NEW · 2026-05-30 Flagship service · MIT source Next theme = multi-agent
SlimeTree-RLM × Platform Integrations Hub
Wraps each SNS / business platform API with an RLM pre-filter (D/µ/R deterministic classification + SHA-256 audit chain). Cuts LLM cost by 60-80% — the same reduction rate whether you choose Gemini, Claude, or OpenAI.
See the 18 public routes ↓ Multi-agent (NEW) ↓ Service page →
A technique that curbs an AI (a large language model like ChatGPT) from saying plausible-but-wrong things (hallucinations). It never touches the model's weights; it supports it from outside as a "record body" to raise answer reliability. A tiny 272 KB part that runs in the browser/phone with no server.
Suppresses LLM hallucinations (plausible lies) without changing any weights. Measured a stable −20.4 ± 0.3 pt improvement across 3 external benchmarks × 3 seeds = 6,870 trials. A "performance equalizer" where 8B-class converges to an 81% ceiling across 4 LLMs. Procedure, rubric and seeds are all public.
A meaning-driven record body. Routes into D (deterministic) / μ (suppression) / R (reasoning): certain parts deterministically, risky parts suppressed, the LLM only when needed. Weights untouched, so it retrofits onto any model. 272 KB WASM, no server in browser/mobile, with an audit WAL.
Hallucination suppression measured as a −20.4 ± 0.3 pt structural constant over 3 bench × 3 seed = 6,870 trials. A performance equalizer where Tier-A 8B-class converges to an 81% ceiling across 4 LLMs. A tier-③ implementation handling non-reproducible stochastic output via meaning-equivalence + convergence + residual. Procedure, LLM settings, seeds and rubric fully public, third-party reproducible.
📋 "Ask your AI at this level" copies this page's explanation with an instruction matched to the level you picked. Paste it into your own AI (Claude · GPT · Gemini · Grok) to dig deeper at that resolution.
Why a single hub?
All 18 routes run the same structure — RLM pre-filter (D/µ/R) → LLM only for R → SHA-256 chain.
Beyond the per-platform hubs (Meta / X / Google), this page is the cross-platform reference.
Common specifications, the Platform-native LLM principle, and the multi-agent extension (next theme) all branch from here.
Public platforms (3)
Each card links to the platform hub. Every platform exposes 6 routes, all MIT source, all browser-only (localStorage-only LLM key).
PublicLLM-neutral
Meta integration ― 6 routes
Facebook / Threads / Messenger / WhatsApp / Instagram / Graph API — all 6 routes. LLM delegation is free choice across Gemini / Claude / OpenAI.
- Prompt Gateway (all 3 providers)
- Threads bot
- Messenger bot
- WhatsApp bot
- Instagram DM bot
- Graph API generic client
PublicXGrok-only
X (formerly Twitter) integration ― 6 routes
Platform-native stack: X API v2 + xAI Grok. Authenticates with the X account, delegates only to Grok to avoid access restrictions placed on other LLMs.
PublicGoogleGemini-only
Google Workspace integration ― 6 routes
Gmail / Calendar / Drive / Sheets / Workspace API — all 6 routes. One Google account, one billing identity, and the Gemini free tier (15 RPM) is usable out of the box.
Planned platforms (3)
Sequenced by demand and how cleanly the Platform-native LLM principle maps to each.
PlannedMicrosoftAzure OpenAI only
Microsoft 365 / Azure integration
Outlook / Teams / SharePoint / OneDrive / Graph API + Azure OpenAI. Entra ID for organization-wide identity; the enterprise main stage.
PlannedSlackLLM-neutral
Slack integration
Channels / DM / App Home / Workflow Builder + any LLM. Slack Marketplace submission, designed for commercial workflows.
PlannedLINELLM-neutral
LINE / LINE WORKS integration
Messaging API / LINE WORKS / LIFF + any LLM. Domestic SMB / municipal / consumer touchpoints; webhook design ready.
Architecture ― RLM pre-filter + Platform-native LLM principle
All platforms share the same processing pipeline:
[input prompt / event]
↓
┌──────────────────┐
│ SlimeTree-RLM │ ← 272 KB WASM, browser-only
│ D/μ/R classify │
└──────────────────┘
↓ (D=instant response / μ=machine-level suppression / R only goes to LLM)
┌──────────────────┐
│ Platform-native │ ← Meta=neutral / X=Grok / Google=Gemini
│ LLM │
└──────────────────┘
↓
┌──────────────────┐
│ SHA-256 WAL chain│ ← every step audited, regulation-ready
└──────────────────┘
↓
[output + audit log]
Platform-native LLM principle
Prioritizes the host platform's access policy, billing model, and SSO alignment. "Any LLM will run," but the LLM that's native to the platform is treated as first-class.
