Content
<div align="center">
<h1>Open Enterprise</h1>
<h3>Enterprise AI Platform & Agent Runtime · Apache-2.0 · Self-Hosted</h3>
**A self-hosted AI agent runtime built on declarative YAML agents.**
No LangChain. No LangGraph. No code. Run agents anywhere with a single binary.
[](LICENSE)
[](https://hub.docker.com/r/openenterprise/open-enterprise-community)
[](https://github.com/openenterprise-info/open-enterprise-community/releases)
[](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/oe-runtime-win.exe)
[](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/oe-runtime-linux)
[](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/oe-runtime-macos)
[](https://discord.gg/vWsZ24Msn)
[Website](https://www.openenterprise.info) · [OE Runtime](https://www.openenterprise.info/runtime.html) · [Docker Hub](https://hub.docker.com/r/openenterprise/open-enterprise-community) · [Discord](https://discord.gg/vWsZ24Msn)
</div>
---
## What is Open Enterprise?
Open Enterprise is an **open-source enterprise AI platform** and **self-hosted AI agent runtime** that lets you build and run autonomous AI agents without writing code. Define complex, multi-step agentic AI workflows in plain YAML. Connect 2,673 enterprise tools — databases, APIs, cloud storage, messaging, SSH, IoT — without custom integration code.
- **No LangChain. No LangGraph. No Python.** Agents are declarative YAML files.
- **Run agents anywhere.** OE Runtime is a single binary for Windows, Linux, and macOS. No Node.js, no Docker, no install.
- **Self-hosted AI.** Deploy on your own infrastructure. Own your data completely. No call-home.
- **Human approval gates.** Build human-in-the-loop agentic workflows without writing code.
- **Enterprise-ready.** Multi-workspace, RAG, MCP, DLP governance, and 2,673 connectors out of the box.
---
## Table of Contents
- [Architecture](#architecture)
- [OE Runtime — Single Binary Agent Executor](#oe-runtime--single-binary-agent-executor)
- [Open Enterprise Platform](#open-enterprise-platform)
- [Quick Start](#quick-start)
- [Writing Agents](#writing-agents)
- [Agent Examples](#agent-examples)
- [Connector Catalog](#connector-catalog)
- [Feature Capabilities](#feature-capabilities)
- [Runtime vs Platform](#runtime-vs-platform)
- [Comparison](#comparison)
- [Environment Variables](#environment-variables)
- [Roadmap](#roadmap)
- [Contributing](#contributing)
- [Community](#community)
- [License](#license)
---
## Architecture
Open Enterprise has two deployment modes — the **Platform** (full web application) and the **Runtime** (standalone CLI binary). Both share the same underlying agent execution engine.
```mermaid
graph TD
subgraph Platform["🖥️ Open Enterprise Platform (Web Application)"]
UI["React Frontend\n(Workspaces, Agents, Chat, RAG)"]
API["Node.js API Server"]
DB[("SQLite / PostgreSQL")]
UI --> API
API --> DB
end
subgraph Runtime["⚡ OE Runtime (CLI Binary — Windows / Linux / macOS)"]
CLI["oe-runtime binary"]
YAML["agent.yaml"]
CONFIG["oe-config.json"]
CLI --> YAML
CLI --> CONFIG
end
subgraph Engine["🔧 OE Engine (Shared Execution Core)"]
EXEC["Agent Executor\n(Tool calling loop)"]
TOOLS["Tool Registry\n(20 capability categories)"]
LLM["LLM Client\n(17+ providers)"]
EXEC --> TOOLS
EXEC --> LLM
end
subgraph Providers["🌐 Connectors & Providers"]
direction LR
P1["OpenAI · Anthropic · Azure · Ollama · Groq"]
P2["PostgreSQL · MySQL · MongoDB · Redis · Snowflake"]
P3["S3 · GDrive · SFTP · OneDrive · Dropbox"]
P4["SSH · REST API · GraphQL · Kafka · MQTT"]
P5["Slack · Email · GitHub · Jira · Notion"]
P6["Images · Video · Speech · Music · OCR · Search"]
end
API --> EXEC
CLI --> EXEC
TOOLS --> Providers
LLM --> P1
```
---
## OE Runtime — Single Binary Agent Executor
OE Runtime is a standalone, cross-platform agent executor. No server. No database. No Node.js install required. Point it at a YAML agent file and run.
