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Enterprise Knowledge & Action Agent

An AI-powered enterprise assistant that answers questions across your company's tools (GitHub, Slack, Notion, HubSpot, and more) and can take actions on your behalf — all through a chat interface, with role-based permissions, citations, and a full audit trail.

Features

  • Conversational knowledge agent — ask natural-language questions ("what's the status of PROJ-123?", "summarize last week's Slack thread on the outage") and get answers grounded in your real company data, with source citations.
  • Multi-agent orchestration — a LangGraph pipeline classifies intent, selects relevant sources, checks permissions, retrieves data, plans tool calls, executes them, and validates citations before responding.
  • 30+ SaaS connectors — real implementations for GitHub, Slack, Notion, and HubSpot, with stub connectors ready to wire up for Jira, Salesforce, Zendesk, Google Workspace, Confluence, and more.
  • Per-user "Connect" flows — each user logs in and connects their own accounts from the UI (no code or env-var edits required); credentials are encrypted at rest and scoped per user.
  • Role-based access control — Admin, Engineer, Analyst, and Viewer roles gate which connectors and which actions (read/write/delete/send) each user can use.
  • Human-in-the-loop actions — write actions (e.g. creating a GitHub issue, posting to Slack) can require explicit human approval before executing.
  • Audit logging & observability — every query and action is logged; LangSmith and OpenTelemetry tracing are built in for debugging and monitoring agent behavior.
  • Streaming responses — answers stream token-by-token with live visibility into which agent step is currently running.

User flow

  1. Sign up / log in at /login with email and password (powered by Supabase Auth).
  2. Connect your accounts at /connectors — click "Connect" next to GitHub, Slack, Notion, HubSpot, etc. to authorize via OAuth (handled by Composio). Disconnect anytime.
  3. Chat at / — ask a question or request an action. The agent figures out which connectors are relevant, checks you have permission, retrieves and cites sources, and streams back an answer.
  4. Approve actions — if the agent wants to take a write action (e.g. create an issue, send a message), it pauses for human approval before proceeding.
  5. Audit trail — every interaction is recorded for compliance and debugging.

Tech stack

Backend

  • FastAPI (Python) — REST API and SSE streaming
  • LangGraph + LangChain — multi-agent orchestration pipeline
  • Anthropic Claude / OpenAI / Gemini / Groq — pluggable LLM providers
  • Supabase (Postgres + pgvector) — auth, vector store for RAG, audit logs, per-user connector credentials
  • Composio — hosted OAuth for per-user connector "Connect" flows
  • LangSmith + OpenTelemetry — tracing and observability
  • Redis — caching

Frontend

  • Next.js 15 (React 19, TypeScript)
  • Tailwind CSS
  • Supabase JS client (auth session management)
  • Vercel AI SDK primitives for streaming chat UI

Infra

  • Docker Compose (backend, frontend, Redis, OpenTelemetry collector)

Why use this platform

  • One place to ask, instead of ten tabs to search. Stop hopping between GitHub, Slack, Notion, and your CRM to piece together an answer — ask once and get a cited, synthesized response.
  • Self-service, not IT-gated. Anyone can log in and connect their own accounts through the UI — no waiting on an admin to hardcode API keys or redeploy the backend.
  • Safe by default. Role-based permissions and human-approval gates mean the agent can be genuinely useful (including taking actions) without becoming a liability.
  • Trustworthy answers. Citation validation means responses point back to the real source document, not a hallucinated summary.
  • Built to extend. New connectors follow a simple base-class interface, and the agent pipeline is a swappable LangGraph — add a data source or a new reasoning step without rearchitecting the system.
  • Observable from day one. Tracing and audit logs are wired in up front, so you can debug agent behavior and demonstrate compliance instead of bolting on observability later.

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