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LangGraph Agent

LangChain

82·Strong

Overall Trust Score

LangChain's graph-based agent framework for building stateful, multi-actor applications with cycles and controllable execution flow. Enables complex, cyclic agent workflows with human-in-the-loop capabilities.

workflow
langchain
open-source
Version: 1.0
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

84
task completion accuracy
85
Methodology
Based on underlying model performance and framework overhead
Evidence
LangGraph Documentation
Supports multiple LLM providers with consistent performance
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
tool use reliability
88
Methodology
Tool integration testing
Evidence
LangGraph Tools
Native integration with LangChain tools ecosystem
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
multi step planning
90
Methodology
Complex task testing with graph execution
Evidence
LangGraph Cycles
Explicit graph structure enables sophisticated multi-step workflows with cycles
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
memory persistence
87
Methodology
Memory persistence testing
Evidence
LangGraph Checkpointing
Built-in checkpointing with multiple backend support (SQLite, PostgreSQL, Redis)
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
error recovery
82
Methodology
Error handling testing
Evidence
LangGraph Error Handling
Supports custom error handling and retry logic in graph nodes
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
latency
Value: Variable (2-8s typical)
Methodology
Performance monitoring
Evidence
Community Benchmarks
Latency depends on graph complexity and LLM provider
Date: 2024-09-15
Confidence: mediumLast verified: 2025-11-09

Security

79
tool sandboxing
72
Methodology
Security architecture review
Evidence
LangGraph Documentation
Tool sandboxing depends on implementation, not enforced by framework
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
access control
78
Methodology
Access control capabilities assessment
Evidence
LangGraph Self-Hosted
Access control implementation is developer's responsibility
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
prompt injection defense
80
Methodology
Injection attack testing
Evidence
LangChain Security
Provides guidance but requires developer implementation
Date: 2024-09-01
Confidence: mediumLast verified: 2025-11-09
data isolation
85
Methodology
Data architecture review
Evidence
LangGraph Persistence
Thread-based isolation when using proper checkpointing
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source transparency
95
Methodology
Source code review
Evidence
LangGraph GitHub
Fully open source with MIT license, code auditable
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09

Privacy & Compliance

82
data retention
88
Methodology
Privacy policy review
Evidence
Self-Hosted Architecture
Full control over data retention when self-hosted
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
gdpr compliance
85
Methodology
Compliance capabilities assessment
Evidence
Open Source Framework
GDPR compliance depends on deployment configuration
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
third party data sharing
75
Methodology
Data flow analysis
Evidence
LLM Provider Integration
Data shared with chosen LLM provider (OpenAI, Anthropic, etc.)
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment option
95
Methodology
Deployment options assessment
Evidence
LangGraph Deployment
Fully supports local deployment with local LLMs
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Trust & Transparency

86
documentation quality
88
Methodology
Documentation completeness review
Evidence
LangGraph Docs
Comprehensive documentation with tutorials and examples
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
execution traceability
90
Methodology
Logging capabilities assessment
Evidence
LangSmith Integration
Excellent tracing via LangSmith integration, shows full graph execution
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
decision explainability
82
Methodology
Explainability features assessment
Evidence
Graph Visualization
Graph structure provides clear execution flow visualization
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
open source code
95
Methodology
Open source assessment
Evidence
GitHub Repository
MIT licensed, 5k+ stars, active development
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09

Operational Excellence

81
ease of integration
78
Methodology
Integration complexity assessment
Evidence
LangGraph Quickstart
Requires understanding of graph concepts, steeper learning curve
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
scalability
85
Methodology
Scalability testing
Evidence
LangGraph Cloud
Supports horizontal scaling with proper infrastructure
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
cost predictability
90
Methodology
Pricing model analysis
Evidence
Open Source Pricing
Free framework, costs only from LLM API usage
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring capabilities
88
Methodology
Monitoring features assessment
Evidence
LangSmith
Excellent monitoring via LangSmith platform
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
community support
75
Methodology
Community activity analysis
Evidence
GitHub Discussions
Growing community but newer framework
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •Full control over agent execution flow with explicit graph structure
  • •Excellent for complex, cyclic workflows with human-in-the-loop
  • •Open source with MIT license, fully auditable code
  • •Built-in checkpointing and state persistence
  • •Strong integration with LangChain ecosystem and tools
  • •Superior observability via LangSmith integration

⚠️ Limitations

  • •Steeper learning curve compared to simpler agent frameworks
  • •Requires more code for simple use cases
  • •Security and sandboxing must be implemented by developer
  • •Relatively newer framework with evolving APIs
  • •Performance overhead from graph execution layer
  • •Limited managed service options (LangGraph Cloud in beta)

📊 Metadata

license: MIT
supported models:
0: Any LangChain-compatible model
1: OpenAI
2: Anthropic
3: Local LLMs
programming languages:
0: Python
1: JavaScript/TypeScript
deployment type: Self-hosted or LangGraph Cloud
tool support:
0: LangChain tools
1: Custom tools
2: Function calling
github stars: 11700+
monthly downloads: 7M+ on PyPI
first release: 2024
pricing: Free (MIT license) - Cloud deployment via LangSmith Plus/Enterprise plans
ga version: 1.0 (October 2025)

Use Case Ratings

customer support

87

Excellent for complex support workflows with human-in-the-loop

code generation

84

Good for multi-step code generation with review cycles

research assistant

88

Graph structure ideal for complex research workflows

data analysis

82

Works well but requires custom tool integration

content creation

85

Good for iterative content workflows with review stages

education

86

Graph-based curriculum paths work well for tutoring

healthcare

78

Requires careful security implementation for healthcare data

financial analysis

80

Self-hosted option good for compliance, needs security hardening

legal compliance

83

Multi-stage review workflows map well to graph structure

creative writing

89

Cyclic graph structure excellent for iterative ideation