + all-time)

Install (community node)

# In your n8n folder:
npm install n8n-nodes-vibo
# Restart n8n → the "ViBo Memory" node appears in the canvas

Or install from the n8n Editor:
Settings → Community Nodes → Install → n8n-nodes-vibo

Setup

  1. Get a key: https://wwwvibo.com — free tier: 500 facts forever, then $5/mo · $30/yr · $60 lifetime (https://wwwvibo.com/pricing).
  2. In n8n: create ViBo API credential, paste your key (starts with VIBO-).
  3. Add the ViBo Memory node to your workflow.

Operations

Memory → Search

Find relevant facts in your memory. Returns facts + savings stats:

{
  "ok": true,
  "facts": [{ "label": "client-anna", "content": "Anna likes coffee, order #42", "level": "L1" }],
  "saved_tokens": 13452,
  "saved_pct": 97.5
}

Memory → Add

Save a fact (deduplicated by exact match):

{ "ok": true, "added": true, "nodes": 45 }

Memory → Usage

Your real savings (today + all-time):

{
  "ok": true,
  "records": 21,
  "saved_tokens": 8428443,
  "saved_usd": 1.18
}

Thread → Add / Compress / Ask / Context

Conversation memory: add messages, compress old ones (-72%),
ask "what did we discuss 3 days ago?", get compressed context for the LLM.

Usage examples

Support bot that remembers clients

[Telegram trigger] → [ViBo Memory: Search "client X"] → [AI Agent] → [Reply]
                        ↓ (no result? add fact)
                    [ViBo Memory: Add "client X likes email"]

Conversation memory for your AI agent

[Chat trigger] → [ViBo Memory: Thread Add (user msg)] → [AI Agent]
                     ↑                          ↓
                     [ViBo Memory: Thread Context] ← chat history

Show your client the savings

[Any trigger] → [ViBo Memory: Usage] → [Message: "You saved X tokens"]

API

The node talks to https://wwwvibo.com:

Endpoint Operation
POST /memory/search Find relevant facts (returns savings stats)
POST /memory/add Save a fact (dedup by exact match)
POST /memory/thread Thread memory: add / compress / ask / context
GET /usage?key= Token savings statistics

Security: data encrypted (L1/L2/L3), per-key memory, key verification
on every call. Secrets never reach the LLM.

Honest about savings

  • Memory: 97.5% fewer tokens on 118 facts (grows with memory, up to 50-150× on 10K+).
  • Web: 96-99% (measured 47,443 → 186 tokens).
  • Threads: -72%.
  • Code generation: little to no savings (it doesn't "remember").
  • Every operation reports its real savings — no marketing numbers.

License

MIT — node itself is free. The memory service requires a ViBo key
($5/month, 2-day free trial).

Site: https://wwwvibo.com · Bot: @ViBomemorybot ·
Docs: https://github.com/vnbochkarev-netizen/ViBo-memory

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