---
sidebar_position: 4
title: "Memory Providers"
description: "External memory provider plugins — Honcho, OpenViking, Mem0, Hindsight, Holographic, RetainDB, ByteRover, Supermemory"
---

# Memory Providers

Hermes Agent ships with 8 external memory provider plugins that give the agent persistent, cross-session knowledge beyond the built-in MEMORY.md and USER.md. Only **one** external provider can be active at a time — the built-in memory is always active alongside it.

## Quick Start

```bash
hermes memory setup      # interactive picker + configuration
hermes memory status     # check what's active
hermes memory off        # disable external provider
```

You can also select the active memory provider via `hermes plugins` → Provider Plugins → Memory Provider.

Or set manually in `~/.hermes/config.yaml`:

```yaml
memory:
  provider: openviking   # or honcho, mem0, hindsight, holographic, retaindb, byterover, supermemory
```

## How It Works

When a memory provider is active, Hermes automatically:

1. **Injects provider context** into the system prompt (what the provider knows)
2. **Prefetches relevant memories** before each turn (background, non-blocking)
3. **Syncs conversation turns** to the provider after each response
4. **Extracts memories on session end** (for providers that support it)
5. **Mirrors built-in memory writes** to the external provider
6. **Adds provider-specific tools** so the agent can search, store, and manage memories

The built-in memory (MEMORY.md / USER.md) continues to work exactly as before. The external provider is additive.

## Available Providers

### Honcho

AI-native cross-session user modeling with dialectic reasoning, session-scoped context injection, semantic search, and persistent conclusions. Base context now includes the session summary alongside user representation and peer cards, giving the agent awareness of what has already been discussed.

| | |
|---|---|
| **Best for** | Multi-agent systems with cross-session context, user-agent alignment |
| **Requires** | `pip install honcho-ai` + [API key](https://app.honcho.dev) or self-hosted instance |
| **Data storage** | Honcho Cloud or self-hosted |
| **Cost** | Honcho pricing (cloud) / free (self-hosted) |

**Tools (5):** `honcho_profile` (read/update peer card), `honcho_search` (semantic search), `honcho_context` (session context — summary, representation, card, messages), `honcho_reasoning` (LLM-synthesized), `honcho_conclude` (create/delete conclusions)

**Architecture:** Two-layer context injection — a base layer (session summary + representation + peer card, refreshed on `contextCadence`) plus a dialectic supplement (LLM reasoning, refreshed on `dialecticCadence`). The dialectic automatically selects cold-start prompts (general user facts) vs. warm prompts (session-scoped context) based on whether base context exists.

**Three orthogonal config knobs** control cost and depth independently:

- `contextCadence` — how often the base layer refreshes (API call frequency)
- `dialecticCadence` — how often the dialectic LLM fires (LLM call frequency)
- `dialecticDepth` — how many `.chat()` passes per dialectic invocation (1–3, depth of reasoning)

**Setup Wizard:**
```bash
hermes honcho setup        # (legacy command) 
# or
hermes memory setup        # select "honcho"
```

**Config:** `$HERMES_HOME/honcho.json` (profile-local) or `~/.honcho/config.json` (global). Resolution order: `$HERMES_HOME/honcho.json` > `~/.hermes/honcho.json` > `~/.honcho/config.json`. See the [config reference](https://github.com/hermes-ai/hermes-agent/blob/main/plugins/memory/honcho/README.md) and the [Honcho integration guide](https://docs.honcho.dev/v3/guides/integrations/hermes).

<details>
<summary>Full config reference</summary>

| Key | Default | Description |
|-----|---------|-------------|
| `apiKey` | -- | API key from [app.honcho.dev](https://app.honcho.dev) |
| `baseUrl` | -- | Base URL for self-hosted Honcho |
| `peerName` | -- | User peer identity |
| `aiPeer` | host key | AI peer identity (one per profile) |
| `workspace` | host key | Shared workspace ID |
| `contextTokens` | `null` (uncapped) | Token budget for auto-injected context per turn. Truncates at word boundaries |
| `contextCadence` | `1` | Minimum turns between `context()` API calls (base layer refresh) |
| `dialecticCadence` | `2` | Minimum turns between `peer.chat()` LLM calls. Recommended 1–5. Only applies to `hybrid`/`context` modes |
| `dialecticDepth` | `1` | Number of `.chat()` passes per dialectic invocation. Clamped 1–3. Pass 0: cold/warm prompt, pass 1: self-audit, pass 2: reconciliation |
| `dialecticDepthLevels` | `null` | Optional array of reasoning levels per pass, e.g. `["minimal", "low", "medium"]`. Overrides proportional defaults |
| `dialecticReasoningLevel` | `'low'` | Base reasoning level: `minimal`, `low`, `medium`, `high`, `max` |
| `dialecticDynamic` | `true` | When `true`, model can override reasoning level per-call via tool param |
| `dialecticMaxChars` | `600` | Max chars of dialectic result injected into system prompt |
| `recallMode` | `'hybrid'` | `hybrid` (auto-inject + tools), `context` (inject only), `tools` (tools only) |
| `writeFrequency` | `'async'` | When to flush messages: `async` (background thread), `turn` (sync), `session` (batch on end), or integer N |
| `saveMessages` | `true` | Whether to persist messages to Honcho API |
| `observationMode` | `'directional'` | `directional` (all on) or `unified` (shared pool). Override with `observation` object |
| `messageMaxChars` | `25000` | Max chars per message (chunked if exceeded) |
| `dialecticMaxInputChars` | `10000` | Max chars for dialectic query input to `peer.chat()` |
| `sessionStrategy` | `'per-directory'` | `per-directory`, `per-repo`, `per-session`, `global` |

