Commit graph

5 commits

Author SHA1 Message Date
ProofOfConcept
998b71e52c flatten: move poc-memory contents to workspace root
No more subcrate nesting — src/, agents/, schema/, defaults/, build.rs
all live at the workspace root. poc-daemon remains as the only workspace
member. Crate name (poc-memory) and all imports unchanged.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
2026-03-25 00:54:12 -04:00
Kent Overstreet
8db59fe2db fix: ensure all agents have both core and subconscious instructions
All 18 agents now include:
- {{node:memory-instructions-core}} — tool usage instructions
- {{node:memory-instructions-core-subconscious}} — subconscious framing
- {{node:subconscious-notes-{agent_name}}} — per-agent persistent notes

The subconscious instructions are additive, not a replacement for
the core memory instructions.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-21 22:51:56 -04:00
ProofOfConcept
b709d58a4f agents: strip old output format, use tool calls exclusively
All 12 agents with WRITE_NODE/REFINE/END_NODE output format blocks
now rely on tool calls (poc-memory write/link-add/etc) via the
Bash(poc-memory:*) tool. Guidelines preserved, format sections removed.

Also changed linker query from type:episodic to all nodes — it was
missing semantic nodes entirely, which is why skills-bcachefs-* nodes
were never getting linked to their hubs.
2026-03-17 00:24:35 -04:00
Kent Overstreet
0e4a65eb98 agents: shared instructions via graph node includes
All 17 agents now include {{node:core-personality}} and
{{node:memory-instructions-core}} instead of duplicating tool
blocks and graph walk instructions in each file. Stripped
duplicated tool/navigation sections from linker, organize,
distill, and evaluate. All agents now have Bash(poc-memory:*)
tool access for graph walking.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-16 17:09:51 -04:00
ProofOfConcept
e12dea503b agent evaluate: sort agent actions by quality using Vec::sort_by with LLM
Yes, really. Rust's stdlib sort_by with an LLM pairwise comparator.
Each comparison is an API call asking "which action was better?"

Sample N actions per agent type, throw them all in a Vec, sort.
Where each agent's samples cluster = that agent's quality score.
Reports per-type average rank and quality ratio.

Supports both haiku (fast/cheap) and sonnet (quality) as comparator.

Usage: poc-memory agent evaluate --samples 5 --model haiku

Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
2026-03-14 19:24:07 -04:00