Ready to deploy
your first agent?
On your infrastructure. Your data never leaves. No vendor lock-in. Open source.
Build autonomous agents that plan before they act, SSH into servers, query databases, manage OpenStack clouds, and delegate to each other. Deep planning loop. Dual-path OCR/RAG architecture with self-healing embeddings (Ollama auto-detect, exponential-backoff retries, ChromaDB → Postgres vector fallback). 6-stage RAG pipeline with semantic chunking, hybrid search (RRF), cross-encoder re-ranking, and Redis-backed query cache. On-spot sandbox skills from chat + an Agent Academy to grow your fleet. On your hardware. Your data never leaves.
git clone -b allinone https://github.com/muhammedali275/AI-Orchestrator-Studio && cd AI-Orchestrator-Studio && sudo bash allinone/setup.sh
Every feature you need to go from "idea" to "autonomous agent running in production" — in one platform.
Visual agent creation with custom system prompts, skill assignments, and multi-LLM routing. No code required.
10+ providers (OpenAI, Azure, Anthropic, Ollama, vLLM, Cohere, HuggingFace, LlamaCpp, TextGen, Custom). Per-agent task routing (code_gen → Codellama, rag_answer → GPT-4) with automatic fallback chains.
Team Leader agents delegate to specialists automatically. Recursive multi-agent execution with depth guards.
Semantic chunking → ingest-time embedding → hybrid BM25 + vector search with RRF fusion → metadata filtering → cross-encoder re-ranking → Redis semantic cache.
Auto-detects local Ollama, retries transient failures with exponential backoff, and falls back ChromaDB → Postgres-backed vector store on RHEL/sqlite-old systems. Bulk reindex + embed-pending endpoints survive 1000+ document corpora.
Path A: sync app-server (PyPDF2/PyMuPDF). Path B: async Celery workers with 3-strategy cascade (Tesseract 5 → easyocr → LLM Vision). 8-step image preprocessing. Auto-promote into RAG vector store.
SSH keys, DB logins, API tokens — bind multiple credentials to one agent. Auto-inject by type into matching skills.
AES-256 vault, RBAC, AD/LDAP login, audit trails, TLS, data governance with PII masking and ISO/NIST/GDPR regulatory references.
Agents plan before they act. 4-tier tool enforcement: native function calls → ReAct text parsing → false-completion re-prompt → intent auto-dispatch. 10-round execution loop with self-correction. Works with ANY LLM provider.
15 built-in skills for Nova, Neutron, Cinder, Glance, Keystone, Heat, Swift. Manage compute, network, and storage with natural language.
Cron-based scheduling for automated checks, daily reports, compliance scans. Full execution history tracking.
MS Teams, Slack, Telegram, REST API, Webhooks. Built-in API Gateway with rate limiting and usage analytics.
Redis-backed cache that detects near-duplicate queries via embedding cosine similarity (>0.95). Sub-millisecond cache hits, 24h TTL, per-agent scoping.
Edit .env config from the UI (App, DB, Worker, Storage). Live health-check panel tests PostgreSQL, Redis, ChromaDB, Celery, and vLLM connectivity.
Create & attach a Python skill to an agent in one API call — sandbox enforced by default. Inline Python skills run in a hardened executor with import allow-list, no-network, CPU + wall-clock limits, and stdout capture.
A guided learning track for new agents: import a .skill pack, auto-ingest its references into RAG, run a self-quiz, and graduate to production. Includes ready-made tracks for Presales, Finance, Infra, Legal, and Tibco-style integration agents.
From user message to tool execution — every layer is deterministic, observable, and provider-agnostic. No black boxes.
Complete message flow from user to final answer — 10 stages, 4 fallback tiers.
Per-agent task-aware model selection with automatic fallback chains. No single-LLM lock-in.
Keyword scoring classifies each message into a task type before selecting the LLM.
Each agent defines its own routing config with primary, task-specific, and fallback LLM connections.
If primary LLM fails (timeout, rate limit, error), the chain automatically tries the next provider.
End-to-end document intelligence — from upload to grounded answer. Self-healing embeddings, vector-store fallback, bulk reindex APIs.
Separated app server, database layer, async workers, and LLM nodes. Designed for horizontal scaling.
React 18 + FastAPI + nginx. Enterprise Orchestrator, UniversalAgentExecutor, ModelRouter, 6-stage RAG, LLMClient.
All relational data, chat memory, rate limiting, task brokering, semantic query cache, vector embeddings.
Async Celery workers. 3-strategy cascade: Tesseract → easyocr → LLM Vision. 8-step image preprocessing. Auto-promotes to RAG.
10+ providers via auto-detection. Per-agent task routing. Primary + fallback chains with cost-aware selection.
From SSH commands to Oracle DBA, from OpenStack cloud to a 6-stage RAG pipeline — agents come ready to work.
Import-ready .skill packages with embedded knowledge bases. Upload → agents start working immediately.
RFP responses, proposals, competitive analysis, objection handling (LAER), ROI/TCO calculations, demo preparation.
Financial analysis, budgeting, forecasting, compliance reporting, revenue recognition, audit preparation.
Server management, monitoring, incident response, capacity planning, patching, backup/recovery procedures.
Contract review, NDA analysis, regulatory compliance, risk assessment, legal terminology, policy drafting.
See how teams across different departments leverage AOS to automate complex, multi-step operations.
Team Leader agent delegates to Linux, VMware, Oracle, and AD sub-agents — all with isolated credentials.
6-stage RAG pipeline: semantic chunking, ingest-time embedding, hybrid RRF search, metadata filtering, cross-encoder re-ranking, and Redis semantic cache.
AD/LDAP enterprise login, regulatory-grade data governance, InfoSec scanning, and full audit trails.
Manage OpenStack/HCS clouds, VMware vCenter, and Kubernetes with natural language. Deep Agent plans multi-step operations before executing.
On your infrastructure. Your data never leaves. No vendor lock-in. Open source.