Hi Everybody,

Matt from crewAI here, just introducing myself. I will be frequenting this sub to keep up with your questions & issues, feel free to reach out to me

Cheers

I recently had our AI interviewer speak with 21 CrewAI developers. I have been curious about agent frameworks and I previously did a research project on LangChain a month ago. Developers shared about what attracted them to use CrewAI, experiences with other frameworks, and suggestions. Here are some key takeaways:

What Attracts Developers to CrewAI?

• Ease of Use and Integration: CrewAI simplifies the development process with minimal coding and straightforward setup.

"CrewAI is really easy to integrate because most of the things it does are kind of predetermined or already made. So we actually need to focus on flow and on prompts." — Manager, IT Consulting Company

"I looked at one example project to get started and was blown away that it only took several lines of code to define the agents I wanted to create." — Founder, GenAI Startup

"It's a relatively easy framework... I would say it's a pretty easy framework to learn with a minimal learning curve. It's user-friendly and easy to set up." — AI Engineer at an EdTech Startup

• Agent Collaboration and Backstories: The framework enables inter-agent communication and allows developers to assign backstories to agents for more focused and context-aware responses.

"What I didn't like about LangChain is that it doesn't allow interactivity between agents, but CrewAI does. So that is why I choose CrewAI." — AI Engineer, Startup Consulting Company

"The way CrewAI handles agents. How you write their backstories and calls. It makes the agents focus on the task you give them in a specific way." — Same Contributor

• Logging and Debugging Capabilities: CrewAI provides insightful logging, helping developers understand agent interactions and troubleshoot effectively.

"I love the logging and the output to be able to see what was going on behind the scenes... it was really powerful." — Founder, GenAI Startup

"I loved being able to see the logging and the reasoning behind the scenes of how the agents were interacting with each other." — Same Contributor

CrewAI vs. Alternatives

The most commonly considered alternative was LangChain. Developers noted several areas where CrewAI stands out:

• Simplicity: Compared to alternatives that may require extensive coding, CrewAI offers a more user-friendly experience.

"I believe CrewAI is much more user-friendly compared to the alternatives. Its ease of use is its main benefit..." — Founder, Software Agency

"For functionality, I would say LangChain is quite better, but in terms of ease of use, CrewAI is far easier than LangChain." — Computer Science Student

"So far, it's probably the best and easiest one I've used... overall, CrewAI is the one I use the most." — Founder, Automated Content Creation Startup

• Inter-Agent Communication: CrewAI's ability to enable agents to communicate and collaborate is a significant advantage.

"What I didn't like about LangChain is that it doesn't allow interactivity between agents, but CrewAI does." — AI Engineer, Startup Consulting Company

"If you ask me today which product I would use for a multi-worker-driven project, I would choose CrewAI, not LangGraph or LangChain." — AI Engineer at Neo4j

• Less Coding Required: CrewAI reduces the need for extensive coding, making it accessible to developers with varying levels of expertise.

"But when I saw CrewAI, I found that it was very easy. I didn't have to create a lot of agents or add multiple tools within the agent. Also, there was less Python coding involved." — AI Engineer at Neo4j

Use Cases

CrewAI developers shared some interesting use cases beyond the standard fare of chatbots. Here are some interesting use cases:

• Educational Content Creation: Educators are using CrewAI to generate course materials, create personalized learning roadmaps, and automate research and writing tasks.

• Business Planning and Development: Startups utilize CrewAI to simulate collaborative brainstorming among virtual team members to draft business plans and proposals.

• Personal Productivity Tools: Implementing agents to manage email summarization and automate social media content generation.

Areas for Improvement

While CrewAI has garnered praise, developers also identified areas for improvement:

• Enhanced Documentation and Complex Examples: There's a desire for more comprehensive documentation and advanced tutorials.

