Data as of Sep 21, 2026 · Based on 341 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
For custom, engineering-led multi-turn customer agents, choose 
Brands AI recommends here
Named in 46% of answers
Best when you want a developer-led, fully custom customer support agent: LangChain (+LangGraph) provides flexible LLM orchestration, memory and tool integration—but it requires engineering effort to build and operate.
Named in 45% of answers
Pick Rasa when you need on‑prem deployment, data privacy, and fine‑grained dialogue control: it gives intent/slot and policy control via ML, at the cost of more build and ops work versus managed platforms.
Named in 33% of answers
Named in 27% of answers
Source | Citation share |
|---|---|
 Top 10 Frameworks for Building AI Agents in 2025https://www.linkedin.com/pulse/top-10-frameworks-building-ai-agents-2025-rachel-grace-okrqc | 3% |
 Top 10 AI Agent Frameworks (2026): Expert-Tested & Reviewed | Lindyhttps://www.lindy.ai/blog/best-ai-agent-frameworks | 2% |
 7 Agent-to-Agent Interaction Frameworks That Transform AI Development | Galileohttps://galileo.ai/blog/agent-to-agent-interaction-frameworks | 2% |
 Top 12 AI Agent Frameworks That Actually Do the Jobhttps://www.kubiya.ai/blog/top-12-ai-agent-frameworks-that-actually-do-the-job | 2% |
How To Build Multi-Turn AI Conversations With Rasahttps://rasa.com/blog/multi-turn-conversation | 2% |
Top Content on LinkedInhttps://www.linkedin.com/pulse/navigating-future-top-frameworks-building-ai-chatbots-levi-s-blown-viiqc | 2% |
 404 - ArticleTed - News and Articleshttps://www.articleted.com/article/988926/323795/Top-Frameworks-for-Building-AI-Chatbots-in-2025 | 1% |
 Enhancing Multi-Turn Conversations: Ensuring AI Agents Provide ...https://www.getmaxim.ai/articles/enhancing-multi-turn-conversations-ensuring-ai-agents-provide-accurate-responses/ | 1% |
 Best Conversational AI Platforms for Customer Service 2025: Enhancing | ReelMindhttps://reelmind.ai/blog/best-conversational-ai-platforms-for-customer-service-2025-enhancing-user-experience | 1% |
 Top 10 AI Agent Frameworks in 2026: The Practical Builder's Guide | AgentForge Hubhttps://www.agentforgehub.com/posts/top-10-ai-agent-frameworks-comparison | 1% |
For a **complex, multi-turn customer-service agent**, I’d start with **LangGraph** if you expect the system to become stateful, tool-heavy, and operationally complex. It is specifically designed for long-running, stateful agents, with persistence, durable execution, human-in-the-loop workflows, and explicit control…
For a complex, multi-turn customer-service agent, I’d start with LangGraph if you expect the system to become stateful, tool-heavy, and operationally complex. It is specifically designed for long-running, stateful agents, with persistence, durable execution, human-in-the-loop workflows, and explicit control over agent state.
That said, OpenAI Agents SDK is an excellent alternative if you want a simpler architecture and are primarily using OpenAI models. It provides agents, tools, sessions, handoffs, guardrails, human-in-the-loop mechanisms, and tracing without requiring you to build as much orchestration infrastructure yourself.
