Exploring Agentic Workflows and Their Frameworks

Exploring Agentic Workflows and Their Frameworks

Introduction to Agentic Workflows

Agentic workflows represent a transformative approach in artificial intelligence, where autonomous AI agents perform complex tasks that traditionally require human intervention. These agents, powered by advanced language models, can understand natural language, reason through problems, and interact with external tools to achieve specific objectives. From automating customer support to streamlining research processes, agentic workflows are reshaping how we approach efficiency and innovation.

The significance of these workflows lies in their ability to reduce human error, enhance productivity, and allow individuals and organizations to focus on high-level strategic goals. By leveraging AI agents, businesses can delegate routine or intricate operations to intelligent systems, freeing up resources for creative and critical tasks. The diagram "Agentic Workflows - Frameworks" provides a comprehensive overview of the tools and frameworks driving this revolution, each offering unique capabilities to build and manage these agents.

Key Frameworks for Agentic Workflows

The diagram highlights nine pivotal frameworks that support the development and implementation of agentic workflows. These frameworks, developed by leading tech companies and specialized platforms, provide diverse tools for creating AI agents tailored to various applications. Below, we explore each framework in detail, including their tools, features, advantages, and limitations.

Agentic Workflows - Frameworks

Figure 1: Agentic Workflows - Frameworks

1. Introducing AutoGen by Microsoft

AutoGen is an open-source framework developed by Microsoft for building multi-agent conversational AI systems powered by Large Language Models (LLMs).

It provides a high-level, modular interface to create and manage autonomous agents that collaborate, reason, and solve complex tasks through dialogue.

🔧 Key Features

  • Multi-Agent Framework – Create agents that communicate, collaborate, and take on roles like planner, coder, or critic.
  • LLM-Oriented Design – Easily integrate with GPT (OpenAI, Azure, etc.), with customizable prompt behaviors.
  • Human-in-the-Loop – Inject human feedback at any point in the conversation loop.
  • Extensible Architecture – Add APIs, tools, memory modules, and external logic to agents.
  • Optimized for Reasoning – Useful in tasks like code generation, research assistance, and decision-making.

🧠 How It Works

AutoGen uses a conversation-based interface where agents exchange messages (prompts/responses) to achieve a goal. Components include:

  • AssistantAgent – A helpful assistant LLM agent.
  • UserProxyAgent – Represents the user or external input.
  • GroupChat – Manages structured communication between agents.

🚀 Example Use Case

A multi-agent coding assistant team could consist of:

  • PlannerAgent: Breaks down the task.
  • CoderAgent: Writes code.
  • CriticAgent: Reviews and suggests improvements.
  • UserProxyAgent: Represents the user’s input or confirmation.

🧪 Research & Industry Applications

  • Scientific paper summarization
  • Bug triage and software generation
  • Financial report analysis
  • Conversational agents and assistants
  • Simulated decision-making environments

📦 Resources

Whether you're building autonomous agents for research, automation, or production systems, AutoGen offers a powerful platform to bring LLMs to life in collaborative settings.

2. OpenAI Agents SDK

OpenAI Agents SDK is a developer toolkit that enables the creation of tool-using AI agents directly integrated into the GPT platform. These agents can use custom functions, maintain memory, interact with users, and execute actions—securely hosted by OpenAI’s infrastructure.

The SDK allows developers to register their own tools (functions), assign them to assistants, and build AI workflows that use reasoning + actions + memory to complete complex tasks automatically.

🔧 Key Features

  • Tool Use – Agents can call developer-defined functions using structured input/output schemas.
  • Memory – Long-term memory support (private alpha) allows agents to recall user history and context.
  • Hosted by OpenAI – Agents run within OpenAI's cloud platform, handling execution, auth, and state securely.
  • Multi-Modal Support – Supports vision, code, and text inputs via GPT-4o and other models.
  • Python SDK – Simple decorators to define tools, register assistants, and deploy workflows.

