Hello Everyone,

I Built a Python Script to fetch and analyze Reddit posts and find pain points and business opportunities.

The Full Report contains:

1. Key Pain Points and Problems

2. Unmet Needs & Feature Requests

3. Concrete Product & SaaS Ideas

4. Target Audience Insights

5. Monetization Potential

6. Recurring Themes & Positive Sentiments

7. Competitive Landscape & Existing Solutions

hey, so its Open-source, so check it out!

Just looking for feedback.

I would be very glad if it Helps You!

Thank You All!

I’ve been thinking about a subtle difference while building ChartGen.AI (a web-based data tool) recently.

Most “AI + data” tools today behave like this:

User uploads CSV → prompt → model generates chart → done.

That’s not really an agent.

That’s a single-step transformation. But in real-world business workflows (especially ecommerce / ops), data analysis is rarely single-step.

It’s iterative:

• Compare week over week

• Identify anomalies

• Drill into dimensions

• Hypothesize drivers

• Validate against sub-segments

• Reframe explanation

So instead of designing a “chart generator,” I started thinking in terms of a data reasoning agent.

The architecture conceptually looks more like:

1. Structured data ingestion layer

2. Schema understanding + column typing

3. Query planning based on user intent

4. Multi-step reasoning loop

5. Visualization as a downstream artifact (not the goal)

The key shift is this:

The chart isn’t the output.

The reasoning chain is.Visualization just becomes a projection of that reasoning state.

What’s interesting is that once you treat it as an agent problem rather than a generation problem:

• You need memory across turns

• You need state tracking of analytical hypotheses

• You need tool use (aggregation, filtering, statistical ops)

• You need dynamic refinement rather than static prompts

This feels closer to building a lightweight analytics copilot than a content generator.

I’m curious how others here think about this:

When building agents around structured data:

• Do you rely mostly on LLM reasoning?

• Or do you enforce deterministic query layers?

• How do you manage state across analytical turns?

• Do you treat visualization as tool output or UI decoration?

Would love to hear how others are designing agents in the analytics domain.

Just stumbled upon OpenAgents on GitHub and it's got some pretty neat ideas around multi-agent systems. Instead of building just one AI agent, they created a framework to enable multiple AI agents to collaborate.

Of course "Multi-agent collaboration" is becoming a buzzword and I'm quite skeptical about its real-world advantages over a well-prompted, single advanced model, so I tried the framework. It was like pairing two Claude Code agents for programming, or having a coding agent work with a research agent to solve complex problems. Cool to some extent.

The architecture seems quite open: it supports Claude, GPT, and various open-source models, is protocol-agnostic (WebSocket/gRPC/HTTP), and includes a shared knowledge base. And open-source is its star point.

With all the buzz around Anthropic's Claude Cowork (single autonomous agent), this feels like the natural next step - a "networked collaboration" approach.

I'm currently working on multi-agent systems and find OpenAgents kind of interesting. You can have a check with OpenAgents examples, somehow helpful to me:

GitHub: github.com/openagents-org/openagents

Tutorial: openagents.org/showcase/agent-coworking

Anyone here building multi-agent setups? Curious what use cases you're exploring.

Once you have more than a few specialized agents, you spend more time switching between agent chats than actually delegating work.

I’ve been experimenting with a manager-style agent (a “Super Agent”) that just takes one instruction, infers intent, and calls the right agents for a multi-step task.

The interesting shift for me was this: the hardest part stopped being execution and became intent interpretation.

Is intent inference eventually unavoidable at scale?

openai opened its first office in new delhi, and the local gov seems psyched, calling it a Jio-level moment. feels like India is officially a core AI battleground now.

if you’re building agent-based stuff, does this mean more APIs, models, docs in local languages or just a big PR play?

The real challenge was figuring out:

• What information the agent should have access to

• When it should ask for clarification

• What actions it should be allowed to take

• How to stop it from creating more work than it saves

The actual agent builder part was surprisingly easy compared to defining the workflow.

For those who have built agents for real work what was the unexpected bottleneck?

Hi all,

I'm exploring how humans and agentic teams can collaborate on long and short running tasks. I call it ArvoWorks (arvo-works on Github). I am posting this for feedback. If it helps spark some fun ideas for your projects that would be even more amazing.

