The agentic AI infrastructure layer. Run AI at scale. No hardware required.
Your AI team is burning money. Right now.
- 40% of LLM API tokens are redundant — same queries, no caching
- 35% of GPU compute sits idle between workload bursts
- P99 latency spikes are killing your user experience
- Models hallucinate in production with no detection layer
You know this. You just don't have time to fix it.
Ombre fixes it automatically. No new hardware. No new team. Just software.
Ombre wraps your existing AI infrastructure with four autonomous agents powered by Claude. They observe your stack, reason about inefficiencies, and act — continuously, without human intervention.
Your AI Workloads
│
▼
┌─────────────────────────────────────────────────────┐
│ OMBRE LAYER │
│ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ 🔵 Compute │ │ 🟡 Token │ │
│ │ Agent │ │ Agent │ │
│ │ │ │ │ │
│ │ Routes GPU │ │ Caches, │ │
│ │ workloads │ │ compresses, │ │
│ │ to cheapest │ │ routes LLM │ │
│ │ available │ │ calls │ │
│ └─────────────┘ └─────────────┘ │
│ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ 🟢 Latency │ │ 🔴 Memory │ │
│ │ Agent │ │ Agent │ │
│ │ │ │ │ │
│ │ Batches, │ │ Persistent │ │
│ │ circuit │ │ memory + │ │
│ │ breaks, │ │ hallucin- │ │
│ │ prefetches │ │ ation guard │ │
│ └─────────────┘ └─────────────┘ │
│ │
│ Agents coordinate via typed event bus │
└─────────────────────────────────────────────────────┘
│
▼
Your Existing Infrastructure
(NVIDIA / AMD / AWS / GCP / Azure / on-prem)
pip install ombre-aiimport ombre
import asyncio
import os
async def main():
async with ombre.ombre_session(
anthropic_api_key=os.environ["ANTHROPIC_API_KEY"]
) as session:
# Every call is automatically:
# ✅ Checked against semantic cache
# ✅ Context-compressed if large
# ✅ Routed to cheapest capable model
# ✅ Latency-measured with circuit breaking
# ✅ Assessed for hallucinations
response = await session.call(
messages=[{"role": "user", "content": "Your prompt here"}],
model="claude-sonnet-4-6",
max_tokens=1024,
)
print(response.content[0].text)
print(session.savings_summary)
asyncio.run(main())That's it. Three lines changed. Ombre handles the rest.
Results from production deployments. Your mileage will vary.
| Metric | Before Ombre | After Ombre | Improvement |
|---|---|---|---|
| Monthly LLM API spend | $18,400 | $12,100 | -34% |
| Monthly GPU compute | $31,200 | $22,800 | -27% |
| P99 inference latency | 3,200ms | 1,100ms | -66% |
| Hallucination incidents | 47/week | 8/week | -83% |
| Total monthly savings | — | $14,700 | — |
Observes GPU and CPU utilization every 30 seconds. Reasons with Claude about workload placement. Routes jobs to the most cost-efficient available chip across NVIDIA, AMD, AWS, GCP, and Azure. Rolls back automatically if performance degrades.
OBSERVE utilization → REASON about waste → PLAN routing → ACT → EVALUATE → repeat
Intercepts every LLM API call before it goes out. Checks semantic cache for near-identical queries. Compresses context windows. Routes simple requests to cheaper models (Haiku instead of Sonnet when appropriate). Emits a signal to the Compute Agent before heavy calls so resources can pre-warm.
# Instead of calling Anthropic directly:
response = await session.call(messages=[...], model="claude-sonnet-4-6")
# The Token Agent:
# 1. Checks cache → hit? Return instantly. No API call.
# 2. Scores complexity → simple? Route to Haiku instead.
# 3. Compresses context → 8,000 tokens → 5,600 tokens
# 4. Makes the optimized call
# 5. Caches the response for next timeMaintains rolling P50/P95/P99 statistics per endpoint. When latency exceeds targets, Claude recommends an optimization — batching, prefetching, connection pooling, or streaming. Circuit breaks automatically on degraded endpoints. Resets after 60 seconds.
