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Acontext vs SkillX

Skill memory layer for agents: auto-captures learnings from runs into plain Markdown skill files you can read, edit, git and share across frameworks — memory without an opaque store. — versus — Research framework that auto-distills agent trajectories into a three-level skill knowledge base (planning, functional, atomic) — pluggable into weaker agents and new environments.

The curated verdict

Same distillation idea at two maturities: skillx is the research framework building a three-level skill knowledge graph from trajectories, acontext the pragmatic version dropping plain Markdown skills into your repo.

AcontextSkillX
Stars3.7k278
Forks33326
LanguageJavaScriptPython
LicenseApache-2.0MIT
Last activity1 months ago6 days ago
Topicsmemory, skillsskills
Curated connections114

Acontext — the curator's take

The "memory should be legible" bet: instead of embeddings in a vector store, learnings from agent runs become Markdown skill files you can read, diff, git and mount into any framework — debuggable memory users can inspect and correct, the exact thing opaque memory layers get wrong. It can also adopt and evolve skills you wrote or downloaded. Trade-off: no semantic recall over thousands of entries; it lives or dies on distilling runs into a curated, manageable skill set. Want scale-out retrieval memory instead? That's memmachine or hindsight territory.

SkillX — the curator's take

The interesting research bet: instead of storing raw trajectories or reflections, distill agent experience into a hierarchy of reusable skills that transfer — a strong backbone agent builds the library, weaker agents plug it in and improve on AppWorld/BFCL/τ2-Bench. Read it if you're building agent-improvement loops; the three-level decomposition (planning/functional/atomic) is a genuinely useful mental model. NOT production tooling — it's an academic codebase (~250 stars) built around benchmarks, so expect to adapt it to your stack rather than pip-install it.