Many AI agent companies offer tools and platforms that help businesses design, train, and deploy custom AI agents for their specific workflows. Some excel at sales tasks, while others focus on research, code, or internal ops.
If you want a fast, no-code tool, Lindy is an ideal choice. Cognition handles full-stack developer tasks. Dust keeps things secure inside Slack.
In this article, we’ll cover:
- 10 best AI agent companies at a glance
- Their features, pros, cons, and pricing
- Tips on how to choose the right AI agent platform
- FAQs
Let’s start with the quick‑glance comparison table below.
10 best AI agent companies: TL;DR
Below is a shortlist of the top AI agent companies we recommend. Here’s a quick breakdown:
1. Lindy: Best AI agent platform for workflow automation
Lindy is a no-code platform for building AI agents that automate common business workflows, from managing inboxes and calendars to handling follow-ups, scheduling, and data extraction.
You can choose from ready-to-use templates to launch workflows fast, like email triager, meeting scheduler, and follow-up email drafter. The drag-and-drop workflow builder lets you modify these templates without writing code.
Who it’s for
Operators, founders, and lean teams looking to offload repetitive tasks without hiring or coding. Especially useful if you’re juggling sales ops, customer support, or internal admin work.
Key features
- Multi-modal agents: Agents can communicate over email, phone, Slack, and web, which is useful if your workflows span channels.
- Human-in-the-loop support: Add approvals or checkpoints when full automation isn’t ideal.
- Prebuilt templates: Dozens of ready-made workflows for lead routing, support responses, scheduling, and more.
- 7,000+ integrations: Integrates with all major apps without complex setup.
Pros
- Fast setup with no-code builder
- Integrates natively with CRMs, support tools, calendars, and more
- SOC 2 and HIPAA-compliant for enterprise security requirements
Cons
- Credit-based pricing on lower tiers may limit power users
- May take a few days to fine-tune agents for your specific workflow logic
Pricing
- Free: 400 monthly credits
- Pro: $49.99/month, billed monthly, 5,000 credits
- Business: $299.99/month, billed monthly, 30,000 credits
- Custom: Custom pricing
Bottom line
If you want a reliable agent that can manage ops tasks without writing code or stitching APIs, Lindy’s a strong choice. Lindy serves busy operators who need time back, not developers experimenting with tooling.
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2. Adept: Best for web-based task automation
Adept builds AI agents that operate your computer like a person, clicking through apps, filling forms, copying data, and completing browser tasks through UI‑based interaction.
Adept’s ACT‑1 model lets users describe a task in plain language (such as ‘create a report from last month’s deals’), and the agent figures out how to do it using the interface itself. Unlike rule-based automation, this agent learns patterns from how humans work inside apps.
Who it’s for
Teams that run complex, repetitive workflows inside tools like Salesforce, Notion, or Excel. Also fits early adopters looking to test what agent-based UI automation looks like in practice.
Key features
- ACT-1 model: Trained to interact with popular SaaS tools visually
- Natural language commands: No need for structured prompts or hard-coded steps
- Autonomous loops: Can retry, scroll, and move across tabs if needed
Pros
- Great for automating legacy systems without APIs
- Doesn’t require integration setup
- Backed by a strong technical team with research roots
Cons
- Not suited for solo users
- Documentation and support are sparse for non-developers
Pricing
- Custom pricing, need to contact their team
Bottom line
Adept is one of the few AI agent development companies focusing on UI-level task execution. It’s still early, but if you need browser automation and don’t want to build brittle scripts, this is worth watching.
3. Cognition: Best for autonomous software engineering
Cognition built Devin, an AI software engineer that can plan, code, debug, and deploy applications with minimal input. It writes entire codebases, sets up environments, and even pushes to GitHub. It’s positioned as a full‑stack autonomous developer.
Who it’s for
Engineering teams, technical founders, or product managers exploring AI-assisted development. Best suited for teams building web apps or dev tooling.
Key features
- End-to-end coding support: From feature spec to working deployment
- Built-in IDE (Devin 2.0): Designed to host, run, and test code internally
- Error recovery: Can detect and fix its own bugs mid-process
Pros
- Ideal for rapid prototyping
- Can run live code in secure sandboxes
- Teams can audit the output before merging it to production
Cons
- Not open-source
- Performance depends on clearly defined goals and prompts
Pricing
- Devin Core: $20/month
- Devin Team: $500/month
- Custom pricing for enterprise deployments
Bottom line
Devin is best if you want to automate software development. It's for technical teams, and can save hours for developer-heavy organizations.
