Praison AI

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Praison AI

PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.

📑 Table of Contents

🚀 Quick Start

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')"

📦 Installation

Python SDK

Lightweight package dedicated for coding:

pip install praisonaiagents

For the full framework with CLI support:

pip install praisonai

JavaScript SDK

npm install praisonai

💻 Usage

Python Code Examples

CLI / No-Code Interface

JavaScript Code Examples

💻 JavaScript Usage

✨ Key Features

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	📖
🔄 Workflows
↳ 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	📖
📚 Add Custom Knowledge	Example	📖
🧠 Memory (Short & Long Term)	Example	📖
📄 Chat with PDF Agents	Example	📖
💻 Code Interpreter Agents	Example	📖
📚 RAG Agents	Example	📖
🤔 Async & Parallel Processing	Example	📖
🔢 Math Agents	Example	📖
🎯 Structured Output Agents	Example	📖
🔗 LangChain Integrated Agents	Example	📖
📞 Callback Agents	Example	📖
🛠️ 100+ Custom Tools	Example	📖
📄 YAML Configuration	Example	📖
💯 100+ LLM Support	Example	📖
🔬 Deep Research Agents	Example	📖
🔄 Query Rewriter Agent	Example	📖
🌐 Native Web Search	Example	📖
📥 Web Fetch (Anthropic)	Example	📖
💾 Prompt Caching	Example	📖
🧠 Claude Memory Tool	Example	📖
💾 File-Based Memory	Example	📖
🔍 Built-in Search Tools	Example	📖
📋 Planning Mode	Example	📖
🔧 Planning Tools	Example	📖
🧠 Planning Reasoning	Example	📖
🔌 MCP Transports	Example	📖
🌐 WebSocket MCP	Example	📖
🔐 MCP Security	Example	📖
🔄 MCP Resumability	Example	📖
⚡ Fast Context	Example	📖
🖼️ Image Generation Agent	Example	📖
📷 Image to Text Agent	Example	📖
🎬 Video Agent	Example	📖
📊 Data Analyst Agent	Example	📖
💰 Finance Agent	Example	📖
🛒 Shopping Agent	Example	📖
⭐ Recommendation Agent	Example	📖
📖 Wikipedia Agent	Example	📖
💻 Programming Agent	Example	📖
📝 Markdown Agent	Example	📖
📝 Prompt Expander Agent	Example	📖
🔀 Router Agent	Example	📖
⛓️ Prompt Chaining	Example	📖
🔍 Evaluator Optimiser	Example	📖
👷 Orchestrator Workers	Example	📖
⚡ Parallelisation	Example	📖
🔁 Repetitive Agents	Example	📖
🤝 Agent Handoffs	Example	📖
🛡️ Guardrails	Example	📖
💬 Sessions Management	Example	📖
✅ Human Approval	Example	📖
🔄 Stateful Agents	Example	📖
🤖 Autonomous Workflow	Example	📖
📜 Rules & Instructions	Example	📖
🪝 Hooks	Example	📖
📈 Telemetry	Example	📖
📹 Camera Integration	Example	📖
📄 Project Docs (.praison/docs/)	Example	📖
🔌 MCP Config Management	Example	📖
💬 AI Commit Messages	Example	📖
@ @Mentions in Prompts	Example	📖
💾 Auto-Save Sessions	Example	📖
📜 History in Context	Example	📖

🌐 Supported Providers

PraisonAI supports 100+ LLM providers through seamless integration:

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

📘 Using Python Code

Light weight package dedicated for coding:

pip install praisonaiagents
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

1. Single Agent

Create app.py file and add the code below:

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

Run:

python app.py

2. Multi Agents

Create app.py file and add the code below:

from praisonaiagents import Agent, PraisonAIAgents

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

Run:

python app.py

3. Agent with Planning Mode

Enable planning for any agent - the agent creates a plan, then executes step by step:

from praisonaiagents import Agent

def search_web(query: str) -> str:
    return f"Search results for: {query}"

agent = Agent(
    name="AI Assistant",
    instructions="Research and write about topics",
    planning=True,              # Enable planning mode
    planning_tools=[search_web], # Tools for planning research
    planning_reasoning=True      # Chain-of-thought reasoning
)

result = agent.start("Research AI trends in 2025 and write a summary")

What happens:

📋 Agent creates a multi-step plan
🚀 Executes each step sequentially
📊 Shows progress with context passing
✅ Returns final result

4. Deep Research Agent

Automated research with real-time streaming, web search, and citations using OpenAI or Gemini Deep Research APIs.

from praisonaiagents import DeepResearchAgent

# OpenAI Deep Research
agent = DeepResearchAgent(
    model="o4-mini-deep-research",  # or "o3-deep-research"
    verbose=True
)

result = agent.research("What are the latest AI trends in 2025?")
print(result.report)
print(f"Citations: {len(result.citations)}")

# Gemini Deep Research
from praisonaiagents import DeepResearchAgent

agent = DeepResearchAgent(
    model="deep-research-pro",  # Auto-detected as Gemini
    verbose=True
)

result = agent.research("Research quantum computing advances")
print(result.report)

Features:

🔍 Multi-provider support (OpenAI, Gemini, LiteLLM)
📡 Real-time streaming with reasoning summaries
📚 Structured citations with URLs
🛠️ Built-in tools: web search, code interpreter, MCP, file search
🔄 Automatic provider detection from model name

5. Query Rewriter Agent

Transform user queries to improve RAG retrieval quality using multiple strategies.

from praisonaiagents import QueryRewriterAgent, RewriteStrategy

agent = QueryRewriterAgent(model="gpt-4o-mini")

# Basic - expands abbreviations, adds context
result = agent.rewrite("AI trends")
print(result.primary_query)  # "What are the current trends in Artificial Intelligence?"

