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Intro

Module 2 shows how to keep agents effective as projects scale: wiring up Multi Repo Search tool so they can browse related repositories and tuning model choices (plus custom API keys) to match each agent’s task.

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Video lesson

Course on Udemy
Preview lesson. The full video is available on Udemy.

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Key takeaways

  • Use the Multi Repo Search tool when questions span multiple repositories, letting agents pull relationships and artifacts without disrupting the current VS Code workspace.
  • Prepare Multi Repo Search tool access by creating a fine-grained GitHub personal access token, adding a connection in the Zencoder dashboard, and registering every repo that agents should consult.
  • Remember that repositories must be indexed before agents can read them, and the dashboard’s indexing log confirms when each sync finishes.
  • Remove linked repositories before deleting a connection; once the connection is gone, you can rebuild it from scratch with fresh credentials.
  • Verify MultiRepoTool availability per agent in the dashboard so the correct tooling is enabled during chats.
  • Set agent models explicitly when needed—Auto is a safe default, but tailoring model choice can optimize for cost, latency, or capability.
  • Add custom API keys for providers like Anthropic or OpenAI when you want usage charged to your own account instead of the workspace allocation.
  • Reference docs at docs.zencoder.ai for current model availability, pricing multipliers, and step-by-step instructions on configuring premium or BYO keys.
  • Follow the module’s model guidance: Grok Code FAST1 for budget tasks, Gemini 2.5 Pro for huge context, Sonnet 4.5 Parallel Thinking for spec workflows, GPT-5 Codecs for specialized code gen, and OPUS for the hardest problems.
  • Collect system/model cards from providers, feed them to an LLM (e.g., Gemini 2.5 Pro’s 1M-token window), and have the agent synthesize comparison tables so you pick the best model per scenario.