Syllabi – Open-Source Agentic AI with Tools, Rag, and Multi-Channel Deploy

Hi HN! I built Syllabi – an open-source platform for creating agentic AI systems that integrate tools, use knowledge bases, and deploy across channels.

The Problem: I kept needing AI that could both answer questions from company knowledge AND take actions (send Slack messages, trigger workflows, call APIs). Existing solutions either don't support agentic tool use well, lock you into their cloud, or require weeks to build from scratch.

What Syllabi Does (Three Pillars):

1. INTEGRATE ANY TOOLS • Call webhooks & custom APIs • Send Slack messages, emails, calendar events • Trigger workflows in external services • Connect YOUR custom tools via API/webhooks • AI intelligently decides WHEN and HOW to use each tool (that's the agentic part)

2. KNOWLEDGE BASE (RAG) • Transform docs, videos, websites into knowledge base • PDFs, Google Drive, Notion, Confluence • Advanced RAG with source citations • Click citations to see exact passages highlighted in original documents • Multi-format processing with smart chunking

3. DEPLOY ANYWHERE • Embed widget on any website • Slack & Discord bots • Microsoft Teams (coming soon) • Standalone web app • REST API for custom integrations • One agent, multiple channels

Key Technical Features: - MIT licensed, self-hosted, privacy-first - Modern AI models (latest GPT-4, GPT-4o, o1 series) - More providers coming (Anthropic, local models) - Agentic tool selection with function calling - Channel-agnostic core with adapter pattern - Async job queue for document processing - Plugin system for custom skills

Tech Stack: Next.js (frontend), Python FastAPI (backend), PostgreSQL, Supabase, OpenAI API

Use Cases: • AI course assistant that answers questions AND books office hours • Support bot trained on docs that can create tickets in Linear/Jira • Team knowledge base in Slack that triggers workflows • API docs helper that generates AND runs code examples

Architecture Highlights: - Modular design with clean separation of concerns - Row-level security for multi-tenancy - Docker deployment for easy self-hosting - Comprehensive API for custom integrations

I started building this 6 months ago because every project seemed to need the same thing: an AI that could access knowledge AND take actions, without vendor lock-in or per-message pricing.

Website: https://www.syllabi-ai.com/ GitHub: https://github.com/Achu-shankar/Syllabi Docs: https://www.syllabi-ai.com/docs

Would love feedback from the HN community – especially on: - Agentic AI architecture approaches - Tool use and function calling strategies - Multi-channel deployment patterns - Self-hosting and security best practices

Happy to answer questions about the tech stack, RAG implementation, agentic tool selection, or anything else!

Congrats! However, it does not look ready to be used. For one, I am missing docker-compose which would spin both frontend and backend with all dependencies. Documentation does not state that but in backend docker-compose file I see a dependency on Redis. Things like that makes it hard for me to try this out (also, not supporting local models - but this might be coming in the future, as you've stated)...

What is your monetization model, e.g. why should I trust this project to still exist in 6 months?

When will you support AWS Bedrock?

I was excited because it looks really good. I looked into the backend code and it's vibe coded with Claude. All the terrible exception handling patterns, all the useless comments and psychopancy is left there. I can't trust this codebase. :(

Here's the deal with releasing early: You release something simple, something with just the core features, in order to validate (sadly most of that validation will be that your marketing sucks but c'est la vie).

If you already have a "large" product that's just very unfinished, that's not a MVP, you're asking people to be alpha testers. You either need to get it into a better state and make sure the docs and onboarding are exemplary, or break the product out into parts and ship them separately.

Quick disclaimer: Some features shown on the landing page are still in active development or testing. I wanted to get this in front of the community early to get feedback on direction before polishing everything.

Specifically: - Multi-channel deployment (Slack/Discord) is working but needs more testing - Some integrations (Teams, advanced webhooks) are in progress - Documentation is being expanded

The core functionality (RAG knowledge base, tool use, web deployment, self-hosting) is solid and ready to use.

Would rather launch early and iterate with community feedback than wait for "perfect"! Happy to be transparent about what's ready and what's coming.

Your README links docs that don’t exist.

Hi everyone, thank you for all the feedback! I want to be completely transparent about what Syllabi is and isn't.

HONEST CONTEXT:

This started as a personal learning project 6 months ago. I kept adding features as I learned, and it grew into something I thought might be useful to others. It's far from polished or complete.

I'm the first to admit: • The codebase has issues (yes, lots of AI-assisted code that needs cleanup) • Documentation is incomplete (some links are broken, sorry!) • Security features aren't fully implemented yet • Docker setup needs work (missing unified docker-compose) • Many features are still in testing/progress

I'm starting a new job tomorrow and won't have time to actively develop this for the foreseeable future. Rather than let it sit on my laptop, I wanted to share it with the community in case: 1. Others find it useful as-is for learning/experimentation 2. People want to collaborate and improve it 3. The architecture/approach helps someone building something similar

This is NOT a product I'm trying to sell or a startup. It's MIT licensed specifically so anyone can fork it, improve it, or learn from it.

LOOKING FOR COLLABORATORS:

If anyone wants to actively work on this project, I'd love that! The codebase needs: - Security hardening - Better documentation - Cleaner exception handling - Unified docker-compose setup - Local model support (Ollama/llama.cpp) - Better testing

I'm happy to answer questions about the architecture and will try to review PRs when I can, but I can't commit to active development right now.

Thanks for the honest feedback - it's exactly what I needed to hear. If this helps even one person learn something or sparks ideas for better implementations, that's a win.

GitHub: https://github.com/Achu-shankar/Syllabi