# HyDE - generates hypothetical document for semantic matching
result = agent.rewrite("What is quantum computing?", strategy=RewriteStrategy.HYDE)

# Step-back - generates broader context question
result = agent.rewrite("GPT-4 vs Claude 3?", strategy=RewriteStrategy.STEP_BACK)

# Sub-queries - decomposes complex questions
result = agent.rewrite("RAG setup and best embedding models?", strategy=RewriteStrategy.SUB_QUERIES)

# Contextual - resolves references using chat history
result = agent.rewrite("What about cost?", chat_history=[...])

Strategies:

BASIC: Expand abbreviations, fix typos, add context
HYDE: Generate hypothetical document for semantic matching
STEP_BACK: Generate higher-level concept questions
SUB_QUERIES: Decompose multi-part questions
MULTI_QUERY: Generate multiple paraphrased versions
CONTEXTUAL: Resolve references using conversation history
AUTO: Automatically detect best strategy

6. Agent Memory (Zero Dependencies)

Enable persistent memory for agents - works out of the box without any extra packages.

from praisonaiagents import Agent
from praisonaiagents.memory import FileMemory

# Enable memory with a single parameter
agent = Agent(
    name="Personal Assistant",
    instructions="You are a helpful assistant that remembers user preferences.",
    memory=True,  # Enables file-based memory (no extra deps!)
    user_id="user123"  # Isolate memory per user
)

# Memory is automatically injected into conversations
result = agent.start("My name is John and I prefer Python")
# Agent will remember this for future conversations

Memory Types:

Short-term: Rolling buffer of recent context (auto-expires)
Long-term: Persistent important facts (sorted by importance)
Entity: People, places, organizations with attributes
Episodic: Date-based interaction history

Advanced Features:

from praisonaiagents.memory import FileMemory

memory = FileMemory(user_id="user123")

# Session Save/Resume
memory.save_session("project_session", conversation_history=[...])
memory.resume_session("project_session")

# Context Compression
memory.compress(llm_func=lambda p: agent.chat(p), max_items=10)

# Checkpointing
memory.create_checkpoint("before_refactor", include_files=["main.py"])
memory.restore_checkpoint("before_refactor", restore_files=True)

# Slash Commands
memory.handle_command("/memory show")
memory.handle_command("/memory save my_session")

Storage Options:

Option	Dependencies	Description
`memory=True`	None	File-based JSON storage (default)
`memory="file"`	None	Explicit file-based storage
`memory="sqlite"`	Built-in	SQLite with indexing
`memory="chromadb"`	chromadb	Vector/semantic search

6. Rules & Instructions

PraisonAI auto-discovers instruction files from your project root and git root:

File	Description	Priority
`PRAISON.md`	PraisonAI native instructions	High
`PRAISON.local.md`	Local overrides (gitignored)	Higher
`CLAUDE.md`	Claude Code memory file	High
`CLAUDE.local.md`	Local overrides (gitignored)	Higher
`AGENTS.md`	OpenAI Codex CLI instructions	High
`GEMINI.md`	Gemini CLI memory file	High
`.cursorrules`	Cursor IDE rules	High
`.windsurfrules`	Windsurf IDE rules	High
`.claude/rules/*.md`	Claude Code modular rules	Medium
`.windsurf/rules/*.md`	Windsurf modular rules	Medium
`.cursor/rules/*.mdc`	Cursor modular rules	Medium
`.praison/rules/*.md`	Workspace rules	Medium
`~/.praison/rules/*.md`	Global rules	Low

from praisonaiagents import Agent

# Agent auto-discovers CLAUDE.md, AGENTS.md, GEMINI.md, etc.
agent = Agent(name="Assistant", instructions="You are helpful.")
# Rules are injected into system prompt automatically

@Import Syntax:

# CLAUDE.md
See @README for project overview
See @docs/architecture.md for system design
@~/.praison/my-preferences.md

Rule File Format (with YAML frontmatter):

---
description: Python coding guidelines
globs: ["**/*.py"]
activation: always  # always, glob, manual, ai_decision
---

# Guidelines
- Use type hints
- Follow PEP 8

7. Auto-Generated Memories

from praisonaiagents.memory import FileMemory, AutoMemory

memory = FileMemory(user_id="user123")
auto = AutoMemory(memory, enabled=True)

# Automatically extracts and stores memories from conversations
memories = auto.process_interaction(
    "My name is John and I prefer Python for backend work"
)
# Extracts: name="John", preference="Python for backend"

8. Agentic Workflows

Create powerful multi-agent workflows with the Workflow class:

from praisonaiagents import Agent, Workflow

# Create agents
researcher = Agent(
    name="Researcher",
    role="Research Analyst",
    goal="Research topics thoroughly",
    instructions="Provide concise, factual information."
)

writer = Agent(
    name="Writer",
    role="Content Writer", 
    goal="Write engaging content",
    instructions="Write clear, engaging content based on research."
)

# Create workflow with agents as steps
workflow = Workflow(steps=[researcher, writer])

# Run workflow - agents process sequentially
result = workflow.start("What are the benefits of AI agents?")
print(result["output"])

Key Features:

Agent-first - Pass Agent objects directly as workflow steps
Pattern helpers - Use route(), parallel(), loop(), repeat()
Planning mode - Enable with planning=True
Callbacks - Monitor with on_step_complete, on_workflow_complete
Async execution - Use workflow.astart() for async

Workflow Patterns (route, parallel, loop, repeat)

from praisonaiagents import Agent, Workflow
from praisonaiagents.workflows import route, parallel, loop, repeat

# 1. ROUTING - Classifier agent routes to specialized agents
classifier = Agent(name="Classifier", instructions="Respond with 'technical' or 'creative'")
tech_agent = Agent(name="TechExpert", role="Technical Expert")
creative_agent = Agent(name="Creative", role="Creative Writer")

workflow = Workflow(steps=[
    classifier,
    route({
        "technical": [tech_agent],
        "creative": [creative_agent]
    })
])

# 2. PARALLEL - Multiple agents work concurrently
market_agent = Agent(name="Market", role="Market Researcher")
competitor_agent = Agent(name="Competitor", role="Competitor Analyst")
aggregator = Agent(name="Aggregator", role="Synthesizer")

workflow = Workflow(steps=[
    parallel([market_agent, competitor_agent]),
    aggregator
])

# 3. LOOP - Agent processes each item
processor = Agent(name="Processor", role="Item Processor")
summarizer = Agent(name="Summarizer", role="Summarizer")

workflow = Workflow(
    steps=[loop(processor, over="items"), summarizer],
    variables={"items": ["AI", "ML", "NLP"]}
)

# 4. REPEAT - Evaluator-Optimizer pattern
generator = Agent(name="Generator", role="Content Generator")
evaluator = Agent(name="Evaluator", instructions="Say 'APPROVED' if good")

workflow = Workflow(steps=[
    generator,
    repeat(evaluator, until=lambda ctx: "approved" in ctx.previous_result.lower(), max_iterations=3)
])

# 5. CALLBACKS
workflow = Workflow(
    steps=[researcher, writer],
    on_step_complete=lambda name, r: print(f"✅ {name} done")
)

# 6. WITH PLANNING & REASONING
workflow = Workflow(
    steps=[researcher, writer],
    planning=True,
    reasoning=True
)

# 7. ASYNC EXECUTION
result = asyncio.run(workflow.astart("input"))

# 8. STATUS TRACKING
workflow.status  # "not_started" | "running" | "completed"
workflow.step_statuses  # {"step1": "completed", "step2": "skipped"}