⚙️ Core Concepts

  • @tool – Decorator to define callable functions your assistant can invoke (e.g., search, fetch, calculate).
  • Assistant – Configurable agent with a profile, instructions, tools, and optionally memory.
  • Thread – Represents a conversation between a user and the assistant.
  • Run – A specific invocation that handles reasoning, tool calls, and response generation.

🚀 Example Use Case

Build a GPT-powered travel planner:

  • Define tools like get_flight_options() and find_hotels().
  • Create an assistant with user instructions like “You are a helpful travel expert.”
  • Let the assistant ask follow-up questions, call tools, and provide curated itineraries.

🧪 Use Cases

  • Custom GPTs with APIs and functions
  • Business operations automation
  • Knowledge assistants with memory
  • Data pipelines and real-time querying agents
  • Support chatbots with document access and logic

📦 Resources

OpenAI Agents SDK is ideal for developers who want to deeply integrate GPT models into tool-using, autonomous applications with hosted execution and scalable interfaces.

3. Introducing LangChain

LangChain is a powerful open-source framework designed to help developers build context-aware applications using Large Language Models (LLMs). It provides a complete toolkit to connect LLMs with external data, tools, and APIs in a modular and scalable way.

LangChain enables complex workflows like retrieval-based QA, agentic reasoning, code generation, and tool-using LLMs—all with just a few lines of code.

🔧 Key Features

  • Chains – Define multi-step workflows that connect prompts, tools, memory, and outputs.
  • Agents – Build LLMs that can make decisions, use tools, and reason in real time.
  • Memory – Give your apps memory of past interactions using built-in memory modules.
  • Retrieval-Augmented Generation (RAG) – Connect LLMs with custom data (e.g. PDFs, databases, websites).
  • Tool Integration – Easily connect APIs, Python functions, SQL, web search, and more.

⚙️ Core Components

  • PromptTemplate – Reusable and dynamic prompts with input variables.
  • LLMChain – A chain that sends prompts to an LLM and returns the response.
  • RetrievalQA – Combines retrieval (from a vector DB) with QA logic using an LLM.
  • Tool and AgentExecutor – For tool-using agents that act based on observations.

🚀 Example Use Case

You can build a document-based chatbot in minutes:

  • Load PDFs and split them into chunks.
  • Embed them using FAISS or Chroma.
  • Use RetrievalQA with a language model.
  • Deploy it via FastAPI, Streamlit, or LangServe.

🧪 Use Cases

  • Custom Chatbots & Assistants
  • Data-Driven LLM Apps (RAG)
  • Autonomous Agents
  • Knowledge Base Search
  • Code Interpretation or Debugging

📦 Resources

LangChain is ideal for developers looking to unlock the full potential of LLMs by chaining reasoning steps, using real-time data, and building intelligent agentic systems.

4. Anthropic & Agentic Workflows

Anthropic is a leading AI research company known for developing the Claude family of language models. With Claude 3, Anthropic has shifted its focus toward enabling more advanced and safe agentic workflows—LLM-powered systems that reason step-by-step, use tools, and operate autonomously or collaboratively.

Claude models are designed with constitutional AI principles, emphasizing safety, transparency, and controllability—making them particularly suited for sensitive multi-agent applications.

🔧 Key Features of Claude for Agentic Systems

  • Structured Function Calling – Supports calling external tools and APIs via well-defined interfaces.
  • Long Context Windows – Up to 200k+ tokens in Claude 3.5, ideal for multi-turn planning and memory-heavy workflows.
  • Tool Use & Function Execution – Claude can simulate agents that reason, delegate tasks, and query external data sources.
  • Safe Delegation – Claude’s alignment and guardrails make it ideal for enterprise agent systems with minimal hallucination.

⚙️ Typical Agentic Architecture with Claude

  • Task Planning: Claude interprets the goal and generates a plan.
  • Tool Selection: Selects or requests a tool from a predefined list (e.g. database, calculator, web search).
  • Multi-Step Reasoning: Executes the workflow with intermediate state tracking.
  • Response Synthesis: Compiles results into a final answer with rationale.

