Conversational AI agent framework in LLM Agent Frameworks and Tooling

Conversational AI agent framework in LLM Agent Frameworks and Tooling | Parse

For your use case, I'd start with LangGraph. If you're building a customer service agent that needs to handle complex, multi-turn conversations, remember previous interactions, execute business workflows, and escalate to human agents, it's one of the strongest frameworks to evaluate.

The key isn't simply generating good responses. It's reliably managing conversation state, making decisions across multiple turns, using business tools correctly, and recovering when a workflow is interrupted. LangGraph is specifically designed for stateful, long-running agent workflows.

The best frameworks to consider

1. LangGraph

(Best overall for complex workflows)

Built for explicit control over conversation state, branching workflows, persistent memory, retries, and human approval. Particularly well suited to customer service cases that span multiple steps or sessions.

Best for: production systems with complex business rules and custom integrations.

Official documentation

2. OpenAI Agents SDK

(Best for a lightweight agent implementation)

Provides agents, tool calling, handoffs between specialists, guardrails, and tracing. Sessions can preserve conversation history across multiple runs. A good choice if you want to build quickly using OpenAI models.

Best for: a code-first support agent with relatively straightforward orchestration.

Official documentation

3. Microsoft Agent Framework

Worth evaluating if your organization relies on Azure, Microsoft 365, .NET, or Microsoft enterprise infrastructure. It brings agent orchestration and workflow capabilities into the Microsoft ecosystem.

Best for: enterprise teams invested in Microsoft's stack.

Official documentation

4. Rasa

Consider it if you want a conversation-centric platform with structured dialogue, business-defined flows, and greater emphasis on predictable support interactions rather than unconstrained agent behavior.

Best for: customer support experiences where controlled dialogue and operational predictability are priorities.

Official website

How I'd architect your customer service agent

For a genuinely complex support agent, I'd use LangGraph to orchestrate the workflow and keep business-critical actions deterministic wherever possible.

1. Conversation intake

Identify intent, authenticate the customer, extract relevant details

2. Persistent conversation state

Customer context, issue status, previous steps, unresolved questions

Knowledge agent

Policies and FAQs

Order agent

Orders and delivery

Billing agent

Refunds and payments

Human escalation

Approvals and complex cases

3. Validate, execute, and verify

Check permissions, call business APIs, confirm results, update case state

4. Respond and resume when needed

Explain the outcome, preserve context, continue the conversation across sessions

LangGraph's persistence and checkpointing support continuing conversations after interruptions and pausing workflows for human intervention.

A few implementation principles matter regardless of framework:

Separate conversation history from business state. Store structured facts such as order ID, refund status, and outstanding questions separately from the chat transcript.
Use APIs as the source of truth. The model should not invent order statuses, refund eligibility, or completed actions.
Require approval for sensitive actions. Define explicit authorization and escalation rules.
Test complete conversations. Evaluate multi-turn scenarios, conflicting instructions, missing information, tool failures, and recovery—not just individual answers.
Instrument every workflow. Track resolution rate, escalation rate, factual accuracy, latency, and cost.

My final recommendation

Start with LangGraph if complex workflows, durable state, and fine-grained control are your main priorities. Choose the OpenAI Agents SDK if you want a simpler implementation and its agent primitives meet your requirements. These aren't mutually exclusive in principle, but I'd avoid adding multiple orchestration layers until you have a demonstrated need.

One question to narrow this down

What environment are you building in?

1. Preferred development stack

Python

TypeScript / Node.js

C# / .NET

No-code / low-code

2. How complex are the conversations?

Mostly FAQs and knowledge retrieval

Multi-step workflows involving orders, billing, refunds, and escalations

Long-running cases that resume across days or channels

3. Model preference

OpenAI models only

Flexibility to switch between model providers

Self-hosted or open-weight models

[Recommend my stack]

Conversational AI agent framework

Which brands AI mentions

Recommendations by AI engine

Questions and answers

See where your brand stands in this ranking