Microsoft Agent Framework (MAF) / AutoGen

As of Oct. 2026, if you want the AI agent frameworks that people actually use—not just demo repos—I’d shortlist these:

My top recommendations

If you want the shortest answer

• Best overall / safest bet: LangGraph + LangChain
• Best if you’re OpenAI-first: OpenAI Agents SDK
• Best for quick multi-agent demos and business workflows: CrewAI
• Best for RAG/document agents: LlamaIndex
• Best for Microsoft enterprise shops: Microsoft Agent Framework
• Best for Google/Gemini shops: Google ADK
• Best for type-safe Python: Pydantic AI
• Best for TypeScript teams: Mastra

What I’d choose by use case

For a real production agent

Use LangGraph unless you have a strong vendor reason not to. Its biggest advantage is explicit state, durable execution, persistence, streaming, and human-in-the-loop control—exactly the stuff that matters after the demo works. (langchain-ai.github.io)

For a simple OpenAI-powered assistant with tools

Use OpenAI Agents SDK. You get a clean programming model for agents, handoffs, tools, sessions, guardrails, and tracing without adopting a large external framework. (openai.github.io)

For “multiple specialists working together”

Use CrewAI if you want fast role-based multi-agent workflows. It is one of the most recognizable open-source agent frameworks and is particularly good for prototypes, internal automations, research crews, sales/ops assistants, and task pipelines. (docs.crewai.com)

For agents over company knowledge

Use LlamaIndex, especially if the hard part is ingestion, retrieval, indexing, query planning, and document-grounded reasoning. (llamaindex.openml.io)

For enterprise cloud alignment

Use Microsoft Agent Framework for Azure/Microsoft/.NET environments and Google ADK for Google Cloud/Gemini environments. Microsoft explicitly positions Agent Framework as the successor to AutoGen and Semantic Kernel; Google positions ADK for building and deploying reliable agents at enterprise scale. (learn.microsoft.com)

One caution

Don’t pick a framework just because of GitHub stars. Recent 2026 comparisons show big communities around AutoGen, CrewAI, LlamaIndex, LangGraph, OpenAI Agents SDK, Semantic Kernel/Microsoft Agent Framework, Mastra, Pydantic AI, and Google ADK, but stars lag behind real production fit. (dreaming.press)

My practical rule: start with the smallest abstraction that solves your problem. For many teams that means OpenAI Agents SDK, Pydantic AI, or LlamaIndex. If workflows become stateful, long-running, auditable, or multi-step, move to LangGraph or the relevant cloud-native framework.

Top AI Agent Frameworks (2026)

Based on current industry reviews and real-world adoption, here's a breakdown of the frameworks developers actually reach for:

🏆 The Big Names

1. LangChain / LangGraph — The most established player in the space. Choose LangChain if you need an open-source framework for rapid prototyping across model providers, paired with LangGraph for stateful multi-agent orchestration, Deep Agents for long-running workflows, and LangSmith for enterprise-grade observability and evaluation across the full application lifecycle. Multiple independent testers rank LangGraph as production-grade — LangGraph is the most production-hardened option on this list.

2. CrewAI — The go-to for role-based multi-agent setups. Choose CrewAI if you need role-based multi-agent prototypes up and running quickly with an intuitive mental model. It's also praised for speed: CrewAI and the OpenAI Agents SDK are the fastest paths to a working prototype.

3. OpenAI Agents SDK — Best if you're committed to OpenAI's ecosystem. Choose OpenAI Agents SDK if you need tightly scoped assistants and clean multi-agent delegation with minimal abstraction.

4. Mastra — The rising star for JavaScript/TypeScript teams. Choose Mastra if you're a TypeScript team building production agents and want workflows, memory, and a Studio environment in one package. One head-to-head benchmark test ranked it #1 overall: Mastra: best full-stack framework for TypeScript teams shipping production agents.

🏢 Enterprise/Cloud-Native Options

5. Microsoft Agent Framework — The unified successor to AutoGen and Semantic Kernel, with graph-based workflows, responsible AI guardrails available through Azure AI Foundry, and Python +.NET runtimes at 1.0 GA.

6. Google ADK (Agent Development Kit) — Best if you're GCP-native and want an opinionated, batteries-included agent runtime with built-in debugging UIs.

7. Claude Agent SDK — Anthropic's offering, noted as Safety-First AI Agent Framework from Anthropic, and recommended as best pre-built agent harness (if you're Claude-native).

