nemo-ai

Neural Evolving Memory Object — but also the fish 🐟

Evolving semantic memory and intent router for AI agent pipelines.

Sub-millisecond intent classification using Hyperdimensional Computing (MAP-HDC) — pure TypeScript, zero dependencies, no GPU, no embedding model. Works in Node.js and the browser. Supports English and Arabic out of the box. Learns with every interaction.

npm install nemo-ai

Overview

nemo sits in front of your LLM (or between agent layers) and answers one question fast:

"Do I know what this input means, and where should it route?"

raw text  (English or Arabic)
    │
    ▼
CST Tokenizer   →  semantic tokens   (CONCEPT · ROLE · NEG · QUERY · MODAL · REL · …)
    │
    ▼
Prep Layer      →  intent frame      (is_question · has_negation · dominant_field · pattern)
    │
    ▼
HDC Encoder     →  hypervector       (10,000-dim bipolar Float32Array, MAP algebra)
    │
    ▼
HDC Agent       →  classification    (nearest prototype · cosine similarity · confidence)
    │
    ▼
Gate            →  skip_llm  |  llm_assist  |  full_llm

The agent updates itself with every accepted input — no retraining, no model weights, no GPU.

Quickstart

import { pipeline, HDCAgent, HDVEncoder, tokenize } from "nemo-ai";

const encoder = new HDVEncoder(); // seeded — deterministic vector space
const agent = new HDCAgent();

// Train on a few examples
const training: Record<string, string[]> = {
  tech: ["fix the bug", "write python code", "debug error"],
  weather: ["rain forecast", "temperature tomorrow", "storm warning"],
  food: ["pasta recipe", "how to cook chicken", "vegan dinner ideas"],
};

for (const [field, phrases] of Object.entries(training)) {
  for (const phrase of phrases) {
    const [hv] = encoder.encode(tokenize(phrase));
    agent.observe(hv, field);
  }
}
agent.calibrate();

// Classify
const result = pipeline("fix the syntax error in my code", agent, encoder);
console.log(result.classification.field); // "tech"
console.log(result.classification.confidence); // 0.78
console.log(result.gate); // "skip_llm"
console.log(result.tool); // "code_assistant"

Arabic Support

nemo ships a full Arabic tokenizer with feature parity to the English one. Both produce identical CSTToken[] output — the same encoder and agent work for both languages without any configuration.

import { tokenizeAr, tokenStreamAr } from "nemo-ai";

// All 18 token types work in Arabic
tokenStreamAr("يمكن أن أساعدك في البرمجة");
// → "MODAL CONCEPT:speak REL:in CONCEPT:tech"

tokenStreamAr("ذكاء اصطناعي يغيّر العالم");
// → "CONCEPT:tech CONCEPT:work"

tokenStreamAr("لماذا فشل النظام؟");
// → "WHY_Q CONCEPT:tech QUERY"

The Arabic tokenizer includes:

• Clitic segmentation — strips و/ف conjunctions, ب/ل/ك prepositions, ال article, object suffixes
• Root-based lookup — 2,100+ stems → 42 semantic fields via trilateral root codes
• Compound phrases — 50+ bigrams (ذكاء اصطناعي → tech, كرة قدم → sport, …)
• Morphological roles — فاعل (agent), مفعول (patient), تفعيل (process), مفعلة (place)
• Normalization resilience — diacritics, ى/ي, آ/أ/إ/ا, tatweel all handled transparently

Using in an Agent Pipeline

Pre-LLM gate

import { pipeline, HDCAgent, HDVEncoder } from "nemo-ai";

async function route(
  input: string,
  agent: HDCAgent,
  encoder: HDVEncoder,
  llm: (prompt: string, opts?: { system?: string }) => Promise<string>,
) {
  const result = pipeline(input, agent, encoder);

  if (result.gate === "skip_llm") {
    return callTool(result.tool, input); // confident — bypass LLM
  }
  if (result.gate === "llm_assist") {
    const system = `Intent: ${result.classification.field}. Tool: ${result.tool}.`;
    return llm(input, { system }); // steer LLM with context
  }
  return llm(input); // unknown — full LLM
}

Persistent session (recommended)

NemoSession wraps encoder + agent + tools into one persistent unit with built-in auto-save:

import { NemoSession } from "nemo-ai";

// Recommended: loads saved state if file exists, starts fresh if not.
// Auto-saves every 100 teach() calls and flushes on SIGTERM — zero config needed.
const session = NemoSession.loadOrCreate("./memory.nemo.json", {
  tools: {
    tech: (input) => codeAssistant(input),
    weather: (input) => weatherService(input),
  },
});

const result = await session.run(userInput);

// Teach from confirmed ground truth — triggers auto-save counter
session.teach("blood pressure monitor", "health");

// Manual flush at any time (e.g. before a planned restart)
session.save();

