ForeSight
August 25, 2026 · View on GitHub
English | 中文
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