### Download
| Platform | Binary | Size |
|----------|--------|------|
| **Windows** | [oe-runtime-win.exe](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/oe-runtime-win.exe) | ~141 MB |
| **Linux** | [oe-runtime-linux](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/oe-runtime-linux) | ~179 MB |
| **macOS** | [oe-runtime-macos](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/oe-runtime-macos) | ~167 MB |
| **Config template** | [oe-config.example.json](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/oe-config.example.json) | — |
| **Sample agent** | [agent.example.yaml](https://github.com/openenterprise-info/open-enterprise-community/releases/latest/download/agent.example.yaml) | — |
> Links always point to the **latest release**. Binaries are built automatically via GitHub Actions on every version tag across all three platforms simultaneously.
### Usage
```bash
# Run an agent
oe-runtime agent.yaml --config oe-config.json
# Pass parameters
oe-runtime market-report.yaml \
--config oe-config.json \
--param company="Tesla" \
--param recipient="ceo@company.com"
# Pass an input message
oe-runtime researcher.yaml \
--config oe-config.json \
--input "Summarise AI trends in healthcare Q3 2026"
# Show help
oe-runtime --help
```
### Config File
Copy `oe-config.example.json` → `oe-config.json` and fill in your credentials:
```json
{
"llm": {
"provider": "openai",
"apiKey": "sk-...",
"model": "gpt-4o"
},
"connectors": [
{
"connection_name": "My Database",
"connection_type": "postgres",
"host": "localhost",
"port": 5432,
"user": "postgres",
"password": "...",
"database": "mydb"
},
{
"connection_name": "Perplexity Search",
"connection_type": "perplexity-search",
"apiKey": "pplx-..."
},
{
"connection_name": "Company Email",
"connection_type": "smtp",
"host": "smtp.zoho.com",
"port": 465,
"secure": true,
"user": "you@yourcompany.com",
"pass": "..."
}
]
}
```
`connection_name` in the config must match `connection_name` in the agent YAML — that is how the runtime links credentials to connectors.
Supported LLM providers: `openai`, `anthropic`, `azure`, `groq`, `gemini`, `ollama`, `mistral`, `deepseek`, `together`, `fireworks`, and more.
### Runtime Capability Categories
| Category | Providers / Protocols |
|----------|-----------------------|
| SQL Databases | PostgreSQL, MySQL, MSSQL, Oracle, SQLite, Snowflake, BigQuery, Redshift |
| NoSQL & Cache | MongoDB, Redis, Elasticsearch |
| Object Storage | AWS S3, GCS, Azure Blob, MinIO, Cloudflare R2, Backblaze B2 |
| Cloud Drives | Google Drive, OneDrive, SharePoint, Dropbox, Box |
| Email | Gmail, Zoho Mail, SMTP, IMAP |
| Messaging | Slack |
| SSH | Any Linux / Unix / Windows server (password or private key) |
| REST API & HTTP | Any HTTP endpoint, custom headers, Bearer / API key / Basic auth |
| GraphQL | GraphQL, Hasura, GraphCMS, Fauna |
| Productivity & CRM | GitHub, Jira, Notion, Confluence, HubSpot, Freshdesk, Zendesk |
| Message Queues | Kafka, RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub |
| Web Search | Perplexity, Google Custom Search, Bing |
| OCR / Vision | Azure Vision, Google Vision, AWS Textract, Tesseract |
| Image Generation | OpenAI gpt-image-1, FLUX, Stable Diffusion, Ideogram |
| Speech & Audio | ElevenLabs, OpenAI TTS, Azure Speech, Google TTS |
| Video Generation | Runway, Kling, Pika (async job polling included) |
| Music Generation | Suno, Udio (async job polling included) |
| Blockchain / Web3 | Ethereum, Polygon, Solana, Avalanche, Infura, Alchemy |
| Directory & Identity | LDAP, Active Directory, Azure AD, OpenLDAP |
| IoT Messaging | MQTT, AWS IoT, HiveMQ, Mosquitto |
---
## Open Enterprise Platform
The full web application — multi-workspace, multi-user, multi-LLM. Deploy once, use across your entire organization.