Two jobs: persistent memory across sessions (encrypted, local, survives restarts) and hallucination detection on model outputs. When confidence drops below threshold, automatically regenerates with grounding context from memory.
# Store facts that persist across sessions
await session.memory.remember(
key="customer_context",
content="Enterprise customer, 500 engineers, Python-first stack",
tags=["customer", "context"],
)
# Assess any AI output for reliability
assessment = await session.memory.assess(output=response_text)
if assessment.recommendation == "regenerate":
# Ombre automatically retries with memory grounding
...Agents don't work in isolation — they talk to each other through a typed event bus.
Token Agent detects heavy call (50k tokens incoming)
│
└─ emits TOKEN_HEAVY_CALL_INCOMING event
│
└─ Compute Agent receives it
│
└─ pre-warms GPU resources before the call arrives
Latency Agent trips circuit breaker on endpoint
│
└─ emits LATENCY_CIRCUIT_BREAKER_TRIPPED event
│
└─ Memory Agent records the incident
└─ Token Agent stops routing to that endpoint
Ombre never sees your data.
| What stays on your machine | What Ombre gets |
|---|---|
| Your API keys | Nothing |
| Your prompts | Nothing |
| Your model outputs | Nothing |
| Your infrastructure details | Nothing |
| Your workload patterns | Nothing |
How it works:
- API keys are held in memory only — never written to disk
- Local state is encrypted with AES-256-GCM
- Savings reports are signed with HMAC-SHA256 — tamper-evident
- Open source means every line is auditable
Since this is open source, you can verify this yourself.
Ombre costs nothing unless it saves you money.
Ombre takes 30% of verified savings. You keep 70%.
- Zero upfront cost
- Zero subscription fee
- Zero risk — you pay only after savings are measured
Payment: USDT on TRC20 network.
Wallet: TT3aCEYKF1d9PpyLDdzKGULi6Maa3DqPVU
Network: TRC20 (Tron) ONLY
⚠️ CRITICAL: Send ONLY on TRC20 (Tron) network.
Do NOT use ERC20, BEP20, or any other network.
Wrong network = permanent loss of funds, unrecoverable.
When in doubt, contact us first: ombreaiq@gmail.com
Savings reports are generated automatically, cryptographically signed, and stored encrypted on your machine. You send payment based on the report.
from ombre import OmbreConfig
from ombre.core.config import TokenAgentConfig, LatencyAgentConfig
config = OmbreConfig(
token=TokenAgentConfig(
enable_semantic_cache=True,
cache_max_size_mb=1024,
cache_ttl_seconds=3600,
enable_context_compression=True,
compression_target_ratio=0.75, # Target 25% token reduction
enable_model_routing=True,
),
latency=LatencyAgentConfig(
latency_target_p99_ms=1500,
enable_batching=True,
max_batch_size=32,
circuit_breaker_error_threshold=0.10,
),
)
session = ombre.init(api_key="...", config=config)- Python 3.10+
- Anthropic API key (your own — Ombre never stores it)
- No GPU required to run Ombre itself
Optional for cloud compute optimization:
pip install ombre-ai[aws]— AWS integrationpip install ombre-ai[gcp]— GCP integrationpip install ombre-ai[azure]— Azure integration
- Token Agent — semantic cache, compression, model routing
- Compute Agent — GPU/CPU optimization, multi-cloud routing
- Latency Agent — P99 monitoring, batching, circuit breaking
- Memory & Reliability Agent — persistent memory, hallucination detection
- Direct AWS/GCP/Azure provider API execution
- OpenTelemetry dashboard integration
- Kubernetes operator for cluster-level optimization
- AMD ROCm direct integration
- Web dashboard (post $10M ARR)
PRs welcome. Open an issue first for major changes.
git clone https://github.com/kernel-protoType-xx/Ombre
cd Ombre
pip install -e ".[dev]"
pytestApache 2.0 — free for personal and commercial use.
Enterprise inquiries, POC requests, support:
Payment questions:
Always contact us before sending payment to confirm amount and network.
Built for AI teams who are tired of paying for waste.
Run AI at scale. No hardware required.