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- Role roadmapsAgentic AI and Forward Deployed
Find your next framework
37Popular
LangChain
The layer under most agents. Models, tools, prompts and messages in one interface.
LangGraph
Build agents as graphs. Loops, memory, human approval and full control of the flow.
Deep Agents
Agents that plan with to-do lists, keep notes in files and hand work to subagents.
Model Context Protocol
Not a framework. The standard that lets any agent plug into any tool or data source.
RAGAS
Scores a RAG bot's answer and its retrieval separately, so you know which half to fix.
NeMo Guardrails
Rails written as files between your app and its model, in a language of their own.
Claude Code
Anthropic's terminal agent. Reads your repo, edits it, runs your commands.
LangMem
Long-term memory for LangGraph agents: extraction, consolidation and retrieval.
Python for AI
Python from your first line to classes, Pydantic, async and pytest, on one AI program.
LLM Fundamentals
Tokens, temperature, prompts, evals, context, cost and speed, on a real model you run.
LlamaIndex
Agents that reason over your documents. Strong on retrieval and indexing.
OpenAI Agents SDK
OpenAI's own agent loop, with handoffs, guardrails and tracing built in.
Pydantic AI
Type safe agents. If you already trust Pydantic for validation, this feels like home.
CrewAI
Give each agent a role, a goal and a backstory, then let the crew work the task.