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PraisonAI

Tue, 03 Jun 2025 15:32:36 +0800

MervinPraison/PraisonAI

Praison AI

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.

Key Features

🤖 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

Using Python Code

Light weight package dedicated for coding:

`1`	`pip install praisonaiagents`

`1`	`export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx`

1. Single Agent

Create app.py file and add the code below:

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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:

`1`	`python app.py`

2. Multi Agents

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:

`1`	`python app.py`

Using No Code

Auto Mode:

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pip install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
praisonai --auto create a movie script about Robots in Mars

Using JavaScript Code

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npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

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const { 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');

AI Agents Flow

  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

AI Agents with Tools

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

AI Agents with Memory

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

AI Agents with Different Processes

Sequential Process

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

Hierarchical Process

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

Workflow Process

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

Agentic Routing Workflow

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

Agentic Orchestrator Worker

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

Agentic Autonomous Workflow

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

Agentic Parallelization

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

Agentic Prompt Chaining

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

Agentic Evaluator Optimizer

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

Repetitive Agents

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

Adding Models

Ollama Integration

`1`	`export OPENAI_BASE_URL=http://localhost:11434/v1`

Groq Integration

Replace xxxx with Groq API KEY:

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export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://api.groq.com/openai/v1

No Code Options

Agents Playbook

Simple Playbook Example

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:

`1`	`praisonai agents.yaml`

Use 100+ Models

https://docs.praison.ai/models/

Development:

Below is used for development only.

Using uv

# 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"

Contributing

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.

Other Features

🔄 Use CrewAI or AG2 (Formerly 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

Star History

Video Tutorials

Topic	Video
AI Agents with Self Reflection
Reasoning Data Generating Agent
AI Agents with Reasoning
Multimodal AI Agents
AI Agents Workflow
Async AI Agents
Mini AI Agents
AI Agents with Memory
Repetitive Agents
Introduction
Tools Overview
Custom Tools
Firecrawl Integration
User Interface
Crawl4AI Integration
Chat Interface
Code Interface
Mem0 Integration
Training
Realtime Voice Interface
Call Interface
Reasoning Extract Agents

glance

Wed, 07 May 2025 15:29:28 +0800

glanceapp/glance

What if you could see everything at a...

Glance

Install • Configuration • Discord • Sponsor

Community widgets • Preconfigured pages • Themes

Features

Various widgets

RSS feeds
Subreddit posts
Hacker News posts
Weather forecasts
YouTube channel uploads
Twitch channels
Market prices
Docker containers status
Server stats
Custom widgets
and many more…

Fast and lightweight

Low memory usage
Few dependencies
Minimal vanilla JS
Single <20mb binary available for multiple OSs & architectures and just as small Docker container
Uncached pages usually load within ~1s (depending on internet speed and number of widgets)

Tons of customizability

Different layouts
As many pages/tabs as you need
Numerous configuration options for each widget
Multiple styles for some widgets
Custom CSS

Optimized for mobile devices

Because you’ll want to take it with you on the go.

Themeable

Easily create your own theme by tweaking a few numbers or choose from one of the already available themes.

Configuration

Configuration is done through YAML files, to learn more about how the layout works, how to add more pages and how to configure widgets, visit the configuration documentation.

Preview example configuration file

pages:
  - name: Home
    columns:
      - size: small
        widgets:
          - type: calendar
            first-day-of-week: monday

          - type: rss
            limit: 10
            collapse-after: 3
            cache: 12h
            feeds:
              - url: https://selfh.st/rss/
                title: selfh.st
                limit: 4
              - url: https://ciechanow.ski/atom.xml
              - url: https://www.joshwcomeau.com/rss.xml
                title: Josh Comeau
              - url: https://samwho.dev/rss.xml
              - url: https://ishadeed.com/feed.xml
                title: Ahmad Shadeed

          - type: twitch-channels
            channels:
              - theprimeagen
              - j_blow
              - piratesoftware
              - cohhcarnage
              - christitustech
              - EJ_SA

