Project Nirvana: Local-First, Privacy-First Inference
A new way of thinking about LLM access. Your agent thinks locally, asks the cloud intelligently, and learns from the response. The cloud never sees your private data.
The Problem
Today's approach leaks your privacy and wastes 85% of your API budget.
Every time you ask your OpenClaw agent a question:
-
Your agent builds a "system prompt" containing:
- Excerpts from its SOUL.md and MEMORY.md
- Your personal information from its USER.md
- Your entire chat history (context window)
-
All of this gets sent to cloud APIs (OpenAI, Anthropic, Google)
-
You pay for thousands of extra tokens
-
The cloud provider trains its next model on your private data
This is the current default. It's inefficient and it's a privacy disaster.
The Solution: Nirvana
Local-first inference that protects privacy and slashes costs.
Nirvana flips the paradigm:
- Your agent thinks locally using Ollama (free, private, on your hardware)
- For complex questions, it asks the cloud — but only sends its own carefully-crafted queries
- Your private data never leaves your system
- The cloud's responses are cached locally — your agent learns from them
The Paradigm Shift
| Aspect | Today (Default) | Nirvana |
|---|
| Where thinking happens | Cloud only | Local first, cloud when needed |
| What gets sent to cloud | Your full context + system prompts | Agent's sanitized query only |
| Who learns from your data | Cloud provider | You (local agent) |
| Token cost per interaction | 2,000–5,000 tokens | 50–300 tokens |
| Savings | — | 85%+ token reduction |
| Privacy | Leaked | Protected |
What Nirvana Does
Local Inference
- Bundled Ollama with qwen2.5:7b (free, 3.5GB model)
- Handles 80%+ of queries locally (no API calls)
- ~200 tokens/second on CPU; 3–5x faster with GPU
- Works offline, no internet required
Privacy Enforcement
- Context stripper — Removes SOUL.md, USER.md, MEMORY.md before cloud queries
- Prompt sanitizer — Agent rewrites its own questions for the cloud (never sends yours)
- Audit trail — Every decision logged; transparent boundary crossing
- Zero telemetry — No data sent to third parties
Intelligent Routing
- Complexity analyzer — Decides: local vs cloud?
- Semantic understanding — "Can qwen2.5:7b handle this, or do I need Claude?"
- Seamless fallback — Cloud APIs used transparently when needed
- User override —
@local or @cloud hints respected
Learning & Caching
- Response integrator — Cloud responses cached locally
- Agent learns — Reuse cached answers for similar future questions
- No repeated payments — Cloud only answers novel questions
How It Works
User asks your agent a question
↓
┌─────────────────────────────────────────┐
│ Nirvana Router │
│ "Can qwen2.5:7b answer this locally?" │
└─────────────────────────────────────────┘
↙ ↘
[LOCAL PATH] [CLOUD PATH]
80%+ of queries 20%- of queries
Ollama (qwen2.5:7b) OpenAI/Anthropic/Google
Free Pay for answer
Private Cloud sees sanitized query only
~1s latency ~3s latency
Result cached locally Result cached locally
↓ ↓
└─────────────┬─────────────────────┘
↓
Agent answers your question using:
- Local inference (primary)
- Cloud intelligence (if needed)
- Cached knowledge (if available)
YOUR PRIVATE DATA NEVER LEFT YOUR SYSTEM
Installation
Prerequisites
- OpenClaw 2026.3.24+
- Docker (for Ollama) — or pre-existing local LLM at any endpoint
Two Paths
Path A: Use Bundled Ollama + qwen2.5:7b (Out-of-box)
# Install the plugin
clawhub install shivaclaw/nirvana
# Start Ollama container (pulls auto on first run)
docker run -d -p 11434:11434 ollama/ollama
# Verify
openclaw nirvana status
Path B: Use Existing Local LLM (Any Provider)
# Install the skill (context stripping only)
clawhub install shivaclaw/nirvana-local
# Configure endpoint
openclaw nirvana configure --local-endpoint http://your-llm:5000
# Verify
openclaw nirvana status
Cost Impact
Token Savings
| Scenario | Today | With Nirvana | Savings |
|---|
| 10 questions/day | 20,000 tokens/day | 3,000 tokens/day | 85% |
| 100 questions/day | 200,000 tokens/day | 30,000 tokens/day | 85% |
| Monthly cost (OpenAI GPT-4) | $500–$1,000 | $75–$150 | 85% |
Local inference is free. Only pay for the 15%–20% of queries that truly need frontier models.