```bash
docker run -d \
-p 3001:3001 \
-e JWT_SECRET=your-secret \
-e SUPER_ADMIN_EMAIL=admin@yourdomain.com \
-e SUPER_ADMIN_PASSWORD=your-password \
-v ./data:/app/server/storage \
openenterprise/open-enterprise-community:latest
```
Open [http://localhost:3001](http://localhost:3001) and log in with your super admin credentials. Configure your LLM provider, embedding model, and vector database from the admin panel — no `.env` changes needed.
### Platform Capabilities
| Feature | Description |
|---------|-------------|
| **AI Assistant / Chat** | SSE streaming chat with RAG, tool calling, @connector and @agent routing |
| **Workspaces** | Multi-tenant workspaces with per-workspace LLM, embedding, and vector DB overrides |
| **RBAC** | Role-based access control — admin, member, viewer per workspace |
| **Agent Builder** | Conversational YAML designer with visual flow and step validation |
| **Marketplace** | Browse and deploy community agent templates (Security, Sales, Marketing, DevOps, Analytics) |
| **Agent Chains** | Sequential agent execution with `always / on_success / on_critical / on_warning` conditions |
| **Cron Scheduling** | Schedule any agent on a cron expression |
| **RAG** | Document ingestion, chunking, embedding, vector upsert, cited retrieval |
| **8 Vector DBs** | LanceDB (default), Pinecone, Qdrant, Chroma, Weaviate, PgVector, Milvus, Zilliz |
| **MCP** | 25 MCP server catalog entries |
| **Human Approval Gates** | Pause any workflow step for human review and approval |
| **DLP / Governance** | Block, warn, redact, and audit content policies *(Enterprise)* |
| **Observability** | Per-user / per-workspace token usage, cost dashboards, activity log *(Enterprise)* |
| **Connector Library** | Browse all 2,673 connectors by category with dynamic credential forms |
| **OE Runtime** | Standalone binary — run any agent YAML locally, cross-platform |
---
## Quick Start
### Option 1 — OE Runtime (Fastest)
No install. No Docker. No Node.js.
```bash
# 1. Download the binary for your OS (see Download table above)
# 2. Make executable (Linux / macOS)
chmod +x oe-runtime-linux # or oe-runtime-macos
# 3. Copy and fill in the config
cp oe-config.example.json oe-config.json
# edit oe-config.json — add your LLM key and connector credentials
# 4. Run the sample agent
./oe-runtime-linux agent.example.yaml \
--config oe-config.json \
--param company="OpenAI"
```
### Option 2 — Docker (Platform)
```bash
docker run -d \
-p 3001:3001 \
-e JWT_SECRET=change-me \
-e SUPER_ADMIN_EMAIL=admin@yourdomain.com \
-e SUPER_ADMIN_PASSWORD=changeme \
-v ./data:/app/server/storage \
openenterprise/open-enterprise-community:latest
```
Then open [http://localhost:3001](http://localhost:3001).
### Option 3 — Source
**Prerequisites:** Node.js v18+, Yarn v1.x
```bash
git clone https://github.com/openenterprise-info/open-enterprise-community.git
cd open-enterprise-community/app
yarn install
cp server/.env.example server/.env
# Edit server/.env — set JWT_SECRET, SUPER_ADMIN_EMAIL, SUPER_ADMIN_PASSWORD
yarn workspace @open-enterprise/server db:push
yarn dev
```
| Service | URL | Env var |
|---------|-----|---------|
| Frontend | http://localhost:3000 | `FRONTEND_PORT` |
| Server | http://localhost:3001 | `SERVER_PORT` |
| Processor | http://localhost:3002 | `PROCESSOR_PORT` |
---
## Writing Agents
Agents are plain YAML files. No Python. No code. No framework to learn.
