Best LLM Agents Architecture Tools (2025)

Building reliable LLM agents requires more than stringing together API calls. You need tool orchestration, memory management, error recovery, planning loops, and often multi-agent coordination. The architecture tools and frameworks you choose determine whether your agents are brittle prototypes or production-grade systems.

This guide reviews the top LLM agent architecture tools available in 2025, comparing their approaches, strengths, and where each fits best.

Key Takeaways

• LangGraph leads for production agents with its stateful graph-based architecture and built-in persistence
• CrewAI is the strongest option for multi-agent collaboration with role-based task assignment
• AutoGen (Microsoft) excels at conversational multi-agent systems with code execution
• SearchHive DeepDive provides the data layer -- real-time web search and scraping that agents need for grounded responses
• Framework choice matters less than architecture patterns -- plan-then-execute, tool-use loops, and human-in-the-loop are universal

1. LangGraph

LangGraph, from the LangChain team, builds agents as directed graphs where nodes are functions and edges define control flow. It supports cycles (for iterative reasoning), conditional branching, and persistent state across turns.

Best for: Production agents that need predictable execution paths, state persistence, and complex multi-step reasoning.

Key features:

• Graph-based agent definition with visual debugging
• Built-in checkpointing and state persistence (SQLite, Postgres, Redis)
• Human-in-the-loop nodes for approval gates
• Streaming support for real-time output
• Subgraph composition for modular agent architectures

Limitations: Steeper learning curve than simpler frameworks. Graph syntax can be verbose for basic agents. Documentation assumes LangChain familiarity.

from langgraph.graph import StateGraph, START, END
import operator
from typing import Annotated, TypedDict

class AgentState(TypedDict):
    query: str
    search_results: Annotated[list, operator.add]
    analysis: str
    iteration: int

def search_node(state: AgentState):
    """Search the web for information using SearchHive."""
    import requests
    resp = requests.post(
        "https://api.searchhive.dev/v1/search",
        headers={"Authorization": "Bearer your_key"},
        json={"query": state["query"], "num_results": 5}
    )
    return {"search_results": resp.json().get("results", [])}

def analyze_node(state: AgentState):
    """Analyze search results and produce final output."""
    results_text = "\n".join(
        f"- {r['title']}: {r['snippet']}" for r in state["search_results"]
    )
    return {"analysis": f"Based on {len(state['search_results'])} sources:\n{results_text}"}

graph = StateGraph(AgentState)
graph.add_node("search", search_node)
graph.add_node("analyze", analyze_node)
graph.add_edge(START, "search")
graph.add_edge("search", "analyze")
graph.add_edge("analyze", END)

agent = graph.compile()
result = agent.invoke({"query": "best web scraping tools 2025", "search_results": [], "analysis": "", "iteration": 0})

2. CrewAI

CrewAI takes a team-based approach to multi-agent systems. You define agents with roles, goals, and backstories, then assign them tasks. Agents collaborate, delegate, and hand off work to each other.

Best for: Teams building multi-agent systems where different specialized agents need to work together on complex tasks.

Key features:

• Role-based agent definition (Researcher, Writer, Analyst)
• Task delegation and handoff between agents
• Built-in memory (short-term and long-term)
• Sequential and hierarchical process flows
• Tool integration via function calling

Limitations: Overhead for simple single-agent use cases. The role/story metaphor can feel forced for technical workflows. Less control over exact execution order compared to LangGraph.

3. AutoGen (Microsoft)

AutoGen, developed by Microsoft Research, focuses on conversational multi-agent interactions. Agents communicate through chat messages and can execute code, use tools, and request human input.

Best for: Research and prototyping conversational agent systems, especially ones involving code generation and execution.

Key features:

• Conversational agent paradigm with message passing
• Built-in code execution sandbox
• Group chat with configurable speaker selection
• Human proxy agent for interactive workflows
• Docker-based code execution isolation

Limitations: Less structured than LangGraph for production use. State management is less explicit. The conversational model doesn't map well to all agent patterns.

4. Semantic Kernel (Microsoft)

Semantic Kernel provides a lightweight, modular approach to building AI agents. It integrates with OpenAI, Azure OpenAI, and local models through a planner-executor pattern.

Best for: Enterprise teams already invested in the Microsoft/Azure ecosystem who need a production-ready agent framework.

Key features:

• Connector ecosystem (OpenAI, Azure, Hugging Face, local models)
• Planner-executor pattern with sequential and stepwise planners
• Native plugin system for tool integration
• Built-in dependency injection
• Strong TypeScript and C# support alongside Python

Limitations: Documentation is uneven across languages. The Azure-centric design can complicate deployment for teams using other cloud providers.

