DEV Community

Hanieh Zahiremami
Hanieh Zahiremami

Posted on

Agentic AI Frameworks Comparison 2025: mcp-agent, LangGraph, AG2, PydanticAI, CrewAI

#ai #python #mcp #agents

Technical comparison of MCP-native and traditional agentic frameworks with production considerations for building AI agents

Framework Selector

Use Case Framework Why
MCP-native development mcp-agent Built for MCP from day one
Visual debugging LangGraph Studio with time-travel debugging
Multi-agent conversations AG2 Agents coordinate autonomously
Type safety PydanticAI Full Pydantic validation
Rapid prototyping CrewAI No-code Studio interface

1. mcp-agent

GitHub: lastmile-ai/mcp-agent | Python

Python framework built for Model Context Protocol. Native MCP implementation, not an adapter.

Key Features:

  • Native MCP implementation - Full protocol support (tools, resources, prompts, notifications, OAuth)
  • Automatic durable execution - Switch to Temporal in one config line, no manual checkpointing
  • Cloud deployment - One command deploys to managed infrastructure
  • Battle-tested patterns - Direct implementation of Anthropic's Building Effective Agents

Code:

from mcp_agent import Agent, MCPApp

# Temporal-backed durability in one line
app = MCPApp(execution_engine="temporal")

agent = Agent(
    name="researcher",
    server_names=["brave-search", "filesystem"],
    instruction="Research and compile reports"
)
Enter fullscreen mode Exit fullscreen mode

Use when:

  • Building for MCP ecosystem
  • Need durable execution without setup complexity
  • Want Temporal reliability for long tasks
  • Prefer Python over graph DSLs

Skip if: Need visual debugging tools

2. LangGraph

GitHub: langchain-ai/langgraph | Python, JavaScript

Graph-based orchestration with visual debugging.

Key Features:

  • LangGraph Studio - Visual debugging with time-travel
  • Graph architecture - Nodes, edges, conditional routing
  • Checkpointing - Built-in state persistence
  • LangSmith integration - Production observability

Code:

from langgraph.graph import StateGraph

workflow = StateGraph(AgentState)
workflow.add_node("research", research_function)
workflow.add_node("analyze", analyze_function)

workflow.add_conditional_edges(
    "research",
    should_continue,
    {"analyze": "analyze", "end": END}
)

app = workflow.compile()
Enter fullscreen mode Exit fullscreen mode

Use when:

  • Need visual debugging of workflow execution
  • Building complex branching logic
  • Team values explicit state management

Skip if: Simple workflows (overkill)

3. AG2 (formerly AutoGen)

GitHub: ag2ai/ag2 | Python

Conversational multi-agent framework. Community fork of AutoGen 0.2 by original creators.

Key Features:

  • Conversational coordination - Agents communicate to solve problems
  • Group chats - Multiple agents collaborate
  • AutoGen Studio - No-code interface
  • Human-in-the-loop - Easy integration

Code:

from autogen import ConversableAgent

coder = ConversableAgent(
    name="coder",
    system_message="You write Python code",
    llm_config=llm_config
)

reviewer = ConversableAgent(
    name="reviewer",
    system_message="You review code quality"
)

result = coder.initiate_chat(reviewer, message="Build a REST API")
Enter fullscreen mode Exit fullscreen mode

Use when:

  • Multi-agent conversations fit your use case
  • Want autonomous coordination
  • Need no-code interface

Skip if: Need deterministic workflow control

4. PydanticAI

GitHub: pydantic/pydantic-ai | Python

Type-safe agents with Pydantic validation. Every input/output is schema-validated.

Key Features:

  • Full type safety - Static type checking
  • Model agnostic - 20+ model providers
  • Structured outputs - Schema compliance guaranteed
  • MCP support - Built-in protocol integration
  • Logfire integration - Real-time observability

Code:

from pydantic import BaseModel
from pydantic_ai import Agent

class SearchResult(BaseModel):
    title: str
    url: str
    relevance_score: float

agent = Agent('openai:gpt-4', result_type=SearchResult)
result = agent.run_sync('Search AI frameworks')
# result.data is guaranteed SearchResult
Enter fullscreen mode Exit fullscreen mode

Use when:

  • Type safety prevents production bugs
  • Building systems requiring schema compliance
  • Team uses FastAPI/Pydantic

Skip if: Type safety not a priority

5. CrewAI

GitHub: crewAIInc/crewAI | Python

Orchestrates agents through Crews (autonomous) and Flows (event-driven).

