Awesome-LangChain-Enterprise is the Cognitive Resilience Backbone — orchestrating stateful, resumable, multi-agent workflows with LangGraph at its core.
┌─────────────────────────────────────────────────────────────────────────────┐ │ AIwork4me Cognitive Trinity │ ├─────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ 🖥️ INTERFACE LAYER │ │ │ │ Awesome-Dify-Workflows (User Interface) │ │ │ │ • API Gateway • Workflow Visualization │ │ │ │ • Human-in-the-Loop UI • Monitoring Dashboard │ │ │ └─────────────────────────────┬───────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ 🧠 COGNITIVE LAYER │ │ │ │ Awesome-LangChain-Enterprise (This Repo) │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ │ │ │ │ LangGraph │ │ Patterns │ │ State Persistence │ │ │ │ │ │ Graphs │ │ (RAG, HITL) │ │ (Memory, Checkpoint)│ │ │ │ │ └──────┬───────┘ └──────┬───────┘ └──────────┬───────────┘ │ │ │ │ │ ┌─────────────┴──────────────────────┘ │ │ │ │ │ │ │ │ │ │ ▼ ▼ │ │ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ │ │ 🔍 EVALUATION LAYER (LangSmith) │ │ │ │ │ │ RAGAS Metrics • Custom Heuristics • Regression Tests │ │ │ │ │ └──────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────┬───────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ 🔧 CAPABILITY LAYER │ │ │ │ Awesome-Claude-Agent-Skills (MCP Tools) │ │ │ │ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │ │ │ │ │ Web │ │ Code │ │ Data │ │Automation │ │ │ │ │ │ Skills │ │ Skills │ │ Skills │ │ Skills │ │ │ │ │ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────────────┘ "An enterprise AI system must be resilient not just to failures, but to uncertainty." — Harrison Chase
| Pillar | Description | Implementation |
|---|---|---|
| Stateful | Every decision is traceable, every state recoverable | LangGraph Checkpointing |
| Resumable | Pause at any point, resume from any checkpoint | MemorySaver + Persistence |
| Observable | Every token, every decision, every latency — measured | LangSmith Integration |
| Pattern | Description | Status |
|---|---|---|
| Adaptive-RAG with HITL | Self-correcting RAG with human fallback | ✅ Production |
| Multi-Agent Supervisor | Hierarchical agent coordination | 🔜 Coming |
| Reflexion | Self-critique and improvement loops | 🔜 Coming |
| Plan-and-Execute | Strategic planning with execution tracking | 🔜 Coming |
Awesome-LangChain-Enterprise/ ├── 📁 graphs/ # LangGraph implementations │ ├── adaptive-rag/ # Adaptive RAG with HITL │ │ ├── graph.py # Graph definition │ │ ├── state.py # State schema │ │ ├── nodes.py # Node implementations │ │ └── edges.py # Conditional edges │ └── multi-agent-supervisor/ # Multi-agent coordination ├── 📁 patterns/ # Reusable cognitive patterns │ ├── adaptive-rag/ │ ├── multi-agent-supervisor/ │ ├── reflexion/ │ └── plan-execute/ ├── 📁 evals/ # LangSmith evaluation │ ├── datasets/ # Test datasets │ ├── evaluators/ # Custom evaluators │ └── run_eval.py # Evaluation runner ├── 📁 persistence/ # State persistence │ ├── memory.py # Long-term memory │ ├── checkpoint.py # Checkpoint management │ └── recovery.py # Resume from checkpoint ├── 📁 infrastructure/ # Deployment configs │ ├── langgraph-cloud/ # LangGraph Cloud configs │ └── docker/ # Self-hosting setup ├── 📁 .claude/ │ └── AGENT_HANDBOOK.md # Engineering standard └── 📄 README.md # This file # Python 3.11+ python --version # Install dependencies pip install langgraph langchain-anthropic langsmith# Required export ANTHROPIC_API_KEY="your-claude-api-key" export LANGSMITH_API_KEY="your-langsmith-api-key" export LANGSMITH_TRACING=true export LANGSMITH_PROJECT="aiwork4me-enterprise" # Optional - MCP Integration export OPENCLAW_API_KEY="your-openclaw-key"from graphs.adaptive_rag import create_adaptive_rag_graph from persistence.checkpoint import CheckpointManager # Create graph with persistence checkpointer = CheckpointManager() graph = create_adaptive_rag_graph(checkpointer=checkpointer) # Execute with tracing result = graph.invoke( {"question": "What are the latest MCP protocol best practices?"}, config={ "configurable": {"thread_id": "user-123"}, "tags": ["domain:research", "pattern:adaptive-rag"] } ) print(result["answer"])Every graph MUST be evaluated before production deployment:
# Run evaluation suite python evals/run_eval.py --graph adaptive-rag --dataset rag-benchmark # Output: # ✅ Faithfulness: 0.94 (target: >0.90) # ✅ Answer Relevance: 0.89 (target: >0.85) # ✅ Context Precision: 0.91 (target: >0.85) # ✅ Hallucination Rate: 0.03 (target: <0.05) # ✅ HITL Trigger Rate: 0.08 (target: <0.15)| Metric | Target | Description |
|---|---|---|
| Faithfulness | > 0.90 | Answer grounded in retrieved context |
| Answer Relevance | > 0.85 | Answer addresses the question |
| Context Precision | > 0.85 | Retrieved context is relevant |
| Hallucination Rate | < 0.05 | Frequency of ungrounded claims |
| HITL Trigger Rate | < 0.15 | Rate of human intervention requests |
| P99 Latency | < 5s | 99th percentile response time |
| Checkpoint Recovery | 100% | Successful resume from interruption |
# Connect to MCP skills from Awesome-Claude-Agent-Skills from langchain.tools import Tool from mcp import MCPClient mcp_client = MCPClient("https://mcp.aiwork4me.io/openclaw") openclaw_tool = Tool( name="deep_research", description="Multi-step web research with OpenClaw", func=mcp_client.call_tool )# Export to Dify as workflow node langgraph: graph: adaptive_rag endpoint: https://langgraph.aiwork4me.io auth: bearer input_mapping: query: question output_mapping: result: answer| Document | Purpose |
|---|---|
| AGENT_HANDBOOK.md | Engineering standard for AI developers |
| State Management Guide | How to design stateful graphs |
| Evaluation Best Practices | LangSmith integration guide |
| Deployment Guide | Production deployment strategies |
| Component | Technology |
|---|---|
| Graph Framework | LangGraph |
| LLM Provider | Anthropic Claude 4 |
| Evaluation | LangSmith + RAGAS |
| Persistence | SQLite / PostgreSQL / Redis |
| Deployment | LangGraph Cloud / Docker |
| Tracing | LangSmith (required) |
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Engineering Standard: Stateful | Resumable | Observable