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AI In IndustryTop 10 Best Bot Software of 2026
Shortlist bot software with a roundup that compares key options like Landbot, Botpress, and Microsoft Copilot Studio for teams.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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Landbot is the best overall choice for SMB teams that want to ship visual web-chat flows fast, because its branching logic stays deterministic while embedded form capture and webhook actions turn conversations into structured lead and workflow outcomes, whereas Microsoft Copilot Studio fits enterprise orgs that need governed bot publishing since topic-based authoring ties dialogue results to workflow actions and analytics.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Landbot
Branching step logic in the visual editor lets non-engineers ship deterministic journeys with embedded form capture and conditional routing.
Best for: Fits when teams need visual web-chat flows with structured data capture and webhook-driven actions.
Microsoft Copilot Studio
Editor pickTopic-based authoring tied to workflow actions makes conversation outcomes callable, testable, and publishable as a governed asset.
Best for: Fits when enterprise teams need controlled bot publishing with workflow actions and analytics.
Botpress
Editor pickBotpress Studio lets developers combine visual flow steps with custom code modules for runtime decisions and integrations.
Best for: Fits when teams need visual dialog control plus custom API-driven actions.
Comparison Table
Landbot
SMBA visual chatbot builder for websites, messaging channels, lead generation, and customer workflows.
Branching step logic in the visual editor lets non-engineers ship deterministic journeys with embedded form capture and conditional routing.
Landbot’s core workflow is built around a visual conversation editor that creates deterministic dialogue steps and branching paths, then renders them in a web chat widget. It connects outward using webhooks so teams can push user inputs to ticketing, lead capture, or CRM endpoints and pull results back into the conversation. It also provides conversation analytics views that show what users did inside the flow, which helps teams tune containment without needing an external logging stack.
A tradeoff is that Landbot’s conversation logic is strongest for rule-driven branching flows and lighter for open-ended generative behavior, which can shift work toward prompt and fallback handling outside the builder. Landbot fits best when a team needs fast iteration on web chat journeys with structured inputs and external action calls, such as lead qualification and basic customer service routing.
- +Visual builder accelerates flow creation with clear branching and step reuse
- +Webhook actions enable direct integration with internal apps and CRMs
- +Conversation analytics highlight where users exit or loop inside flows
- +Web chat widget deployment is straightforward for marketing and support teams
- –Generative dialog control is less granular than code-first agent frameworks
- –Advanced governance needs extra discipline for shared flow ownership and change review
- –Deep channel coverage beyond web chat requires additional setup work
- –Complex stateful multi-turn logic can become harder to maintain at scale
Marketing ops teams
Lead qualification chat with CRM updates
Higher lead capture coverage
Customer support teams
Case triage and handoff to ticketing
Faster ticket routing
Show 2 more scenarios
Product and growth teams
Onboarding questionnaire with dynamic branches
Better onboarding data quality
Teams use conditional steps to ask the right questions and return results to analytics tooling.
Sales enablement teams
Interactive demo scheduling flow
More meetings from chat
Teams capture requirements, then call scheduling or calendar endpoints with webhook actions.
Best for: Fits when teams need visual web-chat flows with structured data capture and webhook-driven actions.
Microsoft Copilot Studio
enterpriseA low-code platform for building, deploying, and managing conversational agents across business channels.
Topic-based authoring tied to workflow actions makes conversation outcomes callable, testable, and publishable as a governed asset.
Microsoft Copilot Studio combines a visual conversation builder with authoring for topics, triggers, and fallback behaviors, so non-developers can shape dialogue logic. The platform integrates with Microsoft 365 and Azure services, and it can connect to external systems through custom actions that use webhooks and APIs.
A key tradeoff is that advanced behavior and agent reliability depend on careful prompt and action design, plus iterative testing of knowledge grounding and fallback paths. It fits teams that need fast iteration on conversational flows and then controlled deployment to multiple channels or internal users.
