<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="docs/logo/dark.png" />
    <source media="(prefers-color-scheme: light)" srcset="docs/logo/light.png" />
    <img alt="PraisonAI Logo" src="docs/logo/light.png" />
  </picture>
</p>

<p align="center">
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" /></a>
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" /></a>
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a>
</p>

<div align="center">

# Praison AI

<a href="https://trendshift.io/repositories/9130" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison%2FPraisonAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>

</div>

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.

<div align="center">
  <a href="https://docs.praison.ai">
    <p align="center">
      <img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
    </p>
  </a>
</div>

## Key Features

- 🤖 Automated AI Agents Creation
- 🔄 Self Reflection AI Agents
- 🧠 Reasoning AI Agents
- 👁️ Multi Modal AI Agents
- 🤝 Multi Agent Collaboration
- 🎭 AI Agent Workflow
- 📚 Add Custom Knowledge
- 🧠 Agents with Short and Long Term Memory
- 📄 Chat with PDF Agents
- 💻 Code Interpreter Agents
- 📚 RAG Agents
- 🤔 Async & Parallel Processing
- 🔄 Auto Agents
- 🔢 Math Agents
- 🎯 Structured Output Agents
- 🔗 LangChain Integrated Agents
- 📞 Callback Agents
- 🤏 Mini AI Agents
- 🛠️ 100+ Custom Tools
- 📄 YAML Configuration
- 💯 100+ LLM Support
- 🔬 **Deep Research Agents** (OpenAI & Gemini)
- 🔄 **Query Rewriter Agent** (HyDE, Step-back, Multi-query)
- 🌐 **Native Web Search** (OpenAI, Gemini, Anthropic, xAI, Perplexity)
- 📥 **Web Fetch** (Retrieve full content from URLs - Anthropic)
- 💾 **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)
- 📋 **Planning Mode** (Plan before execution - Agent & Multi-Agent)
- 🔧 **Planning Tools** (Research with tools during planning)
- 🧠 **Planning Reasoning** (Chain-of-thought planning)

## Using Python Code

Light weight package dedicated for coding:
```bash
pip install praisonaiagents
```

```bash
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
```

### 1. Single Agent

Create app.py file and add the code below:
```python
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:
```bash
python app.py
```

### 2. Multi Agents

Create app.py file and add the code below:
```python
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:
```bash
python app.py
```

### 3. Agent with Planning Mode

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

```python
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:**
1. 📋 Agent creates a multi-step plan
2. 🚀 Executes each step sequentially
3. 📊 Shows progress with context passing
4. ✅ Returns final result

### 4. Deep Research Agent

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

```python
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)}")
```

```python
# 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.

```python
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.

```python
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:**
```python
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 |

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

**Rule File Format (with YAML frontmatter):**
```markdown
---
description: Python coding guidelines
globs: ["**/*.py"]
activation: always  # always, glob, manual, ai_decision
---

# Guidelines
- Use type hints
- Follow PEP 8
```

### 7. Auto-Generated Memories

```python
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. Workflows

Create reusable multi-step workflows in `.praison/workflows/`:

```python
from praisonaiagents.memory import WorkflowManager

manager = WorkflowManager()

# Execute a workflow
result = manager.execute(
    "deploy",
    executor=lambda prompt: agent.chat(prompt),
    variables={"environment": "production"}
)
```

### 9. Hooks

Configure in `.praison/hooks.json`:

```python
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"})
```

## Using No Code

### Auto Mode:
```bash
pip install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
praisonai --auto create a movie script about Robots in Mars
```

### Query Rewriting (works with any command):
```bash
# 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:
```bash
# 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:
```bash
# 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:
```bash
# 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:
```bash
# 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:
```bash
# List available workflows
praisonai workflow list

# Execute a workflow
praisonai workflow run deploy

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

# Show workflow details
praisonai workflow show deploy

# Create a new workflow template
praisonai workflow create my_workflow
```

### Hooks CLI:
```bash
# List configured hooks
praisonai hooks list

# Show hooks statistics
praisonai hooks stats

# Create hooks.json template
praisonai hooks init
```

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

## Using JavaScript Code

```bash
npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
```

```javascript
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');
```

![PraisonAI CLI Demo](docs/demo/praisonai-cli-demo.gif)

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)

## AI Agents Flow

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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.

```mermaid
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
```

## Adding Models

<div align="center">
  <a href="https://docs.praison.ai/models">
    <p align="center">
      <img src="https://img.shields.io/badge/%F0%9F%93%9A_Models-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Models" />
    </p>
  </a>
</div>

## Ollama Integration
```bash
export OPENAI_BASE_URL=http://localhost:11434/v1
```

## Groq Integration
Replace xxxx with Groq API KEY:
```bash
export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
```

## No Code Options

## Agents Playbook

### Simple Playbook Example

Create `agents.yaml` file and add the code below:

```yaml
framework: praisonai
topic: Artificial Intelligence
roles:
  screenwriter:
    backstory: "Skilled in crafting scripts with engaging dialogue about {topic}."
    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:*
```bash
praisonai agents.yaml
```

## Use 100+ Models

- https://docs.praison.ai/models/
<div align="center">
  <a href="https://docs.praison.ai">
    <p align="center">
      <img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
    </p>
  </a>
</div>


## Custom Tools

### Using `@tool` Decorator

```python
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

```python
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)

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

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

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


## Prompt Expansion

Expand short prompts into detailed, actionable prompts:

### CLI Usage

```bash
# 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

```python
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

```bash
# 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

```python
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 |


## Development:

Below is used for development only.

### Using uv
```bash
# 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

```bash
# 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
```

## Contributing

- 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.

## Other Features

- 🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
- 💻 Chat with ENTIRE Codebase
- 🎨 Interactive UIs
- 📄 YAML-based Configuration
- 🛠️ Custom Tool Integration
- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
- 🖼️ Vision Language Model (VLM) Support
- 🎙️ Real-time Voice Interaction

## Video Tutorials

| Topic | Video |
|-------|--------|
| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
| Reasoning Data Generating Agent | [![Reasoning Data](https://img.youtube.com/vi/fUT332Y2zA8/0.jpg)](https://www.youtube.com/watch?v=fUT332Y2zA8) |
| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
| Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
| Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
| Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
| Firecrawl Integration | [![Firecrawl](https://img.youtube.com/vi/UoqUDcLcOYo/0.jpg)](https://www.youtube.com/watch?v=UoqUDcLcOYo) |
| User Interface | [![UI](https://img.youtube.com/vi/tg-ZjNl3OCg/0.jpg)](https://www.youtube.com/watch?v=tg-ZjNl3OCg) |
| Crawl4AI Integration | [![Crawl4AI](https://img.youtube.com/vi/KAvuVUh0XU8/0.jpg)](https://www.youtube.com/watch?v=KAvuVUh0XU8) |
| Chat Interface | [![Chat](https://img.youtube.com/vi/sw3uDqn2h1Y/0.jpg)](https://www.youtube.com/watch?v=sw3uDqn2h1Y) |
| Code Interface | [![Code](https://img.youtube.com/vi/_5jQayO-MQY/0.jpg)](https://www.youtube.com/watch?v=_5jQayO-MQY) |
| Mem0 Integration | [![Mem0](https://img.youtube.com/vi/KIGSgRxf1cY/0.jpg)](https://www.youtube.com/watch?v=KIGSgRxf1cY) |
| Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
| Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
| Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
| Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |

