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Neura
FeaturesUse Cases

Agent Runtime

ReAct engine with multi-step iteration

200+ Connections

MCPX native handlers + Runtime sandbox

Knowledge & RAG

Memory buckets with hybrid search

GlassBox IDE

Chat-driven agent customization

Dataset Creation Lab

290M+ scholarly works · 9 AI agents

Training Pipeline

Fine-tune 33+ open-source models

Marketplace

Publish and monetize agents & datasets

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Agent Runtime
200+ Connections
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Dataset Creation Lab
Training Pipeline
Marketplace
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Neura

Autonomous AI agents that connect to your world and actually get work done.

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© 2026 Neura. All rights reserved.

7 Layers, One Platform

Everything you need to deploy intelligent agents

Seven deeply integrated layers — from runtime to marketplace — so your agents can reason, connect, remember, create data, learn, and earn.

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The full stack for AI agents

Each layer is powerful alone. Together, they form the most complete agent platform available.

Layer 1

Agent Runtime

Think, plan, act, verify, repeat

ReAct engine that reasons, plans, executes, and iterates up to 50 times until the goal is achieved. Powered by 100+ LLMs from 9 providers.

Multi-step iteration loopSpawnable sub-agentsSelf-learning from errors

Layer 2

200+ Connections

Every API, one protocol

Dual-tier architecture: MCPX native handlers for critical services at sub-100ms, Runtime sandbox for 200+ others. Services auto-promote based on usage.

Native + sandbox tiersEncrypted credentialsAuto-promotion pipeline

Layer 3

Knowledge & RAG

Agents that remember everything

Per-job memory buckets with vector embeddings. Hybrid RAG search combines dense similarity with keyword matching for maximum recall.

Scoped memory bucketsAuto-context injectionWeb research agent

Layer 4

GlassBox IDE

Customize through conversation

Chat-driven IDE to customize agent pipelines. Edit system prompts, tool configs, and workflows through natural language — no code required.

Visual pipeline builderLive execution viewVersion control

290 million+ scholarly works powering your datasets

Peer-reviewed papers, patents, and academic publications across every discipline — medicine, law, engineering, physics, biology, and beyond. Research-grade data at scale.

290M+

Scholarly Works

All

Disciplines

Layer 5

Dataset Creation Lab

290M+ scholarly works at your fingertips

9 specialized agents collaborate to create research-grade datasets. Tap into 290 million+ scholarly works, scrape the web, or synthesize from LLM reasoning — with automatic fact-checking across every sample.

290M+ scholarly papers9-agent pipeline6 sourcing modes

Layer 6

Training Pipeline

Your model, your data, your edge

One-click fine-tuning on 33+ open-source models using datasets from the Factory. LoRA or full fine-tuning with automatic evaluation. Your custom model in hours.

33+ fine-tunable modelsLoRA & full fine-tuningAutomatic evaluation

Layer 7

Marketplace

Build, publish, earn

Publish and monetize agents, datasets, knowledge bases, and fine-tuned models. Deploy community-built assets in one click. 80% revenue share for creators.

4 asset types80% revenue shareOne-click deploy

Built for real work

Three innovations that set Neura apart from every other AI platform.

Self-Learning Agents

Every failure makes future agents smarter. Mistakes are recorded with trigger patterns and corrections. At 80%+ confidence, learnings become permanent rules — shared across all your agents.

Unlimited

Error patterns tracked

80%

Auto-promotion threshold

Dual-Tier Execution

MCPX native handlers for critical services deliver sub-100ms latency. Runtime sandbox handles 200+ others with dynamic code generation. High-usage sandbox services are automatically promoted to native.

<100ms

Native handler latency

200+

Sandbox services

Goal-Driven Iteration

Agents don't stop after one tool call. They loop through UNDERSTAND, PLAN, EXECUTE, VERIFY, and CHECK GOAL until the deliverable is complete — up to 50 iterations with automatic self-correction.

50

Max iterations per run

Every step

Built-in verification

Enterprise-grade architecture

Execution Chain Audit

Hash-linked operation log for every action

Parallel Workers

Spawn isolated workers for concurrent tasks

Real-time Cost Tracking

Per-operation cost breakdown with credit deduction

Cross-Agent Learning

Learnings propagate across all agents automatically

How Neura compares

CapabilityNeuraChatGPTLangChainZapier AI
Multi-step iteration—Partial—
Self-learning from errors———
200+ API connections—Partial
Persistent memory (RAG)PartialPartial—
Dataset creation lab (290M+ papers)———
Fine-tuning pipeline———
Visual agent IDE———
Agent & dataset marketplaceGPT Store——
No code required—

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