```yaml
name: My First Agent
description: Searches the web and emails a summary
instructions: |
You are a research assistant. Search for the given topic,
summarise the findings, and email the report to the recipient.
Be accurate, cite your sources, and keep the report concise.
params:
- name: topic
default: "AI trends 2026"
- name: recipient
default: "you@example.com"
# Connectors reference names from oe-config.json — no secrets here
connectors:
- connection_name: Perplexity Search
connection_type: perplexity-search
- connection_name: Company Email
connection_type: smtp
steps:
- name: Research
content: |
Search for: {{topic}}
Run 2-3 searches from different angles.
Collect the 5 most important findings with sources.
- name: Send Report
content: |
Write a structured email report and send it to {{recipient}}.
Subject: Research Report — {{topic}}
Sections: Executive Summary · Key Findings · Sources
```
Run it:
```bash
oe-runtime agent.yaml --config oe-config.json --param topic="LLM benchmarks 2026"
```
### YAML Agent Reference
| Field | Required | Description |
|-------|----------|-------------|
| `name` | No | Display name shown in terminal |
| `description` | No | Short description |
| `instructions` | Yes | System prompt — what the agent does and how |
| `params` | No | Named parameters with optional defaults; substituted via `{{name}}` in prompt and steps |
| `connectors` | No | Connector references matched to credentials in `oe-config.json` |
| `steps` | No | Named workflow steps injected sequentially into the system prompt |
| `maxRounds` | No | Max LLM tool-call iterations (default: 25) |
---
## Agent Examples
### Database Health Check
```yaml
name: Database Health Check
description: Queries MySQL and reports row counts for all tables
instructions: |
You are a database inspector. Query the database and clearly
report the row count for every table, sorted by size.
connectors:
- connection_name: MySQL DB
connection_type: mysql
steps:
- name: Get table counts
content: |
Run:
SELECT table_name, table_rows
FROM information_schema.tables
WHERE table_schema = DATABASE()
ORDER BY table_rows DESC;
Report each table name and row count clearly.
```
### Market Intelligence Briefing
```yaml
name: Market Intelligence Briefing
description: >
Researches a company and industry, pulls internal metrics from
a database, and emails a structured intelligence report.
instructions: |
You are a senior market intelligence analyst. Gather accurate,
up-to-date information from multiple sources and produce a
concise executive briefing that drives decisions.
Always cite sources. Focus on actionable insights.
params:
- name: company
default: "OpenAI"
- name: industry
default: "Generative AI"
- name: recipient
default: "team@yourcompany.com"
connectors:
- connection_name: Perplexity Search
connection_type: perplexity-search
- connection_name: Postgres DB
connection_type: postgres
- connection_name: Company Email
connection_type: smtp
steps:
- name: Research latest news
content: |
Search for the latest news about {{company}} in {{industry}}.
Focus on: product launches, funding, partnerships, executive changes.
Collect the 5 most significant developments from the past 7 days.
- name: Research competitor landscape
content: |
Find the top 3 competitors of {{company}} in {{industry}}.
For each: note one recent move and what it means for {{company}}.
- name: Pull internal performance data
content: |
Query:
SELECT metric_name, value, recorded_at
FROM metrics
WHERE recorded_at >= NOW() - INTERVAL '30 days'
ORDER BY recorded_at DESC
LIMIT 20;
Identify the 3 most notable trends in the results.
- name: Compose and send briefing
content: |
Write and email a Market Intelligence Briefing to {{recipient}}.
