AKB — Agent Knowledge Base

Organizational memory for AI agents. Git-backed knowledge base served over the Model Context Protocol (MCP) — agents read and write directly with hybrid semantic + keyword search, structured tables, files, and a URI graph. Drop-in alternative to Confluence / Notion for Claude Code, Cursor, Windsurf, and any MCP-aware agent.

Works with

Any agent client that speaks MCP (Streamable HTTP or stdio):

• Claude Code — CLI / VS Code / JetBrains
• Claude Desktop — macOS / Windows
• Cursor, Windsurf, Cline, Continue — via the akb-mcp stdio proxy
• Custom agents — direct HTTP POST /mcp/ with a Bearer token

Try it live

A public demo runs at akb-demo.agent.seahorse.dnotitia.ai. Browse and search a small fictional-organization knowledge base — product docs, a company handbook, agent session notes, and an engineering wiki, cross-linked by the URI graph — right in your browser, no signup. To wire it into your own agent, sign up with any email (a throwaway address is fine) and point the akb-mcp proxy at https://akb-demo.agent.seahorse.dnotitia.ai/mcp/.

⚠️ Throwaway demo. It is public, wiped and re-seeded weekly, and runs on minimal resources with no uptime, privacy, or data guarantees. Don't put anything real or sensitive in it — treat every write as public and ephemeral. For real use, self-host in three containers.

Why AKB

Most knowledge tools are built for humans clicking through a UI. Agents need a different shape: structured documents, semantic + keyword search in one call, explicit relations, and full version history. AKB gives agents a single set of tools (akb_put, akb_search, akb_browse, akb_relations, …) over a backing store of Git bare repos and a PostgreSQL hybrid index.

Retrieval quality

Memory is only useful if the right note comes back. AKB's hybrid retrieval (dense + BM25, source-level dedup) was benchmarked on LongMemEval-S — 500 long-context questions, ~50 chat sessions per question. Recall@5 = 98.4%, with no reranker in the loop.

Methodology, per-category breakdown, and a one-command reproducible harness live in eval/longmemeval/. The embedding model differs across systems (AKB: bge-m3@1024), so read this as a stack-level comparison.

Design philosophy

Core stays small; flexibility comes from extension, not built-in automation. AKB does not ship its own consolidator, summariser, or "knowledge gardener" — instead every write emits a structured event to a Redis Stream (akb:events). Operators wire any external consumer (periodic synthesis bot, doc-rot reaper, weekly-digest agent, audit trail, …) on top, with no patches to the core. The base contract is a read/write store; opinions about what to do with the knowledge live outside.

Architecture

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... [View full README on GitHub](https://github.com/dnotitia/akb#readme)