| If your priority is… | Consider |
|---|---|
| Complex workflows and explicit state machines | LangGraph |
| Long-running conversations with resumability | LangGraph |
| Human approval at specific workflow points | LangGraph |
| Multiple specialized support agents | Either |
| Simple, clean OpenAI-native implementation | OpenAI Agents SDK |
| Minimal orchestration code | OpenAI Agents SDK |
| Maximum control over model/provider infrastructure | LangGraph |
For customer service specifically, I'd structure the system roughly like this:
┌──────────────────┐
│ Customer message │
└────────┬─────────┘
↓
┌──────────────────┐
│ Triage / Router │
└────────┬─────────┘
↓
┌────────────────────┼────────────────────┐
↓ ↓ ↓
Order Agent Billing Agent Support Agent
│ │ │
└────────────────────┼────────────────────┘
↓
┌──────────────────┐
│ Tool execution │
│ CRM / orders / │
│ payments / KB │
└────────┬─────────┘
↓
┌──────────────────┐
│ State + Memory │
└────────┬─────────┘
↓
┌──────────────────┐
│ Customer reply │
└──────────────────┘
The important architectural decision isn't actually "which framework?" It's how you represent state. For example, your state might contain:
{
"customer_id": "...",
"conversation_summary": "...",
"current_intent": "refund",
"order_id": "...",
"authenticated": True,
"pending_action": "issue_refund",
"tools_used": [...],
"requires_human": False
}
That lets the agent survive a conversation such as:
"My package never arrived." → finds the order → discovers it was marked delivered → asks for confirmation → customer says "Yes, that's the wrong address" → investigates the shipping address → offers the appropriate resolution → asks for approval before issuing a refund → resumes after approval.
That's where a stateful orchestration framework becomes much more valuable than a simple "chatbot + prompt" architecture. LangGraph's persistence layer, for example, supports thread-scoped checkpoints as well as longer-term memory across conversations.
I'd avoid starting with a swarm of autonomous agents.
Instead, use:
one customer-facing agent + specialized capabilities/tools, and introduce specialist agents only where they genuinely help.
The OpenAI Agents SDK explicitly supports both patterns: a manager can call specialist agents as tools, or the conversation can be handed off to a specialist.
For customer service, I'd generally favor the manager pattern:
Customer-facing Agent
│
├── order_lookup()
├── account_lookup()
├── refund_policy()
├── issue_refund()
├── create_ticket()
└── billing_specialist()
rather than:
Customer
↓
Triage Agent
↓
Order Agent
↓
Billing Agent
↓
Refund Agent
↓
Escalation Agent
The latter can become difficult to debug and maintain surprisingly quickly.
If I were building this today, I'd prototype both LangGraph and OpenAI Agents SDK for a small representative set of conversations, then choose based on how much explicit workflow control you actually need.
If you already know you'll have long-running conversations, persistent state, approvals, retries, complex branching, and external system side effects, I'd lean toward LangGraph. Its design is explicitly aimed at those requirements.
If the goal is to get a clean production customer-service agent running quickly, with relatively straightforward orchestration, I'd lean toward OpenAI Agents SDK. It has built-in sessions, handoffs, guardrails, tools, human-in-the-loop support, and tracing.
One other thing I'd treat as first-class from day one: tool-level guardrails and observability. Customer-service agents can cause real-world side effects—refunds, cancellations, address changes—so you want validation around those operations rather than relying solely on the model's instructions. The Agents SDK, for example, supports guardrails around individual tool calls as well as agent inputs/outputs.
If you tell me your stack (Python/TypeScript), model provider, and the kinds of customer-service actions the agent needs to perform, I can lay out a concrete production architecture—including state schema, agent/tool boundaries, memory, RAG, human escalation, and evaluation strategy.
For a **complex, multi-turn customer service agent**, I’d start with **LangGraph** (often paired with LangChain) or **OpenAI Agents SDK**, depending on your architecture. [LangChain](https://www.langchain.com/resources/ai-agent-frameworks?utm_source=chatgpt.com) - **LangGraph** — best when you need durable state,…
For a complex, multi-turn customer service agent, I’d start with LangGraph (often paired with LangChain) or OpenAI Agents SDK, depending on your architecture.
A production customer-service architecture usually needs:
For a new build, a common stack would be LangGraph + a strong LLM + vector database + CRM APIs + evaluation tooling.
For complex, multi-turn customer service conversations—where the agent needs to maintain strict state, handle looping paths (e.g., asking clarifying questions, validating user input), integrate human-in-the-loop escalation, and safely execute backend tools (like processing refunds or modifying accounts)—the industry…
For complex, multi-turn customer service conversations—where the agent needs to maintain strict state, handle looping paths (e.g., asking clarifying questions, validating user input), integrate human-in-the-loop escalation, and safely execute backend tools (like processing refunds or modifying accounts)—the industry consensus points to a few top-tier frameworks.
