PraisonAI

PraisonAI 🦞

PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous, self-improving agents that research, plan, and execute tasks across your apps. From one agent to an entire organization, deployed in 5 lines of code.

curl -fsSL https://praison.ai/install.sh | bash

pip install praisonai

<div align="center">
  <a href="https://docs.praison.ai">
</a>
</div>

---

## 🎯 Use Cases

AI agents solving real-world problems across industries:

| Use Case | Description |
|----------|-------------|
| 🔍 **Research & Analysis** | Conduct deep research, gather information, and generate insights from multiple sources automatically |
| 💻 **Code Generation** | Write, debug, and refactor code with AI agents that understand your codebase and requirements |
| ✍️ **Content Creation** | Generate blog posts, documentation, marketing copy, and technical writing with multi-agent teams |
| 📊 **Data Pipelines** | Extract, transform, and analyze data from APIs, databases, and web sources automatically |
| 🤖 **Customer Support** | Deploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses |
| ⚙️ **Workflow Automation** | Automate multi-step business processes with agents that hand off tasks, verify results, and self-correct |

---

## 🚀 Meet your first Agent (Under 1 Minute)

1. Install the lightweight core SDK:
```bash
pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"

2. Run your first autonomous agent:

from praisonaiagents import Agent

# Give your agent a goal, and watch it work.
agent = Agent(instructions="You are a senior data analyst.")
agent.start("Analyze the top 3 tech trends of 2026 and format as a markdown table.")

🧬 The Five-Layer Agent Stack

Most frameworks hand you one or two layers and leave the rest as homework. PraisonAI covers all five — plus the outer layer that decides where your agent actually runs.

Each layer wraps the one inside it. When an agent misbehaves, the layer tells you where to look.

┌─────────────────────────────────────────────────────────────────┐
│ ⬡ MANAGED AGENTS — Where does it actually run?                  │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ 5 · GRAPH — Who runs when, and who checks whom?             │ │
│ │ ┌─────────────────────────────────────────────────────────┐ │ │
│ │ │ 4 · LOOP — When do we stop?                             │ │ │
│ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │
│ │ │ │ 3 · HARNESS — Can it act, and be checked?           │ │ │ │
│ │ │ │ ┌─────────────────────────────────────────────────┐ │ │ │ │
│ │ │ │ │ 2 · CONTEXT — Is the right thing in the window? │ │ │ │ │
│ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ │ │
│ │ │ │ │ │ 1 · PROMPT — Did I say it clearly?          │ │ │ │ │ │
│ │ │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ │
│ │ │ │ └─────────────────────────────────────────────────┘ │ │ │ │
│ │ │ └─────────────────────────────────────────────────────┘ │ │ │
│ │ └─────────────────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Layer 1 · Prompt — Did I say it clearly?

Role, instructions, examples, output format.

from praisonaiagents import Agent

agent = Agent(
    role="Senior Data Analyst",
    goal="Turn raw numbers into decisions",
    output="verbose",              # markdown-formatted output
)
agent.start("Summarise Q3 revenue trends")

Layer 2 · Context — Is the right thing in the window?

Write, select, compress, isolate — the four context operations, one parameter each.

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a support engineer.",
    memory={"user_id": "u-42"},    # write    — persists across runs (needs a user_id)
    knowledge=["docs/"],           # select   — retrieves only what's relevant
    context="summarize",           # compress — auto-compacts before the limit
)

Isolate is handoffs=[specialist] — a sub-agent inherits the last few messages and the intersection of your tools, not your whole transcript. 📖 Handoffs

Layer 3 · Harness — Can it act, and be checked?

Agent = Model + Harness. Tool dispatch, plus the guides that steer before acting and the sensors that observe after.

from praisonaiagents import Agent, MCP, tool

@tool
def deploy(env: str) -> str:
    """Deploy the current build to an environment."""
    return f"Deployed to {env}"

agent = Agent(
    name="ReleaseEngineer",
    instructions="You are a release engineer.",
    tools=[deploy, MCP("npx -y @modelcontextprotocol/server-filesystem /tmp")],
    approval=True,                 # guide — human gate before risky tools run
)
agent.start("Deploy to staging, then list the files you can read")

Layer 4 · Loop — When do we stop?

Hard iteration caps, budget ceilings, no-progress detection and completion checks — every brake is explicit.