Subject: Market Intelligence — {{company}} — TODAY'S DATE
Sections:
## Executive Summary (3 sentences)
## Key Developments (5 bullets with source URLs)
## Competitor Watch (1 paragraph per competitor)
## Internal Performance Highlights (3 bullets)
## Recommended Actions (3 specific next steps)
```
### Logo Generator
```yaml
name: Logo Generator
description: Generates professional logo variations from a brief
instructions: |
You are a creative director. Generate professional logo variations
based on the style brief. Each logo must work on light and dark
backgrounds. Save each image and report the output paths.
params:
- name: company_name
default: "TechFlow"
- name: style
default: "minimalist"
- name: count
default: "3"
connectors:
- connection_name: OpenAI Image
connection_type: openai-image
steps:
- name: Generate logos
content: |
Generate {{count}} logo variations for "{{company_name}}".
Style: {{style}}
Requirements: clean, scalable, works at small and large sizes.
Generate each one and report all file paths when done.
```
### SSH Server Audit
```yaml
name: Server Audit
description: SSHs into a server and checks disk, memory, and running services
instructions: |
You are a DevOps engineer. Run diagnostic commands on the server
and produce a clear health report. Flag any issues found.
connectors:
- connection_name: Production Server
connection_type: ssh
steps:
- name: Check system health
content: |
Run these commands on the server and collect output:
df -h # disk usage
free -h # memory usage
uptime # load average
systemctl list-units --failed # failed services
ps aux --sort=-%cpu | head # top CPU processes
- name: Generate report
content: |
Compile a Server Health Report from the command outputs.
Flag: disk usage >80%, memory usage >90%, any failed services.
Format: one-paragraph summary, then a table of findings.
```
---
## Connector Catalog
**2,673 connectors across 45+ categories** — the largest open-source connector catalog for enterprise AI.
| Category | Connectors |
|----------|-----------|
| **Databases** | PostgreSQL, MySQL, MSSQL, Oracle, SQLite, Snowflake, BigQuery, Redshift, CockroachDB, TiDB, Supabase, PlanetScale |
| **NoSQL** | MongoDB, Redis, Elasticsearch, DynamoDB, Cassandra, Couchbase, FaunaDB |
| **Object Storage** | AWS S3, Google Cloud Storage, Azure Blob, MinIO, Cloudflare R2, Backblaze B2, Wasabi, DigitalOcean Spaces |
| **Cloud Drives** | Google Drive, OneDrive, SharePoint, Dropbox, Box |
| **CRM** | HubSpot, Salesforce, Pipedrive, Zoho CRM, Freshsales, Close |
| **Support** | Zendesk, Freshdesk, Intercom, ServiceNow, Help Scout |
| **Project Management** | Jira, Asana, Linear, Monday.com, Trello, ClickUp, Basecamp |
| **Developer Tools** | GitHub, GitLab, Bitbucket, CircleCI, Jenkins, Vercel |
| **Communication** | Slack, Microsoft Teams, Discord, Telegram, WhatsApp Business |
| **Email** | Gmail, Outlook, Zoho Mail, SMTP/IMAP, SendGrid, Mailchimp, Postmark |
| **Finance** | Stripe, QuickBooks, Xero, Plaid, Square, Paddle, Chargebee |
| **HR** | BambooHR, Workday, ADP, Greenhouse, Lever |
| **E-commerce** | Shopify, WooCommerce, BigCommerce, Magento, PrestaShop |
| **AI / ML** | OpenAI, Anthropic, HuggingFace, Replicate, Together AI, Fireworks |
| **Image Generation** | OpenAI gpt-image-1, FLUX, Stable Diffusion, Ideogram |
| **Speech & Audio** | ElevenLabs, OpenAI TTS, Azure Speech, Google TTS |
| **Video Generation** | Runway, Kling, Pika |
| **Music Generation** | Suno, Udio |
| **Search** | Perplexity, Google Custom Search, Bing |
| **OCR / Vision** | Azure Vision, Google Vision, AWS Textract, Tesseract |
| **Message Queues** | Kafka, RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub |
| **IoT** | MQTT, AWS IoT, HiveMQ, Mosquitto |
| **Blockchain / Web3** | Ethereum, Polygon, Solana, Avalanche, Infura, Alchemy |
| **Directory** | LDAP, Active Directory, Azure AD, OpenLDAP |
| **Healthcare** | FHIR, Epic, Cerner |
| **ERP** | SAP, Oracle ERP, Microsoft Dynamics |
| **Marketing** | Google Ads, Facebook Ads, HubSpot Marketing, Marketo, ActiveCampaign |
| **Analytics** | Google Analytics, Mixpanel, Amplitude, Segment, PostHog |
| **+ 17 more** | Legal, Education, GIS, Logistics, Real Estate, Government, ... |
All connectors are browsable from the **Connectors** tab inside the Platform.