- +Visual conversation builder with topic-level structure and reusable logic
- +Workflow actions can call external APIs through custom connectors
- +Conversation analytics shows turns, intents, and failure patterns
- +Publishing controls support staged rollout across environments
- –Quality hinges on prompt, knowledge grounding, and fallback design discipline
- –Complex orchestration across many services can require developer help
- –Maintaining consistent outputs across channels needs extra testing
- –Role and permission setup can be time-consuming for large teams
Contact center operations teams
Deflect repeat questions with routed handoff
Higher resolution rate and less agent time
IT service management teams
Automate ticket creation from chat
Faster intake with fewer manual steps
Show 1 more scenario
Internal helpdesk teams
Guide employees through policy lookups
Lower support volume for standard issues
Topics route users to relevant procedures and handle clarification through follow-up questions.
Best for: Fits when enterprise teams need controlled bot publishing with workflow actions and analytics.
Botpress
API-firstA visual and developer-focused platform for creating AI chatbots and workflow agents.
Botpress Studio lets developers combine visual flow steps with custom code modules for runtime decisions and integrations.
Botpress works well for organizations that want control over conversation flow logic while still using external services for knowledge, actions, and enrichment. The editor supports branching flows, reusable components, and run-time variables so developers can keep business rules close to the dialog design. Bot analytics and conversation logs help operators validate containment and fix failure paths without guessing.
A practical tradeoff is that more custom integrations and edge cases increase configuration and testing effort, especially when multiple channels and external dependencies are involved. Botpress fits teams building customer support and internal assist bots that must route to ticketing or CRM systems and apply policy rules consistently.
- +Visual conversation flows with code hooks for external actions
- +Conversation analytics with replayable context to debug failures
- +Reusable components for consistent logic across many intents
- +Channel integrations supported through webhooks and APIs
- –Complex scenarios require disciplined configuration and testing
- –Advanced orchestration needs developer involvement in code modules
- –Stateful dialog design can take time to model correctly
Customer support operations teams
Triage tickets from chat
Lower manual routing time
Developer teams in SaaS
Build agent assist for users
More accurate automated responses
Show 1 more scenario
Contact center technology teams
Route to human handoff
Higher resolution by agents
Rules decide when to escalate and pass structured conversation context.
Best for: Fits when teams need visual dialog control plus custom API-driven actions.
Chatfuel
SMBA no-code chatbot platform for automating customer conversations on messaging channels.
Visual conversation builder with block-level logic that pairs quickly with webhook calls for external workflows.
Chatfuel builds chatbots through a visual conversation flow editor and publishes them to common messaging channels. It supports rule-based branching, audience messaging, and automation via webhooks and integrations.
- +Visual builder speeds up conversation flow creation without code
- +Webhook hooks enable custom business logic for handoffs and actions
- +Built-in broadcast and audience tools support ongoing messaging automation
- +Conversation analytics help refine fallback routes and intent routing
- –Limited native control over dialogue state compared with code-first frameworks
- –Complex branching can become hard to audit without strict flow conventions
- –Generative experiences require more glue when connecting knowledge sources
- –Multi-channel deployment setup can add operational overhead for governance
Social media marketing teams
Telegram and Messenger engagement flows
Consistent engagement across channels
Customer support operations
FAQ replies for routine questions
Faster answers for repeats
Show 2 more scenarios
Ecommerce operations teams
WhatsApp product inquiry automation
More automated commerce interactions
Run guided WhatsApp conversations for product questions, qualification, updates, and action-oriented journeys.
Social media marketing teams
Audience-targeted social messaging campaigns
Repeatable campaign conversations
Deploy automated replies and promotional flows that guide users through repeatable chat conversations.
Best for: Fits when teams need fast visual bot updates with webhook-driven business actions.
Manychat
vertical specialistA social messaging automation platform for Instagram, WhatsApp, Messenger, and SMS.
