Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/vpeetla-ai/react-agent-pattern/langgraph-orchestrationnpx skills add vpeetla-ai/react-agent-pattern --skill langgraph-orchestrationgit clone --depth 1 https://github.com/vpeetla-ai/react-agent-patternWrote 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/vpeetla-ai/react-agent-pattern/langgraph-orchestration)<a href="https://agentmods.dev/skills/vpeetla-ai/react-agent-pattern/langgraph-orchestration"><img src="https://agentmods.dev/badge/skills/vpeetla-ai/react-agent-pattern/langgraph-orchestration.svg" alt="Measured on agentmods" 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 | $0.00052 | $0.00366 |
| Opus 5 | $0.00026 | $0.00183 |
| Sonnet 5 | $0.00010 | $0.00073 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
langgraph-orchestration 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 3d 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-orchestration — 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.
What it actually says
LangGraph Orchestration
Standard pattern
START → observe/plan → specialist nodes → evaluate → route (retry|pass|hitl) → END
State
- Use
TypedDictwithtotal=Falsefor optional fields - Accumulate
trace_eventsexplicitly (list replacement per node unless using reducer) - Set
max_iterationsin state; enforce in routing
Nodes
- Async wrappers:
async def n_x(state): return sync_node(state, deps) - One responsibility per node (orchestrate, act, review, quality)
- Return partial state updates only
Routing
builder.add_conditional_edges("quality", route_fn, {"retry": "plan", "pass": "ship", "escalate": "hitl"})
- Security failures → escalate/HITL
- Max iterations → escalate, never infinite loop
HITL
graph.compile(checkpointer=MemorySaver(), interrupt_before=["hitl"])
Resume with graph.ainvoke(update, config) after human approval.
Reference implementations
loop-engine-agent-platform/src/loop_engine/graph/build.pyloop-engine-agent-platform/src/loop_engine/graph/repo_build.py- venkat-ai-platform orchestrators
Tests
- Mock LLM for deterministic paths
pytest.mark.asyncio+tmp_pathfor filesystem side effects
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.
- 3d ago First seen · 56 lines · 52 tokens per session scan A ff51bf677029
langgraph-orchestration is a skill published in the GitHub repository vpeetla-ai/react-agent-pattern (2 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 366 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-orchestration, differing in 0 lines, and is treated as a copy.
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hitl-side-effects
Add human-in-the-loop gates for irreversible actions: LangGraph interruptbefore, AegisAI approval queue, UI approve/resume endpoints. Use when shipping, publishing, notifying, or merging agent-generated changes.
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Build or modify LangGraph StateGraph agents in vpeetla-ai repos: typed state, nodes, conditional edges, MemorySaver, interruptbefore HITL. Use when adding orchestrators, coding loops, or multi-agent graphs.
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