📊 Specialized Tools

8. LlamaIndex Workflows — Best if you need event-driven orchestration for document-heavy, data-intensive pipelines.

9. Pydantic AI — Great for type safety in Python. Recommended as best for production systems, teams already using Pydantic, and anyone burned by unvalidated LLM outputs.

10. Vercel AI SDK — Best AI toolkit for JS/TS apps that are growing agent features.

11. smolagents — Hugging Face's entry: a lightweight framework for agents that think in code.

12. AutoGen/AG2 — Historically significant but losing relevance. One 2026 benchmark bluntly called it historically important, hard to recommend for new projects in 2026.

Quick Recommendation by Use Case

The landscape of AI agent frameworks has matured significantly. Developers and enterprise teams have moved away from fragile, "toy" prototypes and converged on a few dominant, production-ready frameworks.

Choosing the right framework depends on three main variables: your language stack (Python vs. TypeScript), your architectural needs (stateful graphs vs. conversational multi-agent), and your tolerance for framework abstraction.

1. LangGraph (by LangChain)

• The Vibe: The heavyweight industry standard for complex, production-grade applications.
• Best For: Branching workflows, state machines, and human-in-the-loop approval processes.

LangGraph is LangChain's answer to the unpredictability of traditional LLM chains. Instead of letting an agent wander aimlessly, LangGraph forces you to define agent actions as a state machine with explicit nodes (actions/computations) and edges (decisions/transitions).

• Why people use it: It gives you absolute control. It has built-in persistence (meaning you can pause an agent mid-execution, wait for a human to approve a tool call, and resume it without losing state).
• The Catch: It has a very steep learning curve. It requires you to write verbose, highly structured code.

2. CrewAI

• The Vibe: The fastest path from zero to a working multi-agent demo.
• Best For: Role-based multi-agent collaboration (e.g., a "researcher" agent passing data to a "writer" agent).

CrewAI excels at coordinate systems where multiple specialized agents need to collaborate. It uses a highly intuitive, almost English-like abstraction where you define agents with specific "roles," "goals," and "backstories," and then assign them to "tasks".

• Why people use it: Out-of-the-box support for multi-agent delegation, memory management, and guardrails. It handles the "glue" code of agent-to-agent communication automatically.
• The Catch: As systems scale, CrewAI can suffer from routing issues or agents overlapping in responsibilities. It is highly opinionated and can be difficult to debug when the underlying abstractions behave unexpectedly.

3. PydanticAI

• The Vibe: Type-safe, clean, and highly predictable backend Python engineering.
• Best For: Teams already using FastAPI, Pydantic, or SQLModel who value type-safety and structured outputs.

Built by the creators of Pydantic (the gold standard for Python data validation), PydanticAI is a model-agnostic agent framework designed for production stability.

• Why people use it: It treats agent outputs as strict schemas. If an LLM returns poorly formatted data, PydanticAI automatically catches it, retries, and forces the model to adhere to the schema. It also features robust dependency injection and is incredibly easy to test and trace.
• The Catch: It doesn't have the massive ecosystem of pre-built integrations (like vector store loaders) that LangChain has. You have to write more of your own tools and integrations.

4. Microsoft AutoGen 2.0 / AG2

• The Vibe: High-autonomy, dynamic conversation-first orchestration.
• Best For: Event-driven, async multi-agent conversations and autonomous code-generation loops.

Microsoft AutoGen v1 was famous for letting agents naturally "ping-pong" ideas off one another. Today, the ecosystem has bifurcated: Microsoft has transitioned AutoGen into its native Microsoft Agent Framework, while the open-source community maintains the highly popular AG2 (formerly AutoGen).

• Why people use it: If you need agents to dynamically debate, collaborate, and autonomously self-correct (like an engineer agent and a QA agent working together until a test passes), AutoGen/AG2 is peerless.
• The Catch: Unbounded agent loops can quickly burn through your API token budget if you don't implement strict termination conditions.

5. LlamaIndex Workflows

• The Vibe: The data-engineering approach to agents.
• Best For: "Document agents" and search-heavy, retrieval-augmented (RAG) applications.

If your agent's primary job is to ingest, search, and reason over massive databases or internal company PDFs, LlamaIndex is the absolute gold standard. Under the hood, Workflows is their event-driven, stateful orchestration system.