🚀 Use Cases

  • Autonomous coding assistants
  • Enterprise report analyzers
  • Customer service agents
  • Legal, financial, and compliance advisors
  • Multi-agent simulations & negotiation bots

🧪 Claude vs Other Frameworks

While Claude itself is not a framework like LangChain or AutoGen, it powers many agentic frameworks through integrations. For example:

  • LangChain + Claude: For structured RAG pipelines with Claude as the reasoning agent.
  • AutoGen + Claude: Enable Claude as an autonomous assistant in multi-agent workflows.
  • Custom APIs: Use Claude’s function calling to simulate full agent-like autonomy.

📦 Resources

Anthropic’s Claude stands out as a reliable, safe, and capable model in agentic workflows, enabling sophisticated use cases across research, industry, and enterprise automation.

5. LlamaIndex & Agentic Workflows

LlamaIndex (formerly GPT Index) is a powerful data framework designed to help LLMs interact with structured and unstructured data. It also supports agentic workflows, where agents powered by LLMs can reason, retrieve data, and take actions using tools and memory.

With its modular and composable architecture, LlamaIndex enables developers to build sophisticated agent systems capable of querying documents, invoking tools, and refining their behavior over time.

🔧 Key Features for Agentic Systems

  • Agents & Tool Use – Supports tool-using agents via a built-in OpenAIAgent or custom agents with plugins.
  • Context-Aware Memory – Offers both short-term memory and vector-based long-term memory using retrieval engines.
  • Data Connectors – Ingest and index data from PDFs, SQL, Notion, websites, APIs, and more.
  • Composable Graphs – Structure your data and logic using query pipelines and knowledge graphs.
  • Observability – Built-in tracing and visual tools for debugging multi-step reasoning flows.

⚙️ Core Components

  • QueryEngine – Core logic unit that enables query-time reasoning with retrieval and synthesis.
  • Tool – Define callable tools that the agent can invoke during reasoning steps.
  • AgentRunner – Orchestrates the conversation between the user, tools, and the LLM agent.
  • KnowledgeGraph – Structure information as a graph for semantic querying and planning.

🚀 Example Use Case

You can build a retrieval-powered research agent:

  • Load a set of academic papers or PDFs.
  • Create a VectorStoreIndex or KnowledgeGraphIndex.
  • Define tools like lookup_term(), summarize_doc(), or cite_sources().
  • Run the agent using OpenAIAgent.from_tools().

🧪 Use Cases

  • RAG agents with structured reasoning
  • Enterprise knowledge assistants
  • Multi-modal document agents
  • Data augmentation and synthesis bots
  • Tool-aware developer agents

📦 Resources

LlamaIndex excels as a data-centric agent framework, making it a top choice when building LLM agents that require deep knowledge grounding, real-time tool access, and transparent multi-step workflows.

6. Letta & MemGPT: Persistent Agentic Framework

Letta is an emerging agentic framework built on top of MemGPT, an advanced memory-augmented LLM system. Letta introduces lifelike, persistent agents that can retain memory across sessions, access tools, and evolve their behavior over time—mimicking human-like long-term cognition.

It enables the development of truly intelligent assistants that remember past interactions, form goals, and carry out long-term tasks through continuous dialogue and tool use.

🧠 What is MemGPT?

  • MemGPT augments LLMs with virtual long-term memory and structured memory systems.
  • It can simulate working memory (short-term) and episodic memory (long-term).
  • Ideal for lifelike AI agents, personalized assistants, and persistent multi-session workflows.

🔧 Key Features of Letta

  • Memory-Driven Agents – Letta agents remember facts, preferences, and goals across interactions.
  • Tool Use – Integrates external tools and APIs for execution, search, computation, etc.
  • Thread-Based Conversations – Each thread acts as a persistent memory stream, like a personal journal.
  • Personalization – Agents can build up knowledge about their users and adapt over time.

