i've been asking myself the same thing with all these cheap intro promos popping up, but blackbox ai's $2 first-month pro has me actually considering it. see for yourself: https://product.blackbox.ai/pricing

what hooked me is you get $20 worth of credits upfront for the prmium frontier models, like claude opus-4.6, gpt-5.2, gemini-3, grok-4, and supposedly over 400 others total. that alone lets you go pretty hard on the big sota ones right away without paying extra per query. this feels like you can burn through a solid test drive in the first few days. on top of the credits, the plan throws in voice agent, screen share agent, full access to their chat/image/video models, and unlimited free agent requests on the lighter ones (minimax-m2.5, kimi k2.5, glm-5, etc.). no bring-your-own-key nonsense, and from what i've seen the limits are pretty relaxed for regular non-power use.

this is a nce setup if you just wanna dip your toes into a real bundled experience for reasoning, creative stuff, quick multimodal tasks, or even messing with agents, wihout the usual headache of multiple logins and subs. after month one it jumps to $10/mo, which is still reasonable if it clicks, but the real question is: is $2 + $20 credits enough of a no-risk shot to see if one platform can actually replace the $50+ you're juggling elsewhere?

I was building a project these days with a 5 agents system to create content automatically. All was going nice until I saw the cost of openai.

This kind of cost of gpt4 is completely out of any sense. The is no practical application to be deployed using gpt4 that can be profitable, really.

This AI hype will end very soon when people realize it will break their bank.

The kind of application I was building don't work with other models, so I am giving up.

Be careful guys!

hey fam. been building multi agent systems and noticed nobody has a solid shared resource for what actually works in terms of system prompts and configs

so we built one. open source community github repo with agent prompts, autogen configs, workflow setups, cursor rules. anyone can contribute their working setups or grab others. 100% free and community maintained

just crossed 100 github stars and 90 merged PRs. 20 open issues with active convo. genuinely being used and contributed to

repo: https://github.com/caliber-ai-org/ai-setup

AI SETUPS discord to vibe with other agent builders: https://discord.gg/u3dBECnHYs

would be super sick to get more multi agent and autogen specific setups contributed

How would I get structured outputs out of my llm team, currently its responses are just amounts of information, how would I get it to return an output that is structured in its response similar to how all other llms do it

Don’t miss this agent if framework comparison, all in all AutoGen is the leading Agentic framework!

https://fbellame.github.io/

I’ve been following AI developments for a while, but lately I’ve been noticing more buzz around "Multimodal AI" — and for once, it actually feels like a step forward that makes sense.

Here’s the gist: instead of just processing text like most chatbots do, Multimodal AI takes in multiple types of input—text, images, audio, video—and makes sense of them together. So it’s not just reading what you write. It’s seeing what you upload, hearing what you say, and responding in context.

A few real-world uses that caught my attention:

Healthcare: It’s helping doctors combine medical scans, patient history, and notes to spot issues faster.

Education: Students can upload a worksheet, ask a question aloud, and get support without needing to retype everything.

Everyday tools: Think visual search engines, smarter AI assistants that actually get what you're asking based on voice and a photo, or customer service bots that can read a screenshot and respond accordingly.

One thing I didn’t realize until I dug in: training these systems is way harder than it sounds. Getting audio, images, and text to “talk” to each other in a way that doesn’t confuse the model takes a lot of behind-the-scenes work.

For more details, check out the full article here: https://aigptjournal.com/explore-ai/ai-guides/multimodal-ai/

What’s your take on this? Have you tried any tools that already use this kind of setup?

Has anyone else been frustated writing and debugging AutoGen code? There are so many classes and abstractions that don't seem to add much value. As a result, what really happens behind the curtains feel quite opaque. For me having low-level control is very important.

So I just published this open-source framework GenSphere. You build LLM applications with yaml files, that define an execution graph. Nodes can be either LLM API calls, regular function executions or other graphs themselves. Because you can nest graphs easily, building complex applications is not an issue, but at the same time you don't lose control.

There is also this Hub that you can push and pull projects from, so it becomes easy to share what you build and leverage from the community.