The best choice depends heavily on your infrastructure preferences and how much low-level control you need over the conversation flow.
To help narrow down the ideal stack, tell me:
For a complex, multi-turn customer service agent—where maintaining exact state, handling erratic user inputs, enforcing guardrails, and executing precise database or API actions are critical—there isn't a single "one-size-fits-all" winner, but industry consensus strongly points to…
For a complex, multi-turn customer service agent—where maintaining exact state, handling erratic user inputs, enforcing guardrails, and executing precise database or API actions are critical—there isn't a single "one-size-fits-all" winner, but industry consensus strongly points to LangGraph as the top tier for production-grade reliability.
Customer service requires deterministic control wrapped around probabilistic Large Language Models (LLMs). If a user changes their mind mid-conversation, injects an unexpected prompt, or needs an escalation path, an unguided "free-flowing" agent will hallucinate or lose its place.
The top framework options break down by how they handle multi-turn complexity:
If your priority is predictability, preventing infinite loops, and flawless state management across long, messy customer service threads, go with LangGraph . If you want to get a prototype running fast with multiple specialist personas collaborating on tickets, start with CrewAI.
To help narrow down the right architectural choice, tell me:
For a **customer service agent that handles complex, multi-turn conversations**, the “best” framework depends on whether you prioritize **conversation control**, **engineering flexibility**, or **speed of deployment**. For most serious customer support systems, I would narrow the choice to a few options rather than…
For a customer service agent that handles complex, multi-turn conversations, the “best” framework depends on whether you prioritize conversation control, engineering flexibility, or speed of deployment. For most serious customer support systems, I would narrow the choice to a few options rather than pick a generic chatbot framework.
| Framework | Best fit | Why |
|---|---|---|
| Rasa | Enterprise customer support, regulated workflows, voice/chat support | Strong conversation management, explicit state handling, self-hosting options, and controls for complex dialogues |
| LangGraph | Engineering teams building custom AI agents | Excellent for stateful, multi-step workflows, tool calling, persistence, and human-in-the-loop flows |
| OpenAI Agents SDK | Teams building OpenAI-centered agents quickly | Simple agent/tool/handoff patterns with less infrastructure |
| Microsoft Agent Framework | Microsoft/Azure-heavy organizations | Good fit if your stack is .NET, Azure, and enterprise Microsoft services |
| LlamaIndex | Knowledge-heavy support bots | Strong when the main challenge is connecting the agent to documents, policies, and data |
LangChain Rasa## Recommendation for your use case
For a complex customer service agent, I would typically choose one of these architectures:
Best when you want maximum customization.
Architecture:
Customer
↓
Chat/Voice Channel
↓
LangGraph Agent
├── Conversation state
├── Memory
├── Tool calls
│ ├── CRM lookup
│ ├── Order system
│ ├── Billing
│ └── Ticket creation
├── Knowledge retrieval (RAG)
└── Human escalation
Why it works:
LangGraph is often selected by teams that want to build their own production agent platform rather than buy a complete conversational platform.
Choose this when:
Traditional customer service has many edge cases where a purely autonomous LLM can behave unpredictably. A dedicated conversation layer can make those flows easier to govern.
Regardless of framework, build around these capabilities:
Persistent conversation state
Customer identity
Previous issues
Current task status
Tool use
CRM
Order management
Knowledge base
Refunds/returns
Scheduling
Hybrid workflow design
Deterministic flows for risky actions
LLM reasoning for open-ended parts
Human handoff
Transfer with conversation summary
Preserve context
Evaluation and tracing
Track failed conversations
Measure resolution rate
Review hallucinations and tool errors
For a new build:
For a large enterprise customer service deployment, I would seriously evaluate Rasa or a Rasa + LangGraph combination.
