Model Context Protocol (MCP)
Open protocol for connecting AI applications with tools, data sources, and prompts.
Frameworks and libraries builders use to make ai agents.
85 listings · 61–80
Open protocol for connecting AI applications with tools, data sources, and prompts.
Databricks MCP services connect ai agents with governed data and enterprise tools.
AI agent platform and toolkit for building, deploying, and managing agent workflows.
Open-source SDK for building agents with tools, handoffs, guardrails, and tracing.
AI evaluation platform for testing agent behavior, traces, and safety.
Open-source LLMOps platform for managing prompts, versions, and application observability.
AI gateway for routing, securing, and observing LLM and agent requests.
Agent builder for designing, testing, and deploying AI workflows with APIs.
Private AI knowledge platform that answers questions about company documents and connected workspaces.
APIs and SDKs for adding and operating conversational ai agents inside applications.
Python framework for building typed ai agents with tools, structured outputs, and model integrations.
Open-source agent framework built on Qwen models with function calling, MCP, code interpreter, and RAG.
Enterprise platform for building controlled ai agents across text, voice, and business workflows.
No-code platform for creating custom AI chatbots from files and embedding them in websites.
AI research platform that generates hypotheses, runs machine learning experiments, and drafts research reports.
AI workforce platform with a Chief of Staff and specialized Co-Worker agents.
Open-source framework and platform for building, deploying, and managing autonomous ai agents.
Natural-language data analysis platform that turns questions into insights, visualizations, and reports.
Enterprise agent platform for voice and digital customer service across contact center systems.
TypeScript platform for deploying ai agents that handle tasks, chat sessions, tools, and document knowledge.
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