Install PraisonAI, set one provider key, and run an Agent in your own app. Fastest start — one line, no Python knowledge needed:

curl -fsSL https://raw.githubusercontent.com/MervinPraison/PraisonAI/main/install.sh | sh

The one-liner installs a managed, isolated CLI and adds praisonai to your PATH. Keep it current with praisonai upgrade and remove it with praisonai uninstall — see Upgrade & Uninstall. Prefer pip? That still works exactly as before — pick a package below.

from praisonaiagents import Agent

agent = Agent(instructions="Be helpful")
agent.start("Summarise the top AI news today")

pip install "praisonai-bot[gateway,bot]"

pip install praisonai

pip install praisonai-code

pip install "praisonai-train[llm]"

pip install "praisonai-browser[all]"

pip install praisonaiagents

npm install praisonai

curl -fsSL https://raw.githubusercontent.com/MervinPraison/PraisonAI/main/install.sh | sh

Six packages, one ecosystem. pip install praisonai pulls code, bot, train, browser, and SDK. Lighter options below.

Terminal-only (smaller)

pip install praisonai-code — agentic CLI without gateway or bots

Bots + gateway only

pip install "praisonai-bot[gateway,bot]" — channel runtime without the wrapper

Full (default)

pip install praisonai — wrapper, code, bot, and SDK together

Training only

pip install "praisonai-train[llm]" — LLM fine-tuning + agent training

Browser automation

pip install "praisonai-browser[all]" — Chrome extension + Playwright engine

Sandbox backends only

pip install "praisonai-sandbox[docker]" — code-execution sandboxes and the compute providers behind tools_run_on= / run_in=, without the wrapper

pip install praisonai transitively installs praisonai-code, praisonai-bot, praisonai-train, praisonai-browser, praisonai-sandbox, and praisonaiagents. You do not need to install them separately.

One-line installer (recommended for CLI users)

If you want the CLI end-to-end, the one-line installer provisions an isolated managed environment (uv tool → pipx) and wires your PATH — no changes to your global Python:

curl -fsSL https://raw.githubusercontent.com/MervinPraison/PraisonAI/main/install.sh | sh

It is idempotent and safe to re-run. pip install praisonai is untouched — keep using it if you embed PraisonAI as a Python library. Environment-variable overrides:

Variable	Purpose
`PRAISONAI_VERSION=x.y.z`	Pin a specific version (default: latest)
`PRAISONAI_INSTALLER=uv\|pipx`	Force a specific tool manager
`PRAISONAI_NONINTERACTIVE=1`	Never prompt — suitable for CI

After installing, configure a provider key:

praisonai setup

Keep the managed install current with praisonai upgrade, or remove it cleanly with praisonai uninstall.

Installing PraisonAI

PraisonAI is published as six PyPI packages. Pick the one that matches what you want to do:

The complete package — CLI, gateway, bots, integrations, YAML-driven multi-bot orchestration. Pulls praisonai-code and praisonaiagents automatically.

For gateway hot reload (event-driven gateway.yaml watching via watchdog), install the gateway extra: pip install "praisonai[gateway]".[claw] extra: pip install "praisonai[claw]" installs the full wrapper plus praisonai-bot[gateway,bot] — the WebSocket gateway and Telegram/Discord/Slack channel bots. Use this when you want the full wrapper and all channel runtime features in one command. For gateway + bots without the wrapper, use pip install "praisonai-bot[gateway,bot]" directly (see Standalone Bot Gateway).Sandbox extras: the sandbox backends live in the standalone praisonai-sandbox package. Install a cloud backend with pip install 'praisonai[docker]', 'praisonai[e2b]', 'praisonai[modal]', 'praisonai[daytona]' (needs DAYTONA_API_KEY), pip install 'praisonai-sandbox[ssh]', or pip install 'praisonai-sandbox[novita]' for the Novita backend (needs NOVITA_API_KEY). See Sandbox Backends for the full matrix. The praisonai[e2b], praisonai[docker], and praisonai[daytona] extras were fixed in PR #4073 — before that release they either did not exist or were empty. If pip warns does not provide the extra 'e2b', upgrade praisonai.

