NocturnusAI

The context engineering engine for AI agents: send only what changed.

Before / After

# ❌ Without NocturnusAI — replay everything, every turn
messages = system_prompt + full_history + tool_outputs   # ~1,259 tokens/turn
response = llm(messages)                                 # $13,600/mo at scale

# ✅ With NocturnusAI — send only what changed
ctx = nocturnus.process_turns(raw_turns)                 # extract → infer → delta
messages = system_prompt + ctx.briefing_delta             # ~221 tokens/turn
response = llm(messages)                                 # $2,400/mo. Same accuracy.

The Numbers

Measured on live APIs. 15-turn product support conversation. Real usage.input_tokens counts. Run it yourself.

Claude Opus 4: 5.7× reduction. Gemini 2.0 Flash: 10.0×. Full calculations.

Install

pip install nocturnusai          # Python
npm install nocturnusai-sdk      # TypeScript
docker run -p 9300:9300 ghcr.io/auctalis/nocturnusai:latest  # Docker

Or use the setup wizard:

curl -fsSL https://raw.githubusercontent.com/Auctalis/nocturnusai/main/install.sh | bash

Why Developers Star This Repo

• Reproducible token reduction — benchmark in the repo, methodology published, run it against your own workload
• Deterministic inference — same query, same result, every time. No embedding drift, no cosine similarity lottery
• Truth maintenance — retract a fact, all derived conclusions auto-retract. No stale context, no hallucination on operational state
• Plugs into existing stacks — LangChain, LlamaIndex, CrewAI, AutoGen, MCP, Vercel AI SDK, OpenAI Agents SDK, Mastra
• Benchmarkable against naive replay — numbers derived, not invented. Every claim traces to a notebook cell

Framework Quickstarts

... View full README on GitHub