Agent frameworks compared: when to use which framework

The development of applications around large language models (LLMs) has shifted from single prompt chains to complex, multi-step systems that make decisions, call tools, and evaluate their own output. While a simple wrapper suffices for linear interactions, advanced workflows require a robust framework to manage state, error handling, and agent interactions. For a clear understanding of the fundamental theory behind these systems, the overview on the difference between an agent and a chatbot explains why state management and tool use are critical.

The choice of an agent framework largely dictates the reliability, maintainability, and operational costs of an AI system. Choosing the wrong abstraction quickly leads to unpredictable infinite loops, opaque debugging, and exploding token costs. In this article, we compare the five leading open-source and enterprise frameworks: LangGraph, CrewAI, AutoGen (AG2), Microsoft Semantic Kernel, and LlamaIndex Workflows. We examine their architecture, deterministic control, fault tolerance, and the specific scenarios where each package excels.

What defines an agent framework in practice?

An agent framework is more than a collection of functions to send an API call to a language model. It is an orchestration layer that takes on four critical software responsibilities:

• State Management: Consistently maintaining short- and long-term memory, intermediate variables, previous tool responses, and conversation history across multiple interactions.
• Control Flow and Routing: Determining the next step in a process. This can be deterministic (fixed program structure via code) or non-deterministic (where the language model autonomously decides which task or tool comes next).
• Tool Execution & Schema Validation: Formatting machine-readable definitions of functions (such as database queries, calculation modules, or web search tools) and validating the inputs and outputs generated by the model.
• Resilience & Human-in-the-loop: Handling API errors, context overflows, and pausing execution when human approval is required for sensitive actions.

For a broader overview of all libraries and adjacent tooling, you can also consult the general registry of agent and LLM frameworks compared , which catalogs broader developments.

Architectural Models: Directed Graphs versus Multi-Agent Swarms

When designing agent systems, we see two fundamentally different architectural philosophies: graph-based orchestration (Directed Graphs) and role-based conversational swarms (Multi-Agent Swarms).

In a graph-based model (such as LangGraph and LlamaIndex Workflows), the flow is modeled as a network of nodes and edges. Each node performs a well-defined task — such as calling an LLM, validating JSON, or executing a Python function — and mutates a central state. Transitions between nodes can be conditional. This model provides maximum control: the developer defines the rules, guardrails, and error handling in code, while the LLM is only granted freedom within its assigned nodes.

In a role-based swarm model (such as CrewAI and AutoGen), one defines individual agents with their own role, backstory, goals, and set of tools. The orchestrator allows these agents to communicate with each other via a chat interface. Agent A (for example, a researcher) generates text and hands it off to Agent B (a reviewer), who provides feedback until a criterion is met. This model is intuitive to set up and excels in creative brainstorming or simulations, but is notorious for its lack of determinism: agents can get stuck in polite pleasantries, overwrite each other's instructions, or enter infinite loops of discussion that burn through tokens unnecessarily.

The Main Frameworks Dissected

1. LangGraph (LangChain Ecosystem)

LangGraph builds on LangChain, but breaks radically with the linear `RunnableSequence` chains of the past. It introduces cyclic graphs with explicit state management. Each node in the graph receives the current state, executes logic, and returns an update. LangGraph supports built-in persistence (checkpointers via SQLite or PostgreSQL), making time-travel debugging, rolling back erroneous actions, and human-in-the-loop intervention straightforward to implement.

Strengths: Extremely robust, complete control over branching, excellent support for complex cyclic loops and asynchronous tasks.
Weaknesses: Steep learning curve, substantial boilerplate code, tightly coupled with LangChain concepts that can sometimes feel abstract.

2. CrewAI

CrewAI focuses on role-based multi-agent collaboration with a strong emphasis on user-friendliness and rapid prototyping. Developers define Agents, Tasks and a Crew that can operate sequentially or hierarchically. Under the hood, CrewAI handles communication and tool delegation via prebuilt prompts. In recent updates, CrewAI has also introduced Flows, which attempt to add more deterministic control on top of the agents.

Strengths: Very quick to set up, intuitive syntax, excellent for scenarios where different personas need to collaborate (such as content production or competitive analysis).
Weaknesses: Less suitable for strict deterministic business processes; debugging can be difficult when agents make unpredictable assumptions in their prompts.

3. AutoGen / AG2 (Microsoft / Community)

AutoGen, originally developed by Microsoft Research and now further developed within the open-source community, pioneered conversation-driven multi-agent systems. In AutoGen, all agents are essentially ConversableAgentinstances that send messages to one another. The framework supports automated code execution in secure Docker environments and flexible group chats with configurable speaker selection.

Strengths: Powerful capabilities for autonomous code generation and direct execution; highly flexible for research and simulation environments.
Weaknesses: Can be unstable in production environments without strict guardrails; high risk of token spill if group conversations are not tightly moderated.

4. Microsoft Semantic Kernel

Semantic Kernel is Microsoft's enterprise-oriented SDK, available in C#, Python, and Java. It integrates seamlessly with native enterprise architectures, Azure OpenAI, and enterprise identity management. Semantic Kernel treats AI functionality as "plugins" and "native functions" that can be linked together via planners.

Strengths: First-class support for C# and .NET ecosystems, solid type safety, enterprise-grade security, and integration with Microsoft infrastructure.
Weaknesses: The Python variant sometimes lags behind the C# version in terms of community and documentation; less flexible for rapid experimentation than purely Python-focused alternatives.