YAML Workflow Template

# .praison/workflows/research.yaml
name: Research Workflow
description: Research and write content with all patterns

agents:
  researcher:
    role: Research Expert
    goal: Find accurate information
    tools: [tavily_search, web_scraper]
  writer:
    role: Content Writer
    goal: Write engaging content
  editor:
    role: Editor
    goal: Polish content

steps:
  # Sequential
  - agent: researcher
    action: Research {{topic}}
    output_variable: research_data

  # Routing
  - name: classifier
    action: Classify content type
    route:
      technical: [tech_handler]
      creative: [creative_handler]
      default: [general_handler]

  # Parallel
  - name: parallel_research
    parallel:
      - agent: researcher
        action: Research market
      - agent: researcher
        action: Research competitors

  # Loop
  - agent: writer
    action: Write about {{item}}
    loop_over: topics
    loop_var: item

  # Repeat (evaluator-optimizer)
  - agent: editor
    action: Review and improve
    repeat:
      until: "quality > 8"
      max_iterations: 3

  # Output to file
  - agent: writer
    action: Write final report
    output_file: output/{{topic}}_report.md

variables:
  topic: AI trends
  topics: [ML, NLP, Vision]

workflow:
  planning: true
  planning_llm: gpt-4o
  memory_config:
    provider: chroma
    persist: true

Loading YAML Workflows

from praisonaiagents.workflows import YAMLWorkflowParser, WorkflowManager

# Option 1: Parse YAML string
parser = YAMLWorkflowParser()
workflow = parser.parse_string(yaml_content)
result = workflow.start("Research AI trends")

# Option 2: Load from file with WorkflowManager
manager = WorkflowManager()
workflow = manager.load_yaml("research_workflow.yaml")
result = workflow.start("Research AI trends")

# Option 3: Execute YAML directly
result = manager.execute_yaml(
    "research_workflow.yaml",
    input_data="Research AI trends",
    variables={"topic": "Machine Learning"}
)

Complete workflow.yaml Reference

# workflow.yaml - Full feature reference
name: Complete Workflow
description: Demonstrates all workflow.yaml features
framework: praisonai  # praisonai, crewai, autogen
process: workflow     # sequential, hierarchical, workflow

workflow:
  planning: true
  planning_llm: gpt-4o
  reasoning: true
  verbose: true
  memory_config:
    provider: chroma
    persist: true

variables:
  topic: AI trends
  items: [ML, NLP, Vision]

agents:
  researcher:
    name: Researcher
    role: Research Analyst
    goal: Research topics thoroughly
    instructions: "Provide detailed research findings"
    backstory: "Expert researcher with 10 years experience"  # alias for instructions
    llm: gpt-4o-mini
    function_calling_llm: gpt-4o      # For tool calls
    max_rpm: 10                        # Rate limiting
    max_execution_time: 300            # Timeout in seconds
    reflect_llm: gpt-4o               # For self-reflection
    min_reflect: 1
    max_reflect: 3
    system_template: "You are a helpful assistant"
    tools:
      - tavily_search

  writer:
    name: Writer
    role: Content Writer
    goal: Write clear content
    instructions: "Write engaging content"

steps:
  - name: research_step
    agent: researcher
    action: "Research {{topic}}"
    expected_output: "Comprehensive research report"
    output_file: "output/research.md"
    create_directory: true
    
  - name: writing_step
    agent: writer
    action: "Write article based on research"
    context:                          # Task dependencies
      - research_step
    output_json:                      # Structured output
      type: object
      properties:
        title: { type: string }
        content: { type: string }

callbacks:
  on_workflow_start: log_start
  on_step_complete: log_step
  on_workflow_complete: log_complete

9. Hooks

Configure in .praison/hooks.json:

from praisonaiagents.memory import HooksManager

hooks = HooksManager()

# Register Python hooks
hooks.register("pre_write_code", lambda ctx: print(f"Writing {ctx['file']}"))

# Execute hooks
result = hooks.execute("pre_write_code", {"file": "main.py"})

10. Field Names Reference (A-I-G-S)

PraisonAI accepts both old (agents.yaml) and new (workflow.yaml) field names. Use the canonical names for new projects:

Canonical (Recommended)	Alias (Also Works)	Purpose
`agents`	`roles`	Define agent personas
`instructions`	`backstory`	Agent behavior/persona
`action`	`description`	What the step does
`steps`	`tasks` (nested)	Define work items
`name`	`topic`	Workflow identifier

A-I-G-S Mnemonic - Easy to remember:

Agents - Who does the work
Instructions - How they behave
Goal - What they achieve
Steps - What they do

# Quick Reference - Canonical Format
name: My Workflow              # Workflow name (not 'topic')
agents:                        # Define agents (not 'roles')
  my_agent:
    role: Job Title            # Agent's role
    goal: What to achieve      # Agent's goal
    instructions: How to act   # Agent's behavior (not 'backstory')
    
steps:                         # Define steps (not 'tasks')
  - agent: my_agent
    action: What to do         # Step action (not 'description')

Note: The parser accepts both old and new names. Run praisonai workflow validate <file.yaml> to see suggestions for canonical names.

11. Extended agents.yaml with Workflow Patterns

Feature Parity: Both agents.yaml and workflow.yaml now support the same features:

All workflow patterns (route, parallel, loop, repeat)
All agent fields (function_calling_llm, max_rpm, max_execution_time, reflect_llm, templates)
All step fields (expected_output, context, output_json, create_directory, callback)
Framework support (praisonai, crewai, autogen)
Process types (sequential, hierarchical, workflow)

You can use advanced workflow patterns directly in agents.yaml by setting process: workflow:

# agents.yaml with workflow patterns
framework: praisonai
process: workflow  # Enables workflow mode
topic: "Research AI trends"

workflow:
  planning: true
  reasoning: true
  verbose: true

variables:
  topic: AI trends

agents:  # Canonical: use 'agents' instead of 'roles'
  classifier:
    role: Request Classifier
    instructions: "Classify requests into categories"  # Canonical: use 'instructions' instead of 'backstory'
    goal: Classify requests
    
  researcher:
    role: Research Analyst
    instructions: "Expert researcher"  # Canonical: use 'instructions' instead of 'backstory'
    goal: Research topics
    tools:
      - tavily_search

steps:
  # Sequential step
  - agent: classifier
    action: "Classify: {{topic}}"
    
  # Route pattern - decision-based branching
  - name: routing
    route:
      technical: [tech_expert]
      default: [researcher]
      
  # Parallel pattern - concurrent execution
  - name: parallel_research
    parallel:
      - agent: researcher
        action: "Research market trends"
      - agent: researcher
        action: "Research competitors"
        
  # Loop pattern - iterate over items
  - agent: researcher
    action: "Analyze {{item}}"
    loop:
      over: topics
      
  # Repeat pattern - evaluator-optimizer
  - agent: aggregator
    action: "Synthesize findings"
    repeat:
      until: "comprehensive"
      max_iterations: 3

Run with the same simple command:

praisonai agents.yaml

🎯 CLI / No-Code Interface

PraisonAI provides a powerful CLI for no-code automation and quick prototyping.