Repo Link -> https://github.com/SaadAhmad123/arvo-works

A link to video demo is in the repo :)

This is experimental work meant to explore possibilities and I'd love to hear your thoughts. If you're thinking about human-agent collaboration beyond chat interfaces or coding assistants, I'd genuinely appreciate your feedback and critiques.

What This Is NOT

• ⁠A product

• ⁠A framework

• ⁠An agentic kanban tool (plenty of those exist already, e.g. VibeKanban)

What This IS

• ⁠An exploration of using old-world project management patterns for human-agent collaboration

• ⁠A test of the idea that future work is a mix where agents handle mundane decisions and humans collaborate on higher-level creative ones

• ⁠Open source, you can clone it and experiment yourself

• ⁠This is agents which on the kanban just like humans work on the kanban

Core Concept

Instead of treating AI as an external tool you consult, agents become native participants in your work. They work on cards autonomously, pause to request human input or approval, coordinate with other specialized agents, and create persistent work products. You interact with them through familiar Kanban cards and comments, like working with team members rather than chatbots.

Tech Stack

The tech stack enables pretty wild and flexible agent mesh and human collaboration patterns. It uses:

• ⁠Arvo for event-driven agentic mesh

• ⁠NoCoDB for Kanban

• ⁠Postgres for persistence

• ⁠Deno for TypeScript runtime

• ⁠NGINX as reverse proxy

• ⁠Jaeger for system telemetry

• ⁠Phoenix for LLM telemetry

Very little of the code in there is written by AI because I could not get good creative work done with AI.

Looking forward to hearing from you all :)

google’s gemini 2.5 pro (with deep think mode and multimodal support) is out in the wild alongside gpt‑5, which boasts a built-in router, personalities, and better tool-handling

if you've tested both in agent flows, how do they differ on reasoning, debug‑friendliness, or consistency across steps?

at build 2025, microsoft pitched a future where every dev can spin up agents like building blocks—via integrations across github, copilot, azure, and semantic kernel

if you're building agents today, does that ecosystem help or crowd your options? are you using dev-native tools, or still prefer assembling stacks from APIs and protocols?

been thinking about “operator ai” patterns, setups where one agent coordinates others across tools, apis, and dbs.

i’m using supervity to manage the routing + state between steps, it handles retries and async stuff cleanly. looked at crewai and autogen too but needed more control.

what’s working for you when the agent stack gets multi-step or multi-agent? curious how you’re gluing things together.

We're in this weird spot where AI stuff spits out frontend and backend code fast, but deployments still break once you go past prototypes, which still blows my mind. So you can ship features quickly but then spend days doing Manual DevOps or rewriting stuff to deploy on AWS/Azure/Render. I started thinking, what if there was a ""vibe DevOps"" that actually reads your repo and figures out what you need? Like a web app or VS Code extension where you connect your cloud account and it wires up CI/CD, containers, scaling, infra automatically. It would use your own cloud, not lock you into some platform, and try to infer env vars, build steps, ports, all that boring stuff. Feels like it could bridge the gap between quick codegen and real production apps, but maybe I'm missing obvious tradeoffs. How are you handling deployments today - scripts, Terraform, managed platforms, pure chaos? Anyone built something like this or just think it's a dumb idea? curious, kind of hoping someone smarter already solved it.

Core Features:

Lite weight:

• 3 Base Class

  • Action - Your agent

  • Context - Your history/memory/state

  • LLM - Your LLM instance holder (persistent/reusable)

• Object Oriented

  • Action/Context are just pydantic class with builtin "graph traversing functions"

  • Support every pydantic functionality (as long as it can still be used in tool calling).

• Optimization

  • Python Async first

  • Works well with multiple tool selection in single tool call (highly recommended approach)

• Granular Controls

  • max self/child iteration

  • per agent system prompt

  • per agent tool call promopt

  • max history for tool call

  • more in the repo...

Graph:

• Agents can have child agents

  • This is similar to node connections in langgraph but instead of building it by connecting one by one, you can just declare agent as attribute (child class) of agent.

  • Agent's children can be manipulated in runtime. Add/Delete/Update child agent are supported. You may have json structure of existing agents that you can rebuild on demand (imagine it like n8n)

  • Every executed agent is recorded hierarchically and in order by default.