4. Vocode: Best for AI-powered phone agents
Vocode is an open‑source platform for building conversational phone agents, including both inbound and outbound call flows.
Vocode lets you create custom phone agents that sound natural and can switch between languages. Developers can deploy agents on Twilio or use their stack. It’s lightweight and flexible, but for coders.
Who it’s for
Developers building voice-driven agent experiences. Works well for support teams, appointment booking, or phone-based data collection.
Key features
- Real-time voice agents: Handles live calls with speech recognition + synthesis
- Multilingual support: Can operate in multiple languages
- Custom APIs: Plug in your own logic or backend flows
Pros
- Impressive voice quality
- Fully open-source and customizable
- Active developer community
Cons
- Need to have technical skills
- Requires setup and infrastructure to deploy
Pricing
- Free to use under an open-source license
- Costs vary by provider, as telephony providers like Twilio set carrier/API pricing
Bottom line
If you’re building voice-first agent tools, Vocode is a strong foundation. Just be prepared to write some code.
5. Dust: Best for Slack-native AI agents
Dust helps teams build secure AI agents that live inside Slack and connect to internal tools like Notion, GitHub, and Google Drive.
It supports connecting private data sources and lets you switch between OpenAI, Anthropic, and Mistral models, which is rare. You’ll need technical comfort to get the most out of it, but it’s powerful once set up.
Who it’s for
Mid-size teams or startups that want internal-facing agents to answer questions, summarize documents, or automate common internal workflows.
Key features
- Slack-first UX: Works natively inside channels
- Model flexibility: Choose from multiple LLM providers
- Secure connectors: Bring in Notion, Google Drive, Linear, and more
Pros
- Built-in templates for fast setup
- Strong security and permissioning
- Ideal for internal use cases
Cons
- Requires some technical lift
- Less suited for customer-facing workflows
Pricing
- Pro plan: $29/user/month
- Custom pricing for enterprise plans
Bottom line
Dust works for teams that want private, secure, internal‑facing agents, especially if Slack is your central workspace.
6. MultiOn: Best for browser-level task automation
MultiOn builds agents that can navigate the web autonomously, searching, clicking, filling forms, and completing multi‑step tasks inside your browser. It runs as a Chrome extension or browser-based agent. You can tell it what to do in plain language, and it gets to work on the open web.
Who it’s for
Early adopters or teams that rely heavily on browser tools that don’t offer APIs. Useful for lead gen, booking, purchasing, or scraping tasks.
Key features
- Web navigation: Operates directly in the browser, no backend required
- Autonomous execution: Can complete full flows like booking or checkouts
- Language input: No prompts or coding required to trigger actions
Pros
- Good for websites without integrations
- Natural interaction model
- Helps research and automation-heavy roles
Cons
- No public pricing information
- Early in development, limited support docs
Pricing
- Hasn’t disclosed pricing yet
- Likely custom or usage-based
Bottom line
MultiOn can be a great AI agent tool for automating real-world browser tasks. Still early, but it’s worth keeping an eye on if you rely on browser-based workflows.
7. CrewAI: Best for multi-agent task orchestration
CrewAI is an open‑source framework for coordinating multiple AI agents, each with defined roles, to work together on complex tasks.
It uses ‘crews’ of agents, like a researcher, writer, and editor, working together with defined responsibilities. While it’s not plug-and-play, it’s flexible and developer-first.
Who it’s for
Developers and teams who want to design multi-step, multi-agent systems. Fits well in research, data analysis, and technical project workflows.
Key features
- Role-based orchestration: Assign custom tasks to each agent
- Python-based framework: Build logic flows and memory sharing
- Open-source: Fully customizable for specific team needs
Pros
- Strong for task delegation at scale
- Works with any LLM
- Ideal for technical builders and prototypers
Cons
- Requires Python knowledge
- Can seem expensive for the hosted plans
Pricing
- Free under an open-source license
- Paid plans start from $99/month
Bottom line
CrewAI is for technical teams exploring how multiple agents can solve layered problems together. It isn’t for beginners, but it’s great for building custom agent tools.
