Navigating the Agent Framework Maze

Analysis of Framework Architectures, Capabilities, and Multi-Agent Dynamics

Comprehensive Analysis of Major Agent-Building Frameworks

An in-depth comparison of architectures and capabilities with special focus on multi-agent systems

We examine 14 leading agent-building frameworks, comparing their architectural approaches, core components, and multi-agent capabilities to help developers select the right framework for their specific needs.

Overview

Agent-building frameworks provide developers with the tools to create autonomous AI systems that can reason, plan, and execute tasks. These frameworks vary significantly in their architectures, capabilities, and approaches to multi-agent orchestration. This analysis focuses on architectural design patterns, component structures, and multi-agent capabilities across major frameworks in the ecosystem.

Modern agent frameworks generally follow one of several architectural patterns:

• Graph-based orchestration: Frameworks where agent workflows are organized as directed graphs of nodes (LangGraph, Flowise)

• Agent-centric messaging: Systems that prioritize message passing between autonomous agents (AutoGen, Langroid)

• Modular component architecture: Frameworks that emphasize reusable, pluggable components (Semantic Kernel, Atomic Agents)

• Hierarchical team structures: Solutions supporting agent hierarchies with varying roles (Swarms, CrewAI)

• Code-centric execution: Lightweight approaches where generated code drives agent behavior (Smolagents)

Framework Comparison Matrix

Multi-Agent System Capabilities

Multi-agent capabilities vary significantly across frameworks, from basic support to sophisticated orchestration systems. The following table focuses specifically on how each framework approaches multi-agent coordination.

Detailed Framework Analysis

LangGraph

LangGraph extends LangChain with a graph-based architecture for building stateful, controllable agent workflows. It uses a Pregel-based execution model to manage graph nodes and state transitions.

Architecture Components:

• StateGraph: Core API for defining computational graphs

• Pregel Runtime: Execution engine based on Pregel model

• Functional API: Decorator-based interface (@entrypoint, @task)

• Checkpointing: State persistence system

• LangGraph Server: Deployment runtime with REST API

• LangGraph Studio: Visual debugging interface

Multi-Agent Capabilities:

LangGraph enables the development of complex multi-agent systems through its stateful graph architecture. Agents can be represented as nodes in the graph with explicit state transitions and communication paths.

Key Features:

• Thread-level persistence and time travel capabilities allow for branching conversations and revisiting previous states, crucial for complex agent interactions.

• Streaming support at multiple levels (token, intermediate steps, specific nodes) allows for real-time observation of multi-agent communication.

• Human-in-the-loop capabilities enable manual intervention in agent workflows, providing a safety mechanism for multi-agent systems.

CrewAI

CrewAI is a lean, high-performance Python framework for orchestrating autonomous AI agents, designed independently of LangChain. It offers developers both simplicity and fine-grained control. CrewAI enables the creation and management of collaborative AI agent teams ("Crews") with specialized roles working autonomously, or through event-driven "Flows" for more control. The framework optimizes for both autonomy and predictability in handling complex tasks.

Architecture Components:

• Crew: orchestrates the collaboration between agents and the execution of tasks

• Agent: defines role, goal, tools, LLM config, allow_delegation capability.

• Task: unit of work with description, expected output, assigned agent.

• Process:

  • Sequential Process: Tasks are executed in order, potentially building on the results of previous tasks.

  • Hierarchical Process: A manager agent delegates tasks to specialized agents based on their expertise.

Flows (Alternative Approach)

CrewAI's Flow system provides an alternative to Crews, offering finer, event-driven control over execution paths along with explicit state management.

Multi-Agent Capabilities:

CrewAI is explicitly designed for multi-agent collaboration through structured crews. Agents with specialized roles work together following a defined process.

Key Features:

• Role-playing agents allow for specialized task handling.

• Defined processes (Sequential, Hierarchical) provide structured workflow control.

• Task delegation enables complex problem decomposition.

• Focus on autonomous collaboration towards a common goal.

