Local-First LLM

Route requests to a local LLM first; fall back to cloud only when necessary. Track every decision to show real token and cost savings.

Quick Start

1. Check if a local LLM is running

python3 skills/local-first-llm/scripts/check_local.py

Returns JSON: { "any_available": true, "best": { "provider": "ollama", "models": [...] } }

2. Route a request

python3 skills/local-first-llm/scripts/route_request.py \
  --prompt "Summarize this meeting transcript" \
  --tokens 800 \
  --local-available \
  --local-provider ollama

Returns: { "decision": "local", "reason": "...", "complexity_score": -1 }

3. Log the outcome

After executing the request, record it:

python3 skills/local-first-llm/scripts/track_savings.py log \
  --tokens 800 \
  --model gpt-4o \
  --routed-to local

4. Show the dashboard

python3 skills/local-first-llm/scripts/dashboard.py

---

Full Routing Workflow

┌─────────────────────────────────────────────────────┐
│  1. check_local.py  →  is a local provider running? │
│                                                      │
│  2. route_request.py  →  local or cloud?             │
│     - sensitivity check  (private data → local)      │
│     - complexity score   (high score → cloud)        │
│     - availability gate  (no local → cloud)          │
│                                                      │
│  3. Execute with the chosen provider                 │
│                                                      │
│  4. track_savings.py log  →  record the outcome      │
│                                                      │
│  5. dashboard.py  →  show cumulative savings         │
└─────────────────────────────────────────────────────┘

---

Routing Rules (Summary)

Condition	Route
No local provider available	☁️ Cloud
Prompt contains sensitive data (password, secret, api key, ssn, etc.)	🏠 Local
Complexity score ≥ 3	☁️ Cloud
Complexity score < 3	🏠 Local

For full scoring details, see references/routing-logic.md.

---

Executing with a Local Provider

Once route_request.py returns "decision": "local", send the request:

Ollama

curl http://localhost:11434/api/generate \
  -d '{"model": "llama3.2", "prompt": "YOUR_PROMPT", "stream": false}'

LM Studio / llamafile (OpenAI-compatible)

curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "local-model", "messages": [{"role": "user", "content": "YOUR_PROMPT"}]}'

---

Dashboard

The dashboard reads from ~/.openclaw/local-first-llm/savings.json (auto-created).

┌─────────────────────────────────────────┐
│    🧠  Local-First LLM — Dashboard      │
├─────────────────────────────────────────┤
│  Local LLM:  ✅  ollama (llama3.2...)   │
├─────────────────────────────────────────┤
│  Total requests:         42             │
│  Routed locally:         31  (73.8%)    │
│  Routed to cloud:        11             │
├─────────────────────────────────────────┤
│  Tokens saved:       84,200             │
│  Cost saved:           $0.4210          │
└─────────────────────────────────────────┘

Reset savings data:

python3 skills/local-first-llm/scripts/track_savings.py reset

---

Additional References

• Routing scoring details: references/routing-logic.md
• Local provider setup (Ollama, LM Studio, llamafile): references/local-providers.md
• Token estimation & cloud cost table: references/token-estimation.md