---
## Feature Capabilities
| Domain | Capabilities |
|--------|-------------|
| **Foundation** | 17+ LLM providers, 7 embedding providers, 8 vector databases |
| **Knowledge** | RAG pipeline, document ingestion, chunking, embedding, citation, memory |
| **Interaction** | AI Assistant / Chat, SSE streaming, conversation threads, @connector routing |
| **Workspaces** | Multi-tenant, RBAC, per-workspace LLM and vector DB overrides |
| **Integration** | 2,673 connectors, tool calling, MCP (25 server catalog entries) |
| **Agents** | Declarative YAML agents, agent planning, multi-agent collaboration, reflection |
| **Workflow** | Sequential chains, conditional branching (`on_success / on_critical`), loops, routers |
| **Scheduling** | Built-in cron scheduler, event-driven triggers |
| **Human-in-the-Loop** | Human approval gates — pause any workflow step for review |
| **OE Runtime** | Standalone binary — run any YAML agent locally, Windows / Linux / macOS |
| **Marketplace** | Community agent templates — browse, deploy, and share |
| **Governance** | DLP policies, content block / warn / redact, violation audit log with CSV export *(Enterprise)* |
| **Observability** | Token usage dashboards, per-user cost breakdowns, activity log *(Enterprise)* |
---
## Runtime vs Platform
| | OE Runtime | Open Enterprise Platform |
|---|-----------|--------------------------|
| **Best for** | Local execution, CI/CD, scripts, developer automation | Teams, organizations, production SaaS |
| **Install** | Single binary download — no install | Docker or source |
| **UI** | None — CLI only | Full web application |
| **Multi-user** | No | Yes — workspaces and RBAC |
| **Persistence** | None | SQLite / PostgreSQL |
| **Agent authoring** | YAML file in any editor | YAML editor + visual builder |
| **RAG** | Not yet *(roadmap)* | Yes — full ingestion and retrieval pipeline |
| **Scheduling** | External cron / task scheduler | Built-in cron expression scheduler |
| **Human approval** | Not yet *(roadmap)* | Yes — built-in |
| **Platforms** | Windows, Linux, macOS | Any Docker host, Kubernetes, bare metal |
You can use both together: author and test agents with OE Runtime locally, then deploy them to the Platform for scheduling, multi-user access, and governance.
---
## Comparison
Architectural comparison only — not a benchmark. Every tool has a different primary use case.
| | Open Enterprise | LangGraph | CrewAI | AutoGen | Dify |
|---|:---:|:---:|:---:|:---:|:---:|
| **Agent definition** | Declarative YAML | Python code | Python code | Python code | UI + YAML |
| **No-code** | ✅ | ❌ | ❌ | ❌ | Partial |
| **Self-hosted** | ✅ | ✅ | ✅ | ✅ | ✅ |
| **Standalone runtime binary** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Cross-platform (Win/Lin/Mac)** | ✅ | ❌ | ❌ | ❌ | ❌ |
| **Built-in connectors** | 2,673 | 0 (write your own) | 0 (write your own) | 0 (write your own) | ~60 |
| **Built-in RAG** | ✅ | ❌ | ❌ | ❌ | ✅ |
| **Human approval gates** | ✅ | Requires code | Requires code | Requires code | ✅ |
| **Multi-workspace** | ✅ | ❌ | ❌ | ❌ | ✅ |
| **MCP support** | ✅ | ✅ | ❌ | ❌ | Partial |
| **Agent marketplace** | ✅ | ❌ | ❌ | ❌ | ✅ |
| **DLP governance** | ✅ *(Enterprise)* | ❌ | ❌ | ❌ | ❌ |
| **License** | Apache-2.0 | MIT | MIT | MIT | Apache-2.0 |
| **Primary language** | None (YAML) | Python | Python | Python | None (UI) |
---
## Environment Variables
| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `JWT_SECRET` | Yes | — | Signs and verifies login tokens. Keep the same value across restarts — changing it logs everyone out. |
| `SUPER_ADMIN_EMAIL` | Yes | — | Email for the super admin account |
| `SUPER_ADMIN_PASSWORD` | Yes | — | Password for the super admin account |
| `FRONTEND_PORT` | No | `3000` | Vite dev server port |
| `SERVER_PORT` | No | `3001` | API server port |
| `PROCESSOR_PORT` | No | `3002` | Document processor port |
| `LICENSE_TYPE` | No | `community` | Edition identifier |
All LLM, embedding, and vector database settings are configured from the admin panel inside the app — no `.env` changes needed after initial setup.
