Harnesses & SDKs

TypeScript framework and product stack for autonomous agents, with a plugin system, persistent memory, a CLI, an app and cloud services.

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

TypeScript agent framework on a Rust runtime, where runs are checkpointed, replayed with zero LLM calls and resumed after a crash.

Official Python implementation of the Universal Tool Calling Protocol, calling HTTP, CLI, MCP and text-file tools through plugins.

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

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

All-in-one Python AI framework for embeddings-based semantic search, LLM pipelines, workflows and agents.

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

C# framework on .NET for building multi-agent AI bots with plugins, RAG, MCP integration and a built-in admin UI.

Kubernetes-native framework and runtime for building and running AI agents as Kubernetes custom resources.

Lightweight Python framework for composing AI agents and tools as small, typed pieces built on Instructor and Pydantic.

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

Python framework for structured LLM outputs and agentic workflows built from tasks, agents and threads.

Open-source reinforcement learning framework that trains LLM agents on multi-step tasks using GRPO.

Python framework for orchestrating multiple LLM agents in sequential, concurrent, hierarchical and graph-based workflows.

Open-source framework that builds, evaluates and self-improves multi-agent LLM workflows from a goal.

Python framework for backtesting and running trading strategies written as Python rules or AI agents, across brokers.

Python framework that models an application as a state machine, with a trace UI and persisted state.

Modular Python agent framework with a tool-calling loop, context management, retrieval, persistent memory and MCP tools.

Python framework for LLM apps built from collaborating agents that hold conversation state, tools and vector-store memory.

Python library that turns files in S3, GCS and Azure into versioned, typed datasets for pipelines and agents.

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

Python and TypeScript toolkit for calling any frontier LLM through one interface, with tools, structured output and tracing.

Python library and CLI for building agents that run on IBM watsonx Orchestrate, with a local Developer Edition for testing.