8. Superagent: Best for API-first agent infrastructure
Superagent is a developer-first platform that helps you build, deploy, and manage AI agents using APIs and modular components.
It supports agent memory, retrieval-augmented generation (RAG), scheduling, and multiple model backends. It emphasizes backend control and suits developer teams, not operators.
Who it’s for
Engineering teams that want full control over how agents work, especially those integrating with internal systems or building custom apps.
Key features
- RAG support: Pulls from databases or files during conversations
- Multi-model support: Works with OpenAI, Anthropic, Hugging Face
- Custom memory and tools: Define your context and logic
Pros
- Strong developer flexibility
- Open-source core for self-hosting
- Modular design for experimentation
Cons
- Doesn’t provide no-code support
- Requires infrastructure and deployment knowledge
Pricing
- Free for open-source use
- No information on enterprise plans
Bottom line
If you’re building agent-powered products from scratch, Superagent gives you the building blocks. But it’s not aimed at everyday operators or non-developer teams.
9. LangChain: Best for custom LLM agent development
LangChain is a development framework for building apps powered by language models, including custom agents that use tools, memory, and reasoning steps.
It supports function-calling, tool chaining, prompt engineering, and long‑term memory, everything needed to build production‑grade agent tools.
Who it’s for
Engineers and AI researchers building LLM-powered systems from the ground up. Ideal for those who need total control over agent logic, chaining, and data routing.
Key features
- Tool chaining: Connects multiple functions in a flow
- Memory modules: Store and retrieve context mid-conversation
- Framework flexibility: Compatible with any LLM or backend
Pros
- Extremely customizable
- Strong open-source community
- Great for POCs and experiments
Cons
- No user interface
- Steep learning curve for non-engineers
Pricing
- Fully open-source under MIT license
- Hosted plans include LangGraph and LangSmith
- Free (hosted): $0/month
- Plus (hosted): $39/month
- Custom pricing for enterprise plans
Bottom line
LangChain is one of the most mature AI agent development companies if you’re building from scratch, but it's only useful if you have engineers on hand.
10. Reka: Best for frontier LLM agent research
Reka is an AI research company developing advanced multimodal foundation models with potential agentic applications, but it is not a plug‑and‑play agent platform.
Reka recently launched Reka Core and Reka Flash, models that show strong performance across language, vision, and reasoning tasks. However, it does not currently offer a product that supports workflow agents or no-code agent tools.
Who it’s for
Enterprise AI teams and researchers exploring high-performance LLMs with future agent capabilities. Reka does not target small teams or operational use cases yet.
Key features
- Multimodal LLMs: Handles text and image inputs
- Competitive benchmarks: Ranked alongside GPT-4 and Claude
- Research-grade tooling: Early access for labs and enterprises
Pros
- High performance across tasks
- Could support more complex agent capabilities in future product releases
- Backed by researchers with published work in top AI conferences
Cons
- Not available for everyday workflows
- No agent builder or integrations today
Pricing
- Hasn’t published pricing or access tiers
- Need to contact their team for more details
Bottom line
Reka isn’t built for direct agent workflows, but has developed a few capabilities across text, speech, and AI workspace. If you want an advanced LLM for future agent design, it’s worth following.
How we tested these AI agent companies
We wanted to understand these tools and what it’s like to use them before recommending any of them, especially from the perspective of an operator or builder. Here’s how:
What we looked for
- Time to deploy: Could we go from zero to a working agent in under an hour? Speed matters, especially for lean teams without dedicated engineering support.
- Task reliability: Did the agent complete workflows accurately and consistently, without breaking on edge cases?
- Integration depth: Could the tool connect to everyday business systems, like CRMs, calendars, or support platforms, without complex setup?
Testing process
We created test flows across sales, support, scheduling, and document workflows. For tools without public access, we reviewed product docs, user demos, and community feedback to assess how the agents behave in real-world use.
Additional factors considered
- Security and compliance: Some agents handle sensitive data, so we looked at SOC 2, HIPAA, and access control options.
- Flexibility for different roles: Not all tools serve the same user. We assessed how well they fit ops teams, developers, and cross‑functional users.
These testing parameters helped us move beyond surface-level features. Now, let’s break down when each platform makes the most sense.
Which AI agent company should you choose?
