Analysis and Comparison of AI Agent Frameworks: From Fundamentals to Multi-Agent Systems

This article is based on an analysis conducted during Addepto’s technical webinar, discussing the architecture and comparison of six leading AI agent frameworks: OpenAI Agents SDK, Google’s agent frameworks, Agnos, CrewAI, Pydantic AI, and LangGraph.

TL;DR

• AI has evolved from ML and DL to LLM-driven agentic systems, enabling memory, multi-step reasoning, and increasingly complex multi-agent architectures.
• Six leading frameworks: OpenAI Agents SDK, Google’s agents, Agnos, CrewAI, Pydantic AI, and LangGraph, were analyzed for architecture, capabilities, and enterprise readiness.
• Reliable agent frameworks must provide strong orchestration, error recovery, state management, safety guardrails, and ideally testing + observability support.
• Most frameworks rely on the ReAct pattern for reasoning and tool use, but single-agent systems degrade as responsibilities grow, requiring sub-agents or structured workflows.
• Multi-agent structures (e.g., Crews/Teams) and workflow-based orchestration enable scalable, deterministic pipelines with shared state and better coordination.
• LangGraph stands out with a low-level, state-centric orchestration model offering maximum flexibility, while high-level frameworks like Agnos and CrewAI excel in rapid prototyping.

Artificial intelligence is evolving at an unprecedented pace. After progressing through the eras of Machine Learning (ML) and Deep Learning (DL), today’s innovation is driven primarily by Large Language Models (LLMs).

In parallel, Natural Language Processing (NLP) has undergone its own transformation: from rule-based and statistical approaches, through vector embeddings, to the breakthrough Transformer architecture that fundamentally reshaped how machines understand language.

Building on these advances, the emergence of memory mechanisms, multi-step reasoning, and Retrieval-Augmented Generation (RAG) has enabled the rise of agentic systems, capable of taking actions, using tools, decomposing complex tasks, and orchestrating sophisticated business workflows. What began as single autonomous agents has now matured into powerful, interconnected multi-agent architectures.

What’s the difference between AI Agent and AI Agent Framework

An AI agent is the actor: a software component powered by an LLM that can reason, make decisions, call tools, and execute tasks. It behaves like a digital employee that performs work: interpreting goals, retrieving information, coordinating steps, and producing outputs. Agents are the runtime units that handle logic, autonomy, and workflow execution.

An AI agent framework, by contrast, is the infrastructure that enables you to design, build, manage, and scale those agents. It provides:

• architectural patterns and abstractions,
• components for memory, state, and orchestration,
• error handling, safety, and evaluation tools,
• interfaces for connecting agents to APIs, databases, and business systems,
• workflows and coordination mechanisms for multi-agent setups.

Today, the AI agent framework landscape is vast and diverse. Dozens of solutions exist, each promising to simplify the development, deployment, and management of agentic systems. In this article, we analyse six representative frameworks:

• OpenAI Agents SDK
• Google frameworks (Rules ADK, CustomAgent patterns)
• Agnos
• CrewAI
• Pydantic AI
• LangGraph

This selection reflects a desire to review frameworks created by industry leaders, offering diverse architectural philosophies, strong community adoption (e.g., on GitHub), and readiness for enterprise-grade production use.When Should You Use an AI Agent Framework

Now that we understand what an AI agent is and how it differs from an AI agent framework, the next question naturally arises: why do we need these capabilities in the first place?

The potential applications are vast. Agents can automate customer support, orchestrate data pipelines, execute research workflows, manage internal operations, generate content, analyze documents, and coordinate entire business processes. Frameworks extend these possibilities by enabling multi-agent collaboration, long-term memory, complex tool use, and reliable integration with enterprise systems.

With so many opportunities, it’s easy to feel overwhelmed or unsure where to begin. This is why it is often wise to start simple.

Adopting a framework introduces new architectural concepts and mental models, which take time to understand. Yet the effort pays off: frameworks provide proven design patterns, robust foundations, and reusable components that significantly improve reliability, maintainability, and scalability.

Evaluating and comparing frameworks also reveals alternative approaches, helping teams choose the most suitable solution and accelerating overall development. 

Essential Features of a Reliable AI Agent Framework

For an agentic system to be dependable, the framework must offer key features:

• Flow control and orchestration – defining how the agent progresses through tasks and interacts with business logic.
• Error recovery – the ability to retry, repair, or take alternative execution paths.
• Environment interaction – performing operations such as API calls or database queries.
• State management – tracking context, session history, and current execution state.
• Safety mechanisms – rules, guardrails, loops, and stop criteria that ensure safe and controlled execution.

Moreover, modern enterprise systems often require more than a single agent. Advanced frameworks therefore support:

• multi-agent coordination, enabling agents to collaborate or delegate tasks,
• Human-in-the-Loop checkpoints, allowing human oversight during critical decisions,
• evaluation and testing tools, ensuring predictable behavior across deployments,
• observability and analytics, giving teams insight into execution paths, performance, and failures.

Notably, among all reviewed frameworks, Pydantic AI and LangGraph are the only ones that offer built-in unit testing for subcomponents, enabling teams to validate small pieces of logic before integrating them into larger workflows.