Auto Mode

pip install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
praisonai --auto create a movie script about Robots in Mars

Query Rewriting (works with any command):

# Rewrite query for better results (uses QueryRewriterAgent)
praisonai "AI trends" --query-rewrite

# Rewrite with search tools (agent decides when to search)
praisonai "latest developments" --query-rewrite --rewrite-tools "internet_search"

# Works with any prompt
praisonai "explain quantum computing" --query-rewrite -v

Deep Research CLI:

# Default: OpenAI (o4-mini-deep-research)
praisonai research "What are the latest AI trends in 2025?"

# Use Gemini
praisonai research --model deep-research-pro "Your research query"

# Rewrite query before research
praisonai research --query-rewrite "AI trends"

# Rewrite with search tools
praisonai research --query-rewrite --rewrite-tools "internet_search" "AI trends"

# Use custom tools from file (gathers context before deep research)
praisonai research --tools tools.py "Your research query"
praisonai research -t my_tools.py "Your research query"

# Use built-in tools by name (comma-separated)
praisonai research --tools "internet_search,wiki_search" "Your query"
praisonai research -t "yfinance,calculator_tools" "Stock analysis query"

# Save output to file (output/research/{query}.md)
praisonai research --save "Your research query"
praisonai research -s "Your research query"

# Combine options
praisonai research --query-rewrite --tools tools.py --save "Your research query"

# Verbose mode (show debug logs)
praisonai research -v "Your research query"

Planning Mode CLI:

# Enable planning mode - agent creates a plan before execution
praisonai "Research AI trends and write a summary" --planning

# Planning with tools for research
praisonai "Analyze market trends" --planning --planning-tools tools.py

# Planning with chain-of-thought reasoning
praisonai "Complex analysis task" --planning --planning-reasoning

# Auto-approve plans without confirmation
praisonai "Task" --planning --auto-approve-plan

Memory CLI:

# Enable memory for agent (persists across sessions)
praisonai "My name is John" --memory

# Memory with user isolation
praisonai "Remember my preferences" --memory --user-id user123

# Memory management commands
praisonai memory show                      # Show memory statistics
praisonai memory add "User prefers Python" # Add to long-term memory
praisonai memory search "Python"           # Search memories
praisonai memory clear                     # Clear short-term memory
praisonai memory clear all                 # Clear all memory
praisonai memory save my_session           # Save session
praisonai memory resume my_session         # Resume session
praisonai memory sessions                  # List saved sessions
praisonai memory checkpoint                # Create checkpoint
praisonai memory restore <checkpoint_id>   # Restore checkpoint
praisonai memory checkpoints               # List checkpoints
praisonai memory help                      # Show all commands

Rules CLI:

# List all loaded rules (from PRAISON.md, CLAUDE.md, etc.)
praisonai rules list

# Show specific rule details
praisonai rules show <rule_name>

# Create a new rule
praisonai rules create my_rule "Always use type hints"

# Delete a rule
praisonai rules delete my_rule

# Show rules statistics
praisonai rules stats

# Include manual rules with prompts
praisonai "Task" --include-rules security,testing

Workflow CLI:

# List available workflows
praisonai workflow list

# Execute a workflow with tools and save output
praisonai workflow run "Research Blog" --tools tavily --save

# Execute with variables
praisonai workflow run deploy --workflow-var environment=staging --workflow-var branch=main

# Execute with planning mode (AI creates sub-steps for each workflow step)
praisonai workflow run "Research Blog" --planning --verbose

# Execute with reasoning mode (chain-of-thought)
praisonai workflow run "Analysis" --reasoning --verbose

# Execute with memory enabled
praisonai workflow run "Research" --memory

# Show workflow details
praisonai workflow show deploy

# Create a new workflow template
praisonai workflow create my_workflow

# Inline workflow (no template file needed)
praisonai "What is AI?" --workflow "Research,Summarize" --save

# Inline workflow with step actions
praisonai "GPT-5" --workflow "Research:Search for info,Write:Write blog" --tools tavily

# Workflow CLI help
praisonai workflow help

YAML Workflow Files:

# Run a YAML workflow file
praisonai workflow run research.yaml

# Run with variables
praisonai workflow run research.yaml --var topic="AI trends"

# Validate a YAML workflow
praisonai workflow validate research.yaml

# Create from template (simple, routing, parallel, loop, evaluator-optimizer)
praisonai workflow template routing --output my_workflow.yaml

Auto-Generate Workflows:

# Auto-generate a sequential workflow from topic
praisonai workflow auto "Research AI trends"

# Generate parallel workflow (multiple agents work concurrently)
praisonai workflow auto "Research AI trends" --pattern parallel

# Generate routing workflow (classifier routes to specialists)
praisonai workflow auto "Build a chatbot" --pattern routing

# Specify output file
praisonai workflow auto "Research AI" --pattern sequential --output my_workflow.yaml

Workflow CLI Options:

Flag	Description
`--workflow-var key=value`	Set workflow variable (can be repeated)
`--var key=value`	Set variable for YAML workflows
`--pattern <pattern>`	Pattern for auto-generation (sequential, parallel, routing, loop)
`--output <file>`	Output file for auto-generation
`--llm <model>`	LLM model (e.g., openai/gpt-4o-mini)
`--tools <tools>`	Tools (comma-separated, e.g., tavily)
`--planning`	Enable planning mode
`--reasoning`	Enable reasoning mode
`--memory`	Enable memory
`--verbose`	Enable verbose output
`--save`	Save output to file

Hooks CLI:

# List configured hooks
praisonai hooks list

# Show hooks statistics
praisonai hooks stats

# Create hooks.json template
praisonai hooks init

Claude Memory Tool CLI:

# Enable Claude Memory Tool (Anthropic models only)
praisonai "Research and remember findings" --claude-memory --llm anthropic/claude-sonnet-4-20250514

Guardrail CLI:

# Validate output with LLM guardrail
praisonai "Write code" --guardrail "Ensure code is secure and follows best practices"

# Combine with other flags
praisonai "Generate SQL query" --guardrail "No DROP or DELETE statements" --save

Metrics CLI:

# Display token usage and cost metrics
praisonai "Analyze this data" --metrics

# Combine with other features
praisonai "Complex task" --metrics --planning

Image Processing CLI:

# Process images with vision-based tasks
praisonai "Describe this image" --image path/to/image.png

# Analyze image content
praisonai "What objects are in this photo?" --image photo.jpg --llm openai/gpt-4o

Telemetry CLI:

# Enable usage monitoring and analytics
praisonai "Task" --telemetry

# Combine with metrics for full observability
praisonai "Complex analysis" --telemetry --metrics

MCP (Model Context Protocol) CLI:

# Use MCP server tools
praisonai "Search files" --mcp "npx -y @modelcontextprotocol/server-filesystem ."

# MCP with environment variables
praisonai "Search web" --mcp "npx -y @modelcontextprotocol/server-brave-search" --mcp-env "BRAVE_API_KEY=your_key"

# Multiple MCP options
praisonai "Task" --mcp "npx server" --mcp-env "KEY1=value1,KEY2=value2"

Fast Context CLI:

# Search codebase for relevant context
praisonai "Find authentication code" --fast-context ./src

# Add code context to any task
praisonai "Explain this function" --fast-context /path/to/project

Knowledge CLI:

# Add documents to knowledge base
praisonai knowledge add document.pdf
praisonai knowledge add ./docs/

# Search knowledge base
praisonai knowledge search "API authentication"

# List indexed documents
praisonai knowledge list

# Clear knowledge base
praisonai knowledge clear

# Show knowledge base info
praisonai knowledge info

# Show all commands
praisonai knowledge help

Session CLI:

# List all saved sessions
praisonai session list

# Show session details
praisonai session show my-project

# Resume a session (load into memory)
praisonai session resume my-project

# Delete a session
praisonai session delete my-project

# Auto-save session after each run
praisonai "Analyze this code" --auto-save my-project

# Load history from last N sessions into context
praisonai "Continue our discussion" --history 5

Session Management (Python):

from praisonaiagents import Agent

# Auto-save session after each run
agent = Agent(
    name="Assistant",
    memory=True,
    auto_save="my-project"
)

# Load history from last 5 sessions
agent = Agent(
    name="Assistant",
    memory=True,
    history_in_context=5
)

Workflow Checkpoints:

from praisonaiagents.memory.workflows import WorkflowManager

manager = WorkflowManager()

# Save checkpoint after each step
result = manager.execute("deploy", checkpoint="deploy-v1")

# Resume from checkpoint
result = manager.execute("deploy", resume="deploy-v1")

# List/delete checkpoints
manager.list_checkpoints()
manager.delete_checkpoint("deploy-v1")

Tools CLI:

# List all available tools
praisonai tools list

# Get info about a specific tool
praisonai tools info internet_search

# Search for tools
praisonai tools search "web"

# Show all commands
praisonai tools help

Handoff CLI:

# Enable agent-to-agent task delegation
praisonai "Research and write article" --handoff "researcher,writer,editor"

# Complex multi-agent workflow
praisonai "Analyze data and create report" --handoff "analyst,visualizer,writer"

Auto Memory CLI:

# Enable automatic memory extraction
praisonai "Learn about user preferences" --auto-memory

# Combine with user isolation
praisonai "Remember my settings" --auto-memory --user-id user123

Todo CLI:

# Generate todo list from task
praisonai "Plan the project" --todo

# Add a todo item
praisonai todo add "Implement feature X"

# List all todos
praisonai todo list

# Complete a todo
praisonai todo complete 1

# Delete a todo
praisonai todo delete 1

# Clear all todos
praisonai todo clear

# Show all commands
praisonai todo help

Router CLI:

# Auto-select best model based on task complexity
praisonai "Simple question" --router

# Specify preferred provider
praisonai "Complex analysis" --router --router-provider anthropic

# Router automatically selects:
# - Simple tasks → gpt-4o-mini, claude-3-haiku
# - Complex tasks → gpt-4-turbo, claude-3-opus

Flow Display CLI:

# Enable visual workflow tracking
praisonai agents.yaml --flow-display

# Combine with other features
praisonai "Multi-step task" --planning --flow-display

Docs CLI:

# List all project docs
praisonai docs list

# Create a new doc
praisonai docs create project-overview "This project is a Python web app..."

# Show a specific doc
praisonai docs show project-overview

# Delete a doc
praisonai docs delete old-doc

# Show all commands
praisonai docs help

MCP Config CLI:

# List all MCP configurations
praisonai mcp list

# Create a new MCP config
praisonai mcp create filesystem npx -y @modelcontextprotocol/server-filesystem .

# Show a specific config
praisonai mcp show filesystem

# Enable/disable a config
praisonai mcp enable filesystem
praisonai mcp disable filesystem

# Delete a config
praisonai mcp delete filesystem

# Show all commands
praisonai mcp help

AI Commit CLI:

# Generate AI commit message for staged changes
praisonai commit

# Generate, commit, and push
praisonai commit --push

@Mentions in Prompts:

# Include file content in prompt
praisonai "@file:src/main.py explain this code"

# Include project doc
praisonai "@doc:project-overview help me add a feature"

# Search the web
praisonai "@web:python best practices give me tips"

# Fetch URL content
praisonai "@url:https://docs.python.org summarize this"

# Combine multiple mentions
praisonai "@file:main.py @doc:coding-standards review this code"

Prompt Expansion

Expand short prompts into detailed, actionable prompts:

CLI Usage

# Expand a short prompt into detailed prompt
praisonai "write a movie script in 3 lines" --expand-prompt

# With verbose output
praisonai "blog about AI" --expand-prompt -v

# With tools for context gathering
praisonai "latest AI trends" --expand-prompt --expand-tools tools.py

# Combine with query rewrite
praisonai "AI news" --query-rewrite --expand-prompt

Programmatic Usage

from praisonaiagents import PromptExpanderAgent, ExpandStrategy

# Basic usage
agent = PromptExpanderAgent()
result = agent.expand("write a movie script in 3 lines")
print(result.expanded_prompt)

# With specific strategy
result = agent.expand("blog about AI", strategy=ExpandStrategy.DETAILED)

# Available strategies: BASIC, DETAILED, STRUCTURED, CREATIVE, AUTO

Key Difference:

--query-rewrite: Optimizes queries for search/retrieval (RAG)
--expand-prompt: Expands prompts for detailed task execution

Web Search, Web Fetch & Prompt Caching

CLI Usage

# Web Search - Get real-time information
praisonai "What are the latest AI news today?" --web-search --llm openai/gpt-4o-search-preview

# Web Fetch - Retrieve and analyze URL content (Anthropic only)
praisonai "Summarize https://docs.praison.ai" --web-fetch --llm anthropic/claude-sonnet-4-20250514

# Prompt Caching - Reduce costs for repeated prompts
praisonai "Analyze this document..." --prompt-caching --llm anthropic/claude-sonnet-4-20250514

Programmatic Usage

from praisonaiagents import Agent

# Web Search
agent = Agent(
    instructions="You are a research assistant",
    llm="openai/gpt-4o-search-preview",
    web_search=True
)

# Web Fetch (Anthropic only)
agent = Agent(
    instructions="You are a content analyzer",
    llm="anthropic/claude-sonnet-4-20250514",
    web_fetch=True
)

# Prompt Caching
agent = Agent(
    instructions="You are an AI assistant..." * 50,  # Long system prompt
    llm="anthropic/claude-sonnet-4-20250514",
    prompt_caching=True
)

Supported Providers:

Feature	Providers
Web Search	OpenAI, Gemini, Anthropic, xAI, Perplexity
Web Fetch	Anthropic
Prompt Caching	OpenAI (auto), Anthropic, Bedrock, Deepseek

MCP (Model Context Protocol)

PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.