Key Features:

  • Crews & Flows - Autonomous and precise control
  • CrewAI Studio - No-code visual builder
  • Built-in observability - Monitoring and metrics
  • Deep customization - Modify behaviors and prompts

Code:

from crewai import Agent, Task, Crew

researcher = Agent(
    role='Market Research Analyst',
    goal='Provide market analysis',
    tools=[search_tool]
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task],
    process=Process.sequential
)

result = crew.kickoff()
Enter fullscreen mode Exit fullscreen mode

Use when:

  • Need both autonomous agents AND workflow control
  • Want rapid prototyping
  • Team includes non-technical members

Skip if: Need MCP-native architecture

Feature Comparison

Feature mcp-agent LangGraph AG2 PydanticAI CrewAI
MCP Native ✅ Integration Integration ✅ Limited
Durability Automatic Configure Manual Built-in Manual
Visual Debug Temporal UI ✅ Studio Studio None Studio
Setup Low Medium Medium Low Low
Type Safety Standard Standard Standard ✅ Full Standard
No-Code None Studio Studio None ✅ Studio

Decision Framework

By Use Case:

Building with MCP? → mcp-agent (native) or PydanticAI (built-in support)

Need visual debugging? → LangGraph Studio (best-in-class)

Conversational agents? → AG2 (natural communication)

Type safety critical? → PydanticAI (Pydantic validation)

Rapid prototyping? → CrewAI Studio

By Technical Requirement:

Durable Execution:

Simplest: mcp-agent

app = MCPApp(execution_engine="temporal")  # Done
Enter fullscreen mode Exit fullscreen mode

More control: LangGraph

from langgraph.checkpoint.sqlite import SqliteSaver
checkpointer = SqliteSaver.from_conn_string(":memory:")
app = workflow.compile(checkpointer=checkpointer)
Enter fullscreen mode Exit fullscreen mode

DIY: AG2, CrewAI

Complex Workflows:

  • Graph-based: LangGraph (explicit control)
  • Code-based: mcp-agent (clean Python)
  • Conversational: AG2 (autonomous coordination)

By Team:

  • Small startup: mcp-agent (less boilerplate) or CrewAI (rapid iteration)
  • Enterprise: LangGraph (mature observability) or mcp-agent (simpler architecture)
  • Non-technical stakeholders: CrewAI Studio or AutoGen Studio

Why MCP Matters

Model Context Protocol launched late 2024. Adoption accelerating:

Ecosystem:

  • Anthropic (creator) - built into Claude
  • Microsoft - integrated into Azure AI
  • Cursor, VS Code - MCP support shipped
  • Hundreds of MCP servers available

Native vs Adapter:

Frameworks built for MCP (mcp-agent, PydanticAI) work directly with the protocol. When Anthropic releases new MCP capabilities, they work immediately.

Frameworks that added MCP later use adapter layers with potential compatibility issues.

Practical Benefit:

Hundreds of MCP servers exist for filesystems, databases, APIs, internal tools. Native MCP support means you use them all without integration code.

Production Considerations

Observability:

What you need: Token usage and costs, latency and performance, tool calls and errors, decision paths.

Available tools:

  • LangSmith (LangGraph) - comprehensive tracing
  • Logfire (PydanticAI) - real-time monitoring
  • Temporal UI (mcp-agent) - execution visibility
  • Built-in (CrewAI) - monitoring and metrics

Summary

For MCP-native development: mcp-agent (designed for it) or PydanticAI (built-in)

For visual debugging: LangGraph Studio

For type safety: PydanticAI

For conversational agents: AG2

For rapid prototyping: CrewAI

The MCP Advantage:

If starting fresh in 2025, MCP support matters:

  • Less adapter code
  • Easier debugging
  • Better interoperability
  • Future-proof as protocol evolves

mcp-agent and PydanticAI are built for MCP. Others added it through integration layers.

Getting Started

Top comments (0)