foresight

ForeSight

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A temporal-aspect long-term memory plugin for DeepSeek Harness (dsh).

Every memory carries explicit temporal semantics — a linguistic aspect
(进行体 / 完成体 / 未然体 / 恒常体) paired with an anchor (time point /
interval / open / none). The framework executes lifecycle mechanically:
decay on anchor expiry, TTL fallback, renewal nudge, prospective review,
prediction verification, and conflict resolution by evidence with a single
conservative/aggressive knob (β).

The problem

Long-term memory is a data-management problem, not a prompt-engineering one.
Left unmanaged, agent memories tend to hit two familiar failure modes:

Stale injection. A project that ended a month ago still competes for the
injection budget as if it were current context.

Lost valid time. "I did that yesterday" comes back as "just now" — nothing
records when a memory is true, let alone when it stops being true.

Why it exists

Unlike chat vendors' rolling memories, ForeSight treats memory as
scheduled first-class data: expiration, injection eligibility, retrieval
weighting and contradiction resolution all derive from one structured policy
file (policy.yaml) — zero hardcoded behavior.

ForeSight approaches this with two axes borrowed from linguistics:

Axis	Values	What it controls
体 (aspect)	progressive / perfect / prospective / gnomic	lifecycle: expires, permanent, to-verify, never-injected
锚 (anchor)	none / point / interval / open	when the statement is true (valid time)

In plain terms, 体 says what stage a fact is at — 进行体: "I am writing
the report" (ongoing, ends someday → expires); 完成体: "the report is
submitted" (concluded, permanent); 未然体: "due next week" (pending, gets
verified later); 恒常体: "I drink coffee daily" (timeless — such facts belong
in your SOUL.md/user.md profile, not the memory store). 锚 says when it
is true — a point ("Aug 20"), an interval ("during my third year"), open
("since March"), or none (timeless).

A few design choices worth noting:

• The aspect taxonomy draws on established linguistic notions, but it is a
  deliberately minimal starting point rather than a complete theory — the
  schema is configurable (policy.yaml) and expected to evolve with use.
• The core is agent-agnostic: dsh is one adapter (platforms/dsh). Embedding
  and LLM services are pluggable interfaces — the default is local Ollama, and
  rules-based fallbacks keep the lifecycle running without a model at all.
• The lifecycle is executed by mechanism, not by model judgment: expiry,
  injection eligibility and conflict resolution are driven by policy; the
  optional LLM is used for classification/derivation only. However, this is an
  early exploratory implementation — the taxonomy, the policy model and the
  mechanics are all under active revision.

Requirements

• Node.js ≥ 20
• DeepSeek Harness (dsh) with the cordis plugin system
• An embedding backend (default: Ollama with
  nomic-embed-text-v2-moe, 768-dim)
• (Optional) An LLM API for classification/derivation (default:
  DeepSeek compatible API; rules-based fallbacks exist)

Installation (dsh profile)

# in your ~/.dsh/profiles/<name>/
pnpm add @foresight/memory

Then add the plugin to your profile's bundle list and wire it in a patch:

# cordis.patch.yml (profile-level)
- id: foresight-core
  config:
    memoryRoot: '<your data directory>'   # e.g. /home/you/.config/foresight
    dbFile: 'foresight.db'
    embedBaseUrl: 'http://localhost:11434'
    embedModel: 'nomic-embed-text-v2-moe'

Configuration — where does my data go?

Your data lives OUTSIDE the repo, in your own data directory. The plugin
never ships any user data; the repository is pure code + templates.

Data directory layout

<memoryRoot>/
├── SOUL.md          # personality (copy from templates/SOUL.md.example)
├── user.md          # user profile (copy from templates/user.md.example)
├── policy.yaml      # all tunables (copy from templates/policy.yaml.example)
└── foresight.db     # SQLite store (created automatically)

On first run the plugin expects the three text files to exist. The plugin
creates the directory if needed but does not silently write policy — copy
the templates, then adjust:

mkdir -p ~/.config/foresight
cp templates/SOUL.md.example  ~/.config/foresight/SOUL.md
cp templates/user.md.example  ~/.config/foresight/user.md
cp templates/policy.yaml.example ~/.config/foresight/policy.yaml

Resolution chain

explicit options (adapter) → environment variables → policy.yaml → defaults

Setting	Env var	Default
data directory	FORESIGHT_MEMORY_DIR	%APPDATA%/foresight (win) / ~/.local/share/foresight (unix)
db file	FORESIGHT_DB_FILE	foresight.db
embed URL	FORESIGHT_EMBED_URL	http://localhost:11434
embed model	FORESIGHT_EMBED_MODEL	nomic-embed-text-v2-moe
LLM base URL	FORESIGHT_LLM_BASE_URL	https://api.deepseek.com/v1
LLM model	FORESIGHT_LLM_MODEL	deepseek-v4-flash

Policy file

policy.yaml is the single source of truth for behavior: permission matrix,
aspect registry (TTL, dwell, injection mode), gate categories, activation β
and decay constants, retrieval factor weights, injection budgets, nudge
cadence, server port/token. You decide your assistant's personality,
profile, and memory policy — none of it is baked into the code.

Layout

src/
├── types.ts            # domain types (aspect×anchor)
├── defaults.ts         # the ONLY place default values live
├── config.ts           # resolution chain + data dir bootstrap
├── policy.ts           # policy.yaml loader + behavior lookup
├── schema.ts           # SQLite DDL + migration + sqlite-vec
├── store.ts            # CRUD, soft-delete, vectors, FTS, audit
├── gate/               # write gate: classify + validate (aspect-text)
├── govern/             # permission model
├── evolve/             # temporal expiry/TTL, activation, β conflict
├── retrieve/           # composable scoring factors + search (3 shapes)
└── store/embed.ts      # embed provider interface (Ollama default)

Verification

pnpm install
pnpm build     # tsc
pnpm test      # node --test tests/ — no external services required

All tests run against an in-memory/temp store and a fake embedder — the suite
passes on a clean machine with no Ollama, no API key, and no data directory.

License

MIT