Tool category
RAG / Knowledge Tools
Retrieval, embeddings, vector search, and knowledge assistant infrastructure.
Use this category when a team needs source-grounded answers from documents, websites, product knowledge, or internal data instead of relying on model memory.
Recommended tools
PineconeManaged vector database for RAG, semantic search, and AI assistants.WeaviateAI-native vector database with free cloud and deployment flexibility.LlamaIndexData and RAG framework for knowledge-heavy AI applications.LangChainAgent engineering framework and observability platform.CohereEnterprise AI platform for Command, Embed, Rerank, and RAG systems.Vertex AIGoogle Cloud model and agent platform for enterprise AI applications.OpenRouterUnified API gateway for routing across hundreds of AI models.OpenAI APIGeneral-purpose model APIs for product builders.ClaudeLong-context assistant for writing, analysis, and coding workflows.FirecrawlWeb data API for search, scraping, crawling, and agent context.GleanWork AI platform for enterprise search, assistant, agents, and automation.Microsoft 365 CopilotAI assistant inside Microsoft 365 for documents, meetings, email, and team work.Slack AIAI summaries, search, meeting notes, translation, and workflow support inside Slack.WriterEnterprise AI agent platform for on-brand, compliant work.
Related workflows
RAG knowledge base evaluation workflowEvaluate a RAG knowledge base by testing ingestion quality, source retrieval, answer faithfulness, and update ownership before scaling infrastructure.Research assistant workflowTurn open web research into source-backed notes, comparison tables, and a decision-ready recommendation.Model API product prototype workflowSelect and test model APIs for a product feature before committing to architecture, pricing, or vendor lock-in.
Related guides
How small teams should choose a RAG stackA practical guide to choosing embeddings, vector search, retrieval evaluation, data ingestion, and model APIs for small-team RAG systems.How to choose AI research tools for source-backed decisionsA selection guide for choosing research assistants, search APIs, scraping tools, and synthesis models without confusing summaries with evidence.Model API selection framework for AI product buildersA method for comparing model APIs by task fit, quality, latency, cost, privacy, and fallback strategy.