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Tech/Sessions/ai-memory-architecture-research.md
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Tech/Sessions/ai-memory-architecture-research.md
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project: ai-memory-architecture-research
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type: session-notes
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status: active
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path: Tech/Sessions
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tags:
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- homelab
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- ai
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- memory
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- architecture
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- research
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created: 2026-05-04
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updated: 2026-05-04
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---
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# AI Memory Architecture Research
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Session exploring the landscape of AI agent harnesses, model memory
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mechanisms, and persistent context layers. Started as a question about
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two products and turned into the architectural backbone for several
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follow-on projects.
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## Outcomes
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- Distinguished harnesses from models as separate layers in the AI
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tooling stack. Harnesses are free or cheap; models are where the cost
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lives. Swapping harnesses doesn't require migrating data or model
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choice.
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- Identified OB1 (by Nate B. Jones) as the right adoption target for a
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personal AI memory layer, validating the self-hosted
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MCP-server-plus-database pattern as the approach to project memory at
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scale.
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- Mapped the conceptual gap between current production AI memory
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(transcript replay plus prefix caching) and the research frontier
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(soft prompts, persona embeddings, weight-level continual learning).
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Confirmed that frontier-API models will never expose substrate access;
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open-weight local models do.
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- Reframed the original "build a harness" instinct as actually "build
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the memory layer." Most existing harnesses are good; what's missing is
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the persistent project memory underneath them.
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## Topics covered
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### Poolside
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Frontier coding-focused AI lab, founded 2023 by Jason Warner
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(ex-GitHub CTO) and Eiso Kant. Up to ~$12B valuation after Nvidia's
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recent investment. Released the Laguna model family — including Laguna
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XS.2, a 33B-total/3B-activated MoE under Apache 2.0 open weights that
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runs on consumer hardware via Ollama. Their thesis: code is the best
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beachhead to AGI because it forces long-horizon reasoning.
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Relevance: Laguna XS.2 is a candidate model for local agentic coding
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workloads on the RTX 3080 and slots cleanly into the Phase 2 OpenClaw
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plan.
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### Pi.dev
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A minimal terminal coding agent harness by Mario Zechner (badlogic),
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MIT-licensed. Notable design choices: no MCP, no sub-agents, no plan
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mode, no built-in todos — extensions instead of features. Supports 15+
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providers including Ollama. Tree-structured sessions with branching
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and gist sharing. Same category as Claude Code and Aider, different
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philosophy.
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Relevance: Useful as a reference architecture for understanding what a
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harness is and isn't. Worth a look as a daily driver for local Ollama
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work, separate from sanctioned `claude -p` for scripted Claude usage.
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### OpenBrain (OB1)
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Personal AI memory layer by Nate B. Jones. Self-hosted
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Postgres-plus-Supabase stack with MCP server, semantic search via
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pgvector, auto-extracted metadata, multiple capture sources (MCP,
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REST, Slack webhook, Obsidian import). Provider-agnostic — any
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MCP-compatible client can read and write through the same database.
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This is the convergence point of the conversation. Independent
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implementation of the same architectural insight reached through
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reasoning: AI memory should be a separate, owned, queryable layer that
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any AI client can read and write through a standard interface, not a
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feature locked inside one vendor's product.
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Adoption is the right call rather than building from scratch. The
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technical-project extension layer (project state, decision logs,
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session notes ingestion) is the gap to fill, and contributing it back
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is plausible.
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## Why this matters
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The cliff-of-projects problem — what happens when there are 100
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projects instead of 5 and Dispatch sessions can't carry coherent
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project memory — is solved at the architecture level by this category
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of solution. Not by a better harness, not by more discipline, but by a
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memory layer that lives alongside whatever tools come and go.
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The research-frontier digression on soft prompts and weight-level
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memory established the boundary: production AI memory is RAG-shaped
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(text retrieved by vector similarity), not weight-shaped or
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activation-shaped. Frontier APIs will never expose the substrate. That
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clarifies what is and isn't worth chasing — pursue the practical
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memory layer (OB1), recognize the exotic stuff (persona embeddings,
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hypernetworks) as research-frontier and not currently buildable on
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closed-weight models.
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## Key learnings
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- Harnesses and models are independent layers. Picking a harness and
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picking a model are separate decisions.
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- "Memory" in current AI is the harness keeping a transcript and
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replaying it. The model itself is stateless. Prefix caching is a cost
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optimization, not a memory mechanism.
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- Frontier API access stops at "send text, get text back." No weight
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access, no activation hooks, no soft-prompt injection. Open-weight
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local models are where substrate experimentation lives.
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- Anthropic actively blocks third-party harnesses from using
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subscription OAuth (April 2026 enforcement). API key with workspace
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spend caps is the only sanctioned path for Claude access from
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non-Anthropic tools. `claude -p` headless mode is the loophole —
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invoking the official binary from a script is fine.
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- The "build my own harness" instinct was actually about needing a
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shared memory layer underneath any harness. Different problem,
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different scope, much smaller project.
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## Follow-on projects
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- [ ] Deploy OB1 on homelab — Supabase stack on Proxmox, MCP server
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reachable via Tailscale
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- [ ] Wire Dispatch pre-compact hook to capture transcripts into OB1 via n8n
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- [ ] Extend existing markdown summary n8n flow to also POST captures into OB1
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- [ ] Build technical-project extension for OB1 (project state schema,
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decision log)
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- [ ] Evaluate Laguna XS.2 on Ollama as local coding model
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- [ ] Authelia in front of OB1 web surfaces (Studio UI), GoTrue stays
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for application identity
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## References
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- Poolside Laguna: open weights via Ollama
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- Pi.dev: github.com/badlogic/pi-mono
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- OB1: github.com/NateBJones-Projects/OB1
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- srnichols OpenBrain (alternative implementation): lighter, plain
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Postgres + pgvector, no Supabase dependency
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