PraisonAI π¦ β Automate and solve complex challenges with AI agent teams that plan, research, code, and deliver results to Telegram, Discord, and WhatsApp β running 24/7. A low-code, production-ready multi-agent framework with handoffs, guardrails, memory, RAG, and 100+ LLM providers, built around simplicity, customisation, and effective human-agent collaboration.
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pip install praisonai
* export TAVILY_API_KEY=xxxxx
Quick Paths:
- π New here? β Quick Start (1 minute to first agent)
- π¦ Installing? β Installation
- π Python SDK? β Python Examples
- π YAML/No-Code? β YAML Examples
- π― CLI user? β CLI Quick Reference
- π€ Contributing? β Contributing
PraisonAI is built for speed, with agent instantiation in under 4ΞΌs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
| Performance Metric | PraisonAI |
|---|---|
| Avg Instantiation Time | 3.77 ΞΌs |
AI agents solving real-world problems across industries:
| Use Case | Description |
|---|---|
| π Research & Analysis | Conduct deep research, gather information, and generate insights from multiple sources automatically |
| π» Code Generation | Write, debug, and refactor code with AI agents that understand your codebase and requirements |
| βοΈ Content Creation | Generate blog posts, documentation, marketing copy, and technical writing with multi-agent teams |
| π Data Pipelines | Extract, transform, and analyze data from APIs, databases, and web sources automatically |
| π€ Customer Support | Deploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses |
| βοΈ Workflow Automation | Automate multi-step business processes with agents that hand off tasks, verify results, and self-correct |
PraisonAI supports 100+ LLM providers through seamless integration:
View all 24 providers with examples
| 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 |
| Feature | How | |
|---|---|---|
| π | MCP Protocol β stdio, HTTP, WebSocket, SSE | tools=MCP("npx ...") |
| π§ | Planning Mode β plan β execute β reason | planning=True |
| π | Deep Research β multi-step autonomous research | Docs |
| π€ | External Agents β orchestrate Claude Code, Gemini CLI, Codex | Docs |
| π | Agent Handoffs β seamless conversation passing | handoff=True |
| π‘οΈ | Guardrails β input/output validation | Docs |
| Web Search + Fetch β native browsing | web_search=True |
|
| πͺ | Self Reflection β agent reviews its own output | Docs |
| π | Workflow Patterns β route, parallel, loop, repeat | Docs |
| π§ | Memory (zero deps) β works out of the box | memory=True |
View all 25 features
| Feature | How | |
|---|---|---|
| π‘ | Prompt Caching β reduce latency + cost | prompt_caching=True |
| πΎ | Sessions + Auto-Save β persistent state across restarts | auto_save="my-project" |
| π | Thinking Budgets β control reasoning depth | thinking_budget=1024 |
| π | RAG + Quality-Based RAG β auto quality scoring retrieval | Docs |
| π | Model Router β auto-routes to cheapest capable model | Docs |
| π§ | Shadow Git Checkpoints β auto-rollback on failure | Docs |
| π‘ | A2A Protocol β agent-to-agent interop | Docs |
| π | Context Compaction β never hit token limits | Docs |
| π‘ | Telemetry β OpenTelemetry traces, spans, metrics | Docs |
| π | Policy Engine β declarative agent behavior control | Docs |
| π | Background Tasks β fire-and-forget agents | Docs |
| π | Doom Loop Detection β auto-recovery from stuck agents | Docs |
| πΈοΈ | Graph Memory β Neo4j-style relationship tracking | Docs |
| ποΈ | Sandbox Execution β isolated code execution | Docs |
| π₯οΈ | Bot Gateway β multi-agent routing across channels | Docs |
"Grok 3 customer support" β Elon Musk quoting PraisonAI's tutorial
Get started with PraisonAI in under 1 minute:
# Install
pip install praisonaiagents
# Set API key
export OPENAI_API_KEY=your_key_here
# Create a simple agent
python -c "from praisonaiagents import Agent; Agent(instructions='You are a helpful AI assistant').start('Write a haiku about AI')"Next Steps: Single Agent Example | Multi Agents | Full Docs
Lightweight package dedicated for coding:
pip install praisonaiagentsFor the full framework with CLI support:
pip install praisonaiπ¦ PraisonAI Claw β full UI with bots, memory, knowledge, and gateway:
pip install "praisonai[claw]"
praisonai clawπ PraisonAI Flow β Langflow Visual Flow Builder:
pip install "praisonai[flow]"
praisonai flowπ€ PraisonAI UI β Clean chat interface:
pip install "praisonai[ui]"
praisonai uinpm install praisonaifrom praisonaiagents import Agent
agent = Agent(instructions="You are a helpful AI assistant")
agent.start("Write a movie script about a robot in Mars")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()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"}
)
)π Full MCP docs β stdio, HTTP, WebSocket, SSE transports
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:
"""Evaluate a math expression."""
