Borrowing it
Nothing to install: this file belongs to andersonlemesc/Oryntra. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/andersonlemesc/Oryntra/main/.agents/skills/langgraph-architecture/SKILL.mdgit clone --depth 1 https://github.com/andersonlemesc/OryntraWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andersonlemesc/oryntra/langgraph-architecture)<a href="https://agentmods.dev/skills/andersonlemesc/oryntra/langgraph-architecture"><img src="https://agentmods.dev/badge/skills/andersonlemesc/oryntra/langgraph-architecture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andersonlemesc/oryntra/langgraph-architecture"><img src="https://agentmods.dev/badge/skills/andersonlemesc/oryntra/langgraph-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.02461 |
| Opus 5 | $0.00020 | $0.01230 |
| Sonnet 5 | $0.00008 | $0.00492 |
| Haiku 4.5 | $0.00004 | $0.00246 |
Grade A, and why
langgraph-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to langgraph-architecture — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangGraph Architecture Decisions
When to Use LangGraph
Use LangGraph When You Need:
- Stateful conversations - Multi-turn interactions with memory
- Human-in-the-loop - Approval gates, corrections, interventions
- Complex control flow - Loops, branches, conditional routing
- Multi-agent coordination - Multiple LLMs working together
- Persistence - Resume from checkpoints, time travel debugging
- Streaming - Real-time token streaming, progress updates
- Reliability - Retries, error recovery, durability guarantees
Consider Alternatives When:
| Scenario | Alternative | Why |
|---|---|---|
| Single LLM call | Direct API call | Overhead not justified |
| Linear pipeline | LangChain LCEL | Simpler abstraction |
| Stateless tool use | Function calling | No persistence needed |
| Simple RAG | LangChain retrievers | Built-in patterns |
| Batch processing | Async tasks | Different execution model |
State Schema Decisions
TypedDict vs Pydantic
| TypedDict | Pydantic |
|---|---|
| Lightweight, faster | Runtime validation |
| Dict-like access | Attribute access |
| No validation overhead | Type coercion |
| Simpler serialization | Complex nested models |
Recommendation: Use TypedDict for most cases. Use Pydantic when you need validation or complex nested structures.
Reducer Selection
| Use Case | Reducer | Example |
|---|---|---|
| Chat messages | add_messages |
Handles IDs, RemoveMessage |
| Simple append | operator.add |
Annotated[list, operator.add] |
| Keep latest | None (LastValue) | field: str |
| Custom merge | Lambda | Annotated[list, lambda a, b: ...] |
| Overwrite list | Overwrite |
Bypass reducer |
State Size Considerations
# SMALL STATE (< 1MB) - Put in state
class State(TypedDict):
messages: Annotated[list, add_messages]
context: str
# LARGE DATA - Use Store
class State(TypedDict):
messages: Annotated[list, add_messages]
document_ref: str # Reference to store
def node(state, *, store: BaseStore):
doc = store.get(namespace, state["document_ref"])
# Process without bloating checkpoints
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 343 lines · 40 tokens per session scan A 418ae33207ba
langgraph-architecture is a skill published in the GitHub repository andersonlemesc/Oryntra (6 stars, last pushed 6d ago), licensed Apache-2.0. It adds 40 tokens to every session and 2,461 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-architecture, differing in 0 lines, and is treated as a copy.
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