Create Virtual Environment (Optional)

python -m venv praisonai-env
source praisonai-env/bin/activate

python -m venv praisonai-env
.\praisonai-env\Scripts\activate

Install

Terminal

pip install praisonai

Configure Environment

Set your API key. PraisonAI auto-detects whichever provider credential is present:

OpenAI

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Anthropic

export ANTHROPIC_API_KEY="${ANTHROPIC_API_KEY:?Set ANTHROPIC_API_KEY in your shell}"

Gemini

export GEMINI_API_KEY="${GEMINI_API_KEY:?Set GEMINI_API_KEY in your shell}"

Groq

export GROQ_API_KEY="${GROQ_API_KEY:?Set GROQ_API_KEY in your shell}"

Ollama

export OLLAMA_HOST=http://localhost:11434

If you only set ANTHROPIC_API_KEY (or GEMINI_API_KEY, GOOGLE_API_KEY, GROQ_API_KEY, COHERE_API_KEY, or OLLAMA_HOST), praisonai run picks the matching provider’s default model automatically — no --model flag required. See Provider Auto-Detection for the full lookup table.

Running Ollama on the default 127.0.0.1:11434? Skip export OLLAMA_HOST — PraisonAI auto-detects a local endpoint when no cloud key is set. See Keyless local-first fallback.

The standalone agent runtime — run, chat, code, and the full CLI backend, without the gateway/bot integrations. Depends only on praisonaiagents.

Create Virtual Environment (Optional)

python -m venv praisonai-env
source praisonai-env/bin/activate

python -m venv praisonai-env
.\praisonai-env\Scripts\activate

Install

Terminal

pip install praisonai-code

Configure Environment

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Any supported provider key works. See Provider Auto-Detection.

Run an agent

from praisonaiagents import Agent

agent = Agent(name="assistant", instructions="Be helpful")
response = agent.start("Summarise the top AI news today")
print(response)

Standalone bots and gateway — no full wrapper. Depends on praisonaiagents only at PyPI level.

Install

Terminal

pip install "praisonai-bot[gateway,bot]"

Configure Environment

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
export TELEGRAM_BOT_TOKEN="${TELEGRAM_BOT_TOKEN:?Set TELEGRAM_BOT_TOKEN in your shell}"

Start gateway

Terminal

praisonai-bot gateway start --host 127.0.0.1 --port 8765

See the praisonai-bot SDK page for Python API details.

Standalone training tier — LLM fine-tuning via Unsloth and iterative agent training, without the full wrapper. Depends on praisonaiagents only at PyPI level.

As of praisonai-train 2026.7+, the [llm] extra requires CUDA 12.4+ and installs the modern Unsloth / TRL / torch 2.6+ stack. The install command is unchanged.

Create Virtual Environment (Optional)

python -m venv praisonai-env
source praisonai-env/bin/activate

python -m venv praisonai-env
.\praisonai-env\Scripts\activate

Install

Terminal

pip install "praisonai-train[llm]"

LLM fine-tuning needs Python 3.11, CUDA 12.4, and torch>=2.6 (plus unsloth>=2025.9.1, trl>=0.18.2, transformers>=4.51.3). Agent training (pip install praisonai-train) has no such requirement.

Configure Environment

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Any supported provider key works. See Provider Auto-Detection.

Run the trainer

Terminal

praisonai-train --help

See the praisonai-train Package page for fine-tuning and agent-training details.

Standalone browser tier — the bridge server, CDP agent, and Playwright engine for Chrome automation, without the full wrapper. Depends on praisonaiagents only at PyPI level.

Create Virtual Environment (Optional)

python -m venv praisonai-env
source praisonai-env/bin/activate

python -m venv praisonai-env
.\praisonai-env\Scripts\activate

Install

Terminal

pip install "praisonai-browser[all]"

Configure Environment

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Any supported provider key works. See Provider Auto-Detection.

