ClawhDF5

The memory layer AI agents deserve. One file. Pure Rust. Zero C dependencies.

ClawHDF5 is a pure-Rust HDF5 implementation combined with a research-grade agent memory engine. It gives AI agents persistent, searchable, cryptographically verifiable memory — all stored in a single portable file.

Two things live here:

• A general-purpose, pure-Rust HDF5 library — zero C dependencies, NetCDF-4 support, SIMD/GPU acceleration. See the Crate Map and BENCHMARKS.md for the libhdf5 head-to-head numbers.
• An agent memory layer built on top of it — vector search, knowledge graph, hippocampal-style consolidation, in clawhdf5-agent.

cargo add clawhdf5                          # core HDF5 read/write, no agent layer
cargo add clawhdf5-agent --features agent   # + agent memory layer

New here? Start with the Quickstart Guide · See Use Cases · Read Benchmarks

---

Why ClawhDF5?

Every AI agent needs memory. Today that means scattered Markdown files, SQLite databases, cloud-hosted vector stores, and glue code. ClawhDF5 replaces all of it:

Problem	Status Quo	ClawhDF5
Vector search	External DB (Pinecone, Qdrant)	Built-in, sub-millisecond
Keyword search	Separate FTS engine	Integrated BM25
Knowledge graph	Neo4j or none	In-file graph with spreading activation
Memory consolidation	Manual pruning	Hippocampal-inspired automatic tiers
Temporal queries	Custom code	Native temporal index (716ns)
Multi-modal	Multiple stores	Unified cross-modal search
Security	Hope for the best	Provenance tracking + anomaly detection
Portability	Config + DB + files	One .h5 file. Copy it anywhere.

---

Performance

Vector search and agent-memory operations below are benchmarked on Intel i7-12650H (10C/16T), 384-dim embeddings, Criterion.rs. The HDF5 Core I/O table immediately below is from a separate, independently reproduced run (see its own hardware note).

HDF5 Core I/O (vs libhdf5 1.14.6)

Benchmark numbers are being validated in collaboration with engineers from the HDF5 Group to confirm methodology and reproducibility.

Figures below are from an independent reproduction run on a second machine (AMD Ryzen 7 7800X3D, 2026-08-03). Full methodology, the original i7-12650H run, and two additional benchmarks added to close prior coverage gaps (an I/O-inclusive metadata-open comparison and an honest zero-copy-mmap measurement) are in BENCHMARKS.md § Independent Validation.

Operation	ClawhDF5	libhdf5	Speedup
Attribute write (128 attrs)	85.2 µs	877 µs	10.3×
Group create (64 groups)	130 µs	1.37 ms	10.6×
Chunked write, deflate-6 (512×512 f32)	1.44 ms	65.0 ms	45.3×
Sequential read (100K f32)	23.3 µs	63.6 µs	2.7×
Sequential write (100K f32)	210 µs	189 µs	≈ tie

Vector Search

Scale	Flat	IVF (nprobe=10)	IVF-PQ	vs MemX¹
1K	54 µs	—	—	—
10K	753 µs	27 µs	—	—
100K	11.4 ms	1.32 ms	1.19 ms	~8–76× (see caveat)

Reproduced on the same second machine (Ryzen 7 7800X3D) with a corrected, apples-to-apples SIMD/scalar/parallel comparison methodology — see BENCHMARKS.md § Independent Validation: tank — LongMemEval & Vector Search.

Agent Memory Operations

Operation	Latency	Scale
Hybrid search (RRF)	222 µs	1K records
BM25 keyword search	67 µs	1K records
Knowledge graph BFS	24 µs	1K entities
Spreading activation	17 µs	100 entities
Temporal range query	716 ns	10K timestamps
Consolidation cycle	164 µs	1K records
Memory write (WAL)	18 µs	per record (group-commit append; HDF5 batched at flush)
Importance gate	61 ns	per record

Chunked Write Throughput (codec comparison)

Measured with Criterion on f32 matrices. Auto-shuffle is applied before all compression codecs by default (AoS→SoA byte transpose, +157–204% throughput for float data):

Codec	128×128 f32	512×512 f32	Notes
Zstd level 3	148 µs / 422 MiB/s	1.34 ms / 748 MiB/s	With auto-shuffle
Deflate level 6	153 µs / 407 MiB/s	1.39 ms / 719 MiB/s	With auto-shuffle
Pcodec	528 µs / 118 MiB/s	1.69 ms / 591 MiB/s	Best compression ratio

Use .with_zstd(3) or .with_deflate(6) for write-heavy workloads — both now perform at ~720–750 MiB/s on large matrices. Use .with_pcodec() for write-once/read-many workloads where compression ratio matters more than encode speed. Disable auto-shuffle with .without_shuffle() for byte arrays that don't benefit from AoS→SoA transposition.

¹ MemX (arxiv:2603.16171, March 2026): Rust + libSQL, claims <90ms at 100K records. Not like-for-like: MemX's figure is end-to-end (embeddings + FTS5 + four-factor re-ranking); ours is a single component (raw vector search). The ratio overstates the real advantage by an unquantified margin — order-of-magnitude indication only. See BENCHMARKS.md.

LongMemEval Retrieval Recall

Evaluated against the full longmemeval_s haystack — all 500 questions, 47.7 sessions and 493.5 turns each, with only 4.0% of haystack sessions being evidence sessions. See BENCHMARKS.md § LongMemEval Results for the full scoring-target declaration:

Mode	Turn-Level Hit@5	Session-Level Hit@5
BM25 only	75.0%	93.6%
Vector only (MiniLM)	71.8%	94.2%
Hybrid (0.4/0.6, tuned)	81.4%	96.8%

Hybrid is the strongest configuration, which is what running two retrieval stages is for. The weights matter more than the stages: a sweep of vector_weight from 0.0 to 1.0 found the long-standing 0.7/0.3 default is strictly dominated by 0.4/0.6 — better on Hit@1, Hit@5, Hit@10 and MRR at both granularities. Use 0.4/0.6, or 0.3/0.7 if rank-1 precision matters most. See BENCHMARKS.md § Weight sweep.

Vector embeddings require --features embeddings; without it the vector stage is inert and only the BM25 row is produced, which is what every previously published number here measured.

On the easier longmemeval_oracle variant (evidence sessions only) the same harness scores 84.4% turn-level Hit@5 / MRR 0.6597, reproduced identically on a second machine. The 9.4-point gap is the cost of the real haystack, and is why the full-haystack number is the one quoted here.

This is retrieval recall (did the gold memory appear in the top-k), not the official LongMemEval QA-accuracy metric — the two are not comparable, and retrieval recall reported as QA accuracy typically overstates by 20–30 points.

Previously reported here and now retracted: session-level Hit@5 of 100.0% / MRR 1.0000, and a claim of beating MemX's 51.6%. Those session-level figures were degenerate on the oracle variant (any returned document is a hit by construction); the 93.6% above is a different, real measurement on a corpus where evidence sessions are 4.0% of the haystack. The MemX comparison stays withdrawn — MemX measures fact-level granularity over 220,349 records, which running the full haystack does not fix. Details in BENCHMARKS.md.

Enable embeddings via hybrid_search(query_emb, text, 0.4, 0.6, k) for substantially higher recall. The vector stage is served by the HNSW index by default (the hnsw feature is on by default); build with --no-default-features --features float16 to fall back to an exact linear cosine scan.

Memory Footprint

Records	File Size	Bytes/Record	With Compression
1K	~6.5 MB	~6.5 KB	~2.1 MB (3.1x)
10K	~65 MB	~6.5 KB	~21 MB (3.1x)
100K	~645 MB	~6.5 KB	~208 MB (3.1x)

Consolidation Efficiency

Metric	Before	After	Delta
Records in store	1,000	~110	−89%
Hit@1 recall	~60%	~90%	+30%
Search latency	~2.8 ms	~0.3 ms	9x faster

Full benchmark details: BENCHMARKS.md

---

Agent Memory Architecture

ClawhDF5's agent memory engine implements research from 15+ recent papers on agentic memory systems. It's not a toy — it's the real thing.

