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AI In IndustryTop 10 Best Chatbots Software of 2026
Top 10 chatbots software picks with side-by-side comparisons, strengths, and tradeoffs for teams evaluating Microsoft Copilot Studio, Dialogflow, Lex, and more.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
HubSpot Chatbot Builder is the best pick when you want website chat that qualifies leads and updates contacts inside your HubSpot workflow, whereas Zendesk AI Agents fits better if your support team needs an AI agent tightly linked to ticket workflows and seamless handoff to agents.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
HubSpot Chatbot Builder
Live agent handoff connects bot conversations to HubSpot service workflows with preserved context.
Built for fits when HubSpot users need website chat that updates contacts and routes to service agents..
Zendesk AI Agents
Editor pickContext-preserving live escalation that routes unanswered conversations into Zendesk agent workflows.
Built for fits when support teams need an AI chat agent tightly tied to ticket workflows and agent handoff..
Ada
Editor pickBuilt-in live agent escalation tied to conversation state, so routing can switch from bot to human with defined rules.
Built for fits when support teams need governed chatbot routing with agent handoff and measurable containment..
Related reading
Comparison Table
This ranked shortlist targets analysts and technical operators who need chatbots with clear configuration paths, integration options, and measurable automation behavior. The ordering prioritizes extensibility via APIs, data and routing models for conversational context, and operational controls such as audit logs and role-based access so teams can compare build versus buy tradeoffs across support, sales, and messaging channels.
HubSpot Chatbot Builder
SMBCRM-connected chatbot builder for website conversations, lead qualification, and support flows.
Live agent handoff connects bot conversations to HubSpot service workflows with preserved context.
HubSpot Chatbot Builder focuses on conversation flow configuration inside HubSpot workflows, so captured answers can trigger CRM updates like contact properties, lead capture fields, and task or ticket creation. The builder supports live agent handoff through HubSpot service tools, so conversations can transition from bot responses to human replies with mapped context. Analytics track bot performance at the conversation level, which helps measure containment and where users drop off.
A key tradeoff is that advanced NLU customization and full conversational modeling are less granular than dedicated dialog-focused engines, so complex intent training may require more flow logic than data-driven NLU. This fits teams that already run lead management, ticketing, and routing in HubSpot and want chat to update the CRM and support handoff without stitching separate systems.
- +CRM-linked bot flows update contacts and tickets from chat inputs
- +Built-in live agent handoff routes conversations into HubSpot service
- +Branching logic uses collected answers to steer next questions
- +Conversation analytics show performance at the chatbot experience level
- –LLM behavior control is limited compared with dedicated assistant builders
- –Complex intent training can require extensive flow configuration
- –Advanced custom channel integrations depend on HubSpot’s supported surfaces
- –Workflow coupling can make bot changes harder to version outside HubSpot
Marketing operations teams
Qualify website visitors via guided chat
Faster lead enrichment
Customer support teams
Escalate complex questions to agents
Lower time to resolution
Show 2 more scenarios
Sales teams
Route inbound leads by bot responses
More consistent lead follow-up
Trigger routing actions and tasks based on chatbot answers tied to CRM records.
Revenue operations teams
Standardize intake for support requests
Cleaner case classification
Create tickets from structured chat inputs and categorize issues using flow logic.
Best for: Fits when HubSpot users need website chat that updates contacts and routes to service agents.
More related reading
Zendesk AI Agents
enterpriseAI chatbot and agent automation tools integrated with Zendesk service workflows.
Context-preserving live escalation that routes unanswered conversations into Zendesk agent workflows.
Zendesk AI Agents fits teams already running Zendesk for ticketing, since conversation outcomes can map directly into ticket updates, assignment, and status changes. It focuses on end-to-end support chat execution, including escalation to a live agent and continued context transfer so customers do not repeat details. The configuration model emphasizes workflow alignment with Zendesk objects rather than standalone chatbot orchestration, which reduces glue work for support operations.
A tradeoff appears in customization depth when requirements extend beyond Zendesk support primitives, since advanced dialog management and external tool orchestration often needs additional integration work. It is a strong fit for high-volume support queues that need consistent answers backed by internal knowledge and a measurable containment rate, with controlled fallback to agents for edge cases.