⚙️ Architecture Highlights

  • LettaAgent – A memory-aware, tool-using agent with its own identity and goals.
  • MemGPT Core – Provides structured memory management and memory compression for long-term use.
  • ToolHandler – Plugin system for integrating custom tools and data pipelines.
  • Session Memory – Preserves agent state, memory, and goals across sessions and reboots.

🚀 Use Cases

  • Persistent AI companions and tutors
  • Autonomous research and planning agents
  • Executive assistants for multi-day tasks
  • Simulated humans in games or training environments

🌐 Letta vs Other Frameworks

Letta stands out by focusing on memory persistence and personality continuity—something missing in traditional prompt-based LLM workflows. It complements frameworks like:

  • LangChain – Offers advanced chaining and RAG logic, but lacks full memory continuity.
  • AutoGen – Great for coordination, but less personalized or persistent.
  • LlamaIndex – Can be combined with Letta for memory indexing and retrieval.

📦 Resources

Letta is an exciting leap forward in the agentic ecosystem—designed not just to solve problems, but to remember, adapt, and grow with its users over time.

7. Google ADK: Agent Development Kit

Google ADK (Agent Development Kit) is a cutting-edge framework released by Google to enable developers to build, deploy, and manage AI agents that use Gemini models and Google tools. ADK provides a structured approach to creating agents that can reason, call tools, remember, and collaborate—designed for integration with Google's cloud and enterprise services.

It supports flexible orchestration of tool-using, multi-modal, and multi-turn agents across various domains like productivity, coding, search, and automation.

🔧 Key Features

  • Gemini Integration – Seamlessly use Google’s Gemini 1.5 models for multi-modal reasoning, planning, and tool calls.
  • Tools & Functions – Agents can call structured functions (tools) with JSON-based inputs/outputs.
  • Memory Management – Context-aware workflows with configurable memory (ephemeral or persistent).
  • Composable Agents – Build agents as modular components with custom logic and tool routing.
  • Secure by Design – Integrates with Google's safety, auth, and permission layers.

⚙️ Core Concepts

  • AgentRunner – Executes an agent loop, handles tool calls, state, and memory.
  • Tool – Encapsulates a callable function (e.g., API, internal service, search query).
  • Orchestration – Combines planner, executor, memory, and tools into intelligent flows.
  • MemoryStore – Optional integration with memory APIs or databases for persistence.

🚀 Example Use Case

Build a Gemini-powered research assistant:

  • Connect Gemini Pro to tool plugins like web search, calculator, and Google Docs API.
  • Define a planner agent that interprets complex queries and routes sub-tasks.
  • Store past questions and user context in a memory backend.
  • Allow the agent to evolve and personalize responses over time.

🧪 Use Cases

  • Enterprise document agents (e.g., summarizing Drive/Docs)
  • Customer support copilots
  • Developer automation agents (e.g., code generation, analysis)
  • Workflow orchestration assistants
  • Voice/chat interfaces for productivity apps

📦 Resources

Google ADK bridges advanced LLM reasoning with robust enterprise infrastructure, giving developers a future-proof platform for building AI agents that are intelligent, tool-using, and grounded in real data and services.

8. CrewAI: Collaborative Agent Framework

CrewAI is an open-source Python framework designed to simplify the development of multi-agent AI systems. It allows you to build, assign roles to, and orchestrate multiple **autonomous LLM agents** that work together as a team—or “crew”—to complete complex tasks collaboratively.

CrewAI emphasizes agent **roles**, **tools**, and **task ownership**, making it easy to implement modular and reusable AI workforces for real-world applications like research, coding, reporting, and operations.

🔧 Key Features

  • Role-Based Agents – Define custom agents with specific names, goals, backstories, and responsibilities.
  • Task Delegation – Assign tasks to agents with well-defined input/output behavior and dependencies.
  • Sequential & Parallel Workflows – Build pipelines where agents execute in order or simultaneously.
  • Tool Integration – Equip agents with tools like APIs, calculators, file readers, or custom functions.
  • Human-in-the-Loop – Allow human review or intervention during execution.