A key design question is whether your agent is mostly answering questions (“Where is my order?”) or performing workflows (“Change my shipping address, issue a refund, verify identity, and update my account”). The second category strongly favors a stateful agent architecture.
For a **complex, multi-turn customer-service agent**, my default recommendation in 2026 is **LangGraph**—especially if you expect long-running conversations, branching workflows, human escalation, persistent state, and multiple model/tool providers. ### My shortlist Framework | Best fit | Why I'd choose it
For a complex, multi-turn customer-service agent, my default recommendation in 2026 is LangGraph—especially if you expect long-running conversations, branching workflows, human escalation, persistent state, and multiple model/tool providers.
| Framework | Best fit | Why I'd choose it |
|---|---|---|
| LangGraph | Complex, stateful customer support | Explicit state machines/graphs, durable workflows, strong control over multi-turn behavior |
| OpenAI Agents SDK | OpenAI-centric, simpler architecture | Excellent built-in sessions, handoffs, guardrails, tools, human-in-the-loop, and tracing OpenAI GitHub OpenAI GitHub |
| Microsoft Agent Framework | Microsoft/Azure stack | Strong choice if your infrastructure is already Microsoft-oriented |
| Google ADK | GCP-native systems | Good if you're deeply invested in Google's ecosystem |
| CrewAI | Fast multi-agent prototypes | Very approachable role/task-based abstraction |
LangChain's own 2026 comparison similarly positions LangGraph for stateful multi-agent orchestration and the OpenAI Agents SDK for tightly scoped assistants with relatively little abstraction.
I'd use a stateful orchestration layer, rather than simply giving an LLM a big system prompt:
┌───────────────┐
│ Customer │
│ conversation │
└───────┬───────┘
↓
┌───────────────┐
│ Triage / │
│ intent agent │
└───────┬───────┘
↓
┌──────────────┼──────────────┐
↓ ↓ ↓
Billing Technical Orders
agent agent agent
│ │ │
└──────────────┼──────────────┘
↓
┌───────────────┐
│ Policy / │
│ safety checks │
└───────┬───────┘
↓
┌───────────────┐
│ Human agent │
│ escalation │
└───────────────┘
The important part is that conversation state lives outside the model. Store things such as:
That makes the agent resilient when a conversation lasts dozens of turns instead of relying on an ever-growing prompt.
If you're primarily using OpenAI models and don't need a highly customized workflow engine, I'd seriously consider the OpenAI Agents SDK. It already provides persistent sessions, tools, handoffs, guardrails, human-in-the-loop mechanisms, and tracing.
For example, you can have a triage agent hand a conversation to specialized refund, billing, or technical-support agents. The SDK explicitly supports this handoff pattern.
It also supports a manager pattern where one agent remains responsible for the customer-facing response while invoking specialist agents as tools.
So my rule of thumb:
If you tell me your stack (Python/TypeScript), model provider, CRM/helpdesk (e.g. Zendesk/Salesforce), and whether the agent needs to take actions like refunds or order changes, I can recommend a concrete architecture and framework rather than just the generic shortlist.
For example, you can have a triage agent hand a conversation to specialized refund, billing, or technical-support agents. The SDK explicitly supports this handoff pattern.
It also supports a manager pattern where one agent remains responsible for the customer-facing response while invoking specialist agents as tools.
That makes the agent resilient when a conversation lasts dozens of turns instead of relying on an ever-growing prompt.
If you're primarily using OpenAI models and don't need a highly customized workflow engine, I'd seriously consider the OpenAI Agents SDK. It already provides persistent sessions, tools, handoffs, guardrails, human-in-the-loop mechanisms, and tracing.
For a complex, multi-turn customer service agent that requires deep state management, conditional branching (e.g., checking order status, processing refunds, or escalating to a human), and robust tool execution, the undisputed front-runner framework is **[LangGraph](https://www.langchain.com/langgraph)** (by the…
For a complex, multi-turn customer service agent that requires deep state management, conditional branching (e.g., checking order status, processing refunds, or escalating to a human), and robust tool execution, the undisputed front-runner framework is LangGraph (by the creators of LangChain).
