> ## Documentation Index > Fetch the complete documentation index at: https://docs.praison.ai/llms.txt > Use this file to discover all available pages before exploring further. # Context Management API > CLI commands, flags, and configuration for context management # Context Management API Complete reference for CLI commands, flags, environment variables, and configuration options. ## CLI Flags ### Auto-Compaction ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Enable automatic compaction (default in interactive mode) praisonai chat --context-auto-compact # Disable automatic compaction praisonai chat --no-context-auto-compact ``` ### Optimization Strategy ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Available strategies: truncate, sliding_window, prune_tools, summarize, smart praisonai chat --context-strategy smart ``` ### Trigger Threshold ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Trigger compaction at 80% utilization (0.0-1.0) praisonai chat --context-threshold 0.8 ``` ### Monitoring ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Enable context monitoring praisonai chat --context-monitor # Set output path praisonai chat --context-monitor-path ./debug/context.txt # Set output format (human or json) praisonai chat --context-monitor-format json # Set update frequency (turn, tool_call, manual, overflow) praisonai chat --context-monitor-frequency turn ``` ### Redaction ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Enable sensitive data redaction (default) praisonai chat --context-redact # Disable redaction praisonai chat --no-context-redact ``` ### Output Reserve ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Reserve tokens for model output praisonai chat --context-output-reserve 8000 ``` ## Interactive Commands | Command | Description | | ------------------------ | ------------------------------- | | `/context` | Show context stats summary | | `/context show` | Show detailed summary + budgets | | `/context stats` | Token ledger table | | `/context budget` | Budget allocation details | | `/context dump` | Write snapshot to disk now | | `/context on` | Enable monitoring | | `/context off` | Disable monitoring | | `/context path ` | Set snapshot output path | | `/context format ` | Set format (human/json) | | `/context frequency ` | Set update frequency | | `/context compact` | Trigger optimization now | ### Examples ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Start chat with monitoring praisonai chat --context-monitor # In session: ❯ /context stats Token Ledger ──────────────────────────────── System Prompt: 1,250 tokens History: 45,000 tokens Tool Outputs: 18,000 tokens ──────────────────────────────── TOTAL: 66,820 tokens ❯ /context budget Budget Allocation (gpt-4o-mini) ──────────────────────────────── Model Limit: 128,000 tokens Output Reserve: 16,384 tokens Usable: 111,616 tokens Utilization: 59.8% ❯ /context dump ✓ Context snapshot written to ./context.txt ❯ /context compact ✓ Optimized: 45,000 → 12,000 tokens (saved 33,000) ``` ## Environment Variables ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Auto-compaction PRAISONAI_CONTEXT_AUTO_COMPACT=true # Strategy PRAISONAI_CONTEXT_STRATEGY=smart # Threshold PRAISONAI_CONTEXT_THRESHOLD=0.8 # Output reserve PRAISONAI_CONTEXT_OUTPUT_RESERVE=8000 # Monitoring PRAISONAI_CONTEXT_MONITOR=true PRAISONAI_CONTEXT_MONITOR_PATH=./context.txt PRAISONAI_CONTEXT_MONITOR_FORMAT=human PRAISONAI_CONTEXT_MONITOR_FREQUENCY=turn # Redaction PRAISONAI_CONTEXT_REDACT=true ``` ## Configuration File ```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # .praison/config.yaml or config.yaml context: auto_compact: true compact_threshold: 0.8 strategy: smart output_reserve: 8000 budgets: system_prompt: 2000 rules: 500 skills: 500 memory: 1000 tools_schema: 2000 tool_outputs: 20000 buffer: 1000 monitor: enabled: false path: ./context.txt format: human frequency: turn redact_sensitive: true ``` ## Precedence Order Configuration is resolved in this order (highest to lowest): 1. **CLI flags** (`--context-strategy smart`) 2. **Environment variables** (`PRAISONAI_CONTEXT_STRATEGY=smart`) 3. **Config file** (`config.yaml`) 4. **Defaults** ## Python SDK ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import ( # Token estimation estimate_tokens_heuristic, estimate_messages_tokens, estimate_tool_schema_tokens, # Budgeting ContextBudgeter, BudgetAllocation, get_model_limit, get_output_reserve, # Ledger ContextLedger, ContextLedgerManager, MultiAgentLedger, # Optimization get_optimizer, OptimizerStrategy, TruncateOptimizer, SlidingWindowOptimizer, PruneToolsOptimizer, SummarizeOptimizer, NonDestructiveOptimizer, SmartOptimizer, # Monitoring ContextMonitor, MultiAgentMonitor, ContextSnapshot, format_human_snapshot, format_json_snapshot, redact_sensitive, ) ``` ## Complete Example ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents import ( ContextBudgeter, ContextLedgerManager, ContextMonitor, get_optimizer, OptimizerStrategy, ) # Set up context management budgeter = ContextBudgeter(model="gpt-4o-mini") ledger = ContextLedgerManager() optimizer = get_optimizer(OptimizerStrategy.SMART) monitor = ContextMonitor(enabled=True, path="./context.txt") # Create agent agent = Agent( instructions="You are a helpful assistant.", llm="gpt-4o-mini", ) # Track system prompt ledger.track_system_prompt(agent.instructions) # Conversation loop messages = [] while True: user_input = input("You: ") messages.append({"role": "user", "content": user_input}) ledger.track_history(messages[-1:]) # Check if optimization needed current = ledger.get_total() budget = budgeter.allocate() if budgeter.get_utilization(current) > 0.8: messages, stats = optimizer.optimize(messages, target_tokens=int(budget.usable * 0.7)) print(f"[Optimized: saved {stats.tokens_saved} tokens]") # Get response response = agent.chat(user_input) messages.append({"role": "assistant", "content": response}) ledger.track_history(messages[-1:]) # Write snapshot monitor.snapshot(ledger=ledger.get_ledger(), budget=budget, messages=messages, trigger="turn") print(f"Assistant: {response}") ``` ## Next Steps * [Context Management Overview](/docs/features/context-management) * [Context Optimizer](/docs/features/context-optimizer) * [Context Monitor](/docs/features/context-monitor)