      - size: full
        widgets:
          - type: group
            widgets:
              - type: hacker-news
              - type: lobsters

          - type: videos
            channels:
              - UCXuqSBlHAE6Xw-yeJA0Tunw # Linus Tech Tips
              - UCR-DXc1voovS8nhAvccRZhg # Jeff Geerling
              - UCsBjURrPoezykLs9EqgamOA # Fireship
              - UCBJycsmduvYEL83R_U4JriQ # Marques Brownlee
              - UCHnyfMqiRRG1u-2MsSQLbXA # Veritasium

          - type: group
            widgets:
              - type: reddit
                subreddit: technology
                show-thumbnails: true
              - type: reddit
                subreddit: selfhosted
                show-thumbnails: true

      - size: small
        widgets:
          - type: weather
            location: London, United Kingdom
            units: metric
            hour-format: 12h

          - type: markets
            markets:
              - symbol: SPY
                name: S&P 500
              - symbol: BTC-USD
                name: Bitcoin
              - symbol: NVDA
                name: NVIDIA
              - symbol: AAPL
                name: Apple
              - symbol: MSFT
                name: Microsoft

          - type: releases
            cache: 1d
            repositories:
              - glanceapp/glance
              - go-gitea/gitea
              - immich-app/immich
              - syncthing/syncthing

Installation

Choose one of the following methods:

Docker compose using provided directory structure (recommended)

Create a new directory called glance as well as the template files within it by running:

`1`	`mkdir glance && cd glance && curl -sL https://github.com/glanceapp/docker-compose-template/archive/refs/heads/main.tar.gz \| tar -xzf - --strip-components 2`

click here to view the files that will be created

Then, edit the following files as desired:

docker-compose.yml to configure the port, volumes and other containery things
config/home.yml to configure the widgets or layout of the home page
config/glance.yml if you want to change the theme or add more pages

Other files you may want to edit

.env to configure environment variables that will be available inside configuration files
assets/user.css to add custom CSS

When ready, run:

`1`	`docker compose up -d`

If you encounter any issues, you can check the logs by running:

`1`	`docker compose logs`

Docker compose manual

Create a docker-compose.yml file with the following contents:

services:
  glance:
    container_name: glance
    image: glanceapp/glance
    restart: unless-stopped
    volumes:
      - ./config:/app/config
    ports:
      - 8080:8080

Then, create a new directory called config and download the example starting glance.yml file into it by running:

`1`	`mkdir config && wget -O config/glance.yml https://raw.githubusercontent.com/glanceapp/glance/refs/heads/main/docs/glance.yml`

Feel free to edit the glance.yml file to your liking, and when ready run:

`1`	`docker compose up -d`

If you encounter any issues, you can check the logs by running:

`1`	`docker logs glance`

Manual binary installation

Precompiled binaries are available for Linux, Windows and macOS (x86, x86_64, ARM and ARM64 architectures).

Linux

Visit the latest release page for available binaries. You can place the binary in /opt/glance/ and have it start with your server via a systemd service. By default, when running the binary, it will look for a glance.yml file in the directory it’s placed in. To specify a different path for the config file, use the --config option:

`1`	`/opt/glance/glance --config /etc/glance.yml`

To grab a starting template for the config file, run:

`1`	`wget https://raw.githubusercontent.com/glanceapp/glance/refs/heads/main/docs/glance.yml`

Windows

Download and extract the executable from the latest release (most likely the file called glance-windows-amd64.zip if you’re on a 64-bit system) and place it in a folder of your choice. Then, create a new text file called glance.yml in the same folder and paste the content from here in it. You should then be able to run the executable and access the dashboard by visiting http://localhost:8080 in your browser.

Other

Glance can also be installed through the following 3rd party channels:

Common issues

Requests timing out

The most common cause of this is when using Pi-Hole, AdGuard Home or other ad-blocking DNS services, which by default have a fairly low rate limit. Depending on the number of widgets you have in a single page, this limit can very easily be exceeded. To fix this, increase the rate limit in the settings of your DNS service.

If using Podman, in some rare cases the timeout can be caused by an unknown issue, in which case it may be resolved by adding the following to the bottom of your docker-compose.yml file:

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networks:
  podman:
    external: true

Broken layout for markets, bookmarks or other widgets

This is almost always caused by the browser extension Dark Reader. To fix this, disable dark mode for the domain where Glance is hosted.

cannot unmarshal !!map into []glance.page

The most common cause of this is having a pages key in your glance.yml and then also having a pages key inside one of your included pages. To fix this, remove the pages key from the top of your included pages.

FAQ

Does the information on the page update automatically?

No, a page refresh is required to update the information. Some things do dynamically update where it makes sense, like the clock widget and the relative time showing how long ago something happened.

How frequently do widgets update?

No requests are made periodically in the background, information is only fetched upon loading the page and then cached. The default cache lifetime is different for each widget and can be configured.

Can I create my own widgets?

Yes, there are multiple ways to create custom widgets:

iframe widget - allows you to embed things from other websites
html widget - allows you to insert your own static HTML
extension widget - fetch HTML from a URL
custom-api widget - fetch JSON from a URL and render it using custom HTML

Can I change the title of a widget?

Yes, the title of all widgets can be changed by specifying the title property in the widget’s configuration:

- type: rss
  title: My custom title

- type: markets
  title: My custom title

- type: videos
  title: My custom title

# and so on for all widgets...

Feature requests

New feature suggestions are always welcome and will be considered, though please keep in mind that some of them may be out of scope for what the project is trying to achieve (or is reasonably capable of). If you have an idea for a new feature and would like to share it, you can do so here.

Feature requests are tagged with one of the following:

Roadmap - will be implemented in a future release
Backlog - may be implemented in the future but needs further feedback or interest from the community
Icebox - no plans to implement as it doesn’t currently align with the project’s goals or capabilities, may be revised at a later date

Building from source

Choose one of the following methods:

Build binary with Go

Requirements: Go >= v1.23

To build the project for your current OS and architecture, run:

`1`	`go build -o build/glance .`

To build for a specific OS and architecture, run:

`1`	`GOOS=linux GOARCH=amd64 go build -o build/glance .`

click here for a full list of GOOS and GOARCH combinations

Alternatively, if you just want to run the app without creating a binary, like when you’re testing out changes, you can run:

`1`	`go run .`

Build project and Docker image with Docker

Requirements: Docker

To build the project and image using just Docker, run:

(replace owner with your name or organization)

`1`	`docker build -t owner/glance:latest .`

If you wish to push the image to a registry (by default Docker Hub), run:

`1`	`docker push owner/glance:latest`

Contributing guidelines

Before working on a new feature it’s preferable to submit a feature request first and state that you’d like to implement it yourself
Please don’t submit PRs for feature requests that are either in the roadmap^[1], backlog^[2] or icebox^[3]
Use dev for the base branch if you’re adding new features or fixing bugs, otherwise use main
Avoid introducing new dependencies
Avoid making backwards-incompatible configuration changes
Avoid introducing new colors or hard-coding colors, use the standard primary, positive and negative
For icons, try to use heroicons where applicable
Provide a screenshot of the changes if UI related where possible
No package.json

^{[1] [2] [3]}

[1] The feature likely already has work put into it that may conflict with your implementation

[2] The demand, implementation or functionality for this feature is not yet clear

[3] No plans to add this feature for the time being

Thank you

To all the people who were generous enough to sponsor the project and to everyone who has contributed in any way, be it PRs, submitting issues, helping others in the discussions or Discord server, creating guides and tools or just mentioning Glance on social media. Your support is greatly appreciated and helps keep the project going.

godotenv

Sat, 26 Apr 2025 15:26:32 +0800

joho/godotenv

GoDotEnv

A Go (golang) port of the Ruby dotenv project (which loads env vars from a .env file).