While linear chains work for simple Q&A, multi-turn conversational agents behave like state machines. Customers change their minds, provide incomplete info, or jump between topics, requiring an architecture that can cycle, pause, and remember context precisely.
Top framework options and how they stack up for this specific use case include:
No matter the framework, a production-grade customer support agent requires:
authenticated=True, order_id=12345, sentiment=frustrated).If you want to proceed, I can:
There is no single "absolute best" framework, but for building production-ready customer service agents capable of managing complex, multi-turn conversations, **[LangGraph](https://www.reddit.com/r/AI_Agents/comments/1i782di/best_tool_for_building_a_complex_conversational/)**,…
There is no single "absolute best" framework, but for building production-ready customer service agents capable of managing complex, multi-turn conversations, LangGraph, Rasa , and enterprise systems like Sierra represent the industry gold standards. Managing multi-turn logic requires sophisticated state management, short-term memory, and robust error handling.
The right platform depends entirely on your team's engineering capacity and the complexity of your custom logic.
Technical Frameworks (For Engineering Teams)
If you have a development team and need complete control over state management, underlying code, and hosting environments, utilize a code-first framework:
Autonomous Enterprise Platforms (For Managed Scaling)
If you prefer an out-of-the-box system that handles infrastructure, LLM routing, and deep helpdesk synchronization, look into these specialized platforms:
Comparative Framework Breakdown
| Feature / Metric | LangGraph | Rasa | Sierra / Ada |
|---|---|---|---|
| Development Speed | Medium (Requires Python/JS) | Medium-Low (Heavy configuration) | High (Low-code/No-code setup) |
| State Flexibility | Maximum (Fully customizable code) | High (Strict conversational flows) | Managed (Platform controlled) |
| Integrations | Manual (Via custom API tools) | Native & Custom (Action server) | Built-in (CRM, Helpdesk hooks) |
| Hosting & Control | Self-hosted or LangGraph Cloud | Self-hosted, Hybrid, or Cloud | Fully SaaS / Managed |
Crucial Elements for Multi-Turn Success
Regardless of the framework you choose, ensuring success in complex customer support relies on specific architectural patterns:
user_authenticated: true, order_id: 12345 ) across the entire interaction so the customer never has to repeat themselves.To provide a more tailored recommendation, what CRM or helpdesk software do you use, and do you want to build this natively with code or configure it via a no-code UI?
For **complex, multi-turn customer-service conversations**, I’d recommend **OpenAI Agents SDK** if you’re primarily using OpenAI models. If you need maximum model/provider independence and very explicit workflow/state control, **LangGraph** is the strongest alternative. ### My recommendation **Start with OpenAI Agents…
For complex, multi-turn customer-service conversations, I’d recommend OpenAI Agents SDK if you’re primarily using OpenAI models. If you need maximum model/provider independence and very explicit workflow/state control, LangGraph is the strongest alternative.
Start with OpenAI Agents SDK for your first production version.
It gives you the pieces that matter for customer support without forcing you to build the orchestration runtime yourself:
A good architecture would look roughly like:
┌──────────────────┐
│ Customer Agent │
│ / Triage Agent │
└────────┬─────────┘
│
┌───────────────┼────────────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌───────────┐
│ Billing │ │ Orders │ │ Technical │
│ Agent │ │ Agent │ │ Agent │
└──────────┘ └──────────┘ └───────────┘
│ │ │
└───────────────┼────────────────┘
▼
┌─────────────────┐
│ Business Tools │
│ CRM / Orders / │
│ Payments / KB │
└─────────────────┘
│
▼
┌─────────────────┐
│ Human Escalation│
└─────────────────┘
The important design choice is not to make every customer interaction a multi-agent interaction. Keep a primary agent responsible for the conversation and delegate only when specialization genuinely helps.
For example, OpenAI's SDK supports two useful patterns: agents-as-tools, where the main agent retains control, and handoffs, where a specialist takes over the conversation.
