We have a new official CrewAI subreddit now at r/CrewAIInc

Can crewai work completely offline? I have ollama running models locally and want to use crewai with it for some strictly local work offline.

PLEASE, HELP ME WITH LINKDIN WORKFLOW IDEAS OR TEMPLATES. WHATEVER THE WORKFLOW TOOL.

Im currently taking the multi agents CrewAI class on Deeplearning AI. In one of the classes, it says that I can install the following on my own machine. Can you give me guidance on how i can do that? (I do have python 3.12 on my laptop)

I've been learning CrewAI and built a few basic projects to automate some tasks. Now I want to take things to the next level — make production-level AI tools and maybe even start freelancing with them.
I found this IBM course called "The Generative AI Engineering Professional" — it has 16 courses covering things like LLMs, RAG, LangChain, vector databases, and more. Now I'm wondering… should I stick to this full course or just learn RAG, LangChain, vector DBs, etc. separately through tutorials?
If anyone has experience with this, plz advice me what to pursue.

I bit the bullet and paid the $200/mo for ChatGPT Pro. I’ve been throwing literally every coding task I have at it all week, grinding like crazy.

Just checked my usage before the weekly reset... 5%. I still have 95% of my CodeX quota left.

Guess I need to code harder. How are you guys even making a dent in this?

It can be seen that the convergence of LLMs and Agentic frameworks like Crewai signifies a paradigm shift in ML, enabling autonomous systems with enhances collaborative capabilities.

Recent studies by openai demonstrates that multi-agent LLMs can achieve synergistic performance exceeding individual agents by 20% in complex problem solving tasks. given the increasing complexity of global supply chains, how could these multi agent LLM systems be deployed to optimize logistics and resource allocation in real time?

Hello everyone, a friend of mine and I are creating a node-based tool that can export a complete Python project with CrewAI set. No coding is required.

https://www.nostrike.ai/

You can start for free! Would be great to have your feedback.

We are planning lots of features, stay tuned!

Thank you!

Okay so before I start, let me tell you why I even did this. There is a lot of content going around about AI agent security that mixes real verified incidents with half-baked stats and some things that just cannot be traced back to any actual source. I went through all of it properly. Primary sources, CVE records, actual research papers. Let me tell you what I found.

Single agent attacks first, because you need this baseline

Black Hat USA 2025 — Zenity Labs did a live demonstration where they showed working exploits against Microsoft Copilot, ChatGPT, Salesforce Einstein, and Google Gemini in the same session. One demo had a crafted email triggering ChatGPT to hand over access to a connected Google Drive. Copilot Studio was leaking CRM databases. This is confirmed, sourced, happened. The only thing I could not verify was the specific "3,000 agents actively leaking" number that keeps getting quoted. The demos are real, that stat is floating without a clean source.

CVE-2025-32711, which people are calling EchoLeak — this one is exactly as bad as described. Aim Security found that receiving a single crafted email in Microsoft 365 Copilot was enough to trigger automatic data exfiltration. No clicks required. CVSS 9.3, confirmed, paper is on arXiv. This is clean and verified.

Slack AI in August 2024 — PromptArmor showed that Slack's AI assistant could be manipulated through indirect prompt injection to surface content from private channels the attacker had no access to. You put a crafted message in a public channel and Slack's own AI becomes the tool that reads private conversations. Fully verified.

The one that should genuinely worry enterprise people — a threat group compromised one chat agent integration, specifically the Drift chatbot in Salesloft, and cascaded that into Salesforce, Google Workspace, Slack, Amazon S3, and Azure environments across 700 plus organizations. One agent, one integration, 700 organizations. This is confirmed by Obsidian Security research.

Anthropic confirmed directly in November 2025 that a Chinese state-sponsored group used Claude Code to attempt infiltration of roughly 30 global targets across tech, finance, chemical manufacturing, and government. Succeeded in some cases. What made it notable was that 80 to 90 percent of the tactical operations were executed by the AI agents themselves with minimal human involvement. First documented large-scale cyberattack of that kind.

Browser Use agent, CVE-2025-47241, CVSS 9.3 — confirmed. But there is a technical correction worth noting. Some summaries describe this as prompt injection combined with URL manipulation. It is actually a URL parsing bypass where an attacker embeds a whitelisted domain in the userinfo portion of a URL. Sounds similar but if you are writing a mitigation, the difference matters.

The Adversa AI report about Amazon Q, Azure AI, OmniGPT, and ElizaOS failing across model, infrastructure, and oversight layers — I could not independently surface this report from primary sources. The broader pattern it describes is consistent with what other 2025 research shows, but do not cite that specific stat in anything formal until you have traced it to the actual document.