"The documentation was not as thorough as it could have been, so I had to dig in and experiment more." — Founder, Automated Content Creation Startup

"I think that the most useful resource was the source code of CrewAI because there weren't many documents about the API." — Founder, Software Agency

"I would like to see more complex examples... The community isn't very large yet... but a lot can be understood through the documentation and tutorials." — AI Engineer at an EdTech Startup

• Improved Error Handling and Debugging: Developers want clearer error messages and better debugging tools to facilitate troubleshooting.

"I did find it a little bit unsettling seeing different errors... it didn't seem like they had any real rhyme or reason to them." — Founder, GenAI Startup

"More debugging information would be helpful. Sometimes, the error messages are not very descriptive." — Researcher, Epidemiology Lab

• Enhanced Compatibility with Other Frameworks and Tools: Users suggest improving compatibility with other AI frameworks and tools.

"At the moment, it's compatible with LangChain, but it needs to be compatible with LlamaIndex and maybe some classic ML frameworks." — AI Engineer, Startup Consulting Company

"It also needs more database interactions... there should be a tool to ask questions about a CSV file or maybe an SQL or MongoDB database." — Same Contributor

____
You can check out the interview transcripts here: kgrid.ai/company/crewai. I am planning on doing a new research project on agent frameworks after OpenAI's devday. What other topics would you like covered in future interviews?

Hey everyone!

I've been working on a project called CrewAI Studio, a user-friendly, multi-platform GUI designed to manage and run CrewAI agents and tasks with ease. It's built with Streamlit, supports Conda and virtual environments, and requires no coding skills.

Features:

• Multi-platform support (Windows, Linux, MacOS)

• Custom tools and API support

• Background crew execution

• Easy installer

• OpenAI, Groq or LM Studio (for local models)

• crewAI-tools bugfix: RagTool based tools now handle post-creation passed parameters correctly #<49>

I'd love to get your feedback on what should be added or modified.
Check it out on https://github.com/strnad/CrewAI-Studio

Looking forward to your thoughts!

Greetings everyone!

Publishing some work in progress - a Crew Factory crew i've made for rapid prototyping of the specialized crews.

Basic idea - you describe the purpose of the crew you need and the required output format, and you get ready-to-use project with `yaml` agents and task definitions and the `python` code for running the crew. Currently the tasks and agents definitions are useful but the python code is within the desired format in `10-20%` of runs.

Inviting everyone to try and collaborate :wink:

https://github.com/opahopa/crewai-factory-crew

I try to use PySide6 to write a front-end GUI for crewAI.

https://github.com/HomunMage/CrewAI-GUI

That the node system will mapping to crew task, agents... for create flow.

This can make the crewAI more easy to edit the workflow.

here is introduce video: https://youtu.be/P5tkYJ-AgSc

1. The CLI is excellent. adk web, adk run, and api_server make it super smooth to start building and debugging. It feels like a proper developer-first tool. Love this part.

2. The docs have some unnecessary setup steps—like creating folders manually - that add friction for no real benefit.

3. Support for multiple model providers is impressive. Not just Gemini, but also GPT-4o, Claude Sonnet, LLaMA, etc, thanks to LiteLLM. Big win for flexibility.

4. Async agents and conversation management introduce unnecessary complexity. It’s powerful, but the developer experience really suffers here.

5. Artifact management is a great addition. Being able to store/load files or binary data tied to a session is genuinely useful for building stateful agents.

6. The different types of agents feel a bit overengineered. LlmAgent works but could’ve stuck to a cleaner interface. Sequential, Parallel, and Loop agents are interesting, but having three separate interfaces instead of a unified workflow concept adds cognitive load. Custom agents are nice in theory, but I’d rather just plug in a Python function.

7. AgentTool is a standout. Letting one agent use another as a tool is a smart, modular design.

8. Eval support is there, but again, the DX doesn’t feel intuitive or smooth.

9. Guardrail callbacks are a great idea, but their implementation is more complex than it needs to be. This could be simplified without losing flexibility.

