Best Context Engineering Tools for OpenAI
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Compare the Top Context Engineering Tools that integrate with OpenAI as of August 2025
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This a list of Context Engineering tools that integrate with OpenAI. Use the filters on the left to add additional filters for products that have integrations with OpenAI. View the products that work with OpenAI in the table below.
What are Context Engineering Tools for OpenAI?
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 OpenAI currently available using the table below. This list is updated regularly.
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1
Zilliz Cloud
Zilliz
Zilliz Cloud is a fully managed vector database based on the popular open-source Milvus. Zilliz Cloud helps to unlock high-performance similarity searches with no previous experience or extra effort needed for infrastructure management. It is ultra-fast and enables 10x faster vector retrieval, a feat unparalleled by any other vector database management system. Zilliz includes support for multiple vector search indexes, built-in filtering, and complete data encryption in transit, a requirement for enterprise-grade applications. Zilliz is a cost-effective way to build similarity search, recommender systems, and anomaly detection into applications to keep that competitive edge.Starting Price: $0 -
2
Weaviate
Weaviate
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. Whether you bring your own vectors or use one of the vectorization modules, you can index billions of data objects to search through. Combine multiple search techniques, such as keyword-based and vector search, to provide state-of-the-art search experiences. Improve your search results by piping them through LLM models like GPT-3 to create next-gen search experiences. Beyond search, Weaviate's next-gen vector database can power a wide range of innovative apps. Perform lightning-fast pure vector similarity search over raw vectors or data objects, even with filters. Combine keyword-based search with vector search techniques for state-of-the-art results. Use any generative model in combination with your data, for example to do Q&A over your dataset.Starting Price: Free -
3
PromptLayer
PromptLayer
The first platform built for prompt engineers. Log OpenAI requests, search usage history, track performance, and visually manage prompt templates. manage Never forget that one good prompt. GPT in prod, done right. Trusted by over 1,000 engineers to version prompts and monitor API usage. Start using your prompts in production. To get started, create an account by clicking “log in” on PromptLayer. Once logged in, click the button to create an API key and save this in a secure location. After making your first few requests, you should be able to see them in the PromptLayer dashboard! You can use PromptLayer with LangChain. LangChain is a popular Python library aimed at assisting in the development of LLM applications. It provides a lot of helpful features like chains, agents, and memory. Right now, the primary way to access PromptLayer is through our Python wrapper library that can be installed with pip.Starting Price: Free -
4
Flowise
Flowise AI
Flowise is an open-source, low-code platform that enables developers to create customized Large Language Model (LLM) applications through a user-friendly drag-and-drop interface. It supports integration with various LLMs, including LangChain and LlamaIndex, and offers over 100 integrations to facilitate the development of AI agents and orchestration flows. Flowise provides APIs, SDKs, and embedded widgets for seamless incorporation into existing systems, and is platform-agnostic, allowing deployment in air-gapped environments with local LLMs and vector databases.Starting Price: Free -
5
Model Context Protocol (MCP)
Anthropic
Model Context Protocol (MCP) is an open protocol designed to standardize how applications provide context to large language models (LLMs). It acts as a universal connector, similar to a USB-C port, allowing LLMs to seamlessly integrate with various data sources and tools. MCP supports a client-server architecture, enabling programs (clients) to interact with lightweight servers that expose specific capabilities. With growing pre-built integrations and flexibility to switch between LLM vendors, MCP helps users build complex workflows and AI agents while ensuring secure data management within their infrastructure.Starting Price: Free -
6
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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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. -
8
Haystack
deepset
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Build semantic search and question-answering applications that can scale to millions of documents. Building blocks for the entire product development cycle such as file converters, indexing functions, models, labeling tools, domain adaptation modules, and REST API.
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