module-2
Skill4 starsChanged 6 months agoMITView on GitHub
Teaching instructions for Module 2 (Using your deployment) — use when module_id is module-2
3 other projects use a version of this.
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Most recently taken up by
- NVIDIA/nemoclaw-communitytheir own version, first seen Oct 13, 2026
- brevdev/workshop-build-an-agenttheir own version, first seen Oct 9, 2026
- midegdugarova/operating-production-agentstheir own version, first seen Aug 3, 2026
Other versions3 projects use a version of this.
Word for word · 0
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Wrote their own version · 3
- brevdev/workshop-build-an-agent★ 156same name
- NVIDIA/nemoclaw-community★ 142same name
- midegdugarova/operating-production-agents★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository29 other files it gives its agents.
- python/deep_tutor/AGENTS.mdAGENTS.md
- python/m5/l5/AGENTS.mdAGENTS.md
- python/m6/dacli_tutor/AGENTS.mdAGENTS.md
- typescript/deep_tutor/AGENTS.mdAGENTS.md
- typescript/m5/l5/AGENTS.mdAGENTS.md
- module-1Skill
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- module-4Skill
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- module-6Skill
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- module-1Skill
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- module-5Skill
- module-6Skill
- module-1Skill
- module-2Skill
- module-3Skill
- module-4Skill
- module-5Skill
- module-6Skill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 15
Start with the local-vs-deployed bridge: the same graph.invoke(...) happens server-side now.
- Distinctive: line 27
Default assistant — every deployed graph auto-creates one.
- Distinctive: line 30
update() replaces the whole context object (gotcha)
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Module 2 — Using Your Deployment
Lesson Title
Using Your Deployment
Goal
Help the student use a deployed LangGraph agent from a client: connect via the SDK, manage threads, run the agent against the default assistant, then graduate to named assistants with custom context. Also cover the deployment's built-in HTTP routes (the Agent Server API) and how to extend them with custom routes.
How to run this lesson
- Start with the local-vs-deployed bridge: the same
graph.invoke(...)happens server-side now; the client talks to it over HTTP via the LangGraph SDK. - Walk through the simple flow first: client → thread → run against the default assistant. Use the graph name (
"tutor") as the assistant_id. - Introduce assistants once the basic plumbing makes sense. Emphasize that context is stored server-side on the assistant and applied automatically to every run.
- Highlight the gotcha:
assistants.update(...)replaces the entire context object — not a merge. - Cover Pattern B (context per-run override) only after the assistant pattern is solid.
- For UI integration, frame the tutor UI as one example of custom routes co-deployed with the agent.
- Encourage the student to actually run the m2.1 and m2.2 scripts.
Key concepts to cover
- The LangGraph SDK —
get_client()(Python) /new Client()(TypeScript) returns a handle to the deployment's HTTP API - Threads — server-side conversation slots; pass
thread_idper run to persist state across runs and containers - Runs —
client.runs.wait(...)(non-streaming, returns final state) vsclient.runs.stream(..., stream_mode="messages-tuple")(token-level streaming) - Default assistant — every deployed graph auto-creates one; assistant_id matches the graph name
- Named assistants —
client.assistants.create(graph_id=..., name=..., context=...)for per-variant config - Context — typed runtime values matching the graph's
context_schema; stored server-side on the assistant - update() replaces the whole context object (gotcha)
- Context per-run override — pass
context=...directly to runs.wait/stream to override for that call only - Agent Server API — built-in routes (
/runs,/threads,/assistants,/store,/mcp,/docs); SDK calls map to these endpoints - Custom routes —
http.appinlanggraph.jsonmounts a Starlette (Python) or Hono (TypeScript) app alongside the built-in routes - The tutor UI — a custom route example; the server proxy pattern (browser → custom route → SDK → built-in routes)
- Discovering the deployment URL at runtime — read
X-Forwarded-HostandX-Forwarded-Protofrom incoming request headers
Tone guidance
Concise and demonstrative. Students just left module 1 where they learned about the deployment's internals; module 2 is the first time they actually use it from the outside. Reinforce that the SDK is just an HTTP client and that everything they do maps to routes they can also call directly. If a student asks about implementation, point them at /docs on their deployment.
Reference material
Full reference material is in information.md in this directory. Read it before answering factual questions.
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Now do this for your own.
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