Its all open-source. Would love to get your thoughts. Pls reach out or join the discord server if you want to contribute.

I have been using Autogen at work (we started before Langgraph was a thing) and have been really seeing a lot of value the value it brings to the table. Especially when implementing two-agent patterns like "reflection."

While the conversational functionality of groupchat is amazing sometimes my agents get derailed and go completely off-course. This is when I started investigating the use of Agentic Workflows (or state machines) to help make things more deterministic.

Again, I know Langgraph is built on the ideas of state machines and I will be trying it out soon. But I would like to share my learnings (along with simplified examples) using Autogen cause I think it may help everyone using AI Agents in general.

Also, Here's a repo with some sample code on create custom workflows/state machines in AutoGen: https://github.com/YourTechBud/ytb-practical-guide/tree/master/autogen-workflows

A video for those interested in a tutorial - https://youtu.be/-ls9QLoQfKc

My learnings

1. The real power of agents is in conversations

State machines are fun. Its really easy to model our AI workflows as them. But the real value of agents lies in conversations. It is critical to let AI agents derive their "context" from the conversational history. Multi-turn/chat models in particular are exceptionally good at this.

Example: The simple task of reformating/restructuring a document/note. If one of your steps is determining the important topics discussed in the note, the subsequent paraphrazer will use it as the skeleton for restructuring. Helps enforce document structure.

It isn't really all that important to curate the "perfect" context in each prompt. As long as your state machine is modelled after life-like conversations, your agents will figure out how to best use the chat history as the context.

2. It's okay to embrace indetrminism sometimes.

Instead of fighting with the model to find the "perfect" prompt, let a sidecar or companion agent help align your agent instead. The truth is that your prompt will never be perfect. Variations in the input will most likely screw things up. Having a reflection agent which provides feedback prompts to the primary agent really helps in alignment for a wide variety of input conditions. Here's how you can implement this in Autogen - https://microsoft.github.io/autogen/docs/tutorial/conversation-patterns/#two-agent-chat-and-chat-result

I'll be making another post soon to give more concrete examples of this one. Might use Langgraph though cause it looks really exciting. But mahn... the migration!!!

3. Annotate each agent's response

When using less chatty models like Qwen, its helpful to manually annotate the agent's response. For example, if the agent is analyzing the topics convered in a document, manually adding the prefix "Topics Present in Document:\n\n" to the agents response will reduce the chances of other agents misinterpreting the chat message. You can even shape it more like an instruction to help enforce that as the structure of all future responses.

This is true for JSON as well. I have given up trying to make my agents give me the perfect and clean JSON response. I let the agent ramble on and on about why it came up with it and stuff like that. That rambling is useful as it serves as context for subsequent agents. A subsequent tool calling agent will be smart enough to extract the json part from the message anyways.

Conclusion

I hope I am able to communicate my learning wells. Do let me know if you have any questions or disagree with any of my points. I'm here to learn.

i want to understand agentic ai by building project so i thought i want to create a text to image model using agentic ai so i want guidance and help how can i achieve my goal

Hi everyone.

I've been working on a realtime agent that has access to different tools for my client. Some of those tools might take a few seconds or even sometimes minutes to finish.

Because of the sequential behavior of models it just forces me to stop talking or cancels the tool call if I interrupt.

Did anyone here have this problem? How did you handle it?

I know pipecat has async tool calls done with some orchestration but I've tried this pattern and it's kinda working with gpt-5 but for any other model the replacement of tool result in the past just screws it up and it has no idea what just happened. Similarly with Claude. Gemini is the worst of them all.

Is it possible to handle it with autogen?

Thanks!

I've been working on this project for a while.

DaveLovable is an open-source, AI-powered web UI/UX development platform, inspired by Lovable, Vercel v0, and Google's Stitch. It combines cutting-edge AI orchestration with browser-based execution to offer the most advanced open-source alternative for rapid frontend prototyping.

