Human-in-the-Loop AI Agent Frameworks Compared: LangGraph, CrewAI, OpenAI and More

Searching for 'human in the loop AI agent platforms' returns a long list of tools that all claim to do the same thing. Underneath, they do share one idea: the agent stops before something consequential, hands a request to a person, and continues when a decision comes back. What differs is how the pause is built, where the paused state lives, how long it can wait, and how much of the surrounding workflow the framework handles for you.

This guide compares six options against their own documentation, checked on 10 November 2026: LangGraph, the OpenAI Agents SDK, the Microsoft Agent Framework, CrewAI, Amazon Bedrock Agents and Temporal. It is not a ranking. The right choice depends on what you already run and how long your approvals wait. If you want the design thinking first, read Designing Human-in-the-Loop AI Agents for Business Workflows. If you want to see one framework in depth, read Pause an AI Agent for Human Approval with LangGraph interrupt().

Frameworks release fast, and some of the pages we read were updated within the last few weeks. Treat the table as a map and confirm details against the version you install.

The columns matter more than the names. 'Where paused state lives' decides whether approvals survive a restart, and 'how the decision comes back' decides how you wire your approval interface.

LangGraph's interrupt() can be called at any point in a node and can be conditional. It saves state through a checkpointer, returns your payload to the caller, and waits indefinitely until you resume with Command(resume=...) on the same thread ID. The value you resume with becomes the return value of the interrupt() call.

Its distinctive trait is that the node restarts from its first line on resume, so code before the interrupt runs again. That makes idempotency a design rule rather than an afterthought. LangGraph also supports multiple pending interrupts, resumed together by interrupt ID, and, from version 1.2.12, an optional response schema to validate the decision.

It fits best when your agent is already a graph with branches and loops, and you want the pause to sit exactly where the decision arises. The trade is that you must pick and operate a durable checkpointer, since an in-memory one loses pending approvals on restart.

In the OpenAI Agents SDK you mark a tool with needs_approval, either as always true or as an async function that decides per call. When the model calls such a tool, the run pauses and the result carries a list of interruptions with the agent name, tool name and arguments. You convert the result to a RunState, approve or reject each item, and run again with that state.

Several details are worth knowing. The same approval flow covers tools on handoff agents and tools inside agents used as tools, and nested approvals surface on the outer run. If the arguments cannot be safely inspected, the SDK treats the call as needing approval rather than guessing. You can set custom rejection messages so the model sees why a call was denied. And decisions can be made sticky with always_approve or always_reject, which the docs warn can leak across agents for shell and file-edit tools that share a name.

RunState is designed to be serialized, so approvals can wait in a database or queue. The documentation is unusually direct about security: keep the complete snapshot in server-side storage, authenticate and authorise the reviewer, take pending items from your own store rather than the client, and stop concurrent or replayed submissions from resuming the same snapshot twice. It also states that approval does not guarantee exactly-once execution, and recommends idempotency keys for actions with consequences.

In the Microsoft Agent Framework, a function tool can require approval, using an approval mode of always_require in Python or by wrapping the function in ApprovalRequiredAIFunction in .NET. When the model wants to call it, the agent run completes with a response that lists the user input required, rather than a final answer. Your code shows the function name and arguments to a person, builds an approval response, and runs the agent again in the same session.

The framework binds an approval response to a pending request recorded in the same session by default, and ignores an unbound response with a logged warning. Its documentation warns that turning this off can let a fabricated or replayed response approve a privileged call. A harness option adds standing approvals saved in the session and optional auto-approval rules. The documentation cautions that standing approvals are not tied to the tool instance that created them, so a replacement tool with the same name can match a saved rule. Scope sessions to a stable trust boundary.

It suits teams already on Azure and .NET or Python with the Microsoft stack, and who want approval as a property of a tool rather than a graph step.

CrewAI's documentation separates two approaches. For development and synchronous work, a Flow-based human feedback decorator, available from version 1.8.0, collects review through the console and routes on the feedback. For production, a webhook-based mode, described as an enterprise feature, sends a notification when a task needs input, holds the run in a Pending Human Input state, and accepts a resume call with the feedback and an approve flag.

