Best AI Agent Builder: 14 Right Tools for Real Work

“Best AI agent builder” sounds like a simple search, but it rarely is. Some builders are great for quick internal helpers. Others shine when the agent needs memory, tool use, approvals, logging, and a clean way to ship changes without breaking everything.

This article pulls the decision down to earth. It lays out what actually matters (beyond flashy demos), then compares a list of agent builders by how they behave in real setups – prototyping, production, teams, and the boring-but-important stuff like security and monitoring.

Snippets AI: A Handy Prompt Optimization Helper

Snippets AI is basically a home for prompts that would otherwise live in five different places at once – a doc, a notes app, a Slack thread, and someone’s memory. Our tool treats prompts like reusable assets: save them, sort them, tweak them, and pull them back out when the same kind of task shows up again. It’s less about “AI agent builder” in the classic sense and more about making prompt work repeatable across models and day-to-day tools.

Where it gets practical is speed and consistency. Instead of hunting for the right prompt, we open a quick-access picker and insert it straight into the app they’re in. Teams can keep shared collections, use variations to adapt a prompt without rewriting from scratch, and rely on version control when prompts evolve (which they always do once more than one person touches them). We also mention API access and using snippets as building blocks for agent-style workflows, including voice and context-focused prompt setups.

Top 14 AI Agent Builders For Different Cases

Now, let’s look through top 14 options of AI agent builders. Depending on your needs and aims, there are many points to cover, as you can choose the right one just for your cases.

1. Lindy 

Lindy is framed like an “AI employee,” but the actual value is more concrete than that – it’s a way to create agents from a plain-language description, then plug them into the apps a business already runs on. Instead of starting with a blank canvas and a bunch of setup steps, it begins with a prompt: describe what the agent should do, then connect the tools it needs to touch.

As a rule, this tool leans hard into the “run it in the real world” side of agents. There’s centralized management, access controls, and built-in memory that turns internal info into something searchable and usable by an agent. The feature list also gets into more hands-on automation: voice agents for phone workflows, plus “computer use” via a virtual machine when a task needs UI interaction rather than clean API steps. It reads like a platform that expects agents to be shared, governed, and iterated on, not just built once and forgotten.

Key Highlights:

• Prompt-based agent creation with a no-code builder
• App integrations to connect agents to daily systems
• Centralized management for deploying and maintaining agents
• Built-in memory for keeping internal knowledge accessible
• Access controls for who can edit, run, or share agents
• Phone and voice agent support
• Virtual machine “computer use” for UI-driven tasks

Who it’s best for:

• Teams running support, sales, or operations workflows across many tools
• Companies that need permissioning and governance around agents
• Use cases that involve email, scheduling, chat, and calls
• Groups that want agents to reference internal knowledge while working
• Workflows that sometimes require UI clicks, not just integrations

Contacts:

• Website: lindy.ai
• E-mail: hello@lindy.ai
• Twitter: x.com/getlindy
• LinkedIn: linkedin.com/company/lindyai

2. Gumloop 

Gumloop feels less like “talk to an agent” and more like “build a workflow that happens to use AI.” The core is a drag-and-drop builder where steps are assembled from nodes – connect apps, move data, apply logic, and add AI where it makes sense. It’s designed so someone can map a real process without needing to translate it into code first.

AI shows up as a practical tool inside those workflows: routing decisions, extracting information, categorizing inputs, generating text, and so on. In addition, it highlights that automations can run in the background, triggered by events from connected apps, which is usually where the time savings actually come from. For teams, the product surfaces the stuff that becomes painful later if it’s missing – credential handling, audit logging, permission controls, and deployment options such as running in a private cloud and routing AI usage through their own keys or proxy.

Key Highlights:

• Visual workflow builder with a library of automation nodes
• AI steps for routing, extraction, categorization, and generation
• Triggers and scheduled runs for background automations
• Templates and interfaces to share workflows inside a team
• Permission controls and credential management for org use
• Audit logging for tracing what ran and where data moved
• Private cloud deployment options and bring-your-own-keys support

Who it’s best for:

• Teams that want repeatable automations, not just chat-based helpers
• Departments juggling lots of apps and handoffs (marketing, ops, sales)
• Orgs that care about visibility into execution and data flow
• Non-technical users who still need structured logic and triggers

Contacts:

• Website: gumloop.com
• LinkedIn: linkedin.com/company/gumloop
• Twitter: x.com/gumloop

3. LangChain Agent Builder

LangChain Agent Builder is a lighter, no-code entry point for setting up agents without jumping straight into frameworks and code. It starts with either a template (like an email helper) or a plain-English goal that gets turned into an editable agent setup. The steps are simple: pick a starting point, connect accounts, run the agent, and adjust instructions when it behaves slightly off – which, realistically, is part of every agent build.

