Enterprise-ready agentic orchestration layer implementing the N-Way Self-Evaluating Deliberation (NSED) protocol. Multiple AI agents work on your task in parallel, having intermediate checkpoints and converging on a final answer. NSED delivers frontier reasoning ability through a Mixture-of-Models architecture that amplifies the strengths of individual models. It shows less sycophancy, cost tracking, optimisation, and a full compliance audit trail managed through NATS as a high-performance communication bus.
Three small open-weight models (20B, 8B, 12B) on consumer hardware score 84% on AIME 2025 through NSED deliberation — matching DeepSeek-R1 and coming within 1 point of GPT-5. The same models score 54% with naive majority voting. (paper)
| Problem | How NSED Solves It |
|---|---|
| Single-model quality ceiling | Mixture of Models: agents from different providers cross-evaluate each other every round — errors get caught, reasoning improves iteratively |
| Black-box AI decisions | Full audit trail: every proposal, evaluation, score, and reasoning trace is persisted and streamable via SSE |
| Data leaves your network | Run entirely on your infrastructure with local models (Ollama, vLLM) — documents never touch a third-party API |
| No human oversight at runtime | Inject guidance mid-deliberation, expose custom tools agents can call back to your systems, set time/token/cost budgets |
| Vendor lock-in | Provider-agnostic: mix Ollama, vLLM, Together AI, or any compatible endpoint in the same session |
| Frontier model hardware costs | A swarm of 8B–20B models in 64 GB total VRAM matches reasoning quality that normally requires 80–140 GB for a single monolithic model |
git clone https://github.com/peeramid-labs/nsed.git && cd nsed
cp .env.example .env # add your API keys (Together AI, etc.)
make setup # install tools, configure git hooks
make docker-up # start NATS + Ollama infrastructure
make run # build and run the orchestratorINFO Dashboard → http://127.0.0.1:8080/dashboard
INFO Swagger UI → http://127.0.0.1:8080/swagger-ui/
Or try simulation mode (no API keys needed): make dev-sim
For binary installation, Docker, Homebrew, and APT options see the orchestrator README.
nsed-agent-sdk <-- nsed-agent <-- nsed-orchestrator
traits implementations server
| Crate | Description |
|---|---|
nsed-agent-sdk |
Trait definitions and data types for building custom agents |
nsed-agent |
Reference agent implementation with NATS worker runtime |
nsed-orchestrator |
API gateway, job broker, deliberation engine, dashboard |
nsed-benchmarks |
Benchmark suite (GSM8K, AIME, LiveCodeBench) |
Each crate has its own Makefile with crate-specific targets (e.g. make run-agent, make test-sdk). The root Makefile includes all four, so every target works from the workspace root. See docs/development.md for the full list.
| Topic | Link |
|---|---|
| Architecture | docs/architecture.md |
| Agent Development | docs/agent-development.md |
| Agent Internals (Library API) | docs/agent-internals.md |
| Authentication | docs/auth.md |
| NATS Integration | docs/NATS.md |
| Development & Configuration | docs/development.md |
| Contributing | CONTRIBUTING.md |
| API (Swagger UI) | http://localhost:8080/swagger-ui/ (while running) |
To reproduce results we've obtained during paper research please switch on to paper-reproduce branch of this repository which is frozen on the old commit "as is". Further guidelines are in the readme of that branch.
| Component | License |
|---|---|
crates/nsed-agent-sdk/ |
MIT |
crates/nsed-agent/ |
BSL 1.1 |
crates/nsed-orchestrator/ |
BSL 1.1 |
crates/nsed-benchmarks/ |
Apache 2.0 |
Source-available under the Business Source License 1.1. Free for entities under $1M USD annual revenue, research, education, and development. Commercial license required above the threshold. Converts to AGPL-3.0 after 4 years.
See NOTICE for patent, AI training, and ethical use terms.
Copyright (c) 2025 Tri Ratna Technologies Limited, trading as Peeramid Labs.