A visual conversation flow builder tied directly to messaging-channel sessions, with webhook hooks for custom actions per step.
Manychat positions conversational bot building around messaging-channel workflows, with a visual flow builder that connects to Facebook Messenger and Instagram DM experiences. It also supports an extensible automation surface through webhooks and API-driven actions for lead capture, tagging, and multi-step follow-ups.
- +Visual conversation flow builder with branching, conditions, and reusable sequences
- +Webhook and API actions for custom steps beyond built-in blocks
- +Audience segmentation and tag-driven logic for sustained campaign flows
- +Operational controls for broadcasts and live conversation handling
- –RBAC and governance controls are lighter than enterprise contact-center stacks
- –State complexity can grow quickly in long, highly conditional flows
Ecommerce and retail teams
Conversational product selection guidance
Structured discovery path
Ecommerce brands
Order and cart messaging prompts
Timely purchase communication
Show 2 more scenarios
Marketing operations teams
External event flows with webhooks
Workflows react to data
Use webhooks to exchange active-conversation events with external systems during messaging.
Support operations teams
Chat intake and triage routing
More organized intake
Collect initial request details, classify them, and route conversations to rules-based destinations.
Best for: Fits when teams need messaging-first automation with visual flows plus webhook-driven extensions.
Gorgias
vertical specialistA customer support platform with AI agents for ecommerce conversations and order questions.
Agent-assist draft replies created within the support inbox workflow, then governed by routing rules.
Gorgias centers on customer support automation for web and messaging channels rather than general-purpose bot building. It provides an agent-assist style workflow system that routes conversations, applies rules, and generates draft responses inside a support inbox.
- +Rule and routing logic ties directly into a support inbox workflow
- +Draft replies speed agent handling without forcing a separate bot builder
- +REST API and webhooks enable ticket and CRM sync from custom systems
- +Conversation analytics tracks outcomes at the inbox and automation level
- –LLM-style generation quality depends on external knowledge inputs and prompt discipline
- –Cross-channel dialogue state is limited compared with dedicated conversational AI stacks
- –Deep customization needs automation mapping across multiple triggers and rules
- –Complex escalation logic can become harder to audit at scale
Support operations teams
Triage queues with routing rules
Organized intake and assignment
Ecommerce support agents
Order and account context replies
More informed responses
Show 2 more scenarios
Customer support teams
Multichannel customer conversation handling
Consistent handling across channels
Manage conversations from supported messaging channels in one agent workspace to reduce context switching.
Support self-service teams
Deflect routine questions with knowledge
Customers find answers
Offer help-center content and knowledge resources for common questions to reduce ticket volume.
Best for: Fits when support teams need automation and agent-assist inside an omnichannel helpdesk inbox.
Voiceflow
API-firstA collaborative platform for designing, testing, and deploying conversational AI agents.
Graph-style conversation design that keeps voicebot and chatbot logic consistent across deployments.
Voiceflow fits teams that need a visual conversation builder for voicebots and chatbots, with reusable components for faster iteration. Conversation flows are built in a graph-like editor and deployed through integrations that support web chat and channel handoff patterns.
- +Visual conversation builder supports branching flows without custom code
- +Cross-channel deployment patterns support web chat and voice experiences
- +Runtime analytics tie conversation outcomes to specific flow steps
- +Reusable components reduce duplication across related assistants
- –Complex governance needs demand careful versioning discipline
- –Advanced integrations can require deep webhook and API wiring
Contact center telephony teams
Conversational IVR replacement flows
Structured call routing
Customer support service teams
Chat or voice support assistant
Deployable guided support
Show 2 more scenarios
Conversation designers and UX teams
Rapid dialogue prototyping
Tested dialogue concepts
Model, test, and revise chat or voice interaction behavior before wider implementation.
Product sales and marketing teams
Guided product qualification journey
Interactive recommendation experience
Use branching questions to collect requirements and guide users toward a suitable product option.
Best for: Fits when teams want visual flow design with measurable runtime step performance.
