akm docs

This is a dated article from AKM's publishing archive. For current product behavior, see the documentation index.

akm 0.7.0 is out. This is the last pre-1.0 ship in the v1 cycle. The headline features are a durable proposal queue that routes all agent-suggested changes through a single reviewable path, three new CLI surfaces (reflect, propose, distill) that write into that queue, a lesson asset type for synthesized knowledge, per-call-site LLM feature gates that are all off by default, and a paired-run benchmarking framework (akm-bench) for measuring whether your stash actually improves agent outcomes. A batch of security, UX, and hygiene hardening rounds out the release.

If you are on 0.6.x, the v1 migration guide covers the per-surface delta. The upgrade is opt-in — everything new requires explicit configuration or a new command invocation.

TL;DR


Proposal queue (akm proposal *)

The fundamental problem with agent-generated suggestions is trust: you want to capture what the agent learned without blindly writing unreviewed content into your stash. The proposal queue solves this by separating generation from promotion.

All proposal-producing commands write to a durable queue that lives outside the asset tree. Unaccepted drafts never appear in search results or get committed. When you're ready to accept a proposal, akm proposal accept runs full validation and then routes the write through the same writeAssetToSource() path used by akm remember and akm import — no special handling, no bypass.

akm proposal list                       # list pending proposals
akm proposal show <id>                  # render one proposal
akm proposal diff <id>                  # diff vs. the live ref
akm proposal accept <id>                # validate, then promote to stash
akm proposal reject <id> --reason "…"  # archive with reason

Multiple proposals for the same ref coexist without filesystem collisions. Auto-accept can be enabled per-source via autoAcceptProposals: true in your stash config (requires a writable source, defaults off).

Three new commands: reflect, propose, distill

These three commands are the primary way to generate proposals.

akm reflect [ref] [--task ...]        # reflect on an asset and propose improvements — doclint:ignore (historical — akm 0.7.0 release announcement; folded into `akm improve` later)
akm propose <type> <name> --task "…"  # generate a new asset as a proposal — doclint:ignore (historical — akm 0.7.0 release announcement; moved to `akm proposal new` in 0.9.0)
akm distill <ref>                     # synthesize a lesson from an asset — doclint:ignore (historical — akm 0.7.0 release announcement; folded into `akm improve` later)

reflect and propose shell out to your configured agent CLI and write only to the proposal queue — they never mutate live stash content. distill is a bounded in-tree LLM call, gated behind llm.features.feedback_distillation, that produces a lesson-type proposal from an existing asset.

All three emit usage events so you can track which workflows you're actually using.

lesson asset type

lesson is a new first-class asset type designed for synthesized knowledge — the kind your agent derives from experience rather than imports from a source. Lessons are stored under lessons/<name>.md in your working stash, parallel to memories/. Required frontmatter: description and when_to_use.

The canonical workflow: akm distill <ref> produces a lesson proposal → akm proposal list shows it → akm proposal accept <id> promotes it to your stash. Direct authoring via akm import or akm remember-style flows is also supported if you want to write lessons manually.

llm.features.* — opt-in LLM gates

Every bounded in-tree LLM call site is now gated behind exactly one feature flag. All defaults are false, so enabling the schema has no effect until you opt in. Seven flags ship in 0.7.0:

Key What it enables
curate_rerank LLM rerank in akm curate
tag_dedup LLM tag dedup during indexer enrichment
memory_consolidation akm remember --enrich consolidation
feedback_distillation akm distill <ref>
embedding_fallback_score Scorer fallback when embeddings unavailable
memory_inference Indexer split of pending memories into atomic facts
graph_extraction Indexer entity/relation extraction → SQLite graph tables

Turn on what you want:

akm config set llm.features.feedback_distillation true
akm config set llm.features.memory_consolidation true

Every gated call site uses the tryLlmFeature() wrapper from src/llm/feature-gate.ts, which guarantees: disabled → fallback returned without ever calling the LLM; throw → error swallowed, fallback returned; timeout → 30-second hard limit, fallback returned.

quality: "proposed" and --include-proposed

SearchHit.quality now has three well-known values: "generated", "curated", and "proposed". The first two appear in default search. Proposed assets are excluded by default — they only surface via akm search ... --include-proposed or via the akm proposal * commands. Unknown quality values parse-warn-include so plugin authors can extend the set without breaking the indexer.

akm-bench v1

Bench grows from a smoke test into a paired-utility framework:

akm-bench compare results/noakm results/akm
akm-bench attribute results/akm

The technical reference for akm-bench now lives in the standalone itlackey/akm-bench repo.

Security hardening (PR #275)

Five security, UX, and hygiene issues landed together in the pre-prod hardening batch:

Security:

UX:

Hygiene:

Upgrade

npm install -g akm-cli@0.7.0
# or
bun install -g akm-cli@0.7.0
# or from an existing install
akm upgrade

Verify:

akm info --format text     # version 0.7.x
akm proposal list          # queue starts empty — that's expected

Try the new surfaces:

akm setup                                            # detects installed agent CLIs
akm config set llm.features.feedback_distillation true
akm distill memory:my-debugging-notes               # produces a lesson proposal — doclint:ignore (historical — akm 0.7.0 release announcement; `distill` command + ref grammar both retired later)
akm proposal list
akm proposal accept <id>

No manual migration is required for users on 0.6.x with no agent or llm.features blocks configured. Everything new is opt-in.

Full historical details are in the pre-1.0 migration plan (pinned to its last commit before the doc was retired) and the 0.7.0 release notes.

Full changelog at CHANGELOG.md.