Package-level reference for the autogen-agentchat / autogen-core / autogen-ext family on PyPI plus the legacy pyautogen — install, rename history, versioning, and alternatives.
Package-level reference for the crewai library on PyPI plus the crewai-tools companion — install, versioning, and multi-agent alternatives.
Package-level reference for haystack-ai on PyPI — install variants, the farm-haystack v1 → haystack-ai v2 rename, integrations, and alternative frameworks.
Package-level reference for the langchain family on PyPI — install variants, partner packages, version churn, and alternatives.
Package-level reference for semantic-kernel on PyPI — install variants, the Python vs .NET split, provider extras, and alternative frameworks.
Spawn isolated subagents with the Task tool — Plan, Explore, general-purpose, project-defined agents, isolation modes including worktrees, background runs, and orchestration patterns.
How Codex CLI supports delegating to child agents — the experimental /agent system, codex exec as a sub-agent pattern, MCP-mediated agent-to-agent calls, isolation boundaries, and orchestration patterns.
Side-by-side comparison of LangChain, LlamaIndex, AutoGen, CrewAI, Haystack, and Semantic Kernel for building LLM-powered applications and agent systems. Covers strengths, weaknesses, and when to pick each.
Build multi-agent AI systems with Microsoft AutoGen. Covers agents, group chats, code execution, tool registration, async runtimes, and LLM configuration.
Orchestrate teams of role-playing AI agents with crewAI. Covers agents, tasks, crews, tools, LLM selection, memory, YAML config, and the kickoff lifecycle.
Build LLM-powered pipelines with LangChain. Covers LCEL chains, chat models, prompts, output parsers, tools, agents, retrievers, memory, and streaming.
Build RAG pipelines and LLM-powered data applications with LlamaIndex. Covers document loading, indexing, query engines, custom LLMs and embeddings, persistent storage, and agents.
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