MCP Client (Consume MCP Servers)

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"))

# SSE (Legacy) - Backward compatibility
agent = Agent(tools=MCP("http://localhost:8080/sse"))

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

MCP Server (Expose Tools as MCP Server)

Expose your Python functions as MCP tools for Claude Desktop, Cursor, and other MCP clients:

from praisonaiagents.mcp import ToolsMCPServer

def search_web(query: str, max_results: int = 5) -> dict:
    """Search the web for information."""
    return {"results": [f"Result for {query}"]}

def calculate(expression: str) -> dict:
    """Evaluate a mathematical expression."""
    return {"result": eval(expression)}

# Create and run MCP server
server = ToolsMCPServer(name="my-tools")
server.register_tools([search_web, calculate])
server.run()  # stdio for Claude Desktop
# server.run_sse(host="0.0.0.0", port=8080)  # SSE for web clients

MCP Features

Feature	Description
Session Management	Automatic Mcp-Session-Id handling
Protocol Versioning	Mcp-Protocol-Version header
Resumability	SSE stream recovery via Last-Event-ID
Security	Origin validation, DNS rebinding prevention
WebSocket	Auto-reconnect with exponential backoff

CLI Features

Feature	Docs
🔄 Query Rewrite - RAG optimization	📖
🔬 Deep Research - Automated research	📖
📋 Planning - Step-by-step execution	📖
💾 Memory - Persistent agent memory	📖
📜 Rules - Auto-discovered instructions	📖
🔄 Workflow - Multi-step workflows	📖
🪝 Hooks - Event-driven actions	📖
🧠 Claude Memory - Anthropic memory tool	📖
🛡️ Guardrail - Output validation	📖
📊 Metrics - Token usage tracking	📖
🖼️ Image - Vision processing	📖
📡 Telemetry - Usage monitoring	📖
🔌 MCP - Model Context Protocol	📖
⚡ Fast Context - Codebase search	📖
📚 Knowledge - RAG management	📖
💬 Session - Conversation management	📖
🔧 Tools - Tool discovery	📖
🤝 Handoff - Agent delegation	📖
🧠 Auto Memory - Memory extraction	📖
📋 Todo - Task management	📖
🎯 Router - Smart model selection	📖
📈 Flow Display - Visual workflow	📖
✨ Prompt Expansion - Detailed prompts	📖
🌐 Web Search - Real-time search	📖
📥 Web Fetch - URL content retrieval	📖
💾 Prompt Caching - Cost reduction	📖

💻 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');

⭐ Star History

📊 Process Types & Patterns

AI Agents Flow

graph LR
    %% Define the main flow
    Start([▶ Start]) --> Agent1
    Agent1 --> Process[⚙ Process]
    Process --> Agent2
    Agent2 --> Output([✓ Output])
    Process -.-> Agent1
    
    %% Define subgraphs for agents and their tasks
    subgraph Agent1[ ]
        Task1[📋 Task]
        AgentIcon1[🤖 AI Agent]
        Tools1[🔧 Tools]
        
        Task1 --- AgentIcon1
        AgentIcon1 --- Tools1
    end
    
    subgraph Agent2[ ]
        Task2[📋 Task]
        AgentIcon2[🤖 AI Agent]
        Tools2[🔧 Tools]
        
        Task2 --- AgentIcon2
        AgentIcon2 --- Tools2
    end

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Start,Output,Task1,Task2 input
    class Process,AgentIcon1,AgentIcon2 process
    class Tools1,Tools2 tools
    class Agent1,Agent2 transparent

AI Agents with Tools

Create AI agents that can use tools to interact with external systems and perform actions.

flowchart TB
    subgraph Tools
        direction TB
        T3[Internet Search]
        T1[Code Execution]
        T2[Formatting]
    end

    Input[Input] ---> Agents
    subgraph Agents
        direction LR
        A1[Agent 1]
        A2[Agent 2]
        A3[Agent 3]
    end
    Agents ---> Output[Output]

    T3 --> A1
    T1 --> A2
    T2 --> A3

    style Tools fill:#189AB4,color:#fff
    style Agents fill:#8B0000,color:#fff
    style Input fill:#8B0000,color:#fff
    style Output fill:#8B0000,color:#fff

AI Agents with Memory

Create AI agents with memory capabilities for maintaining context and information across tasks.

flowchart TB
    subgraph Memory
        direction TB
        STM[Short Term]
        LTM[Long Term]
    end

    subgraph Store
        direction TB
        DB[(Vector DB)]
    end

    Input[Input] ---> Agents
    subgraph Agents
        direction LR
        A1[Agent 1]
        A2[Agent 2]
        A3[Agent 3]
    end
    Agents ---> Output[Output]

    Memory <--> Store
    Store <--> A1
    Store <--> A2
    Store <--> A3

    style Memory fill:#189AB4,color:#fff
    style Store fill:#2E8B57,color:#fff
    style Agents fill:#8B0000,color:#fff
    style Input fill:#8B0000,color:#fff
    style Output fill:#8B0000,color:#fff

AI Agents with Different Processes

Sequential Process

The simplest form of task execution where tasks are performed one after another.

graph LR
    Input[Input] --> A1
    subgraph Agents
        direction LR
        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
    end
    A3 --> Output[Output]

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Input,Output input
    class A1,A2,A3 process
    class Agents transparent

Hierarchical Process

Uses a manager agent to coordinate task execution and agent assignments.

graph TB
    Input[Input] --> Manager
    
    subgraph Agents
        Manager[Manager Agent]
        
        subgraph Workers
            direction LR
            W1[Worker 1]
            W2[Worker 2]
            W3[Worker 3]
        end
        
        Manager --> W1
        Manager --> W2
        Manager --> W3
    end
    
    W1 --> Manager
    W2 --> Manager
    W3 --> Manager
    Manager --> Output[Output]