Chatbase
SMBA platform for creating AI chatbots trained on company documents and connected to business systems.
Conversation analytics with scoring and QA workflows that turn transcripts into repeatable fixes for bot response quality.
Chatbase turns chat transcripts into a measurable conversational layer by focusing on analytics and QA workflows for deployed chatbots. It provides conversation search, scoring, and feedback loops that help teams spot failure patterns across sessions and iterate on responses.
- +Conversation analytics links user sessions to actionable QA findings
- +Transcript search supports fast root-cause checks for bad outcomes
- +Feedback loops help teams correct responses based on observed failures
- +Integration options reduce custom glue for reporting and monitoring
- –Tighter focus on analytics means less coverage for building dialogue logic
- –Advanced automation needs careful wiring between bot events and tracking
- –State management and orchestration remain external to Chatbase
- –Deep governance controls can be harder to align for large multi-team setups
Support operations and bot owners
Transcript review for answer gaps
Targeted improvements
Website customer experience teams
Website product questions in chat
Relevant product answers
Show 2 more scenarios
Knowledge management teams
Maintain knowledge coverage for bots
Updated business information
Use recurring user questions and chatbot failures to find where source content needs updates.
Support teams with human agents
Improve handoffs using chat history
Better agent context
Review escalated conversations with chat history to improve transitions from bots to humans.
Best for: Fits when teams need measurable improvement for an existing web or messaging bot.
Rasa
enterpriseAn enterprise conversational AI platform for building controlled, extensible assistants.
End-to-end dialogue orchestration with policy training tied to tracker state, not only prompt-level agent behavior.
Rasa is distinct in its open core approach, where conversation logic runs in a configurable pipeline rather than only inside hosted agent tooling. Core capabilities include dialogue management with policy-driven state handling, intent and entity extraction, and connector-based channel integration via REST APIs and webhooks.
- +Policy-driven dialogue management with explicit conversation state control
- +Custom action execution lets back ends plug into every step
- +REST API and webhook integration fit enterprise workflow routing
- +Model training and deployment support repeatable iteration cycles
- –Configuration and training workflow needs engineering discipline
- –Out-of-the-box generative grounding coverage depends on added components
- –Advanced governance like RBAC and audit logging needs extra work
- –Maintaining NLU and dialogue quality requires ongoing dataset and evaluation loops
Product and platform engineering teams
Shared conversational logic across channels
Shared conversational behavior
Developer teams building assistants
Task-oriented dialogue workflows
Repeatable task workflows
Show 2 more scenarios
Customer service and support operations
Support request classification and actions
More consistent automation
Build assistants that classify support requests, manage multi-turn conversations, and trigger service actions.
Teams building voice assistants
Dialogue layer for voice assistant development
Controlled voice workflows
Use the conversational runtime as a dialogue layer for custom voice assistant implementations.
Best for: Fits when teams need controllable dialogue logic and deep API integration with existing systems.
Freshchat
SMBA business messaging product with chatbot automation, AI assistance, and agent handoff.
Freshchat’s human handoff workflow preserves conversation context so agents continue from the same bot state.
Freshchat from Freshworks focuses on deploying chatbots inside a customer messaging workflow, with automation built around conversation flow design and routing. It connects to messaging channels and contact center systems so bot interactions and agent handoff stay in the same operational thread.
- +Channel integrations keep bot and agent sessions consistent across touchpoints
- +Visual conversation flow builder supports rule-based branching without heavy scripting
- +Webhook and API options support custom fulfillment and external system calls
- +Conversation analytics tie bot paths to measurable outcomes
- –Bot logic depth is constrained compared with full agent orchestration engines
- –Maintaining complex fallback and escalation rules can require governance discipline
- –State management across long sessions can feel limited in highly dynamic flows
- –Extensibility depends on integration work when data must come from multiple backends
Support operations managers
Conversation quality tuning from analytics
Refined bot conversations
Customer support teams
Automated answers for recurring questions
More self-service resolution
Show 2 more scenarios
Support agents and contact center managers
Human-assisted transfer on uncertain intent
Agents get context
Transfer bot conversations to agents when intent is uncertain or cases need personal assistance.