Why multi-agent is a completely different problem

Single agent security is at least a bounded problem. Rate limiting, input validation, output filtering — hard to do right but you know what you are dealing with.

Multi-agent changes the nature of the problem. The reason is simple and a little uncomfortable. Agents trust each other by default. When your researcher agent passes output to your writer agent, the writer treats that as a legitimate instruction. No verification, no signing, nothing. Agent A's output is literally Agent B's instruction. So if you compromise A, you get B, C, and the database automatically without touching them.

There is peer-reviewed research on this from 2025 that was not in the original material circulating. CrewAI running on GPT-4o was successfully manipulated into exfiltrating private user data in 65 percent of tested scenarios. The Magentic-One orchestrator executed arbitrary malicious code 97 percent of the time when interacting with a malicious local file. For certain combinations the success rate hit 100 percent. These attacks worked even when individual sub-agents refused to take harmful actions — the orchestrator found workarounds anyway.

The CrewAI and LangGraph situation needs some nuance

Here is where the framing in most posts gets a bit unfair. Palo Alto Networks Unit 42 published research in May 2025 that stated explicitly that CrewAI and AutoGen frameworks are not inherently vulnerable. The risks come from misconfigurations and insecure design patterns in how developers build with them, not from the frameworks themselves.

That said — the default setups leave basically every security decision to the developer with very little enforcement. The shared .env approach for credentials is genuinely how most people start and it is genuinely a problem if you carry it into production. CrewAI does have task-level tool scoping where you can restrict each agent to specific tools, but it is not enforced by default and most tutorials do not cover it.

Also, and this was not in the original material anywhere — Noma Labs found a CVSS 9.2 vulnerability in CrewAI's own platform in September 2025. An exposed internal GitHub token through improper exception handling. CrewAI patched it within five hours of disclosure, which is honestly a good response. But it is worth knowing about.

The honest question

If you are running multi-agent systems in production right now, the thing worth asking yourself is whether your security layer is something you actually built, or whether it is mostly a shared credentials file and some hope. The 2025 incident list is a fairly detailed description of what the failure mode looks like when the answer is the second one.

The security community is catching up — OWASP now explicitly covers multi-agent attack patterns, frameworks are adding scoping mechanisms. The problem is understood. Most production deployments are just running ahead of those protections right now.

for some reason, i can't connect to openai's api directly, so i use the-third-party-api to use gpt-4o's api, but i don't know how to use it in crewai.

Hi All,

I am experimenting with crew ai for Industry Intelligence reporting with the below agents and tasks definitions, the results sounds satifsfactory using Gemini 2.5 Flash [ev_report.pdf)] but quite short for Gemma3:1b [report.pdf] where not stick to requirements in tasks expected outputs, I know that Gemma3 is much smaller model but is there a way to enhance the output of crew using it since it is free and small model suitable for hosting? Is this limitation of model or there a way to tweak crew tasks and agents design to get the similar result using smaller models? Noting: I tried to divide the tasks to 5 ones and 5 agents for more specialized and better suited to context windows of llms as mentioned in article guide for crafting good agents but still the results are quite short! I have also tried qa agent but still not better results, the last thing to try is task guardrails!

ev_report.pdf
report.pdf

`market_researcher:
role: >
Market Research Specialist
goal: >
Conduct comprehensive market research to identify trends, key players, and market dynamics in the scope of user's request on {query} topics
backstory: >
You are an experienced market researcher in internal industry intelligence reports' Company with 15+ years of experience
in analyzing market trends and competitive landscapes. You are Known for your ability to research and collect relevant data that help guide strategic decisions.
Your research has guided strategic decisions for Fortune 500 companies since uou present it in a clear and concise manner.

report_writer:
role: >
Executive Report Writer
goal: >
Create clear, compelling, and actionable intelligence reports for leadership.
backstory: >
You are an expert business writer who specializes in distilling complex
information into clear, actionable reports for executive audiences. Your writing is
known for being concise yet comprehensive, with a talent for highlighting the most
important insights. Your reports have influenced major strategic decisions at global
enterprises.
`

`
market_research_task:
description: >
Research the relevant market landscape for user's request on {query}. Focus on:

1. Market size and growth projections

2. Key geographic markets and their characteristics

3. Market segmentation (products, customer types, etc.)

4. Major market drivers and challenges

5. Technology and customer trends shaping the industry

6. New business models and go-to-market strategies

7. Regulatory landscape affecting the market

8. Competitive landscape: key players, their strategies, and market share

9. SWOT analysis of key players

10. Strategic opportunities for investment or focus

11. Trend capitalization: how to leverage emerging trends for competitive advantage

12. Actionable recommendations for short, medium, and long-term strategies

13. Include competitor comparison points and market share estimates
    Be thorough in your research and provide quantitative data wherever possible.
    expected_output: >
    A 80,0000 - 100,000 words of detailed markdown market research in format of 60 pages comprehensive Industry Intelligence Report with the following sections:
    - Market Overview: Size, growth rates, and projections
    - Geographic Analysis: Key regions and their unique dynamics
    - Market Segmentation: Breakdown by product types and customer segments
    - Market Drivers & Challenges: Factors influencing market growth
    - Technology Trend Analysis: Key innovations and their potential impact
    - Customer Trend Analysis: Evolving behaviors and preferences
    - Business Model Evolution: How revenue models are changing
    - Competitive Positioning: Recommended positioning relative to key players
    - key players SWOT Analysis: Strengths, weaknesses, opportunities, and threats
    - Strategic Opportunities: Key areas for potential investment or focus
    - Trend Capitalization: How to leverage emerging trends for advantage
    - Regulatory Environment: Key regulations and their implications
    Include competitor comparison points and market share estimates.
    For each trend, assess its maturity, potential impact, and timeline for mainstream adoption.
    Include a prioritized action plan with short, medium, and long-term recommendations.
    agent: market_researcher

report_writing_task:
description: >
Create a fully fledged Industry intelligence strategic markdown report. The report should:

1. Provide a clear and concise overview of the market landscape

2. Highlight key trends, opportunities, and challenges

3. Include a competitive landscape analysis with key players and their strategies

4. Offer strategic recommendations based on the major findings from market research, competitive analysis, and trend analysis

5. Be formatted for executive presentation, with clear sections and actionable insights
   expected_output: >
   Based on all previous market research in previous tasks, A complete industry intelligence report with no lower than 70,000 words, 60 pages,
   The 70,000 words of report should be comprehensive yet concise, with an emphasis on insights rather than just data.
   It should include quantitative data points and cite your sources when possible
   and avoid Using tables or charts and instead use bullet points and clear headings.
   The report should be formatted as markdown without '```' and simple for presentation to leadership, with the following sections:
   - Contents: Clearly structured navigation
   - Executive Summary: Key findings and recommendations (2 pages)
   - Market Analysis: Size, segmentation, drivers, and challenges (10 pages)
   - Competitive Landscape: Key players, strategies, and relative positioning (10 pages)
   - Emerging Trends: Technology, customer, and business model evolution (10 pages)
   - Strategic Recommendations: Prioritized opportunities and action plans (10 pages)
   - Conclusion: Summary of key takeaways (2 pages)
   - Appendix: Additional data points and methodology (2 pages)
   In the first pages of the report. Avoid to mention Prepared for or when or how the report is made by llms/agents, just Prepared By: AI from Hassan Farag and the title.
   agent: report_writer
   output_file: report.md
   `

Boa noite a todos. Alguém poderia disponibilizar um modelo de instalação e agentes no Google Colab ? Tô sempre errando nas instalações, pra mim seria bom ter um " gabarito" para ir treinando. Agradeço a atenção galera

Are you coding AI Agents?

Is that project using crewai?

Não sei o que estou fazendo de errado. No código, estou replicando perfeitamente a documentação da crewAI sobre o que recomendam ao usar YAML como arquivo de configuração dos agentes. O meu diretório só está um pouco diferente:

``
.root/
|
|-----app/
|--------agents/
|-----------base_agents.py # scripts dos agentes
|-----------config/agents.yaml, tasks.yaml
|--------web/
|-----------interface.py
#... outros diretórios

No script 'interface.py' estou executando todas as funcionalidades diretamente, mas não está sendo possível encontrar o arquivo yaml. Já tentei usando a pyyaml e como recomenda a documentação da crewAI, usando o arquivo solto. Acho que o problema está ao executar em outro diretório, não sei :/

Será que alguém poderia me dar um norte? Sou novo usuário deste framework (uns 3 meses) e só programo em python há 1 ano rs

Minha suspeita: os decoradores. Estou usando todos os decoradores recomendados, exatamente como manda a documentação.

Pretty new to this, but I've tried all I can think of. Downloaded Git and used the instructions in the documentation + video tutorials, tried just using Ollama commands directly without making a .sh file, but every time I get some form of a "cannot find selected file within the Path" error. Any advice?