Run a goal

Terminal

praisonai-browser run "Go to google and search praisonai"

See the praisonai-browser Package page and the Browser CLI for the full command set.

The pure Python SDK — no CLI, no gateway, no heavy dependencies. Import directly in your own application.

Create Virtual Environment (Optional)

python -m venv praisonai-env
source praisonai-env/bin/activate

python -m venv praisonai-env
.\praisonai-env\Scripts\activate

Install

Terminal

pip install praisonaiagents

Configure Environment

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Use in your app

from praisonaiagents import Agent

agent = Agent(name="assistant", instructions="Be helpful")
response = agent.start("Hello!")
print(response)

Create Virtual Environment (Optional)

python -m venv praisonai-env
source praisonai-env/bin/activate

python -m venv praisonai-env
.\praisonai-env\Scripts\activate

Install PraisonAI

pip install praisonai

Set API Key

Set your API key for the provider you want to use. PraisonAI auto-detects whichever credential is present:

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Use Anthropic, Gemini, Groq, or Ollama instead? Just set that provider’s key and praisonai run picks the right model automatically. See Provider Auto-Detection.

Install PraisonAI

npm install praisonai

Set API Key

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Install PraisonAI

npm install praisonai

Set API Key

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

All existing praisonai CLI verbs (run, chat, code, gateway, bot, …) continue to work unchanged — the wrapper routes them through the praisonai-code runtime. You do not need to change any scripts.

Package dependency chain

Each layer depends on the one to its left. Installing praisonai also pulls praisonai-sandbox (the code-execution backends) alongside praisonai-code; installing praisonai-code pulls only praisonaiagents; installing praisonaiagents has no PraisonAI dependencies. Generate your OpenAI API key from OpenAI You can also use other LLM providers like Anthropic, Google, etc. Please refer to the Models for more information.

Which Install Do You Need?

Terminal-only (smaller)

pip install praisonai-code — agentic CLI: run, chat, code, runtime. No bots or gateway.

Full (default)

pip install praisonai — everything: bots, gateway, kanban, dashboard, and the agentic CLI.

pip install praisonai transitively installs praisonai-code, praisonai-sandbox, and praisonaiagents. You do not need to install them separately.

How It Works

Pip installs the package, you export one provider key, and the CLI or SDK resolves the matching model at run time.

Best Practices

Install only what you need

Use praisonaiagents for a pure Python SDK, praisonai-code for the terminal runtime, and praisonai for the full wrapper with bots and gateway.

Use a virtual environment

Create a venv before installing to keep PraisonAI isolated from your system Python.

Export keys, don't hard-code them

Set OPENAI_API_KEY (or your provider’s key) in your shell or .env. Never inline the raw value in code.

Skip --model on first runs

PraisonAI auto-detects the provider from whichever key is present and picks a sensible default model.

Quick Start

Build your first AI agent.

Models

Choose a provider and model.

Quick Install

The one-liner installer uses an isolated tool manager (uv tool → pipx → bootstrap uv) and exposes praisonai via a ~/.local/bin/praisonai shim — your global Python environment is untouched. See Isolation Backends for details.

macOS / Linux / WSL
Windows

curl -fsSL https://raw.githubusercontent.com/MervinPraison/PraisonAI/main/install.sh | sh

iwr -useb https://praison.ai/install.ps1 | iex

Installs praisonai[all] into %USERPROFILE%\.praisonai\venv, wires PATH, and runs praisonai setup on first install. See Installer Internals for parameters and env-var overrides.To install with specific extras only:

& ([scriptblock]::Create((iwr -useb https://praison.ai/install.ps1))) -Extras "ui,chat"

Alternative (no installer): pipx install praisonai or pip install "praisonai[all]".

