Clawsaver

Reduce model API costs by 20–40% through intelligent message batching. Buffer related messages, send once.

Reduce model API costs by 20–40% through intelligent message batching and buffering.

Most agent systems waste money on redundant API calls. When users send follow-up messages, you call the model separately for each one. ClawSaver fixes this by waiting ~800ms to collect related messages, then sending them together in a single optimized request. Same response quality. Lower cost. No user friction.

How It Works: Batching & Buffering

WITHOUT CLAWSAVER (Context Overhead Hidden):
User:  "What is ML?"
Model: → API Call #1 [Context: system prompt, chat history] (cost: $X)
       Returns: definition

User:  "Give an example"
Model: → API Call #2 [Context: system prompt, chat history, Q1, A1] (cost: $X)
       Returns: example

User:  "Apply to finance?"
Model: → API Call #3 [Context: system prompt, chat history, Q1–A2] (cost: $X)
       Returns: finance application

Total: 3 calls × full context = 3X cost, each call repeats context overhead

───────────────────────────────────────

WITH CLAWSAVER (Single Context Load):
User:  "What is ML?"          ← Buffer (800ms wait)
User:  "Give an example"      ← Buffer (800ms wait)
User:  "Apply to finance?"    ← Flush: Send all 3 together

Model: → API Call #1 [Context loaded ONCE: system prompt, chat history]
       Processes all 3 questions together
       Returns: comprehensive answer addressing all three

Total: 1 call × full context = 1X cost, context overhead paid once

Actual savings (with context): 67% reduction
Cost per token: 1/3 (fewer context re-loads + consolidation)

Why it matters: Context (system prompts, history, instructions) gets re-sent on every API call. With ClawSaver, you pay that context overhead once per batch instead of three times. This compounds the savings beyond just "fewer calls."

Example (4K token context, 200 output tokens):

• Without ClawSaver: 3 calls × 4,200 tokens = 12,600 tokens
• With ClawSaver: 1 call × 4,600 tokens = 4,600 tokens
• Actual savings: 63% token reduction (even better than call reduction)

The Problem

User: "What is machine learning?"
(pause)
User: "Give an example"
(pause)
User: "How does that apply to healthcare?"

Without optimization: 3 API calls = 3x cost
With ClawSaver: 1 batched call = 1/3 the price

Across thousands of conversations, this compounds fast.

How It Works

1. User sends message → ClawSaver buffers it
2. Waits ~800ms for follow-ups from same user
3. If more messages arrive → keep buffering
4. Timer expires → send all messages together
5. Model responds once → you get complete answer

Why users don't notice: They're already waiting for your model response. Buffering input doesn't feel slower because the response comes right after the batch sends.

Install

clawhub install clawsaver

Quick Start (10 lines)

import SessionDebouncer from 'clawsaver';

const debouncers = new Map();

function handleMessage(userId, text) {
  if (!debouncers.has(userId)) {
    debouncers.set(userId, new SessionDebouncer(
      userId,
      (msgs) => callModel(userId, msgs)
    ));
  }
  debouncers.get(userId).enqueue({ text });
}

Impact

Metric	Value
Cost reduction	20–40% typical
Setup time	10 minutes
Code added	~10 lines
Dependencies	0
File size	4.2 KB
Latency added	+800ms (user-imperceptible)
Maintenance	None

Three Profiles

Choose based on your use case:

Balanced (Default)

• 25–35% savings
• 800ms buffer
• Chat, Q&A, general conversation

Aggressive

• 35–45% savings
• 1.5s buffer
• Batch workflows, high-volume ingestion

Real-Time

• 5–10% savings
• 200ms buffer
• Interactive, voice-first systems

When to Use

✅ Chat applications
✅ Customer support bots
✅ Multi-turn Q&A
✅ Any conversation with follow-ups

❌ Single-request workflows
❌ Sub-100ms response requirements

API

new SessionDebouncer(userId, handler, {
  debounceMs: 800,      // wait time
  maxWaitMs: 3000,      // absolute max
  maxMessages: 5,       // batch size cap
  maxTokens: 2048       // reserved
})

// Methods
debouncer.enqueue(message)      // add to batch
debouncer.forceFlush(reason)    // send now
debouncer.getState()            // buffer + metrics
debouncer.getStatusString()     // human-readable

Docs

• START_HERE.md — Navigation (pick your role/timeline)
• AUTO-INTEGRATION.md — ⭐ Drop-in middleware wrapper (2 min setup)
• QUICKSTART.md — 5-minute integration
• INTEGRATION.md — Patterns, edge cases, full config
• SUMMARY.md — Metrics and ROI (decision makers)
• SKILL.md — Full API reference
• example-integration.js — Copy-paste templates

Security

• No telemetry — Doesn't phone home
• No network calls — Runs locally
• No dependencies — Pure JavaScript
• You control output — You decide what goes to your model

Data never leaves your machine.

License

MIT

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Start here: Pick your path in START_HERE.md, or jump to QUICKSTART.md for 5-minute setup.