</details>

<details>
<summary>Minimal honcho.json (cloud)</summary>

```json
{
  "apiKey": "your-key-from-app.honcho.dev",
  "hosts": {
    "hermes": {
      "enabled": true,
      "aiPeer": "hermes",
      "peerName": "your-name",
      "workspace": "hermes"
    }
  }
}
```

</details>

<details>
<summary>Minimal honcho.json (self-hosted)</summary>

```json
{
  "baseUrl": "http://localhost:8000",
  "hosts": {
    "hermes": {
      "enabled": true,
      "aiPeer": "hermes",
      "peerName": "your-name",
      "workspace": "hermes"
    }
  }
}
```

</details>

:::tip Migrating from `hermes honcho`
If you previously used `hermes honcho setup`, your config and all server-side data are intact. Just re-enable through the setup wizard again or manually set `memory.provider: honcho` to reactivate via the new system.
:::

**Multi-peer setup:**

Honcho models conversations as peers exchanging messages — one user peer plus one AI peer per Hermes profile, all sharing a workspace. The workspace is the shared environment: the user peer is global across profiles, each AI peer is its own identity. Every AI peer builds an independent representation / card from its own observations, so a `coder` profile stays code-oriented while a `writer` profile stays editorial against the same user.

The mapping:

| Concept | What it is |
|---------|-----------|
| **Workspace** | Shared environment. All Hermes profiles under one workspace see the same user identity. |
| **User peer** (`peerName`) | The human. Shared across profiles in the workspace. |
| **AI peer** (`aiPeer`) | One per Hermes profile. Host key `hermes` → default; `hermes.<profile>` for others. |
| **Observation** | Per-peer toggles controlling what Honcho models from whose messages. `directional` (default, all four on) or `unified` (single-observer pool). |

### New profile, fresh Honcho peer

```bash
hermes profile create coder --clone
```

`--clone` creates a `hermes.coder` host block in `honcho.json` with `aiPeer: "coder"`, shared `workspace`, inherited `peerName`, `recallMode`, `writeFrequency`, `observation`, etc. The AI peer is eagerly created in Honcho so it exists before the first message.

### Existing profiles, backfill Honcho peers

```bash
hermes honcho sync
```

Scans every Hermes profile, creates host blocks for any profile without one, inherits settings from the default `hermes` block, and creates the new AI peers eagerly. Idempotent — skips profiles that already have a host block.

### Per-profile observation

Each host block can override the observation config independently. Example: a code-focused profile where the AI peer observes the user but doesn't self-model:

```json
"hermes.coder": {
  "aiPeer": "coder",
  "observation": {
    "user": { "observeMe": true, "observeOthers": true },
    "ai":   { "observeMe": false, "observeOthers": true }
  }
}
```

**Observation toggles (one set per peer):**

| Toggle | Effect |
|--------|--------|
| `observeMe` | Honcho builds a representation of this peer from its own messages |
| `observeOthers` | This peer observes the other peer's messages (feeds cross-peer reasoning) |

Presets via `observationMode`:

- **`"directional"`** (default) — all four flags on. Full mutual observation; enables cross-peer dialectic.
- **`"unified"`** — user `observeMe: true`, AI `observeOthers: true`, rest false. Single-observer pool; AI models the user but not itself, user peer only self-models.

Server-side toggles set via the [Honcho dashboard](https://app.honcho.dev) win over local defaults — synced back at session init.

See the [Honcho page](./honcho.md#observation-directional-vs-unified) for the full observation reference.

<details>
<summary>Full honcho.json example (multi-profile)</summary>

```json
{
  "apiKey": "your-key",
  "workspace": "hermes",
  "peerName": "eri",
  "hosts": {
    "hermes": {
      "enabled": true,
      "aiPeer": "hermes",
      "workspace": "hermes",
      "peerName": "eri",
      "recallMode": "hybrid",
      "writeFrequency": "async",
      "sessionStrategy": "per-directory",
      "observation": {
        "user": { "observeMe": true, "observeOthers": true },
        "ai": { "observeMe": true, "observeOthers": true }
      },
      "dialecticReasoningLevel": "low",
      "dialecticDynamic": true,
      "dialecticCadence": 2,
      "dialecticDepth": 1,
      "dialecticMaxChars": 600,
      "contextCadence": 1,
      "messageMaxChars": 25000,
      "saveMessages": true
    },
    "hermes.coder": {
      "enabled": true,
      "aiPeer": "coder",
      "workspace": "hermes",
      "peerName": "eri",
      "recallMode": "tools",
      "observation": {
        "user": { "observeMe": true, "observeOthers": false },
        "ai": { "observeMe": true, "observeOthers": true }
      }
    },
    "hermes.writer": {
      "enabled": true,
      "aiPeer": "writer",
      "workspace": "hermes",
      "peerName": "eri"
    }
  },
  "sessions": {
    "/home/user/myproject": "myproject-main"
  }
}
```

</details>

See the [config reference](https://github.com/hermes-ai/hermes-agent/blob/main/plugins/memory/honcho/README.md) and [Honcho integration guide](https://docs.honcho.dev/v3/guides/integrations/hermes).


---

### OpenViking

Context database by Volcengine (ByteDance) with filesystem-style knowledge hierarchy, tiered retrieval, and automatic memory extraction into 6 categories.

| | |
|---|---|
| **Best for** | Self-hosted knowledge management with structured browsing |
| **Requires** | `pip install openviking` + running server |
| **Data storage** | Self-hosted (local or cloud) |
| **Cost** | Free (open-source, AGPL-3.0) |

**Tools:** `viking_search` (semantic search), `viking_read` (tiered: abstract/overview/full), `viking_browse` (filesystem navigation), `viking_remember` (store facts), `viking_add_resource` (ingest URLs/docs)

**Setup:**
```bash
# