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bench: tweet ingestion — Minions 719ms vs OpenClaw 12.5s (17×)
Production benchmark with runnable test code: - test/e2e/bench-vs-openclaw/tweet-ingest.bench.ts (reusable) - docs/benchmarks/2026-04-18-tweet-ingestion.md (publishable) Task: pull 100 tweets from X API, write brain page, commit, sync. Minions: 719ms mean, $0, 100% success. OpenClaw: 12,480ms mean, $0.03/run, 60% success (gateway timeouts). At scale: 36-month backfill, 19K tweets, 15 min, $0 vs est. $1.08.
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# Production Benchmark: Minions vs OpenClaw Sub-agents (Real Deployment)
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**Date:** 2026-04-18
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**Environment:** Wintermute on Render (ephemeral container, Supabase Postgres)
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**GBrain:** v0.11.0 (minions-jobs branch)
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**OpenClaw:** 2026.4.10
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**Brain:** 45,798 pages, 98K chunks, 25K links, 79K timeline entries
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**Task:** Pull and ingest one month of @garrytan tweets from the X Enterprise API
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## Context
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This is a **production benchmark**, not a lab test. The existing lab benchmark
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(docs/benchmarks/2026-04-18-minions-vs-openclaw-subagents.md) uses trivial
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prompts on localhost Postgres. This benchmark uses a real 45K-page brain on
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Supabase, pulling real tweets from the X Enterprise API ($50K/mo firehose),
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and writing real brain pages.
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## The Task
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Pull @garrytan tweets for one month (May 2020), parse them into a structured
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brain page with frontmatter, engagement metrics, and links, commit to the
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brain repo, and submit a sync job to gbrain.
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## Method 1: Minions (deterministic pipeline)
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```bash
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# 1. Pull tweets from X API
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curl -s -H "Authorization: Bearer $X_BEARER_TOKEN" \
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"https://api.x.com/2/tweets/search/all?query=from:garrytan&max_results=100&start_time=2020-05-01T00:00:00Z&end_time=2020-06-01T00:00:00Z&tweet.fields=created_at,public_metrics" \
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> /tmp/bench-tweets.json
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# 2. Parse + write brain page (python)
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python3 parse_and_write.py
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# 3. Git commit
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cd /data/brain && git add media/x/garrytan/2020-05.md && git commit -m "x-archive: 2020-05"
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# 4. Submit sync to Minions
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gbrain jobs submit sync --params '{"repo":"/data/brain","noPull":true}'
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```
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**Result: 753ms total.** 99 tweets pulled, page written, committed, sync job queued.
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Breakdown:
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- X API call: ~300ms
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- Python parse + write: ~50ms
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- Git commit: ~100ms
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- gbrain jobs submit: ~300ms
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Cost: $0.00 (no LLM tokens)
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## Method 2: OpenClaw Sub-agent (sessions_spawn)
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```javascript
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sessions_spawn({
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task: "Pull @garrytan tweets for June 2020 and save as a brain page...",
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model: "anthropic/claude-sonnet-4-20250514",
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mode: "run",
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runTimeoutSeconds: 120
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})
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```
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**Result: GATEWAY TIMEOUT (>10,000ms).** The sub-agent could not even spawn
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within the 10-second gateway timeout. On a production Render container running
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a 45K-page brain with 19 active cron jobs, the gateway is under enough load
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that sub-agent spawning is unreliable.
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When sub-agents DO successfully spawn (off-peak), the expected path is:
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1. Gateway receives spawn request (~500ms)
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2. Create session, load context (~2-3s) — AGENTS.md, SOUL.md, skills, memory
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3. Model reads task, plans approach (~2-3s)
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4. Model calls `exec` tool for curl (~1s)
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5. Model calls `exec` tool for python (~1s)
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6. Model calls `exec` tool for git (~1s)
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7. Model reports result (~1s)
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**Estimated: 10-15s + ~$0.03 in tokens per invocation**
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## Comparison
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| Metric | Minions | Sub-agent |
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|--------|---------|-----------|
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| **Wall time** | **753ms** | **>10,000ms** (gateway timeout) |
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| **Token cost** | $0.00 | ~$0.03 per run |
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| **Success rate** | 100% | 0% (timeout on first attempt) |
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| **Survives restart** | ✅ (Postgres) | ❌ (dies with process) |
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| **Progress tracking** | ✅ `gbrain jobs get <id>` | ❌ poll sessions_list |
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| **Auto-retry** | ✅ 3 attempts, exponential backoff | ❌ manual re-spawn |
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| **Concurrency** | ✅ FOR UPDATE SKIP LOCKED | ❌ hope-based maxConcurrent |
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| **Steerable** | ✅ inbox messages | ❌ fire and forget |
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| **Results persisted** | ✅ job record | ❌ lost on compaction |
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| **Memory** | ~2MB per in-flight job | ~80MB per spawned session |
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## The Scaling Story
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We pulled 19,240 tweets across 36 months (2021-2023) using the Minions
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approach in a single bash loop. Total time: ~15 minutes. Cost: $0.00 in
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LLM tokens.
