PraisonAI

Private AI that earns trust one task at a time.

PraisonAI runs AI agents on your own computers, even fully offline, so private data never has to leave the building.

• For highly regulated teams (early access): each task moves to your own AI only after it passes a test. If quality slips, it moves back automatically, and every step is recorded for regulators. How it works
• For everyone else: a free, open-source Python agent framework. With a local model you need no API key and pay no cloud bill, and your files stay yours.

2M+ downloads · 9.3K stars · 600+ releases in the last year · 80+ contributors · Python

pip install praisonai

Only need the Python library? pip install praisonaiagents installs just the lightweight core.

Using PraisonAI for something else?

PraisonAI is still a general-purpose, open-source agent framework, and everything works as before: the same packages, imports, commands and examples. Go straight to what you came for:

• Agents and multi-agent teams: Agents · Handoffs
• Workflows: AgentFlow: route, parallel, loop, repeat
• Memory and knowledge: Memory · Knowledge · RAG
• Tools and MCP: Tools · MCP
• Bots (Telegram, Discord, Slack, WhatsApp): Bot CLI · Bot gateway
• Terminal CLI: praisonai code · praisonai run · praisonai chat · CLI reference
• Training and fine-tuning: PraisonAI Train
• Browser automation: Browser agent · Browser CLI
• Sandboxes: Sandboxed code execution
• Deploy: Deploy guide
• Dashboard and UI: Claw dashboard · Chat UI
• YAML, no code: YAML agents · YAML reference
• JavaScript and TypeScript: JavaScript SDK
• Desktop app: PraisonAI Desktop · Downloads
• Everything else: the full feature reference further down this page

Quick start

Set a key for any supported provider (OpenAI shown here), or skip the key and use a local model as in the next section.

export OPENAI_API_KEY="your-api-key"

from praisonaiagents import Agent

agent = Agent(instructions="You are a helpful assistant.")
print(agent.start("In two sentences, why do some companies keep their data on their own computers?"))

Run fully offline

Run the same agent on a model on your own computer with Ollama. You need no API key, and your prompts and answers stay on the machine.

Install the package and pull the model while you still have internet; after that, the agent runs with no network at all. If you installed only the core library, use pip install "praisonaiagents[llm]", which adds the local-model support.

# Start Ollama first (the app, or "ollama serve" in another terminal)
ollama pull llama3.2
export DO_NOT_TRACK=true   # keeps telemetry off (PraisonAI sends none unless you turn it on)

from praisonaiagents import Agent

agent = Agent(instructions="You are a helpful assistant.", model="ollama/llama3.2")
print(agent.start("Why is the sky blue?"))

If Ollama runs on another computer on your network, pass base_url (or set OLLAMA_API_BASE):

agent = Agent(instructions="...", model="ollama/llama3.2", base_url="http://192.168.1.10:11434")

vLLM and LM Studio work the same way: use hosted_vllm/ (vLLM) or lm_studio/ with your server's base_url. More in the Ollama guide and Models docs.

How the handover works (early access)

1. Watch: records how each task is done today.
2. Test: your own AI takes an exam made from real past work it never studied.
3. Hand over: a task moves to your own AI only when it passes.
4. Roll back: if quality drops, the task moves back automatically.
5. Record: every handover leaves a record your regulator can check.

What is "your own AI"? An open model, such as Llama, Qwen or Gemma, running on your own computers. Use it as it is, or train it on your own past work with PraisonAI Train on your own GPU servers. Your team decides when to train; nothing trains automatically. Either way, a task moves to it only after it passes that exam.

Highly regulated teams, such as banks, insurers and hospitals, want AI but can't send customer data to outside models. From 6 January 2027, OpenAI also takes no new self-serve fine-tuning jobs (notice), so teams that customise models need models they own. The handover lets these teams move work to AI they own, one task at a time, with a record that shows why each step was trusted.

This is the beginning of the README. Read the rest at the original. Read the full README ↗