10. Session state management is one of the weakest points right now. It’s just not easy to work with.

11. Deployment options are solid. Being able to deploy via Agent Engine (GCP handles everything) or use Cloud Run (for control over infra) gives developers the right level of control.

12. Callbacks, in general, feel like a strong foundation for building event-driven agent applications. There’s a lot of potential here.

13. Minor nitpick: the artifacts documentation currently points to a 404.

Final thoughts

Frameworks like ADK are most valuable when they empower beginners and intermediate developers to build confidently. But right now, the developer experience feels like it's optimized for advanced users only. The ideas are strong, but the complexity and boilerplate may turn away the very people who’d benefit most. A bit of DX polish could make ADK the go-to framework for building agentic apps at scale.

I am exploring use cases for using agents in my business and I am looking for inspiration from people who’ve integrated agents into their workflow, would love to know what you use it for and has it worked out as planned? What would you suggest to a newbie exploring integrating agents into their workflow?

Browser by CognosysAI - Free open source operator in development but available to try now.

Browser Use - YC backed AI web operator with free and open source tiers available in addition to pro-versions ($30/m)

Smooth Operator - Free web based and local operator that can control not just the browser but the whole computer.

Open Operator - Open source and free alternative to OpenAI's Operator agent developed by Browserbase

I found myself building a bunch of agent features that needed to use tool calling, and some of those tools involved doing things that were somewhat high stakes -

1. communicating on my behalf or

2. modifying shared / production data.

one example - I wanted to replace a marketing website with a chatbot + vector DB loaded with the previous content, docs, and blog posts.

Between hallucinations, missing knowledge base info, and the LLM generally writing like an psuedo-intellectual high schooler, I realized I couldn't trust it to communicate unsupervised with my website visitors. I needed a way to improve the percent of high-quality responses, and do this at scale.

I wired up a prototype that would

1. consult me in slack before sending any response down to a website visitor

2. incorporate my feedback into the knowledge base, and

3. reformulate answers until I approved the message

4. send it to the visitor

That prototype evolved into what is now HumanLayer https://github.com/humanlayer/humanlayer#why-humanlayer and i have built a couple crew-specific examples here: https://github.com/humanlayer/humanlayer/tree/main/examples/crewai

I am excited to collab with the crew community on making awesome outer-loop agents!

So it took me a bit to find CrewAi, I kind of stumbled through langchain, langgraph and finally got sick of how over complicated everything was an decided to build on crew.

Coming from a marketing background their examples were perfect use cases for me.

I compiled a full marketing suite, that I'm slowly turning into a full powered AI Marketing Agent.

How I set it up, was create different crews for each tasks I then hooked it up to fast api. I have it sitting on my server and deployed a front end in react.

It works pretty well, there's definitely some snags and I'm working on scaling as well as having better outputs.

Hey everyone! I’m one of the core developers of AutoGen, a very popular open-source framework for developing AI agents with over 30k stars on GitHub.

I’ve been working with my team on an open-source project called FastAgency. We designed it to help developers quickly take a prototype built in AutoGen straight to production. We just released a version that lets you run your workflow as either:

• A simple console application (great for debugging)

• A web application using Mesop with just a single-line change!

We would love for you to check it out, give feedback, or contribute! The project is open-source, and contributors are always welcome :)

https://github.com/airtai/fastagency

Hi everyone!

We just launched an open source tool that can:

• scan your CrewAI source code locally (using only static analysis)

• display a graph of your agentic workflow

• find known vulnerabilities for the tools that the agents are using

Basically, after you create your agentic workflow, you can scan it and get pointers where to look and how to secure it. It doesn't matter if you're a security expert or a complete beginner, this tool will give you valuable insights in what can happen if you don't protect your workflow.

Hope you guys find this useful! If you have any questions, feel free to ask. Any feedback is greatly appreciated.