What stands out is how it keeps a human in the loop without making it annoying. Approvals can be used for actions that shouldn’t fire automatically, which makes the builder more usable for “real work” instead of experiments. It also sits in a broader ecosystem where teams can move from basic setup into more serious tooling around observability, evaluation, and deployment. In other words, it works as a front door – not the entire building.

Key Highlights:

• Start from templates or describe an agent goal in plain English
• Connect accounts to let agents pull context and take actions
• Run and iterate quickly by editing instructions
• Optional approvals for sensitive or important steps
• Can be used in chat or in work tools like Slack
• Fits into a broader stack for testing and deployment

Who it’s best for:

• Teams that want to spin up internal helpers without building from scratch
• Workflows that need guardrails and approval steps
• Groups already using LangChain tools and wanting a simpler builder layer
• People who want quick iteration before committing to deeper engineering

Contacts:

• Website: langchain.com 
• LinkedIn: linkedin.com/company/langchain
• Twitter: x.com/LangChainAI

4. CrewAI 

CrewAI is built around the idea that one agent isn’t always enough. As it is clear from the name, this tool focuses on “crews” – sets of agents that can take different roles and work through a task in stages, with orchestration underneath. Teams can build these workflows through a visual editor and AI copilot, or go straight to APIs when they want tighter control. That dual approach matters in practice: it lets non-engineers participate, while still giving engineers a way to get precise.

A lot of its attention goes into what happens after the build phase. Tracing shows exactly how agents interpreted a task and which tools they touched. Training helps make outcomes more repeatable. Guardrails put boundaries around behavior so workflows don’t wander into weird territory. On the operational side, it calls out role-based access control, monitoring, and deployment options that can run in cloud or private environments, which is usually where agent projects start to feel “real” inside larger orgs.

Key Highlights:

• Multi-agent orchestration for role-based workflows
• Visual editor plus API-first build path
• Integrated tools and triggers for workflow automation
• Tracing to follow tool calls and steps in detail
• Training and guardrails to stabilize outcomes
• Centralized management with role-based access control
• Deployment options for cloud or private infrastructure

Who it’s best for:

• Teams building multi-step processes that benefit from separate agent roles
• Orgs that need governance, monitoring, and permissions around agents
• Mixed teams where non-engineers build in a studio and engineers extend via API
• Workflows that need tracing and oversight to be maintained over time

Contacts:

• Website: crewai.com 
• LinkedIn: linkedin.com/company/crewai-inc
• Twitter: x.com/crewaiinc

5. LlamaIndex 

LlamaIndex approaches agents like a practical piece of software, not a magic assistant. An agent gets a task, looks at the tools available, picks what fits the next step, runs it, then checks if the work is finished. Tools can be as small as a couple of Python functions or as heavy as full query engines, and the agent leans on details like docstrings and type hints to understand what each tool is meant to do. If the docs are sloppy, the agent gets sloppy too, which is a very real-world detail they don’t hide.

The ecosystem is built to grow with the problem. Someone can start with a basic function-calling setup, then move into agent workflows that support looping, parallel paths, and multi-agent coordination. A lot of the platform’s DNA is tied to documents and pipelines, so the agent work isn’t just “answer from memory” – it’s parsing, extracting, indexing, and retrieving from actual files when the job demands it.

Key Highlights:

• Step-by-step tool selection with a clear “loop until done” pattern
• Tools can be simple functions or full query engines
• Uses docstrings and type hints to make tool behavior legible
• Pre-built agent patterns for common starting points
• Workflow engine that supports async, event-driven runs
• Building blocks for memory, state, and human review
• Strong focus on document parsing and retrieval pipelines

Who it’s best for:

• Developers who want agents that behave like predictable software
• Teams automating document-heavy work where extraction matters
• Projects that may start small but need room for multi-step workflows

Contacts:

• Website: llamaindex.ai 
• LinkedIn: linkedin.com/company/llamaindex
• Twitter: x.com/llama_index

6. Vellum 

Vellum is built around a “brief it like a teammate” workflow, then turns that into something teams can actually maintain. The interesting part is not the initial draft – it’s the way the build experience splits cleanly between non-technical and technical work. Non-technical teams can adjust logic in a visual builder without turning everything into a dev ticket, while engineers still have an SDK path when the agent needs custom behavior or deeper wiring.