⚙️ Core Concepts

  • Crew – The overall manager that executes agent workflows and maintains shared memory/context.
  • Agent – An LLM-powered persona with a role and capability (can use tools).
  • Task – Defines what an agent must accomplish, optionally with outputs consumed by others.
  • Tool – Functions or actions the agent can call when reasoning (e.g., database search, API call).

🚀 Example Use Case

Build a market analysis crew:

  • ResearcherAgent: Gathers and summarizes market data.
  • AnalystAgent: Reviews and interprets the research.
  • ReporterAgent: Generates a structured report or slide deck.
  • All agents share memory and collaborate within a single Crew.

🧪 Use Cases

  • Multi-step document processing (summarization, extraction, synthesis)
  • Autonomous research and reporting
  • Code analysis and testing teams
  • Startup-style product agents (PM, dev, tester, UX)
  • Financial analysis and proposal generation

📦 Resources

CrewAI is ideal for developers looking to build fast, structured, multi-agent workflows with LLMs. It shines in scenarios that require cooperation, tool use, and modular task design in a lightweight but powerful framework.

Conclusion

🔚 Conclusion: Comparing Modern Agentic Frameworks

The rise of agentic workflows marks a transformative shift in how we build intelligent systems. From multi-step reasoning to tool usage and memory persistence, each framework contributes a unique piece to the evolving landscape of autonomous agents. Here's a synthesis of the strengths, use cases, and considerations for each:

✅ LangChain

Strength: Modular pipelines, RAG, and tool chaining.

Best for: Developers building complex, multi-component applications with structured logic.

Weakness: Can get overly complex; requires tight integration discipline.

✅ AutoGen (Microsoft)

Strength: Multi-agent collaboration, LLM-to-LLM messaging, coordination protocols.

Best for: Research, simulation, or real-world cooperative agents.

Weakness: Higher overhead; not ideal for simple agent flows.

✅ Claude (Anthropic)

Strength: Safe, long-context reasoning and tool use; ideal for aligned enterprise agents.

Best for: Planning, document analysis, and low-hallucination workflows.

Weakness: Not a full framework; requires LangChain or custom orchestration.

✅ LlamaIndex

Strength: Data-aware agents, vector and graph indexes, tool-based retrieval.

Best for: RAG applications, document-querying agents, semantic memory-based flows.

Weakness: Less support for agent collaboration or persona-based flows.

✅ Letta (MemGPT)

Strength: Memory-rich, persistent agents with continuity and identity.

Best for: Companions, tutors, multi-session agents.

Weakness: Still maturing; less standardized for tooling and integration.

✅ Google ADK

Strength: Gemini-powered orchestration, security, and enterprise API integration.

Best for: Production deployment inside the Google ecosystem.

Weakness: Limited availability; best suited to Google-native environments.

✅ CrewAI

Strength: Team-based collaboration, role-based agents, and task pipelines.

Best for: Scenarios involving collaborative AI (e.g., research teams, coding pipelines).

Weakness: Limited memory management; less ideal for long-term personalization.

✅ OpenAI Agents SDK

Strength: Native GPT-4 tool use, hosted execution, memory support, simple SDK.

Best for: Developers building production-ready agents directly on the OpenAI platform.

Weakness: Less control over runtime; bound to OpenAI's infrastructure.

🧠 Final Thought

There is no one-size-fits-all framework. Your choice should depend on the type of agentic system you are building:

  • Multi-agent collaboration: AutoGen, CrewAI
  • Data-grounded workflows: LlamaIndex, LangChain
  • Persistent memory & user modeling: Letta / MemGPT
  • Production deployment: OpenAI SDK, Google ADK
  • Long-form aligned reasoning: Claude by Anthropic

These tools are complementary. Combining them—like Claude with LangChain, or CrewAI with LlamaIndex—can result in even more powerful and flexible agentic systems. As the ecosystem evolves, hybrid architectures will become the norm in intelligent application development.