Episode 1 The 2025 Agent War

# Agentic Frameworks: A Quick Guide to the 2025 Agent War

7 min read
ai agentic-framework agentstackinsider agentic-frameworks
Table of Contents
Agentic Framework Title

Introduction to the Agentic Framework Series

Lately it feels like the world of AI moves so fast that if you blink, you miss an entire generation of breakthroughs. As someone who loves digging into emerging technologies, I finally gathered the courage to steal a little time from a busy schedule and kick off a series I’ve been wanting to write for ages. And what better place to start than the booming world of agentic frameworks? With LangGraph, LlamaIndex Agents, OpenAI’s Agents SDK, Google’s ADK, and Microsoft’s Agent Framework all evolving at lightning speed, the agentic AI ecosystem is turning into a full-on 2025 “Agent War.” Since I’m constantly tracking these updates anyway, I figured—why not share the journey and explore this rapidly shifting landscape together?

What Is an Agentic Framework?

An agentic framework is a software toolkit designed to help developers build AI agents—systems that can reason, take actions, use tools, and complete multi-step tasks with a level of autonomy. Instead of treating an LLM as a “single prompt in, single answer out” model, an agentic framework gives it structure: memory, tools, workflows, decision loops, and the ability to interact with data or external systems. Frameworks like LangGraph, LlamaIndex Agents, OpenAI’s Agent SDK, and Google’s ADK make it possible to create agents that can research, retrieve information, write code, operate APIs, and even coordinate with other agents. In short, an agentic framework transforms a passive model into an active problem-solver.

BTW, agentic frameworks — often called AI agent frameworks as well.

Enough talk — let’s get to it!

Let’s Summon Popular Agentic Frameworks

LangChain + LangGraph

Positioning:
Probably still the most widely recognized OSS agent stack; LangChain for the agent loop and tools, LangGraph for durable, stateful workflows. Link: LangChain Blog

Latest status (late 2025):

  • LangChain and LangGraph both hit v1.0 with a tightened “core agent loop” and a middleware system for flexible control.

  • LangGraph adds graph-structured workflows, persistence, debugging, and visual tools for complex agent interactions and long-running processes.

Best for:
General-purpose agent apps: RAG copilots, workflow agents, multi-step tool use, where you want a large ecosystem and lots of examples.

LlamaIndex (LlamaAgents & Workflows)

Positioning:
Started as a data/RAG library, now a full “data-centric agent framework” with strong connectors, parsing (LlamaParse), and high-quality RAG pipelines.Links: LlamaIndex+2

Latest status:

  • LlamaAgents early-access: full-stack templates for building agents, including TypeScript workflows and CopilotKit integrations.

  • 2025 comparisons show improved retrieval quality and strong performance for document-heavy workloads.

Best for:
Knowledge-intensive agents: enterprise search copilots, contract analysis, tech docs assistants, any system where the “data plane” is the hard part.

OpenAI Agents SDK (successor to Swarm)

Positioning:
OpenAI’s first-party agentic framework for building agents over their Responses API: a minimal set of primitives (agents, handoffs, guardrails, sessions) with tight GPT integration. Link: OpenAI Agents SDK

Latest status:

  • Designed as a production-ready agentic framework of the earlier “Swarm” multi-agent experiment.

  • Integrated with OpenAI’s new Responses API (web search, computer use, document search), replacing the older Assistants API over 2025-26.

Best for:
Teams already standardized on OpenAI: quick path to agents with web search, tools, and multi-agent delegation without heavy orchestration code.

Microsoft Agent Framework (AutoGen + Semantic Kernel)

Positioning:
Microsoft is merging AutoGen’s multi-agent orchestration with Semantic Kernel’s enterprise integration into a single “Microsoft Agent Framework” for Python and .NET. Link: Microsoft Agent Framework

Latest status:

  • AutoGen is now in maintenance mode; new feature development is happening in Microsoft Agent Framework.

  • Provides agents, planners, and orchestration with hooks into Azure/OpenAI, Office 365, and other Microsoft services.

Best for:
Enterprise .NET/Python shops on Azure that want multi-agent workflows tied into existing Microsoft infra, identity, and DevOps.

Google Agent Development Kit (ADK) / Vertex AI Agent Builder

Positioning:
Google’s open-source Agent Development Kit plus Vertex AI Agent Builder: ADK for local/dev usage, Agent Builder for managed, scalable deployment. Link: Google Agent Build

Latest status:

  • ADK announced in 2025 as a modular, model-agnostic agentic framework (though optimized for Gemini and Google Cloud).

  • Recent updates add prebuilt plugins (including “self-heal”), more language support (Go, Python, Java), and richer observability & security for production agents.

Best for:
GCP-centric teams: data & MLOps agents (BigQuery, Dataflow, etc.), multi-system enterprise agents, and workloads that need Vertex AI governance features.

