Flue vs Claude Code vs Mastra — Which Agent Framework Wins?

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: Flue vs Claude Code vs Mastra — Which Agent Framework Wins?
- URL: https://www.youtube.com/watch?v=4IIqiFuCE-4
- Topic: Codex + Claude Workflows
- My current learning frame: Build a Flue workflow that runs a small script-based skill (like the video's YouTube title scorer), deliberately hit the in-memory sandbox failure, fix it with a custom tool instead of local file access, then compile to the single server file and trigger it with a curl POST.
- Why this matters: New playlist item from DIY Smart Code; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:34 / Evidence 1: "artificial intelligence agent frameworks around. And it runs thousands of agents for almost nothing. It's called Flu. It's open source and it comes from the team behind Astro. Here's the twist. They never set out to build an..."
- 2:12 / Evidence 2: "a fully autonomous no human workflow. Flu drops that assumption. You can build agents that wait for human input, exactly like cloud code, or you can build workflows that run start to finish with nobody watching, which is..."
- 5:09 / Evidence 3: "Same idea as an agent, except instead of exporting the agent, you export a run function and you hand it the skill. This skill scores YouTube titles FitIQ style by running a Python script. So, you point the..."
- 6:50 / Evidence 4: "the results. Prefer streaming? Flu speaks WebSockets, too. Which, by the way, is a completely different approach than Mastra. The last time the sources also reached for Mastra, you wired everything by hand. Sessions, memory, sandbox, tool loading,..."

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A routing matrix for when to use Codex, Claude, browser checks, or manual review.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Inspect -> Plan -> Edit -> Verify -> Review -> Route
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "Flue vs Claude Code vs Mastra — Which Agent Framework Wins?", not a generic Codex + Claude Workflows essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.