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Input,Output input
    class Manager,W1,W2,W3 process
    class Agents,Workers transparent

Workflow Process

Advanced process type supporting complex task relationships and conditional execution.

graph LR
    Input[Input] --> Start
    
    subgraph Workflow
        direction LR
        Start[Start] --> C1{Condition}
        C1 --> |Yes| A1[Agent 1]
        C1 --> |No| A2[Agent 2]
        A1 --> Join
        A2 --> Join
        Join --> A3[Agent 3]
    end
    
    A3 --> Output[Output]

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
    classDef transparent fill:none,stroke:none

    class Input,Output input
    class Start,A1,A2,A3,Join process
    class C1 decision
    class Workflow transparent

Agentic Routing Workflow

Create AI agents that can dynamically route tasks to specialized LLM instances.

flowchart LR
    In[In] --> Router[LLM Call Router]
    Router --> LLM1[LLM Call 1]
    Router --> LLM2[LLM Call 2]
    Router --> LLM3[LLM Call 3]
    LLM1 --> Out[Out]
    LLM2 --> Out
    LLM3 --> Out
    
    style In fill:#8B0000,color:#fff
    style Router fill:#2E8B57,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

Agentic Orchestrator Worker

Create AI agents that orchestrate and distribute tasks among specialized workers.

flowchart LR
    In[In] --> Router[LLM Call Router]
    Router --> LLM1[LLM Call 1]
    Router --> LLM2[LLM Call 2]
    Router --> LLM3[LLM Call 3]
    LLM1 --> Synthesizer[Synthesizer]
    LLM2 --> Synthesizer
    LLM3 --> Synthesizer
    Synthesizer --> Out[Out]
    
    style In fill:#8B0000,color:#fff
    style Router fill:#2E8B57,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Synthesizer fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

Agentic Autonomous Workflow

Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.

flowchart LR
    Human[Human] <--> LLM[LLM Call]
    LLM -->|ACTION| Environment[Environment]
    Environment -->|FEEDBACK| LLM
    LLM --> Stop[Stop]
    
    style Human fill:#8B0000,color:#fff
    style LLM fill:#2E8B57,color:#fff
    style Environment fill:#8B0000,color:#fff
    style Stop fill:#333,color:#fff

Agentic Parallelization

Create AI agents that can execute tasks in parallel for improved performance.

flowchart LR
    In[In] --> LLM2[LLM Call 2]
    In --> LLM1[LLM Call 1]
    In --> LLM3[LLM Call 3]
    LLM1 --> Aggregator[Aggregator]
    LLM2 --> Aggregator
    LLM3 --> Aggregator
    Aggregator --> Out[Out]
    
    style In fill:#8B0000,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Aggregator fill:#fff,color:#000
    style Out fill:#8B0000,color:#fff

Agentic Prompt Chaining

Create AI agents with sequential prompt chaining for complex workflows.

flowchart LR
    In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
    Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
    Gate -->|Fail| Exit[Exit]
    
    style In fill:#8B0000,color:#fff
    style LLM1 fill:#2E8B57,color:#fff
    style LLM2 fill:#2E8B57,color:#fff
    style LLM3 fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
    style Exit fill:#8B0000,color:#fff

Agentic Evaluator Optimizer

Create AI agents that can generate and optimize solutions through iterative feedback.

flowchart LR
    In[In] --> Generator[LLM Call Generator] 
    Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
    Evaluator -->|REJECTED + FEEDBACK| Generator
    
    style In fill:#8B0000,color:#fff
    style Generator fill:#2E8B57,color:#fff
    style Evaluator fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

Repetitive Agents

Create AI agents that can efficiently handle repetitive tasks through automated loops.

flowchart LR
    In[Input] --> LoopAgent[("Looping Agent")]
    LoopAgent --> Task[Task]
    Task --> |Next iteration| LoopAgent
    Task --> |Done| Out[Output]
    
    style In fill:#8B0000,color:#fff
    style LoopAgent fill:#2E8B57,color:#fff,shape:circle
    style Task fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff

🔧 Configuration & Integration

Ollama Integration

export OPENAI_BASE_URL=http://localhost:11434/v1

Groq Integration

Replace xxxx with Groq API KEY:

export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://api.groq.com/openai/v1

100+ Models Support

PraisonAI supports 100+ LLM models from various providers. Visit our models documentation for the complete list.

📋 Agents Playbook

Simple Playbook Example

Create agents.yaml file and add the code below:

framework: praisonai
topic: Artificial Intelligence
agents:  # Canonical: use 'agents' instead of 'roles'
  screenwriter:
    instructions: "Skilled in crafting scripts with engaging dialogue about {topic}."  # Canonical: use 'instructions' instead of 'backstory'
    goal: Create scripts from concepts.
    role: Screenwriter
    tasks:
      scriptwriting_task:
        description: "Develop scripts with compelling characters and dialogue about {topic}."
        expected_output: "Complete script ready for production."

To run the playbook:

praisonai agents.yaml

🛠️ Custom Tools / Create Plugins

PraisonAI supports multiple ways to create and integrate custom tools (plugins) into your agents.

Using `@tool` Decorator

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")

Using `BaseTool` Class

from praisonaiagents import Agent, BaseTool

class WeatherTool(BaseTool):
    name = "weather"
    description = "Get current weather for a location"
    
    def run(self, location: str) -> str:
        return f"Weather in {location}: 72°F, Sunny"

agent = Agent(
    instructions="You are a weather assistant",
    tools=[WeatherTool()]
)
agent.start("What's the weather in Paris?")

Creating a Tool Package (pip installable)

# pyproject.toml
[project]
name = "my-praisonai-tools"
version = "1.0.0"
dependencies = ["praisonaiagents"]

[project.entry-points."praisonaiagents.tools"]
my_tool = "my_package:MyTool"

# my_package/__init__.py
from praisonaiagents import BaseTool

class MyTool(BaseTool):
    name = "my_tool"
    description = "My custom tool"
    
    def run(self, param: str) -> str:
        return f"Result: {param}"

After pip install, tools are auto-discovered:

agent = Agent(tools=["my_tool"])  # Works automatically!

🧠 Memory & Context

PraisonAI provides zero-dependency persistent memory for agents. For detailed examples, see section 6. Agent Memory in the Python Code Examples.

