Memory

hillclimb learns across searches, and the memory is file-based and git-versionable — it lives in your hillclimb dir, under knowledge/.

• Cards (knowledge/<family>/*.yaml) — every finished search distills a statistical card (operator stats, top approaches, failure modes; no model calls) that future searches on the family receive as a "prior experience" prompt section. Concurrent searches in one run also share live cards mid-flight.
• Claims (learning.claims, default on) — after distilling the card, one cheap agent pass (routing key distill, default model haiku) turns the search into typed claims: histgradientboosting helps on this family, with confidence and candidate-id evidence. Claim subjects are canonical entities (knowledge/entities.yaml, alias-deduped) classified closed-set into a small curated concept ontology (knowledge/concepts.yaml — tabular / time-series / decision-trees / neural-networks / …; the agent may only propose additions, which you promote by flipping proposed: false).
• Graph (knowledge/graph.json) — a derived index rebuilt deterministically from the YAML (never hand-edit; hillclimb knowledge rebuild regenerates it, and it is gitignored). Every node/edge carries first_seen, claims gain superseded_at when a newer belief displaces them, so any historical view is a pure filter.
• Retrieval (learning.graph_retrieval, default on) — new searches also get the top graph-ranked claims: same-family first, then cross-family claims that share a concept with the problem.
• Credit (learning.credit, default on) — injected claims share the search's outcome (did it beat the best prior score on the problem?), so every claim accumulates a measured track record that adjusts its retrieval ranking; chronically failing claims retire. Memory that learns whether it's right.
• Playbooks (learning.playbooks, default on) — hillclimb knowledge consolidate is the sleep phase: multi-family claims generalize up the concept hierarchy, and each concept with enough evidence gets an agent-written playbook (knowledge/playbooks/<concept>.md, a reviewable git diff) that replaces the raw claims block in draft prompts; credit flows to the playbook's source claims.
• Skills (learning.skills, default on) — winning solutions are harvested into knowledge/skills/ (2 best per family) and the best match lands in the next search's first draft as reference_solution.py: proven scaffolds, not prose hints.
• Query tool (operators.knowledge_tool, default on) — operator agents are told they can run hillclimb knowledge query "<keywords>" mid-search to consult the memory before re-deriving something expensive.
• Does it help? — an experiment with a memory-on and a memory-off arm (learning.enabled: false) answers it on holdout; see Experiments.

Exploring it

Explore it interactively with hillclimb knowledge graph (or g inside hillclimb watch): a true-3D scene rendered by plotui (Rust rasterizer; full-pixel Kitty graphics — kitty, Ghostty, iTerm2 ≥ 3.5, and WezTerm are supported). Drag rotates, shift-drag pans, scroll zooms — and zoom doubles as semantic level-of-detail: zoom out and entities fold into concept supernodes. Every node type has its own marker (rings for problems and families, triangles for searches, squares for libraries, diamonds for techniques, open diamonds for claims) — the legend in the top-left corner is the key, and each entry is a toggle: click it or press its number (1–8) to hide that type. Click a node for the detail panel (re-click or Enter opens a search's candidates), scrub through time search by search or change by change (g flips the timeline between one tick per finished search and one per graph change — per candidate, since claims are stamped with their evidencing candidate's finish), filter and color by concept from the sidebar. ? slides out a panel with every key and gesture — the footer carries only the few worth a permanent slot. Node positions come from a 3D spring layout cached in graph.json (pos3; the 2D pos stays for hillclimb-go).

Design notes and rationale: docs/memory-graph.md.