Privacy Guarantee
What Never Leaves Your System
- ✅ SOUL.md (agent identity)
- ✅ USER.md (your personal info)
- ✅ MEMORY.md (agent memories)
- ✅ Chat history (your actual questions)
- ✅ Code snippets, documents, secrets
What Optionally Goes to Cloud
- ✅ Agent's own sanitized query (no personal data)
- ✅ Task-specific context (never your full context)
- ✅ Result gets cached locally for future reuse
Privacy Audit Trail
# View what was sent to cloud this session
openclaw nirvana audit-log
# Output:
# 2026-04-24 14:23:45 — CLOUD API CALL
# Original query: [REDACTED]
# Sanitized query sent: "Explain quantum entanglement"
# Response cached: Yes
# User data in request: None
Platform Support
| Platform | Status | Notes |
|---|
| Linux (Ubuntu/Debian) | ✅ Full | Ollama container + native binaries |
| macOS (Intel/ARM) | ✅ Full | Ollama via Docker or native |
| Windows (WSL2) | ✅ Full | Ollama in WSL2 container |
| VPS (Hostinger, DigitalOcean, AWS) | ✅ Full | Docker Compose ready |
| Docker container | ✅ Full | Orchestrated via docker-compose |
| Air-gapped (offline) | ✅ Full | Local-only mode (no cloud fallback) |
Configuration
Basic Setup
{
"nirvana": {
"mode": "local-first",
"local_model": {
"provider": "ollama",
"endpoint": "http://ollama:11434",
"model": "qwen2.5:7b",
"timeout_ms": 180000
},
"routing": {
"local_threshold": 0.75,
"max_local_tokens": 8000,
"cloud_fallback": true
},
"privacy": {
"strip_soul": true,
"strip_user": true,
"strip_memory": true,
"audit_logging": true
}
}
}
Custom Local LLM (Non-Ollama)
{
"nirvana": {
"local_model": {
"provider": "custom",
"endpoint": "http://your-llm-server:5000",
"api_format": "openai-compatible",
"model": "your-model-name",
"timeout_ms": 120000
}
}
}
Use Cases
✅ Perfect For
- Personal AI agents (maximize budget, minimize cost)
- Private/sensitive workloads (code, healthcare, legal, finance)
- Latency-critical tasks (local response < 2s)
- Air-gapped environments (fully offline)
- Cost-conscious organizations (85% savings)
- Privacy-first deployments (zero external data exposure)
⚠️ When to Use Cloud
- Advanced reasoning (Claude Opus for complex problems)
- Specialized tasks (image generation, audio synthesis)
- Extreme scale (millions of tokens/day)
Philosophy
Your agent should train itself. The cloud should not train on you.
Today's default paradigm:
- Cloud provider gains knowledge from every interaction with you
- You pay for the privilege of training their next model
- Your private data becomes their training data
Nirvana's paradigm:
- Your agent gains knowledge from selective cloud interactions
- You pay only for what you actually need
- Your private data never leaves your system
- Cloud providers contribute intelligence; you keep the learning
What's Included
| Component | Purpose |
|---|
| router.ts | Decides local vs cloud routing |
| context-stripper.ts | Removes private data before cloud API calls |
| privacy-auditor.ts | Logs all boundary crossings |
| response-integrator.ts | Caches cloud responses locally |
| ollama-manager.ts | Handles Ollama lifecycle + model management |
| metrics-collector.ts | Tracks performance + cost + privacy |
| config.schema.json | Configuration validation |
Performance
Benchmarks (qwen2.5:7b on 4-core CPU)
- Latency (P50): 800ms–1.2s per response
- Throughput: 180–220 tokens/second
- Memory: 4.6GB RAM running
- Accuracy: 85–92% accuracy vs Claude 3.5 on typical tasks
- GPU acceleration: 3–5x faster with CUDA/Metal
Optimization
- Use GPU (CUDA/Metal) for production
- Upgrade to qwen3.5:9b for complex reasoning
- Enable response caching for repeated patterns
- Monitor metrics dashboard for bottlenecks
Support & Community
License
MIT-0 — Free to use, modify, and redistribute. No attribution required.
Your agent deserves privacy. Nirvana makes it real.