🔬 Advanced Features

Research & Intelligence

🔬 Deep Research Agents - OpenAI & Gemini support for automated research
🔄 Query Rewriter Agent - HyDE, Step-back, Multi-query strategies for RAG optimization
🌐 Native Web Search - Real-time search via OpenAI, Gemini, Anthropic, xAI, Perplexity
📥 Web Fetch - Retrieve full content from URLs (Anthropic)
📝 Prompt Expander Agent - Expand short prompts into detailed instructions

Memory & Caching

💾 Prompt Caching - Reduce costs & latency (OpenAI, Anthropic, Bedrock, Deepseek)
🧠 Claude Memory Tool - Persistent cross-conversation memory (Anthropic Beta)
💾 File-Based Memory - Zero-dependency persistent memory for all agents
🔍 Built-in Search Tools - Tavily, You.com, Exa for web search, news, content extraction

Planning & Workflows

📋 Planning Mode - Plan before execution for agents & multi-agent systems
🔧 Planning Tools - Research with tools during planning phase
🧠 Planning Reasoning - Chain-of-thought planning for complex tasks
⛓️ Prompt Chaining - Sequential prompt workflows with conditional gates
🔍 Evaluator Optimiser - Generate and optimize through iterative feedback
👷 Orchestrator Workers - Distribute tasks among specialised workers
⚡ Parallelisation - Execute tasks in parallel for improved performance
🔁 Repetitive Agents - Handle repetitive tasks through automated loops
🤖 Autonomous Workflow - Monitor, act, adapt based on environment feedback

Specialised Agents

🖼️ Image Generation Agent - Create images from text descriptions
📷 Image to Text Agent - Extract text and descriptions from images
🎬 Video Agent - Analyse and process video content
📊 Data Analyst Agent - Analyse data and generate insights
💰 Finance Agent - Financial analysis and recommendations
🛒 Shopping Agent - Price comparison and shopping assistance
⭐ Recommendation Agent - Personalised recommendations
📖 Wikipedia Agent - Search and extract Wikipedia information
💻 Programming Agent - Code development and analysis
📝 Markdown Agent - Generate and format Markdown content
🔀 Router Agent - Dynamic task routing with cost optimisation

MCP Protocol

🔌 MCP Transports - stdio, Streamable HTTP, WebSocket, SSE (Protocol 2025-11-25)
🌐 WebSocket MCP - Real-time bidirectional connections with auto-reconnect
🔐 MCP Security - Origin validation, DNS rebinding prevention, secure sessions
🔄 MCP Resumability - SSE stream recovery via Last-Event-ID

Safety & Control

🤝 Agent Handoffs - Transfer context between specialised agents
🛡️ Guardrails - Input/output validation and safety checks
✅ Human Approval - Require human confirmation for critical actions
💬 Sessions Management - Isolated conversation contexts
🔄 Stateful Agents - Maintain state across interactions

Developer Tools

⚡ Fast Context - Rapid parallel code search (10-20x faster)
📜 Rules & Instructions - Auto-discover CLAUDE.md, AGENTS.md, GEMINI.md
🪝 Hooks - Pre/post operation hooks for custom logic
📈 Telemetry - Track agent performance and usage
📹 Camera Integration - Capture and analyse camera input

Other Features

🔄 CrewAI & AG2 Integration - Use CrewAI or AG2 (Formerly AutoGen) Framework
💻 Codebase Chat - Chat with entire codebase
🎨 Interactive UIs - Multiple interactive interfaces
📄 YAML Configuration - YAML-based agent and workflow configuration
🛠️ Custom Tools - Easy custom tool integration
🔍 Internet Search - Multiple providers (Tavily, You.com, Exa, DuckDuckGo, Crawl4AI)
🖼️ VLM Support - Vision Language Model support
🎙️ Voice Interaction - Real-time voice interaction

🎓 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 from the community! Here's how you can contribute:

Fork on GitHub - Use the "Fork" button on the repository page
Clone your fork - git clone https://github.com/yourusername/praisonAI.git
Create a branch - git checkout -b new-feature
Make changes and commit - git commit -am "Add some feature"
Push to your fork - git push origin new-feature
Submit a pull request - Via GitHub's web interface
Await feedback - From project maintainers

🔧 Development

Using uv

# Install uv if you haven't already
pip install uv

# Install from requirements
uv pip install -r pyproject.toml

# Install with extras
uv pip install -r pyproject.toml --extra code
uv pip install -r pyproject.toml --extra "crewai,autogen"

Bump and Release

# From project root - bumps version and releases in one command
python src/praisonai/scripts/bump_and_release.py 2.2.99

# With praisonaiagents dependency
python src/praisonai/scripts/bump_and_release.py 2.2.99 --agents 0.0.169

# Then publish
cd src/praisonai && uv publish

Made with ❤️ by the PraisonAI Team

Documentation • GitHub • Issues

Name		Name	Last commit message	Last commit date
Latest commit History 2,339 Commits
.github		.github
docker		docker
examples		examples
src		src
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md

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MervinPraison/PraisonAI

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Praison AI

📑 Table of Contents

🚀 Quick Start

📦 Installation

Python SDK

JavaScript SDK

💻 Usage

Python Code Examples

CLI / No-Code Interface

JavaScript Code Examples

✨ Key Features

🌐 Supported Providers

📘 Using Python Code

1. Single Agent

2. Multi Agents

3. Agent with Planning Mode

4. Deep Research Agent

5. Query Rewriter Agent

6. Agent Memory (Zero Dependencies)

6. Rules & Instructions

7. Auto-Generated Memories

8. Agentic Workflows

Workflow Patterns (route, parallel, loop, repeat)

YAML Workflow Template

Loading YAML Workflows

Complete workflow.yaml Reference

9. Hooks

10. Field Names Reference (A-I-G-S)

11. Extended agents.yaml with Workflow Patterns

🎯 CLI / No-Code Interface

Auto Mode

Query Rewriting (works with any command):

Deep Research CLI:

Planning Mode CLI:

Memory CLI:

Rules CLI:

Workflow CLI:

YAML Workflow Files:

Auto-Generate Workflows:

Hooks CLI:

Claude Memory Tool CLI:

Guardrail CLI:

Metrics CLI:

Image Processing CLI:

Telemetry CLI:

MCP (Model Context Protocol) CLI:

Fast Context CLI:

Knowledge CLI:

Session CLI:

Session Management (Python):

Workflow Checkpoints:

Tools CLI:

Handoff CLI:

Auto Memory CLI:

Todo CLI:

Router CLI:

Flow Display CLI:

Docs CLI:

MCP Config CLI:

AI Commit CLI:

@Mentions in Prompts:

Prompt Expansion

CLI Usage

Programmatic Usage

Web Search, Web Fetch & Prompt Caching

CLI Usage

Programmatic Usage

MCP (Model Context Protocol)

MCP Client (Consume MCP Servers)

MCP Server (Expose Tools as MCP Server)

MCP Features

CLI Features

💻 Using JavaScript Code

Using `@tool` Decorator

Using `BaseTool` Class

Packages