return eval(expression)
agent = Agent(
instructions="You are a helpful assistant",
tools=[search, calculate]
)
agent.start("Search for AI news and calculate 15*4")π Full tools docs β BaseTool, tool packages, 100+ built-in tools
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, tracesπ Full persistence docs β PostgreSQL, MySQL, SQLite, MongoDB, Redis, and 20+ more
Connect your AI agents to Telegram, Discord, Slack, WhatsApp and more β all from a single command.
pip install "praisonai[claw]"
praisonai clawOpen http://localhost:8082 β the dashboard comes with 13 built-in pages: Chat, Agents, Memory, Knowledge, Channels, Guardrails, Cron, and more. Add messaging channels directly from the UI.
π Full Claw docs β platform tokens, CLI options, Docker, and YAML agent mode
Build multi-agent workflows visually with drag-and-drop components in Langflow.
pip install "praisonai[flow]"
praisonai flowOpen http://localhost:7861 β use the Agent and Agent Team components to create sequential or parallel workflows. Connect Chat Input β Agent Team β Chat Output for instant multi-agent pipelines.
π Full Flow docs β visual agent building, component reference, and deployment
Lightweight chat interface for your AI agents.
pip install "praisonai[ui]"
praisonai uiCreate 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.yamlThe agents automatically work together sequentially
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_numberstools.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 + bRun with:
praisonai agents.yamlπ‘ Tips:
- Use the function name (e.g.,
add_numbers) in the tools list, not the file name- Tools in
tools.pyare automatically discovered- The function's docstring helps the AI understand how to use it
| 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 |
π Full CLI reference
π€ Core Agents
| Feature | Code | Docs |
|---|---|---|
| Single Agent | Example | π |
| Multi Agents | Example | π |
| Auto Agents | Example | π |
| Self Reflection AI Agents | Example | π |
| Reasoning AI Agents | Example | π |
| Multi Modal AI Agents | Example | π |
π Workflows
| Feature | Code | Docs |
|---|---|---|
| Simple Workflow | Example | π |
| Workflow with Agents | Example | π |
Agentic Routing (route()) |
Example | π |
Parallel Execution (parallel()) |
Example | π |
Loop over List/CSV (loop()) |
Example | π |
Evaluator-Optimizer (repeat()) |
Example | π |
| Conditional Steps | Example | π |
| Workflow Branching | Example | π |
| Workflow Early Stop | Example | π |
| Workflow Checkpoints | Example | π |
π» Code & Development
| Feature | Code | Docs |
|---|---|---|
| Code Interpreter Agents | Example | π |
| AI Code Editing Tools | Example | π |
| External Agents (All) | Example | π |
| Claude Code CLI | Example | π |
| Gemini CLI | Example | π |
| Codex CLI | Example | π |
| Cursor CLI | Example | π |
π§ Memory & Knowledge
| Feature | Code | Docs |
|---|---|---|
| Memory (Short & Long Term) | Example | π |
| File-Based Memory | Example | π |
| Claude Memory Tool | Example | π |
| Add Custom Knowledge | Example | π |
| RAG Agents | Example | π |
| Chat with PDF Agents | Example | π |
| Data Readers (PDF, DOCX, etc.) | CLI | π |
| Vector Store Selection | CLI | π |
| Retrieval Strategies | CLI | π |
| Rerankers | CLI | π |
| Index Types (Vector/Keyword/Hybrid) | CLI | π |
| Query Engines (Sub-Question, etc.) | CLI | π |
π¬ Research & Intelligence
| Feature | Code | Docs |
|---|---|---|
| Deep Research Agents | Example | π |
| Query Rewriter Agent | Example | π |
| Native Web Search | Example | π |
| Built-in Search Tools | Example | π |
| Unified Web Search | Example | π |
| Web Fetch (Anthropic) | Example | π |
π Planning & Execution
| Feature | Code | Docs |
|---|---|---|
| Planning Mode | Example | π |
| Planning Tools | Example | π |
| Planning Reasoning | Example | π |
| Prompt Chaining | Example | π |
| Evaluator Optimiser | Example | π |
| Orchestrator Workers | Example | π |
π₯ Specialized Agents
| Feature | Code | Docs |
|---|---|---|
| Data Analyst Agent | Example | π |
| Finance Agent | Example | π |
| Shopping Agent | Example | π |
| Recommendation Agent | Example | π |
| Wikipedia Agent | Example | π |
| Programming Agent | Example | π |
