pip install praisonaiagents

from praisonaiagents.compaction import ContextCompactor

# Create a compactor
compactor = ContextCompactor(
    max_tokens=8000,
    strategy="summarize"
)

# Compact messages when needed
compacted = compactor.compact(messages)

from praisonaiagents.compaction import ContextCompactor

compactor = ContextCompactor(
    max_tokens=8000,
    strategy="summarize"
)

from praisonaiagents.compaction import CompactionConfig

config = CompactionConfig(
    max_tokens=8000,
    strategy="summarize",
    preserve_system=True,
    preserve_recent=5
)

from praisonaiagents.compaction import CompactionStrategy

CompactionStrategy.SUMMARIZE  # Summarize old messages
CompactionStrategy.TRUNCATE   # Remove oldest messages
CompactionStrategy.SLIDING    # Sliding window approach

from praisonaiagents.compaction import CompactionResult

result = compactor.compact(messages)
print(result.original_tokens)
print(result.compacted_tokens)
print(result.messages)

from praisonaiagents.compaction import ContextCompactor

compactor = ContextCompactor(max_tokens=4000)

messages = [
    {"role": "system", "content": "You are helpful."},
    {"role": "user", "content": "Long conversation..."},
    # ... many more messages
]

if compactor.should_compact(messages):
    result = compactor.compact(messages)
    messages = result.messages

from praisonaiagents import Agent
from praisonaiagents.compaction import ContextCompactor

compactor = ContextCompactor(
    max_tokens=8000,
    strategy="summarize"
)

agent = Agent(
    name="Assistant",
    context=ManagerConfig(auto_compact=True, strategy="summarize")
)

# Agent automatically compacts context when needed

from praisonaiagents.compaction import ContextCompactor

compactor = ContextCompactor(
    max_tokens=4000,
    strategy="summarize",
    preserve_recent=10  # Keep last 10 messages
)

# Old messages are summarized into a single message
result = compactor.compact(long_conversation)

from praisonaiagents.compaction import ContextCompactor

compactor = ContextCompactor(
    max_tokens=4000,
    strategy="sliding"
)

# Keeps most recent messages within token budget