v2.0.1 ยท MCP-native ยท Python 3.10+

Memory that makes
AI agents
smarter over time.

Persistent memory that follows your AI across sessions, instances, and machines.

Get started โ†’ View on GitHub
Open source No vendor lock-in Human-readable MCP-native
terminal
โฏ /rem the postgres pool exhausts at 50+ concurrent req/s
โœ“ Saved โ†’ infra.postgres#1742389200
 
user: what do I know about connection pooling?
Found 3 entries across 2 files...
infra.postgres โ€” "Pool exhausts at 50+ req/s" (2d ago)
infra.redis โ€” "Redis maxes at 10k connections" (1w ago)
โฏ

You talk.
It remembers.

Save anything with /rem or just tell your AI what to remember in plain language. Ask questions later and the right context surfaces automatically.

You never need to learn the underlying tools. Crafted skill files teach AI agents when to search, when to save, and how to route entries to the right file โ€” so you just talk.

No database to migrate. No vendor to depend on. Delete the index and rebuild it in seconds.

// flow
You
โ”œโ”€โ”€ /rem <finding> โ”€โ”€ quick save
โ””โ”€โ”€ "remember that..." โ”€โ”€ natural chat
 
โ†“
 
AI Agent (learns via skill files)
โ”œโ”€โ”€ knows when to search vs save
โ””โ”€โ”€ routes to the right file automatically
 
โ†“
 
~/.hkb/storage/
โ”œโ”€โ”€ project.architecture.md
โ”œโ”€โ”€ infra.postgres.md
โ””โ”€โ”€ security.threat-intel.md

Proven tech, not reinvented.

Four architecture decisions that make it work โ€” built on tools that already exist and already scale.

Dot Namespaces

domain.topic.subtopic.md replaces folder hierarchy. The first segment is your project for natural filtering. Two to four segments: infra.postgres, security.threat-intel, project.architecture.decisions.

Hybrid Search

Ripgrep for exact and regex matches โ€” the fastest grep on earth. BM25 via SQLite FTS5 for keyword ranking. Results are merged, deduplicated, and scored. Configurable weights, no custom search engine.

Append-Only Entries

Entries are never mutated โ€” only appended with epoch timestamps. The markdown files are the source of truth, not the index. Delete the SQLite database and rebuild it in seconds.

Smart Scoring

A 9-stage pipeline ranks every result: recency decay, staleness penalties, type and status boosts. Decisions and high-weight entries never go stale. The best match always surfaces first.

Built different
where it matters.

Most AI memory tools trade durability for convenience. HyperKB doesn't make you choose.

Files You Can Actually Read

Your knowledge lives in infra.postgres.md, not a3f2b8c9.json. Open any file in any editor. Read it without the tool โ€” today, five years from now. No database required to access your own data.

Search That Ranks, Not Just Matches

A 9-stage scoring pipeline with recency decay, staleness penalties, type boosts, and session anchoring. The best result surfaces first โ€” not just the newest, not just a substring match.

Context Your AI Can Use

Token-budgeted retrieval packs exactly what fits. Session briefings orient the agent on startup. Topic anchoring biases ongoing searches. Your AI gets signal, not noise.

No Lock-In, Ever

SQLite is a rebuildable cache, not a prison. Delete it and rebuild in seconds. Sync across machines with git-backed three-way merge over S3. Your markdown files are always yours, always portable.

One KB,
every machine.

S3-compatible sync with git-backed three-way merge. Your knowledge base follows you across machines and AI sessions โ€” encrypted credentials at rest, conflict resolution built in.

AWS S3 MinIO Backblaze B2 Any S3-compatible
Sync topology: two machines pushing and pulling from an encrypted S3 bucket with three-way merge

Installs in minutes.

1 โ€” Install
brew install ripgrep
git clone https://github.com/calvincs/hyperkb ~/src/hyperkb
python3 -m venv ~/.hkb/venv
~/.hkb/venv/bin/pip install -e "~/src/hyperkb[all]"
export PATH="$HOME/.hkb/venv/bin:$PATH"  # add to ~/.zshrc
hkb init
sudo apt install ripgrep    # or: dnf, cargo
git clone https://github.com/calvincs/hyperkb ~/src/hyperkb
python3 -m venv ~/.hkb/venv
~/.hkb/venv/bin/pip install -e "~/src/hyperkb[all]"
export PATH="$HOME/.hkb/venv/bin:$PATH"  # add to ~/.bashrc
hkb init
2 โ€” Register the MCP server
// Add to ~/.claude/.mcp.json (or project .mcp.json)
{
  "mcpServers": {
    "hyperkb": {
      "command": "~/.hkb/venv/bin/hkb-mcp"
    }
  }
}

Restart Claude Code and approve the MCP server when prompted. The skill files teach your AI agent how to use the KB automatically.
For permissions, skill files, and OpenCode setup, see the full setup guide โ†’

Give your AI
a memory worth keeping.

Plain markdown you can read without the tool โ€” today, five years from now, always.

โ˜… View on GitHub Read the docs