5. Haystack (deepset)

Haystack is an end-to-end framework for building NLP pipelines, with strong agent capabilities built on top of its retrieval and document processing tools.

Best for: RAG-heavy agent systems where document retrieval and processing are core to the agent's functionality.

Key features:

• Pipeline-based architecture with reusable components
• Built-in document stores (Elasticsearch, PostgreSQL, in-memory)
• RAG-optimized retrieval with dense and sparse embeddings
• Agent support with tool calling and conversational memory
• Good documentation with practical examples

Limitations: Best suited for retrieval-heavy use cases. Less flexible than LangGraph for non-RAG agent patterns. The pipeline model doesn't handle cycles as naturally as graph-based frameworks.

6. LlamaIndex

LlamaIndex started as a RAG framework and has evolved into a full agent platform with its "Workflows" feature for multi-step agent orchestration.

Best for: Teams building data-driven agents that need sophisticated retrieval, synthesis, and reasoning over large document collections.

Key features:

• Advanced RAG with query routing, sub-question decomposition, and document summarization
• Workflow engine for multi-step agent orchestration
• 200+ data connectors for ingestion
• Agent capabilities with tool use and planning
• Strong integration with vector databases

Limitations: The framework has grown large, making it hard to know which components to use. Documentation sprawl across multiple concepts can be confusing for newcomers.

7. Smolagents (Hugging Face)

Smolagents is Hugging Face's lightweight agent framework. It focuses on code-generating agents that write and execute Python code as their primary action.

Best for: Developers who want minimal framework overhead and prefer agents that write code rather than use rigid tool schemas.

Key features:

• Code-generation-first approach (agents write Python to solve tasks)
• Minimal dependencies -- ships as a small pip package
• Managed and local model support
• Tool creation via simple Python function decorators
• Built-in memory and logging

Limitations: The code-generation approach requires careful sandboxing. Less structured than graph-based frameworks for predictable production workflows. Limited multi-agent support.

8. Pydantic AI

Pydantic AI, from the Pydantic team, brings type-safe agent development to Python. Agents define their inputs and outputs as Pydantic models, and the framework handles validation and structured responses.

Best for: Teams that prioritize type safety, structured outputs, and developer experience in their agent systems.

Key features:

• Type-safe agent definitions using Pydantic models
• Built-in structured output validation
• Model-agnostic (OpenAI, Anthropic, Google, local models)
• Dependency injection for tool configuration
• Clean, Pythonic API design

Limitations: Newer framework with a smaller ecosystem. Less battle-tested in production compared to LangGraph or AutoGen.

The Data Layer: SearchHive DeepDive and SwiftSearch

Agent frameworks handle orchestration, but agents need access to real-time data to be useful. SearchHive provides two APIs that plug into any agent architecture:

• SwiftSearch: Real-time web search API for retrieving current information. Agents call SwiftSearch to find relevant sources, then reason over the results.
• DeepDive: Multi-source research API that searches, scrapes, and synthesizes information from multiple pages into a structured report. Agents use DeepDive for complex research tasks that require reading multiple sources.

import requests

API_KEY = "your_searchhive_api_key"

# SwiftSearch: fast web search for agent tool use
def web_search(query: str) -> list:
    resp = requests.post(
        "https://api.searchhive.dev/v1/search",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"query": query, "num_results": 5}
    )
    return [{"title": r["title"], "url": r["url"], "snippet": r["snippet"]}
            for r in resp.json().get("results", [])]

# DeepDive: multi-page research for complex queries
def deep_research(query: str) -> dict:
    resp = requests.post(
        "https://api.searchhive.dev/v1/deepdive",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"query": query, "max_pages": 5}
    )
    return resp.json()

# Use in any framework -- LangGraph tool, CrewAI tool, AutoGen function
def search_tool(state):
    results = web_search(state["query"])
    return {"context": results}

Comparison Table

Our Recommendation

For production agent systems in 2025, LangGraph is the strongest choice due to its explicit control flow, state persistence, and human-in-the-loop support. If you're building multi-agent collaboration systems, CrewAI provides the cleanest abstractions for role-based team workflows.

Pair your framework with SearchHive DeepDive for the data layer. Agents that can't access current information are limited to their training data. DeepDive gives agents real-time web research capabilities through a simple API call -- no framework lock-in, no complex configuration.

Get started with SearchHive's free tier -- 500 API credits per month. The developer documentation includes integration guides for LangGraph, CrewAI, and other frameworks. For more on building data-driven applications, see /compare/firecrawl and /blog/complete-guide-to-automation-for-data-collection.