| Platform | Default LLM | Rationale |
|---|---|---|
| Meta (FB / Threads / WhatsApp / Instagram) | Free choice across Gemini / Claude / OpenAI | Meta has no native LLM binding; we support whichever LLM the user prefers neutrally. |
| X (formerly Twitter) | Grok-only (xAI) | X-native; avoids the direct-access restrictions placed on competitor LLMs; aligns with X Premium / API tiering. |
| Google Workspace | Gemini-only | One Google account manages both Workspace + Gemini; the free tier (15 RPM) is available out of the box. |
| Microsoft 365 (planned) | Azure OpenAI only | Integrates with Entra ID / Microsoft Purview; consolidates enterprise billing. |
| Slack (planned) | Neutral (any LLM) | Slack has no native LLM binding; respects the enterprise's existing LLM contracts. |
| LINE (planned) | Neutral (any LLM) | Same as above; domestic SMB LLM preferences are fragmented. |
NEXT THEME · 2026-05-30 Drops on top of all 18 routes
★ LLM multi-agent support
Promotes RLM from a single LLM delegator into a multi-agent orchestrator.
The structure of the existing 18 routes is unchanged; only the R-verdict path expands to multiple agents.
6 candidate patterns
Priority 1stB
Cost-tier escalation
Try with a cheap LLM (Gemini Flash) → RLM judges quality (R-meta verdict) → escalate to a premium LLM (Claude Opus / GPT-5) only if insufficient.
Adds another 50-70% on top of the existing 73% cut. The numbers hit hardest here.
Priority 2ndA
Cross-validation
Delegate to 2 LLMs (e.g. Gemini + Claude) in parallel → compare outputs. Match = unified answer; mismatch = human-review flag.
For high-precision domains (finance / medical / legal).
Priority 3rdF
Voting / consensus
N LLMs respond independently → RLM aggregates (vote / average).
Agreement ratio = confidence score. For classification / fact-checking.
C
Specialist routing
A second-layer RLM classifies the domain (code / legal / medical / general) → routes to the matching agent + system prompt → picks the LLM.
For enterprise multi-domain support.
D
Orchestrator-worker
One LLM plans → multiple workers run in parallel → RLM merges the results.
For parallelizing large tasks (research / multi-step problems).
E
Debate / critique loop
LLM A proposes → LLM B critiques → A revises → iterate. RLM detects convergence / divergence and caps the loop.
For code review and document drafting.
Why RLM excels as an orchestrator
| Aspect | Generic multi-agent framework (e.g. LangGraph) | RLM extension |
|---|---|---|
| Existing LLM calls | Locked inside the framework | Subdivides the R verdict of the existing D/μ/R; drops in without modification |
| Audit trail | Framework-specific log | SHA-256 WAL chain records every agent call; regulation-ready by default |
| Cost reduction | Depends on agent design | On top of the existing D/μ cut (73%), Pattern B saves another 50-70% |
| Platform integration | Wire separately | Rides on the existing 18 routes (shared across Meta / X / Google) |
| LLM choice | Often provider-locked | Preserves Platform-native principle (X → multi-Grok, Meta → Gemini + Claude in parallel, etc.) |
★ Running implementation ― all 18 demos + dedicated page + shared module shipped (2026-05-31)
B mode (cost-tier escalation) is implemented across shared module + 18 demos + dedicated page, with JS syntax verified (local + live). AI agents like Claude Code / OpenAI Codex can import it directly.
★ Shared module (MIT, ES module)
/integrations/slimetree-rlm-multi-llm.js (250 lines, 10 exports, 4 providers = Gemini/Claude/OpenAI/Grok)
import { callLLMWith, judgeResponseQuality, MODEL_CATALOG } from '...'
Dedicated demo pages (6-pattern showcase + measurement dashboard)
- /integrations/multi-agent-demo/ ― A/B/C/D/E/F run in parallel on a single page; one Gemini key (free tier works).
- ★ NEW: /integrations/measurement/ ― batch runner dashboard for replacing estimates with measured values. Runs 150 sample prompts through the Flash → Pro escalation, measures the escalation rate, exports JSON.
All 18 demos (3 Gateways + 15 bots, every one with B mode)
Every bot ships with a bot-specific extra judge (18 variants: PII request / over-assertion / competitor mention / sarcasm / executive keyword / confidential+public / credit-card pattern / etc.). A baseline for domain-specific escalation in enterprise PoCs.
★ Local LM extension — extend pattern B / pattern C to consumer GPUs (RTX 5060 Ti class)
SlimeTree-RLM's R-meta verdict scores cloud LLM and local LLM outputs through the same interface.