Flowise

Flowise is a low-code framework for building AI workflows with a visual, drag-and-drop interface. It features a pluggable architecture with components for LLM integrations, memory systems, and tools.

Architecture Components:

• Server: Node.js backend for API endpoints and flow execution

• UI: React-based frontend with visual flow builder

• Components: Collection of node types and integrations

• Chatflows: Directed graphs for processing inputs and outputs

• Memory System: Flexible storage for conversation history

• Queue System: Redis/BullMQ for high-throughput processing

Multi-Agent Capabilities:

Flowise supports multi-agent workflows through its component system, allowing developers to create complex flows where multiple agent nodes can be connected and orchestrated in the visual editor.

Key Features:

• Directed graph execution enables complex multi-step, multi-agent workflows with clear data flow between components.

• Scalability via queue-based execution allows for handling multiple agent instances across distributed systems.

Langroid

Langroid provides intuitive abstractions for building LLM applications with a focus on modularity. It uses an Agent-Task architecture inspired by the Actor Framework.

Architecture Components:

• Agent: Message transformers with LLM conversation state

• Task: Orchestration of agents and workflow management

• Responder Methods: llm_response, agent_response, user_response

• Specialized Agents: ChatAgent, DocChatAgent, SQLChatAgent, etc.

• Task Loop: Manages turn-taking between responders

• Control Flow: Tools for managing task execution

Multi-Agent Capabilities:

Langroid enables robust multi-agent systems through hierarchical task delegation and addressable messaging. Agents can collaborate by exchanging messages and delegating subtasks.

Key Features:

• Hierarchical, recursive task delegation allows complex problems to be broken down and distributed among specialized agents.

• Addressable messaging system enables targeted communication between specific agents in multi-agent workflows.

• Control flow tools (DoneTool, PassTool, SendTool) provide mechanisms for coordinating task completion and agent communication.

Smolagents

Smolagents is a lightweight library for building autonomous agents powered by Large Language Models (LLMs). It focuses on code generation and execution rather than complex orchestration.

Architecture Components:

• Agent Classes: Coordinate reasoning and action cycles

• Model Interfaces: Abstraction for different LLMs

• Tool System: Interface for agent-tool interaction

• Agent Memory: Storage for interaction history

• Code Executors: Sandboxed environments for code execution

• Monitoring: System for logging and debugging

Multi-Agent Capabilities:

Smolagents is primarily focused on single-agent architectures, with limited explicit support for multi-agent systems. However, individual agents can be programmatically combined for more complex workflows.

Key Features:

• Implements the ReAct (Reasoning and Acting) pattern for systematic problem-solving through reasoning about tasks before taking actions.

• Strong focus on code generation and secure execution allows agents to solve tasks through dynamic code rather than predefined workflows.

• Modality-agnostic design supports text, vision, and audio inputs, enabling versatile agent capabilities.

Semantic Kernel

Semantic Kernel is Microsoft's model-agnostic SDK for building, orchestrating, and deploying AI agents and multi-agent systems with a focus on enterprise applications.

Architecture Components:

• Kernel: Central orchestrator coordinating all components

• Plugins: Collections of related functions

• Functions: Semantic, native, and OpenAPI functions

• Memory System: Semantic search using vector embeddings

• Agent System: Framework for single and multi-agent systems

• AI Service Connectors: Integrations with various AI providers

Multi-Agent Capabilities:

Semantic Kernel provides a unified agent framework for creating both single agents and multi-agent systems with specialized agents that collaborate to solve complex problems.

Key Features:

• The agent system allows constructing multiple specialized agents with different capabilities and access to specific plugins.

• Cross-platform design supports building consistent multi-agent systems across multiple programming languages (C#, Python, Java).

• Semantic memory system enables agents to store and retrieve information based on meaning, facilitating knowledge sharing between agents.

Atomic Agents

Atomic Agents is a lightweight, modular framework built around the concept of atomicity—creating small, focused components that can be combined into complex systems with predictable behavior.