Two cautions come straight from the documentation. Negative feedback makes the crew retry the task with your feedback added as context, so careless or noisy feedback can steer the next attempt. And webhook URLs must be supplied again in the resume call, because they are not carried over from the kickoff.

CrewAI's enterprise platform also describes flow review management with responder assignment, permissions, escalation policies, SLA management, dynamic routing and analytics. That is the most complete built-in approval management of the six, if you accept the platform. In the open-source mode, those operational pieces would be yours to build.

Bedrock Agents offers two mechanisms. User confirmation can be enabled for a specific action in an action group, and the agent then asks the end user to confirm or deny before the function is called, with the answer sent back as a confirmation state. AWS positions this as protection against prompt injection, noting that an agent will follow instructions that appear in tool results.

Return of control is the other route. The action group is configured so that, instead of calling a Lambda function, the agent returns the elicited parameters and an invocation ID to your application. Your application performs the action, which is the natural place for a human decision, and sends the result back in the next invocation using the same invocation ID.

It fits when your agent already lives in Bedrock and you want approval without adding another framework. We did not find, in these pages, built-in features for deadlines, reviewer assignment or audit, so plan those in your application. The same mechanisms are what you reach through Amazon Lex when you attach a Bedrock agent to a bot. For that route, see Amazon Lex vs AI Agent in Amazon Connect.

Temporal is a durable execution platform rather than an agent framework, and it handles the waiting part unusually well. In its human-in-the-loop recipe, a workflow asks a model to propose an action, and if the action is risky it waits on a condition, with a timeout, until an external system sends a decision as a Signal. Temporal's documentation says the wait can last hours, days or indefinitely while consuming no compute, that timers survive disruptions, and that all decisions are logged for audit.

The recipe also sends a notification through an activity, which is where an email or chat message would go, and lists extensions such as multiple approvers, escalation and query handlers to check status. You write the agent logic yourself or combine Temporal with another framework's agent loop.

It fits when approvals can wait a long time, when you need timeouts and escalation to be reliable, and when the approval is one step in a larger business process. It asks for more infrastructure and a different programming model than a library inside your agent.

Pausing is the easy part. The table lists the surrounding pieces, and whether the pages we read describe them as built in. Where we found nothing, that means we found nothing in those pages, not that the feature cannot be built.

Whichever you choose, keep an approval request record in your own database. It is the source of truth for who was asked, what they saw, who decided and when, and it survives a change of framework.

• Prompt injection: tool results and retrieved documents can contain instructions. Bedrock's documentation says as much, and it is why the approval must sit in code outside the model, on the action, not in the prompt.
• Forged or replayed decisions: bind each decision to a specific pending request and consume it once. The Microsoft and OpenAI documentation both address this directly.
• Reviewer identity: take the approver from your authentication layer, never from the request body, and check they are allowed to approve this action and amount.
• Stale approvals: if an approved action runs hours later, check that its preconditions still hold, and record the framework and prompt versions alongside paused work, as the OpenAI documentation suggests.
• Sensitive payloads: paused state can contain customer data and arguments. Encrypt it, limit access and set retention.
• Duplicate effects: approval permits a call, it does not guarantee it happens once. Use idempotency keys for payments and messages.

Our own service page lists LangGraph and CrewAI as typical stack components, and we choose by fit rather than loyalty. If you already run one of these, adding approvals to it is usually cheaper than switching to another for that feature alone.

1. Build the same small flow in your top two choices: propose a refund, pause for approval, then act.
2. Kill the process while a request is pending, restart, and resume. Note what survives.
3. Resume twice with the same decision, and confirm the action happens once.
4. Let a request sit past your deadline and see what you must write to handle it.
5. Try to approve as the wrong user and with a forged request ID.
6. Measure how many lines of your own code sit around the framework's pause.

A day on this will tell you more than any table. If the workflow is in financial services, also read Human Approval Checkpoints for AI Agents in Regulated Finance. For the wider build, see Agentic Workflows: Human in the Loop or Fully Autonomous, and for help designing approval steps into your own agent, see our agentic workflow orchestration service.