{"id":"spillwavesolutions-mastering-langgraph-agent-skill","name":"mastering-langgraph-agent-skill","homepage":null,"repo_url":"https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill","category":"ai-ml","subcategories":[],"tags":["ai-ml","langgraph","langgraph-python","agentic-workflows","tool-use","multi-agent","human-in-the-loop","persistence","python"],"what_it_does":"A Python educational skill/repository that teaches common LangGraph patterns for building stateful, tool-using, multi-step AI agents (including persistence/memory, human-in-the-loop, and multi-agent workflows), with a focus on agent “Agent Skill Standard” packaging and deployment guidance.","use_cases":["Learning and implementing LangGraph StateGraph-based agent workflows","Building tool-using agent loops and branching/conditional routing workflows","Adding conversation persistence via checkpointers and thread_id usage","Designing human-in-the-loop checkpoints using interrupt()","Coordinating multi-agent systems (supervisor/swarm patterns)","Debugging and monitoring agent graphs (testing, LangSmith, visualization)"],"not_for":["A turnkey hosted API service with managed authentication and rate limits","Production-grade SDK/API consumer for third-party integration without writing Python code","Use as a compliance-certified security or data-processing product"],"best_when":"You want practical guidance for implementing LangGraph agent skills in Python and integrating those graphs into your own applications.","avoid_when":"You need a network API surface (REST/GraphQL/gRPC) or managed deployment with guaranteed SLAs from this specific repository/package.","alternatives":["LangGraph official documentation and templates","LangChain/LangGraph community examples and pattern repos","Other agent framework docs such as Microsoft Semantic Kernel or LlamaIndex agent workflows (where applicable)"],"af_score":37.5,"security_score":26.0,"reliability_score":23.8,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-30T15:29:13.103703+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":true,"sdk_languages":["python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["None specified for this repo/package itself (relies on underlying LLM/provider credentials, e.g., OpenAI/Anthropic)"],"oauth":false,"scopes":false,"notes":"The repository is an educational skill/template for LangGraph; authentication is not described for any hosted API. LLM provider credentials are implied as necessary by the example code (not detailed here)."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing for the repository content is stated. Costs would be driven by your LLM provider usage if you run the examples."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":37.5,"security_score":26.0,"reliability_score":23.8,"mcp_server_quality":0.0,"documentation_accuracy":65.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":95.0,"rate_limit_clarity":0.0,"tls_enforcement":20.0,"auth_strength":20.0,"scope_granularity":10.0,"dependency_hygiene":40.0,"secret_handling":45.0,"security_notes":"No hosted service or transport layer is provided by the repo itself. Security guidance is not detailed in the provided README beyond general best practices (e.g., mentioning debugging/monitoring). Any real security posture depends on how you configure LLM provider credentials, checkpointers/storage, and tool execution in your application.","uptime_documented":0.0,"version_stability":35.0,"breaking_changes_history":30.0,"error_recovery":30.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["Examples rely on in-memory checkpointers (InMemorySaver) which may not provide persistence across processes; production usage requires appropriate checkpointer/storage.","Agent workflows can be sensitive to state schema design (e.g., message aggregation via operator.add) and thread_id consistency.","Tool-using agents require careful tool error handling and guardrails; the repo content shown here is conceptual and may not include robust patterns for all failure modes."]}}