wiki-vault/Sources/Dev/ai-memory-architecture-research.md

---
created: 2026-05-04
path: Sources/Dev
project: ai-memory-architecture-research
status: active
tags:
- homelab
- ai
- memory
- architecture
- research
type: session-notes
updated: 2026-05-04
---

# AI Memory Architecture Research

Session exploring the landscape of AI agent harnesses, model memory
mechanisms, and persistent context layers. Started as a question about
two products and turned into the architectural backbone for several
follow-on projects.

## Outcomes

- Distinguished harnesses from models as separate layers in the AI
tooling stack. Harnesses are free or cheap; models are where the cost
lives. Swapping harnesses doesn't require migrating data or model
choice.
- Identified OB1 (by Nate B. Jones) as the right adoption target for a
personal AI memory layer, validating the self-hosted
MCP-server-plus-database pattern as the approach to project memory at
scale.
- Mapped the conceptual gap between current production AI memory
(transcript replay plus prefix caching) and the research frontier
(soft prompts, persona embeddings, weight-level continual learning).
Confirmed that frontier-API models will never expose substrate access;
open-weight local models do.
- Reframed the original "build a harness" instinct as actually "build
the memory layer." Most existing harnesses are good; what's missing is
the persistent project memory underneath them.

## Topics covered

### Poolside

Frontier coding-focused AI lab, founded 2023 by Jason Warner
(ex-GitHub CTO) and Eiso Kant. Up to ~$12B valuation after Nvidia's
recent investment. Released the Laguna model family — including Laguna
XS.2, a 33B-total/3B-activated MoE under Apache 2.0 open weights that
runs on consumer hardware via Ollama. Their thesis: code is the best
beachhead to AGI because it forces long-horizon reasoning.

Relevance: Laguna XS.2 is a candidate model for local agentic coding
workloads on the RTX 3080 and slots cleanly into the Phase 2 OpenClaw
plan.

### Pi.dev

A minimal terminal coding agent harness by Mario Zechner (badlogic),
MIT-licensed. Notable design choices: no MCP, no sub-agents, no plan
mode, no built-in todos — extensions instead of features. Supports 15+
providers including Ollama. Tree-structured sessions with branching
and gist sharing. Same category as Claude Code and Aider, different
philosophy.

Relevance: Useful as a reference architecture for understanding what a
harness is and isn't. Worth a look as a daily driver for local Ollama
work, separate from sanctioned `claude -p` for scripted Claude usage.

### OpenBrain (OB1)

Personal AI memory layer by Nate B. Jones. Self-hosted
Postgres-plus-Supabase stack with MCP server, semantic search via
pgvector, auto-extracted metadata, multiple capture sources (MCP,
REST, Slack webhook, Obsidian import). Provider-agnostic — any
MCP-compatible client can read and write through the same database.

This is the convergence point of the conversation. Independent
implementation of the same architectural insight reached through
reasoning: AI memory should be a separate, owned, queryable layer that
any AI client can read and write through a standard interface, not a
feature locked inside one vendor's product.

Adoption is the right call rather than building from scratch. The
technical-project extension layer (project state, decision logs,
session notes ingestion) is the gap to fill, and contributing it back
is plausible.

## Why this matters

The cliff-of-projects problem — what happens when there are 100
projects instead of 5 and Dispatch sessions can't carry coherent
project memory — is solved at the architecture level by this category
of solution. Not by a better harness, not by more discipline, but by a
memory layer that lives alongside whatever tools come and go.

The research-frontier digression on soft prompts and weight-level
memory established the boundary: production AI memory is RAG-shaped
(text retrieved by vector similarity), not weight-shaped or
activation-shaped. Frontier APIs will never expose the substrate. That
clarifies what is and isn't worth chasing — pursue the practical
memory layer (OB1), recognize the exotic stuff (persona embeddings,
hypernetworks) as research-frontier and not currently buildable on
closed-weight models.

## Key learnings

- Harnesses and models are independent layers. Picking a harness and
picking a model are separate decisions.
- "Memory" in current AI is the harness keeping a transcript and
replaying it. The model itself is stateless. Prefix caching is a cost
optimization, not a memory mechanism.
- Frontier API access stops at "send text, get text back." No weight
access, no activation hooks, no soft-prompt injection. Open-weight
local models are where substrate experimentation lives.
- Anthropic actively blocks third-party harnesses from using
subscription OAuth (April 2026 enforcement). API key with workspace
spend caps is the only sanctioned path for Claude access from
non-Anthropic tools. `claude -p` headless mode is the loophole —
invoking the official binary from a script is fine.
- The "build my own harness" instinct was actually about needing a
shared memory layer underneath any harness. Different problem,
different scope, much smaller project.

## Follow-on projects

- [ ] Deploy OB1 on homelab — Supabase stack on Proxmox, MCP server
reachable via Tailscale
- [ ] Wire Dispatch pre-compact hook to capture transcripts into OB1 via n8n
- [ ] Extend existing markdown summary n8n flow to also POST captures into OB1
- [ ] Build technical-project extension for OB1 (project state schema,
decision log)
- [ ] Evaluate Laguna XS.2 on Ollama as local coding model
- [ ] Authelia in front of OB1 web surfaces (Studio UI), GoTrue stays
for application identity

## References

- Poolside Laguna: open weights via Ollama
- Pi.dev: github.com/badlogic/pi-mono
- OB1: github.com/NateBJones-Projects/OB1
- srnichols OpenBrain (alternative implementation): lighter, plain
Postgres + pgvector, no Supabase dependency