Context Budgeter CLI

Configure context budget allocation from the CLI

Reserve output tokens and inspect per-segment budgets so long agent runs don’t exhaust context before the model can reply.

from praisonaiagents import Agent

agent = Agent(name="budget-cli", instructions="Respect output token reserves.")
agent.start("Run a long task without exhausting the context budget.")

The user runs the agent from the CLI and checks /context budget to see segment allocation.

How It Works

Quick Start

1. Run an agent, then inspect budget

   from praisonaiagents import Agent
   
   agent = Agent(
       name="Assistant",
       instructions="Answer helpfully within context limits.",
   )
   
   agent.start("Summarise the key points of context budgeting")
   

   From the CLI session:

   praisonai chat
   > /context budget
   
2. Increase output reserve for long replies

   praisonai chat --context-output-reserve 16000
   

   Default output reserve is 8000 tokens. Raise it when the model truncates final answers.

CLI Flags

Flag	Default	Description
--context-output-reserve	8000	Tokens reserved for model output

Interactive Commands

> /context budget   # Allocation breakdown
> /context stats    # Current usage vs budget

Example /context budget output:

Budget Allocation
  Model Limit:     128,000
  Output Reserve:  8,000
  Usable:          120,000

  Segment Budgets:
    System Prompt: 2,000
    History:       84,616
    Tool Outputs:  20,000
    ...

config.yaml

context:
  output_reserve: 8000
  default_tool_output_max: 10000

Environment variable: PRAISONAI_CONTEXT_OUTPUT_RESERVE.

Best Practices

Raise output reserve before raising compaction threshold

If replies are cut off, increase --context-output-reserve first — compaction may not be the issue.

Check /context stats after heavy tool use

Tool outputs consume the largest share; stats show which segment is filling fastest.

Pair with token estimation mode

Use Token Estimation CLI accurate mode when validating budget numbers.

Related

Token Estimation CLI

Configure estimation accuracy

Context Compaction

Automatic overflow protection