Proprietary Architecture

RapidCore AI (AgentOS)

The foundational architectural framework powering RapidStackLab's enterprise agent deployments. Engineered for stateful persistence, zero hallucination tolerance, and seamless enterprise tool integration.

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Core Modules

Inside the RapidCore AI Framework

Every client system we build is grounded in modular, production-tested components that eliminate the common pitfalls of agentic software.

Module 01

Stateful Graph Orchestration

LangGraph + Temporal.io

Cyclic graph execution with state checkpointing, automated recovery, and human-in-the-loop approval gates. Agents can pause execution for human verification without losing memory.

Supervisor-worker and swarm topologies

Deterministic state transitions and rollback

Async human-in-the-loop escalation

Persistent checkpointing via PostgreSQL

Module 02

Unified Model Gateway

LiteLLM Proxy + OpenRouter

Smart dynamic routing across 100+ LLMs with fallback cascades, load balancing, latency optimization, and automated token budgeting to slash inference costs.

Automatic failover across OpenAI, Anthropic, and Gemini

Cost-aware routing: Fast models for triage, reasoning models for execution

Local model hosting via Ollama / vLLM for air-gapped data

Exact token usage tracing and per-tenant budgeting

Module 03

Hybrid Enterprise Memory & RAG

pgvector + BM25 + Cohere Rerank

Multi-stage retrieval pipeline combining dense vector embeddings with sparse keyword BM25 search, cross-encoder reranking, and contextual compression.

Sub-150ms hybrid dense/sparse search latency

Semantic chunking preserving table and document structures

Multi-tenant metadata isolation in PostgreSQL

Episodic conversation memory with summary compaction

Module 04

Deterministic Tool Execution Fabric

OpenAPI + Pydantic + Docker Sandboxes

Strict schema validation, idempotency keys, rate limiting, and isolated Docker sandboxes for executing code, database queries, and enterprise API calls safely.

Pydantic runtime validation for tool call parameters

Idempotency keys preventing accidental duplicate actions

Read-only database replica routing for analytical queries

Pre-built connectors for Salesforce, SAP, Stripe, Jira, and Slack

Module 05

Guardrails & Continuous Evals

NeMo Guardrails + DeepEval + OpenTelemetry

Comprehensive security and quality harness enforcing prompt injection defenses, PII masking, toxic content blocking, and automated regression evals.

Pre-execution prompt injection & jailbreak detection

Real-time PII anonymization and masking

CI/CD automated regression evals before new agent deployment

Distributed OpenTelemetry tracing for every agent span

Code Pattern

LangGraph State Machine Pattern

How RapidCore AI structures deterministic supervisor-worker topologies with human escalation.

rapidcore/agent_graph.py

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated, Sequence
import operator

class AgentState(TypedDict):
    task: str
    context: list[str]
    messages: Annotated[Sequence[dict], operator.add]
    requires_human_approval: bool
    confidence_score: float

# Initialize StateGraph with deterministic schema
workflow = StateGraph(AgentState)

# Define modular nodes
workflow.add_node("supervisor", supervisor_router)
workflow.add_node("rag_worker", hybrid_retrieval_agent)
workflow.add_node("tool_worker", deterministic_tool_executor)
workflow.add_node("human_gate", human_approval_queue)

# Conditional routing based on confidence and policy
workflow.add_conditional_edges(
    "supervisor",
    route_by_intent,
    {
        "retrieve": "rag_worker",
        "execute_action": "tool_worker",
        "escalate": "human_gate"
    }
)

workflow.add_edge("rag_worker", "supervisor")
workflow.add_edge("tool_worker", END)
workflow.add_edge("human_gate", "tool_worker")

# Compile with persistent PostgreSQL checkpointer
app = workflow.compile(checkpointer=PostgresCheckpointer())

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from langgraph.graph import StateGraph, END from typing import TypedDict, Annotated, Sequence import operator class AgentState(TypedDict): task: str context: list[str] messages: Annotated[Sequence[dict], operator.add] requires_human_approval: bool confidence_score: float # Initialize StateGraph with deterministic schema workflow = StateGraph(AgentState) # Define modular nodes workflow.add_node("supervisor", supervisor_router) workflow.add_node("rag_worker", hybrid_retrieval_agent) workflow.add_node("tool_worker", deterministic_tool_executor) workflow.add_node("human_gate", human_approval_queue) # Conditional routing based on confidence and policy workflow.add_conditional_edges( "supervisor", route_by_intent, { "retrieve": "rag_worker", "execute_action": "tool_worker", "escalate": "human_gate" } ) workflow.add_edge("rag_worker", "supervisor") workflow.add_edge("tool_worker", END) workflow.add_edge("human_gate", "tool_worker") # Compile with persistent PostgreSQL checkpointer app = workflow.compile(checkpointer=PostgresCheckpointer())