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AI Agents

Agentic AI Tools Compared: 7 Platforms for Autonomous Workflows

We set up a customer support pipeline with three AI agents: one that triages tickets, one that drafts responses, and one that escalates edge cases to a human. It ran for a week without intervention and resolved about 70% of incoming requests correctly.

Key Takeaways:

  • • CrewAI is easiest to learn — role-based multi-agent framework with simple Python API, best for straightforward pipelines
  • • LangGraph offers the most production control — stateful graph-based orchestration with cycles, branching, and persistent memory for complex workflows
  • • Relevance AI is the no-code option — visual drag-and-drop agent builder starting at $19/month, no engineering required
  • • Costs range from free to hundreds/month — open-source frameworks are free but LLM API calls run $0.50-15 per complex agent session

That pipeline took two days to build using LangGraph. The same concept took four days with AutoGen and about half a day with Relevance AI's visual builder (though the Relevance version handled fewer edge cases). The point is not which tool was fastest — it's that building autonomous AI workflows is no longer a research project. It's an afternoon.

The agentic AI space has split into distinct categories since we last covered it. Open-source frameworks for developers who want full control. No-code platforms for teams that need agents without hiring engineers. Enterprise solutions baked into cloud providers. And a few wild cards that don't fit neatly into any box.

We spent four weeks testing seven platforms across real business workflows — not the "make me a poem" demos you see in launch videos. Here is what actually works, what breaks, and what costs more than the marketing pages suggest.

How We Tested These Platforms

We designed three workflows that mirror how companies actually use agentic AI, not isolated toy examples. Each platform was given the same objectives and judged on the same criteria.

Test Workflows

  1. Customer support triage: An agent that reads incoming support tickets, classifies urgency, drafts a response, and either sends it directly or routes to a human. Tested with 200 real (anonymized) tickets.
  2. Research-to-report pipeline: Three agents collaborating to research a topic, compile findings, fact-check against sources, and produce a formatted report. We ran this for five different topics.
  3. Data extraction and enrichment: An agent that scrapes product listings from three e-commerce sites, normalizes the data into a consistent schema, and flags anomalies (missing prices, duplicate entries).

For each workflow, we measured:

  • Time from setup to first successful run
  • Accuracy rate on the 200-ticket support test
  • Total LLM API costs per workflow run
  • How gracefully the platform handled errors and retries
  • Whether the output was good enough to use without manual editing

We did not test coding-specific agents here — that is covered in our agentic AI tools explainer, which focuses on Cursor Agent Mode, Claude Code, and similar developer tools.

7 Agentic AI Platforms Tested

1. CrewAI

crewai.com

CrewAI remains the most approachable multi-agent framework for Python developers. You define agents with roles ("researcher," "writer," "reviewer"), assign them tasks, and the framework manages handoffs. It has around 25,000 GitHub stars and a community that ships tutorials faster than most startups ship features.

Our research-to-report pipeline was up and running in about 90 minutes. The researcher agent pulled data from web searches, the writer structured it into sections, and the reviewer flagged three factual errors the writer had introduced. The whole pipeline cost roughly $0.80 in API calls per run using GPT-4o.

The support triage workflow exposed a real limitation: CrewAI processes tasks sequentially by default. Running 200 tickets through a three-agent pipeline took over 40 minutes. You can enable parallel execution, but the documentation for that feature is sparse and we hit a race condition that corrupted output on about 8% of tickets.

Strengths

  • +Fastest time to first working pipeline of any framework we tested
  • +Role-based agent design is intuitive — non-ML engineers get it quickly
  • +Model-agnostic: works with OpenAI, Anthropic, Groq, local models
  • +CrewAI Enterprise adds monitoring and deployment for teams

Weaknesses

  • -Sequential execution makes it slow for batch processing
  • -Parallel mode is buggy — race conditions on shared state
  • -Limited observability — debugging multi-agent failures means reading raw logs

G2 rating: 4.5/5 (28 reviews) | Pricing: Open-source (free), Enterprise from $99/mo | GitHub: ~25k stars

2. AutoGen (Microsoft)

microsoft.github.io/autogen

Microsoft's AutoGen framework takes a conversation-first approach to multi-agent systems. Agents interact through structured dialogues — they can debate, critique, and build on each other's work. The v0.4 rewrite (shipped in late January) changed the architecture significantly, and the ecosystem is still catching up.