Architecture Components:

• BaseAgent: Coordinates execution, prompting, memory management

• BaseIOSchema: Foundation for input/output data structures

• AgentMemory: Manages conversation history and state

• SystemPromptGenerator: Creates structured prompts for LLMs

• ContextProviders: Dynamically inject information at runtime

• BaseTool: Foundation for executing external functionality

Multi-Agent Capabilities:

Atomic Agents focuses on building robust, controllable agent pipelines rather than fully autonomous multi-agent systems. Multiple atomic agents can be composed into larger applications.

Key Features:

• Strong emphasis on structured inputs and outputs through Pydantic models ensures reliable communication between agent components.

• Multi-provider support allows seamless switching between different LLM providers with minimal code changes.

• Tool ecosystem with standardized input/output schemas enables modular extension of agent capabilities.

Agno

Agno is a framework for building reasoning agents, multimodal agents, and teams of agents with multiple coordination modes and first-class reasoning capabilities.

Architecture Components:

• Agent: Core entity with model, tools, memory, and knowledge

• Team System: Enables multiple agents to work together

• Memory: Short-term and long-term memory capabilities

• Knowledge: Storage and retrieval of information

• Tools: Extensions to enhance agent capabilities

• Instructions: Guidelines for agent behavior

Multi-Agent Capabilities:

Agno's Team system is specifically designed for multi-agent collaboration with three distinct coordination modes for different types of team interaction.

Key Features:

• Route Mode directs requests to the most appropriate specialist agent, optimizing resource allocation in multi-agent teams.

• Collaborate Mode enables multiple agents to work together with shared context, facilitating collaborative problem-solving.

• Coordinate Mode distributes subtasks across different agents, allowing parallel execution of complex workflows.

PydanticAI

PydanticAI is an agent framework that brings the ergonomic developer experience of FastAPI to generative AI development with type-safe agent definitions and structured data validation.

Architecture Components:

• Agent: Generic class parameterized with dependency and output types

• Model Integration Layer: Unified interface to LLM providers

• Tools System: Function-based tools with schema generation

• Graph System: Node-based execution model for workflows

• Evaluation Framework: Tools for testing agent performance

• Observability: Integration with Pydantic Logfire

Multi-Agent Capabilities:

PydanticAI supports multi-agent architectures through its graph system, which enables building complex workflows using typed nodes and edges for agent communication.

Key Features:

• Typed nodes and edges ensure reliable communication between agents in multi-agent workflows.

• State management and persistence allows maintaining context across agent interactions.

• Visualization capabilities through Mermaid diagrams help developers understand and debug multi-agent interactions.

Mastra

Mastra is a TypeScript framework for building AI applications, centered around a main orchestrator class that coordinates multiple subsystems for a complete development platform.

Architecture Components:

• Mastra Class: Central orchestrator managing all components

• Agent: AI entities interacting with language models

• Workflows: Orchestration of agent interactions

• Storage: Persistence of data and state

• Memory: Conversation and knowledge storage

• Telemetry: Logging and monitoring

Multi-Agent Capabilities:

Mastra supports multi-agent systems through its orchestrator architecture, allowing registration and coordination of multiple agents in complex workflows.

Key Features:

• The central Mastra class can register and retrieve multiple agents, facilitating multi-agent coordination.

• Workflow system enables composition of multiple agents into graph-based, stateful workflows with branching and parallel execution.

• Memory systems allow agents to share information through persistent and semantic memory, enhancing multi-agent collaboration.

AutoGen

AutoGen is a framework for building autonomous multi-agent AI applications with a flexible, modular architecture based on message passing between collaborating agents.

Architecture Components:

• Core API: Message passing infrastructure and event-driven architecture

• AgentChat API: Pre-built agent types and team coordination

• Extensions API: Model clients, code execution, specialized agents

• Developer Tools: AutoGen Studio and AutoGen Bench

• Agent Types: AssistantAgent, UserProxyAgent, specialized agents

• Team Coordination: Different mechanisms for agent interaction

Multi-Agent Capabilities:

AutoGen is specifically designed for multi-agent collaboration, with team coordination systems enabling different patterns of agent interaction and communication.

