LangGraph Architecture Analysis Skill

A skill for analyzing LangGraph application architecture, identifying bottlenecks, and proposing multiple improvement strategies.

📋 Overview

This skill analyzes existing LangGraph applications and proposes graph structure improvements:

1. Current State Analysis: Performance measurement and graph structure understanding
2. Problem Identification: Organizing bottlenecks and architectural issues
3. Improvement Proposals: Generate 3-5 diverse improvement proposals (all candidates for parallel exploration)

Important:

• This skill only performs analysis and proposals. It does not implement changes.
• Output all improvement proposals. The arch-tune command will implement and evaluate them in parallel.

🎯 When to Use

Use this skill in the following situations:

1. When performance improvement of existing applications is needed

   • Latency exceeds targets
   • Cost is too high
   • Accuracy is insufficient

2. When considering architecture-level improvements

   • Prompt optimization (fine-tune) has limitations
   • Graph structure changes are needed
   • Considering introduction of new patterns

3. When you want to compare multiple improvement options

   • Unclear which architecture is optimal
   • Want to understand trade-offs

📖 Analysis and Proposal Workflow

Step 1: Verify Evaluation Environment

Purpose: Prepare for performance measurement

Actions:

1. Verify existence of evaluation program (

   .langgraph-master/evaluation/

   or specified directory)
2. If not present, confirm evaluation criteria with user and create
3. Verify test cases

Output: Evaluation program ready

Step 2: Measure Current Performance

Purpose: Establish baseline

Actions:

1. Run test cases 3-5 times
2. Record each metric (accuracy, latency, cost, etc.)
3. Calculate statistics (mean, standard deviation, min, max)
4. Save as baseline

Output:

baseline_performance.json

Step 3: Analyze Graph Structure

Purpose: Understand current architecture

Actions:

1. Identify graph definitions with Serena MCP

   • Search for StateGraph, MessageGraph with

     find_symbol

   • Identify graph definition files (typically

     graph.py

     ,

     main.py

     , etc.)

2. Analyze node and edge structure

   • List node functions with

     get_symbols_overview

   • Verify edge types (sequential, parallel, conditional)
   • Check for subgraphs

3. Understand each node's role

   • Read node functions
   • Verify presence of LLM calls
   • Summarize processing content

Output: Graph structure documentation

Step 4: Identify Bottlenecks

Purpose: Identify performance problem areas

Actions:

1. Latency Bottlenecks

   • Identify nodes with longest execution time
   • Verify delays from sequential processing
   • Discover unnecessary processing

2. Cost Issues

   • Identify high-cost nodes
   • Verify unnecessary LLM calls
   • Evaluate model selection optimality

3. Accuracy Issues

   • Identify nodes with frequent errors
   • Verify errors due to insufficient information
   • Discover architecture constraints

Output: List of issues

Step 5: Consider Architecture Patterns

Purpose: Identify applicable LangGraph patterns

Actions:

1. Consider patterns based on problems

   • Latency issues → Parallelization
   • Diverse use cases → Routing
   • Complex processing → Subgraph
   • Staged processing → Prompt Chaining, Map-Reduce

2. Reference langgraph-master skill

   • Verify characteristics of each pattern
   • Evaluate application conditions
   • Reference implementation examples

Output: List of applicable patterns

Step 6: Generate Improvement Proposals

Purpose: Create 3-5 diverse improvement proposals (all candidates for parallel exploration)

Actions:

1. Create improvement proposals based on each pattern

   • Change details (which nodes/edges to modify)
   • Expected effects (impact on accuracy, latency, cost)
   • Implementation complexity (low/medium/high)
   • Estimated implementation time

2. Evaluate improvement proposals

   • Feasibility
   • Risk assessment
   • Expected ROI

Important: Output all improvement proposals. The arch-tune command will implement and evaluate all proposals in parallel.

Output: Improvement proposal document (including all proposals)

Step 7: Create Report

Purpose: Organize analysis results and proposals

Actions:

1. Current state analysis summary
2. Organize issues
3. Document all improvement proposals in

   improvement_proposals.md

   (with priorities)
4. Present recommendations for reference (first recommendation, second recommendation, reference)

Important: Output all proposals to

improvement_proposals.md

. The arch-tune command will read these and implement/evaluate them in parallel.

