
orra
Build production-ready multi-agent applications that handle complex real-world interactions - across any language, agent framework or deployment platform.
Stars: 155

Orra is a tool for building production-ready multi-agent applications that handle complex real-world interactions. It coordinates tasks across existing stack, agents, and tools run as services using intelligent reasoning. With features like smart pre-evaluated execution plans, domain grounding, durable execution, and automatic service health monitoring, Orra enables users to go fast with tools as services and revert state to handle failures. It provides real-time status tracking and webhook result delivery, making it ideal for developers looking to move beyond simple crews and agents.
README:
Move beyond simple Crews and Agents. Use orra to build production-ready multi-agent applications that handle complex real-world interactions.
orra coordinates tasks across your existing stack, agents and any tools run as services using intelligent reasoning — across any language, agent framework or deployment platform.
- 🧠 Smart pre-evaluated execution plans
- 🎯 Domain grounded
- 🗿 Durable execution
- 🚀 Go fast with tools as services
- ↩️ Revert state to handle failures
- ⛑️ Automatic service health monitoring
- 🔮 Real-time status tracking
- 🪝 Webhook result delivery
- Agent replay and multi-LLM consensus planning
- Continuous adjustment of Agent workflows during runtime
- Additional language SDKs - Ruby, DotNet and Go very soon!
- Installation
- How The Plan Engine Works
- Guides
- Explore Examples
- Docs
- Self Hosting
- Join Our Alpha Testing Community
- License
- Docker and Docker Compose - For running the Plan Engine
- Set up Reasoning and Embedding Models to power task planning and execution plan caching/validation
Select between Groq's deepseek-r1-distill-llama-70b model or OpenAI's o1-mini / o3-mini models.
Update the .env file with one of these:
Groq
# GROQ Reasoning
REASONING_PROVIDER=groq
REASONING_MODEL=deepseek-r1-distill-llama-70b
REASONING_API_KEY=xxxx
O1-mini
# OpenAI Reasoning
REASONING_PROVIDER=openai
REASONING_MODEL=o1-mini
REASONING_API_KEY=xxxx
O3-mini
# OpenAI Reasoning
REASONING_PROVIDER=openai
REASONING_MODEL=o3-mini
REASONING_API_KEY=xxxx
Update the .env file with:
# Execution Plan Cache and validation OPENAI API KEY
PLAN_CACHE_OPENAI_API_KEY=xxxx
Download the latest CLI binary for your platform from our releases page:
# macOS
curl -L https://github.com/orra-dev/orra/releases/download/v0.2.3/orra-darwin-arm64 -o /usr/local/bin/orra
chmod +x /usr/local/bin/orra
# Linux
curl -L https://github.com/ezodude/orra/releases/download/v0.2.3/orra-linux-amd64 -o /usr/local/bin/orra
chmod +x /usr/local/bin/orra
# Verify installation
orra version
Clone the repository and start the Plan Engine:
git clone https://github.com/ezodude/orra.git
cd orra/planengine
# Start the Plan Engine
docker compose up --build
The Plan Engine powers your multi-agent applications through intelligent planning and reliable execution:
Your agents stay clean and simple (wrapped in the orra SDK):
Python
from orra import OrraAgent, Task
from pydantic import BaseModel
class ResearchInput(BaseModel):
topic: str
depth: str
class ResearchOutput(BaseModel):
summary: str
agent = OrraAgent(
name="research-agent",
description="Researches topics using web search and knowledge base",
url="https://api.orra.dev",
api_key="sk-orra-..."
)
@agent.handler()
async def research(task: Task[ResearchInput]) -> ResearchOutput:
results = await run_research(task.input.topic, task.input.depth)
return ResearchOutput(summary=results.summary)
JavaScript
import { initAgent } from '@orra.dev/sdk';
const agent = initAgent({
name: 'research-agent',
orraUrl: process.env.ORRA_URL,
orraKey: process.env.ORRA_API_KEY
});
await agent.register({
description: 'Researches topics using web search and knowledge base',
schema: {
input: {
type: 'object',
properties: {
topic: { type: 'string' },
depth: { type: 'string' }
}
},
output: {
type: 'object',
properties: {
summary: { type: 'string' }
}
}
}
});
agent.start(async (task) => {
const results = await runResearch(task.input.topic, task.input.depth);
return { summary: results.summary };
});
Features:
- AI analyzes intent and creates execution plans that target your components
- Automatic service discovery and coordination
- Parallel execution where possible
# Define domain constraints
name: research-workflow
domain: content-generation
use-cases:
- action: "Research topic {topic}"
capabilities:
- "Web search access"
- "Knowledge synthesis"
constraints:
- "Verify sources before synthesis"
- "Maximum research time: 10 minutes"
Features:
- Full semantic validation of execution plans
- Capability matching and verification
- Safety constraints enforcement
- State transition validation
# Execute an action with the Plan Engine
orra verify run "Research and summarize AI trends" \
--data topic:"AI in 2024" \
--data depth:"comprehensive"
The Plan Engine ensures:
- Automatic service health monitoring
- Stateful execution tracking
- Built-in retries and recovery
- Real-time status updates
- Webhook result delivery
- 🛒 E-commerce AI Assistant (JavaScript) - E-commerce customer service with a delivery specialized agent
- 👻 Ghostwriters (Python) - Content generation example showcasing how to use orra with CrewAI 🆕🎉
- 📣 Echo Tools as Service (JavaScript) - Simple example showing core concepts using JS
- 📣 Echo Tools as Service (Python) - Simple example showing core concepts using Python
- Rapid Multi-Agent App Development with orra
- What is an Agent in orra?
- Orchestrating Actions with orra
- Domain Grounding Execution
- Execution Plan Caching
- Core Topics & Internals
- Storage: We use BadgerDB to persist all state
- Deployment: Single-instance only, using containers designed for development and self-hosted deployments
We're looking for developers who:
- Are building multi-agent applications
- Want to help shape orra's development
- Are comfortable working with Alpha software
- Can provide feedback on real-world use cases
Connect With Us:
- GitHub Discussions - Share your experience and ideas
- Office Hours - Weekly calls with the team every Friday
Orra is MPL-2.0 licensed.
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