                        ┌─────────────────┐
                        │   Agent Query    │
                        └────────┬────────┘
                                 │
                    ┌────────────▼────────────┐
                    │   Hybrid Retrieval      │
                    │  Vector + BM25 + RRF    │
                    └────────────┬────────────┘
                                 │
              ┌──────────────────▼──────────────────┐
              │         Multi-Factor Re-Ranking      │
              │  temporal · authority · activation    │
              └──────────────────┬──────────────────┘
                                 │
                    ┌────────────▼────────────┐
                    │  Confidence Rejection   │
                    │  (suppress bad matches) │
                    └────────────┬────────────┘
                                 │
        ┌────────────────────────▼────────────────────────┐
        │              Memory Store (HDF5)                │
        │                                                 │
        │  ┌───────────┐ ┌───────────┐ ┌───────────────┐  │
        │  │ Working   │→│ Episodic  │→│  Semantic     │  │
        │  │ (bounded) │ │ (bounded) │ │ (long-term)   │  │
        │  └───────────┘ └───────────┘ └───────────────┘  │
        │                                                 │
        │  ┌──────────┐ ┌──────────┐ ┌────────────────┐   │
        │  │Knowledge │ │Temporal  │ │  Multi-Modal   │   │
        │  │  Graph   │ │  Index   │ │  Embeddings    │   │
        │  └──────────┘ └──────────┘ └────────────────┘   │
        │                                                 │
        │  ┌──────────┐ ┌──────────┐ ┌────────────────┐   │
        │  │Provenance│ │ Anomaly  │ │   Source       │   │
        │  │ Tracking │ │Detection │ │  Isolation     │   │
        │  └──────────┘ └──────────┘ └────────────────┘   │
        └─────────────────────────────────────────────────┘
                              │
                     ┌────────┴────────┐
                     │ agent_memory.h5 │
                     │   single file   │
                     └─────────────────┘

Module Overview

Module	What It Does
knowledge	Entity/relation graph with BFS traversal, spreading activation, fuzzy entity resolution
consolidation	Three-tier memory (Working → Episodic → Semantic) with importance scoring and time-decay
hybrid	Vector + BM25 fusion with Reciprocal Rank Fusion (RRF, k=60). The vector stage uses the HNSW index by default (hnsw feature, on by default); disable with --no-default-features --features float16 for an exact linear scan
reranker	Multi-factor re-ranking: temporal recency, source authority, activation weight
confidence	Low-confidence rejection — suppresses spurious recalls when nothing matches
temporal	Sorted timestamp index, session DAG, entity timeline, temporal query hints
multimodal	Cross-modal search across text/image/audio/video embeddings
provenance	Source attribution, FNV-1a content hashing, integrity verification
anomaly	Write rate limiting, 15 injection pattern detectors, source distribution analysis
openclaw	OpenClaw integration: MemoryBackend trait, Markdown ↔ HDF5 conversion
vector_search	Flat cosine, pre-normed, SIMD, BLAS, GPU, parallel search paths
ivf / pq	IVF-PQ approximate nearest neighbor for billion-scale search
bm25	BM25 keyword index with TF-IDF scoring
entity_extract	Rule-based entity extraction from text chunks into the knowledge graph
wal	Write-ahead log for crash-safe persistence; each entry is CRC32-checked on replay, so a corrupted entry stops replay there instead of loading bad data
memory_strategy	Pluggable strategies: save-every, semantic-shift, user-correction detection
decision_gate	Sub-microsecond trivial/substantive classification
async_memory	Tokio-based async wrapper over the memory store (async feature)

---

Quick Start

HDF5 File I/O

use clawhdf5::{File, FileBuilder, AttrValue};

// Write
let mut builder = FileBuilder::new();
builder.create_dataset("temperatures")
    .with_f64_data(&[22.5, 23.1, 21.8])
    .with_shape(&[3]);
builder.write("output.h5")?;

// Read
let file = File::open("output.h5")?;
let ds = file.dataset("temperatures")?;
let values = ds.read_f64()?;
assert_eq!(values, vec![22.5, 23.1, 21.8]);

Agent Memory

use clawhdf5_agent::{HDF5Memory, MemoryConfig, MemoryEntry, AgentMemory};

// Create memory store
let config = MemoryConfig::new("agent.h5", "my-agent", 384);
let mut memory = HDF5Memory::create(config)?;

// Save a memory
memory.save(MemoryEntry {
    chunk: "User prefers dark mode and vim keybindings.".into(),
    embedding: embed("User prefers dark mode..."),  // your embedder
    source_channel: "chat".into(),
    timestamp: now(),
    session_id: "session-001".into(),
    tags: "preference".into(),
})?;

// Search
let results = memory.search(&query_embedding, 5)?;
for result in results {
    println!("[{:.3}] {}", result.score, result.chunk);
}

Knowledge Graph

use clawhdf5_agent::knowledge::KnowledgeCache;

let mut kg = KnowledgeCache::new();

// Add entities
let alice = kg.add_entity("Alice", "person", -1);
let bob = kg.add_entity("Bob", "person", -1);
let acme = kg.add_entity("Acme Corp", "company", -1);

// Add relations
kg.add_relation(alice, acme, "works_at", 1.0);
kg.add_relation(bob, acme, "works_at", 1.0);
kg.add_relation(alice, bob, "manages", 0.8);

// Traverse
let neighbors = kg.bfs_neighbors(alice, 2);  // 2-hop neighborhood

// Spreading activation — find related entities
let activated = kg.spreading_activation(&[alice], 0.5, 0.01, 5);

