--- sidebar_position: 6 --- # Memory Banks Memory banks are isolated containers that store all memory-related data for a specific context or use case. import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import CodeSnippet from '@site/src/components/CodeSnippet'; {/* Import raw source files */} import memoryBanksPy from '!!raw-loader!@site/examples/api/memory-banks.py'; import memoryBanksMjs from '!!raw-loader!@site/examples/api/memory-banks.mjs'; import directivesPy from '!!raw-loader!@site/examples/api/directives.py'; import directivesMjs from '!!raw-loader!@site/examples/api/directives.mjs'; ## What is a Memory Bank? A memory bank is a complete, isolated storage unit containing: - **Memories** — Facts and information retained from conversations - **Documents** — Files and content indexed for retrieval - **Entities** — People, places, concepts extracted from memories - **Relationships** — Connections between entities in the knowledge graph - **Directives** — Hard rules the agent must follow during reflect operations Banks are completely isolated from each other — memories stored in one bank are not visible to another. You don't need to pre-create a bank. Hindsight will automatically create it with default settings when you first use it. :::tip Prerequisites Make sure you've completed the [Quick Start](./quickstart) to install the client and start the server. ::: ## Creating a Memory Bank ```bash hindsight bank create my-bank ``` ## Bank Configuration Each memory bank can be configured independently per operation. Configuration can be set via the [bank config API](#updating-configuration), the [Control Plane UI](/), or [server-wide environment variables](/developer/configuration). ### retain_mission {#retain-configuration} A plain-language description of what this bank should pay attention to during extraction. The mission is injected into the extraction prompt alongside the built-in rules — it steers focus without replacing the extraction logic. ``` e.g. Always include technical decisions, API design choices, and architectural trade-offs. Ignore meeting logistics, greetings, and social exchanges. ``` Works alongside any extraction mode. Leave blank for general-purpose extraction. ### retain_extraction_mode Controls how aggressively facts are extracted: | Mode | Description | |------|-------------| | `concise` *(default)* | Selective — only facts worth remembering long-term | | `verbose` | Captures more detail per fact; slower and uses more tokens | | `custom` | Write your own extraction rules via `retain_custom_instructions` | ### retain_custom_instructions Only active when `retain_extraction_mode` is `custom`. Replaces the built-in extraction rules entirely with your own instructions. See [Retain configuration](/developer/configuration#retain) for environment variable names and defaults. ### entity_labels {#entity-labels} Defines a controlled vocabulary of `key:value` classification labels extracted at retain time and stored as entities. Because labels become entities, they automatically link memories in the knowledge graph (two memories with `pedagogy:scaffolding` are linked), improve semantic and BM25 retrieval, and optionally filter memories via the standard `tags`/`tags_match` API when `tag: true` is set on a group. Each entry in `entity_labels` is a **label group** — one classification dimension: ```json { "entity_labels": [ { "key": "engagement", "description": "Student engagement level during the session", "type": "value", "optional": true, "values": [ { "value": "active", "description": "Student is actively participating" }, { "value": "passive", "description": "Student is listening but not participating" } ] }, { "key": "pedagogy", "description": "Teaching strategies used", "type": "multi-values", "values": [ { "value": "scaffolding", "description": "Breaking complex tasks into smaller steps" }, { "value": "direct_instruction", "description": "Explicit explanation by the teacher" }, { "value": "socratic_questioning", "description": "Guiding through questions rather than answers" } ] } ] } ``` | Field | Default | Description | |-------|---------|-------------| | `key` | — | Label group identifier. Becomes the prefix in `key:value` entities. | | `description` | `""` | Shown to the LLM to guide label assignment. | | `type` | `"value"` | `"value"` → pick one enum value; `"multi-values"` → pick multiple; `"text"` → free-form string. | | `values` | `[]` | Allowed values for `"value"` and `"multi-values"` types. Ignored for `"text"`. | | `optional` | `true` | When `true` the LLM may skip the label if not applicable. When `false` the LLM must always assign a value. Has no effect on `"multi-values"` groups (always optional). | | `tag` | `false` | When `true`, extracted `key:value` labels are also written as tags on the memory unit, enabling filtering via `tags`/`tags_match` in recall/reflect. | **Enum groups** (`type: "value"` or `type: "multi-values"`): the LLM picks from the predefined `values` list; anything outside the list is silently dropped. Vocabulary stays