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Release v3.11.0
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‎api.md‎

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‎docker/Dockerfile.chat‎

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@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
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# Install Python packages (using latest versions)
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RUN pip install --no-cache-dir \
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praisonai_tools \
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"praisonai>=3.10.27" \
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"praisonai>=3.11.0" \
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"praisonai[chat]" \
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"embedchain[github,youtube]"
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‎docker/Dockerfile.dev‎

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@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
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# Install Python packages (using latest versions)
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RUN pip install --no-cache-dir \
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praisonai_tools \
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"praisonai>=3.10.27" \
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"praisonai>=3.11.0" \
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"praisonai[ui]" \
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"praisonai[chat]" \
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"praisonai[realtime]" \

‎docker/Dockerfile.ui‎

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@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
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# Install Python packages (using latest versions)
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RUN pip install --no-cache-dir \
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praisonai_tools \
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"praisonai>=3.10.27" \
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"praisonai>=3.11.0" \
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"praisonai[ui]" \
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"praisonai[crewai]"
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‎examples/python/bot_example.py‎

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"""
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Messaging Bot Example - Deploy Agents to Telegram, Discord, Slack
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This example demonstrates how to configure bots for different
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messaging platforms.
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"""
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import os
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from praisonaiagents import Agent, BotConfig
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# Telegram Bot Configuration
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telegram_config = BotConfig(
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token=os.environ.get("TELEGRAM_BOT_TOKEN", ""),
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command_prefix="/",
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mention_required=True,
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typing_indicator=True,
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max_message_length=4096,
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metadata={"platform": "telegram"}
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)
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# Discord Bot Configuration
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discord_config = BotConfig(
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token=os.environ.get("DISCORD_BOT_TOKEN", ""),
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command_prefix="!",
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mention_required=True,
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typing_indicator=True,
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metadata={
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"platform": "discord",
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"guild_ids": [] # Add your guild IDs here
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}
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)
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# Slack Bot Configuration
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slack_config = BotConfig(
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token=os.environ.get("SLACK_BOT_TOKEN", ""),
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command_prefix="/",
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mention_required=True,
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typing_indicator=True,
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metadata={
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"platform": "slack",
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"app_token": os.environ.get("SLACK_APP_TOKEN", "")
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}
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)
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# Create the agent that will handle messages
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assistant = Agent(
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name="assistant",
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instructions="""You are a helpful assistant that responds to messages
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from users on messaging platforms. Be friendly and concise.""",
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llm="gpt-4o-mini"
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)
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if __name__ == "__main__":
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print("Bot Configurations:")
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print()
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print("Telegram Config:")
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print(f" Token configured: {bool(telegram_config.token)}")
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print(f" Command prefix: {telegram_config.command_prefix}")
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print(f" Mention required: {telegram_config.mention_required}")
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print()
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print("Discord Config:")
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print(f" Token configured: {bool(discord_config.token)}")
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print(f" Command prefix: {discord_config.command_prefix}")
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print()
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print("Slack Config:")
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print(f" Token configured: {bool(slack_config.token)}")
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print(f" Command prefix: {slack_config.command_prefix}")
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print()
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print("To start a bot, use the CLI:")
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print(" praisonai bot telegram --token $TELEGRAM_BOT_TOKEN")
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print(" praisonai bot discord --token $DISCORD_BOT_TOKEN")
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print(" praisonai bot slack --token $SLACK_BOT_TOKEN")

