Collective memory for Ollama. The first person who asks waits. Everyone else gets the answer instantly. The more people use it, the faster it gets.
DLocker sits between your agents and Ollama. Every response gets stored in a vector database. When someone asks something similar (>=95% match), DLocker returns the cached response instantly without touching Ollama.
First request: 30 seconds (goes to Ollama) Second request: instant (from cache) 1000th request: instant (from cache)
The cache grows with every user. The system gets faster over time.
Linux / macOS:
curl -fsSL https://dlocker.app/install.sh | shWindows (PowerShell):
powershell -c "irm dlocker.app/install.ps1 | iex"Note: if you get a 503 error, wait 10 seconds and run the command again. The server may be cold-starting.
Then point your OpenAI-compatible client at http://localhost:8000/v1 instead of http://localhost:11434/v1.
# Before
client = OpenAI(base_url="http://localhost:11434/v1")
# After -- that's it.
client = OpenAI(base_url="http://localhost:8000/v1")| Other libraries | DLocker | |
|---|---|---|
| Installation | pip install + rewrite code | One command |
| Code changes | Import + modify LLM calls | Zero |
| Gets faster over time | No | Yes |
| Method | Path | Description |
|---|---|---|
GET |
/ |
Dashboard UI |
GET |
/health |
Health check |
GET |
/stats |
Cache statistics |
POST |
/v1/chat/completions |
OpenAI-compatible chat endpoint |
POST |
/admin/cache/clear |
Clear the cache |
pip install -r requirements.txt
uvicorn app.main:app --port 8000 --reload
# Run tests
pytest tests/DLocker is not affiliated with Ollama.
MIT