This is one of the few tools in the list that seems to assume agents will change constantly, and it designs around that reality. Versioning is part of the normal workflow, evaluations and regression-style checks help catch quality drift, and observability makes it possible to look at what the agent did instead of guessing. Once an agent is stable enough, it can be packaged into an internal AI app so people can run it without touching the setup.

Key Highlights:

• Prompt-to-agent workflow that drafts logic from plain-English instructions
• Visual builder for editing steps without living in code
• Python and TypeScript SDK for engineering extensions
• Built-in evaluations and regression-style checks for quality drift
• Versioning and rollback for controlled iteration
• Observability features for reviewing runs and agent behavior

Who it’s best for:

• Orgs that want business teams building, with engineering oversight available
• Teams that need controlled updates instead of “edit and pray”
• Internal automations that need monitoring and repeatability
• Groups with governance and access control requirements

Contacts:

• Website: vellum.ai
• LinkedIn: linkedin.com/company/vellumai
• Twitter: x.com/vellum_ai

7. Decagon 

Decagon is very clearly built for customer experience agents, not generic “do anything” assistants. The platform reads like it was designed by people who have seen what happens when a support bot meets real customers. Their Agent Operating Procedures idea is basically SOPs for agents – written in natural language, but compiled into code so the logic stays strict and repeatable. It’s a nice middle ground where CX operators can shape behavior without turning every tweak into a custom engineering project.

A lot of the product detail sits around running agents across channels without fragmenting the experience. Chat, email, voice, SMS, and API surfaces are treated like different doors into the same system, with cross-channel memory so the agent doesn’t reset its brain every time the channel changes. For tuning and trust, it pushes traceable observability, testing and QA workflows, plus reporting and insights that help teams see where the agent is actually failing instead of relying on anecdotes.

Key Highlights:

• Agent Operating Procedures that translate natural language into code-level logic
• Omnichannel support across chat, email, voice, and SMS
• Cross-channel memory to keep interactions connected
• Traceable observability to inspect logic during conversations
• Guardrails designed for production CX environments
• Testing, QA, and reporting tools for ongoing tuning
• Integrations and API surfaces for custom customer touchpoints
• Unified customer context via a knowledge and memory layer

Who it’s best for:

• Support orgs that need one consistent agent across multiple channels
• Teams that want CX operators iterating on logic without losing rigor
• Companies that care about visibility into how the agent behaved in real conversations
• Businesses where deflection is important but quality still matters

Contacts:

• Website: decagon.ai
• LinkedIn: linkedin.com/company/decagon-ai
• Twitter: x.com/DecagonAI

8. Kore.ai 

Kore.ai is less “build a single agent” and more “set up an enterprise environment where a lot of agents can exist without chaos.” In this tool’s setup, no-code builder tools let business teams design agent logic visually using modular blocks and templates, while technical teams keep a path open for deeper integrations and more advanced configuration. That split is practical – it avoids turning every small workflow idea into an engineering backlog item.

This platform details lean into the realities of enterprise rollout: connectors, channels, model flexibility, retrieval and knowledge graph patterns, and multi-agent orchestration with memory and tools. Then there’s the control layer that tends to decide whether the tool survives in a big org – prompt and evaluation tooling, tracing, analytics, monitoring events, guardrails, RBAC, versioning, audit logs, and security and compliance controls. It’s built for places where deployment has rules and oversight isn’t optional.

Key Highlights:

• Visual no-code builder designed for structured agent logic
• Pro-code extensions and SDK-style inputs for engineering teams
• Modular building blocks for prompts, tools, connectors, and workflows
• Templates and marketplace-style reuse across departments
• Multi-agent orchestration with memory and tool usage
• Retrieval and knowledge graph patterns for enterprise context
• Prompt, evaluation, and model tooling for experimentation and control
• Tracing, analytics, monitoring, and governance controls

Who it’s best for:

• Larger orgs rolling out agents across many teams and functions
• Teams that need governance, audit logs, and permissioning from day one
• Companies with scattered data sources and lots of integrations to manage

Contacts:

• Website: kore.ai
• Facebook: facebook.com/KoreDotAI
• Twitter: x.com/koredotai
• LinkedIn: linkedin.com/company/kore-inc
• Phone: +1 844 924 8973

9. Glean 

Glean’s agent builder keeps circling back to one idea: an agent that isn’t grounded in real company knowledge is going to drift, confidently, and at the worst possible moment. Basically, the setup starts with natural language or a quickstart template, then moves into a drag-and-drop builder where the agent becomes more like a workflow than a chatbot. The grounding piece is permission-aware, so the agent can pull from live company systems without turning access into a manual mess.