---
## Roadmap
### OE Runtime
- [ ] `--watch` mode — re-run agent on file change (dev loop)
- [ ] Output to file (`--output report.md`)
- [ ] Built-in RAG — bring your own vector DB
- [ ] Agent chaining via CLI (`--chain followup.yaml`)
- [ ] WebAssembly target for browser / edge execution
### Open Enterprise Platform
- [ ] Event-driven agent triggers (webhook, queue, file watch)
- [ ] Visual flow builder — drag-and-drop workflow designer
- [ ] Agent versioning and rollback
- [ ] Agent-to-agent pub/sub communication
- [ ] Multi-region deployment support
- [ ] Plugin SDK for custom connector adapters
### Marketplace
- [ ] Community-submitted agent templates with ratings
- [ ] One-click deploy from marketplace
- [ ] Agent forking and remixing
- [ ] Usage stats and trending agents
### API & Integration
- [ ] Public REST API for agent execution
- [ ] Node.js and Python SDK
- [ ] GitHub Action: run any agent in CI/CD
- [ ] OCI container image per agent
---
## Contributing
Contributions are welcome. Before opening a PR:
1. **Open an issue** to discuss the change — especially for new features
2. **Fork** the repository
3. **Branch** from `main`
4. **Test** your changes locally
5. **Open a PR** with a clear description of what and why
**Where contributions are most valuable:**
- New connector adapters (see `app/server/src/utils/tools/adapters/`)
- Agent YAML examples for the community marketplace
- Bug fixes with clear reproduction steps
- Documentation improvements and translations
---
## Community
<a href="https://discord.gg/vWsZ24Msn"><img src="https://img.shields.io/badge/Discord-Join%20the%20Community-5865F2?style=for-the-badge&logo=discord&logoColor=white" height="50" /></a>
| | Link |
|---|------|
| 💬 Discord | [discord.gg/vWsZ24Msn](https://discord.gg/vWsZ24Msn) |
| 🌐 Website | [openenterprise.info](https://www.openenterprise.info) |
| ⚡ OE Runtime | [openenterprise.info/runtime.html](https://www.openenterprise.info/runtime.html) |
| 🐳 Docker Hub | [hub.docker.com/r/openenterprise/open-enterprise-community](https://hub.docker.com/r/openenterprise/open-enterprise-community) |
| 🐛 Issues | [github.com/…/issues](https://github.com/openenterprise-info/open-enterprise-community/issues) |
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## License
[Apache-2.0](LICENSE) — free to use, modify, and deploy for any purpose, including commercial use.
No usage limits. No telemetry. No call-home. Self-host it on your own infrastructure and own your data completely.
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<div align="center">
**[⭐ Star this repo](https://github.com/openenterprise-info/open-enterprise-community)** · **[🐳 Docker Hub](https://hub.docker.com/r/openenterprise/open-enterprise-community)** · **[💬 Discord](https://discord.gg/vWsZ24Msn)** · **[🌐 Website](https://www.openenterprise.info)**
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MCP Config
Below is the configuration for this MCP Server. You can copy it directly to Cursor or other MCP clients.
mcp.json
Connection Info
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