AutoGen produced the highest-quality output in our research pipeline. We set up a "group chat" where the researcher proposed claims, the fact-checker challenged weak ones, and the writer revised. The resulting reports were noticeably more nuanced than single-pass alternatives. But this quality came at a cost: each report used roughly 3x more tokens than the same workflow in CrewAI.

For the support triage test, AutoGen's code executor agent handled ticket classification well, but the setup was painful. Getting three agents to coordinate on a consistent output format required around 120 lines of configuration. The equivalent in CrewAI was about 35 lines.

Strengths

  • +Conversational patterns (debate, peer review) produce higher-quality output
  • +Built-in Docker sandboxing for code execution agents
  • +Microsoft backing suggests long-term support and Azure integration

Weaknesses

  • -Steep learning curve — documentation reads like a research paper
  • -v0.4 breaking changes mean most community tutorials are outdated
  • -Multi-turn debates burn through tokens fast — 3x cost vs. single-pass
  • -Verbose setup: even simple workflows need 100+ lines of boilerplate

Pricing: Open-source (free) | GitHub: ~38k stars | Requires Python 3.10+

3. LangGraph (LangChain)

langchain-ai.github.io/langgraph

LangGraph takes a fundamentally different approach from CrewAI and AutoGen. Instead of defining agents with roles, you build a directed graph where nodes are functions (LLM calls, tool use, data transforms) and edges define the flow between them. Think of it as a state machine for AI workflows.

This graph-based model made it the strongest performer in our support triage test. We built a workflow where tickets enter a classification node, branch to either an auto-response path or an escalation path based on urgency, and loop back for refinement if the drafted response does not pass a quality check. The branching and looping logic that was awkward in CrewAI was natural in LangGraph.

The downside is complexity. LangGraph requires you to think in graphs, which is not intuitive for most developers. Our first attempt at the support pipeline took almost a full day, compared to 90 minutes with CrewAI. But once it worked, it was rock-solid — we ran 200 tickets through it with zero crashes and a 74% accuracy rate on auto-responses.

Strengths

  • +Graph-based control flow handles branching, loops, and conditionals cleanly
  • +Built-in persistent state — agents remember context across sessions
  • +LangSmith integration provides excellent observability and tracing
  • +Most production-ready option — used by companies like Elastic and Replit

Weaknesses

  • -Steepest learning curve of any tool on this list
  • -Tied to the LangChain ecosystem — you are adopting a full stack, not just a library
  • -Overkill for simple sequential agent pipelines
  • -LangChain's abstraction layers add overhead that some developers find frustrating

Pricing: Open-source (free), LangSmith from $39/mo for tracing | Part of LangChain ecosystem (~100k combined GitHub stars)

4. Relevance AI

relevanceai.com

Relevance AI is the no-code option on this list. You build agents by dragging and dropping tool blocks — web scraping, LLM calls, API requests, data transforms — onto a visual canvas and connecting them. No Python required. No terminal. Just a browser.

We rebuilt our support triage pipeline in about three hours using their visual builder. The classification accuracy was comparable to the coded solutions (around 68%), which surprised us. The auto-responses were shorter and more templated than what LangGraph produced, but they were perfectly adequate for tier-1 support.

Where Relevance AI fell short was flexibility. When we wanted a ticket to loop back through the classification step if the initial response scored below a confidence threshold, the visual builder could not express that logic cleanly. We ended up duplicating nodes as a workaround, which made the workflow brittle and hard to maintain.

Strengths

  • +Genuinely no-code — operations teams can build and manage agents themselves
  • +Pre-built integrations with Slack, Gmail, HubSpot, Salesforce, and more
  • +Built-in scheduling — agents can run on cron jobs without external tooling
  • +Usage dashboard shows token costs per agent, which is rare in this space

Weaknesses

  • -Visual builder cannot express loops or conditional branching well
  • -Agent responses tend to be more generic than coded alternatives
  • -Pricing scales with usage — can get expensive at volume

G2 rating: 4.6/5 (40 reviews) | Pricing: Free tier, Pro from $19/mo, Scale custom | Platform: Cloud-based

5. AgentGPT

agentgpt.reworkd.ai

AgentGPT is the simplest tool on this list. You open it in a browser, type a goal ("research the competitive landscape for project management tools"), and watch it break the goal into subtasks, execute them using web searches, and compile results. No account required for basic use. No setup at all.

That simplicity is also the ceiling. In our research pipeline test, AgentGPT produced a decent overview of the topic in about 12 minutes, but the depth was shallow — more like a summary of Google's first page than original research. It could not access APIs, run code, or interact with any external systems beyond basic web browsing.

