AEVS vs RightNow CUDA Editor: Comparison (2026) | linkgo
AEVS vs RightNow CUDA Editor: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AEVS and RightNow CUDA Editor — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AEVS
Fetch.ai
Free
Open-source SDK that creates tamper-evident, cryptographically signed receipts for every tool call an AI agent makes.
Key features
Signed Receipts: Records every tool call and seals it with an ECDSA P-256 signature backed by KMS.
Hash-Chained Logs: Links each receipt to the previous one so tampering or skipped steps are detectable.
Independent Verification: Confirms signatures via a public API or explorer using only a reference ID.
Drop-In SDK: Installs with pip and wraps existing tools without changing them.
Framework Auto-Detection: Automatically integrates with LangChain and MCP-based agents.
Open Source: Released as fetchai/AEVS-sdk for Python 3.10–3.13.
Best for
Agent Auditing: Keep a verifiable record of exactly what an agent did and when.
High-Stakes Actions: Prove execution of sensitive operations such as payments or refunds.
Compliance Evidence: Provide tamper-evident logs for regulated or accountable workflows.
Debugging Agents: Inspect tool inputs, outputs, timing, and errors for each call.
Third-Party Verification: Let external parties confirm an action occurred without sharing source code.
All-in-one AI-powered code editor for CUDA with hardware-aware agents, GPU emulation/virtualization, real-time profiling and enterprise benchmarking.
Key features
Agentic Hardware-Aware Assistant: An AI agent that reasons about NVIDIA GPU architecture (memory hierarchy, warp scheduling, occupancy) to suggest kernel-level optimizations, launch configuration changes, and micro-architectural fixes tailored to the target GPU.
GPU Emulation and CPU Simulation Mode: Built-in GPU emulator allowing developers to run and test CUDA code on machines without physical GPUs by simulating GPU behavior and validating kernel logic before deployment.
GPU Virtualization Support: Virtualized GPU environments for remote testing and multi-tenant workflows, enabling developers to run GPU workloads in isolated virtual GPUs for reproducible experiments.
Real-time Profiling with Smart Terminal: Live profiling integrated into the editor that surfaces hotspots, stalls, memory transfers, and kernel timelines in the terminal while code runs, allowing rapid iterative tuning.
Line-by-Line Performance Analysis: Fine-grained cost annotations that attribute runtime and memory behavior to specific lines or blocks of CUDA code to pinpoint bottlenecks and inefficient constructs.
Benchmarking Terminal with Sweep Configurations: Enterprise-grade benchmarking tooling that runs parameter sweeps (grid/search) across kernel launch parameters, inputs, and device targets and produces reproducible reports.
Open-source CLI and Easy Install: Community-facing CLI (rightnow-cli) available via pip for quick setup, enabling a lightweight GPU-native AI code assistant and integration into developer workflows and CI.
GPU Profiler Visualization: Web-based visualization transforms NVIDIA profiling data into timeline views, flame graphs, heatmaps and includes AI-powered bottleneck detection to accelerate root-cause analysis.
Agentic hardware-aware assistants that reason about GPU architecture and propose optimizations
GPU emulator and virtualization allowing code execution without physical GPUs (CPU simulation mode)
Real-time profiling with line-by-line performance analysis
Enterprise-grade benchmarking across NVIDIA GPUs (supports GTX 1060 to H100)
Integrated debugging and code completion tailored for CUDA
Open-source CLI (rightnow-cli) installable via pip
Multi-agent interactive tools and professional UI for GPU development
Supports running and testing in GPU-native environments and simulated environments
Community resources: GitHub repos, Discord, and documentation (INSTALLATION.md, CONTRIBUTING.md)
Best for
CUDA Kernel Optimization: Iteratively tune kernels with the agentic assistant and line-by-line performance feedback to reduce execution time and increase occupancy on target NVIDIA GPUs.
Developing Without Hardware: Use the GPU emulator/CPU simulation mode to write and validate CUDA code on developer laptops or CI runners that lack physical GPUs before running on real devices.
Fleet Benchmarking and Regression Testing: Run sweep benchmarks across multiple GPU models (GTX 1060 through H100) to compare performance, detect regressions, and generate reproducible benchmarking reports for releases.
Performance Debugging and Bottleneck Detection: Combine real-time profiling and profiler visualizations to trace memory transfer stalls, warp divergence, and synchronization issues, with AI-suggested fixes.
Enterprise Workflows and CI Integration: Integrate the CLI and benchmarking terminal into continuous integration pipelines to automatically run performance sweeps, collect metrics, and gate commits on performance thresholds.
Educational and Research Use: Provide students and researchers with a free, GPU-aware coding environment and visualization tools to learn CUDA programming and analyze kernel performance without needing physical GPUs.
Developing and optimizing CUDA kernels with hardware-aware suggestions
Profiling and diagnosing GPU performance issues using timeline views and flame graphs
Benchmarking CUDA workloads across a range of NVIDIA GPUs for enterprise reporting
Debugging and iterating CUDA code on machines without GPUs using CPU simulation/emulation
Educational use for learning CUDA, performance analysis, and GPU programming patterns
Integrating into CI or dev workflows via CLI for automated performance checks
