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namiqsarxanli-creator/el-regreso

namiqsarxanli-creator/el-regreso

A stateful LangGraph agent with checkpointing: a spirit that returns home every Día de los Muertos and remembers past visits via thread_id — nodes, edges, persistent memory, live graph visualization
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0 ★ · namiqsarxanli-creator/el-regreso · read from the repository page · (checked 2026-10-16 20:00:20)
Source
read directly from its GitHub page · type: open-source project
Harvest history
first seen 2026-10-10 · last seen 2026-10-10 · seen 1× · listed by 1 sources
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score 103 · 1 sources · repo
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El Regreso

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

> *One night, every year.*
> A stateful **LangGraph** agent built around Día de los Muertos: nodes, edges, interrupts, subgraphs
> and persistent **checkpointing**.
🇦🇿 Azərbaycan dilində
What it does
A spirit returns home on the night of 1–2 November. It goes through these steps:
1. finds the marigold petal path
2. crosses the levels of Mictlán
3. "tastes" the altar offerings
4. talks to its family
5. leaves again, until next year
When the same thread_id returns the following year, the spirit **remembers previous visits**:
> *"I recognized the door, Abuela Remedios. The table was empty, but the road I came by was not.
> **You haven't brought a photo for 3 years.** I'm not asking, just saying."*
The "3 years" is **not generated by the model**. It is computed from the visits field restored by the
checkpointer, and the LLM only turns facts into a sentence. That is the core design principle: **facts
come from state, not from the model**, so even the offline fallback tells the same truth.
LangGraph features used
| Concept | LangGraph |
|---|---|
| Altar (ofrenda) | State (TypedDict) with three different reducers |
| Placing an item on the altar | update_state() / Command(resume=...) |
| Petal path | edges |
| 1 vs 2 November route | add_conditional_edges(START, ...) |
| A lost spirit waits for a human | interrupt() |
| Levels of Mictlán | subgraph |
| A spirit waiting all year | SqliteSaver + thread_id |
| Past visits | get_state_history() |
| Alternate timeline | update_state() on an old checkpoint_id → new branch |
| The "third death" (being forgotten) | delete_thread() |
Other details:
- **Node types:** five kinds of node: pure function, tool, interrupt, parallel and LLM.
graph.py contains only nodes and edges, with no business logic.
- **Structured output:** LLM judgement via with_structured_output(...).
- **Crash recovery:** solution/chaos.py kills the process mid-run and resumes it from the last
checkpoint.
- **Provider failover chain:**
1. Groq gpt-oss-120b
2. Groq gpt-oss-20b
3. Gemini
4. OpenAI gpt-4o-mini
5. offline templates
On a quota or rate-limit error, the next provider takes over automatically. Without any key, the
app never crashes.
Live visualization
A FastAPI + WebSocket web UI, hand-made with no frontend library, shows:
- **The live graph and a LangGraph journal** with the real call behind every event, e.g.
graph.stream(...), interrupt(...), update_state(checkpoint_id=...), delete_thread(...).
- **A clickable checkpoint timeline:** pick any moment, change the altar, and fork an alternate story.
Source
github · https://github.com/namiqsarxanli-creator/el-regreso
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