Context tools for AI products and agents, from retrieval frameworks and managed context engines to vector databases, memory layers, document parsing, reranking APIs, and evaluation stacks.
20 tools found
47K+ GitHub stars
Data framework for agent and RAG applications spanning parsing, extraction, indexing, retrieval, and knowledge workflows across many data sources.
100M+ monthly open-source users
Application framework for chaining retrieval, memory, prompts, models, and tools into context-aware LLM systems with a broad integration ecosystem.
Trusted by world's leading companies
Managed vector database for semantic search and hybrid retrieval with serverless operations, metadata filters, and production-ready indexing for AI workloads.
21.5K+ GitHub stars, 20M+ downloads
Vector database with hybrid search, built-in vectorizers, and AI-native indexing for teams that want retrieval infrastructure with richer search behavior.
Trusted by millions of developers
Open-source vector database for embeddings, metadata filtering, and local-to-cloud retrieval workflows that need a simple AI-native storage layer.
29K+ GitHub stars, 250M+ downloads
High-performance vector search engine with payload filtering and production control for teams building semantic retrieval and recommendation systems.
Used by 100,000+ developers
Persistent memory layer for AI assistants and agents that stores user preferences, long-term facts, and compressed context across sessions and workflows.
14K+ GitHub stars, 25K weekly PyPI
Long-term memory system for AI assistants that stores conversation history, user facts, and temporal knowledge for more personalized future interactions.
Trusted by leading AI companies
Tracing, evaluation, and monitoring platform for LLM, agent, and retrieval systems that need visibility into context flow, regressions, and production failures.
Popular document ETL solution
Document ETL platform for parsing, chunking, enrichment, and connector-driven ingestion so messy enterprise content becomes retrieval-ready context.
Trusted by industry leaders worldwide
Semantic reranking API that improves retrieval relevance by reordering candidate results before answer generation in grounded AI and search systems.
Used by thousands of companies
Embedding API for multilingual, long-context, and multimodal retrieval tasks where teams need higher quality representations for search and grounding.
Trusted by Anthropic & LangChain
Embeddings and rerankers tuned for high-quality retrieval, including domain-specific models for code, legal, finance, and multilingual content.
Popular open-source RAG evaluation
Evaluation framework for RAG systems that measures faithfulness, context precision, recall, and answer quality across offline tests and production monitoring.
Trusted by Qualcomm & innovators
Enterprise retrieval and grounding platform focused on high-accuracy RAG over business data, with context orchestration and production-ready retrieval quality controls.
Used by Uber, LinkedIn & Klarna
Stateful workflow framework for multi-step LLM and retrieval graphs where context, memory, branching, and repeated tool use need explicit orchestration.
40K+ GitHub stars
Distributed vector database for large-scale similarity search, GPU acceleration, and production retrieval systems that need more control over performance and scale.
10M+ users, #1 on G2
Prompt management workbench with versioning, regression testing, usage monitoring, and evaluation workflows for teams iterating on prompts and context behavior in production.
Production-ready LLM framework
Open-source framework for building production RAG pipelines, search systems, and question-answering workflows with pluggable retrievers, stores, and evaluation hooks.
Trusted by Broadcom & enterprises
Managed retrieval and grounding platform for enterprise AI with built-in chunking, indexing, retrieval, evaluation, and policy-aware answer generation.
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