Observability, in turn, is most commonly delivered through integrations with external platforms such as Langfuse or LangSmith, typically via OpenTelemetry, which provides standardized tracing and monitoring capabilities.

Read more: Understanding AI Agents: A Guide to AI Agentic Workflow

How AI Agents Execute Tasks and Maintain Autonomy

Except LangGraph, nearly every analyzed framework defines an Agent class responsible for giving the system autonomy and nondeterministic behavior.

Agent behavior is controlled by:

• Instructions and context – goal, scope, constraints, tone
• Model selection and parameters – specifying the LLM and its settings
• Output rules – e.g., Pydantic schemas, markdown formatting
• Tools and external knowledge sources
• Pre- and post-hooks, guardrails – for validation, error handling, and self-correction

Agents can even call other agents by wrapping an agent as a tool, as seen in Google’s Rules ADK.

Frameworks automatically assemble the LLM prompt by adding context, instructions, and output schemas. Understanding which elements are injected automatically is crucial – these additions can improve or degrade output quality.

Additional elements shown, for example, in Agnos include:

• Tool preparation – selecting tools based on session state
• Optional reasoning step – generating plans or checklists before execution
• Session summarization – compressing long context windows

The Role of ReAct in AI Agent Behavior and Tool Use

All five frameworks that use an Agent class (except LangGraph) rely on the ReAct pattern (Reason + Act).

The loop consists of:

• LLM performs reasoning
• If tool calls are included, tools are executed
• Tool results become input for subsequent reasoning

This iterative loop continues until the model produces a final answer. ReAct is popular due to native support in modern LLMs, iterative self-correction, and conceptual simplicity. In many cases, ReAct outperforms complex upfront planning.

Limitations of Single Agents and How to Scale with Sub-Agents

A single agent is fundamentally a well-crafted prompt enhanced with ReAct, tool use, and hooks. However:

• The ReAct loop cannot easily be replaced
• Too many instructions, tools, or responsibilities cause degradation in reasoning
• Agents begin choosing incorrect tools or misinterpreting instructions

To manage complexity, developers rely on:

• Sub-agents
• Hooks
• Checklist-style tools
• Custom agent classes (e.g., Google’s CustomAgent)
• Orchestration and Multi-Agent Architectures Explained

To overcome the limitations of single agents, frameworks introduce multi-agent architectures. It’s a system in which several autonomous agents collaborate to solve complex tasks. Instead of one agent doing everything, responsibilities are divided across specialized roles, similar to a cross-functional business team.

This architecture provides:

• Shared system state and memory enabling agents to build on each other’s outputs,
• Parallelism for faster execution and diverse reasoning,
• Coordination mechanisms such as managers, routers, or schedulers,
• Improved reliability by distributing responsibilities across simpler, focused agents.

Implemented in Agnos and CrewAI, this structure includes a manager agent overseeing multiple specialized sub-agents.

These systems offer:

• Shared unified system state, memory, and knowledge
• Parallel execution of specialists, useful for brainstorming or distributed tasks
• This differs from simple agent-calling-agent patterns by providing shared context and coordination primitives.
• The workflow of a multi-agent system

Workflows compose agents into deterministic execution pipelines. Unlike ReAct loops, workflows always follow a predefined order. Each step may contain agents, teams, or custom logic.

Common workflow patterns include:

• Graphs (Pydantic AI’s Pydantic graphs)
• Sequential, parallel, loops, conditional, and routing structures
• Composite advanced patterns

Workflows share execution state, typically stored as dictionaries or Pydantic classes, enabling:

• Step-level unit testing
• Structured reasoning
• Post-hooks for validation and automatic correction

Read more: LangChain vs. LlamaIndex: Strategic Framework Selection Guide for AI Leaders

LangGraph vs. Traditional Agent Frameworks

LangGraph takes a different path: instead of orchestrating agents, it orchestrates state transformations. It controls how data flows and evolves.

LangGraph does not have an Agent class, but integrates tightly with LangChain. The LangChain helper create_agent is essentially a graph-based ReAct implementation within LangGraph, offering:

• Conversation summarization
• Recovery patterns
• Hooks and guardrails

Its main advantage: if the built-in create_agent function is insufficient, you can drop to low-level graph primitives and build custom logic. Also, LangGraph supports multi-agent patterns through subgraphs, which can share or isolate state.

Trade-offs: Unlike other frameworks that bundle many features into an Agent class, LangGraph requires manually composing these features. This gives tremendous flexibility but results in a steeper learning curve.

Choosing the Right AI Agent Framework

There is no universal “best” framework. The optimal choice depends entirely on project requirements. However, the recommended implementation pathways show:

• Minimalist approach: If speed is critical or LLM calls must be minimized, you can skip complex patterns like ReAct.
• High-level frameworks (Agnos, CrewAI): Ideal for chat systems requiring user interaction and for projects with multiple simple, specialized agents (e.g., helpdesk technician, marketing researcher). These frameworks provide powerful built-in components (context management, session summarization, memory) and are excellent for rapid prototyping.
• LangGraph or custom architecture: When high-level frameworks reach their limits, LangGraph provides the granular control needed for complex systems.

Regardless of the framework, one principle is key: “If you use a framework, make sure you understand the underlying source code.” A deep understanding of the internals is essential for building reliable agentic systems.