dnotitia/akb

Overview

AKB is an organizational memory system for AI agents that scopes documentation, tables, and files within vaults and unifies them via a URI graph. It serves this unified knowledge over the Model Context Protocol (MCP), enabling agents to query structured and unstructured data from a single endpoint.

Best for

Best for
Developers building AI agent systems that need structured, vault-scoped knowledge retrieval

Use cases

• Give an AI agent access to company documentation and policy files scoped to a specific vault
• Provide agents with a unified, graph-linked interface to query tables and documents for data analysis
• Let an agent retrieve files and metadata from multiple vaults through a single MCP connection

Notes

AKB is an organizational memory system for AI agents that scopes documentation, tables, and files within vaults and unifies them via a URI graph. It serves this unified knowledge over the Model Context Protocol (MCP), enabling agents to query structured and unstructured data from a single endpoint.

44 stars on GitHub. Last updated 2026-06-01.

Use cases

• Give an AI agent access to company documentation and policy files scoped to a specific vault
• Provide agents with a unified, graph-linked interface to query tables and documents for data analysis
• Let an agent retrieve files and metadata from multiple vaults through a single MCP connection

Pros

• Unifies docs, tables, and files under a single URI graph for coherent agent access
• Uses vault scoping to keep knowledge boundaries clean and secure
• Exposes knowledge via the standard MCP, making it compatible with many agent frameworks

Cons

• Small community with only 44 stars, indicating early-stage maturity and limited support
• Requires Python runtime and MCP integration, adding deployment overhead
• As an organizational memory tool, it depends on consistent vault structuring to function effectively

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.