Windows terminals: PraisonAI automatically detects legacy Windows code pages (CP1252, CP850, etc.) and falls back to ASCII-safe output. For full emoji and box-drawing rendering, switch your terminal to UTF-8:

$env:PYTHONIOENCODING='utf-8'
chcp 65001

set PYTHONIOENCODING=utf-8
chcp 65001

The installer detects your platform, picks a tool manager (uv tool → pipx → bootstrap uv), installs praisonai into an isolated environment, and drops a ~/.local/bin/praisonai shim on your PATH.

Requirements

Python 3.10 or higher
macOS, Linux, or WSL (the one-liner is POSIX; on Windows use pipx/pip)

Manage Your Install

Upgrade

praisonai upgrade — update the CLI in place, or --check for a newer version

Uninstall

praisonai uninstall — remove the managed install cleanly

Package Structure

PraisonAI ships as three separate PyPI packages. Understanding which to install avoids surprises at runtime.

Install command	What you get	Agentic CLI (`run`, `chat`, `code`, …)	Bot / gateway / pairing
`pip install praisonai`	Wrapper + code + agents	✅ Full	✅ Yes
`pip install praisonai-code`	Terminal-native agent CLI + LLM runtime	✅ Default `run "…"`, `run —output plain	verbose	silent	actions	json	stream	stream-json`,` chat`,` code`(resident TUI, PR #3818),`daemon`,` doctor`,` config`,` version`	❌ No — needs `pip install praisonai`
`pip install praisonaiagents`	Core SDK — Python API only	❌ No CLI	N/A

Standalone pip install praisonai-code supports the actions/daemon path as of PraisonAI 4.6.123+ (C7 + C9 four-tier). Use praisonai-code run --output actions|json|stream|stream-json "your prompt" for the standalone in-process Agent path, and praisonai-code daemon start --background (spawns python -m praisonai_code.runtime) for the warm runtime. run --help, config, doctor, and version also work standalone.

pip install praisonai-code alone now delivers run, chat, and code — chat and code run resident on the built-in split-pane TUI (PR #3818), and default run "prompt" plus all --output modes route through the in-process Agent (PR #2853). Installing the wrapper (pip install praisonai) adds the richer legacy dispatch for code; wrapper-only features (bot, claw, dashboard, gateway, identity, kanban, onboard, pairing, framework adapters, YAML --framework crewai/autogen) still require it — they degrade gracefully rather than raising raw import errors. See Standalone LLM Modules and the praisonai-code CLI.

The stub command groups agents, workflow, registry, memory, skills, hooks, rules, eval, package, templates, todo, research, commit, and call show a single-line install hint (<group> requires the full wrapper. Install the full wrapper: pip install praisonai) and exit 1 on standalone (PR #2854). Their native standalone-safe subcommands (memory learn, skills bundle/check/eligible, eval list-judges, eval list) are unaffected. See the praisonai-code CLI reference.

PyPI publish order

Packages are published in dependency order:

1. praisonaiagents  →  2. praisonai-code + praisonai-bot + praisonai-train + praisonai-browser  →  3. praisonai

If you pin versions, ensure all six packages resolve to the same release cycle. Since 4.6.104, praisonai pins praisonai-code>=0.0.2, so pip install praisonai always pulls a compatible runtime automatically. See praisonaiagents SDK page for the release-cycle notes.

Terminal-only (smaller)

Bots + gateway only

Full (default)

Training only

Browser automation

Sandbox backends only

​One-line installer (recommended for CLI users)

​Installing PraisonAI

​Package dependency chain

​Which Install Do You Need?

Terminal-only (smaller)

Full (default)

​How It Works

​Best Practices

​Related

Quick Start

Models

​Quick Install

​Manage Your Install

Upgrade

Uninstall

​Package Structure

​PyPI publish order

One-line installer (recommended for CLI users)

Installing PraisonAI

Package dependency chain

Which Install Do You Need?

How It Works

Best Practices

Related

Quick Install

Manage Your Install

Package Structure

PyPI publish order