• Why people use it: Seamless integration with hundreds of vector databases, retrievers, and data parsers. It makes building highly accurate search agents remarkably robust.
• The Catch: While excellent at data retrieval, it is less suited for "autonomous acting" (like interacting with web browsers or desktop control) than frameworks like CrewAI or AutoGen.

6. Mastra (TypeScript/JavaScript)

• The Vibe: The full-stack, backend-first TypeScript agent framework.
• Best For: Modern JS/TS teams who want a Python-grade agent ecosystem without having to write Python.

Mastra has quickly become the favorite for modern TypeScript applications. It provides a full-stack, unified framework for managing agent states, tool calling, and integrations natively in TS.

• Why people use it: Outstanding developer experience. If your main app is built on Next.js, Remix, or Express, Mastra allows you to deploy agents directly inside your existing codebase without maintaining a separate Python microservice.
• The Catch: The JS/TS ecosystem for machine learning and data parsing is still smaller than Python's, meaning you may have to build some data-heavy pipelines from scratch.

How to Choose (The 10-Second Guide)

The most widely used AI agent frameworks today are LangGraph, LangChain, CrewAI, Microsoft AutoGen, LlamaIndex Workflows, OpenAI Agents SDK, Google ADK, and Mastra. If you want the safest “top brands” list based on current adoption and production mention, LangGraph and LangChain are the most established, while CrewAI, AutoGen, and OpenAI Agents SDK are among the most commonly recommended for real agent builds.

• LangGraph — best overall for production-grade, stateful, controllable agents and complex workflows.
• LangChain — best for broad integrations and the largest general-purpose ecosystem around LLM apps and agents.
• CrewAI — best for multi-agent “team” style workflows and fast prototypes with specialized roles.
• Microsoft AutoGen — strong for multi-agent conversation patterns and research-to-prototype agent collaboration.
• LlamaIndex Workflows — strong when your agent needs RAG, retrieval, and data-heavy workflows.
• OpenAI Agents SDK — a strong choice if you want an OpenAI-native framework with a simpler path to agent orchestration.
• Google ADK — best for teams building on Google Cloud / GCP-native infrastructure.
• Mastra — a good pick for TypeScript-native teams building autonomous systems.

A practical way to choose is:

• Pick LangGraph if you care most about control, reliability, and production workflows.
• Pick LangChain if you want the widest ecosystem and integrations.
• Pick CrewAI if your mental model is a group of collaborating agents.
• Pick AutoGen if you want agent-to-agent dialogue and experimentation.
• Pick OpenAI Agents SDK if you are already centered on OpenAI’s platform.
• Pick Google ADK if your stack is GCP-first.

If you want, I can also give you a ranked shortlist by use case:

• best for beginners
• best for production
• best open-source
• best for Python
• best for TypeScript

Short answer: there’s no single “best” AI agent framework. The right pick depends on whether you want code-first orchestration, multi-agent teams, low-code automation, enterprise cloud agents, or coding agents.

The ones with the strongest real-world adoption right now are roughly:

For coding agents/products specifically, the most used are Claude Code, Cursor, GitHub Copilot, OpenHands, Devin, and Aider. These are products rather than general frameworks.

If I had to pick a practical shortlist for most teams:

1. LangGraph — best overall for production-grade, controllable agents.
2. OpenAI Agents SDK — best if OpenAI-native and you want speed.
3. Claude Agent SDK + MCP — best if you want Anthropic/Claude tooling and interoperable tools.
4. CrewAI — best for quick multi-agent experiments and role-based workflows.
5. LlamaIndex — best for RAG/data-heavy agents.
6. Dify or n8n — best low-code options for real business automation.
7. Microsoft Agent Framework / Azure AI Foundry Agent Service — best for Microsoft-centric enterprises.
8. AWS Strands + Bedrock AgentCore — best for AWS-centric enterprises.
9. Google ADK + Vertex AI Agent Builder — best for GCP/Gemini-centric teams.

A few cautions: AutoGPT, BabyAGI, and SuperAGI were historically important but are mostly not what production teams build on today. OpenAI Swarm is deprecated in favor of the Agents SDK. Also, MCP and A2A are protocols, not frameworks, but supporting MCP is increasingly a strong signal of ecosystem compatibility.

Choose based on: model support, tool calling, durable state, memory, human-in-the-loop, observability/evals, deployment, security, and cost — not just GitHub stars.