stable and graph links stay tight. Use `"multi-values"` when a fact can belong to several values at once. **Free-text groups** (`type: "text"`): the LLM writes any string. Use the `description` field to provide examples and guidance. Graph clustering is less reliable than with enum groups because the model may phrase the same concept differently across sessions. ```json { "key": "topic", "description": "Specific subject being discussed. Examples: algebra, quadratic equations, geometry.", "type": "text", "optional": true, "values": [] } ``` ### entities_allow_free_form By default, entity labels are extracted **alongside** regular named entities (people, places, concepts). Set to `false` to disable free-form extraction so only label entities are stored: ```json { "entity_labels": [...], "entities_allow_free_form": false } ``` ### enable_observations {#observations-configuration} Toggles automatic observation consolidation on or off. Defaults to `true` when the observations feature is enabled on the server. ### observations_mission Defines what this bank should synthesise into durable observations. Replaces the built-in consolidation rules entirely — leave blank to use the server default. ``` e.g. Observations are stable facts about people and projects. Always include preferences, skills, and recurring patterns. Ignore one-off events and ephemeral state. ``` See [Observations configuration](/developer/configuration#observations) for environment variable names and defaults. ### mission A first-person narrative that provides identity and framing context for `reflect`. The agent uses this to ground its reasoning and apply a consistent perspective. ``` e.g. You are a senior engineering assistant. Always ground answers in documented decisions and rationale. Ignore speculation. Be direct and precise. ``` ### disposition_skepticism How skeptical vs trusting the bank is when evaluating claims during `reflect`. Scale 1–5. | Value | Behaviour | |-------|-----------| | `1` | Trusting — accepts information at face value | | `3` *(default)* | Balanced | | `5` | Skeptical — questions and doubts claims | ### disposition_literalism How literally to interpret information during `reflect`. Scale 1–5. | Value | Behaviour | |-------|-----------| | `1` | Flexible — reads between the lines, considers context | | `3` *(default)* | Balanced | | `5` | Literal — takes things exactly as stated | ### disposition_empathy How much to weight emotional context when reasoning during `reflect`. Scale 1–5. | Value | Behaviour | |-------|-----------| | `1` | Detached — focuses on facts and logic | | `3` *(default)* | Balanced | | `5` | Empathetic — considers emotional context | :::info Disposition traits and `mission` only affect the `reflect` operation. `retain_mission` and `observations_mission` are separate per-operation settings. ::: --- ## Updating Configuration Bank configuration fields (retain mission, extraction mode, observations mission, etc.) are managed via a **separate config API**, not the `create_bank` call. This lets you change operational settings independently from the bank's identity and disposition. ### Setting Configuration Overrides You can update any subset of fields — only the keys you provide are changed. ### Reading the Current Configuration The response distinguishes: - **`config`** — the fully resolved configuration (server defaults merged with bank overrides) - **`overrides`** — only the fields explicitly overridden for this bank ### Resetting to Defaults This removes all bank-level overrides. The bank reverts to server-wide defaults (set via environment variables). You can also update configuration directly from the [Control Plane UI](/) — navigate to a bank and open the **Configuration** tab. --- ## Directives Directives are hard rules that the agent must follow during [reflect](./reflect) operations. Unlike disposition traits which influence *how* the agent reasons, directives are explicit instructions that are *always* enforced. :::info Directives only affect the `reflect` operation. They are injected into prompts and the agent is required to comply with them in all responses. ::: ### When to Use Directives Use directives for rules that must never be violated: - **Language/style constraints**: "Always respond in formal English" - **Privacy rules**: "Never share personal data with third parties" - **Domain constraints**: "Prefer conservative investment recommendations" - **Behavioral guardrails**: "Always cite sources when making claims" ### Creating Directives ### Listing Directives ### Updating Directives ### Deleting Directives ### Directives vs Disposition | Aspect | Directives | Disposition | |--------|------------|-------------| | **Nature** | Hard rules, must be followed | Soft influence on reasoning style | | **Enforcement** | Strict — responses are rejected if violated | Flexible — shapes interpretation | | **Use case** | Compliance, guardrails, constraints | Personality, character, tone | | **Example** | "Never recommend specific stocks" | High skepticism: questions claims |