from praisonaiagents import Agent, ExecutionConfig

agent = Agent(
    instructions="Fix the failing tests.",
    execution=ExecutionConfig(max_iter=30, max_budget=0.50, on_budget_exceeded="stop"),
    autonomy=True,                 # required to drive the loop with run_autonomous()
)
result = agent.run_autonomous("Refactor the auth module", max_iterations=5)

print(result.completion_reason)
# goal | no_tool_calls | max_iterations | timeout | doom_loop | needs_help | error
# (with on_budget_exceeded="stop", hitting the cap raises BudgetExceededError,
#  surfaced here as completion_reason="error")

Doom-loop detection is on by default. Repeated identical tool calls and A→B→A→B oscillation get caught — while a poller whose output keeps changing does not. 📖 Doom Loop Detection

Layer 5 · Graph — Who runs when, and who checks whom?

Topology as a versionable artifact: prompt chaining, routing, parallelisation, orchestrator-worker.

from praisonaiagents import AgentFlow
from praisonaiagents.workflows import route, parallel, repeat

flow = AgentFlow(steps=[
    classifier,
    route({"bug": [bug_agent], "feature": [feature_agent], "default": [triage]}),
    parallel([reviewer, tester]),                      # fan out, join automatically
    repeat(editor, until=lambda ctx: "approved" in ctx.previous_result.lower(),
           max_iterations=3),                          # evaluator–optimizer
])
flow.run("Ticket #123: login fails on Safari")

The same graph is expressible in YAML with no Python at all. 📖 AgentFlow

⬡ Outside the stack: Managed Agents — Where does it actually run?

The harness is commoditising; where the agent executes is the next multiplier. Rather than burning your laptop's CPU, hand an agent a short-lived cloud sandbox — repo, tools and tests run there.

pip install praisonai

The simplest way in is tools_run_on= — one whole team or workflow shares one sandbox, so a file written by step 1 is there for step 2. Thinking stays on your machine:

from praisonaiagents import Agent, AgentFlow

writer = Agent(name="Writer", instructions="You write files.")
reader = Agent(name="Reader", instructions="You read files.")

flow = AgentFlow(tools_run_on="docker", steps=[writer, reader])  # or e2b | modal | daytona | flyio
flow.run("Write 'hello' to /workspace/note.txt, then read it back")

Same thing with no Python at all:

name: remote-demo
tools_run_on: docker      # every step shares one sandbox
agents:
  writer: {role: Writer, goal: Write files}
  reader: {role: Reader, goal: Read files}
steps:
  - agent: writer
    action: "Write 'hello' to /workspace/note.txt"
  - agent: reader
    action: "Read /workspace/note.txt"

For a single agent, two words cover it — and they answer different questions:

from praisonaiagents import Agent

# A. Only the TOOLS move. Thinking stays on your machine.
agent = Agent(name="builder", instructions="You build things.",
              tools_run_on="docker")   # docker | e2b | modal | daytona | flyio
                                       # tenki | sandlock | ssh | novita | subprocess

# B. The WHOLE agent moves — model calls, loop and tools
agent = Agent(name="teacher", instructions="You teach.", run_on="anthropic")  # hosted
agent = Agent(name="builder", instructions="You build.", run_on="docker")     # self-hosted
agent.start("Write a Python script that prints the first 10 primes, then run it")

Ask any object where it runs, and it will tell you:

>>> Agent(name="builder", instructions="x", tools_run_on="docker")
Agent(name='builder', thinks_on='this machine', tools_run_on='a Docker container')

>>> agent.where_does_it_run()
Thinking (the AI model calls) happens on this machine.
Tools run on a Docker container.
Your own tools (check_db) still run on this machine -- only shell, file and
code tools move. They read and write this machine's files.

Naming a place that cannot do the job is a typo, not a preference, so it says so:

>>> Agent(name="x", instructions="i", run_on="e2b")
TypeError: Agent(run_on='e2b') is not valid: run_on= places the whole agent
-- model calls, loop and tools -- on a managed runtime, and 'e2b' runs
commands but cannot host an agent loop.
  To run only the tools there:  Agent(tools_run_on='e2b')

To run one block of code somewhere else, name the place on that call:

agent.execute_code_sync("print(6 * 7)", run_in="sandlock")   # kernel-enforced

See what is running and reclaim strays:

praisonai managed ps          # list running sandboxes
praisonai managed stop --all  # reclaim them