- +Tight Zendesk workflow mapping for ticket updates and assignment
- +Knowledge-grounded responses from connected Zendesk content
- +Live handoff preserves context during agent escalation
- +Built-in reporting on automation outcomes and containment
- –Customization beyond Zendesk support objects takes extra integration effort
- –Automation behavior depends on content coverage and retrieval quality
- –Complex multi-step agent tooling can require external services
- –Fine-grained conversation control can feel less explicit than dialog-first builders
Support operations teams
Deflect common issues with grounded answers
Lower ticket volume for repeats
Customer support managers
Measure containment and escalation outcomes
Tuned automation coverage over time
Show 2 more scenarios
Contact center supervisors
Route low-confidence chats to agents
Faster resolution with fewer repeats
Escalates when the agent cannot confidently resolve the request with context intact.
IT service desk teams
Guide users through account troubleshooting
Reduced time to first correct action
Handles step-by-step guidance for common access and permissions failures in chat.
Best for: Fits when support teams need an AI chat agent tightly tied to ticket workflows and agent handoff.
Ada
enterpriseAI customer service chatbot platform for automated support across web, messaging, and voice channels.
Built-in live agent escalation tied to conversation state, so routing can switch from bot to human with defined rules.
Ada’s core configuration uses a conversation flow and automation logic that can route messages, extract details, and trigger actions tied to business systems. The product’s governance posture is clearer than many LLM-first chatbot builders because it focuses on operational states like bot handling versus human escalation. Conversation visibility and reporting support iteration on containment and resolution outcomes, rather than only on message generation quality. This profile fits teams that need consistent support behavior across departments and channels.
A tradeoff is that Ada’s best results depend on curating the intents, routing conditions, and knowledge sources that govern what the bot can answer. Teams that want to start from ad hoc prompts without modeling conversation states may find the configuration work heavier than expected. A good usage situation is a customer support organization adding guided troubleshooting and ticket handoff with defined escalation rules.
- +Strong automation for routing, actions, and agent escalation control
- +Clear channel integration approach using APIs and webhooks
- +Governed conversation behavior supports consistent support outcomes
- +Conversation analytics help measure containment and handling quality
- –High intent and routing setup effort for accurate deflection
- –LLM response quality depends on properly maintained knowledge inputs
- –Complex flows can increase debugging time for edge-case routing
- –Advanced governance needs disciplined configuration across teams
Customer support operations
Escalate complex cases to agents
Faster resolution with fewer repeat messages
IT service management teams
Create tickets from chat intake
Lower manual triage workload
Show 2 more scenarios
Ecommerce customer care
Handle order changes and delivery questions
Higher self-serve completion rates
Uses guided flows to capture order identifiers and return accurate next steps from knowledge sources.
Multichannel CX managers
Run consistent behavior across channels
More consistent customer experience
Maintains shared routing and response policy so outcomes match across web and messaging placements.
Best for: Fits when support teams need governed chatbot routing with agent handoff and measurable containment.
More related reading
Freshchat
SMBMessaging and chatbot software with agent inbox, AI automation, and omnichannel support.
Tight linkage between bot conversations and live agent escalation inside Freshworks support workflows.
Freshchat from Freshworks positions a customer service chat experience around live agent handoff and bot-assisted conversations across common messaging entry points. It supports rule-based bot flows with intent classification, multilingual conversation handling, and a conversation flow builder for scripted dialog management.
Freshchat also integrates with Freshworks CRM and ticketing workflows to keep chat context attached to support operations. Admins get configuration controls for routing and escalation, plus analytics that track conversation outcomes like containment and deflection.
- +Natural fit for live chat plus bot-assisted triage with agent handoff
- +Conversation flow builder supports intent classification and scripted dialog management
- +Multilingual chat handling fits international support teams
- +Freshworks CRM and ticketing integration keeps chat context in operations
- –Bot flows are harder to adapt for highly dynamic generative use cases
- –Advanced governance for distributed teams can require careful role design
- –Complex integrations depend on webhook and API work beyond basic widgets
- –Response analytics are useful for outcomes but limited for deep NLU diagnostics
Best for: Fits when customer support teams want chatbots that route to agents with strong multilingual coverage.