Output:

• analysis_report.md

  - Current state analysis and issues

• improvement_proposals.md

  - All improvement proposals (Proposal 1, 2, 3, ...)

📊 Output Formats

baseline_performance.json

{
  "iterations": 5,
  "test_cases": 20,
  "metrics": {
    "accuracy": {
      "mean": 75.0,
      "std": 3.2,
      "min": 70.0,
      "max": 80.0
    },
    "latency": {
      "mean": 3.5,
      "std": 0.4,
      "min": 3.1,
      "max": 4.2
    },
    "cost": {
      "mean": 0.020,
      "std": 0.002,
      "min": 0.018,
      "max": 0.023
    }
  }
}

analysis_report.md

# Architecture Analysis Report

Execution Date: 2024-11-24 10:00:00

## Current Performance

| Metric | Mean | Std Dev | Target | Gap |
|--------|------|---------|--------|-----|
| Accuracy | 75.0% | 3.2% | 90.0% | -15.0% |
| Latency | 3.5s | 0.4s | 2.0s | +1.5s |
| Cost | $0.020 | $0.002 | $0.010 | +$0.010 |

## Graph Structure

### Current Configuration

\```
analyze_intent → retrieve_docs → generate_response
\```

- **Node Count**: 3
- **Edge Type**: Sequential only
- **Parallel Processing**: None
- **Conditional Branching**: None

### Node Details

#### analyze_intent
- **Role**: Classify user input intent
- **LLM**: Claude 3.5 Sonnet
- **Average Execution Time**: 0.5s

#### retrieve_docs
- **Role**: Search related documents
- **Processing**: Vector DB query + reranking
- **Average Execution Time**: 1.5s

#### generate_response
- **Role**: Generate final response
- **LLM**: Claude 3.5 Sonnet
- **Average Execution Time**: 1.5s

## Issues

### 1. Latency Bottleneck from Sequential Processing

- **Issue**: analyze_intent and retrieve_docs are sequential
- **Impact**: Total 2.0s delay (57% of total)
- **Improvement Potential**: -0.8s or more reduction possible through parallelization

### 2. All Requests Follow Same Flow

- **Issue**: Simple and complex questions go through same processing
- **Impact**: Unnecessary retrieve_docs execution (wasted Cost and Latency)
- **Improvement Potential**: -50% reduction possible for simple cases through routing

### 3. Use of Low-Relevance Documents

- **Issue**: retrieve_docs returns only top-k (no reranking)
- **Impact**: Low Accuracy (75%)
- **Improvement Potential**: +10-15% improvement possible through multi-stage RAG

## Applicable Architecture Patterns

1. **Parallelization** - Parallelize analyze_intent and retrieve_docs
2. **Routing** - Branch processing flow based on intent
3. **Subgraph** - Dedicated subgraph for RAG processing (retrieve → rerank → select)
4. **Orchestrator-Worker** - Execute multiple retrievers in parallel and integrate results

improvement_proposals.md

# Architecture Improvement Proposals

Proposal Date: 2024-11-24 10:30:00

## Proposal 1: Parallel Document Retrieval + Intent Analysis

### Changes

**Current**:
\```
analyze_intent → retrieve_docs → generate_response
\```

**After Change**:
\```
START → [analyze_intent, retrieve_docs] → generate_response
      ↓ parallel execution ↓
\```

### Implementation Details

1. Add parallel edges to StateGraph
2. Add join node to wait for both results
3. generate_response receives both results

### Expected Effects

| Metric | Current | Expected | Change | Change Rate |
|--------|---------|----------|--------|-------------|
| Accuracy | 75.0% | 75.0% | ±0 | - |
| Latency | 3.5s | 2.7s | -0.8s | -23% |
| Cost | $0.020 | $0.020 | ±0 | - |

### Implementation Complexity

- **Level**: Low
- **Estimated Time**: 1-2 hours
- **Risk**: Low (no changes to existing nodes required)