The top AI agent frameworks in 2026, based on actual usage (GitHub stars, PyPI/npm downloads, developer surveys, production mentions, and expert roundups), are led by the LangChain ecosystem (especially LangGraph), CrewAI, and the OpenAI Agents SDK.

These stand out for real adoption rather than hype. LangChain/LangGraph dominate in stars (~147K for LangChain, ~42K for LangGraph) and downloads (millions weekly for the ecosystem), while CrewAI excels in multi-agent collaboration (~59K stars). OpenAI’s SDK is favored for simplicity with their models.

Here are the leading options people actually use, grouped by strengths:

Top Recommendations

• LangGraph (LangChain ecosystem): Best for production-grade, complex, stateful agents. It models workflows as graphs with checkpoints, persistence, human-in-the-loop interrupts, branching, retries, and observability (via LangSmith). Ideal for reliable, long-running tasks with compliance needs. It has strong enterprise traction (e.g., mentions at companies like Uber, LinkedIn, Klarna) and high downloads. Use this when control, durability, and scalability matter most. LangChain itself provides the broader toolkit for chains, tools, and integrations.

• CrewAI: Best for role-based multi-agent teams and rapid prototyping. Define agents with roles, goals, and backstories; they collaborate on tasks. It’s beginner-friendly, Python-based (MIT license), and popular for marketing/content/research workflows. It has very high community adoption and stars.

• OpenAI Agents SDK: Best lightweight/production option for OpenAI models (GPT-4o, o1, etc.). It offers simple primitives for agents, tool use, handoffs, sessions, tracing, and guardrails. Minimal overhead makes it fast for prototypes that scale; strong for OpenAI-centric stacks.

Strong Contenders by Use Case

• Microsoft Agent Framework (successor to AutoGen): Top choice for Microsoft/.NET/Azure enterprises. Focuses on multi-agent conversations, governance, and deep Azure/M365 integration. AutoGen is now in maintenance mode.

• LlamaIndex (Workflows): Excellent for RAG/data-heavy or document-grounded agents. Strong ecosystem for retrieval and knowledge integration (~52K stars).

• Pydantic AI: Great for type-safe, reliable Python agents with structured outputs and validation. Growing fast for production reliability.

• Mastra: Leading TypeScript/JavaScript-native option for web/app-integrated agents.

• Google ADK: Strong for Google Cloud/Vertex AI and Gemini users, with multi-language support.

Other Notable Mentions

• smolagents and Agno: Minimalist or high-performance options for specific needs (e.g., code-first or lightweight agents).
• DSPy: Optimization-focused (“programming not prompting”) for self-improving systems.

No-code/low-code platforms (e.g., n8n, Dify, Langflow, Flowise) see heavy use for quicker builds or non-developers, but they are more platforms than pure code frameworks.

Quick Guidance on Choosing

• Complex/production workflows needing reliability: Start with LangGraph.
• Collaborative agent teams/rapid starts: CrewAI.
• OpenAI models + simplicity: OpenAI Agents SDK.
• Enterprise Microsoft stack: Microsoft Agent Framework.
• Data/RAG focus: LlamaIndex.
• TypeScript/JS: Mastra.
• Match to your ecosystem (cloud provider, language, model preference) for best results—many lists note that the “best” depends on your specific constraints more than raw popularity.

The landscape evolves quickly (new SDKs from OpenAI, Google, and Anthropic emerged or matured recently), so check GitHub activity, PyPI downloads, and recent case studies for the latest. LangChain’s ecosystem remains the most battle-tested for serious use.

When selecting an AI agent framework, the choice depends heavily on your language stack, workflow complexity, production requirements, and ecosystem preference. Based on recent surveys, technical evaluations, and community adoption patterns, the top AI agent frameworks and platforms currently in use are as follows:

1. LangChain

Type: Open-source framework for LLM applications Best For: Rapid prototyping of complex agentic workflows, multi-step reasoning, RAG pipelines Key Features: Modular components for chain creation, tool calling, and retrieval Native pairing with LangGraph (stateful multi-agent orchestration) and Deep Agents (long-running workflows) Observability via LangSmith with debugging, cost, latency, and response monitoring Provider-agnostic: supports OpenAI, Anthropic, Google Gemini, AWS Bedrock, and others Use Cases: Research assistants, automated summarization, multi-agent orchestration in production Considerations: Heavy dependencies may require careful version control; abstraction layers can complicate debugging in very complex workflows Website: langchain.com