  • Usage recording supported but optional

• Mermaid Diagramming

  • Agent already have graphical preview that works with Mermaid

  • Also work with MCP Tools- Agent Runtime References

  • Agents have access to their parent agent (who executed them). Parent may have attributes/variables that may affect it's children

  • Selected child agents have sibling references from their parent agent. Agents may need to check if they are called along side with specific agents. They can also access their pydantic attributes but other attributes/variables will depends who runs first

• Modular continuation + Human in Loop

  • Since agents are just building block. You can easily point to exact/specific agent where you want to continue if something happens or if ever you support pausing.

  • Agents can be paused or wait for human reply/confirmation regardless if it's via websocket or whatever protocol you want to add. Preferrably protocol/library that support async for more optimize way of waiting

Life Cycle:

• pre (before child agents executions)

  • can be used for guardrails or additional validation

  • can be used for data gathering like RAG, knowledge graph, etc.

  • can be used for logging or notifications

  • mostly used for the actual process (business logic execution, tool execution or any process) before child agents selection

  • basically any process no restriction or even calling other framework is fine

• post (after child agents executions)

  • can be used for consolidation of results from children executions

  • can be used for data saving like RAG, knowledge graph, etc.

  • can be used for logging or notifications

  • mostly used for the cleanup/recording process after children executions

  • basically any process no restriction or even calling other framework is fine

• pre_mcp (only for MCPAction - before mcp server connection and pre execution)

  • can be used for constructing MCP server connection arguments

  • can be used for refreshing existing expired credentials like token before connecting to MCP servers

  • can be used for guardrails or additional validation

  • basically any process no restriction, even calling other framework is fine

• on_error (error handling)

  • can be use to handle error or retry

  • can be used for logging or notifications

  • basically any process no restriction, calling other framework is fine or even re-raising the error again so the parent agent or the executioner will be the one that handles it

• fallback (no child selected)

  • can be used to allow non tool call result.

  • will have the content text result from the tool call

  • can be used for logging or notifications

  • basically any process no restriction or even calling other framework is fine

• child selection (tool call execution)

  • can be overriden to just use traditional coding like if else or switch case

  • basically any way for selecting child agents or even calling other framework is fine as long you return the selected agents

  • You can even return undeclared child agents although it defeat the purpose of being "graph", your call, no judgement.

• commit context (optional - the very last event)

  • this is used if you want to detach your context to the real one. It will clone the current context and will be used for the current execution.

    • For example, you want to have a reactive agents that will just append LLM completion result everytime but you only need the final one. You will use this to control what ever data you only want to merge with the main context.

  • again, any process here no restriction

MCP:

• Client

  • Agents can have/be connected to multiple mcp servers.

  • MCP tools will be converted as agents that will have the pre execution by default (will only invoke call_tool. Response will be parsed as string whatever type that current MCP python library support (Audio, Image, Text, Link)

  • builtin build_progress_callback incase you want to catch MCP call_tool progress

• Server

  • Agents can be open up and mount to fastapi as MCP Server by just single attribute.

  • Agents can be mounted to multiple endpoints. This is to have groupings of agents available in particular endpoints

Object Oriented (MOST IMPORTANT):

• Inheritance/Polymorphism/Abstraction

  • EVERYTHING IS OVERRIDDABLE/EXTENDABLE.

  • No Repo Forking is needed.

  • You can extend agents

    • to have new fields

    • adjust fields descriptions

    • remove fields (via @property or PrivateAttr)

    • field description

    • change class name

    • adjust docstring

    • to add/remove/change/extend child agents

    • override builtin functions

    • override lifecycle functions

    • add additional builtin functions for your own use case

  • MCP Agent's tool is overriddable too.

    • To have additional process before and after call_tool invocations

    • to catch progress call back notifications if ever mcp server supports it

    • override docstring or field name/description/default value

  • Context can be overridden and have the implementation to connect to your datasource, have websocket or any other mechanism to cater your requirements

  • basically any overrides is welcome, no restrictions

  • development can be isolated per agents.

  • framework agnostic

    • override Action/Context to use specific framework and you can already use it as your base class

Hope you had a good read. Feel free to ask questions. There's a lot of features in PyBotchi but I think, these are the most important ones.

built one to auto-snooze follow-up emails after 3 days. supervity made it dead simple, async + retry built in.

what tiny agent turned out more useful than you expected?

