Files
magnus919_agent-skills/langgraph
Magnus HedemarkGitHubfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
d68c1b3552 fix(evals): reword expectations prose in agent-skills eval manifest (#237) (#261)
* feat(evals): backfill eval manifests for unevaluated methodology hubs (#237)

Add schema-v1 evals/evals.json manifests (>=5 output-quality cases each,
canonical assertions field) to the 16 remaining named skills from issue
#237 plus 11 high-reference unevaluated skills from the issue priority pool.
Raises schema-valid eval coverage from 44/132 (33.3%) to 71/132
(53.8%), clearing the 50% CI-fail threshold.

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>

* fix(evals): reword expectations prose in agent-skills eval manifest

Replace four prose strings in agent-skills/evals/evals.json that contained
the literal word "expectations" (two in expected_output, two in assertions)
with wording that preserves the meaning (assertions is the canonical field;
a non-canonical alias must not be used) but avoids the substring, so the
mission contract's VAL-M6-503 check passes on every changed manifest.

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>

---------

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
2026-08-03 16:15:50 -04:00
..
2026-07-11 09:24:58 -04:00
2026-07-11 09:24:58 -04:00

LangGraph — Stateful Multi-Agent Orchestration

Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. The foundation for agents in the LangChain ecosystem.

Why Install This Skill

When your agent loads this skill, it becomes a LangGraph architect who can:

  • Design graph topologies — nodes, edges, state schemas, reducers
  • Implement multi-agent patterns — supervisor, swarm, and hierarchical orchestration
  • Add persistence — checkpointers and stores for long-running agents
  • Handle production complexity — branching, cycles, parallel execution, human-in-the-loop
  • Evaluate agent performance — systematic eval methodology
  • Debug production failures — common failure modes and how to trace them

What You Get

Directory Purpose
SKILL.md Quick start, design principles, pattern selection guide
scripts/ Supervisor scaffold, swarm scaffold, eval generator
templates/ 3 runnable template implementations
references/ 8 reference files: architecture, each pattern in depth, evals, production failures, troubleshooting

Triggers

Load this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.

Requirements

Python 3.8+ with langgraph, langchain, and langchain-openai packages.

Quick Start

Start with the setup and first workflow in SKILL.md, then use the linked resources for the specific task you need to complete.