“TypeScript”

Open-source toolkit for building, evaluating and deploying agents in Python, TypeScript, Go, Java and Kotlin, tuned for Gemini but model-agnostic.

JavaScript and TypeScript framework of interoperable components for building LLM apps and agents, with LangGraph.js and Deep Agents above it.

Multi-language SDKs for embedding the GitHub Copilot agent runtime, with planning, tool calls and file edits, in your apps.

Open-source agent harness that runs the model loop for you, with MCP tools, skills, sandboxing, approvals and a chat UI.

TypeScript SDK for persistent AI agents on Cloudflare Durable Objects, with state sync, scheduling and MCP support.

Open-source Firebase framework for AI features and agent workflows in JavaScript, Go, Python and Dart, with model plugins.

TypeScript SDK with reference implementations of Hashgraph Consensus Standards and a Registry Broker client.

Python and TypeScript SDK that gives an AI agent a phone number, with pluggable LLM, speech and telephony providers.

Python and TypeScript SDK that lets an agent message people on email, Slack, Discord, Telegram and other channels through one handler.

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

Lightweight Python framework and starting template for agents that let the model choose its own approach to each task.

Open-source SDK that connects AI agents to app front ends with chat UI, generative UI, shared state and human approvals; creator of the AG-UI protocol.

The batteries included agent harness.

Opinionated agent harness on LangGraph with sub-agents, filesystem access, context management, persistent memory and approval of tool calls.

Python platform that gives agents a cryptographic identity, A2A communication and x402 payment requirements behind one bindufy() call.

Open-source agent harness from LangChain for building agents with code, with LangSmith for testing and monitoring.

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

Open-source access layer that connects AI agents to 1200+ integrations with shared auth, permissions and audit logs.

Policy enforcement, identity, sandboxing and reliability tooling for autonomous AI agents, installable with one pip command.

Visualization language and MCP server that lets AI agents create charts from editable chart specs.

XState library where agent logic is a state machine, so a model can only pick events the machine allows.

Open agent harness for AI applications, with a YAML-configured coding agent, sessions, tools, MCP support and a CLI.

Framework for AI agents and multi-agent networks where a router picks which agent runs, with tools and MCP support.

Minimal Python graph framework of about 100 lines for LLM agents, workflows, RAG and map-reduce pipelines.