Key Features:

• RoundRobinGroupChat implements a simple turn-taking mechanism where agents speak in a fixed order, providing predictable conversation flows.

• SelectorGroupChat uses an LLM to dynamically select the next speaker based on context, creating adaptive, situation-aware agent conversations.

• Configurable termination conditions allow precise control over when multi-agent conversations should end, based on specific phrases or message counts.

LlamaIndex

LlamaIndex is a data framework designed to connect Large Language Models with external data sources, with component packages for building autonomous agents using its data capabilities.

Architecture Components:

• Data Connectors: Tools to ingest data from various sources

• Data Structuring: Methods to parse, chunk, and index data

• Retrieval Interfaces: Components to retrieve relevant context

• Response Synthesis: Generate knowledge-augmented responses

• Agent Frameworks: Packages for building autonomous agents

• Modular Package Structure: Core and integration packages

Multi-Agent Capabilities:

LlamaIndex provides agent framework packages that enable the building of autonomous agents using its data retrieval and processing capabilities.

Key Features:

• The llama-index-agent-* packages provide frameworks for building autonomous agents integrated with the data capabilities of LlamaIndex.

• Data-centric approach enables agents to share and access common knowledge repositories, facilitating collaborative information processing.

• Modular package structure allows flexible composition of data retrieval, processing, and agent capabilities.

Letta

Letta (formerly MemGPT) is an open-source framework for building stateful LLM applications with advanced reasoning capabilities and transparent long-term memory.

Architecture Components:

• Client-Server Architecture: Server manages agents, memory, tools

• Agent System: Stateful entities with memory and configuration

• Hierarchical Memory: Core, archival, and recall memory

• Tool System: Tool manager, execution sandbox, and rules

• LLM Integration: Support for multiple providers

• Deployment Options: Local server or production environments

Multi-Agent Capabilities:

Letta supports multi-agent systems through its tool system, which includes tools specifically designed for multi-agent communication and collaboration.

Key Features:

• Multi-agent communication tools facilitate interaction between agents, enabling collaborative workflows and information sharing.

• Three-tier hierarchical memory system (core, archival, recall) provides efficient context management for complex multi-agent interactions.

• Client-server architecture supports the deployment and management of multiple agents in a shared environment with persistent state.

Swarms

Swarms is an enterprise-grade framework for orchestrating multi-agent systems, enabling the creation, management, and execution of various agent workflows for collaborative AI systems.

Architecture Components:

• SwarmRouter: Central controller directing tasks to workflows

• Agent System: Building blocks integrating LLMs, tools, and memory

• Workflow Systems: Patterns for agent collaboration

• Tool System: External system interaction capabilities

• Memory Systems: Short-term and long-term information storage

• Model Integration: Support for various LLM providers

Multi-Agent Capabilities:

Swarms is explicitly designed for multi-agent orchestration, offering several workflow patterns for different types of agent collaboration and interaction.

Key Features:

• SequentialWorkflow enables agents to work in sequence, with outputs from one agent becoming inputs for the next, creating process chains.

• ConcurrentWorkflow allows agents to work in parallel on the same task, facilitating collective intelligence and redundancy.

• HierarchicalSwarm organizes agents in a management hierarchy, with director agents coordinating worker agents for complex task execution.

Architectural Patterns and Tradeoffs

The agent framework ecosystem shows several distinct architectural patterns, each with specific tradeoffs and advantages:

Graph-Based Orchestration

Frameworks: LangGraph, Flowise, PydanticAI

These frameworks model agent workflows as directed graphs with nodes representing computation steps and edges defining data flow.

Advantages:

• Explicit visualization of workflow logic

• Fine-grained control over execution paths

• Clear state transitions and error handling

Challenges:

• More upfront design required

• Less adaptable to emergent behaviors

• Potentially complex for very dynamic workflows

Agent-Centric Messaging

Frameworks: AutoGen, Langroid, Letta

These frameworks focus on message passing between autonomous agents, each with their own state, capabilities, and decision-making.

