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NeuBirdNeuBird is the Agentic Operations Center. As production outgrows human understanding and agents arrive to fill the gap, NeuBird gives the enterprise one secure, audited point of access to its telemetry and its LLMs, queried in place with no data copied and tokens spent once, and a central memory that records every investigation, by human or agent, versioned and cited inside the customer's own environment. Working alongside the engineers who run production, NeuBird uses Context Engineering to catch incidents before the page and resolve them in minutes with the causal chain shown. Managers see every piece of agentic work in one view, and the enterprise's own agents connect over MCP to inherit the same context, memory, guardrails and audit trail. Backed by Xora Innovation, Mayfield and M12, NeuBird is headquartered in Redwood City, California. For more information, visit neubird.ai
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RaimaDBRaimaDB is an embedded time series database designed specifically for Edge and IoT devices, capable of operating entirely in-memory. This powerful and lightweight relational database management system (RDBMS) is not only secure but has also been validated by over 20,000 developers globally, with deployments exceeding 25 million instances. It excels in high-performance environments and is tailored for critical applications across various sectors, particularly in edge computing and IoT. Its efficient architecture makes it particularly suitable for systems with limited resources, offering both in-memory and persistent storage capabilities. RaimaDB supports versatile data modeling, accommodating traditional relational approaches alongside direct relationships via network model sets. The database guarantees data integrity with ACID-compliant transactions and employs a variety of advanced indexing techniques, including B+Tree, Hash Table, R-Tree, and AVL-Tree, to enhance data accessibility and reliability. Furthermore, it is designed to handle real-time processing demands, featuring multi-version concurrency control (MVCC) and snapshot isolation, which collectively position it as a dependable choice for applications where both speed and stability are essential. This combination of features makes RaimaDB an invaluable asset for developers looking to optimize performance in their applications.
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Google Compute EngineGoogle's Compute Engine, which falls under the category of infrastructure as a service (IaaS), enables businesses to create and manage virtual machines in the cloud. This platform facilitates cloud transformation by offering computing infrastructure in both standard sizes and custom machine configurations. General-purpose machines, like the E2, N1, N2, and N2D, strike a balance between cost and performance, making them suitable for a variety of applications. For workloads that demand high processing power, compute-optimized machines (C2) deliver superior performance with advanced virtual CPUs. Memory-optimized systems (M2) are tailored for applications requiring extensive memory, making them perfect for in-memory database solutions. Additionally, accelerator-optimized machines (A2), which utilize A100 GPUs, cater to applications that have high computational demands. Users can integrate Compute Engine with other Google Cloud Services, including AI and machine learning or data analytics tools, to enhance their capabilities. To maintain sufficient application capacity during scaling, reservations are available, providing users with peace of mind. Furthermore, financial savings can be achieved through sustained-use discounts, and even greater savings can be realized with committed-use discounts, making it an attractive option for organizations looking to optimize their cloud spending. Overall, Compute Engine is designed not only to meet current needs but also to adapt and grow with future demands.
What is MemMachine?
MemMachine represents a state-of-the-art open-source memory system designed specifically for sophisticated AI agents, facilitating the capacity of AI-driven applications to gather, store, and access information along with user preferences from prior interactions, which significantly improves future conversations. Its memory architecture ensures a seamless flow of continuity across multiple sessions, agents, and expansive language models, resulting in a rich and evolving user profile over time. This groundbreaking advancement transforms conventional AI chatbots into tailored, context-aware assistants, empowering them to understand and respond with enhanced precision and depth. Consequently, users benefit from a fluid interaction that becomes progressively intuitive and personalized with each engagement, ultimately fostering a deeper connection between the user and the AI. By leveraging this innovative system, the potential for meaningful interactions is elevated, paving the way for a new era of AI assistance.
What is EverOS?
EverOS is a persistent memory platform for AI agents that combines long-term context, multimodal ingestion, self-evolving skills, and cross-platform compatibility. The system is designed to sit inside an existing agent loop and retrieve relevant context before each large language model call. Rather than sending an entire conversation or knowledge history into the model every time, EverOS selects the most relevant stored memories for the current task. Its memory architecture is built to support low-latency retrieval while reducing token usage and maintaining continuity across sessions. EverOS also introduces self-evolving skills, which turn successful task executions into cases that can later be consolidated into reusable procedural knowledge. Repeated successful patterns can be promoted into shared skills that are available to multiple agents without requiring manual curation or brittle workflow logic. The platform accepts multimodal sources including PDFs, images, spreadsheets, documents, slides, URLs, and Markdown and automatically parses, chunks, and indexes them for retrieval. EverOS works with agent environments and interfaces such as Claude Code, Codex, OpenClaw, Hermes, MCP, and OpenAI- and Anthropic-compatible SDKs. Its Markdown-first portability model allows organizations to export memories in a readable, version-controllable format and move between hosted and self-managed deployments. Teams can start with EverOS Cloud or deploy the Apache 2.0 open source version on their own infrastructure using the same API and memory format. EverOS is designed for developers, AI infrastructure teams, and enterprises that want agents to retain context, learn reusable procedures, and operate more efficiently across tools and environments. EverOS achieves 93.05% on the LoCoMo long-term memory benchmark (research: arXiv:2601.02163).
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Integrations Supported
API Availability
API Availability
Has API
Pricing Information
$2,500 per month
Free Version
Free Trial Offered?
Pricing Information
Free
Free: $0/month. 3 Memory Spaces, 50,000 MCU/month, 100,000 Retrieval API calls/month.
Pro: $25/month (free during Early Access). 8 Memory Spaces, 250,000 MCU/month, 500,000 Retrieval API calls/month, includes Self-Evolving Skills.
Enterprise: Custom pricing. Custom limits, dedicated account manager, private deployment available.
Pro: $25/month (free during Early Access). 8 Memory Spaces, 250,000 MCU/month, 500,000 Retrieval API calls/month, includes Self-Evolving Skills.
Enterprise: Custom pricing. Custom limits, dedicated account manager, private deployment available.
Free Version
Supported Platforms
SaaS
Supported Platforms
SaaS
On-Prem
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Company Facts
Organization Name
MemVerge
Date Founded
2017
Company Location
United States
Company Website
www.memverge.ai
Company Facts
Organization Name
EverMind
Date Founded
2025
Company Location
United States
Company Website
evermind.ai/everos
Categories and Features
AI Memory Layers
Not specified
Categories and Features
AI Memory Layers
Not specified