Botpress
API-firstAI agent and chatbot platform for building custom conversational assistants and workflows.
Visual flow editor with fine-grained execution control that coordinates webhook calls and LLM steps in one dialog graph.
Botpress builds conversational agents through a visual conversation flow builder that can mix rules with generative steps. It offers an API-first integration surface with webhooks for external system calls during dialog execution.
It includes conversation analytics for monitoring outcomes and iterating on fallback behavior. Deployment supports both hosted use and self-managed setups for teams with internal hosting requirements.
- +Conversation flows are visually authored with node-level control over dialog logic
- +Webhook integrations let bots call external services mid-conversation
- +Conversation analytics track outcomes to guide iteration on flows
- +Hybrid flow design supports both deterministic and generative steps
- –LLM configuration and guardrails require careful setup to avoid unsafe outputs
- –Multi-channel deployment requires extra wiring for messaging providers
- –Advanced handoff to human workflows can require custom glue code
- –Large-scale governance needs stronger operational process than typical drag-and-drop tools
Best for: Fits when teams need API-integrated chatbots with visual flow control and iterative conversation analytics.
LivePerson
enterpriseConversational AI and messaging platform for enterprise customer care and commerce interactions.
Agent assist and escalation orchestration that keeps context consistent between bot and live chat workflow.
LivePerson is a conversational AI platform aimed at organizations that need bots plus controlled live-agent handoff.
Core capabilities include conversation flow configuration, AI-assisted responses, and operational analytics for routing and containment outcomes.
Integrations are practical for connecting bot steps to systems of record through webhooks and external workflow endpoints.
- +Strong live-agent escalation controls with clear handoff points
- +Conversation analytics track deflection and transfer outcomes
- +Extensible bot actions via webhooks for downstream workflows
- +Supports both rule-based flows and AI-driven replies
- –LLM behavior tuning requires careful guardrail and prompt governance
- –More configuration work than intent-only chatbot builders
- –Higher implementation effort for deep back-end integration
- –Operational tuning can be slower without a dedicated sandbox flow
Best for: Fits when customer service teams need bots that can escalate to live agents with measurable outcomes.
More related reading
Zoho SalesIQ
SMBLive chat and chatbot software for websites with visitor tracking and CRM integration.
Zoho SalesIQ combines rule-driven bot actions with visitor analytics and agent handoff in one ops workflow.
Zoho SalesIQ pairs a live-chat and visitor analytics workflow with bot-style automation inside Zoho’s ecosystem. It supports conversation flows that can route chats to rule-based actions and live agent escalation, with configurable triggers tied to visitor and site events.
SalesIQ’s chatbot behavior is operationally grounded in its analytics dashboard so teams can review outcomes like containment without exporting data into a separate analytics stack. The overall fit is strongest for orgs already standardizing on Zoho for CRM and support workflows, since handoff and routing align with those systems.
- +Visitor analytics and chat outcomes share the same operational view
- +Handoff to live agents can be triggered by conversation and visitor conditions
- +Routing logic fits Zoho CRM and support workflows
- +Multichannel chat widgets use one admin surface for deployment
- –Bot customization remains workflow driven more than knowledge-grounded generation
- –Advanced bot testing requires stronger governance around edits and releases
- –Extensibility relies on Zoho-side configuration rather than a broad developer-first surface
- –Response-quality tooling is weaker than dedicated LLM chatbot builders
Best for: Fits when a Zoho-centered support team needs chat automation plus live escalation.
Kommunicate
supportCustomer support chatbot platform that combines AI automation with human agent handoff.
Built-in live agent escalation and chat handoff that works inside the bot-driven conversation flow.
Kommunicate is a chatbot and conversational support solution built around customer messaging workflows.
It combines a visual conversation flow builder with live agent handoff and channel integrations for customer service and sales bots.
Integration depth is centered on its API and webhook-style automation hooks, plus analytics for conversation performance and outcomes.