Sandboxes shut themselves down when idle (auto_shutdown, idle_timeout_s), and a post-setup snapshot is reused so the next run skips the image pull and dependency install. Commit a .praisonai/environment.yaml and the environment travels with the repo.

📖 20 runnable examples · manage sessions with praisonai managed sessions list <agent-id> or praisonai managed sessions resume <session-id> "<prompt>"

Stack framing adapted from The Five-Layer Agent Stack and Agent Harnesses vs Orbs.

🌌 The PraisonAI Ecosystem

Start simple with the core SDK, or expand to full visual builders and dashboards when you're ready.

• Core SDK (praisonaiagents): For pure Python development. pip install praisonaiagents
• 💻 PraisonAI CLI (praisonai): For terminal-based developers. pip install praisonai
• 🦞 Claw Dashboard: Connect agents directly to Telegram, Slack, or Discord. pip install "praisonai[claw]"
• 🔗 Flow Visual Builder: Drag-and-drop workflow creation. pip install "praisonai[flow]"
• 🤖 PraisonAI UI: Clean chat interface. pip install "praisonai[ui]"

JavaScript SDK

npm install praisonai

🧠 Supported Providers & Features

Powered by 100+ LLMs (OpenAI, Anthropic, Gemini & local models).

🌟 Why PraisonAI?

📘 Using Python Code

Unsure whether a result is a bug or model randomness? Check the SDK Behavior Matrix for stream, memory prefetch, structured output, teams and AG-UI.

1. Single Agent

from praisonaiagents import Agent
agent = Agent(instructions="You are a helpful AI assistant")
agent.start("Write a movie script about a robot in Mars")

2. Multi Agents

from praisonaiagents import Agent, Agents

research_agent = Agent(instructions="Research about AI")
summarise_agent = Agent(instructions="Summarise research agent's findings")
agents = Agents(agents=[research_agent, summarise_agent])
agents.start()

3. MCP (Model Context Protocol)

from praisonaiagents import Agent, MCP

# stdio - Local NPX/Python servers
agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))

# Streamable HTTP - Production servers
agent = Agent(tools=MCP("https://api.example.com/mcp"))

# WebSocket - Real-time bidirectional
agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))

# With environment variables
agent = Agent(
    tools=MCP(
        command="npx",
        args=["-y", "@modelcontextprotocol/server-brave-search"],
        env={"BRAVE_API_KEY": "your-key"}
    )
)

📖 Full MCP docs — stdio, HTTP, WebSocket, SSE transports

4. Custom Tools

from praisonaiagents import Agent, tool

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

@tool
def calculate(expression: str) -> float:
    """Safely evaluate a numeric arithmetic expression."""
    import ast
    import operator
    
    # Define allowed operations
    _OPS = {
        ast.Add: operator.add,
        ast.Sub: operator.sub,
        ast.Mult: operator.mul,
        ast.Div: operator.truediv,
        ast.Pow: operator.pow,
        ast.USub: operator.neg,
        ast.UAdd: operator.pos,
    }
    
    def _safe_eval(node):
        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
            return node.value
        elif isinstance(node, ast.BinOp) and type(node.op) in _OPS:
            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
        elif isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
            return _OPS[type(node.op)](_safe_eval(node.operand))
        else:
            raise ValueError("Unsupported expression")
    
    try:
        return _safe_eval(ast.parse(expression, mode="eval").body)
    except (ValueError, SyntaxError, TypeError, ZeroDivisionError, OverflowError):
        raise ValueError("Invalid arithmetic expression")

agent = Agent(
    instructions="You are a helpful assistant",
    tools=[search, calculate]
)
agent.start("Search for AI news and calculate 15*4")

⚠️ Security Note: Never use eval(), exec(), or subprocess in tool functions that process LLM-generated or user-supplied input. Always validate and sanitize inputs to prevent code injection attacks. 📖 Full tools docs — BaseTool, tool packages, 100+ built-in tools

5. Persistence (Databases)

from praisonaiagents import Agent, db

agent = Agent(
    name="Assistant",
    memory={
        "db": db(database_url="postgresql://localhost/mydb"),
        "session_id": "my-session",
    },
)
agent.chat("Hello!")  # Auto-persists messages, runs, traces

📖 Full persistence docs — PostgreSQL, MySQL, SQLite, MongoDB, Redis, and 20+ more

6. PraisonAI Claw 🦞 (Dashboard UI)

Connect your AI agents to Telegram, Discord, Slack, WhatsApp and more — all from a single command.

pip install "praisonai[claw]"
praisonai claw

Required Environment Variables

Copy .env.example to .env and configure the following variables:

Open http://localhost:8082 — the dashboard comes with 13 built-in pages: Chat, Agents, Memory, Knowledge, Channels, Guardrails, Cron, and more. Add messaging channels directly from the UI.