‎examples/python/failover_example.py‎

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"""
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Model Failover Example - Automatic Provider Switching
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This example demonstrates how to configure model failover
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for reliability and cost optimization.
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"""
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import os
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from praisonaiagents import (
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AuthProfile,
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FailoverConfig,
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FailoverManager,
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)
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# Create auth profiles for different providers
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openai_profile = AuthProfile(
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name="openai-primary",
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provider="openai",
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api_key=os.environ.get("OPENAI_API_KEY", "sk-..."),
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priority=1, # Highest priority
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rate_limit=100,
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)
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anthropic_profile = AuthProfile(
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name="anthropic-backup",
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provider="anthropic",
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api_key=os.environ.get("ANTHROPIC_API_KEY", "sk-ant-..."),
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priority=2, # Second priority
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rate_limit=50,
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)
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groq_profile = AuthProfile(
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name="groq-fallback",
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provider="groq",
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api_key=os.environ.get("GROQ_API_KEY", "gsk-..."),
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priority=3, # Third priority (fast but limited)
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rate_limit=30,
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)
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# Configure failover behavior
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failover_config = FailoverConfig(
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max_retries=3,
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retry_delay=1.0,
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exponential_backoff=True,
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max_retry_delay=60.0,
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failover_on_rate_limit=True,
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failover_on_timeout=True,
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failover_on_error=True,
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)
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# Create failover manager
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manager = FailoverManager(failover_config)
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manager.add_profile(openai_profile)
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manager.add_profile(anthropic_profile)
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manager.add_profile(groq_profile)
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def demonstrate_failover():
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"""Demonstrate failover functionality."""
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print("Failover Manager Status:")
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print()
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# Get current status
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status = manager.status()
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for name, info in status.items():
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print(f" {name}:")
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print(f" Status: {info.get('status', 'unknown')}")
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print(f" Priority: {info.get('priority', 'N/A')}")
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print(f" Failures: {info.get('failure_count', 0)}")
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print()
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# Get next available profile
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next_profile = manager.get_next_profile()
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if next_profile:
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print(f"Next profile to use: {next_profile.name}")
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print(f" Provider: {next_profile.provider}")
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print(f" Priority: {next_profile.priority}")
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print()
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# Simulate marking a failure
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print("Simulating failure on primary provider...")
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manager.mark_failure("openai-primary", "Rate limit exceeded")
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# Get next profile after failure
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next_profile = manager.get_next_profile()
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if next_profile:
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print(f"After failure, next profile: {next_profile.name}")
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print()
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# Get retry delay
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delay = manager.get_retry_delay("openai-primary")
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print(f"Retry delay for openai-primary: {delay}s")
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print()
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# Reset all providers
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print("Resetting all providers...")
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manager.reset_all()
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next_profile = manager.get_next_profile()
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if next_profile:
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print(f"After reset, next profile: {next_profile.name}")
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def cost_optimization_example():
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"""Example of cost-optimized failover."""
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print("\nCost Optimization Example:")
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print("-" * 40)
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# Create cost-optimized manager
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cost_manager = FailoverManager()
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# Cheaper model first
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cost_manager.add_profile(AuthProfile(
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name="gpt-4o-mini",
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provider="openai",
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api_key=os.environ.get("OPENAI_API_KEY", ""),
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priority=1,
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metadata={"model": "gpt-4o-mini", "cost_per_1k": 0.00015}
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))
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# Premium model as fallback
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cost_manager.add_profile(AuthProfile(
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name="gpt-4o",
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provider="openai",
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api_key=os.environ.get("OPENAI_API_KEY", ""),
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priority=2,
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metadata={"model": "gpt-4o", "cost_per_1k": 0.005}
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))
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# Most expensive as last resort
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cost_manager.add_profile(AuthProfile(
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name="claude-opus",
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provider="anthropic",
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api_key=os.environ.get("ANTHROPIC_API_KEY", ""),
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priority=3,
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metadata={"model": "claude-3-opus", "cost_per_1k": 0.015}
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))
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print("Cost-optimized failover chain:")
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for profile in [
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cost_manager.get_profile("gpt-4o-mini"),
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cost_manager.get_profile("gpt-4o"),
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cost_manager.get_profile("claude-opus"),
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]:
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if profile:
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cost = profile.metadata.get("cost_per_1k", "N/A")
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print(f" {profile.priority}. {profile.name} (${cost}/1K tokens)")
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if __name__ == "__main__":
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print("Model Failover Configuration")
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print("=" * 40)
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print()
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print("Failover Config:")
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print(f" Max retries: {failover_config.max_retries}")
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print(f" Initial delay: {failover_config.retry_delay}s")
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print(f" Exponential backoff: {failover_config.exponential_backoff}")
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print(f" Max delay: {failover_config.max_retry_delay}s")
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print()
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print("Auth Profiles:")
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for profile in [openai_profile, anthropic_profile, groq_profile]:
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print(f" {profile.name}:")
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print(f" Provider: {profile.provider}")
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print(f" Priority: {profile.priority}")
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print(f" Rate limit: {profile.rate_limit} req/min")
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print()
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demonstrate_failover()
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cost_optimization_example()

‎examples/python/gateway_example.py‎

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"""
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Gateway Example - Multi-Agent Coordination
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This example demonstrates how to use the Gateway for coordinating
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multiple agents and managing real-time communication.
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"""
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from praisonaiagents import Agent, GatewayConfig, SessionConfig
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# Configure the gateway
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gateway_config = GatewayConfig(
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host="127.0.0.1",
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port=8765,
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max_connections=100,
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heartbeat_interval=30,
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session_config=SessionConfig(
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timeout=3600, # 1 hour session timeout
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max_messages=500,
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)
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)
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# Create specialized agents
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researcher = Agent(
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name="researcher",
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instructions="You research topics thoroughly and provide detailed information.",
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llm="gpt-4o-mini"
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)
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writer = Agent(
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name="writer",
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instructions="You write clear, engaging content based on research.",
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llm="gpt-4o-mini"
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)
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# Example: Simple agent interaction
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if __name__ == "__main__":
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print("Gateway Configuration:")
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print(f" Host: {gateway_config.host}")
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print(f" Port: {gateway_config.port}")
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print(f" WebSocket URL: {gateway_config.ws_url}")
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print(f" Max Connections: {gateway_config.max_connections}")
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print()
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# Test agent
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response = researcher.start("What are the key benefits of multi-agent systems?")
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print("Researcher Response:")
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print(response)

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