“observability”

TypeScript framework for AI agents and apps with graph-based workflows, memory, suspend-and-resume approvals, MCP servers and model routing.

SDK for browser agents with act, observe and extract calls, used from TypeScript and Python to automate sites and pull out structured data.

AI application runtime framework for backends with structured outputs, observable actions, MCP capabilities and recoverable workflows.

Python SDK for deploying agents to Amazon Bedrock AgentCore, a managed runtime with memory, gateway, tools and observability.

Workflow SDK: Build durable, reliable, and observable apps and AI Agents in TypeScript

Open-source Python framework for multi-agent workflows, with role-based Crews and event-driven Flows.

TypeScript platform to build and deploy long-running AI agents and workflows with retries, queues and human approval steps.

TypeScript framework for AI agents with tools, memory, RAG, MCP and sub-agents, paired with a Cloud or self-hosted console.

Browser infrastructure for AI agents: local and cloud Playwright-based browser sessions, scraping and deployable browser functions.

Quarkus extensions that connect LangChain4j to Quarkus apps, with declarative AI services and LLM, tool and embedding support.

Python framework for generative AI agents, pipelines and parallel workflows, with RAG engines, tools, memory and swappable drivers.

Python SDK for building agents on Volcengine AgentKit, with a bundled Web UI, Studio deployment and short- and long-term memory.

Python framework for data labeling agents that learn skills from a ground-truth dataset through iterative feedback.

Python framework for MCP-based agents with composable workflow patterns, MCP server creation and Temporal-backed durable execution.

Go framework for multimodal AI agents with models, tools, memory, middleware, chains and structured output.