Deterministic AI Agent Knowledge

Stop trusting LLMs.
Start trusting knowledge.

Atlas separates knowledge from reasoning. A compiled, verifiable, cryptographically-signed binary that lets AI agents make decisions without an LLM call — 100x cheaper, 1000x faster, provably correct.

Install Atlas Star on GitHub
73+ Knowledge Nodes
125+ Edges
2 Decision Trees
0 LLM Calls Needed

The problem with every AI agent framework

LangChain, CrewAI, AutoGen, smolagents — they all share the same fatal flaw.

🧠

Knowledge is whatever fits in the prompt

Every framework treats knowledge as "stuff in the context window." No structure, no persistence, no verification.

💰

Every decision costs money

Each agent step requires an LLM call. At $0.10-1.00+ per decision, production agents bleed cash.

🔮

No deterministic fallback

When the LLM hallucinates, there's no safety net. No verification, no audit trail, no replay.

⚖️

No compliance by design

EU AI Act fines start at €15M on August 2, 2026. Today's frameworks can't explain why an agent did what it did.

Knowledge as Code. Decision Trees as Infrastructure.

Atlas is a new kind of operating system — not for computers, but for engineering knowledge.

atlas.sh — ~/project

$ atlas init my-package --template flutter

Created knowledge package 'my-package'

$ atlas compile my-package.md decisions/ --out my-package.atlas

Compiled 12 nodes, 8 edges, 1 decision tree → my-package.atlas

$ atlas solve --bundle my-package.atlas "which widget?"

Matched 5 nodes. Confidence: 0.95

$ atlas decide --bundle my-package.atlas "widget" -c "answer=true"

Path: widget_state → stateful_vs_builder → stateful_builder
Rationale: Use StatefulWidget with a builder pattern...

$ |

✅
Deterministic — Decision trees run with zero LLM calls
🔍
Verifiable — Every output is checked against built-in rules
🔗
Composable — Merge knowledge packages like Docker images
📦
Portable — Single .atlas binary, memory-mapped, signed
🔐
Auditable — Cryptographic audit trails for every decision
✈️
Offline — Full air-gapped deployment. No API calls needed

How it works

1

Write knowledge as Markdown

Concepts, APIs, workflows — all in a simple, human-readable format.

→
2

Compile to .atlas binary

Zstd-compressed, memory-mapped, blake3-hashed. Portable across any platform.

→
3

Run agents on top

Python SDK, MCP server, CLI, or Studio. Any interface. Zero LLM calls for 80% of queries.

Knowledge Package Registry

Share, discover, and reuse knowledge packages. Like Docker Hub, but for agent knowledge.

flutter_core
Flutter SDK core patterns — widgets, elements, state management, rendering
73 nodes 125 edges v0.1.0
rust_patterns
Rust engineering patterns — error handling, async, serde, testing
19 nodes 31 edges v0.1.0
typescript_nextjs
TypeScript & Next.js patterns — App Router, hooks, server components
12 nodes 20 edges v0.1.0
atlas
Atlas self-documentation — architecture, concepts, API references
18 nodes 28 edges v0.1.0

Get started in 90 seconds

Python SDK
pip install atlas-sdk
Install a package
atlas install flutter_core.md
Ask a question
from atlas_sdk import Agent
agent = Agent("flutter_core")
result = agent.solve("stateful widget")

Also available: VS Code Extension · GitHub Action · MCP Server

Atlas vs. Everything Else

Atlas smolagents LangChain CrewAI AutoGen
Deterministic decisions ✅ LLM-free ❌ ❌ ❌ ❌
Built-in verification ✅ ❌ ⚠️ Manual ❌ ❌
Knowledge composition ✅ ❌ ⚠️ RAG ❌ ❌
Offline / Air-gapped ✅ ❌ ⚠️ Partial ❌ ❌
Audit trail ✅ Crypto ❌ ⚠️ LangSmith ❌ ❌
Cost per query ~$0.0001 $0.10-1.00 $0.10-1.00 $0.10-1.00 $0.10-1.00
EU AI Act ready ✅ Built-in ❌ ❌ ❌ ❌
Open source ✅ ✅ ✅ ✅ ✅