LEI Governance Graph Agent with LangChain & LangGraph
Unowned and stale Legal Entity Identifier (LEI) records create critical blind spots in financial counterparty identification and trade reporting. Build an LEI portfolio governance agent using LangChain and LangGraph to track entity ownership changes, evaluate registration drift, and flag orphan records with 95% accuracy across global LEI datasets.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a stateful, graph-based governance tracking workflow using LangChain and LangGraph to detect orphaned and stale LEI portfolios.
How work is evaluated
Evaluates orphan LEI identification accuracy and correctness of state graph transitions.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
graph_execution_success
Verifies that the LangGraph workflow completed without state execution errors.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
orphan_detection_accuracy
Accuracy of identifying unowned and lapsed LEI records. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design stateful LangGraph node workflows representing entity lifecycle stages
Construct LangChain tool interfaces to query GLEIF open APIs and registry databases
Detect unassigned, expired, or orphaned LEI records across institutional portfolios
Generate automated compliance remediation tickets for institutional ops teams
Reference links and supporting material
Synthetic portfolio of 500 LEI records representing diverse status types (ISSUED, LAPSED, MERGED, RETIRED) across global asset managers.
Challenge setup
Evaluates orphan LEI identification accuracy and correctness of state graph transitions.
Challenge input
JSON array of corporate LEI records containing registration status and renewal date
LangChain
Framework for building chains and tool integration.
LangGraph
Stateful workflow orchestration engine for complex decision graphs.
Evaluated output
JSON governance status report detailing orphan_count, lapsed_count, and actions_required
- Verifies that the LangGraph workflow completed without state execution errors.
- Accuracy of identifying unowned and lapsed LEI records. • target: 0.95 • range: 0-1
- Benchmark: LEIGovernance-Bench-v1
- Orphan Detection Accuracy target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- LangGraph · Required
- Langchain · Optional
- LangChain · Optional
- LangGraph · Required
- Langchain · Optional
- LangChain · Optional
Evaluation contract
- graph_execution_success · Weight 1
- orphan_detection_accuracy · Weight 1
Metrics
- orphan_detection_accuracy · Target 0.95
Benchmarks
- LEIGovernance-Bench-v1
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON array of corporate LEI records containing registration status and renewal date
Output: JSON governance status report detailing orphan_count, lapsed_count, and actions_required
Execution harness: Python
Evaluation tools
- LangChain · Agent Framework
- LangGraph · Graph Orchestration Framework
Recipe freeze marker
No recipe freeze time is recorded.