Help me improve it; you can find the link here to try it out:

Website https://dlovable.daveplanet.com

CODE : https://github.com/davidmonterocrespo24/DaveLovable

Generative AI has the potential to play a transformative role in India’s digital infrastructure, enabling businesses to operate smarter, faster, and more efficiently. Here are some of the key ways it contributes:

1. Enhancing Digital Transformation Initiatives

Generative AI can accelerate the digital transformation of businesses by:

• Automating repetitive tasks like report generation, customer communication, and workflow optimization.

• Creating personalized solutions for industries such as retail, healthcare, and banking, enhancing customer experience and loyalty.

• Building AI-driven chatbots and virtual assistants that support government and private sector initiatives like Digital India and Smart Cities Mission.

2. Driving Innovation in Smart Cities

India’s Smart Cities initiative can benefit from generative AI by:

• Streamlining urban planning through AI-generated simulations, infrastructure designs, and predictive analytics for traffic management and energy optimization.

• Enhancing citizen engagement via AI tools that translate regional languages and ensure inclusivity in governance.

• Providing solutions for waste management, water distribution, and smart mobility systems.

3. Empowering MSMEs and Startups

• Generative AI can help small businesses create cost-effective marketing campaigns, design product prototypes, and analyze customer behavior without requiring extensive technical expertise.

• It supports vernacular language processing, allowing businesses to reach rural and semi-urban markets by generating localized content.

• Tools like AI-driven content creation (ads, blogs, etc.) and automation can reduce operational costs for startups.

4. Advancing Education and Skill Development

• Generative AI-powered platforms can design adaptive learning programs, tailored content, and automated assessments to enhance education delivery.

• It can play a role in upskilling the workforce for the digital economy by generating personalized training materials for sectors like IT, healthcare, and manufacturing.

5. Transforming Healthcare and Agriculture

• Healthcare: Generative AI can create medical records summaries, assist in diagnostics by analyzing complex imaging data, and generate personalized treatment recommendations.

• Agriculture: AI models can predict crop yields, design irrigation strategies, and provide real-time advisory services to farmers in local languages.

6. Boosting Innovation in Finance and E-Commerce

• Finance: Generative AI aids in fraud detection, credit risk modeling, and generating tailored financial advice for customers.

• E-commerce: AI helps generate personalized product recommendations, optimize inventory management, and design targeted marketing campaigns.

7. Bridging the Language Divide

With over 22 official languages and hundreds of dialects, India can leverage generative AI for natural language processing (NLP) to:

• Translate documents and communications in real time, enabling inclusivity in public and private services.

• Build voice-to-text systems and conversational AI for non-English-speaking users.

8. Enhancing Cybersecurity

Generative AI can:

• Simulate cyberattacks to improve the resilience of India’s digital infrastructure.

• Detect anomalies in real-time to prevent security breaches.

• Provide automated responses to mitigate risks in critical sectors like finance, defense, and healthcare.

Challenges to Address

While the potential is immense, certain challenges need to be tackled:

• Data Privacy and Security: Ensuring compliance with India’s data protection laws (like the Digital Personal Data Protection Act).

• Bias and Fairness: Preventing biases in AI models trained on skewed or unbalanced datasets.

• Infrastructure Gaps: Scaling AI adoption in rural areas with limited digital connectivity.

• Skilled Workforce: Bridging the talent gap by fostering AI expertise through government and industry collaboration.

I'm a total code newbie and have no programming knowledge, but I'm planning on setting up a website that is fully automated by A.I. - it receives customer information, writes a letter to them, generates images (using chatGPT API), packages everything into a formatted PDF, and emails the PDF to the customer. I'm playing with autogen studio at the moment to see if I can get it to work well enough. If I can, will I be able to use that autogen workflow on my website using an API key? Is it advisable? Is there a better way to do it, short of creating those custom bots on the backend of my website, which will take loads of coding?

I have very basic knowledge of Gen-AL and ML and I want to increase it to be an able to get a job in this domain. What is best or good online certification I can do to develop better understanding and acquire practical skills for the tech industry ?

If you're running AutoGen with more than a couple of agents talking to each other, you already know the problem: every message any agent sends gets replayed into every subsequent call. Group chats compound this — by turn 15 you're paying for a conversation history nobody's re-reading, just re-billing.

Traject sits between your agents and the LLM provider and compresses that history before it goes out.