We could not run the support triage or data extraction tests at all — AgentGPT does not support custom tools, file access, or persistent data. It is an agent in the loosest sense: it chains web searches and summarization. Useful for quick research, not for production workflows.

Strengths

  • +Zero setup — open in browser and start immediately
  • +Good for quick exploratory research and brainstorming
  • +Open-source — can self-host with your own API keys

Weaknesses

  • -No custom tools, API access, or file system interaction
  • -Output quality is surface-level — fine for summaries, not for serious analysis
  • -Gets stuck in loops on ambiguous goals
  • -Not suitable for any production use case

Pricing: Free (open-source), hosted version has usage limits | GitHub: ~32k stars | No Python required

6. Vertex AI Agent Builder (Google)

cloud.google.com

Google's Vertex AI Agent Builder is the enterprise play in this list. It lives inside Google Cloud Platform, integrates with BigQuery, Cloud Functions, and Dialogflow, and targets teams that are already deep in the Google ecosystem. If you are not on GCP, this is probably not for you.

For the data extraction workflow, Vertex Agent Builder was the most reliable platform we tested. We connected it to BigQuery for storage, used Cloud Functions as tool endpoints, and built an agent that scraped, normalized, and loaded product data with proper error handling. The integration between agent actions and Google Cloud services felt seamless in a way that open-source tools cannot replicate without significant plumbing.

The cost was the problem. Our test workflow — which ran once daily for a week — generated a GCP bill of about $47. The same workflow running through LangGraph with direct API calls to GPT-4o-mini cost roughly $3.20 for the week. The convenience tax for staying inside GCP is steep.

Strengths

  • +Deep integration with Google Cloud services (BigQuery, Cloud Functions, Dialogflow)
  • +Enterprise-grade security, IAM, and audit logging out of the box
  • +Grounding with Google Search reduces hallucinations in research tasks
  • +Low-code interface lets non-engineers contribute to agent design

Weaknesses

  • -Expensive — GCP pricing makes simple workflows surprisingly costly
  • -Tightly coupled to GCP — nearly impossible to migrate away
  • -Gemini models lag behind GPT-4o and Claude on complex reasoning tasks
  • -Setup requires GCP familiarity — not beginner-friendly

Pricing: Pay-per-use (GCP billing), costs vary widely by usage | Platform: Google Cloud only

7. Claude Computer Use (Anthropic)

anthropic.com

Claude Computer Use is the oddest entry on this list. It is not a framework or a platform. It is a capability baked into Anthropic's Claude model: the ability to see your screen, move the mouse, click buttons, and type text. It turns Claude into an agent that operates your computer the way a human would.

We tested it on the data extraction workflow by pointing it at three e-commerce sites and asking it to collect product information. It opened Chrome, navigated to each site, scrolled through listings, and extracted data into a spreadsheet. The accuracy was around 85% — it missed some items that loaded via infinite scroll and occasionally misread prices on product cards with complex layouts.

What makes Claude Computer Use fascinating is its generality. It is not limited to pre-defined tools or APIs. If a human can do it by clicking through an application, Claude can attempt it. We used it to fill out a Google Form, manage a Trello board, and update a row in Airtable — all without any integration setup. But it is slow (each action takes 2-4 seconds), expensive (roughly $0.12 per screenshot analysis), and brittle when pages change layout.

Strengths

  • +Works with any application — no APIs or integrations needed
  • +Can automate tasks on legacy systems that have no API
  • +Backed by Anthropic's strong reasoning capabilities

Weaknesses

  • -Slow — each click-and-observe cycle takes several seconds
  • -Expensive at scale — every screenshot costs tokens
  • -Brittle on dynamic or frequently changing UIs
  • -Security risk — giving an AI full computer control requires trust

Pricing: Requires Claude Pro ($20/mo) or API access ($3/MTok input, $15/MTok output for Opus) | Platform: macOS, Linux, Windows

Side-by-Side Comparison

PlatformTypePricingCoding RequiredBest For
CrewAIMulti-agent frameworkFree / $99/mo EnterprisePythonContent pipelines, research workflows
AutoGenMulti-agent frameworkFree (open-source)PythonComplex agent conversations, quality-critical tasks
LangGraphGraph orchestrationFree / $39/mo LangSmithPython / JSProduction systems with complex control flow
Relevance AINo-code builderFree / $19/mo ProNoneOperations teams, simple automations
AgentGPTBrowser agentFree (open-source)NoneQuick research, brainstorming
Vertex AI Agent BuilderEnterprise platformPay-per-use (GCP)Low-codeEnterprises on Google Cloud
Claude Computer UseComputer-controlling agent$20/mo (Claude Pro)NoneLegacy system automation, general desktop tasks

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Picking the Right Platform for Your Use Case

These tools serve different users solving different problems. Here is a direct mapping based on what we learned during testing.