That lets pattern B (cost-tier escalation) insert a new "Local LLM tier" below Gemini Flash, driving the per-token rate to effectively zero.
Pattern B extended — 4-tier escalation
| tier | LLM | token rate | Behaviour when R-meta verdict passes |
|---|---|---|---|
| Tier 0 (new) | Local LM (Gemma 4 12B Q4_K_M / Gemma 3 12B etc.) | ¥0 / 1M tok (electricity only) | Most D/µ-processed prompts answered locally; cloud billing skipped |
| Tier 1 | Gemini Flash (existing) | ~¥30 / 1M tok | Local verdict insufficient → promote to Flash |
| Tier 2 | Gemini Pro / Claude Sonnet | ~¥500 / 1M tok | Flash insufficient → escalate to Pro |
| Tier 3 | Claude Opus / GPT-5 | ~¥5,000-15,000 / 1M tok | Frontier reasoning needed → final escalate to Opus |
Reduction effect: out of the R-fraction remaining after D/µ already cut 60-80%, tiers 0 and 1 absorb 70-95%. Frontier billing (tier 3) is 3-10% of actual traffic. ¥1M / month in cloud LLM spend lands at ¥30-100k.
RTX 5060 Ti × Gemma 4 12B (2026-06-05, in-house measurement)
| Metric | gemma3:12b | gemma4:12b Q4_K_M | gemma4:12b Q8_0 |
|---|---|---|---|
| Decode tok/s | 46.3 | 43.5 | 27.6 |
| Peak VRAM | 9.7 GB | 8.6 GB | 13.7 GB |
| RLM judge p99 latency | ~100 µs | ~100 µs | ~100 µs |
| Quality sufficient rate (n=50) | 49/50 | 47/50 | 47/50 |
Setup: NVIDIA GeForce RTX 5060 Ti (16 GB) / CUDA 13.1 / ollama 0.30.5 (gemma4 architecture native) / WSL2 Ubuntu / Phase B judge via SlimeTree-RLM R-meta verdict layer.
4 viable patterns for enterprise Local LM migration
A
Compliance-bound domains
Healthcare / legal / finance / defence — sectors where cloud LLMs are regulated out. At n=50 the model already passes 47/50 sufficient, fit for first-draft + human-review workflows. SHA-256 audit chain matches audit requirements immediately.
B
High-volume routine inference
Monthly throughput above 10M tokens for routine work (classification / summarisation / drafting / RAG ingestion). One RTX 5060 Ti sustains 3.6M tokens/day; capex recovers in ~3 months on electricity alone.
C
Narrow-domain specialist
Tax Q&A, manufacturing SOP, internal policy lookup. LoRA fine-tuning lifts the 12B base to frontier-general parity inside the domain. SlimeTree-RLM D/µ/R gates output quality.
D
Hybrid (the headline)
SlimeTree-RLM R-meta verdict routes 90-95% local / 5-10% cloud frontier. Frontier-class quality at 1/10 - 1/20 of the bill, measured on real traffic.
Position of Gemma 4 12B: not a replacement for frontier LLMs (Claude / GPT-5 / Gemini Pro) but a specialist tier sitting next to cloud frontier.
Frontier reasoning / long context (100k+) / sub-2s dialogue UX stay on cloud. Everything else goes local.
Common specifications (all 18 routes)
| License | MIT (covers all 18 demos and the RLM mock module); commercial use permitted |
|---|---|
| Runtime | Browser-only (static HTML + JS; no server-side code required) |
| RLM core | 272 KB WASM single file; parallelized via SharedArrayBuffer + Atomics |
| LLM key storage | localStorage only; never transmitted outside the browser. Each LLM provider is called directly. |
| Audit chain | SHA-256 hash chain (WAL); exportable; tamper-evident |
| Error handling | Rollback (only non-commutative side propagates); patent claims 21, 35-37 |
| Pipeline visualization | Live indicator embedded in every demo; D/μ/R verdicts shown in real time |
| Security boundary | RLM judgment is contained in the browser; only R-verdict items are forwarded to the LLM |
| Cost reduction | 60-80% LLM-cost cut (D/μ items are handled deterministically, generating no LLM call) |
Related pages
- As a service: /service/rlm-integrations/ ― 6-tier cost comparison, delivery options
- SlimeTree-RLM primary materials: /resource/slimetree-rlm/ ― 3 benchmarks × 4 LLMs, paper v10, patent claims 1-44
- SlimeTree-RLM product page: /products/device/slimetree-rlm/ ― applied scenarios, enterprise
- Platform hubs: Meta / X / Google
- Explainer blog (JA): Cutting LLM hallucinations to 1/3 with just 272 KB
- Sales / NDA: Contact / Partners