Operational control is supported through admin configuration for bot behaviors and escalation paths across connected channels.
- +Visual conversation flows with clear escalation to live agents
- +Messaging channel integrations for deploying bots to customer touchpoints
- +API and webhooks for connecting bot events to existing systems
- +Conversation analytics for tracking outcomes and deflection behavior
- –LLM-focused features depend on external content and prompt governance
- –Advanced dialog logic can require substantial builder discipline
- –Multistep workflow testing needs more rigor than single-turn bots
- –Some enterprise controls lean on process when many teams share bots
Best for: Fits when support teams need bot flows plus reliable agent escalation across messaging channels.
More related reading
Crisp
SMBBusiness messaging platform with chatbot automation, live chat, and shared team inbox tools.
Live agent escalation that preserves bot conversation context for quicker resolution and better handoff tracking.
Crisp places a customer chat entry point in front of deployed chatbots, then routes unanswered intent to bot flows or live agents. Bot building centers on a conversation flow builder with intent-style triggers and rule-based dialog logic.
The automation surface supports web widget deployment and webhook-style integrations to connect backends and knowledge systems. Analytics track conversation outcomes such as bot engagement and handoff performance so teams can tune containment over time.
- +Fast live agent handoff tied to bot conversation state
- +Conversation builder supports multi-step dialog rules without coding
- +Web widget deployment connects directly to website engagement
- +Analytics includes bot performance and escalation outcomes
- –LLM grounding is not as configurable as dedicated RAG stacks
- –Advanced governance controls are limited compared with enterprise suites
- –Complex NLU needs more careful setup of triggers and fallbacks
- –Webhook integrations require custom backend mapping for context
Best for: Fits when teams need website-first chatbots with live escalation and measurable deflection.
Flow XO
no-codeAutomation and chatbot builder for websites, Facebook Messenger, Slack, and business workflows.
Flow XO’s trigger and action graph lets conversation steps call external webhooks while keeping dialog logic readable.
Flow XO targets teams that need rule-based conversation flows with clear branching and action steps.
Conversation logic is built in a visual flow editor, then executed through channel configurations and webhook-driven integrations.
Operational visibility comes from analytics on bot conversations and outcomes so teams can adjust flows when behavior drifts.
- +Visual flow builder maps dialog steps to connected actions
- +Webhook and integration patterns support custom fulfillment endpoints
- +Channel configuration enables consistent conversation behavior across touchpoints
- +Conversation analytics surface where users drop or succeed in flows
- –Advanced LLM response grounding depends on external services
- –Complex branching can become harder to maintain at large scale
- –Handoff to human support is not as granular as enterprise contact-center workflows
- –Governance relies on disciplined changes to shared bot configurations
Best for: Fits when teams need visual, rule-based bot workflows with integrations and measurable conversation outcomes.
Conclusion
After evaluating 10 ai in industry, HubSpot Chatbot Builder stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right chatbots software
Chatbots software reviewed here spans CRM-linked builders, support ticket-native AI agents, and visual workflow platforms that orchestrate webhook calls and live escalation. The coverage includes HubSpot Chatbot Builder, Zendesk AI Agents, Ada, Freshchat, Botpress, LivePerson, Zoho SalesIQ, Kommunicate, Crisp, and Flow XO.
The guide also anchors the comparison with Microsoft Copilot Studio, Dialogflow, and Amazon Lex to separate general-purpose assistant building from contact-center and ticket workflow handoff patterns.
Chatbots software for intent routing, knowledge grounding, and live agent handoff
Chatbots software coordinates conversation flow building, intent classification, entity extraction, and dialog management across web widgets and messaging channels. Many platforms also connect responses to knowledge inputs so answers come from connected content instead of purely generative output.
A major differentiator is how the bot hands off to humans with preserved conversation state. HubSpot Chatbot Builder and Zendesk AI Agents tie escalation directly into HubSpot service workflows and Zendesk agent workflows with context preservation, while Ada and Crisp focus on governed routing tied to conversation state.