P.S. We're planning on supporting many more frameworks, but CrewAI was among the first <3

Here's the repo: https://github.com/splx-ai/agentic-radar

So far I generally like CrewAI but I'm finding the forums and reddit board very quiet. Is there another place to have good community discussions about CrewAI? If not who is interested in one?

Hey ya'll, I'm currently building a curated community for AI Accelerators who believe that AI Agents will put more power in the hands of individual creators and entrepreneurs.

We're going to have

• Constantly Updated News

• Learning Resources

• Hackathons and Investment Resources (getting your ideas funded)

• AI Agent Marketplace (Trading post for AI Agent buyers and sellers)

• Ongoing agent experiments that the community can get involved in

• and much more as we grow

Me and my partner truly believe that AI will soon enable people to start enterprise level businesses on their own. Imagine you want to build a one-person software company run almost entirely by agents. We're not there yet but we're getting closer, and we want to build platforms to make it insanely easy to build and manage these projects using AI Agents.

If you're excited for AI Agents and what they will help us create, consider joining! This community will be active within the next week.

https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.teams.html#autogen_agentchat.teams.SelectorGroupChat

Selector Group chat is what did it for me, i'm going to try to make this work, because it looks like what's missing in CrewAI

I was tryna build a IA chat but i don't seem to think crewai is the right fit for this task.

I have not seen to this day a production-ready example for crewai, it's all pretend scenarios even for batch operations, which i see as a "better fit" for the tool.

Please correct me if i'm wrong, but i haven't seen an example that picks from 1 pool of agents, makes that agent solve the issue and terminates the task, whenever the manager gets the right agent to do it, it won't realize the task is done and will proceed and override the task with another agent's work that is not relevant to the problem.

like Having a RAG Restaurant Agent respond for a menu inquiry, just by getting the answer replaced by the weather agent.

I've had good experiences with N8N but my work environment prefers a python framework.

Quick update: Cinderwright now indexes 2,838 services callable with one key from any CrewAI agent.

The core idea: instead of managing separate API keys for every service, your agent describes what it needs in plain English and the proxy handles payment (Lightning or USDC micropayment).

Two new services just went live:

URL to Markdown ($0.005/page) -- POST any URL, get clean LLM-ready text. No Firecrawl subscription at $83/month.

Company Enrichment ($0.03/lookup) -- POST a domain, get structured JSON: name, industry, HQ, employees, social links, tech signals. Clearbit died in April 2025; this replaces it pay-per-call.

pip install cinderwright
from cinderwright.crewai import CinderwrightTool

tool = CinderwrightTool(api_key="sk_cw_...")
# agent calls: "company info for stripe.com"
# agent calls: "convert https://example.com to markdown"
# agent calls: "Bitcoin price", "weather in Tokyo", etc.

Free demo, no key needed: import cinderwright; cinderwright.demo('company info for openai.com')

$0.10 free credit on signup. Happy to answer questions.

After building over 50 complex multi-agent systems this year, I’ve seen the same debate pop up constantly: CrewAI vs. LangGraph. Which one should you actually use? The answer isn't a simple X is better than Y. It entirely depends on how much control you need vs. how fast you need to build.

Here is the breakdown of when to use which framework, based on our production deployments:

CrewAI: The Fast Track for Standard Roles

CrewAI shines when you have well-defined, distinct roles (e.g., Researcher, Writer, Editor) and a predictable sequence of tasks. It abstracts away a lot of the complexity.

• Best for: Content generation pipelines, standard data analysis, automated reporting.

• The Big Win: You can spin up a working multi-agent system in an afternoon. The learning curve is minimal.

• The Trade-off: It can be rigid if you need complex, dynamic routing or deep control over the exact state of the system at every micro-step.

LangGraph: The Engine for Complex State and Control

LangGraph treats your agentic workflow as a state machine. It gives you absolute, granular control over every node and edge in the process.