| Math Agents | Example | π |
| Markdown Agent | Example | π |
| Prompt Expander Agent | Example | π |
π¨ Media & Multimodal
| Feature | Code | Docs |
|---|---|---|
| Image Generation Agent | Example | π |
| Image to Text Agent | Example | π |
| Video Agent | Example | π |
| Camera Integration | Example | π |
π Protocols & Integration
| Feature | Code | Docs |
|---|---|---|
| MCP Transports | Example | π |
| WebSocket MCP | Example | π |
| MCP Security | Example | π |
| MCP Resumability | Example | π |
| MCP Config Management | Docs | π |
| LangChain Integrated Agents | Example | π |
π‘οΈ Safety & Control
| Feature | Code | Docs |
|---|---|---|
| Guardrails | Example | π |
| Human Approval | Example | π |
| Rules & Instructions | Docs | π |
βοΈ Advanced Features
| Feature | Code | Docs |
|---|---|---|
| Async & Parallel Processing | Example | π |
| Parallelisation | Example | π |
| Repetitive Agents | Example | π |
| Agent Handoffs | Example | π |
| Stateful Agents | Example | π |
| Autonomous Workflow | Example | π |
| Structured Output Agents | Example | π |
| Model Router | Example | π |
| Prompt Caching | Example | π |
| Fast Context | Example | π |
π οΈ Tools & Configuration
| Feature | Code | Docs |
|---|---|---|
| 100+ Custom Tools | Example | π |
| YAML Configuration | Example | π |
| 100+ LLM Support | Example | π |
| Callback Agents | Example | π |
| Hooks | Example | π |
| Middleware System | Example | π |
| Configurable Model | Example | π |
| Rate Limiter | Example | π |
| Injected Tool State | Example | π |
| Shadow Git Checkpoints | Example | π |
| Background Tasks | Example | π |
| Policy Engine | Example | π |
| Thinking Budgets | Example | π |
| Output Styles | Example | π |
| Context Compaction | Example | π |
π Monitoring & Management
| Feature | Code | Docs |
|---|---|---|
| Sessions Management | Example | π |
| Auto-Save Sessions | Docs | π |
| History in Context | Docs | π |
| Telemetry | Example | π |
| Project Docs (.praison/docs/) | Docs | π |
| AI Commit Messages | Docs | π |
| @Mentions in Prompts | Docs | π |
π₯οΈ CLI Features
| Feature | Code | Docs |
|---|---|---|
| Slash Commands | Example | π |
| Autonomy Modes | Example | π |
| Cost Tracking | Example | π |
| Repository Map | Example | π |
| Interactive TUI | Example | π |
| Git Integration | Example | π |
| Sandbox Execution | Example | π |
| CLI Compare | Example | π |
| Profile/Benchmark | Docs | π |
| Auto Mode | Docs | π |
| Init | Docs | π |
| File Input | Docs | π |
| Final Agent | Docs | π |
| Max Tokens | Docs | π |
π§ͺ Evaluation
| Feature | Code | Docs |
|---|---|---|
| Accuracy Evaluation | Example | π |
| Performance Evaluation | Example | π |
| Reliability Evaluation | Example | π |
| Criteria Evaluation | Example | π |
npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxxconst { 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');Learn PraisonAI through our comprehensive video series:
View all 22 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 |  |
We welcome contributions! Fork the repo, create a branch, and submit a PR β Contributing Guide.
ModuleNotFoundError: No module named 'praisonaiagents'
Install the package:
pip install praisonaiagentsAPI key not found / Authentication error
Ensure your API key is set:
export OPENAI_API_KEY=your_key_hereFor other providers, see Models docs.
How do I use a local model (Ollama)?
# Start Ollama server first
ollama serve
# Set environment variable
export OPENAI_BASE_URL=http://localhost:11434/v1See Models docs for more details.
How do I persist conversations to a database?
Use the db parameter:
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.
How do I enable agent memory?
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.
How do I run multiple agents together?
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.
How do I use MCP tools?
from praisonaiagents import Agent, MCP
agent = Agent(
tools=MCP("npx @modelcontextprotocol/server-memory")
)See MCP docs for all transport options.
- π Full Documentation
- π Report Issues
- π¬ Discussions
Made with β€οΈ by the PraisonAI Team
π Documentation β’
GitHub β’
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