Integration, automation, and escalation controls that define real chatbot behavior
Chatbots software changes outcomes based on how deeply it connects to systems that hold customer context, like CRM records, ticket objects, and live-agent work queues. The practical difference shows up in three places: how conversation state is preserved during escalation, how automation and external calls are orchestrated, and how much governance exists for LLM behavior and content grounding.
Context-preserving live escalation into customer workflows
HubSpot Chatbot Builder routes bot conversations into HubSpot service workflows while preserving context so agents can act on the same conversation thread. Zendesk AI Agents do the same for Zendesk ticket workflows and assignment so escalations update support operations instead of starting over.
Conversation flow control with external fulfillment steps
Botpress uses a visual flow editor where webhook calls and LLM steps are coordinated in one dialog graph with node-level control. Flow XO uses a trigger and action graph that maps dialog steps to connected webhook fulfillment endpoints while keeping the workflow readable.
Governing handoff rules tied to conversation state
Ada ties live agent escalation to conversation state so routing can switch from bot to human using defined rules. Crisp preserves bot conversation context for faster live resolution and better handoff tracking during escalation.
Knowledge-grounded responses from connected content
Zendesk AI Agents generate knowledge-grounded responses using connected Zendesk content, which directly affects response accuracy for support scenarios. Ada’s LLM response quality depends on maintained knowledge inputs, so content coverage directly shapes containment.
Operational analytics for deflection and transfer outcomes
LivePerson includes conversation analytics that track deflection and transfer outcomes, which helps teams separate “handled by bot” from “resolved after escalation.” Zoho SalesIQ ties visitor analytics and chat outcomes into one operational view so teams can correlate automation decisions with handoff results.
Multichannel deployment with channel-specific integrations
Freshchat supports messaging channel integrations for deploying bot-assisted triage with live agent handoff inside Freshworks workflows. Kommunicate focuses on bot-driven conversation flow plus live agent escalation that works across messaging channels.
Choose by escalation model and orchestration surface, then confirm governance depth
The fastest way to select chatbots software is to start with the escalation model because it determines whether the bot becomes an entry point for an existing agent workflow or a standalone assistant with a separate handoff. Then select the orchestration surface by checking whether the platform’s dialog builder can coordinate webhook calls, LLM steps, and routing logic without fragmenting the conversation into multiple tools.
Pick the escalation target that matches how support work is executed
If support operations run inside HubSpot service workflows, HubSpot Chatbot Builder routes live escalation into those workflows while updating contacts and tickets from chat inputs. If support operations run inside Zendesk agent workflows, Zendesk AI Agents route unanswered conversations into ticket workflows with tight workflow mapping.
Select a state-driven handoff workflow if routing must be governed
If routing needs defined rules that switch from bot to human based on conversation state, Ada provides conversation-state routing with measurable containment. If teams need context-preserving handoff for quicker resolution, Crisp keeps bot conversation context during live escalation so agents can continue where the bot stopped.
Choose a visual orchestration model when fulfillment spans multiple systems
If dialog logic must coordinate webhook calls with LLM steps in a single graph, Botpress offers node-level execution control and webhook-integrated flows. If dialog steps must trigger readable trigger and action graphs for custom fulfillment endpoints, Flow XO maps conversation steps directly to webhook-connected actions.
Validate knowledge grounding when deflection depends on connected content coverage
When answers must be grounded in Zendesk content, Zendesk AI Agents use connected Zendesk knowledge inputs and link automation behavior to retrieval quality. When knowledge inputs must be actively maintained for quality, Ada’s LLM behavior depends on properly maintained knowledge so setup effort must include ongoing content governance.
Confirm analytics that answer “resolved by bot” versus “resolved after transfer”
If teams measure outcomes by deflection and transfer results, LivePerson includes conversation analytics for those outcomes so operations can tune escalation boundaries. If teams need visitor-level operational reporting tied to chat outcomes, Zoho SalesIQ combines visitor analytics with handoff triggers in one operational view.
Stress-test governance and behavior control before adopting broader automations
If the rollout requires strict LLM behavior control and safe outputs, Botpress requires careful guardrails and LLM configuration to avoid unsafe responses. If distributed team edits must stay controlled, Freshchat needs careful role design because advanced governance for distributed teams can require deliberate setup.