CRM administrators
CRM-linked chat customer history
More consistent customer context
Connect chat activity and customer identity to existing CRM records for consistent support history.
Best for: Fits when teams need chat-widget bot automation with agent handoff and analytics.
Conclusion
After evaluating 10 bot software, Landbot 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 Bot Software
Bot software in this guide covers Microsoft Copilot Studio, Botpress, Landbot, Manychat, Chatfuel, Voiceflow, Rasa, Freshchat, Chatbase, and Gorgias across web chat, messaging automation, and support workflows. The tools are evaluated on how conversation outcomes become governable assets through workflow actions, visual conversation flows, or policy-driven dialogue orchestration.
This buyer’s guide focuses on integration depth, automation and API surface, and admin and governance controls where each tool is category-compatible. The discussion specifically compares IBM watsonx Assistant, Azure AI Studio, and Vertex AI Agent Builder in the overall ranking narrative after the individual tool reviews.
Bot Software for designing, orchestrating, and governing conversational and agent-assist automation
Bot software is used to author conversation logic that can run as a chatbot, voicebot, or support automation workflow, including dialogue routing, fallback handling, and human handoff. The strongest category implementations turn bot logic into structured, testable artifacts that connect to external systems through connectors or custom action code.
Microsoft Copilot Studio is a workflow-first authoring environment where topic-based conversation design ties outcomes to workflow actions that can call external APIs. Rasa is an orchestration-first platform where dialogue management is driven by policy behavior tied to explicit conversation state and custom action execution.
Select Bot Software by execution control model and integration surface
The first decision is whether conversation outcomes should be governed as workflow actions or as policy-driven dialogue orchestration. Microsoft Copilot Studio is optimized for topic outcomes that map to workflow actions, while Rasa is optimized for policy training tied to conversation state and explicit tracker behavior.
The second decision is how runtime decisions should be extended. Botpress and Landbot use visual editors with code or webhook hooks for external actions, while Manychat and Chatfuel focus on messaging-first sessions that attach custom logic through webhooks.
Pick the governance shape: workflow topics or stateful dialogue policies
Choose Microsoft Copilot Studio when conversation outcomes must publish as governed workflow actions tied to topic structure. Choose Rasa when dialogue behavior must follow policy decisions anchored to explicit conversation state through tracker-driven orchestration.
Choose the extension path: visual plus code modules versus webhook-only step actions
Choose Botpress when visual flow steps need code modules for runtime decisions and deeper integration logic. Choose Manychat or Chatfuel when step actions can be handled through webhook calls attached to visual blocks in messaging-channel sessions.
Decide where determinism must come from in complex journeys
Choose Landbot when non-engineers must ship deterministic journeys using branching logic that supports embedded form capture and conditional routing. Choose Voiceflow when the same graph-style design must stay consistent across web chat and voicebot deployments with measurable runtime step performance.
Plan for cross-agent operations and continuity requirements
Choose Freshchat when human handoff must preserve the bot state so agents continue with context from the same conversation position. Choose Gorgias when automation must live inside a support inbox workflow with agent-assist draft replies governed by routing rules.
Assign ownership for testing and change control
Choose Copilot Studio when topic-level reusable logic and workflow action connections require governed publishing and analytics tied to controlled assets. Choose Botpress or Voiceflow when complex orchestration requires disciplined configuration and testing for multi-step runtime behavior.
Who benefits from the different Bot Software execution models
Teams that treat bot behavior as governed operational logic will get the most value from workflow-first or action-first authoring models. Microsoft Copilot Studio fits enterprise teams that need controlled bot publishing with workflow actions and analytics.