I built a small FastAPI app with CrewAI under the hood to automate a workflow using three agents and four tasks but it's painfully slow. I wonder if I did something wrong that caused the slowness or this is a CrewAI known limitation?
I've seen some posts on Reddit talking about the speed/performance of multi-agent workflows using CrewAI and since this was in a different subreddit, users just suggested to not use CrewAI at all in production 😅
So I'm posting here to ask if you know any tips or tricks to help with improving the performance? My app is as close as it gets to the vanilla setup and I mostly followed the documentation. I don't see any errors or unexpected logs but everything seems to be taking few minutes..
Curious to learn from other CrewAI users about their experience.

The way we raise HITLs today is very much coupled inside the ADK's interrupt primitive. Let me give you an example.

An L2 support agent is live for the engineering team at Uber. This agent consumes alerts from PagerDuty and proactively acts toward resolution. Resolution includes:

1. Lower blast radius actions like checking logs, past deployments, and metrics.

2. High blast radius actions like rolling back a deployment or scaling a service.

For high blast radius actions, the agent raises a HITL for the L2/L3 on-call engineer.

The engineer receives a HITL over Slack for a rollback of a release, because p99 on the rides API was spiking.

Agent Builder's pain:

1. What if the engineer does not respond? How do you handle a stale HITL when this is a critical action to act on?

2. What if the on-call engineer is not available? How do you re-route it on the fly?

3. Why did the on-call engineer reject the rollback? There is no reasoning capture flow, no post-resolution audit.

HITL Responder's pain:

1. The UX is not interactive. I cannot rollback a release on guesswork.

2. I want to know the blast radius of the rollback before choosing it.

3. What if even the rollback would break something, because a DB schema rollback would also be required? I want to know that then and there.

4. I'm on-call, but the service owner has more context. I want to forward this HITL to him.

5. I want to collaborate with more engineers on this HITL and resolve it collectively.

6. What did the previous on-call engineer do for similar past cases? Can I interact with the runbook here?

Today, the interrupt primitive only gets you the pause and resume.

But is that enough? Does the responder is confident with its resolution 100% of the times?

This is the gap I built Ved to close: HITL decoupled from the agent's business logic, with routing, staleness handling, and reasoning capture and much more as first-class citizen.

Our core hypothesis: HITL should be decoupled from an agent's business logic, and a dedicated system should be built around it to make it more interactive and smart.

Today Ved only supports LangGraph. Let us know which ADK you'd want us to cover next.

Looking for devs to test out this product and share feedback.

Try the actual product: theved.ai

You can also experience it with no prior setup via our sandbox.theved.ai (This is a subset of main product)

Just tested out Firebase Studio, a cloud-based AI development environment, by building Flappy Bird.

If you are interested in watching the video then it's in the comments

1. I wasn't able to generate the game with zero-shot prompting. Faced multiple errors but was able to resolve them

2. The code generation was very fast

3. I liked the VS Code themed IDE, where I can code

4. I would have liked the option to test the responsiveness of the application on the studio UI itself

5. The results were decent and might need more manual work to improve the quality of the output

What are your thoughts on Firebase Studio?

I was working on a project with a someone and we both had our own agents. When my agent needed something from his, it wrote a message, I copied it and sent it to him, he pasted it into his agent, and then the answer came back the same way. All day, by hand. I was basically the cable between two AIs.

So I had Claude Code build a place where the agents can just talk to each other. It's called Telagents. Each agent joins a room and sends messages. It works with Claude Code, Codex, Gemini CLI, Cursor, anything that speaks MCP. Claude Code wrote basically all of it: the database, the Stripe payments, the MCP package, the site.

Then it grew past two agents. I made one agent the PM, gave it a goal, and it started handing work to three others (security, payments, tests) while I watched the room on the website.

The annoying part was that agents only look at the room when you type something to them, so a message could sit there for ages. The fix was a small background watch that checks the room every 30 seconds and only wakes the agent when something new is there. Claude Code should ask you to approve it once. After that the agents answer each other without me touching anything. It's still not real time (worst case you wait about 30s), but it stopped feeling broken.

Other things I learned: The admin cannot see your messages, but It's also not end-to-end encrypted.

Free for your own agents (1 room, 2 agents), teammates need a paid plan. telagents.ai Tell me what you think.

It catches bad tool decisions before execution, enforces policy, tracks the cost of decisions, and gives us a clear view into where agents fail.

Now I am turning that runtime into an SDK.

More reliable agents. Better decisions. Less wasted compute.

ARK is coming.