### Recommendation Level

⭐⭐⭐⭐ (High) - Effective for Latency improvement with low risk

---

## Proposal 2: Intent-Based Routing

### Changes

**Current**:
\```
analyze_intent → retrieve_docs → generate_response
\```

**After Change**:
\```
analyze_intent
    ├─ simple_intent → simple_response (lightweight)
    └─ complex_intent → retrieve_docs → generate_response
\```

### Implementation Details

1. Conditional branching based on analyze_intent output
2. Create new simple_response node (using Haiku)
3. Routing with conditional_edges

### Expected Effects

| Metric | Current | Expected | Change | Change Rate |
|--------|---------|----------|--------|-------------|
| Accuracy | 75.0% | 82.0% | +7.0% | +9% |
| Latency | 3.5s | 2.8s | -0.7s | -20% |
| Cost | $0.020 | $0.014 | -$0.006 | -30% |

**Assumption**: 40% simple cases, 60% complex cases

### Implementation Complexity

- **Level**: Medium
- **Estimated Time**: 2-3 hours
- **Risk**: Medium (adding routing logic)

### Recommendation Level

⭐⭐⭐⭐⭐ (Highest) - Balanced improvement across all metrics

---

## Proposal 3: Multi-Stage RAG with Reranking Subgraph

### Changes

**Current**:
\```
analyze_intent → retrieve_docs → generate_response
\```

**After Change**:
\```
analyze_intent → [RAG Subgraph] → generate_response
                  ↓
            retrieve (k=20)
                  ↓
            rerank (top-5)
                  ↓
            select (best context)
\```

### Implementation Details

1. Convert RAG processing to dedicated subgraph
2. Retrieve more candidates in retrieve node (k=20)
3. Evaluate relevance in rerank node (Cross-Encoder)
4. Select optimal context in select node

### Expected Effects

| Metric | Current | Expected | Change | Change Rate |
|--------|---------|----------|--------|-------------|
| Accuracy | 75.0% | 88.0% | +13.0% | +17% |
| Latency | 3.5s | 3.8s | +0.3s | +9% |
| Cost | $0.020 | $0.022 | +$0.002 | +10% |

### Implementation Complexity

- **Level**: Medium-High
- **Estimated Time**: 3-4 hours
- **Risk**: Medium (introducing new model, subgraph management)

### Recommendation Level

⭐⭐⭐ (Medium) - Effective when Accuracy is priority, Latency will degrade

---

## Recommendations

**Note**: The following recommendations are for reference. The arch-tune command will **implement and evaluate all Proposals above in parallel** and select the best option based on actual results.

### 🥇 First Recommendation: Proposal 2 (Intent-Based Routing)

**Reasons**:
- Balanced improvement across all metrics
- Implementation complexity is manageable at medium level
- High ROI (effect vs cost)

**Next Steps**:
1. Run parallel exploration with arch-tune command
2. Implement and evaluate Proposals 1, 2, 3 simultaneously
3. Select best option based on actual results

### 🥈 Second Recommendation: Proposal 1 (Parallel Retrieval)

**Reasons**:
- Simple implementation with low risk
- Reliable Latency improvement
- Can be combined with Proposal 2

### 📝 Reference: Proposal 3 (Multi-Stage RAG)

**Reasons**:
- Effective when Accuracy is most important
- Only when Latency trade-off is acceptable

🔧 Tools and Technologies Used

MCP Server Usage

• Serena MCP: Codebase analysis

  • find_symbol

    : Search graph definitions

  • get_symbols_overview

    : Understand node structure

  • search_for_pattern

    : Search specific patterns

Reference Skills

• langgraph-master skill: Architecture pattern reference

Evaluation Program

• User-provided or auto-generated
• Metrics: accuracy, latency, cost, etc.

⚠️ Important Notes

1. Analysis Only

   • This skill does not implement changes
   • Only outputs analysis and proposals

2. Evaluation Environment

   • Evaluation program is required
   • Will be created if not present

3. Serena MCP

   • If Serena is unavailable, manual code analysis
   • Use ls, read tools

🔗 Related Resources

• langgraph-master skill - Architecture patterns
• arch-tune command - Command that uses this skill
• fine-tune skill - Prompt optimization