📖 Full Claw docs — platform tokens, CLI options, Docker, and YAML agent mode

7. Langflow Integration 🔗 (Visual Flow Builder)

Build multi-agent workflows visually with drag-and-drop components in Langflow.

pip install "praisonai[flow]"
praisonai flow

Open http://localhost:7861 — use the Agent and Agent Team components to create sequential or parallel workflows. Connect Chat Input → Agent Team → Chat Output for instant multi-agent pipelines.

📖 Full Flow docs — visual agent building, component reference, and deployment

8. PraisonAI UI 🤖 (Clean Chat)

Lightweight chat interface for your AI agents.

pip install "praisonai[ui]"
praisonai ui

📄 Using YAML (No Code)

Example 1: Two Agents Working Together

Create agents.yaml:

framework: praisonai
topic: "Write a blog post about AI"

agents:
  researcher:
    role: Research Analyst
    goal: Research AI trends and gather information
    instructions: "Find accurate information about AI trends"
    
  writer:
    role: Content Writer
    goal: Write engaging blog posts
    instructions: "Write clear, engaging content based on research"

Run with:

praisonai agents.yaml

The agents automatically work together sequentially

Example 2: Agent with Custom Tool

Create two files in the same folder:

agents.yaml:

framework: praisonai
topic: "Calculate the sum of 25 and 15"

agents:
  calculator_agent:
    role: Calculator
    goal: Perform calculations
    instructions: "Use the add_numbers tool to help with calculations"
    tools:
      - add_numbers

tools.py:

def add_numbers(a: float, b: float) -> float:
    """
    Add two numbers together.
    
    Args:
        a: First number
        b: Second number
    
    Returns:
        The sum of a and b
    """
    return a + b

Run with:

praisonai agents.yaml

💡 Tips:

• Use the function name (e.g., add_numbers) in the tools list, not the file name
• Tools in tools.py are automatically discovered
• The function's docstring helps the AI understand how to use it

🎯 CLI Quick Reference

📖 Full CLI reference

✨ Key Features

💻 Using JavaScript Code

npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

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');

⚡ Performance

PraisonAI is built for speed, with agent instantiation in around 14μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.

⭐ Star History

* export TAVILY_API_KEY=xxxxx

🔍 Langfuse Tracing

pip install "praisonai[langfuse]"
praisonai langfuse

🎓 Video Tutorials

Learn PraisonAI through our comprehensive video series:

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

👥 Contributing

We welcome contributions! Fork the repo, create a branch, and submit a PR → Contributing Guide.

❓ FAQ & Troubleshooting

Install the package:

pip install praisonaiagents

Ensure your API key is set:

export OPENAI_API_KEY=your_key_here

For other providers, see Models docs.

# Start Ollama and pull a model
ollama serve
ollama pull llama3.2

from praisonaiagents import Agent

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

The ollama/ prefix is what selects Ollama's handling — tool-call repair, tool-result formatting and the streaming rules small local models need. Or set it by environment instead:

export OPENAI_MODEL_NAME=ollama/llama3.2

Setting only OPENAI_BASE_URL is not enough: with no model named, the OpenAI default (gpt-4o-mini) is sent to Ollama, which answers 404 model 'gpt-4o-mini' not found. Always name the model.

Point at a non-default host with base_url= or OLLAMA_HOST:

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

The same shape works for other local runtimes — lm_studio/, vllm/ and hosted_vllm/ with their server's base_url.

See Models docs for more details.

Use the db parameter:

from praisonaiagents import Agent, db

agent = Agent(
    name="Assistant",
    memory={
        "db": db(database_url="postgresql://localhost/mydb"),
        "session_id": "my-session",
    },
)

See Persistence docs for supported databases.

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    # Enables file-based memory (no extra deps!)
    memory={"user_id": "user123"},
)

See Memory docs for more options.

from praisonaiagents import Agent, Agents

agent1 = Agent(instructions="Research topics")
agent2 = Agent(instructions="Summarize findings")
agents = Agents(agents=[agent1, agent2])
agents.start()

See Agents docs for more examples.

from praisonaiagents import Agent, MCP

agent = Agent(
    tools=MCP("npx @modelcontextprotocol/server-memory")
)

See MCP docs for all transport options.

Getting Help

• 📚 Full Documentation
• 🐛 Report Issues
• 💬 Discussions