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 skills add soba-labs/langchain-agent-skills --skill langgraph-agent-patternsgit clone --depth 1 https://github.com/soba-labs/langchain-agent-skillsWrote 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/soba-labs/langchain-agent-skills/langgraph-agent-patterns)<a href="https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-agent-patterns"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-agent-patterns/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/soba-labs/langchain-agent-skills/langgraph-agent-patterns"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-agent-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00118 | $0.03344 |
| Opus 5.5 | $0.00047 | $0.01338 |
| Sonnet 5.5 | $0.00024 | $0.00669 |
| Haiku 4.5 | $0.00012 | $0.00334 |
Grade A, and why
langgraph-agent-patterns 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 566 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangGraph Agent Patterns
Implement and configure multi-agent coordination patterns for LangGraph applications.
Pattern Selection
Choose the right pattern based on your coordination needs:
| Pattern | Best For | When to Use |
|---|---|---|
| Supervisor | Complex workflows, dynamic routing | Agents need to collaborate, routing is context-dependent |
| Router | Simple categorization, independent tasks | One-time routing, deterministic decisions |
| Orchestrator-Worker | Parallel execution, high throughput | Independent subtasks, results need aggregation |
| Handoffs | Sequential workflows, context preservation | Clear sequence, each agent builds on previous |
Quick Decision:
- Dynamic routing needed? → Supervisor
- Tasks can run in parallel? → Orchestrator-Worker
- Simple categorization? → Router
- Linear sequence? → Handoffs
For detailed comparison: See references/pattern-comparison.md
Pattern Implementation Guides
Supervisor-Subagent Pattern
Overview: Central coordinator delegates to specialized subagents based on context.
Quick Start:
# Generate supervisor graph boilerplate
uv run scripts/generate_supervisor_graph.py my-team \
--subagents "researcher,writer,reviewer"
# TypeScript
uv run scripts/generate_supervisor_graph.py my-team \
--subagents "researcher,writer,reviewer" \
--typescript
Key Components:
- State with routing:
nextfield for routing decisions - Supervisor node: Makes routing decisions based on context
- Subagent nodes: Specialized agents with distinct capabilities
- Conditional edges: Route from supervisor to subagents
Example Flow:
User Request → Supervisor → Researcher → Supervisor → Writer → Supervisor → FINISH
For complete implementation: See references/supervisor-subagent.md
Router Pattern
Overview: One-time routing to specialized agents based on initial request.
Key Components:
- State with route: Single routing decision field
- Router node: Categorizes request (keyword, LLM, or semantic)
- Specialized agents: Independent agents for each category
- Conditional routing: Route to agent, then END
What ships with it
25 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/examples/handoff-example/js/index.js 2.1 KB runs code
- assets/examples/handoff-example/js/package.json 215 B
- assets/examples/handoff-example/python/graph.py 2.5 KB runs code
- assets/examples/handoff-example/python/requirements.txt 25 B
- assets/examples/orchestrator-example/js/index.js 2.0 KB runs code
- assets/examples/orchestrator-example/js/package.json 220 B
- assets/examples/orchestrator-example/python/graph.py 2.1 KB runs code
- assets/examples/orchestrator-example/python/requirements.txt 25 B
- assets/examples/router-example/js/index.js 1.8 KB runs code
- assets/examples/router-example/js/package.json 214 B
- assets/examples/router-example/python/graph.py 2.1 KB runs code
- assets/examples/router-example/python/requirements.txt 25 B
- assets/examples/supervisor-example/js/index.js 2.3 KB runs code
- assets/examples/supervisor-example/js/package.json 218 B
- assets/examples/supervisor-example/python/graph.py 2.8 KB runs code
- assets/examples/supervisor-example/python/requirements.txt 25 B
- references/handoffs.md 11 KB
- references/orchestrator-worker.md 12 KB
- references/pattern-comparison.md 11 KB
- references/router-pattern.md 11 KB
- references/state-management-patterns.md 12 KB
- references/supervisor-subagent.md 18 KB
- scripts/generate_supervisor_graph.py 16 KB runs code
- scripts/validate_agent_graph.py 18 KB runs code
- scripts/visualize_graph.py 14 KB runs code
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.
- 2mo ago First seen · 566 lines · 118 tokens per session scan A a25c90f818e5
langgraph-agent-patterns is a skill published in the GitHub repository soba-labs/langchain-agent-skills (106 stars, last pushed 18d ago), licensed MIT. It adds 118 tokens to every session and 3,344 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
langgraph-agent-patterns compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| langgraph-agent-patternssoba-labs/langchain-agent-skills | Skill | 106 | 18d ago | MIT | A | 118 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |