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PraisonAI
PraisonAI is a production-ready Multi AI Agents framework, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
Stars: 3251
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Praison AI is a low-code, centralised framework that simplifies the creation and orchestration of multi-agent systems for various LLM applications. It emphasizes ease of use, customization, and human-agent interaction. The tool leverages AutoGen and CrewAI frameworks to facilitate the development of AI-generated scripts and movie concepts. Users can easily create, run, test, and deploy agents for scriptwriting and movie concept development. Praison AI also provides options for full automatic mode and integration with OpenAI models for enhanced AI capabilities.
README:
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, 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.
- 🤖 Automated AI Agents Creation
- 🔄 Self Reflection AI Agents
- 🧠 Reasoning AI Agents
- 👁️ Multi Modal AI Agents
- 🤝 Multi Agent Collaboration
- 🎭 AI Agent Workflow
- 📚 Add Custom Knowledge
- 🧠 Agents with Short and Long Term Memory
- 📄 Chat with PDF Agents
- 💻 Code Interpreter Agents
- 📚 RAG Agents
- 🤔 Async & Parallel Processing
- 🔄 Auto Agents
- 🔢 Math Agents
- 🎯 Structured Output Agents
- 🔗 LangChain Integrated Agents
- 📞 Callback Agents
- 🤏 Mini AI Agents
- 🛠️ 100+ Custom Tools
- 📄 YAML Configuration
- 💯 100+ LLM Support
Light weight package dedicated for coding:
pip install praisonaiagents
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
Create 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
graph LR
%% Define the main flow
Start([▶ Start]) --> Agent1
Agent1 --> Process[⚙ Process]
Process --> Agent2
Agent2 --> Output([✓ Output])
Process -.-> Agent1
%% Define subgraphs for agents and their tasks
subgraph Agent1[ ]
Task1[📋 Task]
AgentIcon1[🤖 AI Agent]
Tools1[🔧 Tools]
Task1 --- AgentIcon1
AgentIcon1 --- Tools1
end
subgraph Agent2[ ]
Task2[📋 Task]
AgentIcon2[🤖 AI Agent]
Tools2[🔧 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/v1
Replace 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.
- 🔄 Use CrewAI or AutoGen Framework
- 💻 Chat with ENTIRE Codebase
- 🎨 Interactive UIs
- 📄 YAML-based Configuration
- 🛠️ Custom Tool Integration
- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
- 🖼️ Vision Language Model (VLM) Support
- 🎙️ Real-time Voice Interaction
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griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.
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zep-python
Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.
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AI-in-a-Box
AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.
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NeMo
NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained models.
For similar jobs
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promptflow
**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.
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deepeval
DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.
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MegaDetector
MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".
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leapfrogai
LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.
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llava-docker
This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.
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carrot
The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.
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TrustLLM
TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.
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AI-YinMei
AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.