The State of AI Agent Frameworks in 2025

The AI agent landscape has evolved rapidly. Here’s our analysis of the major frameworks and what they mean for developers building production AI systems.

The Big Picture

graph TB
    subgraph Orchestration
        LG[LangGraph]
        CA[CrewAI]
        AG[AutoGen]
    end

    subgraph Memory
        M0[Mem0]
        LT[Letta]
        ZP[Zep]
    end

    subgraph Observability
        LF[Langfuse]
        HL[Helicone]
    end

    subgraph Inference
        VL[vLLM]
        LC[llama.cpp]
    end

    Orchestration --> Memory
    Orchestration --> Observability
    Orchestration --> Inference

Framework Comparison

LangGraph

Graph-based state machines with explicit control flow. Best for complex workflows requiring fine-grained state management.

CrewAI

Role-based multi-agent collaboration. Powers 60% of Fortune 500 AI deployments with its intuitive crew metaphor.

AutoGen

Microsoft’s message-passing architecture. Merging with Semantic Kernel for Q1 2026 GA.

Haystack

Pipeline-based NLP from deepset. Excels at search, RAG, and document processing workflows.

When to Use Each

Choose LangGraph when:

You need explicit state machine control
Complex branching logic is required
You want LangChain ecosystem integration

from langgraph.graph import StateGraph

graph = StateGraph(AgentState)
graph.add_node("research", research_agent)
graph.add_node("write", writing_agent)
graph.add_edge("research", "write")

Choose CrewAI when:

Tasks map naturally to roles (researcher, writer, reviewer)
You want quick prototyping (ship in under 2 weeks)
Sequential or hierarchical execution fits your use case

from crewai import Agent, Crew, Task

researcher = Agent(role="Researcher", goal="Find data")
crew = Crew(agents=[researcher], tasks=[...])

Choose AutoGen when:

You need auditable, observable agent interactions
Human-in-the-loop is critical
You’re building for Azure/enterprise environments

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent("assistant")
user = UserProxyAgent("user")

Key Developments in 2025

Microsoft Agent Framework

The biggest news is Microsoft merging AutoGen with Semantic Kernel into a unified Microsoft Agent Framework:

GA Expected: Q1 2026
Languages: C#, Python, Java
Features: Production SLAs, multi-language support, deep Azure integration

This signals enterprise commitment to standardized agent development.

CrewAI Enterprise Growth

CrewAI raised $18M and now powers agents for 60% of Fortune 500 companies. However, its opinionated design can be limiting as requirements grow beyond sequential/hierarchical execution.

Haystack Voice Support

Haystack Agents now extend multimodal capabilities to support both text and voice workflows, positioning it for the growing voice-first agent market.

Our Recommendations

Use Case	Recommendation
Quick prototype with clear roles	CrewAI
Complex stateful workflows	LangGraph or DuraGraph
Enterprise with Azure	Wait for Microsoft Agent Framework
Search/RAG applications	Haystack
Self-hosted production	DuraGraph