“Python”

Python multi-agent framework with PEER and DOE collaboration patterns and configurable LLM vendors such as Qwen, DeepSeek and OpenAI.

Low-code Python framework for YAML-defined agents and multi-agent Cogs, with personas, prompt templates and memory nodes.

Python framework for LLM applications built on Qwen models, with function calling, MCP, a code interpreter and RAG options.

Python framework for autonomous agents that work in a sandboxed workspace, and task agents with tools and MCP support.

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

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

Lightweight Python framework for LLM agents built as PyTorch-style layers, with agent memory kept as session state.

Open-source framework for real-time multimodal conversational AI, with RTC and WebSocket voice assistant examples.

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

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

Open-source infrastructure for continually self-improving agents that serves requests, learns from feedback and versions updates.

Reliability layer for tool calling with self-hosted LLMs, offered as a proxy, a workflow runner and middleware.

Framework for building, running and evaluating autonomous agents, with a planner-based multi-agent mode, skills and agent RL training.

Java SDK for calling LLMs such as OpenAI, Azure OpenAI and Hugging Face, with plugins that chain functions and planners.