Context Management Overview

Complete visual guide to context management architecture, terminology, and optimization strategies

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    instructions="You are a helpful assistant.",
    context_window=8000,
)
agent.start("Summarise the last 10 messages without losing context.")

Context Management Overview

This page provides a comprehensive visual guide to how context management works in PraisonAI Agents.

The user opens this overview to see how budgeting, compaction, and monitoring fit together before tuning an agent.

What Makes PraisonAI Context Management Best-in-Class

Feature Comparison

Architecture

Multi-Agent Context Flow

Terminology Reference

Core Concepts

Optimization Strategies

Token Segments

Defaults Reference

ContextConfig Defaults

ContextConfig(
    auto_compact=True,           # Auto-optimize when threshold reached
    compact_threshold=0.8,       # Trigger at 80% usage
    strategy="smart",            # Use smart optimization
    output_reserve=8000,         # Reserve for LLM response
    history_ratio=0.6,           # 60% of usable for history
    tool_output_max=10000,       # Max tokens per tool output
    prune_after_tokens=40000,    # Start pruning after 40K
    keep_recent_turns=5,         # Keep last 5 turns intact
    tool_limits={},              # Per-tool output limits
)

Per-Tool Limits

Configure different limits for different tools:

context:
  auto_compact: true
  tool_limits:
    tavily_search: 2000      # Search results: 2000 chars
    tavily_extract: 5000     # Full page: 5000 chars
    code_executor: 10000     # Code output: 10000 chars

Overflow Handling

Session Deduplication

Prevents duplicate content across agents in multi-agent workflows:

CLI Commands

Analytics Dashboard

# View analytics for recent sessions
praisonai replay dashboard

# Analyze specific session
praisonai replay dashboard <session_id>

# JSON output
praisonai replay dashboard --json

Session Statistics

# View session stats
praisonai replay context <session_id> --stats

Output:

TOKEN USAGE BY AGENT:
  deep_researcher      ██████████████████████████████    956,146 (75.5%)
  content_writer       ███                               113,023 (8.9%)

CONTEXT EFFICIENCY:
  ⚠️  Duplicates Found:  236
  ⚠️  Wasted Tokens:     135,279 (10.7%)

Smart Default Context

New in v1.0: Context management is now automatically enabled when your agent has tools:

from praisonaiagents import Agent

# Context auto-enabled because tools are present
agent = Agent(
    instructions="You are helpful",
    tools=[my_search_tool]  # context=True automatically
)

# No tools = no context overhead
simple_agent = Agent(
    instructions="You are helpful"
    # context=None (disabled, zero overhead)
)

# Explicitly disable even with tools
agent = Agent(
    instructions="You are helpful",
    tools=[my_search_tool],
    context=False  # Override smart default
)

Quick Start Examples

1. Enable or Customise

   from praisonaiagents import Agent
   
   agent = Agent(
       instructions="You are helpful",
       context=True  # Enable with defaults (or auto-enabled with tools)
   )

   from praisonaiagents import Agent, ManagerConfig
   
   agent = Agent(
       instructions="You are helpful",
       context=ManagerConfig(
           auto_compact=True,
           compact_threshold=0.7,
           strategy="smart",
           llm_summarize=True,  # Enable LLM-powered summarization
           smart_tool_summarize=True,  # Summarize tool outputs before truncating (default)
           tool_summarize_limits={"tavily_search": 1000, "web_scraper": 5000},  # Per-tool thresholds
           tool_limits={"tavily_search": 2000}
       )
   )

   context:
     auto_compact: true
     compact_threshold: 0.7
     strategy: smart
     llm_summarize: true
     smart_tool_summarize: true
     tool_summarize_limits:
       tavily_search: 1000
       web_scraper: 5000
     tool_limits:
       tavily_search: 2000
       tavily_extract: 5000

Process Flow Diagrams

Sequential Process Flow

How context flows through agents in sequential execution:

Hierarchical Process Flow

How context flows in manager-worker hierarchies:

Workflow Process Flow

How context flows through workflow steps:

Optimization Strategy Diagrams

Truncate Strategy

Sliding Window Strategy

Prune Tools Strategy

Summarize Strategy

Smart Strategy (Combined)

Context Overflow Handling

What Happens When Context Exceeds Limits

Tool Output Overflow Handling

LLM Summarization

Enable intelligent summarization using the agent’s LLM:

from praisonaiagents import Agent, ContextConfig

agent = Agent(
    instructions="You are helpful",
    context=ContextConfig(
        auto_compact=True,
        llm_summarize=True,  # Enable LLM-powered summarization
        strategy="smart"
    )
)

When llm_summarize=True:

• Old messages are summarized by the LLM instead of truncated
• Key facts, decisions, and context are preserved
• More intelligent compression than simple truncation

Best Practices

Related Pages