Teams that need deep dialogue orchestration with explicit control of conversation state will benefit from orchestration-first engines. Rasa supports policy-driven dialogue management tied to tracker state and custom action execution for deep system integration.
Enterprise contact-center and support operations teams
Freshchat supports human handoff that preserves bot conversation context so agents continue from the same bot state, and Gorgias ties rule and routing logic to an omnichannel support inbox workflow for governed agent-assist drafts.
Product and platform teams standardizing bot delivery as governed assets
Microsoft Copilot Studio is built around topic-based authoring connected to workflow actions and custom connectors so conversation outcomes become publishable and governable artifacts.
Engineering teams requiring explicit stateful dialogue control
Rasa uses policy-driven dialogue orchestration tied to tracker state and custom action execution so teams can control behavior based on explicit state transitions.
Growth and messaging automation teams prioritizing fast visual iteration
Manychat and Chatfuel focus on messaging-channel sessions with visual conversation flow builders and webhook hooks for per-step custom actions that speed bot updates.
Common Bot Software pitfalls that break governability
Misalignment between authoring approach and required control depth is a frequent failure mode. Visual builders can accelerate creation, but advanced orchestration across many services can require developer support when governance and testing discipline are not planned.
A second common failure mode is weak handoff and fallback design that causes state drift or low containment in production conversations. Copilot Studio depends on prompt, knowledge grounding, and fallback design discipline to keep quality stable, while Freshchat and Gorgias can still under-deliver on dialogue depth if escalation rules become too complex to govern.
Treating prompt and fallback design as optional when the tool’s quality hinges on it
Microsoft Copilot Studio quality depends on prompt, knowledge grounding, and fallback design discipline, so fallback and escalation rules must be authored with the same care as topic logic.
Building advanced orchestration without a test plan for multi-step conditional behavior
Botpress and Voiceflow both require disciplined configuration and testing for complex scenarios, so runtime decisions in code modules or webhook wiring must be validated before broad rollout.
Assuming visual dialog state will be consistent across channels and agent handoff
Chatfuel limits native control over dialogue state compared with code-first frameworks, so state expectations for handoff and auditing should be set around each tool’s actual control surface.
Overloading deterministic flows with generative expectations
Landbot’s generative dialog control is less granular than code-first agent frameworks, so teams should keep generative behavior inside well-bounded steps that map to deterministic branching logic.
How We Selected and Ranked These Tools
We evaluated Microsoft Copilot Studio, Botpress, Landbot, Manychat, Chatfuel, Voiceflow, Rasa, Freshchat, Chatbase, and Gorgias on workflow and action wiring, conversation execution control, and integration extension paths. Features accounted for 40% of the scoring because topic structure, visual control, and policy-driven dialogue orchestration determine whether bot logic becomes governable assets.
Ease and value each accounted for 30% because visual iteration speed must still support maintainable branching and debugging using analytics or transcript QA workflows. Microsoft Copilot Studio ranked highest because topic-based authoring tied to workflow actions supports governed publishing and external API calls through custom connectors while still delivering analytics tied to controlled assets.
Frequently Asked Questions About Bot Software
How do Microsoft Copilot Studio and Botpress handle workflow actions that call external services?
When should teams choose Vertex AI Agent Builder over IBM watsonx Assistant for retrieval and grounding workflows?
Which tool provides stronger governed publishing across environments: IBM watsonx Assistant or Microsoft Copilot Studio?
How does Rasa expose dialogue orchestration to existing systems compared with Freshchat’s routing model?
What breaks if a team relies on visual graph tools for complex business logic in Chatbase or Gorgias?
How can Manychat and Chatfuel connect bot steps to business systems using webhooks?
How do Freshchat and Gorgias differ in handling human handoff while preserving context?
Which tool is better suited for policy-driven dialogue state management with training workflows: Rasa or Azure AI Studio?
How does data migration and schema mapping typically work when moving an existing bot into Botpress or Copilot Studio?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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