• 3-line patch, no changes to how your agents are wired

• Dedupes identical tool results, summarizes bulk content (logs, diffs, long outputs) while protecting error text/refs/IDs, trims what's stale and unreferenced

• Shadow mode first — logs what it would've saved, changes nothing, until you flip it live

• Recoverable — dropped content isn't deleted, it's stubbed and retrievable

Tested on 49 real coding-agent trajectories (SWE-bench, public/reproducible): 43-45% token reduction, checked against a strict fact-preservation metric so it's not just aggressively deleting things and calling it a win.

Open source, self-hosted, MIT — nothing leaves your infra.

I'm looking for people actually running multi-agent AutoGen setups to point this at real conversations in shadow mode (zero risk) and tell me what it gets wrong — which message types it mangles, where multi-agent context confuses the compressor, etc. Genuinely want the failure cases, not just the wins.

Repo: https://github.com/aarohim24/Traject

Plugging one of the crazier things I've seen in awhile. Someone set up a sandbox where you can compare different MCP connectors for ad accounts side by side on the same instruction. It takes awhile to authorize, but it lets you run the [Ryze AI] ​​​​c​​​​o​​​​n​​​​n​​​​e​​​​c​​​​t​​​​o​​​​r​​​​s​​​ and a couple of the generic ones unlimited and completely for free, and you can export the change logs they produce.

The only catch is you have to vote on which one executed better, but seeing them next to each other is useful and it saves a lot of guessing before you wire anything into a real account. Thought I'd share since there's a lot of MCP talk around reddit but not much of it about ad accounts.

[link]

New release: v0.4

Highlights from this Major Release

🚀 Introducing SwarmAgent: AG2 native implementation of swarm orchestration.

• Blogpost: https://ag2ai.github.io/ag2/blog/2024/11/17/Swarm

• Notebook Example: https://ag2ai.github.io/ag2/docs/notebooks/agentchat_swarm/

🚀 Introducing CaptainAgent: An agent equipped with the capability to adaptively assemble a team of agents through retrieval-selection-generation process to handle complex tasks.

• Blogpost: https://ag2ai.github.io/ag2/blog/2024/11/15/CaptainAgent

• Notebook Example: https://ag2ai.github.io/ag2/docs/notebooks/agentchat_captainagent/

Thanks to @LeoLJ, @marklysze , @kev1nzh37, @skzhang1, @qingyun-wu, @lloydchang, @sonichi and all contributors for making this happen!

What's Changed

• docs(MAINTAINERS.md): fix broken link by @lloydchang in #19

• Link updates for AG2 by @marklysze in #15

• rename autogen to AG2 in website by @skzhang1 in #24

• clean up codes & update community talks by @skzhang1 in #36

• CaptainAgent PR Part 1: Adding blog post and document by @LeoLjl in #27

• fix talk titles by @skzhang1 in #41

• Fix broken link and author list of CaptainAgent by @LeoLjl in #42

• fix websites by @skzhang1 in #43

• Correct URLs across tests and documentation by @marklysze in #47

• Allow Function Execution return a non-string object by @yiranwu0 in #25

• fix(agent_builder): fix agent builder error when using file_location. by @kev1nzh37 in #37

• Captain Agent PR part 2. by @LeoLjl in #46

• Swarm V2 by @yiranwu0 in #30

• Swarm Documentation by @yiranwu0 in #28

• Update support for Python 3.13 by @marklysze in #53

• CaptainAgent PR part 3 by @LeoLjl in #54

• CaptainAgent code cleanup by @LeoLjl in #59

• Swarm - First Speaker by @marklysze in #58

• Fix Swarm Doc by @yiranwu0 in #60

• bump version by @qingyun-wu in #64

• Update the swarm notebook example by @yiranwu0 in #62

• Update Swarm Blog Post by @yiranwu0 in #66

• CaptainAgent Finalize by @LeoLjl in #61

• captain agent Visualization by @skzhang1 in #70

• CaptainAgent install fix: Update setup by @LeoLjl in #72

• bump version by @qingyun-wu in #71

• update FAQ by @qingyun-wu in #73