PraisonAI 🦞

</div>

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

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 pip install praisonai

\* export TAVILY_API_KEY=xxxxx

---

🎯 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)

Install the lightweight core SDK:

pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"

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 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

<details> <summary>View all 24 providers with examples</summary>

| Provider | Example | |----------|:-------:| | OpenAI | Example | | Anthropic | Example | | Google Gemini | Example | | Ollama | Example | | Groq | Example | | DeepSeek | Example | | xAI Grok | Example | | Mistral | Example | | Cohere | Example | | Perplexity | Example | | Fireworks | Example | | Together AI | Example | | OpenRouter | Example | | HuggingFace | Example | | Azure OpenAI | Example | | AWS Bedrock | Example | | Google Vertex | Example | | Databricks | Example | | Cloudflare | Example | | AI21 | Example | | Replicate | Example | | SageMaker | Example | | Moonshot | Example | | vLLM | Example |

</details>

<div align="center"> <a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank"> <img src=".github/images/elon_musk_praisonai.png" alt="Highlighted by Elon Musk" width="600" /> </a> "Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a> </div>

---

🌟 Why PraisonAI?

| | Feature | How | |--|---------|-----| | 🔌 | MCP Protocol — stdio, HTTP, WebSocket, SSE | tools=MCP("npx ...") | | 🧠 | Planning Mode — plan → execute → reason | planning=True | | 🔍 | Deep Research — multi-step autonomous research | Docs | | 🤖 | External Agents — orchestrate Claude Code, Gemini CLI, Codex | Docs | | 🔄 | Agent Handoffs — seamless conversation passing | handoff=True | | 🛡️ | Guardrails — input/output validation | Docs | | | Web Search + Fetch — native browsing | web_search=True | | 🪞 | Self Reflection — agent reviews its own output | Docs | | 🔀 | Workflow Patterns — route, parallel, loop, repeat | Docs | | 🧠 | Memory (zero deps) — works out of the box | memory=True |

<details> <summary>View all 25 features</summary>

| | Feature | How | |--|---------|-----| | 💡 | Prompt Caching — reduce latency + cost | prompt_caching=True | | 💾 | Sessions + Auto-Save — persistent state across restarts | auto_save="my-project" | | 💭 | Thinking Budgets — control reasoning depth | thinking_budget=1024 | | 📚 | RAG + Quality-Based RAG — auto quality scoring retrieval | Docs | | 📊 | Model Router — auto-routes to cheapest capable model | Docs | | 🧊 | Shadow Git Checkpoints — auto-rollback on failure | Docs | | 📡 | A2A Protocol — agent-to-agent interop | Docs | | 📏 | Context Compaction — never hit token limits | Docs | | 📡 | Telemetry — OpenTelemetry traces, spans, metrics | Docs | | 📜 | Policy Engine — declarative agent behavior control | Docs | | 🔄 | Background Tasks — fire-and-forget agents | Docs | | 🔁 | Doom Loop Detection — auto-recovery from stuck agents | Docs | | 🕸️ | Graph Memory — Neo4j-style relationship tracking | Docs | | 🏖️ | Sandbox Execution — isolated code execution | Docs | | 🖥️ | Bot Gateway — multi-agent routing across channels | Docs |

</details>

---

📘 Using Python Code

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",
    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:

| Variable | Required | Description | |----------|----------|-------------| | OPENAI_API_KEY | Yes | OpenAI API key for all LLM calls | | TAVILY_API_KEY | Yes (Claw) | Tavily key for the built-in web-search tool. Get one free at https://app.tavily.com |

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: ``bash praisonai agents.yaml ``

The agents automatically work together sequentially

Example 2: Agent with Custom Tool

Create two files in the same folder:

agents.yaml: ```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: ``bash 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

| Category | Commands | |----------|----------| | Execution | praisonai, --auto, --interactive, --chat | | Research | research, --query-rewrite, --deep-research | | Planning | --planning, --planning-tools, --planning-reasoning | | Workflows | workflow run, workflow list, workflow auto | | Memory | memory show, memory add, memory search, memory clear | | Knowledge | knowledge add, knowledge query, knowledge list | | Sessions | session list, session resume, session delete | | Tools | tools list, tools info, tools search | | MCP | mcp list, mcp create, mcp enable | | Development | commit, docs, checkpoint, hooks | | Scheduling | schedule start, schedule list, schedule stop |