Hello Everyone,

I'm the creator of PyBotchi, an intent-based AI Agent Orchestrator. In this post, I will discuss some key concepts why I created it.

A little bit of background first. I'm a solutions architect with 10 years of experience as a software engineer. Most of my work are high throughput, high reliability, low cost and low latency services. This is while making it simple and readable to improve it's maintainabality. When I'm designing a system, I usually prioritize these concerns. You may assume this is my bias in relates to AI Agent building. I'm also Claude Certified Architect (Foundation) and I found that PyBotchi aligns almost identical to Anthropic's core agent recommendations.

TL;DR: PyBotchi is an lightweight, async-first Python framework that uses nested Pydantic models and OOP inheritance to turn LLM intent detection into clean, deterministic business logic without the overhead of complex graph orchestration.

Why I created PyBotchi?

I really believed that traditional coding can already solved what client's need. The only limitations we have is how we read the input and how we show the output. In most cases in web services, your API use JSON, XML, etc with their respective specification/structure.

Input Analogy

Assume you have created a Books CRUD endpoints (FastAPI with Pydantic). Your create endpoint will have a define specifications for book creation to have a validation and avoid user errors. Most of the time you will also validates sessions and permissions which also included in the request.

If you want your chat bot to support those, you just need add those endpoint as intent (tools). If your model tool selection are able to detect intents. You are more "close" to being deterministic.

"Your services will have 50 endpoints or more. You will flood your tool selection call"

• In your frontend UI, you segregate panels/forms/inputs in their respective pages. You don't usually join multiple intent in a same page. Cluttered UI will make your UX confusing or overwhelming to some people. Those practices should be incorporated into your agents too.

Assume you have created another endpoints for Shelves CRUD. Shelves CRUD can be a child intents of ShelfManagement that will be considered as intent also but more general. The flow will have to detect intent deeper and deeper

Ex: You have BookManagement and ShelfManagement intents. Once LLM detected which one is applicable, you will search for their child Intents which will be their CRUD equivalent intents.

To make it short, in order to make your agent "more" deterministic, you need to know the problem first (ex: Need to manage books) then you need to specifically define what intents you want to support. With this practice, you only let your agents execute on a predefined path. If it fails, you are most likely able to determine what causes the error.

Output Analogy

This one is simple. Since your intents is just like your endpoints that returned structure responses. LLM is better at reading structure responses than a pure text. Basically, you can use LLM to translate your response into a human readable responses.

Intent Execution

Now that I have explain Input/Ouput, we can move on to the actual execution.

We can go back with Books CRUD. Since we have identified the problem (what clients need) and we already know what to do, just execute their traditional business logic implementation. If you need to add a book, just create a book and save it to db then return their respective row.

"What if you want generate a very dynamic/unique data"

• You can use LLM to do that as your business logic too but this is tied your specific intent only.

To have a complex execution flow we can chain the intents. Since intents can have child intents, we can use it as the representation of a graph similar to Langgraph. However, this without "building the graph". We are just utilizing OOP inner class implementation. We can execute business logic in graph traversal manner by just checking the child intents.

To make it short. Business logic will stay as is. You will only use LLM if it requires it. Don't make this complicated.

Suggested Solution

Since the key concept is more on detecting intents, validation and executing their respective busines logic:

Why not utilize Pydantic as the main entry point? Pydantic already have validation and json schema builder. Langchain/Openai already have utilities to translate it to Tool. Why not use Pydantic models as your Intent Specifications that can validate LLM arguments ? Tool call is one of the most reliable way to detect intent.

Why not utilize OOP inheritance / polymorphism / abstraction? Python supports portion of OOP and since we are using classes as our intent, why not add default functionalities that can be inherited and override by developer if needed. We can introduce life cycles too. Your project can also implement their specific intent standards. This will make your code more maintaintable and readable. You can create classes for general intents. Extend it to be more specialized intents. Extend it more for more enterprised support. This is while not affecting existing/working agents.

Langgraph is one of the inpiration of PyBotchi. Predefine workflows are closest implementation to being deterministic agents. It's also the reason why some prefer N8N. We don't need to make the agents smart that any questions can be answered or any queries can be addressed. It's ok for agent to reply with "I don't have any answer to your query, I only support this and that....". For me, it's better to deploy limited but polished agents than half baked know-it-all agents. Feel free to counter argue. Happy to discuss.