2. Mastra

Type: TypeScript-first AI agent framework Best For: Production-grade, full-stack AI applications in TypeScript/JavaScript environments Key Features: Integrated workflows, evaluations, observability, and multi-agent management Model routing across hundreds of LLMs Apache 2.0 open-source license Use Cases: Teams shipping production AI products with TypeScript, prioritizing reliability and observability Tradeoffs: Newer ecosystem, fewer integrations compared to Python-native frameworks Website: mastra.ai

3. CrewAI

Type: Multi-agent orchestration framework Best For: Role-based multi-agent collaboration workflows Key Features: Agents assigned distinct roles, personas, and task responsibilities Integrates with PostgreSQL, MongoDB, Qdrant, Weaviate, web scraping tools Supports local model runtimes (Ollama) and MCP protocol Use Cases: Research pipelines, collaborative AI systems, workflow automation Tradeoffs: Optimal only when task division across agents is natural; otherwise, single-agent architecture may be simpler Website: crewai.com

4. Microsoft Agent Framework (MAF) / AutoGen

Type: Multi-agent orchestration framework and SDK Best For: Enterprise AI applications within the Microsoft ecosystem Key Features: Unified successor to AutoGen and Semantic Kernel Python +.NET support with Azure integration and responsible AI guardrails Graph-based workflows and orchestrations for multi-agent systems Use Cases: Enterprise chatbots, virtual assistants, AI-enhanced productivity tools Tradeoffs: Best for teams already invested in Microsoft infrastructure Website: github.com/microsoft/agent-framework

5. Google ADK 2.0

Type: Google Cloud-native agent development kit Best For: Multi-agent systems with GCP integration Key Features: Graph-based execution engine, human-in-the-loop support, state persistence Cross-language support: Python, TypeScript, Go, Java, Kotlin Managed Agent APIs and Agent2Agent interoperability Use Cases: Enterprise-grade AI orchestrations leveraging Google Cloud (Vertex AI, Gemini models) Considerations: Best for GCP-aligned teams requiring opinionated agent runtime Website: cloud.google.com

6. OpenAI Agents SDK

Type: Lightweight multi-agent SDK Best For: Applications closely integrated with OpenAI models Key Features: Primitives for Agents, Handoffs, Guardrails, Sandbox Agents Control over multi-agent workflows with minimal abstraction Python + TypeScript support Use Cases: Developers needing direct access to OpenAI APIs, small-to-medium multi-agent projects Tradeoffs: Limited standalone production tooling — works best within OpenAI ecosystem Website: openai.com

7. LlamaIndex (Workflows)

Type: Retrieval-augmented agent framework Best For: Data-heavy, document-centric pipelines requiring enterprise retrieval Key Features: Integration with PDFs, structured databases, knowledge bases Supports Python and TypeScript Use Cases: Knowledge management systems, RAG applications, intelligent document search Tradeoffs: Limited workflow/orchestration capabilities compared to LangChain or Mastra Website: llamaindex.ai

8. RASA (CALM)

Type: Conversational AI + chatbot framework Best For: Enterprises needing customizable, LLM-native dialogue systems Key Features: Flexible conversation design via CALM (replaces intent pipelines) Supports rule-based and ML-based workflows Use Cases: Scalable chatbots, virtual assistants, enterprise conversational interfaces Tradeoffs: Requires substantial ML/NLP expertise and compute resources for operation Website: rasa.com

9. smolagents (Hugging Face Transformers Agents)

Type: Minimalist, Python-native agent framework Best For: Code-first agent development for multi-step workflows Key Features: Direct Python tool integration, lightweight (~1,000 lines of code) Flexible transformer orchestration, model fine-tuning, open-source Use Cases: Research-heavy AI applications, lightweight or auditable agent deployments Tradeoffs: Less opinionated; requires familiarity with Python and LLM development Website: huggingface.co

How to Choose

Rapid prototyping across diverse LLMs: LangChain + LangGraph Production-grade TypeScript applications: Mastra Role-based multi-agent orchestration: CrewAI Microsoft Stack + Enterprise workflows: MAF Google Cloud ecosystem: Google ADK 2.0 OpenAI-centered apps: OpenAI Agents SDK Document-heavy retrieval workflows: LlamaIndex Conversational AI: RASA Lightweight, code-first research agents: smolagents

Choosing the right framework depends on your team’s language ecosystem, workflow complexity, required production readiness, and model provider preferences. Many enterprise setups combine a core framework (LangChain, Mastra) with observability/monitoring tools within the same ecosystem for safer deployment.

