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Edgehdf5 Memory

HDF5-backed persistent cognitive memory for AI agents.

Rating
3.8 (405 reviews)
Downloads
8,513 downloads
Version
1.0.0

Overview

HDF5-backed persistent cognitive memory for AI agents.

Complete Documentation

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EdgeHDF5 Memory

Persistent HDF5-backed memory with vector search, BM25 hybrid retrieval, Hebbian learning, and temporal decay.

Setup

bash
# Install the CLI (one-time)
cargo install edgehdf5-cli
# Or from source:
cargo install --path crates/edgehdf5-cli

Set EDGEHDF5_PATH env var or pass --path to every command.

Commands

All output is JSON for easy parsing.

Create a memory file

bash
edgehdf5 --path agent.h5 create --agent-id myagent --dim 384 --wal

Save an entry

Pass JSON via --json or stdin:

bash
edgehdf5 --path agent.h5 save --json '{"chunk":"User asked about weather","embedding":[0.1,0.2,...],"source_channel":"discord","timestamp":1700000000.0,"session_id":"s1","tags":"weather"}'

Embedding must match the dimension specified at creation.

Search memory

bash
edgehdf5 --path agent.h5 search --embedding '[0.1,0.2,...]' --query 'weather forecast' -k 5

Optional: --vector-weight 0.7 --keyword-weight 0.3 (defaults).

Recall a specific entry

bash
edgehdf5 --path agent.h5 recall 42

Stats

bash
edgehdf5 --path agent.h5 stats

Returns: count, active entries, WAL pending, config details.

Flush WAL

bash
edgehdf5 --path agent.h5 flush-wal

Generate AGENTS.md

bash
edgehdf5 --path agent.h5 agents-md
# Or write to file:
edgehdf5 --path agent.h5 agents-md --output AGENTS.md

Export all entries

bash
edgehdf5 --path agent.h5 export

Outputs one JSON object per line (JSONL).

Snapshot

bash
edgehdf5 --path agent.h5 snapshot backup.h5

Workflow: Saving Conversations

  • After each exchange, construct a MemoryEntry JSON with the conversation chunk and its embedding vector
  • Pipe to edgehdf5 save
  • The WAL (if enabled) ensures low-latency writes — flush periodically with flush-wal

Workflow: Recalling Context

  • Embed the current query using your embedding model
  • Run edgehdf5 search --embedding '[...]' --query 'user text' -k 10
  • Use returned chunks as context for the response

Notes

  • Embeddings must be generated externally (e.g., via an embedding API or local model)
  • The .h5 file is a standard HDF5 file readable by any HDF5 library
  • WAL files are stored alongside the .h5 file as .h5.wal

Installation

Terminal bash 

openclaw install edgehdf5-memory
Copied!

💻Code Examples

cargo install --path crates/edgehdf5-cli

cargo-install---path-cratesedgehdf5-cli.txt
Set `EDGEHDF5_PATH` env var or pass `--path <file.h5>` to every command.

## Commands

All output is JSON for easy parsing.

### Create a memory file

edgehdf5 --path agent.h5 create --agent-id myagent --dim 384 --wal

edgehdf5---path-agenth5-create---agent-id-myagent---dim-384---wal.txt
### Save an entry

Pass JSON via `--json` or stdin:

edgehdf5 --path agent.h5 save --json '{"chunk":"User asked about weather","embedding":[0.1,0.2,...],"source_channel":"discord","timestamp":1700000000.0,"session_id":"s1","tags":"weather"}'

edgehdf5---path-agenth5-save---json-chunkuser-asked-about-weatherembedding0102sourcechanneldiscordtimestamp17000000000sessionids1tagsweather.txt
Embedding must match the dimension specified at creation.

### Search memory

edgehdf5 --path agent.h5 search --embedding '[0.1,0.2,...]' --query 'weather forecast' -k 5

edgehdf5---path-agenth5-search---embedding-0102---query-weather-forecast--k-5.txt
Optional: `--vector-weight 0.7 --keyword-weight 0.3` (defaults).

### Recall a specific entry

edgehdf5 --path agent.h5 stats

edgehdf5---path-agenth5-stats.txt
Returns: count, active entries, WAL pending, config details.

### Flush WAL

edgehdf5 --path agent.h5 export

edgehdf5---path-agenth5-export.txt
Outputs one JSON object per line (JSONL).

### Snapshot
example.sh
# Install the CLI (one-time)
cargo install edgehdf5-cli
# Or from source:
cargo install --path crates/edgehdf5-cli
example.sh
edgehdf5 --path agent.h5 agents-md
# Or write to file:
edgehdf5 --path agent.h5 agents-md --output AGENTS.md

Tags

#search_and-research

Quick Info

Category Web Scrapers
Model Claude 3.5
Complexity Multi-Agent
Author osobh
Last Updated 3/10/2026
🚀
Optimized for
Claude 3.5
🧠

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openclaw install edgehdf5-memory

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