Advantages:

• Highly flexible and adaptable interactions

• Natural modeling of conversational systems

• Easier to implement emergent behaviors

Challenges:

• More difficult to predict and debug

• Potential for communication inefficiencies

• Complex state management across agents

Modular Component Architecture

Frameworks: Semantic Kernel, Atomic Agents, LlamaIndex

These frameworks emphasize reusable, pluggable components (plugins, skills, tools) that can be assembled into agent workflows.

Advantages:

• High reusability of components

• Clear separation of concerns

• Easier to test individual components

Challenges:

• Integration complexity between components

• Potential for interface mismatches

• Overhead of component management

Structured Teams

Frameworks: CrewAI, Swarms, Agno, Mastra

These frameworks organize agents into structured teams with defined roles, coordination mechanisms, and leadership hierarchies.

Advantages:

• Clear division of responsibilities

• Scalable to complex problems

• Efficient task allocation

Challenges:

• Bottlenecks at coordination points

• Overhead of coordination mechanisms

• More complex to configure and optimize

Framework Selection Guide

Selecting the right agent framework depends on your specific requirements, use case, and development preferences. Consider the following dimensions when making your choice:

Task Complexity and Workflow Structure

• Simple, single-purpose agents: Consider Smolagents or Atomic Agents for lightweight, focused solutions.

• Complex, multi-step workflows: LangGraph, CrewAI, PydanticAI, or Flowise provide powerful orchestration.

• Dynamic, adaptive systems: AutoGen, Langroid, or Swarms offer flexible agent interaction models.

Multi-Agent Collaboration Requirements

• Conversation-based collaboration: AutoGen's group chat mechanisms excel at natural agent dialogues.

• Structured teams: CrewAI, Swarms, Agno provide explicit hierarchy and role-based coordination.

• Process-oriented workflows: LangGraph, Mastra support clear process steps with defined transitions.

• Independent, message-passing agents: Langroid, Letta enable autonomous agents with addressable messaging.

Development Experience and Integration

• Visual development: Flowise offers a no-code, visual builder experience.

• Type-safe development: PydanticAI, Atomic Agents provide strong typing and schema validation.

• Enterprise integration: Semantic Kernel has robust enterprise capabilities and cross-language support.

• Data-centric applications: LlamaIndex specializes in connecting agents with extensive data sources.

Other Considerations

• Memory requirements: Letta, Semantic Kernel, Swarms have robust memory systems.

• Model flexibility: Agno (23+ providers), Atomic Agents, Semantic Kernel offer broad model support.

• Performance considerations: Agno highlights optimization, Swarms offers concurrent processing.

• Deployment environments: Consider language support, runtime requirements, and hosting options.

Conclusion

The agent framework ecosystem continues to evolve rapidly, with each framework adopting different architectural approaches to solve the challenge of creating reliable, powerful AI agents and multi-agent systems. As this analysis has shown, there is no single "best" framework. Rather, each offers distinct advantages for particular use cases and development styles.

Graph-based frameworks like LangGraph provide explicit control and visualization, while messaging-based systems like AutoGen enable more dynamic agent conversations. Modular approaches like Semantic Kernel and Atomic Agents emphasize component reusability, and team-based frameworks like Swarms and Agno excel at structured multi-agent collaboration.

When selecting a framework, consider your specific requirements for multi-agent capabilities, memory systems, state management, tool integration, and model support. The right choice depends on your application's complexity, the nature of agent collaboration needed, and your team's development preferences.

As the field continues to mature, we can expect these frameworks to evolve, potentially converging on common patterns or specializing further into distinct niches. The future of agent frameworks will likely include more sophisticated multi-agent orchestration, improved reasoning capabilities, and tighter integration with emerging model paradigms.

References and Further Reading

• DeepWiki static code analysis of public repos

  • LangGraph

  • Flowise

  • Langroid

  • Smolagents

  • Semantic Kernel

  • Atomic Agents

  • Agno

  • PydanticAI

  • Mastra

  • Autogen

  • LlamaIndex

  • Letta

  • Swarms

• “Awesome LLM Resources” curated list

• “Agentic AI: Comparing New Open-Source Frameworks”