Run multiple local LLMs in parallel. Coreling divides tasks, manages memory, and unifies your AI workflow — without a single byte leaving your machine.
Compatible with Ollama · LM Studio · llama.cpp
Capabilities
Coreling's router analyzes complexity, domain, and compute — then assigns each subtask to the best local model. Zero config.
A persistent local vector store enriches every session. Context survives restarts, model switches, and project changes.
Zero telemetry. Zero cloud calls. Every prompt, completion, and memory artifact stays on-device. Fully air-gap compatible.
Run Gemma 3, Llama 3.2, Mistral, and Phi-3 in parallel as a single unified agent — not isolated chatbots.
The Coreling orchestrator adds less than 50 ms overhead. Multi-agent intelligence at native local inference speed.
Task history, memory graphs, and configs stored as SQLite + JSON on disk. Export, inspect, or version-control everything.
Architecture
Input
Natural language task
Task Splitter
Decomposes into subtasks
Local LLMs
Best model per subtask
Memory DB
Context written locally
Output
Unified coherent result
No account. No telemetry. No model provider reading your prompts. Coreling was built air-gap-first — your data stays exactly where it belongs.
Open Source
Coreling is and will always be free for everyone. Our mission is to democratize local AI orchestration.
Stop sending your most sensitive work to someone else's server. Multi-agent AI running entirely on your hardware.