CrewAI

Positioning:
A popular OSS “multi-agent crew” agentic framework: define specialized agents (roles), share context, and let them collaborate on tasks. Link: CrewAI

:::successSneak peek — the next episode will likely be a hands-on dive into CrewAI!:::

Latest status:

  • Active development and strong marketing as a “multi-agent platform” with business-oriented workflows.

  • Frequently cited (alongside LangChain & AutoGen) as a top choice in 2025 industry roundups because of its clean Python API and real-world focus.

Best for:
Multi-agent experiments and startup-style stacks: e.g., “researcher + planner + executor” teams for content, lead-gen, or coding tasks.

Haystack

Positioning:
RAG + agentic framework aimed squarely at production: modular pipelines and “agents” that can call tools, retrieve data, and generate answers.Link: Heystack

Latest status:

  • Marketed specifically for “agentic, compound AI systems” with end-to-end observability and debugging.

  • Supports agents that choose between web search, vector stores, and other tools to resolve complex queries.

Best for:
Teams that want a transparent, production-grade RAG/agent stack with strong search roots (Elastic/OpenSearch, vector DBs) and clear pipelines.

OpenHands Software Agent SDK (software-dev agents)

Positioning:
A toolkit spun out of the popular OpenHands code-assistant framework, specifically for reliable software development agents (coding, debugging, PRs). Link: OpenHands Software Agent SDK

Latest status:

  • 2025 paper describes a redesigned SDK for flexible, secure software agents: sandboxed execution, multi-LLM routing, and integration with editors (VS Code, browser, CLI).

Best for:
Engineering teams wanting production code agents (e.g., SWE-Bench style tasks) with strong execution sandboxes and lifecycle controls.

Research / Training-oriented frameworks (Agent Lightning, etc.)

Positioning:
These are less “app frameworks” and more training stacks, but relevant for anyone planning RL-fine-tuned agents. The paper link is here.

  • Agent Lightning: RL training framework that decouples training from agent execution, plugging into existing agent stacks like LangChain, AutoGen, OpenAI Agents SDK with minimal changes.

Best for:
R&D teams working on agent RL, evaluation, and fine-tuning rather than pure orchestration.

Dominant Use Cases

Across vendors and OSS, you see a few consistent patterns:

  1. RAG copilots / knowledge assistants

    • Internal “Chat with docs” for policies, support docs, codebases (LangChain, LlamaIndex, Haystack).

    • Industry: customer support, legal analysis, technical documentation.

  2. Agentic Process Automation (APA) / workflow agents

    • Multi-step agents that call APIs, write files, trigger workflows, etc. (LangGraph, CrewAI, OpenAI Agents SDK, MS Agent Framework, Google ADK). Link: Top 7 AI Agentic Frameworks in 2025: The Ultimate Guide.

    • Example: end-to-end lead processing, back-office ops, report generation.

  3. Software engineering agents

    • Coding, refactoring, test-generation, PR review (OpenHands SDK; OpenAI computer-use agents; some AutoGen/CrewAI patterns).

  4. Data & analytics agents

  5. Ops / infra & enterprise workflows

What is the best AI agent framework ?!

Haha! I know you will ask this question!

Choosing the best AI agent framework isn’t as simple as crowning a single winner—because the “best” depends entirely on what you’re building. LangGraph excels at complex, stateful workflows with fine-grained control. LlamaIndex dominates when your agent needs powerful data retrieval and document intelligence. OpenAI’s Agents SDK is unbeatable for rapid development with built-in web search, tool use, and multi-agent orchestration. Google’s ADK shines in enterprise environments that lean heavily on GCP data pipelines. And Microsoft’s Agent Framework integrates seamlessly with Azure and the broader Microsoft ecosystem. Instead of looking for a universal champion, the smarter question is: Which agentic framework aligns with your stack, your data, and your use case? Because in 2025’s Agent War, “context”—not hype—decides the winner.

Where we can go deeper next ?

We’ve only scratched the surface of what the agentic ecosystem is becoming. From production-ready orchestration with LangGraph, to data-centric workflows in LlamaIndex, to OpenAI’s emerging Agent SDK and Google’s ADK reshaping enterprise automation—the real excitement starts when we dig into how these agentic frameworks actually behave in the wild.

In the next episodes, I’d like to break down architectures, explore real-world use cases, and walk through hands-on builds you can follow step by step. If the 2025 “Agent War” or “Agentic Framework War” or whatever has your curiosity sparked, stick around—this is just the beginning, and the most interesting battles are still ahead.

:::success You’re on a roll! Don’t stop now—check out the other series and level up your AI skills. Make sure you haven’t missed anything! 👇

🚀 Daily AI Insights

🚀 Maching Learning :::

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