from praisonaiagents import Workflow, WorkflowStep, ContextConfig

# Workflow with context management
workflow = Workflow(
    steps=[...],
    context=True,  # Enable with defaults
)

# Or with custom config
workflow = Workflow(
    steps=[...],
    context=ContextConfig(
        auto_compact=True,
        compact_threshold=0.8,
        strategy="smart",
        tool_output_max=10000,  # Limit tool outputs (e.g., search results)
    ),
)

name: Research Workflow
context: true  # Enable auto-compaction

# Or detailed config
context:
  auto_compact: true
  compact_threshold: 0.8
  strategy: smart
  tool_output_max: 10000

agents:
  researcher:
    role: Researcher
    tools:
      - tavily_search

steps:
  - agent: researcher
    action: "Research {{topic}}"

from praisonaiagents import Agent, ContextConfig, OptimizerStrategy

agent = Agent(
    instructions="You are a helpful assistant",
    context=ContextConfig(
        strategy=OptimizerStrategy.SLIDING_WINDOW,
        keep_recent_turns=10,
    )
)

from praisonaiagents.context import SessionContextTracker

# Create tracker
tracker = SessionContextTracker(
    session_id="user123",
    track_summary=True,
    track_goal=True,
    track_plan=True,
    track_progress=True,
)

# Update state
tracker.update_goal("Build a Python web app")
tracker.update_plan([
    "1. Create Flask project",
    "2. Add routes",
    "3. Add database",
    "4. Deploy"
])

# Mark progress
tracker.add_progress("Created Flask project")
tracker.add_progress("Added routes")

# Get context for prompt
context = tracker.to_system_prompt_section()
print(context)

<session_context>
This is a continuation of an ongoing session. Here's where things stand:

**User's Goal**: Build a Python web app

**Plan**:
  1. Create Flask project
  2. Add routes
  3. Add database
  4. Deploy

**Progress**:
  ✓ Created Flask project
  ✓ Added routes

<session_context_guidelines>
Use this context to maintain continuity:
- Reference earlier decisions and conclusions naturally
- Don't re-ask questions that have already been answered
- Build on established understanding rather than starting fresh
</session_context_guidelines>
</session_context>

from praisonaiagents.context import ContextAggregator
import asyncio

# Create aggregator
aggregator = ContextAggregator(max_tokens=4000)

# Register sources with priorities (lower = higher priority)
aggregator.register_source("memory", memory.search_short_term, priority=10)
aggregator.register_source("knowledge", knowledge.search, priority=20)
aggregator.register_source("rag", rag.retrieve, priority=30)

# Aggregate (async)
async def get_context(query):
    result = await aggregator.aggregate(query)
    print(f"Sources: {result.sources_used}")
    print(f"Tokens: {result.tokens_used}")
    return result.context

# Or sync
result = aggregator.aggregate_sync("user question")
print(result.context)

from praisonaiagents import Agent
from praisonaiagents.context import ContextConfig, MonitorConfig

# Enable context monitoring
agent = Agent(
    instructions="You are a helpful assistant",
    context=ContextConfig(
        monitor=MonitorConfig(
            enabled=True,
            path="./context_logs/",
            format="human",  # or "json"
            frequency="turn",  # or "tool_call", "overflow"
            redact_sensitive=True,
        )
    )
)

# DON'T DO THIS - ContextAgent is removed
from praisonaiagents import ContextAgent
agent = ContextAgent(...)  # ❌ ImportError

from praisonaiagents import Agent
from praisonaiagents.context import ContextConfig

agent = Agent(
    instructions="You are helpful",
    context=ContextConfig(
        session_tracking=True,
        aggregate_memory=True,
    )
)

from praisonaiagents.context.fast import FastContextAgent

# Create agent for code search
with FastContextAgent(
    workspace_path="/path/to/project",
    max_parallel=8,
    model="gpt-4o-mini"
) as agent:
    # Simple pattern search
    result = agent.search_simple("def main")
    
    # Intelligent LLM-powered search
    result = agent.search("Find all database connection handling")
    
    for match in result.matches[:5]:
        print(f"{match.file}:{match.line_number}")