The build experience also assumes iteration is constant. Agents can include branching and looping for processes that aren’t linear, and model choice can vary step by step depending on what’s happening in the workflow. Version control is baked in, which matters more than people admit – it’s the difference between “we improved the agent” and “we shipped a new problem.” The overall vibe is: keep it connected to real data, keep changes trackable, and make agents usable by more than just the builders.

Key Highlights:

• No-code creation from natural language or templates
• Drag-and-drop builder aimed at workflow-style agents
• Permission-aware connections to live company knowledge and systems
• Branching and looping for non-linear processes
• Step-level model selection based on the task
• Conversational refinement for adjusting logic and steps
• Automatic versioning with rollback support

Who it’s best for:

• Teams that need agents grounded in internal knowledge with permissions respected
• Workflows that look like processes, not single-step answers
• Orgs that want safe iteration without breaking live automations
• Internal agent libraries shared across departments

Contacts:

• Website: glean.com
• Instagram: instagram.com/gleanwork
• LinkedIn: linkedin.com/company/gleanwork
• Twitter: x.com/glean
• Address: 260 Sheridan Ave, Suite 300, Palo Alto, CA 94306, United States

10. Flowise 

Flowise is basically a visual workbench for building agent workflows. Instead of stitching logic together in code first, teams connect blocks into a flow that can run – anything from a simple chat assistant to a setup where multiple agents coordinate and hand tasks off. It’s the kind of tool that makes sense when someone wants to “see the system” while building it, not just hope the orchestration works after the fact.

The platform keeps one foot in prototype-land and the other in production. There’s support for tool calling and knowledge retrieval (RAG), plus human-in-the-loop steps when an agent should pause and ask for review. Once the flow is working, it can be exposed through an API, embedded into an app as a chat widget, or extended through SDKs, which makes it easier to move from an internal experiment to something other people can actually use.

Key Highlights:

• Visual builder for chatflows and agent workflows
• Multi-agent orchestration for more complex setups
• Tool calling and knowledge retrieval (RAG) from different data sources
• Human-in-the-loop review steps when automation needs a checkpoint
• Execution traces that can plug into common observability tools
• API, SDK, and embedded chat options for integration

Who it’s best for:

• Teams that like building workflows visually instead of writing orchestration code
• Chatbot projects that may grow into multi-agent setups
• Use cases that need an embedded assistant inside a product or portal

Contacts:

• Website: flowiseai.com
• LinkedIn: linkedin.com/company/flowiseai
• Twitter: x.com/FlowiseAI

11. Workato 

Workato’s “Genies” agents are built around the idea that an agent should not just talk – it should reason, take actions across systems, and follow the same rules the business already runs on. Underneath the naming, the tool feels like an extension of enterprise automation: agents connect to existing workflows, use pre-built skills, and can be assembled in a low-code studio without rebuilding the same building blocks every time.

The strongest theme is control. Actions run under role-based access control, users authenticate at runtime, and everything is meant to be auditable after the fact. In addition, the platform puts emphasis on governance details like data isolation, encryption, guardrails that limit what an agent can do, and operational logs so teams can trace what happened when an agent made a decision.

Key Highlights:

• Low-code Agent Studio for building and deploying custom agents
• Reusable skills and an agent library to speed up common work
• Connections across a large set of business apps and systems
• Runtime authentication, RBAC, and audit trails for agent actions
• Guardrails that restrict actions to approved skills and workflows
• Dashboards and logs for visibility into runs and decisions

Who it’s best for:

• Enterprises already running a lot of automation across many tools
• Teams that need strict access control and auditability
• Departments that want agents executing real actions, not just answering questions

Contacts:

• Website: workato.com
• E-mail: info@workato.com
• LinkedIn: linkedin.com/workato
• Twitter: x.com/workato
• Phone: +1 (844) 469-6752

12. Voiceflow 

Voiceflow is built for customer service agents, and it shows. The platform is centered around designing conversations that work in real channels, not just in a demo chat window. It supports both voice agents for phone calls and chat agents with deeper customization, which matters when customer support is spread across different touchpoints and each one has its own quirks.

It also feels team-oriented by design. Visual flows give product and support teams a shared way to build and iterate, while developer APIs and technical hooks handle the parts that need real integrations and data access. Security and model choice come up as practical concerns too, which fits the audience – customer-facing agents are not the place for “just connect everything and hope.”

Key Highlights:

• Built for customer service agents across voice and chat channels
• Visual flow builder with room for customization
• Developer APIs for data, knowledge, and interface integration
• Centralized workspace built for team collaboration
• Support for approved models and controlled data access

Who it’s best for:

• Product teams building customer-facing agents, especially for support
• Companies that need both voice and chat in one platform
• Teams that want collaboration plus developer-level extensibility

Contacts:

• Website: voiceflow.com
• LinkedIn: linkedin.com/company/voiceflowhq
• Twitter: x.com/VoiceflowHQ

13. Tray.ai 

Tray’s Merlin Agent Builder is a no-code workspace that’s meant to keep agents governed from day one. The idea is simple: agents should be able to use company data, take actions across systems, and still stay inside IT’s rules. The build flow is structured – define tools, set governance, connect data sources, then choose where the agent lives, like Slack, Teams, or an API endpoint.

A lot of the interesting detail sits in the “ops side” of agent building. It is all about reasoning setup, memory, governed data ingestion, testing before launch, and monitoring through their broader platform. There’s also a clear push toward reusable components – hubs, gateways, templates – which is usually what separates a one-off agent experiment from something that can be rolled out across teams without turning into a mess.

Key Highlights:

• No-code environment for building and managing agents under governance
• Agent tools and connectors, including CRUD and custom logic options
• Guardrails for scope, authentication, and approved actions
• Memory options plus governed knowledge access via Smart Data Sources
• Data ingestion for structured and unstructured sources
• Deployment targets like Slack, Teams, and APIs
• Testing and controlled rollout workflow
• Agent Gateway and Agent Hub for governed tool and template reuse
• Support for MCP and A2A style interoperability patterns

Who it’s best for:

• IT-led teams deploying agents across many systems with strict controls
• Organizations that need reusable building blocks and shared templates
• Workflows where agents must take actions, not just provide answers
• Teams that want one platform for building, deploying, and monitoring agents

Contacts:

• Website: tray.ai
• E-mail: people@tray.ai
• LinkedIn: linkedin.com/company/tray-ai
• Twitter: x.com/tray

14. IBM watsonx.ai 

IBM’s watsonx.ai positions agent building as part of a full development lifecycle, not a separate “agent toy box.” Thus. teams can experiment in a playground, wire up tools, deploy quickly through a UI or command line, and then keep an eye on behavior through tracing and evaluation. It’s structured like a developer studio that expects agents to move into production, not stay as prototypes.

The tool library is where it becomes more concrete. Agents can be given web search, document search, code execution, and data connectors for databases and warehouses, plus custom tools for outside services. Model choice is flexible as well, including IBM and third-party options, and the overall framing stays rooted in security, scalability, and compliance – the constraints that usually show up first in enterprise environments.

Key Highlights:

• Studio-style workflow from experimentation to deployment
• Playground for testing agent behavior before rollout
• One-click or single-command deployment options
• Tracing and evaluation tools for monitoring performance and behavior
• Tool library including web search, document search, and code execution
• Data connectors plus support for custom tools
• Flexible model choice across IBM and third-party models

Who it’s best for:

• Teams that want one environment to build, deploy, and monitor agents
• Orgs where tracing, evaluation, and governance are expected
• Use cases that need tools like retrieval, web search, or code execution

Contacts:

• Website: ibm.com
• Instagram: instagram.com/ibm
• LinkedIn: linkedin.com/company/ibm
• Twitter: x.com/ibm 
• Address: 1 New Orchard Road Armonk, New York 10504-1722 United States
• Phone: 1-800-426-4968

Final Thoughts

A “best” agent builder is mostly a myth once the real-world constraints show up. What matters is where the agent needs to live, what it’s allowed to touch, and how messy the work is going to get after week two. Some builders feel right when the goal is to sketch workflows quickly and keep the logic visible. Others make more sense when the agent has to operate inside strict rules, pull from real internal data, and leave a clear trail behind every action.

The honest way to choose is to think in boring questions, not hype. Does the team need a visual builder or a developer-first setup? Will the agent run in chat, in voice, in internal tools, or across all of them? Does it need approvals, version control, and monitoring, or is it a lightweight helper that can be rebuilt easily? Answer those, and the “best” option usually becomes obvious – not because it’s perfect, but because it fits the actual job.