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langchain-langgraph-human-in-loop

Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before / interrupt_after and Command(resume=...) — JSON-serializable state, clean resume semantics, and UI wiring for approval decis

langchain-langgraph-streaming

Pick the correct LangGraph 1.0 stream_mode ("messages" vs "updates" vs "values"), wire it into SSE or WebSocket without proxy-buffering gotchas, and filter astream_events(v2) server-side before forwar

langchain-langgraph-subgraphs

Compose LangGraph 1.0 subgraphs correctly — shared state key propagation, Send / Command(graph=...) dispatch, callback scoping, per-subgraph recursion budgets, and testing each subgraph in isolation.

langchain-local-dev-loop

Build a fast, deterministic local test loop for LangChain 1.0 / LangGraph 1.0 — FakeListChatModel fixtures, pytest config, VCR cassettes with key redaction, warning-filter policy. Use when adding test

langchain-middleware-patterns

Build composable middleware for LangChain 1.0 chains and LangGraph 1.0 agents — PII redaction, caching, retry, token budgets, guardrails — with ORDERING rules that avoid cache-key leakage and double-c

langchain-model-inference

Invoke Claude, GPT-4o, and Gemini through LangChain 1.0 without tripping on the content-block, token-accounting, and structured-output quirks that silently break production code. Use when initializing

langchain-multi-env-setup

Build reliable dev / staging / prod isolation for LangChain 1.0 services — Pydantic Settings + SecretStr, cloud Secret Manager in prod, per-env prompt and model version pinning, env-specific checkpoin

langchain-observability

Wire LangSmith tracing and custom metric callbacks into a LangChain 1.0 chain or LangGraph 1.0 agent correctly — env-var spelling, subgraph propagation, per-tenant dimensions, cost and latency counter

langchain-otel-observability

Wire LangChain 1.0 / LangGraph 1.0 traces into an OpenTelemetry-native backend (Jaeger, Honeycomb, Grafana Tempo, Datadog) with LLM-specific SLOs, safe prompt-content policy, and subgraph-aware span p

langchain-performance-tuning

Tune LangChain 1.0 / LangGraph 1.0 Python chains and agents for throughput, latency, and cost — streaming modes, explicit batch concurrency, semantic plus exact caches, persistent message history, and

langchain-prompt-engineering

Manage LangChain 1.0 prompts like code — LangSmith prompt hub versioning, XML-tag conventions for Claude, few-shot example selection, discriminated-union extraction schemas, and A/B test wiring. Use w

langchain-rate-limits

Rate-limit LangChain 1.0 calls correctly across multi-worker deployments — Redis-backed limiters, asyncio.Semaphore, narrow exception whitelists, and provider-specific throttle handling. Use when hitt

langchain-reference-architecture

A reference layered architecture for production LangChain 1.0 / LangGraph 1.0 services — LLM factory with version-safe defaults, chain/graph registry, retriever and tool DI, Pydantic-validated config,

langchain-sdk-patterns

Compose LangChain 1.0 Python runnables with the production defaults the docs do not warn about: parallel batching, narrow fallbacks, and brace-safe prompts. Use when building an LCEL chain with Runnab

langchain-security-basics

Harden a LangChain 1.0 chain or LangGraph agent against prompt injection, tool abuse, PII leakage in traces, and secrets exfiltration — wrap user content in XML tags, enforce the tool allowlist via pr

langchain-upgrade-migration

Migrate a LangChain 0.3.x Python codebase to LangChain 1.0 / LangGraph 1.0 without breaking production — named breaking changes, codemod patterns, and a phased rollout. Use when upgrading LangChain or

langchain-webhooks-events

Dispatch LangChain 1.0 chain/agent events to external systems — webhooks, Kafka, Redis Streams, SNS — via async fire-and-forget callbacks, subgraph-aware wiring, and HMAC-signed delivery with idempote

langgraph

LangGraph 1.x (LTS) Python workflow patterns for state management, delta channels, resilience (node timeouts, error handlers, graceful drain), routing, parallel execution, supervisor-worker, tool call

by yonatangrossskills.sh

goalflow

Route goalflow (wanmol/goal-flow) work — a LangGraph framework that combines workflow graphs with agent loops — into exactly one mode: fit check, transpiling a Dify DSL export into runnable LangGraph

by akillnessskills.sh

by terminalskillsskills.sh

langgraph

Build stateful, multi-step AI agents and workflows with LangGraph. Use when a user asks to create AI agents with complex logic, build multi-agent systems, implement human-in-the-loop workflows, create

multi-agent-architect

Design and optimize production-grade multi-agent systems with LangGraph, LangChain, and DeepAgents for complex AI workflows.

langchain-langgraph-basics

Build a correct LangGraph 1.0 StateGraph — typed TypedDict state with reducers, nodes, edges, compile, and recursion budgets — without hitting the silent-termination and state-replacement traps. Use w

by gabrielmoreiraGitHub

langchain-enterprise-rbac

Enforce tenant isolation and role-based access across LangChain 1.0 chains and LangGraph 1.0 agents — per-request retriever construction, tenant-scoped rate limits, role-scoped tool allowlists, and st

langgraph-workflows

Design and implement multi-agent workflows with LangGraph 0.2+ - state management, supervisor-worker patterns, conditional routing, and fault-tolerant checkpointing

by majiayu000GitHub