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Turn codebases into queryable graphs for persistent AI architectural understanding.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "OpenLore" yet — see the docs or source repo.
Use OpenLore to analyze this repository and produce an architecture overview covering core services, module dependencies, key data flows, and main design constraints. Also identify the best files for a new developer to read first.
A structured architecture summary with module relationships, data flow notes, design constraints, and a recommended reading path.
Use OpenLore to compare the current code against existing specifications. Find drift in interfaces, behaviors, or dependencies, rank issues by severity, and recommend whether code or documentation should be updated.
A prioritized drift report listing mismatches, affected areas, and recommended fixes.
Use OpenLore to answer this: which modules and functions does a user login request pass through from entry point to database? Provide the call chain, related configuration, and nodes that may affect performance or security.
A traceable call-chain analysis with related configuration, risk points, and supporting evidence.
Retrieve past project decisions and constraints by tracing code changes to conversations.
Store and retrieve long-term memory across agents and sessions.
Query and understand large codebases with a fast knowledge graph for AI agents.
Provides local persistent memory for coding agents with low-cost context retrieval.
Provide AI agents shared team context, session captures, and structured documentation.
Index codebases into a knowledge graph for search, analysis, and code understanding.