r/crewai

Hey everyone!

Over the past year, as I tried to scale our agentic coding workflows and software factories at my company, I kept hitting the same set of problems. So I built HAR to solve them.

Repo: github.com/os-factory/har

Getting a single coding agent to work in a repo is easy. Scaling to a real multi-agent workflow, where several run at once and where you verify and trust the output, is where it breaks down. A few things go wrong:

1. No standard way to run or verify a repo. That knowledge is scattered across a README, a CLAUDE.md, editor rules, and CI config, all drifting out of sync with each other and the actual code.
2. Agents on one repo collide. Shared dev server, shared database, shared ports, conflicting git state.
3. Trusting a change means re-verifying it yourself. Which defeats the point of running a fleet.
4. Vendor sandboxes lock you in. If the setup lives in someone's hosted dashboard, switching agents later means rebuilding the whole thing.

What HAR does

HAR is a CLI and an MCP server. It works with Claude Code, Cursor, Codex, or any MCP agent, and it closes each of those gaps:

1. Isolation. Each agent gets its own git worktree, branch, ports, and database. Nothing is shared with the main checkout or another agent's slot, so a fleet runs in parallel without colliding on a dev server, DB, or ports.
2. Deterministic validation gates. HAR runs your project's real checks through a fixed pipeline, same result every time. The result is bound to the exact code that passed and enforced at commit time, so an unverified tree cannot land.
3. Verifiable proof. Every run leaves logs, artifacts, and a validated tree hash tied to the exact code checked. A reviewer inspects the evidence instead of trusting the agent's self-report.
4. Full observability. Mission Control is a local dashboard showing every repo, worktree, run, and validation in one place, so you can watch a whole fleet as it works.

All of this lives in one contract committed to your repo, which every agent reads the same way. It replaces the usual scatter of a README, a CLAUDE.md, editor rules, and CI config that drift apart. You start from a profile that matches your stack, your agent adapts it to the real repo, and you extend verification with plugins (like Playwright) or with any command you already run.

Give it a try and let me know what you think :)

Hi everyone,

I've been building an open-source project called Kodiak.

The goal of Kodiak is to become an autonomous AI software engineering platform that can:

• Understand an entire codebase

• Build a semantic repository index

• Retrieve relevant context using RAG

• Plan multi-step development tasks

• Execute tools safely

• Learn from previous executions

• Improve through reflection and memory

Current progress:

✅ Repository indexing

✅ Semantic search

✅ Embedding pipeline

✅ RAG retrieval

✅ Memory system

🚧 Workflow engine

🚧 Multi-agent orchestration

My current focus is making the architecture modular so future components (planner, coding agents, memory, tool execution, GitHub integration) can evolve independently.

I'm not looking for someone to build the project for me.

Instead, I'd really appreciate feedback from developers who have built AI agents, developer tools, or large Python systems.

I'd especially love opinions on:

• Overall architecture

• RAG pipeline design

• Repository indexing strategy

• Memory architecture

• Scalability concerns

• Features that would make Kodiak genuinely useful

GitHub:

https://github.com/ShamGaneshan2008/Kodiak

Any honest feedback—positive or critical—is appreciated. I'm building this to learn how large AI systems are designed, so I'd love to hear from experienced engineers.

Thanks!

Hey fellow AI builders,

I absolutely love CrewAI, but as my workflows got more complex, managing the relationships between Agents, Tasks, and Tools in raw Python started driving me crazy.

I originally built this to visually map out my own tasks (like setting up agents to verify destination counts from file imports and generate English contact lists), and I just wanted a simple UI that generates standard Python code I can run locally.

So I created AgentGraph Studio. You design your crew on the canvas, set up your LLMs (it supports local models like Ollama too), and it exports a ready-to-run main.py, .env.example, and requirements.txt. It's completely free and runs entirely in the browser (no API keys required on the site).

I attached a quick 30-sec demo of how it works. I’d love to know what other features or tools you guys would want me to add next!

I built a 15-question AI Mastery Challenge on my platform to test who actually understands prompt engineering, multi-agent systems, and LLM behavior. 

 THE CONTEST: 

The person with the highest score on the leaderboard by next Sunday wins a $25 Cash Prize (or local equivalent) and a free permanent shoutout for their portfolio on our homepage!

How to enter:

1. Comment CHALLENGE below.

2. Below is the access link to the Quiz.

3. Take the quiz, register your username, and lock in your spot on the live leaderboard.

Quiz Link:

https://interconnectd.com/quiz/67/the-ultimate-ai-mastery-challenge-are-you-smarter-than-an-llm/

May the best prompt engineer win. Tag a friend who thinks they are an AI expert. 

#AI #PromptEngineering #GenerativeAI #LLM #NoCodeAI #IndieHackers #TechChallenge #ArtificialIntelligence #SoftwareEngineering #BuildInPublic