You should choose the right AI agent company based on what kind of work you’re trying to offload and how technical your team is. Here’s how to think about fit based on your use cases:
Choose Lindy if you
- Run everyday business tasks at a startup or mid-size team
- Want to automate email, scheduling, CRM updates, or support tasks
- Don’t have engineering resources, but still need reliable automation
Lindy works well if you want practical, business-focused agent tools. It’s especially useful for lean ops teams who need to move fast.
Choose other platforms like
- Adept: You need to automate interactions in legacy or browser-based tools without integrations
- Cognition: You want an autonomous coding agent to ship internal tools
- CrewAI: You’re building multi-agent systems for research or technical workflows
- LangChain: You have engineers who want full control over agent logic and tool chaining
- Dust: You want secure, internal-facing Slack agents connected to private company data
Avoid these tools if you
- Want no-code deployment, but the platform requires coding, like Superagent, Vocode, and LangChain
- Need out-of-the-box workflows, but the tool is still in beta, like MultiOn or Reka
- Don’t have time or resources to train or customize an agent from scratch
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Final verdict
If you’re looking for the best AI agent platform to automate everyday tasks and workflows, Lindy is the most complete option on this list. It’s built for operators, not developers, and it handles workflows most teams need, like inbox triage, meeting scheduling, follow-ups, and system updates.
If your needs are highly technical or research-focused, other tools may fit better. Cognition is strong for autonomous coding. CrewAI and LangChain offer flexibility for developer teams building from scratch. And Adept looks promising for your workflows that live inside browser UIs.
The space is evolving quickly, but the best agent platforms ship usable outcomes, not demos.
How Lindy helps you create customized AI agents
Lindy is an AI automation tool that lets you create AI agents without writing code. These agents help you with emails, meetings, and sales.
Lindy stands out among other AI agent companies for three key reasons:
- Drag-and-drop workflow builder for non-coders: You don’t need any technical skills to build workflows with Lindy. It offers a drag-and-drop visual workflow builder.
- Create AI agents for your use cases: You can create AI agents and give them written instructions in everyday language without prompt engineering, and automate repetitive tasks like lead sourcing and email scheduling. For instance, create an assistant that finds leads from websites and business intelligence sources like People Data Labs. Create another agent that sends emails to each lead and schedules meetings with members of your sales team.
- Free to start, affordable to scale: Build your first few automations with Lindy’s free version and get up to 400 tasks. With the Pro plan, you can automate up to 5,000 tasks, which offers much more value than Lindy’s competitors.
Frequently asked questions
What is the best AI agent company for businesses in 2025?
Some of the best AI agent companies for businesses include Lindy, Cognition, LangChain, and CrewAI. Lindy is a strong choice for workflow automation. Cognition is better for autonomous coding. LangChain and CrewAI are built for dev-heavy setups.
Which companies are leading in AI agent development?
Lindy, Cognition, Adept, Dust, and LangChain lead AI agent development, each focusing on different parts of the stack from workflow orchestration to LLM tooling.
What’s the difference between an AI agent and a chatbot?
An AI agent is designed to complete tasks for users, while a chatbot primarily responds to user messages or inquiries. Agents can handle email, update CRMs, book meetings, or summarize documents, often without needing user prompts.
Can AI agents integrate with CRMs and calendars?
Yes, AI agent tools can integrate with CRMs and calendars, like Google Calendar, HubSpot, Salesforce, and others. Lindy, for example, supports over 7,000 integrations out of the box.
Are AI agent tools safe for enterprise use?
Yes, some AI agent tools are safe and meet enterprise standards, like SOC 2 and HIPAA, while others require self‑hosting or are too early for enterprise use. Lindy, for example, is SOC 2 and HIPAA-compliant.
What industries benefit most from AI agents?
Industries that benefit the most from AI agents are healthcare, SaaS, professional services, real estate, and finance. These sectors rely on repetitive digital tasks that AI agents can help automate.
How customizable are AI agents for specific business needs?
AI agents are quite customizable, depending on the platform you use. They range from plug‑and‑play templates to fully customizable frameworks. Some tools are template‑driven, like Lindy, while others let you define every step, like LangChain or CrewAI.
What should I look for when choosing an AI agent platform?
You should look for ease of setup, task reliability, integration support, and whether it fits your team’s technical level while choosing an AI agent platform. The best tools are the ones every team member can use.
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