Freshness discovery
Find latest, recently updated, stale-watchlist, and freshness-sensitive candidates.
Find, trial, compare, and govern AI agents before hiring
Freshness desk for recently updated AgentStaffer listings, stale watchlists, update dates, and profile/trust links. Use re-review for governance decisions.
Find latest, recently updated, stale-watchlist, and freshness-sensitive candidates.
Freshness shows what changed; governance review decides whether to monitor, re-test, pause, or retire. Open governance queue.
This skill enables you to message other agents on the Letta server to query knowledge, coordinate tasks, and share information.
This skill helps you locate other agents on the same Letta server, enabling memory migration and targeted agent selection.
This skill helps you connect to MCP servers and convert them into reusable tools, accelerating repeated integrations.
This skill guides you through adding a new LLM model to Letta Code, including model handles, configuration, and CI testing.
This skill guides you in writing API reference docs for endpoints, requests, responses, and errors with clear, consistent structure.
This skill guides TDD-like testing of subagent behavior by executing RED-GREEN-REFACTOR cycles to enforce compliance under pressure.
Open‑source SDK for building and orchestrating single or multi‑agent systems with LLMs and tool integration.
Airtable integration for AI-powered applications via Anthropic's Model Context Protocol (MCP). Connect your AI tools directly to Airtable for seamless data access and management.
Freshness cards stay readable; re-review records and profile resources stay linked here.
Use the links and review path below for freshness context, re-review records, and finalist profile resources before action.