Best Context Engineering Tools for OpenLIT
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Compare the Top Context Engineering Tools that integrate with OpenLIT as of September 2025
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This a list of Context Engineering tools that integrate with OpenLIT. Use the filters on the left to add additional filters for products that have integrations with OpenLIT. View the products that work with OpenLIT in the table below.
What are Context Engineering Tools for OpenLIT?
Context engineering tools are specialized frameworks and technologies that manage the information environment surrounding large language models (LLMs) to enhance their performance in complex tasks. Unlike traditional prompt engineering, which focuses on crafting individual inputs, context engineering involves dynamically assembling and structuring relevant data—such as user history, external documents, and real-time inputs—to ensure accurate and coherent outputs. This approach is foundational in building agentic AI systems, enabling them to perform multi-step reasoning, maintain state across interactions, and integrate external tools or APIs seamlessly. By orchestrating the flow of information and memory, context engineering tools help mitigate issues like hallucinations and ensure that AI systems deliver consistent, reliable, and context-aware responses. Compare and read user reviews of the best Context Engineering tools for OpenLIT currently available using the table below. This list is updated regularly.
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Pinecone
Pinecone
The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Developer-friendly, fully managed, and easily scalable without infrastructure hassles. Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval. Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results. Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely. -
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Qdrant
Qdrant
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. -
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LlamaIndex
LlamaIndex
LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.
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