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The praisonai managed command group provides terminal-based management of Anthropic-hosted sessions, agents, and environments.

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Quick Start

1

Set API Key

Export your Anthropic API key:
export ANTHROPIC_API_KEY=sk-ant-...
2

List Sessions

View sessions for an agent:
praisonai managed sessions list agent_01AbCdEf
3

Delete with Confirmation

Clean up resources safely:
praisonai managed sessions delete sesn_01AbCdEf --yes

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How It Works

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Commands

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Sessions

Manage Anthropic-hosted agent sessions:
CommandPurposeExample
sessions list <agent_id>List sessions for an agentpraisonai managed sessions list agent_01AbCdEf
sessions get <session_id>Get session details and usagepraisonai managed sessions get sesn_01AbCdEf
sessions resume <session_id> "<prompt>"Resume session with new promptpraisonai managed sessions resume sesn_01AbCdEf "Continue the task"
sessions delete <session_id>Delete a session (with confirmation)praisonai managed sessions delete sesn_01AbCdEf --yes
# List recent sessions
praisonai managed sessions list agent_01AbCdEf --limit 5

# Get detailed session info
praisonai managed sessions get sesn_01AbCdEf

# Resume previous conversation
praisonai managed sessions resume sesn_01AbCdEf "What did we discuss?"

# Clean up old sessions
praisonai managed sessions delete sesn_01AbCdEf --yes

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Agents

Manage your Anthropic-hosted agents:
CommandPurposeExample
agents listList all your agentspraisonai managed agents list
agents get <agent_id>Get agent configurationpraisonai managed agents get agent_01AbCdEf
agents createCreate new agent (via Python SDK)See ManagedAgent docs
agents update <agent_id>Update agent name, system, or modelpraisonai managed agents update agent_01AbCdEf --name "New Name" --version 1
agents delete <agent_id>Delete an agent (with confirmation)praisonai managed agents delete agent_01AbCdEf --yes
# List your agents
praisonai managed agents list

# View agent details
praisonai managed agents get agent_01AbCdEf

# Update agent configuration  
praisonai managed agents update agent_01AbCdEf --system "You are a data analyst." --version 2

# Remove unused agent
praisonai managed agents delete agent_01AbCdEf --yes

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Environments

Manage sandbox environments for code execution:
CommandPurposeExample
envs listList your environmentspraisonai managed envs list
envs get <env_id>Get environment detailspraisonai managed envs get env_01AbCdEf
envs update <env_id>Update packages or networkingpraisonai managed envs update env_01AbCdEf --packages "pandas,numpy" --networking full
envs delete <env_id>Delete an environment (with confirmation)praisonai managed envs delete env_01AbCdEf --yes
# List environments
praisonai managed envs list

# Install packages
praisonai managed envs update env_01AbCdEf --packages "requests,beautifulsoup4"

# Change networking mode
praisonai managed envs update env_01AbCdEf --networking limited

# Clean up environment
praisonai managed envs delete env_01AbCdEf --yes
Environment Update Options:
  • --packages: Comma-separated pip packages (whitespace is trimmed, empty entries filtered)
  • --networking: Either full or limited (case-sensitive)

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IDs Helper

Save and restore Anthropic-assigned resource IDs locally:
CommandPurposeExample
ids save <agent_id> <env_id> <session_id>Save IDs to JSON filepraisonai managed ids save agent_01... env_01... sesn_01... --version 1
ids restore "<prompt>"Restore IDs and run promptpraisonai managed ids restore "Continue previous task"
ids showDisplay saved IDspraisonai managed ids show
# Save current resource IDs
praisonai managed ids save agent_01AbCdEf env_01AbCdEf sesn_01AbCdEf --version 1

# View saved IDs
praisonai managed ids show

# Resume with saved state
praisonai managed ids restore "What was I working on?"

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Choosing a Subcommand

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Safety: Confirmation Prompts

All delete commands prompt for confirmation unless --yes is provided:
# Shows prompt: "Are you sure you want to delete sesn_01AbCdEf?"
praisonai managed sessions delete sesn_01AbCdEf

# Skips prompt
praisonai managed sessions delete sesn_01AbCdEf --yes
Typing anything non-affirmative prints Deletion cancelled. and exits with code 0.

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Authentication

The CLI requires the anthropic Python package and one of these environment variables: 
# Primary (checked first)
export ANTHROPIC_API_KEY=sk-ant-api-...

# Fallback
export CLAUDE_API_KEY=sk-ant-api-...
Installation: 
pip install 'anthropic>=0.94.0'
Without the package or API key, commands exit with code 1 and an error message.

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Common Patterns

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Cleanup Script

Remove old sessions and unused resources: 
#!/bin/bash
# cleanup-resources.sh

AGENT_ID="agent_01AbCdEf"

# List and delete old sessions
echo "Cleaning sessions for $AGENT_ID..."
praisonai managed sessions list $AGENT_ID --limit 20 | tail -n +3 | while read id status title; do
  if [[ $status == "completed" ]]; then
    praisonai managed sessions delete $id --yes
    echo "Deleted session: $id"
  fi
done

# List and delete unused environments  
echo "Cleaning environments..."
praisonai managed envs list | tail -n +3 | while read id status name; do
  if [[ $status == "inactive" ]]; then
    praisonai managed envs delete $id --yes
    echo "Deleted environment: $id"
  fi
done

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Environment Setup

Install packages and configure networking: 
# Update development environment
ENV_ID="env_01AbCdEf"
praisonai managed envs update $ENV_ID \
  --packages "pandas,numpy,matplotlib,jupyter" \
  --networking full

# Verify installation
praisonai managed envs get $ENV_ID

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Session Backup

Save session state before major changes: 
# Backup current session state
praisonai managed ids save agent_01AbCdEf env_01AbCdEf sesn_01AbCdEf \
  --version 1 --file backup-$(date +%Y%m%d).json

# Show what was backed up
praisonai managed ids show --file backup-$(date +%Y%m%d).json

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Best Practices

  • Use --yes in scripts to avoid interactive prompts
  • Regularly clean up completed sessions to manage costs
  • Save important session IDs before deleting resources
  • Monitor environment status before making updates
  • Check exit codes in scripts (0 = success, 1 = error)
  • Handle API timeouts with retry logic
  • Verify API key is set before running commands
  • Use --limit to prevent large resource listings
  • Test package installations in development environments first
  • Use --networking limited for security-sensitive workloads
  • Keep package lists minimal to reduce startup time
  • Update packages gradually to identify compatibility issues
  • Use sessions resume instead of creating new sessions
  • Save session IDs for important conversations
  • Monitor session usage with sessions get
  • Implement session rotation for long-running applications

ManagedAgent + DB persistence (Python SDK)

Library-level persistence with database backends

Sessions

Advanced session management concepts