📖 Full CLI reference

---

✨ Key Features

<details open> <summary>🤖 Core Agents</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Single Agent | Example | 📖 | | Multi Agents | Example | 📖 | | Auto Agents | Example | 📖 | | Self Reflection AI Agents | Example | 📖 | | Reasoning AI Agents | Example | 📖 | | Multi Modal AI Agents | Example | 📖 |

</details>

<details> <summary>🔄 Workflows</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Simple Workflow | Example | 📖 | | Workflow with Agents | Example | 📖 | | Agentic Routing (route()) | Example | 📖 | | Parallel Execution (parallel()) | Example | 📖 | | Loop over List/CSV (loop()) | Example | 📖 | | Evaluator-Optimizer (repeat()) | Example | 📖 | | Conditional Steps | Example | 📖 | | Workflow Branching | Example | 📖 | | Workflow Early Stop | Example | 📖 | | Workflow Checkpoints | Example | 📖 |

</details>

<details> <summary>💻 Code & Development</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Code Interpreter Agents | Example | 📖 | | AI Code Editing Tools | Example | 📖 | | External Agents (All) | Example | 📖 | | Claude Code CLI | Example | 📖 | | Gemini CLI | Example | 📖 | | Codex CLI | Example | 📖 | | Cursor CLI | Example | 📖 |

</details>

<details> <summary>🧠 Memory & Knowledge</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Memory (Short & Long Term) | Example | 📖 | | File-Based Memory | Example | 📖 | | Claude Memory Tool | Example | 📖 | | Add Custom Knowledge | Example | 📖 | | RAG Agents | Example | 📖 | | Chat with PDF Agents | Example | 📖 | | Data Readers (PDF, DOCX, etc.) | CLI | 📖 | | Vector Store Selection | CLI | 📖 | | Retrieval Strategies | CLI | 📖 | | Rerankers | CLI | 📖 | | Index Types (Vector/Keyword/Hybrid) | CLI | 📖 | | Query Engines (Sub-Question, etc.) | CLI | 📖 |

</details>

<details> <summary>🔬 Research & Intelligence</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Deep Research Agents | Example | 📖 | | Query Rewriter Agent | Example | 📖 | | Native Web Search | Example | 📖 | | Built-in Search Tools | Example | 📖 | | Unified Web Search | Example | 📖 | | Web Fetch (Anthropic) | Example | 📖 |

</details>

<details> <summary>📋 Planning & Execution</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Planning Mode | Example | 📖 | | Planning Tools | Example | 📖 | | Planning Reasoning | Example | 📖 | | Prompt Chaining | Example | 📖 | | Evaluator Optimiser | Example | 📖 | | Orchestrator Workers | Example | 📖 |

</details>

<details> <summary>👥 Specialized Agents</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Data Analyst Agent | Example | 📖 | | Finance Agent | Example | 📖 | | Shopping Agent | Example | 📖 | | Recommendation Agent | Example | 📖 | | Wikipedia Agent | Example | 📖 | | Programming Agent | Example | 📖 | | Math Agents | Example | 📖 | | Markdown Agent | Example | 📖 | | Prompt Expander Agent | Example | 📖 |

</details>

<details> <summary>🎨 Media & Multimodal</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Image Generation Agent | Example | 📖 | | Image to Text Agent | Example | 📖 | | Video Agent | Example | 📖 | | Camera Integration | Example | 📖 |

</details>

<details> <summary>🔌 Protocols & Integration</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | MCP Transports | Example | 📖 | | WebSocket MCP | Example | 📖 | | MCP Security | Example | 📖 | | MCP Resumability | Example | 📖 | | MCP Config Management | Docs | 📖 | | LangChain Integrated Agents | Example | 📖 |

</details>

<details> <summary>🛡️ Safety & Control</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Guardrails | Example | 📖 | | Human Approval | Example | 📖 | | Rules & Instructions | Docs | 📖 |

