langgraph

LangGraph Example — Testing LangGraph Agents with EvalView

Test LangGraph agents with EvalView — capture tool calls, verify execution sequences, measure latency and cost, detect regressions with golden baselines, and run in CI.

Example Output

Text version

                               📊 Evaluation Summary
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┓
┃ Test Case           ┃ Backend   ┃ Score ┃ Status    ┃    Cost ┃ Tokens ┃ Latency ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━┩
│ Conversational Test │ Langgraph │  80.0 │ ✅ PASSED │ $0.0014 │    321 │  6533ms │
│ Search Test         │ Langgraph │  85.0 │ ✅ PASSED │ $0.0024 │    720 │  7244ms │
│ Multi-Step Research │ Langgraph │  90.0 │ ✅ PASSED │ $0.0089 │  2,450 │ 12340ms │
└─────────────────────┴───────────┴───────┴───────────┴─────────┴────────┴─────────┘

Execution Flow (3 steps)
├── Step 1: tavily_search [green]✓[/green]  [2100ms | $0.0020]
│   └── → params: {"query": "AI agents 2024 trends"}
├── Step 2: tavily_search [green]✓[/green]  [1800ms | $0.0020]
│   └── → params: {"query": "LangGraph vs AutoGPT comparison"}
└── Step 3: summarize [green]✓[/green]  [3200ms | $0.0049]
    └── → params: {"content": "Based on the search results..."}

Quick Start

1. Install Dependencies

# Python 3.11+ required
pip install "langgraph-cli[inmem]" langchain-openai langchain-anthropic tavily-python

2. Set API Keys

export OPENAI_API_KEY=sk-...
export TAVILY_API_KEY=tvly-...  # Get free key at tavily.com

3. Start LangGraph Server

Option A: Use the included example agent

cd examples/langgraph/agent
langgraph dev

Option B: Use your own LangGraph agent

cd /path/to/your/langgraph/project
langgraph dev

Server runs at: http://localhost:2024

4. Run Tests

# From EvalView root
evalview run --pattern examples/langgraph/

Test Cases

Test	What it checks
conversational.yaml	Basic Q&A without tools
search.yaml	Web search tool usage
multi-step.yaml	Multi-tool research workflow

Configuration

EvalView auto-detects LangGraph Cloud API on port 2024. To configure manually:

# .evalview/config.yaml
adapter: langgraph
endpoint: http://localhost:2024
assistant_id: agent  # Your graph name from langgraph.json
timeout: 90

Writing Test Cases

name: "My Test"
adapter: langgraph
endpoint: http://localhost:2024

input:
  query: "What are the latest AI trends?"
  context:
    assistant_id: agent  # Optional: override default assistant

expected:
  tools:
    - tavily_search  # Expected tools to be called
  output:
    contains:
      - "AI"
      - "trends"

thresholds:
  min_score: 70
  max_cost: 0.10
  max_latency: 30000

Troubleshooting

"Python 3.11+ required"

# Use conda or pyenv
conda create -n langgraph python=3.12
conda activate langgraph

"TAVILY_API_KEY not found"

• Get a free key at tavily.com
• Or modify the agent to remove the search tool

"Connection refused on port 2024"

• Make sure langgraph dev is running
• Check for errors in the server terminal

Links

• LangGraph Docs
• LangGraph GitHub
• EvalView Docs