agentUniverse/examples/context_engineering/default_context_manager.yaml

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YAML

# Example ContextManager Configuration
# Central orchestrator for context engineering with proactive budget management
name: 'default_context_manager'
description: 'Default context manager with task-adaptive strategies'
# Storage backends (multi-tier architecture)
hot_store_name: 'ram_context_store' # Hot tier: fast in-memory access
warm_store_name: 'redis_context_store' # Warm tier: persistent with TTL
cold_store_name: 'chroma_context_store' # Cold tier: long-term archival with vector search
# LLM for token counting
llm_name: 'default_llm'
# Compression and routing (Phase 2)
compressor_name: 'adaptive_compressor' # Intelligent compression strategy selector
router_name: 'context_router' # Task-specific routing engine
enable_compression: true # Enable intelligent compression in _make_room()
# Default budget configuration
default_max_tokens: 8000
default_reserved_tokens: 1000
# Task-specific configurations
# These override defaults based on task_type
task_configs:
code_generation:
max_tokens: 10000
reserved_tokens: 1500
budget_ratios:
workspace: 0.5 # 50% for code files
knowledge: 0.2 # 20% for documentation
memory: 0.15 # 15% for conversation history
system: 0.05 # 5% for system prompts
task: 0.1 # 10% for current task
compression_strategy: 'selective'
data_analysis:
max_tokens: 12000
reserved_tokens: 2000
budget_ratios:
background: 0.4 # 40% for data context
workspace: 0.3 # 30% for notebooks/scripts
knowledge: 0.15 # 15% for documentation
memory: 0.1 # 10% for conversation
system: 0.05 # 5% for system prompts
compression_strategy: 'summarize'
dialogue:
max_tokens: 8000
reserved_tokens: 1000
budget_ratios:
conversation: 0.5 # 50% for chat history
background: 0.25 # 25% for context
system: 0.15 # 15% for system prompts
memory: 0.1 # 10% for long-term memory
compression_strategy: 'adaptive'
metadata:
type: 'CONTEXT_MANAGER'
module: 'agentuniverse.agent.context.context_manager'
class: 'ContextManager'