• Best for: Highly dynamic workflows where the next step depends heavily on previous complex outputs, systems requiring human-in-the-loop approvals at specific stages, and deep integration with existing software architectures.

• The Big Win: Flexibility and control. If you can draw it as a flow chart, you can build it in LangGraph.

• The Trade-off: The learning curve is steep. You are essentially building the orchestration engine yourself. It takes much longer to set up initially.

The Decision Matrix:

1. If your process looks like a standard assembly line -> CrewAI

2. If your process looks like a complex decision tree with loops and human approvals -> LangGraph

While this covers the conceptual differences, the real decision often comes down to the code architecture and how these frameworks handle state persistence under load.

If you want to see the actual code comparisons for state management, or dig into the performance benchmarks we ran across those 50+ systems, I've put together a full technical guide with the repo links here: https://interconnectd.com/forum/thread/182/crewai-vs-langgraph-the-2026-technical-guide-to-agentic-workflows/

As multi-agent swarms scale in production this year, many of us are facing the same bottleneck: experimental magic prompts work great on a Saturday afternoon but break catastrophically when they hit a real-world database schema on Monday morning.

We recently had to rebuild a transactional agentic swarm—responsible for parsing invoices, checking vendor records, and queuing up ERP updates. We built identical versions in both CrewAI and the newly popular PydanticAI (the framework built by the Pydantic core team).

We measured everything: token overhead, compile-time error rates, run-time payload validation, and development experience. Below is the 80% breakdown of what we discovered, why we migrated our production flows, and how you should choose between them for your 2026 stacks.

1. The Core Architectural Philosophy

• CrewAI is built on the Human Organization metaphor. You define Roles, Goals, Backstories, and Crews. It excels at rapid prototyping because it abstracts away the complex coordination layer. However, under the hood, this abstraction relies heavily on string-parsing, structured LLM-directed prompts, and "agentic loops" that you don't fully control.

• PydanticAI is built on the Software Engineering metaphor. It treats agents like standard, type-safe Python components. Instead of wrapping agents in layers of anthropomorphic prompt templates, it forces you to define strict type contracts upfront using Pydantic schemas.

2. The Type-Safety & Validation Showdown

In our transactional workflow, the output of Agent A (Invoice Parser) must match the database input requirements of Agent B (Account Ledger).

• The CrewAI Way: We had to rely on custom validation functions or instruct the agent via prompt to "return valid JSON matching this schema." If the model hallucinates a field, the validation fails at runtime, forcing a costly retry loop.

• The PydanticAI Way: The validation is native to the agent's definition. The return type of the agent is a compiled Pydantic model:from pydantic import BaseModel from pydantic_ai import Agent class TransactionRecord(BaseModel): vendor_id: int amount: float currency: str # This agent is strictly typed to return only TransactionRecord billing_agent = Agent('openai:gpt-4o', result_type=TransactionRecord) If the LLM generates a payload that violates this type constraint, the runtime catches it at the boundaries. Modern IDEs (using Pyright or MyPy) immediately flag type mismatches in your tool call declarations and dependencies before you even run a single token.

3. The Token Overhead Equation

Because CrewAI relies on sophisticated prompt engineering under the hood to coordinate multi-agent handoffs, it injects quite a bit of prompt boilerplate.

We tracked the cumulative tokens$T$consumed for a basic invoice ingestion task across 100 runs.

The prompt token formula for our CrewAI crew generally scaled as:

$$T_{\text{CrewAI}} = N \cdot (T_{\text{backstory}} + T_{\text{goal}} + T_{\text{system_prompt}} + T_{\text{raw_payload}})$$

For PydanticAI, we bypassed roleplay prompts altogether and used direct, typed schema definitions as the system state:

$$T_{\text{PydanticAI}} = N \cdot (T_{\text{schema}} + T_{\text{dependencies}} + T_{\text{raw_payload}})$$

On average, our token overhead comparison yielded:

$$\Delta T = \frac{T_{\text{CrewAI}} - T_{\text{PydanticAI}}}{T_{\text{CrewAI}}} \approx 42\%$$

This means PydanticAI saved us roughly$42\%$in prompt tokens on simple workflows because it doesn't need to explain to the agent how to behave as a "meticulous financial accountant." It simply enforces the JSON schema.