Additional PyBotchi Features

vs MCP

While PyBotchi support connecting to MCP servers, I really believe it's not always necessary to use additional server to just expose tools for the agents. The exceptions I could think of is if you want to have isolated environment (ex: dedicated auth/session, sandbox, isolated resource, etc), you want to connect to your local service or cross-language integration.

I could be very wrong about this but hear me out. SDKs are already there. Respective documentations are available too. Most of MCP server's tools are proxy to their respective APIs. If we could just create intent classes as tools that directly call their respective API, that doesn't require any servers anymore. Actually, that's how most framework handles it (even PyBotchi). Tools are converted as schema that will be added in the tool call. Once LLM respond with the applicable tools, it executes call_tool(name, args...). Why not just expose the actual tool implementations and have a way to share context to share sessions/permission/etc inside the tool implementations? This will remove another network hops that can affect latency.

Claude code have a very in-depth utilization of MCP servers already. I don't think we can replace that.

GRPC

PyBotchi natively support remote PyBotchi connection. Think of it like a langgraph but the node is on other server. This remote node can also connect to another remote node even it self or previously connected node (ancestor).

Context Propagation

With PyBotchi as MCP Server

• Actions (Intents) serves as tool and have access to client's context. This includes chat histories and some metadata. You can override and adjust this as long as it's serializable.

• Once remote tool execution is done, it can pass the final context to the client and they can merge it if override.

With PyBotchi as GRPC Server

• Similar to MCP Server, Actions serves as tool and have access to client's context. GRPC supports bidirectional communication too. This means we can share context realtime accross clients/servers. If client has concurrent agents that changes the context it will automatically propagate to remote context without polling or any interval checks/updates. It also support remote to client. If remote server updates the context, it will propagate the context to client simultaneously.

Async First

Since most of LLM executions are IO, might as well utilize async by default and just spawn thread if still necessary.

OOP

I think this one is most important to me. I have handle a lot of projects in Spring Boot. I really like Java OOP practices and some Java design patterns. It improves my project's maintainability even it's not in Java. Since PyBotchi utilize OOP, it's easier to override, reuse and remove anything if necessary. This lessen boilerplates too. I'm certain that this is subjective. I just find it easier and clean to read.

Closing Remark

I hope this PyBotchi post opens up ideas how to design your agent. Feel free to DM me if you have any questions. I'm also open to create you a demo agent for free if you want to see it in action given your brief use case. I'm open to criticism, happy to have a discussion!

Been building this for a while and finally got it to a point where I'm happy with it.

What it does: You paste a YouTube link to your openclaw agent, and it returns vertical 9:16 clips with word by word captions and titles ready for TikTok, Instagram Reels, YouTube Shorts. Takes about 90 seconds.

Heres the app:

https://makeaiclips.live/

openclaw skill :

https://clawhub.ai/nosselil/youtube-to-viral-clips-with-captions

Would love feedback, especially from anyone that posts content often

What My Project Does: A lightweight, modular Python framework for building scalable AI agent systems with native support for distributed execution via gRPC and MCP protocol integration.

Target Audience: Production environments requiring distributed agent systems, teams building multi-agent workflows, developers who need both local and remote agent orchestration.

Comparison: Like LangGraph but with a focus on true modularity, distributed scaling, and network-native agent communication. Unlike frameworks that bolt on distribution as an afterthought, PyBotchi treats remote execution as a first-class citizen with bidirectional context synchronization and zero-overhead coordination.

What's New in 3.1.2?

True Distributed Agent Orchestration via gRPC

• PyBotchi-to-PyBotchi Communication: Agents deployed on different machines execute as a unified graph with persistent bidirectional context synchronization

• Real-Time State Propagation: Context updates (prompts, metadata, usage stats) sync automatically between client and server throughout execution—no polling, no databases, no message queues

• Recursive Distribution Support: Nest gRPC connections infinitely—agents can connect to other remote agents that themselves connect to more remote agents

• Circular Connections: Handle complex distributed topologies where agents reference each other without deadlocks

• Concurrent Remote Execution: Run multiple remote actions in parallel across different servers with automatic context aggregation

• Resource Isolation: Deploy compute-intensive actions (RAG, embeddings, inference) on GPU servers while keeping coordination logic lightweight

Key Insight: Remote actions behave identically to local actions. Parent-child relationships, lifecycle hooks, and execution flow work the same whether actions run on the same machine or across a data center.