From the original Library:

Storing configuration in the environment is one of the tenets of a twelve-factor app. Anything that is likely to change between deployment environments–such as resource handles for databases or credentials for external services–should be extracted from the code into environment variables.

But it is not always practical to set environment variables on development machines or continuous integration servers where multiple projects are run. Dotenv load variables from a .env file into ENV when the environment is bootstrapped.

It can be used as a library (for loading in env for your own daemons etc.) or as a bin command.

There is test coverage and CI for both linuxish and Windows environments, but I make no guarantees about the bin version working on Windows.

Installation

As a library

`1`	`go get github.com/joho/godotenv`

or if you want to use it as a bin command

go >= 1.17

`1`	`go install github.com/joho/godotenv/cmd/godotenv@latest`

go < 1.17

`1`	`go get github.com/joho/godotenv/cmd/godotenv`

Usage

Add your application configuration to your .env file in the root of your project:

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S3_BUCKET=YOURS3BUCKET
SECRET_KEY=YOURSECRETKEYGOESHERE

Then in your Go app you can do something like

package main

import (
    "log"
    "os"

    "github.com/joho/godotenv"
)

func main() {
  err := godotenv.Load()
  if err != nil {
    log.Fatal("Error loading .env file")
  }

  s3Bucket := os.Getenv("S3_BUCKET")
  secretKey := os.Getenv("SECRET_KEY")

  // now do something with s3 or whatever
}

If you’re even lazier than that, you can just take advantage of the autoload package which will read in .env on import

`1`	`import _ "github.com/joho/godotenv/autoload"`

While .env in the project root is the default, you don’t have to be constrained, both examples below are 100% legit

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godotenv.Load("somerandomfile")
godotenv.Load("filenumberone.env", "filenumbertwo.env")

If you want to be really fancy with your env file you can do comments and exports (below is a valid env file)

# I am a comment and that is OK
SOME_VAR=someval
FOO=BAR # comments at line end are OK too
export BAR=BAZ

Or finally you can do YAML(ish) style

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FOO: bar
BAR: baz

as a final aside, if you don’t want godotenv munging your env you can just get a map back instead

var myEnv map[string]string
myEnv, err := godotenv.Read()

s3Bucket := myEnv["S3_BUCKET"]

… or from an io.Reader instead of a local file

1
2

reader := getRemoteFile()
myEnv, err := godotenv.Parse(reader)

… or from a string if you so desire

1
2

content := getRemoteFileContent()
myEnv, err := godotenv.Unmarshal(content)

Precedence & Conventions

Existing envs take precedence of envs that are loaded later.

The convention for managing multiple environments (i.e. development, test, production) is to create an env named {YOURAPP}_ENV and load envs in this order:

env := os.Getenv("FOO_ENV")
if "" == env {
  env = "development"
}

godotenv.Load(".env." + env + ".local")
if "test" != env {
  godotenv.Load(".env.local")
}
godotenv.Load(".env." + env)
godotenv.Load() // The Original .env

If you need to, you can also use godotenv.Overload() to defy this convention and overwrite existing envs instead of only supplanting them. Use with caution.

Command Mode

Assuming you’ve installed the command as above and you’ve got $GOPATH/bin in your $PATH

`1`	`godotenv -f /some/path/to/.env some_command with some args`

If you don’t specify -f it will fall back on the default of loading .env in PWD

By default, it won’t override existing environment variables; you can do that with the -o flag.

Writing Env Files

Godotenv can also write a map representing the environment to a correctly-formatted and escaped file

1
2

env, err := godotenv.Unmarshal("KEY=value")
err := godotenv.Write(env, "./.env")

… or to a string

1
2

env, err := godotenv.Unmarshal("KEY=value")
content, err := godotenv.Marshal(env)

Contributing

Contributions are welcome, but with some caveats.

This library has been declared feature complete (see #182 for background) and will not be accepting issues or pull requests adding new functionality or breaking the library API.

