We describe Hearthstone, A persistent cache for idempotent agent tool calls keyed on the hash of their inputs.. Agents frequently re-call the same tool with the same inputs across runs and even within a single run.
We describe Inkwell, A streaming repairer that converts almost-valid LLM JSON into valid JSON without a second model call.. LLM JSON output frequently has small errors: trailing commas, unescaped quotes inside string values, missing closing braces, or truncated tail.
We describe Ledger, A line-oriented, grep-able structured trace format for agent runs that diffs cleanly.. Agent traces today are either opaque proprietary formats (vendor-specific, non-portable) or deeply nested JSON that is unreadable by grep and produces terrible diffs on tool-output changes.
We describe Kerf, A minimum-viable, single-process Python sandbox tuned for short-lived agent snippets.. Most agent stacks execute LLM-generated Python either in the main process (catastrophic) or in a full container (expensive).
We describe Rampart, A thin declarative front-end that compiles simple allowlists to seccomp-bpf filters for agent sandboxes.. Agents executing generated code need a sandbox, but configuring seccomp-bpf or equivalent is error-prone.
We describe Nettle, A tiny artifact store that makes every agent tool call cite its inputs and outputs by hash.. Agent traces are unreadable because tool inputs and outputs are either dumped inline (blowing up trace size) or elided (destroying reviewability).
We describe Halberd, A deterministic fault-injection harness that lets you grade agent recovery against a pre-specified failure taxonomy.. Agents are evaluated mostly on happy-path tasks; their behaviour under tool failure (timeout, partial output, garbled JSON, rate-limit, auth revoke) is measured anecdotally.
We describe Lethe-2, A per-agent forgetting controller that treats eviction as a budgeted, auditable operation.. Multi-agent swarms accumulate shared context that, over long runs, drifts from the actual task and silently inflates token cost across every agent.
We describe Aphex, A content-addressed, token-budgeted working memory for coding agents that doesn't balloon the context window.. Long-horizon coding agents repeatedly re-read large files and recompute summaries across turns because their working memory has no durable, addressable index.
We describe Obol, A reproducible, hash-based fingerprint for single-cell identity that lets two studies compare cell populations without sharing raw counts.. Cross-study comparisons in scRNA-seq commonly rely on re-integrating raw count matrices, which is slow, requires raw data access, and re-opens batch-correction choices already made by the original authors.