5. LlamaIndex Workflows

While LlamaIndex started as a specialized data and RAG library, with LlamaIndex Workflows it offers a full-fledged event-driven framework for complex agent architectures. Instead of a static graph, you define steps that listen for specific events and emit new events. This makes the system asynchronous, scalable, and exceptionally well-suited for data-intensive agents that need to query complex RAG pipelines.

Strengths: Seamless integration with advanced retrieval techniques, clean event-driven syntax, minimal overhead.
Weaknesses: Less built-in tooling for multi-agent dialog patterns; requires a solid understanding of event-driven architectures.

Comparison table: technical properties and suitability

The table below compares the key architectural aspects side by side to facilitate a well-reasoned evaluation.

State management and routing: a concrete code example

To illustrate how graph-based control works in practice, the Python example below shows a conceptual implementation of a validation loop in LangGraph. Here, a node evaluates whether the generated output meets predefined quality requirements before it reaches the end user.

from typing import TypedDict, Literal
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    opdracht: str
    concept_tekst: str
    revisies_nodig: int
    is_goedgekeurd: bool

def genereer_concept(state: AgentState) -> dict:
    # Simuleer LLM-generatie op basis van de opdracht
    return {
        "concept_tekst": f"Concept voor: {state['opdracht']}",
        "revisies_nodig": state.get("revisies_nodig", 0) + 1
    }

def valideer_kwaliteit(state: AgentState) -> dict:
    # Controleer of het concept voldoet aan de eisen
    goedgekeurd = state["revisies_nodig"] >= 2
    return {"is_goedgekeurd": goedgekeurd}

def beslis_volgende_stap(state: AgentState) -> Literal["genereer", "__end__"]:
    if state["is_goedgekeurd"] or state["revisies_nodig"] > 3:
        return END
    return "genereer"

# Bouw de graaf op
workflow = StateGraph(AgentState)
workflow.add_node("genereer", genereer_concept)
workflow.add_node("valideer", valideer_kwaliteit)

workflow.set_entry_point("genereer")
workflow.add_edge("genereer", "valideer")
workflow.add_conditional_edges("valideer", beslis_volgende_stap)

app = workflow.compile()

In this pattern, we see why graph-based agents are more reliable than open chat loops: the condition to stop (revisies_nodig > 3) is hard-coded in Python, ensuring the system can never loop indefinitely and incur unnecessary costs.

Error handling, guardrails, and privacy considerations

When agents operate autonomously and invoke external tools, new security and compliance risks emerge. An agent granted unconstrained access to an SQL database or email client can cause irreversible damage in the event of hallucinations.

Because autonomous agents can continuously send context, user input, and payload data to external APIs, it is essential to strictly enforce the guidelines regarding AI models and GDPR privacy compliance with every network call. This includes data minimization in tool outputs and masking personal data before it enters the agent's memory.

Additionally, every production system should be equipped with three lines of defense:

1. Schema validation: Enforce strict validation of tool calls using libraries such as Pydantic before the native function is executed.
2. Human-in-the-loop (HITL): For destructive actions (such as transferring money, deleting records, or sending public messages), implement a mandatory pause where a human supervisor must approve the action.
3. Timeouts and budget limits: Set a hard cap on the number of loops, total execution time, and consumed tokens per session.

Evaluation and monitoring of agent systems

Testing an agent architecture differs fundamentally from traditional software testing. Because the behavior of a language model is non-deterministic, a simple unit test is rarely sufficient. Quantitatively measuring success rates, tool selection accuracy, and trajectory efficiency requires specific methodologies; read more about how to evaluate an AI agent for an in-depth measurement setup with benchmarks and trajectory analysis.

In addition to upfront evaluation, continuous observability in production is essential. By logging every step in the decision graph (including latency per node, prompt tokens, and tool output), anomalies can be detected early before they reach users.

Anyone who also wants to verify how software creators and API providers structure their partner models and referrals can consult the registry of AI referral and affiliate programs to check for potential commercial dependencies and bias in tooling.

Decision tree: which framework fits which situation?

To make a well-considered choice, it helps to evaluate your use case against the following criteria:

• Choose LangGraph if: The application must follow strict business processes, deterministic branching is required, state persistence via databases is essential, and human-in-the-loop approvals are critical.
• Choose CrewAI if: The goal is to quickly build a prototype where different virtual roles (such as researcher, writer, and editor) collaborate on a creative or analytical deliverable.
• Choose Semantic Kernel if: The existing IT infrastructure runs primarily on C# / .NET, enterprise governance and identity management are decisive, and AI needs to be directly integrated into enterprise backends.
• Choose LlamaIndex Workflows if: The agent primarily functions as a data analyst or advanced RAG router over large volumes of unstructured documents and databases.
• Choose AutoGen if: The focus is on autonomous code generation and iterative execution in secure sandbox environments or scientific multi-agent simulations.

Conclusion

There is no universally superior agent framework; the right choice depends entirely on the balance between flexibility and maintainability. While multi-agent swarms are appealing for quick demonstrations, graph- and event-driven architectures dominate production environments due to their predictability and robust error handling.

Overview verified on 2026-08-15. llmnet.nl provides independent technical dossiers free of commercial interests.