Memory Architecture

Each node reads only from its ancestor chain:

root ──▶ memory["root"]
  ├─ node_A ──▶ memory["node_A"]
  │    ├─ node_A1  (reads: root + node_A)
  │    └─ node_A2  (reads: root + node_A, NOT node_A1)
  └─ node_B  (reads: root only, NOT node_A branch)

search_memory is invoked with query = node.eval_summary (a one- sentence direction text). On Letta 0.16.7 the skill calls passages.search (GET /archival-memory/search, embed_query=True) with top_k = max(letta_overfetch, limit*40), then post-filters the ranked window by ancestor_ids, ari_checkpoint, and kind == "node_scope" locally. The embedding-rank order returned by the server is preserved — children see entries most relevant to their query first. The deliberately-skipped sibling endpoint passages.list(search=q) is not semantic — it routes to a SQL substring filter (LOWER(text) LIKE LOWER(%q%)) which silently returns 0 against long natural-language queries on structured passages like RESULT SUMMARY metrics=[...]. See ari-skill-memory/src/ari_skill_memory/backends/letta_backend.py for the live verification.

v0.6.0: backed by Letta

Both layers live in the same per-checkpoint Letta agent:

ari_node_<ckpt_hash> — node-scope archival collection with the ancestor-scope metadata filter above.
ari_react_<ckpt_hash> — flat per-checkpoint ReAct trace (LettaMemoryClient, not ancestor-filtered).

The read and write paths through these two collections (HASH = checkpoint hash; the write guard and the post-filter are what enforce ancestor scope):

mermaid

flowchart LR
    node["Executing node<br/>(= ARI_CURRENT_NODE_ID)"]
    subgraph letta["Per-checkpoint Letta agent"]
        nodecol["ari_node_HASH<br/>ancestor-scoped archival"]
        reactcol["ari_react_HASH<br/>flat ReAct trace"]
    end
    node -->|"add_memory (CoW: node_id must == current)"| nodecol
    node -->|"ReAct steps"| reactcol
    node -->|"search_memory(query, ancestor_ids)"| search["passages.search<br/>embed_query, top_k = max(overfetch, limit*40)"]
    search --> nodecol
    search -->|"post-filter: ancestor_ids + ari_checkpoint + kind == node_scope"| result["ranked, ancestor-only entries"]

The agent also seeds a core-memory block (persona + human + ari_context) with experiment goal, primary metric, and hardware spec once the first node's generate_ideas completes (the point at which primary_metric is known). Skills can read it via get_experiment_context() without paying for a search; the call returns {} until that seed runs.

Copy-on-Write: write-side tools reject node_id ≠ $ARI_CURRENT_NODE_ID so ancestor entries are byte-stable across siblings; Letta self-edit is disabled by default for the same reason.

Portability: each checkpoint carries a memory_backup.jsonl.gz snapshot that is restored automatically on ari resume when the target Letta is empty — keeping cp -r checkpoints/foo /elsewhere/ + ari resume working.

Memory Architecture ​

v0.6.0: backed by Letta ​

See also ​

Memory Architecture

v0.6.0: backed by Letta

See also