AI agents have
no persistent memory
When AI agents operate across sessions, projects, and teams, they lose context constantly. Every conversation starts from zero. Institutional knowledge stays trapped in individual interactions, never compounding into organizational intelligence.
No persistent context
Each agent session starts blank. Prior decisions, context, and accumulated knowledge vanish the moment a session ends.
No structured retrieval
Even when context exists in files, agents have no way to search, rank, or retrieve the most relevant knowledge for a given query.
No causal reasoning
Agents cannot trace cause-and-effect chains across knowledge entries. Understanding why something happened requires manual reconstruction.
No access governance
Any agent can access any knowledge. No per-agent, per-domain, or per-operation access controls exist to protect sensitive organizational context.
Six layers of intelligence
Every query flows through a multi-tier pipeline. Each tier adds progressively deeper understanding.
Knowledge Compiler
Parses Markdown with YAML frontmatter, validates 19 structured fields, and compiles into a Tantivy full-text search index. Watch mode enables incremental compilation as knowledge evolves.
Multi-Tier Cache
Exact match cache (MD5, 60s TTL), fuzzy cache (Jaccard similarity at 0.6 threshold), and generation-aware invalidation ensure fast, consistent query responses.
BM25 Search
Multi-field search with configurable boosts — title 3x, tags 2x, domain tags 1.5x. Compound scoring combines BM25, confidence, importance, and recency.
Causal Graph
Traverses cause-and-effect relationships up to 6 hops deep. Triggered automatically by causal signal words like 'caused by', 'leads to', 'why did'.
Temporal Queries
CurrentState, SinceTimestamp, and EventHistory patterns. Automatically triggered by keywords like 'current', 'latest', 'since', and 'history'.
LLM Synthesis
Opt-in Tier 3 that fires when search scores fall below threshold. Synthesizes answers from retrieved knowledge using Claude, with full governance controls.
Query pipeline
Every knowledge query flows through a deterministic, cached, and auditable pipeline — from exact match to LLM synthesis.
Each tier short-circuits when a high-confidence result is found — most queries resolve at Tier 0 or 1.
Three modes, any agent
Engram adapts to your agent workflow. Direct CLI, Claude Code plugin, or existing ByteRover corpora — one knowledge engine handles all.
| Mode | Interface | Use Case |
|---|---|---|
| CLI Direct | compile / query / curate | Local development, CI pipelines |
| Claude Code Plugin | Hook-based (UserPromptSubmit) | Auto-retrieval on every prompt |
| ByteRover Corpus | Transparent field aliasing | Existing knowledge bases |
Built in Rust, 6 crates
A modular Rust workspace where each concern is its own crate — knowledge compilation, query execution, governance, and agent integration.
How Honto
engineered Engram
Engram represents Honto's commitment to AI systems that compound knowledge over time. Every design decision prioritizes retrieval accuracy, query performance, and governance.
Knowledge Modeling
Defined 19 structured frontmatter fields covering fact types, confidence levels, causal links, temporal metadata, and domain tags.
Multi-Tier Pipeline
Designed a query pipeline that balances speed and depth — exact cache for repeated queries, BM25 for discovery, causal graphs for understanding.
Modular Rust Workspace
6 independent crates, each owning a capability boundary. 513 tests across 16 integration files ensure correctness at every layer.
Open-Source Release
Built to be transparent, extensible, and community-driven. Full compatibility with existing ByteRover knowledge corpora.
Need persistent memory
for your AI agents?
Engram is open-source and ready to deploy. For enterprise knowledge systems, custom ontology design, or integration support — Honto's engineering team is here to help.