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Context Optimizer

The Context Optimizer provides multiple strategies for reducing context size when approaching model limits, preventing overflow errors and reducing costs.

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Agent-Centric Quick Start

from praisonaiagents import Agent
from praisonaiagents.context import ManagerConfig

# Configure optimization strategy via context param
agent = Agent(
    instructions="You are helpful.",
    context=ManagerConfig(
        auto_compact=True,
        compact_threshold=0.8,  # Trigger at 80% usage
        strategy="smart",  # truncate, sliding_window, prune_tools, summarize, smart
    ),
)

# Optimization happens automatically when threshold is reached
response = agent.chat("Hello!")

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Optimization Strategies

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Low-Level API

from praisonaiagents.context import get_optimizer, OptimizerStrategy

# Get an optimizer
optimizer = get_optimizer(OptimizerStrategy.SMART)

# Optimize messages to target token count
messages = [...]  # Your conversation history
optimized, stats = optimizer.optimize(messages, target_tokens=50000)

print(f"Reduced from {len(messages)} to {len(optimized)} messages")
print(f"Saved {stats.tokens_saved} tokens")

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Strategy Reference

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Truncate

Removes oldest messages first, preserving system prompt and recent context. 
from praisonaiagents.context import TruncateOptimizer

optimizer = TruncateOptimizer()
result, stats = optimizer.optimize(messages, target_tokens=10000)
Best for: Simple cases where old context is not important.

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Sliding Window

Keeps the N most recent messages within a token window. 
from praisonaiagents.context import SlidingWindowOptimizer

optimizer = SlidingWindowOptimizer()
result, stats = optimizer.optimize(messages, target_tokens=10000)
Best for: Conversations where recent context matters most.

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Prune Tools

Truncates old tool outputs while preserving recent ones. 
from praisonaiagents.context import PruneToolsOptimizer

optimizer = PruneToolsOptimizer(
    protect_recent=5,  # Keep last 5 tool outputs intact
    max_output_tokens=500,  # Truncate older outputs to 500 tokens
)
result, stats = optimizer.optimize(messages, target_tokens=10000)
Best for: Tool-heavy conversations with large outputs.

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Summarize

Uses LLM to create a summary of older conversation. 
from praisonaiagents.context import SummarizeOptimizer

optimizer = SummarizeOptimizer(
    keep_recent=4,  # Keep last 4 turns intact
    model="gpt-4o-mini",
)
result, stats = optimizer.optimize(messages, target_tokens=10000)
Best for: Long conversations where context continuity matters.

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Non-Destructive

Tags messages for exclusion without deleting them (enables undo). 
from praisonaiagents.context import NonDestructiveOptimizer

optimizer = NonDestructiveOptimizer()
result, stats = optimizer.optimize(messages, target_tokens=10000)

# Messages are tagged with 'excluded': True
# Use get_effective_history() to filter
Best for: When you need to preserve full history for audit/undo. Combines all strategies intelligently based on content analysis. 
from praisonaiagents.context import SmartOptimizer

optimizer = SmartOptimizer()
result, stats = optimizer.optimize(messages, target_tokens=10000)
Order of operations:
  1. Prune old tool outputs
  2. Apply sliding window
  3. Summarize if still over limit

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Factory Function

from praisonaiagents.context import get_optimizer, OptimizerStrategy

# Available strategies
strategies = [
    OptimizerStrategy.TRUNCATE,
    OptimizerStrategy.SLIDING_WINDOW,
    OptimizerStrategy.PRUNE_TOOLS,
    OptimizerStrategy.SUMMARIZE,
    OptimizerStrategy.NON_DESTRUCTIVE,
    OptimizerStrategy.SMART,
]

for strategy in strategies:
    optimizer = get_optimizer(strategy)
    result, stats = optimizer.optimize(messages, target_tokens=10000)
    print(f"{strategy.value}: {len(result)} messages")

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Optimization Result

from praisonaiagents.context import OptimizationResult

# stats returned from optimize()
stats: OptimizationResult
print(f"Original tokens: {stats.original_tokens}")
print(f"Optimized tokens: {stats.optimized_tokens}")
print(f"Tokens saved: {stats.tokens_saved}")
print(f"Strategy used: {stats.strategy_used}")
print(f"Messages removed: {stats.messages_removed}")
print(f"Tool outputs pruned: {stats.tool_outputs_pruned}")

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Tool Call Preservation

The optimizer preserves tool_call/tool_result pairs to maintain API validity: 
# These pairs are kept together or removed together
{"role": "assistant", "tool_calls": [{"id": "call_123", ...}]}
{"role": "tool", "tool_call_id": "call_123", "content": "..."}

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CLI Usage

# Set optimization strategy
praisonai chat --context-strategy smart

# Set trigger threshold
praisonai chat --context-threshold 0.8

# Manual optimization in session
/context compact

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Configuration

# config.yaml
context:
  auto_compact: true
  compact_threshold: 0.8
  strategy: smart

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Next Steps