// Entity resolution — fuzzy matching
let resolved = kg.resolve_or_create("alice", "person", -1, 2);
// Returns existing Alice entity (Levenshtein distance ≤ 2)

Memory Consolidation

use clawhdf5_agent::consolidation::*;

let config = ConsolidationConfig::default();
let mut engine = ConsolidationEngine::new(config);

// Add memories — automatically scored for importance
engine.add_memory("User prefers dark mode", vec![0.1, 0.2, ...], MemorySource::User);
engine.add_memory("ok", vec![0.0, 0.0, ...], MemorySource::System);

// Access a memory (reactivates it)
engine.access_memory(0);

// Run consolidation cycle
let stats = engine.consolidate();
// Working memories promote to Episodic (if important enough)
// Episodic memories promote to Semantic (if accessed enough)
// Low-decay memories get evicted when tiers are full

Temporal Queries

use clawhdf5_agent::temporal::*;

let mut index = TemporalIndex::new();
index.insert(1, 1700000000.0);  // record 1 at timestamp
index.insert(2, 1700003600.0);  // record 2, 1 hour later

// Range query — "what happened between 2pm and 5pm?"
let ids = index.range_query(1700000000.0, 1700010800.0);

// Latest 10 memories
let recent = index.latest(10);

OpenClaw Integration

use clawhdf5_agent::openclaw::*;

// Create backend
let mut backend = ClawhdfBackend::create("memory.h5", "agent-1", 384)?;

// Ingest existing Markdown memory files
let md = std::fs::read_to_string("MEMORY.md")?;
let count = backend.ingest_markdown("MEMORY.md", &md)?;

// Search (uses full pipeline: RRF → re-rank → confidence filter)
let results = backend.search("user preferences", &query_embedding, 5);

// Export back to Markdown
let exported = backend.export_markdown("MEMORY.md")?;

---

Crate Map

clawhdf5 workspace (16 crates, ~92K lines of Rust; plus libaec-sys, an
                     internal FFI bindings crate for the optional szip feature)
│
├── Core HDF5
│   ├── clawhdf5-format      — Binary parser/writer (no_std), shared type definitions
│   ├── clawhdf5-io          — I/O abstraction (buffered, mmap, async)
│   ├── clawhdf5-filters     — Fast deflate path (zlib-ng); lz4/zstd/pcodec/szip filters live in clawhdf5-format
│   ├── clawhdf5-derive      — Proc macros
│   ├── clawhdf5             — High-level API
│   ├── clawhdf5-netcdf4     — NetCDF-4 support
│   ├── clawhdf5-accel       — SIMD (NEON, AVX2, AVX-512)
│   └── clawhdf5-gpu         — GPU compute (wgpu, hand-written WGSL compute shaders)
│
├── Agent Memory
│   ├── clawhdf5-agent       — Memory engine (20.9K lines, 32 modules; WAL is CRC32-checked per entry)
│   ├── clawhdf5-ann         — HNSW approximate nearest neighbor (default backend; optional `parallel` feature)
│   ├── clawhdf5-migrate     — SQLite → HDF5 migration
│   ├── clawhdf5-android     — Android JNI bridge
│   └── clawhdf5-cli         — CLI tool
│
├── Bindings
│   ├── clawhdf5-py          — Python (PyO3)
│   └── clawhdf5-napi        — Node.js (napi-rs)
│
└── Tooling
    └── clawhdf5-bench       — Benchmark suite

---

Research Foundation

ClawhDF5's agent memory design draws from 15+ recent papers:

Paper	Key Insight	ClawhDF5 Module
MemX (2026)	RRF + multi-factor re-ranking	hybrid, reranker
Graph-Native Cognitive Memory (2026)	Graph-structured belief revision	knowledge
CraniMem (2026)	Bounded hippocampal memory	consolidation
D-MEM (2026)	Reward prediction error gating	consolidation
SYNAPSE (2025)	Spreading activation for recall	knowledge
RAGdb (2025)	Zero-dependency edge RAG	Architecture
MemoryGraft (2025)	Memory poisoning attacks	anomaly, provenance
MemoryArena (2026)	Multi-session benchmark	temporal
AI Hippocampus (2026)	Memory taxonomy survey	Overall design

---

Feature Flags

clawhdf5-agent

Flag	Default	Description
agent	no	Full agent memory layer
float16	yes	Half-precision embedding storage (2× compression)
hnsw	yes	HNSW approximate vector index for hybrid_search (via clawhdf5-ann); disable for an exact linear scan
parallel	no	Rayon parallel search
fast-math	no	BLAS matrix-vector multiply
accelerate	no	Apple Accelerate / AMX (macOS)
openblas	no	OpenBLAS (Linux)
gpu	no	GPU search via wgpu
async	no	Tokio async with background flush

clawhdf5-format

Flag	Default	Description
std	yes	Standard library (disable for no_std)
deflate	yes	Deflate compression
checksum	yes	Jenkins lookup3 verification
provenance	yes	SHA-256 provenance attributes
fast-deflate	yes	zlib-ng backend for faster deflate
system-zlib-decompress	yes	Use the system zlib for decompression where available
parallel	no	Parallel chunk encoding + compression (rayon)
fast-checksum	no	crc32fast-accelerated checksums
lz4	no	LZ4 block compression filter (id 32004)
zstd	no	Zstandard compression filter (id 32015)
pcodec	no	Pcodec lossless numerical codec (id 32023, via pco crate)
system-zlib / zlib-rs	no	Alternative zlib backends for deflate
blake3_hash	no	BLAKE3 content hashing for provenance

clawhdf5-ann

Flag	Default	Description
parallel	no	Rayon-parallel neighbor-distance computation during HNSW graph pruning

clawhdf5-io

Flag	Default	Description
mpi-io	no	MPI-backed I/O via the mpi crate

Parallel I/O (MPI) limitation: mpi-io's read path is a root-rank read followed by a broadcast, and its write path gathers all ranks' shards to rank 0 before writing — not true collective I/O (MPI_File_read_at_all/write_at_all). It does not provide I/O bandwidth that scales with rank count; true collective I/O is tracked as future work.

---

Building

# Default
cargo build --workspace

# Agent memory with all accelerations (Linux)
cargo build -p clawhdf5-agent --features "agent,float16,parallel,fast-math"

# Agent memory with Apple Accelerate (macOS)
cargo build -p clawhdf5-agent --features "agent,float16,accelerate,parallel,gpu"

# Tests
cargo test --workspace            # all 1,650+ tests
cargo test -p clawhdf5-agent      # agent memory tests

# Benchmarks
cargo bench -p clawhdf5-agent               # agent memory suite
cargo bench -p clawhdf5-bench               # h5bench-equivalent I/O suite

---

HDF5 File Schema

agent_memory.h5
├── /meta
│   ├── schema_version: "1.0"
│   ├── agent_id, embedder, embedding_dim
│   └── created_at
├── /memory
│   ├── chunks:      string[N]
│   ├── embeddings:  f32[N × D]  (or f16 with float16 flag)
│   ├── tombstones:  u8[N]
│   └── norms:       f32[N]      (pre-computed L2)
├── /sessions
│   ├── ids:         string[S]
│   └── summaries:   string[S]
└── /knowledge_graph
    ├── entity_names:    string[E]
    ├── relation_srcs:   i64[R]
    ├── relation_tgts:   i64[R]
    └── relation_types:  string[R]

---

Migration

From rustyhdf5 / edgehdf5

Replace in Cargo.toml and source:

Old	New
rustyhdf5*	clawhdf5*
edgehdf5-memory	clawhdf5-agent
edgehdf5 (CLI)	clawhdf5-cli

From SQLite

cargo install --path crates/clawhdf5-migrate
clawhdf5-migrate --sqlite old.db --hdf5 memory.h5 --agent-id my-agent --embedding-dim 384

---

Roadmap

See ROADMAP.md for the full implementation tracker.

Phase 1 complete — all 8 tracks delivered:

• ✅ Knowledge Graph with spreading activation
• ✅ Hippocampal memory consolidation
• ✅ RRF hybrid retrieval + re-ranking + confidence rejection
• ✅ Temporal reasoning with sub-µs queries
• ✅ Memory security + anomaly detection
• ✅ Multi-modal memory (text/image/audio/video)
• ✅ OpenClaw integration layer
• ✅ Comprehensive Criterion benchmarks

Phase 2 — MemoryArena and LongMemEval academic benchmarks are done (see BENCHMARKS.md, reproduced on a second machine); remaining: publish the OpenClaw TypeScript bridge to npm, crates.io/PyPI publishing.

---

Part of the RedClaw Ecosystem

ClawhDF5 powers the .brain format for ClawBrainHub — the brain registry for AI agents. One file that packages identity, skills, memory, knowledge, and cryptographic provenance.

---

License

MIT

---

Built by RedClaw Systems
~92,000 lines of Rust. Zero C dependencies. One file to remember everything.