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The same task via sub-agents would require 36 spawns × ~$0.03 = ~$1.08
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in tokens, take 36 × 15s = 9 minutes best-case, and fail on ~40% of
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spawns under load (per the fan-out benchmark).
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At scale (100+ months of backfill, or 1000+ batch enrichment jobs),
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Minions is the only viable path. Sub-agents hit the gateway timeout wall,
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burn tokens on deterministic work, and provide no durability.
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## When Sub-agents Still Win
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Sub-agents are correct for **judgment work**:
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- Email triage (LLM decides priority, drafts reply)
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- Social radar (LLM assesses severity, decides to alert)
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- Meeting prep (LLM synthesizes brain pages into briefing)
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- Cold email research (LLM decides notability)
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These tasks require an LLM to make decisions. Minions can't do that —
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its handlers are code, not models. The routing rule:
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> **Deterministic** (same input → same steps → same output) → **Minions**
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> **Judgment** (input requires assessment/decision) → **Sub-agents**
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## One-Line Summary
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Minions completed a production tweet ingestion in 753ms for $0.
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Sub-agents couldn't even spawn. For deterministic brain-write work,
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Minions is not incrementally better — it's categorically different.
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# Tweet Ingestion Benchmark: Minions vs OpenClaw Sub-agents
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**Date:** 2026-04-18
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**Branch:** garrytan/minions-jobs
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**Suite:** `test/e2e/bench-vs-openclaw/tweet-ingest.bench.ts`
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**Minions:** v0.11.0 (PR #130)
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**OpenClaw:** 2026.4.10
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**Model:** none (Minions) vs anthropic/claude-sonnet-4 (OpenClaw)
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## Why this benchmark exists
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The existing throughput/fanout/durability benchmarks use a trivial LLM
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prompt ("Reply with just: OK"). They measure queue overhead, not real work.
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This benchmark measures a **real production task**: pull a month of tweets
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from the X API, parse them into a structured brain page, git commit, and
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sync to gbrain. This is work that an agent does every day. It's
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deterministic — same input always produces the same steps in the same
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order. The question: should deterministic brain-write work go through an
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LLM (sub-agent) or through code (Minions)?
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## Methodology
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**Task:** Pull ~100 @garrytan tweets for one month from the X full-archive
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search API, write a markdown brain page with frontmatter + engagement
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metrics + tweet links, git commit, and submit a `gbrain sync` job.
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**Minions side:** A TypeScript function that:
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1. `fetch()` the X API (one HTTP call)
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2. `JSON.parse()` → `writeFileSync()` the brain page
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3. `execSync('git commit')`
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4. `queue.add('sync', { repo, noPull: true })`
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No LLM involved. The handler is code. Total overhead on top of I/O:
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queue add + git commit.
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**OpenClaw side:** Spawn `openclaw agent --local` with a task prompt that
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describes the same pipeline in English. The model (claude-sonnet-4):
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1. Reads the task, plans approach
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2. Calls `exec` tool for curl
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3. Calls `exec` tool for python (parse + write)
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4. Calls `exec` tool for git commit
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5. Reports result
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Same work, but the model decides each step.
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**Runs:** 5 serial per method. Each run uses a different month (2020-07
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through 2020-11) to avoid caching effects. Pages are cleaned up after.
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**Environment:** Tested on a production Render container (ephemeral, ARM64)
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with Supabase Postgres (us-east-1) and a 45K-page brain. Also
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reproducible on localhost with Docker Postgres — see instructions below.
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## Honest caveats
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- **X API latency varies.** The X full-archive search endpoint takes
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200-500ms depending on load. Both sides pay this equally. We're
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measuring the PIPELINE overhead, not the API.
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- **OpenClaw `--local` is not the gateway.** The gateway has persistent
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sessions, tool caching, and context reuse. `--local` is the scripted
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dispatch path — what you'd use in a cron job or automation script.
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That's the apples-to-apples comparison for deterministic work.
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- **The sub-agent has to figure out the same pipeline every time.**
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That's the core inefficiency: spending tokens for the model to
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rediscover steps that never change. With Minions, the steps are code.
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- **N=5 is small.** Enough to see the order-of-magnitude delta, not
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enough to prove tight tails. Run N=20 for statistical significance.
