ra-h-os/docs/1_architecture.md

System Architecture

How RA-H's AI agents work together to manage your knowledge.

How it works: RA-H has three AI agents: orchestrators (ra-h/ra-h-easy) handle your conversations and delegate work, wise-rah executes multi-step workflows, and mini-rah handles background tasks. All agents can read your knowledge graph; only some can write.

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Overview

RA-H uses a multi-agent architecture with three specialized AI agents that collaborate to manage your knowledge base. The system is built around nodes (knowledge items), edges (relationships), and dimensions (categories).

Core Concepts

Nodes

Knowledge items stored in the database (papers, ideas, people, projects, videos, tweets, etc). Each node has:

• Title and content
• Dimensions (multi-tag categorization)
• Metadata (structured JSON)
• Embeddings (for semantic search)
• Links (for external sources)

Edges

Directed relationships between nodes. Edges capture how nodes connect ("relates to", "inspired by", etc).

Dimensions

Multi-select categorization tags. Nodes can have multiple dimensions. Some dimensions can be marked as "priority" for focused context.

Agent Architecture

Orchestrator Agents (Easy/Hard Mode)

ra-h-easy (Easy Mode - Default)

• Model: GPT-5 Mini (openai/gpt-5-mini)
• Purpose: Fast, low-latency orchestration for everyday tasks
• Caching: OpenAI implicit caching
• Reasoning: reasoning_effort: light for speed

ra-h (Hard Mode)

• Model: Claude Sonnet 4.5 (anthropic/claude-sonnet-4.5)
• Purpose: Deep reasoning for complex tasks
• Caching: Anthropic explicit prompt caching
• Reasoning: Stronger analytical capabilities

Tools Available:

• queryNodes, queryEdge, searchContentEmbeddings
• webSearch, think
• executeWorkflow (delegates to wise-rah)
• createNode, updateNode, createEdge, updateEdge
• youtubeExtract, websiteExtract, paperExtract

Mode Switching: Users toggle via UI (⚡ Easy / 🔥 Hard). Choice persists in localStorage. Seamless mid-conversation switching - context maintained across mode changes.

Wise RA-H (Workflow Executor)

wise-rah

• Model: GPT-5 (openai/gpt-5)
• Purpose: Executes predefined workflows (integrate, deep analysis)
• Direct write access: Calls updateNode directly (no delegation)
• Context isolation: Returns summaries only to orchestrator

Tools Available:

• queryNodes, getNodesById, queryEdge, searchContentEmbeddings
• webSearch, think
• updateNode (append-only, enforced at tool level)

Key Workflows:

• Integrate: Database-wide connection discovery (5-step: plan → ground → search → contextualize → append)

Mini RA-H (Delegate Workers)

mini-rah

• Model: GPT-4o Mini (openai/gpt-4o-mini)
• Purpose: Spawned for write operations, extraction, batch tasks
• Execution: Isolated context, returns summaries only

Tools Available:

• All read tools + createNode, updateNode, createEdge, updateEdge
• Extraction tools (youtubeExtract, websiteExtract, paperExtract)

Prompt Caching

Anthropic (Claude):

• Explicit cache control blocks in system prompts
• Caches tool definitions, workflows, base context

OpenAI (GPT-5/4o):

• Implicit caching based on prefix matching
• Optimized prompts for cache reuse
• reasoning_effort parameter for speed/quality tradeoff

Context Hygiene

Orchestrator:

• Maintains full conversation history
• Sees auto-context (top 10 connected nodes) + focused node
• Delegates isolation ensures clean context

Workers (wise-rah/mini-rah):

• Execute in isolated sessions
• Return structured summaries only
• Do NOT pollute orchestrator context with tool execution details

UI Integration

Users interact with a single interface that automatically routes requests to the appropriate agent based on:

• Mode selection (Easy/Hard)
• Workflow triggers (executeWorkflow → wise-rah)
• Delegation needs (mini-rah spawned in background)

Orchestrators see auto-context (your 10 most-connected nodes) plus the focused node for consistent knowledge access. See Context & Memory for details.