You want multi-agent workflows and you can write Python

Start with CrewAI if your pipeline is straightforward (agents work sequentially on defined tasks). Move to LangGraph when you need branching, looping, or persistent state. Consider AutoGen when output quality matters more than token costs and you want agents to challenge each other's work.

You need agents but your team does not code

Relevance AI is the clear winner. The visual builder handles 80% of common agent workflows without writing a line of code. The remaining 20% (complex loops, custom logic) will either require a developer or a creative workaround.

You are already on Google Cloud

Vertex AI Agent Builder integrates tightly with your existing GCP services and adds enterprise security features. The premium you pay in GCP costs may be worth it if compliance, IAM, and audit logging are hard requirements.

You need to automate tasks on apps with no API

Claude Computer Use is the only option that can interact with arbitrary desktop and web applications by controlling mouse and keyboard. It is slow, expensive, and requires trust, but nothing else on this list can fill out a legacy CRM or navigate a government portal.

A pattern we noticed:

Every team that got real value from agentic AI started with a narrow, well-defined task — not a vague goal like "automate our customer support." The teams that tried to boil the ocean with agents ended up spending more time debugging the agent than doing the work manually. Start small. Prove it works. Then expand.

Related Reading

For a deeper look at what agentic AI is and how it works under the hood, see Agentic AI Tools Explained: What They Are and How They Work, which covers the concepts behind these platforms.

If you are specifically interested in AI tools for software development, our AI Coding Tools Compared article covers Cursor, GitHub Copilot, Claude Code, and other developer-focused options.

Frequently Asked Questions

What is an agentic AI tool?

An agentic AI tool is software that can autonomously plan, execute, and iterate on multi-step tasks without constant human input. Unlike chatbots that respond to one prompt at a time, agentic tools break goals into subtasks, use external tools like APIs and file systems, and self-correct when they encounter errors. Examples include multi-agent frameworks like CrewAI and LangGraph, no-code builders like Relevance AI, and enterprise platforms like Google Vertex AI Agent Builder.

Which agentic AI framework has the most GitHub stars?

As of February 2026, Microsoft AutoGen leads with around 38,000 GitHub stars, followed by LangGraph (part of the LangChain ecosystem with over 100,000 combined stars) and CrewAI with roughly 25,000 stars. Star counts reflect community interest but not necessarily production readiness. CrewAI has a more active contributor community relative to its size, while AutoGen benefits from Microsoft's backing.

Can I build an AI agent without coding?

Yes. Relevance AI offers a fully visual, no-code agent builder where you drag and drop tools, set triggers, and deploy agents without writing any code. Google Vertex AI Agent Builder also provides a low-code interface for enterprises already on Google Cloud. AgentGPT lets you run browser-based agents with no setup. However, no-code platforms typically offer less flexibility and customization than coding frameworks.

How much does it cost to run agentic AI tools?

Costs vary widely. Open-source frameworks like CrewAI, AutoGen, and LangGraph are free to use, but you pay for the underlying LLM API calls — ranging from $0.50 to $15 per complex agent session depending on the model and token usage. Relevance AI starts at $19 per month. Google Vertex AI Agent Builder uses pay-per-use pricing that can reach hundreds of dollars monthly at scale. Claude Computer Use requires a Claude Pro subscription ($20/month) or API access.

What is the difference between CrewAI, AutoGen, and LangGraph?

CrewAI focuses on role-based multi-agent collaboration with a simple Python API, making it the easiest to learn. AutoGen (Microsoft) specializes in conversational agent patterns where agents debate and peer-review each other's work, offering the most flexibility but with a steeper learning curve. LangGraph provides stateful graph-based orchestration with fine-grained control over agent workflows, supporting cycles, branching, and persistent memory. CrewAI is ideal for straightforward multi-agent pipelines, AutoGen for complex agent interactions, and LangGraph for production systems that need deterministic control flow.

Last updated: February 18, 2026 | Published by OpenAI Tools Hub Team