The best AI agent frameworks that developers actually use in production are LangGraph, CrewAI, and Microsoft AutoGen (along with vendor SDKs like the OpenAI Agents SDK).

Top AI Agent Frameworks

LangGraph Best for: Complex, stateful, and cyclical production workflows. Why people use it: Built by the creators of LangChain, it offers fine-grained architectural control, built-in persistence, time-travel debugging, and human-in-the-loop checkpoints. It is widely considered the industry standard for scaling secure, deterministic agents. CrewAI Best for: Role-based multi-agent collaboration and fast prototyping. Why people use it: It has one of the lowest learning curves and uses a clear "crew/role/task" abstraction (e.g., assigning researcher, writer, and editor roles to different agents). It is the fastest way to spin up collaborative multi-agent demos. Microsoft AutoGen (and AG2) Best for: Conversational multi-agent systems and code-heavy automation. Why people use it: Agents converse, negotiate, and execute code dynamically to solve open-ended exploratory or research tasks. It remains popular for scientific workflows and Microsoft-stack environments. OpenAI Agents SDK Best for: Lightweight, minimal-abstraction workflows tied to GPT models. Why people use it: It provides a minimal surface area for agent delegation and tool handling with very low overhead. LlamaIndex Workflows Best for: Data-heavy and retrieval-augmented generation (RAG) agent pipelines. Why people use it: It excels when an agent's primary bottleneck is querying, parsing, and reasoning over massive proprietary document repositories.

Would you like to share what kind of workflow or task your agent needs to perform (e.g., document search, automated coding, multi-role teamwork) so I can recommend the exact framework that fits your project?

The AI agent framework landscape has matured past simple proof-of-concept scripts into robust tools built for complex, production-grade workflows. Developers and enterprises actually deploying agents lean toward a few dominant frameworks depending on whether they need complex control flows, fast prototyping, or TypeScript support.

The top-used AI agent frameworks break down by their core strengths:

LangGraph (Python / TypeScript) Best for: Complex, stateful, multi-agent orchestration and cyclic workflows. Why people use it: Built by the LangChain team, LangGraph treats agent execution as a state machine. It gives you fine-grained control over loops, interruptions, human-in-the-loop approvals, and precise error recovery, making it the industry standard for production-grade, deterministic agent architectures. CrewAI (Python) Best for: Rapid prototyping and role-based "team of specialists" setups. Why people use it: It lets you quickly spin up autonomous multi-agent crews where each agent has a specific role, backstory, and set of tools. It bridges autonomous task delegation ("Crews") with structured, deterministic steps ("Flows"), making it exceptionally fast for getting vertical AI applications off the ground. Microsoft Agent Framework / AutoGen (Python /.NET) Best for: Enterprise environments and mixed.NET/Python stacks. Why people use it: Unifying previous iterations like AutoGen and Semantic Kernel, Microsoft's framework is built explicitly for heavy enterprise routing, secure integration with Azure, and production-ready human-in-the-loop primitives. OpenAI Agents SDK (Python / TypeScript) Best for: Lightweight, minimalist agent loops and handoffs. Why people use it: If you want to avoid heavy abstractions, OpenAI's official lightweight SDK provides clean primitives for multi-agent handoffs and function calling without the steep learning curve of larger frameworks. Mastra (TypeScript) Best for: JavaScript/TypeScript-native web stacks. Why people use it: Most agent frameworks lean heavily on Python, leaving JS/TS developers behind. Mastra fills this gap by offering built-in developer tooling, workflow suspend/resume primitives, and native integration with modern frameworks like Next.js. LlamaIndex Workflows (Python) Best for: Data-heavy, RAG-centric (Retrieval-Augmented Generation) pipelines. Why people use it: If your agent's primary job is reasoning over massive proprietary data corpora, document parsing, and structured retrieval, LlamaIndex's event-driven workflow engine provides a streamlined way to coordinate those steps.

Are you building in Python or TypeScript, and what is the primary goal of your agent (e.g., data retrieval, multi-agent coding, customer support)? I can help you pick the exact framework that fits your tech stack.