RadOS — Recursive Architecture Development Operating System

RADOS is a deployable framework where agent systems design, validate, and execute their own architectures — then improve them over time.

Most orchestration frameworks (LangGraph, CrewAI, AutoGen) require you to design the agent graph. RadOS flips this: agents architect the graph, validate it against contracts, execute it with full resilience, and evolve successful patterns into reusable Architecture DNA.

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Core Idea

Intent → Decompose → Resolve → Synthesize → Validate → Execute → Evaluate → Evolve
                                                                          │
                                                                          └── 🔁

Given a goal, RadOS recursively decomposes it into atomic capabilities, resolves them against a registry, synthesizes a validated DAG, executes it with retry/circuit-breaker/fallback logic, scores the result against your intent — then persists winning architectures as DNA for instant reuse.

If the system underperforms, it generates a self-improvement intent and recursively re-architects itself. Always gated by human oversight.

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Features

• 3-Tier Decomposition — DNA pattern matching → rule templates → structured LLM prompting with schema validation
• Beam Search Synthesis — finds optimal capability compositions using a compatibility graph with cost/latency pruning
• 9-Point Static Validation — cycle detection, schema compatibility, connectivity, governance, budgets, fallbacks, security boundaries, side-effect conflicts, gate ordering
• Resilient Execution — async topological execution, parallel node groups, exponential backoff retries, per-capability circuit breakers, checkpoint/resume
• Evolutionary Loop — EMA-based fitness tracking, DNA persistence, automatic self-improvement intent generation
• Multi-Layer Memory — working (ephemeral), episodic (persistent history), semantic (knowledge graph), all SQLite-backed
• 9 Seed Capabilities — text summarizer, classifier, entity extractor, data validator/transformer, web search, document retriever, report generator, human review gate
• Framework Adapters — LangGraph, CrewAI, MCP server ready
• OpenTelemetry-Compatible Tracing — span-level observability out of the box

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Quick Start

pip install rados

from rads import RADS, Intent
from rads.capabilities.seed import bootstrap_registry

# Initialize and load seed capabilities
rads = RADS()
bootstrap_registry(rads.registry)

# Define what you want done — not how
intent = Intent(
    description="Analyze Q3 earnings and generate an investor memo",
    domain="financial_analysis",
    quality_gates=[{"metric": "accuracy", "operator": ">", "threshold": 0.85}],
    human_oversight="review_final",
    budget={"max_latency_ms": 60000},
)

# RadOS decomposes, synthesizes, validates, executes, and learns
result = rads.process_sync(intent, context={"earnings_data": {...}})

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Architecture

┌─────────┐    ┌──────────────┐    ┌─────────────┐    ┌──────────┐
│  Intent  │───▶│  Decomposer  │───▶│  Synthesizer│───▶│Validator │
└─────────┘    │  (Hybrid)    │    │  (Beam      │    │(9 checks)│
               └──────────────┘    │   Search)   │    └────┬─────┘
                                   └─────────────┘         │
                          ┌──────────────┐                 ▼
                          │  DNA Registry│◀────── ┌──────────────┐
                          │  (Templates) │        │  Executor    │
                          └──────────────┘        │ (Resilient)  │
                                                  └──────┬───────┘
                                                         │
                          ┌──────────────┐              ▼
                          │  Capability  │◀────── ┌──────────────┐
                          │   Registry   │        │  Evaluator   │
                          │  + Fitness   │        │  + Evolution │
                          └──────────────┘        └──────────────┘
                                                         │
                                                    🔁 Recursive
                                                       Self-Improve

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Key Concepts

Concept	Description
Intent	A structured goal with quality gates, constraints, and oversight level
Capability	The smallest executable unit — a typed contract with fitness metrics
Architecture DNA	A reusable, persisted blueprint for a successful agent graph
Decomposition	Breaking a goal into sub-requirements via patterns, templates, or LLM
Beam Search	Finding optimal capability compositions under cost/latency constraints
Evolution	Self-improvement loop: score → update fitness → persist DNA → retry

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Minimum Requirements

• Python ≥ 3.10
• pydantic, sqlalchemy, numpy
• Optional: openai for embeddings/LLM, langgraph for adapter

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RADOS v2.0 — The system that can architect itself can architect anything.