Agent Package Manager (APM) automates agent workflows, enabling operations teams to deploy and manage AI agents efficiently. It connects to Python-based tools and workflows, streamlining agent interactions and package management.
git clone https://github.com/danielmeppiel/apm.gitAgent Package Manager (APM) automates agent workflows, enabling operations teams to deploy and manage AI agents efficiently. It connects to Python-based tools and workflows, streamlining agent interactions and package management.
[{"step":"Install APM and authenticate","action":"Run `pip install apm-cli` and log in using `apm auth --api-key YOUR_API_KEY`. Ensure your Python environment has access to the agent's repository (e.g., GitHub or a private PyPI server).","tip":"Use `apm whoami` to verify your authentication status before proceeding."},{"step":"Specify the agent and environment","action":"Define the agent name (e.g., `customer_support_agent`) and target environment (e.g., `production`, `staging`) in your command or configuration file. For example: `apm deploy customer_support_agent --env production`.","tip":"Use `apm list-agents` to browse available agents and their versions before deployment."},{"step":"Resolve dependencies and validate","action":"Let APM handle dependency resolution by running `apm deploy [AGENT_NAME] --resolve-deps`. APM will check for conflicts and install required packages. Validate the deployment with `apm health [AGENT_NAME]`.","tip":"For complex workflows, use a `apm-config.yaml` file to specify version constraints or custom hooks."},{"step":"Monitor and rollback if needed","action":"Use the APM dashboard or CLI to monitor agent performance. If issues arise, roll back using `apm rollback [AGENT_NAME] --version [BACKUP_TAG]` or trigger an automatic rollback if thresholds are breached.","tip":"Set up alerts in your monitoring tool (e.g., Prometheus, Datadog) to notify your team of deployment failures."}]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/danielmeppiel/apmCopy the install command above and run it in your terminal.
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Use APM to [ACTION] for [AGENT_NAME] in [ENVIRONMENT]. Include [VERSION_SPECIFICATION] if applicable. For example: 'Deploy the latest stable version of the customer support agent to the production environment using APM. Ensure all dependencies are resolved and the agent is configured with the provided API keys.'
Successfully deployed **Customer Support Agent v2.1.4** to the **production environment** using APM. The deployment included: - **Resolved dependencies**: Updated `langchain==0.1.16`, `requests==2.31.0`, and `pydantic==2.5.3` to compatible versions. - **Configuration**: Applied the following settings from the environment file: ```yaml api_key: "sk-live_abc123xyz" timeout: 30 max_retries: 3 ``` - **Validation**: APM verified the agent's health endpoint returned `status: "healthy"` with a response time of **120ms**. - **Rollback plan**: A snapshot of the previous version (**v2.1.3**) was automatically created and tagged as `backup/2024-05-15`. Next steps: 1. Monitor the agent's performance in the APM dashboard for the next 24 hours. 2. If errors exceed 0.1% in the first hour, trigger the rollback using `apm rollback customer_support_agent --version backup/2024-05-15`. 3. Report any issues to the #agent-ops Slack channel for triage.
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