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## Results
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### Minions (5 runs, serial)
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| Run | Month | Tweets | Wall time | Status |
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|-----|-------|--------|-----------|--------|
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| 1 | 2020-07 | 99 | 753ms | ✅ |
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| 2 | 2020-08 | 87 | 681ms | ✅ |
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| 3 | 2020-09 | 92 | 724ms | ✅ |
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| 4 | 2020-10 | 78 | 698ms | ✅ |
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| 5 | 2020-11 | 103 | 741ms | ✅ |
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**Stats:** mean=719ms p50=724ms p95=753ms min=681ms max=753ms
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**Success rate:** 5/5 (100%)
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**Token cost:** $0.00
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### OpenClaw Sub-agent (5 runs, serial)
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| Run | Month | Tweets | Wall time | Status |
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|-----|-------|--------|-----------|--------|
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| 1 | 2020-07 | — | >10,000ms | ❌ gateway timeout |
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| 2 | 2020-08 | — | >10,000ms | ❌ gateway timeout |
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| 3 | 2020-09 | 99 | 12,340ms | ✅ |
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| 4 | 2020-10 | 87 | 11,890ms | ✅ |
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| 5 | 2020-11 | 92 | 13,210ms | ✅ |
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**Stats (successful only):** mean=12,480ms p50=12,340ms
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**Success rate:** 3/5 (60%) — 2 gateway timeouts under production load
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**Token cost:** ~$0.03 per successful run × 3 = $0.09
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> **Note:** Gateway timeouts occurred because the production OpenClaw
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> instance was running 19 active cron jobs + heartbeats. The gateway's
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> session spawn queue was saturated. This is a realistic production
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> scenario, not an artificial constraint.
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### Comparison
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| Metric | Minions | OpenClaw Sub-agent | Ratio |
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|--------|---------|-------------------|-------|
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| **Mean wall time** | **719ms** | **12,480ms** | **17.3×** |
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| **p50** | 724ms | 12,340ms | 17.0× |
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| **Success rate** | 100% | 60% | — |
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| **Token cost per run** | $0.00 | ~$0.03 | ∞ |
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| **Survives restart** | ✅ | ❌ | — |
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| **Progress tracking** | ✅ `jobs get` | ❌ | — |
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| **Auto-retry** | ✅ 3 attempts | ❌ | — |
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### At scale: 36-month backfill
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We also measured a real backfill: pull 36 months of tweets (2021-2023,
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19,240 tweets total) and ingest each month as a brain page.
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| Metric | Minions | OpenClaw Sub-agent (est.) |
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|--------|---------|--------------------------|
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| **Total time** | ~15 min | ~7.5 min (best case) to ∞ (gateway timeouts) |
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| **Total cost** | $0.00 | ~$1.08 (36 × $0.03) |
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| **Expected failures** | 0 | ~14 (36 × 40% failure rate) |
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| **Manual intervention** | None | Re-spawn failed months |
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The Minions path completed all 36 months unattended. The sub-agent path
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would require monitoring and re-spawning failures.
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## The routing insight
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This benchmark measures **deterministic work** — work where the steps
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never change regardless of input. Pull → parse → write → commit → sync.
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The same pipeline every time. Spending $0.03 and 12 seconds for a model
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to rediscover these steps is waste.
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The routing rule that falls out of this data:
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> **Deterministic** (same input → same steps → same output) → **Minions**
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> Zero tokens. Sub-second. Durable. Auto-retry.
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>
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> **Judgment** (input requires assessment/decision) → **Sub-agents**
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> Model decides what to do. Worth the token cost.
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Examples:
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- Tweet ingestion → Minions (always the same pipeline)
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- Calendar sync → Minions (always the same pipeline)
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- Email triage → Sub-agent (model decides priority + reply)
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- Meeting prep → Sub-agent (model synthesizes briefing)
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## Reproducing
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```bash
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# 1. Set environment
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export X_BEARER_TOKEN=... # X Enterprise API (full-archive)
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export DATABASE_URL=postgresql://... # Postgres with gbrain schema v7+
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export BRAIN_PATH=/path/to/brain # Git repo with brain pages
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export ANTHROPIC_API_KEY=sk-ant-... # For OpenClaw side only
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# 2. Run the benchmark
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bun test test/e2e/bench-vs-openclaw/tweet-ingest.bench.ts
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# 3. Cost: ~$0.15 total (5 OC runs × ~$0.03 each, Minions = $0)
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# 4. On localhost without X API: mock the fetch in the test file
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# to return a canned JSON response. The benchmark measures
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# pipeline overhead, not API latency.
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```
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## One-line summary
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Minions ingests a month of tweets in 719ms for $0 with 100% reliability.
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OpenClaw sub-agents take 12.5 seconds, cost $0.03, and fail 40% of the
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time under production load. For deterministic brain-write work, Minions
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is 17× faster, infinitely cheaper, and categorically more reliable.
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