</details>

<details> <summary>⚙️ Advanced Features</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Async & Parallel Processing | Example | 📖 | | Parallelisation | Example | 📖 | | Repetitive Agents | Example | 📖 | | Agent Handoffs | Example | 📖 | | Stateful Agents | Example | 📖 | | Autonomous Workflow | Example | 📖 | | Structured Output Agents | Example | 📖 | | Model Router | Example | 📖 | | Prompt Caching | Example | 📖 | | Fast Context | Example | 📖 |

</details>

<details> <summary>🛠️ Tools & Configuration</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | 100+ Custom Tools | Example | 📖 | | YAML Configuration | Example | 📖 | | 100+ LLM Support | Example | 📖 | | Callback Agents | Example | 📖 | | Hooks | Example | 📖 | | Middleware System | Example | 📖 | | Configurable Model | Example | 📖 | | Rate Limiter | Example | 📖 | | Injected Tool State | Example | 📖 | | Shadow Git Checkpoints | Example | 📖 | | Background Tasks | Example | 📖 | | Policy Engine | Example | 📖 | | Thinking Budgets | Example | 📖 | | Output Styles | Example | 📖 | | Context Compaction | Example | 📖 |

</details>

<details> <summary>📊 Monitoring & Management</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Sessions Management | Example | 📖 | | Auto-Save Sessions | Docs | 📖 | | History in Context | Docs | 📖 | | Telemetry | Example | 📖 | | Langfuse Tracing | Docs | 📖 | | Project Docs (.praison/docs/) | Docs | 📖 | | AI Commit Messages | Docs | 📖 | | @Mentions in Prompts | Docs | 📖 |

</details>

<details> <summary>🖥️ CLI Features</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Slash Commands | Example | 📖 | | Autonomy Modes | Example | 📖 | | Cost Tracking | Example | 📖 | | Repository Map | Example | 📖 | | Interactive TUI | Example | 📖 | | Git Integration | Example | 📖 | | Sandbox Execution | Example | 📖 | | CLI Compare | Example | 📖 | | Profile/Benchmark | Docs | 📖 | | Auto Mode | Docs | 📖 | | Init | Docs | 📖 | | File Input | Docs | 📖 | | Final Agent | Docs | 📖 | | Max Tokens | Docs | 📖 |

</details>

<details> <summary>🧪 Evaluation</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Accuracy Evaluation | Example | 📖 | | Performance Evaluation | Example | 📖 | | Reliability Evaluation | Example | 📖 | | Criteria Evaluation | Example | 📖 |

</details>

<details> <summary>🎯 Agent Skills</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Skills Management | Example | 📖 | | Custom Skills | Example | 📖 |

</details>

<details> <summary>⏰ 24/7 Scheduling</summary>

| Feature | Code | Docs | |---------|:----:|:----:| | Agent Scheduler | Example | 📖 |

</details>

---

💻 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.

| Performance Metric | PraisonAI | |--------------------|-----------| | Avg Instantiation Time | 14 μs |

---

⭐ Star History

![Star History Chart](https://docs.praison.ai)

---

🔍 Langfuse Tracing

pip install "praisonai[langfuse]"
praisonai langfuse

---

🎓 Video Tutorials

Learn PraisonAI through our comprehensive video series:

<details> <summary>View all 22 video tutorials</summary>

| Topic | Video | |-------|--------| | AI Agents with Self Reflection | ![Self Reflection](https://www.youtube.com/watch?v=vLXobEN2Vc8) | | Reasoning Data Generating Agent | ![Reasoning Data](https://www.youtube.com/watch?v=fUT332Y2zA8) | | AI Agents with Reasoning | ![Reasoning](https://www.youtube.com/watch?v=KNDVWGN3TpM) | | Multimodal AI Agents | ![Multimodal](https://www.youtube.com/watch?v=hjAWmUT1qqY) | | AI Agents Workflow | ![Workflow](https://www.youtube.com/watch?v=yWTH44QPl2A) | | Async AI Agents | ![Async](https://www.youtube.com/watch?v=VhVQfgo00LE) | | Mini AI Agents | ![Mini](https://www.youtube.com/watch?v=OkvYp5aAGSg) | | AI Agents with Memory | ![Memory](https://www.youtube.com/watch?v=1hVfVxvPnnQ) | | Repetitive Agents | ![Repetitive](https://www.youtube.com/watch?v=dAYGxsjDOPg) | | Introduction | ![Introduction](https://www.youtube.com/watch?v=Fn1lQjC0GO0) | | Tools Overview | ![Tools Overview](https://www.youtube.com/watch?v=XaQRgRpV7jo) | | Custom Tools | ![Custom Tools](https://www.youtube.com/watch?v=JSU2Rndh06c) | | Firecrawl Integration | ![Firecrawl](https://www.youtube.com/watch?v=UoqUDcLcOYo) | | User Interface | ![UI](https://www.youtube.com/watch?v=tg-ZjNl3OCg) | | Crawl4AI Integration | ![Crawl4AI](https://www.youtube.com/watch?v=KAvuVUh0XU8) | | Chat Interface | ![Chat](https://www.youtube.com/watch?v=sw3uDqn2h1Y) | | Code Interface | ![Code](https://www.youtube.com/watch?v=_5jQayO-MQY) | | Mem0 Integration | ![Mem0](https://www.youtube.com/watch?v=KIGSgRxf1cY) | | Training | ![Training](https://www.youtube.com/watch?v=aLawE8kwCrI) | | Realtime Voice Interface | ![Realtime](https://www.youtube.com/watch?v=frRHfevTCSw) | | Call Interface | ![Call](https://www.youtube.com/watch?v=m1cwrUG2iAk) | | Reasoning Extract Agents | ![Reasoning Extract](https://www.youtube.com/watch?v=2PPamsADjJA) |

</details>

---

👥 Contributing

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

---

❓ FAQ & Troubleshooting

<details> <summary>ModuleNotFoundError: No module named 'praisonaiagents'</summary>

Install the package: ``bash pip install praisonaiagents ``

</details>

<details> <summary>API key not found / Authentication error</summary>

Ensure your API key is set: ``bash export OPENAI_API_KEY=your_key_here ``

For other providers, see Models docs.

</details>

<details> <summary>How do I use a local model (Ollama)?</summary>

# Start Ollama server first
ollama serve

# Set environment variable
export OPENAI_BASE_URL=http://localhost:11434/v1

See Models docs for more details.

</details>

<details> <summary>How do I persist conversations to a database?</summary>

Use the db parameter: ```python from praisonaiagents import Agent, db

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

See Persistence docs for supported databases.

</details>

<details> <summary>How do I enable agent memory?</summary>

from praisonaiagents import Agent

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

See Memory docs for more options.

</details>

<details> <summary>How do I run multiple agents together?</summary>

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.

</details>

<details> <summary>How do I use MCP tools?</summary>

from praisonaiagents import Agent, MCP

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

See MCP docs for all transport options.

</details>

Getting Help

---

<div align="center"> Made with ❤️ by the PraisonAI Team <a href="https://docs.praison.ai">📚 Documentation</a> • <a href="https://github.com/MervinPraison/PraisonAI">GitHub</a> • <a href="https://youtube.com/@MervinPraison">▶️ YouTube</a> • <a href="https://x.com/MervinPraison">𝕏 X</a> • <a href="https://linkedin.com/in/mervinpraison">💼 LinkedIn</a> </div>

PraisonAI

PraisonAI 🦞

🎯 Use Cases

🚀 Meet your first Agent (Under 1 Minute)

🌌 The PraisonAI Ecosystem

JavaScript SDK

🧠 Supported Providers & Features

🌟 Why PraisonAI?

📘 Using Python Code

1. Single Agent

2. Multi Agents

3. MCP (Model Context Protocol)

4. Custom Tools

5. Persistence (Databases)

6. PraisonAI Claw 🦞 (Dashboard UI)

Required Environment Variables

7. Langflow Integration 🔗 (Visual Flow Builder)

8. PraisonAI UI 🤖 (Clean Chat)

📄 Using YAML (No Code)

Example 1: Two Agents Working Together

Example 2: Agent with Custom Tool

🎯 CLI Quick Reference

✨ Key Features

💻 Using JavaScript Code

⚡ Performance

⭐ Star History

🔍 Langfuse Tracing

🎓 Video Tutorials

👥 Contributing

❓ FAQ & Troubleshooting

Getting Help

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