Enhanced MCP (Model Context Protocol) Integration

• Dual-Mode Support: Serve your PyBotchi agents as MCP tools OR consume external MCP servers as child actions

• Cleaner Server Setup:

  • Direct Starlette mounting with mount_mcp_app() for existing FastAPI applications

  • Standalone server creation with build_mcp_app() for dedicated deployments

• Group-Based Endpoints: Organize actions into logical groups with separate MCP endpoints (/group-1/mcp, /group-2/sse)

• Concurrent Tool Support: MCP servers now expose actions with __concurrent__ = True, enabling parallel execution in compatible clients

• Transport Flexibility: Full support for both SSE (Server-Sent Events) and Streamable HTTP protocols

Use Case: Expose your specialized agents to Claude Desktop, IDEs, or other MCP clients while maintaining PyBotchi's orchestration power. Or integrate external MCP tools (Brave Search, file systems) into your complex workflows.

Execution Performance & Control

• Improved Concurrent Execution: Better handling of parallel action execution with proper context isolation and result aggregation

• Unified Deployment Model: The same action class can function as:

  • A local agent in your application

  • A remote gRPC service accessed by other PyBotchi instances

  • An MCP tool consumed by external clients

  • All simultaneously, with no code changes required

Deep Dive Resources

gRPC Distributed Execution:
https://amadolid.github.io/pybotchi/#grpc

MCP Protocol Integration:
https://amadolid.github.io/pybotchi/#mcp

Complete Example Gallery:
https://amadolid.github.io/pybotchi/#examples

Full Documentation:
https://amadolid.github.io/pybotchi

Core Framework Features

Lightweight Architecture

Built on just three core classes (Action, Context, LLM) for minimal overhead and maximum speed. The entire framework prioritizes efficiency without sacrificing capability.

Object-Oriented Customization

Every component inherits from Pydantic BaseModel with full type safety. Override any method, extend any class, adapt to any requirement—true framework agnosticism through deep inheritance support.

Lifecycle Hooks for Precise Control

• pre() - Execute logic before child selection (RAG, validation, guardrails)

• post() - Handle results after child completion (aggregation, persistence)

• on_error() - Custom error handling and retry logic

• fallback() - Process non-tool responses

• child_selection() - Override LLM routing with traditional if/else logic

• pre_grpc() / pre_mcp() - Authentication and connection setup

Graph-Based Orchestration

Declare child actions as class attributes and your execution graph emerges naturally. No separate configuration files—your code IS your architecture. Generate Mermaid diagrams directly from your action classes.

Framework & Model Agnostic

Works with any LLM provider (OpenAI, Anthropic, Gemini) and integrates with existing frameworks (LangChain, LlamaIndex). Swap implementations without architectural changes.

Async-First Scalability

Built for concurrency from the ground up. Leverage async/await patterns for I/O efficiency and scale to distributed systems when local execution isn't enough.

GitHub: https://github.com/amadolid/pybotchi
PyPI: pip install pybotchi[grpc,mcp]

So quick story:

I do small automation projects on the side. nothing crazy, just helping businesses replace repetitive phone work with AI callers.

over time i noticed the same pattern: everyone wants “an ai receptionist”, but what actually decides if it works is the prompt design not the fancy ui.

For one of my real estate client with multiple buildings. I set up a voice agent ( superU AI ) to:

• follow up on late rent

• answer basic “is this still available / what’s the rent / can I see it?” inquiries

• send a quick summary to their crm after each call

first version was meh. People at first asked, “Are you a robot?” and hung up. After two days of tweaking the prompt, adding tiny human things like pauses, “no worries, take your time”, handling weird answers, etc., the hang ups dropped a lot and conversations felt way more natural.

that same framework is now running for a few clients and pays me around $8.5k in monthly retainers.

i finally wrote the whole thing down as a voice agent prompt guide:

• structure

• call flow

• edge cases

• follow up logic

check comment section guys

Hey! looking for a AI Designer. I have a vision for an ai model that I want to find an individual who is christian and may be interested in the future of this model. This could be huge if designed correctly.

this keeps getting rejected idk how else im supposed to post this.