Control how many tokens an agent spends on extended reasoning before producing a response.
from praisonaiagents import Agent from praisonaiagents . thinking import ThinkingBudget agent = Agent ( name = " DeepThinker " , instructions = " Solve problems step by step. " , ) agent . thinking_budget = ThinkingBudget . high () agent . start ( " Solve this optimisation problem... " )
The user asks a hard reasoning question; the agent spends extended thinking tokens up to the configured ThinkingBudget before answering.
Quick Start
Set a Budget Level
Use predefined levels via the thinking_budget property after creating the agent. from praisonaiagents import Agent from praisonaiagents . thinking import ThinkingBudget agent = Agent ( name = " Thinker " , instructions = " Reason carefully. " ) agent . thinking_budget = ThinkingBudget . medium () # 8,000 tokens agent . start ( " Explain quantum entanglement simply. " )
Custom Budget
Fine-tune token limits and adaptive scaling. from praisonaiagents import Agent from praisonaiagents . thinking import ThinkingBudget budget = ThinkingBudget ( max_tokens = 12000 , min_tokens = 1000 , adaptive = True , max_time_seconds = 120.0 , ) agent = Agent ( name = " CustomThinker " , instructions = " Solve complex problems. " ) agent . thinking_budget = budget agent . start ( " Design a load-balancing strategy. " )
How It Works Phase What happens 1. Set You assign a ThinkingBudget to the agent 2. Think The model reasons up to the token limit 3. Answer The agent returns the reasoned response
Budget Levels Pre-configured levels for different task complexity:
Level Tokens Use case minimal()2,000 Quick answers low()4,000 Simple reasoning medium()8,000 Default balance high()16,000 Complex analysis maximum()32,000 Deep research
from praisonaiagents . thinking import ThinkingBudget budget = ThinkingBudget . high () tokens = budget . get_tokens_for_complexity ( 0.9 ) # scales with complexity
CLI Usage praisonai thinking status # Show current budget praisonai thinking set high # Set budget level praisonai thinking stats # Show usage statistics
praisonai run --thinkingPass --thinking <budget> on the praisonai run command to set an exact token budget for a one-off prompt:
# Use a numeric token budget praisonai run --thinking 4096 " Explain quantum entanglement " # Use a named level praisonai run --thinking high " Solve this optimisation problem: ... " # Use in --command mode praisonai run --command my_agent.py --thinking 8000
--thinking is now correctly threaded through the direct-prompt path (praisonai run) in both --command and bare-prompt modes. Earlier releases raised a NameError on the direct-prompt path.
Value Tokens Notes off0 Disable extended thinking minimal2,000 Quick answers low4,000 Simple reasoning medium8,000 Balanced high16,000 Complex analysis <int>Exact Any positive integer token budget
For persistent sessions, use praisonai thinking set <level> instead.
Usage Tracking Track thinking utilisation across sessions:
from praisonaiagents . thinking import ThinkingBudget , ThinkingTracker tracker = ThinkingTracker () session = tracker . start_session ( budget_tokens = 8000 ) tracker . end_session ( session , tokens_used = 5000 , time_seconds = 30.0 ) summary = tracker . get_summary () print ( f "Average utilisation: { summary [ ' average_utilization ' ] :.1% } " )
Best Practices
Match budget to task complexity
Use minimal() or low() for quick lookups; reserve high() or maximum() for multi-step analysis where reasoning depth matters.
Set via property, not constructor
Assign agent.thinking_budget after creating the agent — budgets are applied lazily with zero overhead when unset.
Set adaptive=True so token allocation scales with task complexity via get_tokens_for_complexity().
Use praisonai thinking stats to review utilisation before increasing budgets in production.
Token Budgeting Manage overall token spend across agent runs
Reflection Self-review loops for higher-quality outputs
