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PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
- π€ Automated AI Agents Creation
- π Self Reflection AI Agents
- π§ Reasoning AI Agents
- ποΈ Multi Modal AI Agents
- π€ Multi Agent Collaboration
- π AI Agent Workflow
- π Add Custom Knowledge
- π§ Agents with Short and Long Term Memory
- π Chat with PDF Agents
- π» Code Interpreter Agents
- π RAG Agents
- π€ Async & Parallel Processing
- π Auto Agents
- π’ Math Agents
- π― Structured Output Agents
- π LangChain Integrated Agents
- π Callback Agents
- π€ Mini AI Agents
- π οΈ 100+ Custom Tools
- π YAML Configuration
- π― 100+ LLM Support
Light weight package dedicated for coding:
pip install praisonaiagents
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxxCreate app.py file and add the code below:
from praisonaiagents import Agent agent = Agent(instructions="Your are a helpful AI assistant") agent.start("Write a movie script about a robot in Mars")
Run:
python app.py
Create app.py file and add the code below:
from praisonaiagents import Agent, PraisonAIAgents research_agent = Agent(instructions="Research about AI") summarise_agent = Agent(instructions="Summarise research agent's findings") agents = PraisonAIAgents(agents=[research_agent, summarise_agent]) agents.start()
Run:
python app.py
pip install praisonai export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx praisonai --auto create a movie script about Robots in Mars
npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxxconst { Agent } = require('praisonai'); const agent = new Agent({ instructions: 'You are a helpful AI assistant' }); agent.start('Write a movie script about a robot in Mars');
graph LR
%% Define the main flow
Start([βΆ Start]) --> Agent1
Agent1 --> Process[β Process]
Process --> Agent2
Agent2 --> Output([β Output])
Process -.-> Agent1
%% Define subgraphs for agents and their tasks
subgraph Agent1[ ]
Task1[π Task]
AgentIcon1[π€ AI Agent]
Tools1[π§ Tools]
Task1 --- AgentIcon1
AgentIcon1 --- Tools1
end
subgraph Agent2[ ]
Task2[π Task]
AgentIcon2[π€ AI Agent]
Tools2[π§ Tools]
Task2 --- AgentIcon2
AgentIcon2 --- Tools2
end
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Start,Output,Task1,Task2 input
class Process,AgentIcon1,AgentIcon2 process
class Tools1,Tools2 tools
class Agent1,Agent2 transparent
Create AI agents that can use tools to interact with external systems and perform actions.
flowchart TB
subgraph Tools
direction TB
T3[Internet Search]
T1[Code Execution]
T2[Formatting]
end
Input[Input] ---> Agents
subgraph Agents
direction LR
A1[Agent 1]
A2[Agent 2]
A3[Agent 3]
end
Agents ---> Output[Output]
T3 --> A1
T1 --> A2
T2 --> A3
style Tools fill:#189AB4,color:#fff
style Agents fill:#8B0000,color:#fff
style Input fill:#8B0000,color:#fff
style Output fill:#8B0000,color:#fff
Create AI agents with memory capabilities for maintaining context and information across tasks.
flowchart TB
subgraph Memory
direction TB
STM[Short Term]
LTM[Long Term]
end
subgraph Store
direction TB
DB[(Vector DB)]
end
Input[Input] ---> Agents
subgraph Agents
direction LR
A1[Agent 1]
A2[Agent 2]
A3[Agent 3]
end
Agents ---> Output[Output]
Memory <--> Store
Store <--> A1
Store <--> A2
Store <--> A3
style Memory fill:#189AB4,color:#fff
style Store fill:#2E8B57,color:#fff
style Agents fill:#8B0000,color:#fff
style Input fill:#8B0000,color:#fff
style Output fill:#8B0000,color:#fff
The simplest form of task execution where tasks are performed one after another.
graph LR
Input[Input] --> A1
subgraph Agents
direction LR
A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
end
A3 --> Output[Output]
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Input,Output input
class A1,A2,A3 process
class Agents transparent
Uses a manager agent to coordinate task execution and agent assignments.
graph TB
Input[Input] --> Manager
subgraph Agents
Manager[Manager Agent]
subgraph Workers
direction LR
W1[Worker 1]
W2[Worker 2]
W3[Worker 3]
end
Manager --> W1
Manager --> W2
Manager --> W3
end
W1 --> Manager
W2 --> Manager
W3 --> Manager
Manager --> Output[Output]
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Input,Output input
class Manager,W1,W2,W3 process
class Agents,Workers transparent
Advanced process type supporting complex task relationships and conditional execution.
graph LR
Input[Input] --> Start
subgraph Workflow
direction LR
Start[Start] --> C1{Condition}
C1 --> |Yes| A1[Agent 1]
C1 --> |No| A2[Agent 2]
A1 --> Join
A2 --> Join
Join --> A3[Agent 3]
end
A3 --> Output[Output]
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Input,Output input
class Start,A1,A2,A3,Join process
class C1 decision
class Workflow transparent
Create AI agents that can dynamically route tasks to specialized LLM instances.