I'd choose LangGraph if your support workflow looks more like a complicated state machine:
Customer
↓
Authenticate
↓
Understand issue
↓
Retrieve account
↓
Determine eligibility
↓
┌───────────────┐
│ Refund? │
│ Replacement? │
│ Troubleshoot? │
└───────┬───────┘
↓
Approval required?
↓
Human review?
↓
Execute action
↓
Verify outcome
↓
Follow up
LangGraph is particularly attractive when you need explicit state, branching, resumability, long-running workflows, and tight control over execution. A current 2026 comparison from LangChain positions LangGraph as its stateful orchestration layer for complex/long-running agents.
| Requirement | My pick |
|---|---|
| Fastest path to a sophisticated support agent | OpenAI Agents SDK |
| Multi-turn conversations | OpenAI Agents SDK |
| Specialist agents / routing | OpenAI Agents SDK |
| Tool calling + guardrails | OpenAI Agents SDK |
| Explicit workflow/state machine | LangGraph |
| Very long-running workflows | LangGraph |
| Maximum model-provider flexibility | LangGraph |
| Python/TypeScript application | Either |
| Complex deterministic business processes | LangGraph |
The biggest mistake I'd avoid is choosing a framework based on the number of "agents" it supports. Customer service is fundamentally a state-management, tool-use, policy, and escalation problem, with an LLM sitting inside that system.
If I were building this today, I'd start with:
OpenAI Agents SDK + Sessions + PostgreSQL/your existing data store + typed business tools + retrieval + tool-level guardrails + human escalation + tracing/evals.
Then I'd introduce LangGraph only if the workflow becomes sufficiently stateful/branch-heavy that explicit graph orchestration is buying you something.
If you tell me your stack (Python vs TypeScript, OpenAI vs multi-model, and what your support agent needs to do—refunds, orders, CRM, etc.), I can sketch the production architecture and folder structure I'd use.
For a **customer service agent that must handle complex, multi-turn conversations**, I would usually prioritize **state management, conversation control, tool orchestration, memory, observability, and human handoff** over just “chatbot” features. There is no single best framework, but these are the strongest choices…
For a customer service agent that must handle complex, multi-turn conversations, I would usually prioritize state management, conversation control, tool orchestration, memory, observability, and human handoff over just “chatbot” features. There is no single best framework, but these are the strongest choices depending on your needs.
Best when: You want a highly capable AI agent that can reason, call tools, maintain state, branch, retry, and involve humans.
Strengths:
Tradeoff:
For a sophisticated support agent, this is often my default engineering recommendation.
Best when: The conversation itself is the product and you need reliability, governance, and predictable behavior.
Strengths:
Tradeoff:
Rasa is especially compelling if your support agent needs strict policies and consistent handling of edge cases.
Best when: You want a clean implementation with tool calling and agent handoffs without adopting a large framework.
Strengths:
Tradeoff:
Best when: Your customer service environment already uses Azure, Microsoft 365, Dynamics, or .NET.
Strengths:
Tradeoff:
A production architecture often looks like:
Customer
|
Chat / Voice Channel
|
Conversation Orchestrator
(LangGraph or Rasa)
|
+-----------------------+
| Intent + Context |
| Memory |
| Policy Checks |
| Tool Selection |
+-----------------------+
|
Tools:
- CRM lookup
- Order system
- Billing
- Knowledge base
- Ticket creation
- Human escalation
Key capabilities to design for:
| Capability | Why it matters |
|---|---|
| Persistent conversation state | Customers return later and expect continuity |
| Tool calling | The agent must take actions, not just answer |
| Guardrails | Prevent incorrect refunds, promises, or policy violations |
| Human handoff | Escalate gracefully with full context |
| Evaluation framework | Measure resolution rate and failure modes |
| Tracing | Debug why an agent made a decision |
For a genuinely complex, multi-turn customer service agent, I would start with LangGraph or Rasa, not a simple chatbot framework.
If you share your expected scale (e.g., 1k vs 1M conversations/month), channels (chat, voice, email), and whether the agent must execute transactions, I can suggest a more specific architecture.
