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Agents - Framework / Tools

Choosing the Right Agentic Framework​

Not all platforms are created equal. Some are no-code, some are open-source, some focus on orchestration, and others on ease of integration.

  • OpenAI Agents API – Great for GPT-native agents, thread-based logic
  • Google Vertex AI – Strong orchestration + enterprise-ready
  • LangGraph – DAG-based workflows for complex multi-agent flows
  • AutoGen / CrewAI – Agent-to-agent communication with tool chaining
  • Make / n8n – Ideal for no-code, scenario-based automation

Understanding MCP (Multi-Agent Communication Protocol), memory integration, and orchestration style is essential when scaling from simple prompts to full systems.

SmolAgent - Agents​

Building your agent​

To initialize a minimal agent, you need at least these two arguments:

  • model, a text-generation model to power your agent - because the agent is different from a simple LLM, it is a system that uses a LLM as its engine. You can use any of these options:

  • tools, a list of Tools that the agent can use to solve the task. It can be an empty list. You can also add the default toolbox on top of your tools list by defining the optional argument add_base_tools=True.

CrewAI​

Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.

Why CrewAI?​

The power of AI collaboration has too much to offer. CrewAI is a standalone framework, built from the ground up without dependencies on Langchain or other agent frameworks. It's designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.

Agno​

Developers use Agno to build Reasoning Agents, Multimodal Agents, Teams of Agents and Agentic Workflows. Agno also provides a beautiful UI to chat with your Agents, pre-built FastAPI routes to serve your Agents and tools to monitor and evaluate their performance.

Use Agno to build the 5 levels of Agentic Systems:

  • Level 1: Agents with tools and instructions.
  • Level 2: Agents with knowledge and storage.
  • Level 3: Agents with memory and reasoning.
  • Level 4: Agent Teams that can reason and collaborate.
  • Level 5: Agentic Workflows with state and determinism.
# pip install -U agno

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools

reasoning_agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    tools=[
        ReasoningTools(add_instructions=True),
        YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True),
    ],
    instructions="Use tables to display data.",
    markdown=True,
)

GitHub - agno-agi/agno: Full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning. ⭐ 39k

A beautiful UI for your Agents - Agno

agno/cookbook/getting_started/05_agent_team.py at main · agno-agi/agno · GitHub ⭐ 39k

Introducing Agno 2.0 and AgentOS: A High-Performance Runtime for Multi-Agent Systems | Ashpreet B. posted on the topic | LinkedIn

AutoGen​

Autogen Studio​

An web-based UI for prototyping with agents without writing code. Built on AgentChat.

AutoGen Studio is a low-code interface built to help you rapidly prototype AI agents, enhance them with tools, compose them into teams and interact with them to accomplish tasks. It is built on AutoGen AgentChat - a high-level API for building multi-agent applications.

pip install -U autogenstudio
autogenstudio ui --port 8080 --appdir ./myapp

autogenstudio serve --team=team-config.json --port=5000

AutoGen — AutoGen

VertexAI​

Frameworks​

AI Agents / Tools​

AI Agents Landscape