Contributions would be gladly accepted that:

bring this library’s parsing into closer compatibility with the mainline dotenv implementations, in particular Ruby’s dotenv and Node.js’ dotenv
keep the library up to date with the go ecosystem (ie CI bumps, documentation changes, changes in the core libraries)
bug fixes for use cases that pertain to the library’s purpose of easing development of codebases deployed into twelve factor environments

code changes without tests and references to peer dotenv implementations will not be accepted

Fork it
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Added some feature')
Push to the branch (git push origin my-new-feature)
Create new Pull Request

Releases

Releases should follow Semver though the first couple of releases are v1 and v1.1.

Use annotated tags for all releases. Example git tag -a v1.2.1

Who?

The original library dotenv was written by Brandon Keepers, and this port was done by John Barton based off the tests/fixtures in the original library.

douyin-downloader

Sat, 12 Apr 2025 15:24:42 +0800

jiji262/douyin-downloader

DouYin Downloader

DouYin Downloader 是一个用于批量下载抖音内容的工具。基于抖音 API 实现，支持命令行参数或 YAML 配置文件方式运行，可满足大部分抖音内容的下载需求。

✨ 特性

多种内容支持
- 视频、图集、音乐、直播信息下载
- 支持个人主页、作品分享、直播、合集、音乐集合等多种链接
- 支持去水印下载
批量下载能力
- 多线程并发下载
- 支持多链接批量下载
- 自动跳过已下载内容
灵活配置
- 支持命令行参数和配置文件两种方式
- 可自定义下载路径、线程数等
- 支持下载数量限制
增量更新
- 支持主页作品增量更新
- 支持数据持久化到数据库
- 可根据时间范围过滤

🚀 快速开始

安装

安装 Python 依赖：

`1`	`pip install -r requirements.txt`

复制配置文件：

`1`	`cp config.example.yml config.yml`

配置

编辑 config.yml 文件，设置：

下载链接
保存路径
Cookie 信息（从浏览器开发者工具获取）
其他下载选项

运行

方式一：使用配置文件（推荐）

`1`	`python DouYinCommand.py`

方式二：使用命令行

`1`	`python DouYinCommand.py -C True -l "抖音分享链接" -p "下载路径"`

使用交流群

使用截图

📝 支持的链接类型

作品分享链接：https://v.douyin.com/xxx/
个人主页：https://www.douyin.com/user/xxx
单个视频：https://www.douyin.com/video/xxx
图集：https://www.douyin.com/note/xxx
合集：https://www.douyin.com/collection/xxx
音乐原声：https://www.douyin.com/music/xxx
直播：https://live.douyin.com/xxx

🛠️ 高级用法

命令行参数

基础参数：

-C, --cmd            使用命令行模式
-l, --link          下载链接
-p, --path          保存路径
-t, --thread        线程数（默认5）

下载选项：

-m, --music         下载音乐（默认True）
-c, --cover         下载封面（默认True）
-a, --avatar        下载头像（默认True）
-j, --json          保存JSON数据（默认True）

更多参数说明请使用 -h 查看帮助信息。

示例命令

下载单个视频：

`1`	`python DouYinCommand.py -C True -l "https://v.douyin.com/xxx/"`

下载主页作品：

`1`	`python DouYinCommand.py -C True -l "https://v.douyin.com/xxx/" -M post`

批量下载：

`1`	`python DouYinCommand.py -C True -l "链接1" -l "链接2" -p "./downloads"`

更多示例请参考使用示例文档。

📋 注意事项

本项目仅供学习交流使用
使用前请确保已安装所需依赖
Cookie 信息需要自行获取
建议适当调整线程数，避免请求过于频繁

🤝 贡献

欢迎提交 Issue 和 Pull Request。

📜 许可证

本项目采用 MIT 许可证。

🙏 鸣谢

TikTokDownload
本项目使用了 ChatGPT 辅助开发，如有问题请提 Issue

📊 Star History

License

MIT