flowchart LR
In[In] --> Router[LLM Call Router]
Router --> LLM1[LLM Call 1]
Router --> LLM2[LLM Call 2]
Router --> LLM3[LLM Call 3]
LLM1 --> Out[Out]
LLM2 --> Out
LLM3 --> Out
style In fill:#8B0000,color:#fff
style Router fill:#2E8B57,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
Create AI agents that orchestrate and distribute tasks among specialized workers.
flowchart LR
In[In] --> Router[LLM Call Router]
Router --> LLM1[LLM Call 1]
Router --> LLM2[LLM Call 2]
Router --> LLM3[LLM Call 3]
LLM1 --> Synthesizer[Synthesizer]
LLM2 --> Synthesizer
LLM3 --> Synthesizer
Synthesizer --> Out[Out]
style In fill:#8B0000,color:#fff
style Router fill:#2E8B57,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Synthesizer fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.
flowchart LR
Human[Human] <--> LLM[LLM Call]
LLM -->|ACTION| Environment[Environment]
Environment -->|FEEDBACK| LLM
LLM --> Stop[Stop]
style Human fill:#8B0000,color:#fff
style LLM fill:#2E8B57,color:#fff
style Environment fill:#8B0000,color:#fff
style Stop fill:#333,color:#fff
Create AI agents that can execute tasks in parallel for improved performance.
flowchart LR
In[In] --> LLM2[LLM Call 2]
In --> LLM1[LLM Call 1]
In --> LLM3[LLM Call 3]
LLM1 --> Aggregator[Aggregator]
LLM2 --> Aggregator
LLM3 --> Aggregator
Aggregator --> Out[Out]
style In fill:#8B0000,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Aggregator fill:#fff,color:#000
style Out fill:#8B0000,color:#fff
Create AI agents with sequential prompt chaining for complex workflows.
flowchart LR
In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
Gate -->|Fail| Exit[Exit]
style In fill:#8B0000,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
style Exit fill:#8B0000,color:#fff
Create AI agents that can generate and optimize solutions through iterative feedback.
flowchart LR
In[In] --> Generator[LLM Call Generator]
Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
Evaluator -->|REJECTED + FEEDBACK| Generator
style In fill:#8B0000,color:#fff
style Generator fill:#2E8B57,color:#fff
style Evaluator fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
Create AI agents that can efficiently handle repetitive tasks through automated loops.
flowchart LR
In[Input] --> LoopAgent[("Looping Agent")]
LoopAgent --> Task[Task]
Task --> |Next iteration| LoopAgent
Task --> |Done| Out[Output]
style In fill:#8B0000,color:#fff
style LoopAgent fill:#2E8B57,color:#fff,shape:circle
style Task fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
export OPENAI_BASE_URL=http://localhost:11434/v1Replace xxxx with Groq API KEY:
export OPENAI_API_KEY=xxxxxxxxxxx export OPENAI_BASE_URL=https://api.groq.com/openai/v1
Create agents.yaml file and add the code below:
framework: praisonai topic: Artificial Intelligence roles: screenwriter: backstory: "Skilled in crafting scripts with engaging dialogue about {topic}." goal: Create scripts from concepts. role: Screenwriter tasks: scriptwriting_task: description: "Develop scripts with compelling characters and dialogue about {topic}." expected_output: "Complete script ready for production."
To run the playbook:
praisonai agents.yaml
Below is used for development only.
# Install uv if you haven't already pip install uv # Install from requirements uv pip install -r pyproject.toml # Install with extras uv pip install -r pyproject.toml --extra code uv pip install -r pyproject.toml --extra "crewai,autogen"
- Fork on GitHub: Use the "Fork" button on the repository page.
- Clone your fork:
git clone https://github.com/yourusername/praisonAI.git - Create a branch:
git checkout -b new-feature - Make changes and commit:
git commit -am "Add some feature" - Push to your fork:
git push origin new-feature - Submit a pull request via GitHub's web interface.
- Await feedback from project maintainers.
- π Use CrewAI or AG2 (Formerly AutoGen) Framework
- π» Chat with ENTIRE Codebase
- π¨ Interactive UIs
- π YAML-based Configuration
- π οΈ Custom Tool Integration
- π Internet Search Capability (using Crawl4AI and Tavily)
- πΌοΈ Vision Language Model (VLM) Support
- ποΈ Real-time Voice Interaction
| Topic | Video |
|---|---|
| AI Agents with Self Reflection | Self Reflection |
| Reasoning Data Generating Agent | Reasoning Data |
| AI Agents with Reasoning | Reasoning |
| Multimodal AI Agents | Multimodal |
| AI Agents Workflow | Workflow |
| Async AI Agents | Async |
| Mini AI Agents | Mini |
| AI Agents with Memory | Memory |
| Repetitive Agents | Repetitive |
| Introduction | Introduction |
| Tools Overview | Tools Overview |
| Custom Tools | Custom Tools |
| Firecrawl Integration | Firecrawl |
| User Interface | UI |
| Crawl4AI Integration | Crawl4AI |
| Chat Interface | Chat |
| Code Interface | Code |
| Mem0 Integration | Mem0 |
| Training | Training |
| Realtime Voice Interface | Realtime |
| Call Interface | Call |
| Reasoning Extract Agents | Reasoning Extract |