WeilaiSun/dsh-hindsight-memory ↗★ 1
dsh-hindsight-memory
Semantic long-term memory for DeepSeek Harness: hindsight_retain / hindsight_recall / hindsight_reflect tools over the local Hindsight daemon (PostgreSQL + pgvector + DeepSeek embeddings), with optional per-turn auto-recall injection. 适合需语义记忆与自动召回的进阶用户，需自建 PostgreSQL 与向量环境。
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README
Read the full README ↗Configuration
| Key | Default | Description |
|---|---|---|
venvPython | F:\Hermes\HERMES_HOME\hermes-agent\venv\Scripts\python.exe | Python with hindsight_embed installed |
configJson | F:\Hermes\HERMES_HOME\hindsight\config.json | JSON with llm_api_key / llm_base_url / llm_model (source of the DeepSeek key; never hardcoded) |
llmBaseUrl / llmModel / llmProvider | DeepSeek defaults | Embedding endpoint; deepseek/openai_compatible/openrouter map to openai |
profile | deepseek | Hindsight profile name (own daemon port + PG instance + bank) |
bank | = profile | Bank id used by retain/recall/reflect |
autoRecall | false | Inject relevant memories before each agent step |
recallTypes | ["observation","experience","world"] | Fact types searched |
recallLimit | 4 | Max memories per recall |
recallMinUserChars | 20 | Min user-message length before auto-recall triggers |
recallBudgetTokens | 1200 | Token budget for auto-recall injection |
Usage
The model calls the tools directly. Example retention policy (mirrors a self-evolution protocol):
- Retain after complex tasks (5+ tool calls), after resolving a pitfall, when the user expresses a durable preference, or after key design decisions.
- Recall at the start of complex tasks, when the user references past work, or when a situation smells like a repeat.