Episodic memory retrieval

const [queryHV] = encoder.encode(tokenize("how do I fix this"));
const episodes = agent.retrieve(queryHV, 3);

for (const ep of episodes) {
  console.log(ep.meta?.text, "→", ep.sim.toFixed(3));
}

Share memory between agents

import fs from "fs";
import { HDCAgent } from "nemo-ai";

// Export
fs.writeFileSync("memory.nemo.json", JSON.stringify(agent.toJSON()));

// Import in another process / service
const agentB = HDCAgent.fromJSON(
  JSON.parse(fs.readFileSync("memory.nemo.json", "utf8")),
);

API Reference

pipeline(text, agent, encoder): PipelineResult

Full stack in one call. English only — for Arabic use tokenizeAr then encoder.encode.

| Field | Type | Description | | ---------------- | ---------------- | ------------------------------------------- | | tokens | CSTToken[] | CST token array | | frame | ReasoningFrame | intent frame (is_question, has_negation, …) | | classification | ClassifyResult | field, confidence, top3 | | tool | string | mapped tool name | | gate | GateDecision | skip_llm / llm_assist / full_llm |

HDCAgent

| Method | Description | | ------------------------------ | ------------------------------------------------------- | | observe(hv, field) | Accumulate a hypervector during training | | calibrate() | Compute per-field thresholds — call once after training | | classify(hv) | Returns ClassifyResult | | update(hv, field, meta?) | Conditional self-update at inference time | | feedback(hv, field, meta?) | Unconditional ground-truth update | | step(hv, meta, groundTruth?) | classify + update in one call | | retrieve(queryHV, k, field?) | Return k nearest episodes from memory | | toJSON() | Serialize full state to a plain JSON-safe object | | HDCAgent.fromJSON(state) | Restore from serialized state | | snapshot() | Return field counts and memory stats |

HDVEncoder

| Method | Description | | ---------------------- | ------------------------------------------------------ | | encode(tokens) | Returns [Float32Array, dominantField \| null] | | atomState() | Export atom hypervectors as Record<string, number[]> | | loadAtomState(state) | Restore a saved atom space |

Tokenizers

| Export | Language | Description | | --------------------- | -------- | --------------------------------------------- | | tokenize(text) | English | Returns CSTToken[] | | tokenStream(text) | English | Returns token values as a space-joined string | | tokenizeAr(text) | Arabic | Returns CSTToken[] — same interface | | tokenStreamAr(text) | Arabic | Returns token values as a space-joined string | | COMPOUND_FIELDS | English | Bigram → field map (exported for extension) | | COMPOUND_FIELDS_AR | Arabic | Bigram → field map (exported for extension) |

Gate thresholds

| Gate | Confidence | Meaning | | ------------ | ----------- | ---------------------------------------------- | | skip_llm | ≥ 0.55 | nemo is confident — skip the LLM entirely | | llm_assist | 0.35 – 0.55 | Inject semantic context into the LLM prompt | | full_llm | < 0.35 | Unknown input — route to the LLM without hints |

import { GATE_HIGH, GATE_MED } from "nemo-ai";
// GATE_HIGH = 0.55,  GATE_MED = 0.35

NemoSession factories

| Factory | When to use | | ---------------------------------------------------- | --------------------------------------------------------------------------------------- | | NemoSession.loadOrCreate(path, opts?) | Recommended. Restores saved state if the file exists; starts fresh if not (ENOENT). | | NemoSession.load(path, opts?) | File must exist — throws if missing. Use when a missing file is a deployment error. | | new NemoSession({ agent, encoder, filePath, ... }) | Manual construction from pre-built instances. |

NemoSession persistence options

NemoSession.loadOrCreate("./.nemo.json", {
  autoSaveEvery: 100,   // save every N teach() calls (default 100 when filePath given, 0 otherwise)
  shutdownHook: true,   // flush on SIGTERM/SIGINT — default true when filePath given
  tools: { ... },       // tool functions (not serialised, reattach after load)
});

| Option | Default | Description | | --------------- | -------- | ---------------------------------------------------------------------------------- | | autoSaveEvery | 100 ¹ | Save every N teach() calls. teach() is the only call that mutates HDC state. | | shutdownHook | true ¹ | Register SIGTERM/SIGINT handlers to flush state on process exit. Node.js only. |

¹ When filePath is provided. Without a path both default to 0/false — suitable for browser environments.

