Weaviate is an open-source vector database that allows users to store data objects and vector embeddings from ML-models and scale to billions of data objects seamlessly.
The tool provides lightning-fast pure vector similarity search over data objects or raw vectors and supports a combination of keyword-based search and vector search techniques for state-of-the-art search results.
Weaviate also enables users to use any generative model in combination with your data to create next-gen search experiences. The tool has integrations with a wide variety of well-known neural search frameworks and provides out-of-the-box support for vectorization.
Users can also choose from Weaviate's modules, which have extensive support for vectorization. Weaviate is designed to give developers an excellent experience and enable them to go from zero to production seamlessly.
The tool is designed with community and open-source principles in mind, and users can join the Weaviate community on Slack. The tool currently has backup and restore capabilities, making it a robust solution for data-intensive applications.
Weaviate has a vast library of resources that help users learn how to use the tool and get inspiration from other users' innovative apps. Finally, Weaviate is available for use anywhere as an open-source tool.
Managed Memory and Context Service: Engram provides a structured memory layer that helps agents remember user interactions and learn over time, enhancing their effectiveness.
Asynchronous Memory Pipelines: Memory operations run in the background, ensuring that application performance is not hindered by memory I/O, with durability and atomicity in data handling.
Templates for Personalization: Ready-to-deploy templates for common use cases like continual learning and multi-agent state management are included, allowing for quick implementation.
Unified Retrieval System: Engram integrates with Weaviate's hybrid search capabilities, enabling efficient retrieval of structured memories alongside traditional data.
Scoped Memory Isolation: Built-in scopes allow for project, user, and property-specific memory management, ensuring that relevant memories are accessible only to authorized agents.
Personalized AI Experiences: Engram allows the creation of AI agents that learn and adapt to individual user interactions over time, enhancing user engagement and satisfaction.
Memory Management: It provides a managed memory service that organizes and reconciles user interactions, preventing context degradation and ensuring high-quality responses.
Multi-Agent Coordination: Engram supports multi-agent systems by enabling shared, scoped memory, facilitating complex workflows that require collaboration between different agents.
Asynchronous Processing: The service runs memory pipelines in the background, allowing applications to operate without being blocked by memory I/O, thus improving performance.
Template-Based Personalization: Engram offers ready-to-deploy templates for common use cases like continual learning and personalization, streamlining the development process for teams.
Engram provides a managed memory and context service that helps agents remember user interactions across sessions, enhancing personalization.
It actively maintains memory by extracting, deduplicating, and reconciling information, ensuring a clean memory state as interactions accumulate.
The service allows for asynchronous memory pipelines, preventing application layer blocking and improving overall performance.
Engram includes built-in scopes for project, user, and property isolation, ensuring that the right memories are accessible to the appropriate users.
It integrates seamlessly with Weaviate's hybrid search capabilities, leveraging the existing infrastructure for efficient memory retrieval.
Free Plan: Always free, includes 1 cluster per user, 100,000 objects, 1 GB memory, 10 GB disk, 1 collection, up to 3 tenants, 2,000 embeddings requests per day, and 1,000 query agent requests per month.
Flex Plan: Starts at $45/month, pay-as-you-go, includes shared cloud cluster, baseline security, 99.5% uptime, and usage-based query agent and embeddings.
Premium Plan: Starts at $400/month, prepaid contract, offers shared or dedicated deployment, up to 99.95% uptime, and enterprise support with a dedicated Technical Account Team.
Query Agent Pricing: Free for the first 1,000 requests per month, $30/month for additional requests, with unlimited usage-based requests thereafter.
Embedding Pricing: Varies by model, e.g., $0.025 per 1M tokens for SNOWFLAKE ARCTIC-EMBED-M-V1.5.