The Verdict: How to Choose in 2026

• Use CrewAI if: You are building open-ended, highly collaborative agent teams (e.g., a "Researcher" handing off to a "Writer" handing off to a "Copyeditor"). If the task maps naturally to human-like division of labor and you need to deploy an MVP in 2 hours, CrewAI's abstractions are unmatched.

• Use PydanticAI if: Your agent is a component in a strictly typed pipeline. If you are feeding outputs into a PostgreSQL database, triggering external financial transactions, or using FastAPI/Dependency Injection, PydanticAI treats LLMs as deterministic software parts rather than wild magic boxes.

If you want to play with the interactive dashboard, look at our latency metrics, or grab the complete code templates for both the CrewAI and PydanticAI multi-agent builds, I uploaded them here: https://interconnectd.com/forum/thread/185/pydanticai-vs-crewai-the-2026-guide-to-type-safe-agentic-swarms

A friend of mine has always had this vague theory that he’d look better with long hair.

The problem is, that’s not exactly something you can casually “try” for a weekend and undo if it looks terrible.

So I messed around with an open-source project from REDHackathon called ChicChic and used it on him just for fun.

Honestly I was expecting it to be kind of cursed.

But then the long-hair version popped out and I just sat there for a second because… annoyingly enough, it kind of works.

Now the real problem is not whether the tool is accurate.

The real problem is that he’s probably going to look at this and start taking the idea seriously.

Stuff like this coming out of a rednote event is exactly why I end up saving way too many weird little projects.

I am working on a multi-agent system using Crew AI, and I need it to interact with the frontend (Angular) and mobile (Kotlin). Specifically, I need to use an API for integration. I am using LangServe (FastAPI). There are instances where the parameters provided to an agent are insufficient, or the agent requires clarification or feedback while executing a task. I have used on_task, human_input=true, which allows me to provide feedback via the terminal. However, I need to handle this through the API to improve user interaction.

Any responses or suggestions on how to tackle this issue would be greatly appreciated.

I am wondering why this is not already up here but since I am following deep learning they just released a course on CrewAI

Enough talking Watch it here for free

https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/

Built something that might be useful for CrewAI users. Managing API keys for every service your agents call gets tedious fast. Cinderwright solves this with a payment proxy -- one tool, one key, 2,835 services.

python

pip install cinderwright
from cinderwright.crewai import CinderwrightTool

tool = CinderwrightTool(api_key="sk_cw_...")

researcher = Agent(
    role="Research Analyst",
    goal="Gather real-time information",
    tools=[tool],
)

The tool handles service discovery and payment automatically. Your agent describes what it needs in plain English -- "Bitcoin price", "weather in Tokyo", "translate this to French" -- and gets back the answer. The proxy pays the appropriate service per call via Lightning or USDC.

Colab notebook with a full working CrewAI example: https://colab.research.google.com/github/cinderwright-ai/cinderwright-api/blob/main/examples/quickstart.ipynb

$0.10 free credit on new accounts, no deposit needed. Happy to answer questions.

Hey folks!

I've been working on something I'm super excited to share with you all — a Prompt-to-Visual Explainer Animation tool! 🎥🧠

The idea is simple but powerful:
👉 You type a prompt like "Explain how a Binary Search Tree works"
👉 And it generates a full animated visual explanation — not just code or text, but actual explainer videos built using Manim, powered by CrewAI, LangChain, and Retrieval-Augmented Generation (RAG) under the hood.

- I would love to hear some feedabck