Manual save / custom strategies

// Explicit save at any point
session.save();

// Save to a different path (e.g. snapshot before upgrade)
session.save("./memory.nemo.backup.json");

// High-traffic: disable built-in auto-save, use your own interval
const session = NemoSession.loadOrCreate("./.nemo.json", {
  autoSaveEvery: 0,
  shutdownHook: false,
});
setInterval(() => session.save(), 5 * 60_000); // every 5 min
process.once("SIGTERM", () => session.save());

Low-level persistence helpers

import { saveToFile, loadFromFile } from "nemo-ai";

// Save agent + encoder directly
saveToFile("./memory.nemo.json", agent, encoder, { myMeta: "v2" });
const { agent, encoder, meta } = loadFromFile("./memory.nemo.json");

State is a plain JSON file — portable to any store:

// Database column
await db.run("UPDATE sessions SET state = ?", [JSON.stringify(agent.toJSON())]);

// Redis
await redis.set(`nemo:${userId}`, JSON.stringify(agent.toJSON()));

What is inside .nemo.json

| Key | Contents | Notes | | -------------- | ------------------------------------------------------------- | -------------------------------------------------------- | | agent | Per-field prototype hypervectors + thresholds + episode store | Grows with teach() calls | | encoderAtoms | Random atom space (seeded at SEED=42) | Deterministic — could be skipped, saved for fast startup | | version | Integer schema version | Currently 1 | | savedAt | ISO timestamp | Informational only |

The built-in vocabulary (keyword tables in tokenizer.ts) is not in the file — it is compiled into the package. Only the learned improvements from teach() are persisted. Losing the file means starting fresh with the built-in vocabulary, not losing the model.

Token Types

nemo's CST (Conceptual Semantic Tokenizer) produces 18 token types shared by both languages:

| Type | Trigger | Example | | --------- | --------------------- | -------------------------------- | | CONCEPT | Semantic field match | CONCEPT:tech, CONCEPT:health | | ROLE | Morphological pattern | ROLE:agent, ROLE:patient | | REL | Preposition | REL:in, REL:to, REL:from | | NEG | Negation word | not, لا, لم | | QUERY | Question mark | ?, ؟ | | WHAT_Q | What-question | what, ماذا | | WHO_Q | Who-question | who, من | | WHERE_Q | Where-question | where, أين | | WHEN_Q | When-question | when, متى | | WHY_Q | Why-question | why, لماذا | | HOW_Q | How-question | how, كيف | | WHICH_Q | Which-question | which, أي | | MODAL | Ability/obligation | should, يمكن, يجب | | COND | Condition | if, إذا | | CAUSE | Causation | because, لأن | | FUTURE | Future marker | will, سـ prefix | | PAST | Past marker | was/were, كان | | LIT | No match (fallback) | unknown terms |

How it learns

nemo uses MAP Hyperdimensional Computing (Multiply-Add-Permute algebra):

• Every token maps to a seeded random 10,000-dim bipolar {-1, +1} Float32Array
• bind(a, b) — elementwise multiply — encodes associations, self-inverse
• bundle([a, b, c]) — majority vote — merges into a category prototype
• similarity(a, b) — cosine similarity — measures semantic distance

The agent keeps one running sum per semantic field. Each accepted input shifts the prototype slightly toward the new example — continual learning with no gradient descent, no stored weights, no retraining cycle.

Semantic Fields

42 shared fields across English and Arabic:

know · think · speak · write · see · feel · create · destroy · fix · work · move · send · give · gather · hold · connect · exist · govern · fight · trade · social · possess · science · health · tech · art · sport · nature · weather · animal · plant · body · food · material · color · time · place · size · measure · quality

Development

git clone https://github.com/msm-core/nemo.git
cd nemo
npm install
npm test       # 90 tests
npm run build  # compiles to dist/

Based on CST

nemo's tokenizer is built on the linguistic principles of Contextual Semantic Tokenization (CST) — a linguistically-grounded alternative to subword segmentation, originally developed for language model training.

CST encodes semantic field and morphological role directly into every token. Arabic morphology defines the algebraic foundation: root × pattern = concept. The root ك-ت-ب (k-t-b, write) + pattern فَاعِل (agent) = كاتب (writer). CST generalises this algebra across languages.

Research project: github.com/emadjumaah/cst

What nemo uses from CST

| CST concept | nemo implementation | | ---------------------------------------------- | ------------------------------------------------- | | Semantic fields (~45 universal) | 42 shared fields, same names | | Morphological role detection | ROLE:agent / patient / process / place | | Triconsonantal root algebra | ROOT_MAP → ROOT_FIELD for Arabic stems | | Structural markers (negation, tense, modality) | NEG, MODAL, PAST, FUTURE, COND, CAUSE | | Cross-lingual field parity | Same 42 fields for English and Arabic |

What is different

The full CST research tokenizer runs a 7-stage pipeline (including named-entity detection) and produces CMP:write:agent / ROOT:move / STR:negation tokens — designed for language model training benchmarks (35–46% BPC reduction over SentencePiece BPE).

nemo implements a runtime-optimized subset of those principles: a 4-stage pipeline (normalize → structural detect → clitic segment → root/field lookup), with a flattened token format (CONCEPT:write + ROLE:agent) that maps efficiently into HDC hypervectors. The goal is sub-millisecond classification in agent pipelines, not LM training.

If you need the full research tokenizer for training or evaluation, see the CST repository.

License

MIT