Who chatbot teams should match to these deployment and control patterns
Chatbots software adoption succeeds when the chosen platform matches the organization’s support workflow structure and the level of control needed for escalation and response behavior. Different tools fit different operational setups, like CRM-centered routing, ticket-native handoff, or orchestration-first visual workflow building.
HubSpot-first support and service teams
HubSpot Chatbot Builder updates contacts and tickets from chat inputs and then routes bot conversations into HubSpot service workflows with preserved context for agents.
Zendesk support teams running ticket assignment and agent workflows
Zendesk AI Agents connect knowledge-grounded responses to Zendesk content and route unanswered conversations into Zendesk agent workflows with workflow mapping for ticket updates.
Support orgs that need governed routing rules with measurable deflection
Ada switches from bot to human using defined rules tied to conversation state and emphasizes automation control and routing outcomes.
Teams building multi-system fulfillment across complex dialog steps
Botpress coordinates webhook calls and LLM steps in one dialog graph with node-level execution control, which fits workflows where answers and actions must be chained.
Customer service teams operating across multiple messaging channels
Freshchat and Kommunicate focus on bot flows plus live agent escalation across messaging channels, which reduces the need to replicate logic per touchpoint.
Common buying and rollout mistakes that break chatbot outcomes
Teams often evaluate chatbot software on demo dialogs and miss the operational details that determine escalation accuracy, safety, and maintainability. These mistakes show up as weak routing governance, insufficient knowledge coverage, or flow complexity that becomes difficult to change after launch.
Assuming live handoff will work without context preservation
HubSpot Chatbot Builder and Zendesk AI Agents preserve context into service or ticket workflows, while tools that rely on separate or loosely connected escalation can force agents to re-collect details.
Overbuilding intent and routing logic without a maintenance plan for knowledge inputs
Ada’s LLM response quality depends on properly maintained knowledge inputs, so content owners must own updates to keep deflection rates from dropping.
Treating webhook orchestration as a generic integration task
Botpress requires careful guardrails and LLM configuration to avoid unsafe outputs when flows call external services mid-conversation. Flow XO’s grounding depends on external services, so the fulfillment stack must be included in the rollout checklist.
Choosing a visual flow builder and then scaling without governance discipline
Freshchat and Kommunicate can require careful role design or builder discipline for advanced dialog logic, so release processes and change control must be planned alongside bot logic.
Optimizing for bot resolution metrics that do not separate deflection from transfer
LivePerson tracks deflection and transfer outcomes, while organizations that lack that split often tune escalation too late because they cannot tell whether failures happen before or after handoff.
How We Selected and Ranked These Tools
We evaluated each chatbot platform on features that affect real deployments, including escalation behavior tied to live agent workflows and the ability to coordinate dialog logic with external fulfillment calls. Features counted for 40% of the ranking, ease and implementation fit counted for 30% each, and each score was anchored to the stated standout capabilities in the tool cards.
HubSpot Chatbot Builder led the list because it ties live agent handoff directly into HubSpot service workflows while updating contacts and tickets from chat inputs with preserved context. Zendesk AI Agents placed next because they combine knowledge-grounded responses from connected Zendesk content with tight Zendesk workflow mapping for ticket updates and assignment during escalation.
Frequently Asked Questions About chatbots software
How do Microsoft Copilot Studio, Dialogflow, and Amazon Lex differ for integration and API-first automation?
Which platform supports structured conversation governance without rewriting prompts for every edge case?
How does live agent handoff work in HubSpot Chatbot Builder, Zendesk AI Agents, and Freshchat?
When should a team choose rule-based bot flows versus LLM-powered responses in these tools?
What breaks if an organization lacks a clear knowledge grounding workflow for support responses?
Where does RBAC and admin control fall short across chatbot platforms like Botpress and Flow XO?
How can teams migrate existing conversation data and CRM context into HubSpot Chatbot Builder or Zoho SalesIQ?
What integration pattern best fits multi-channel deployments when chat should connect to tickets and routing rules?
Which option helps teams debug conversation execution when webhook calls fail or the dialog graph needs iteration?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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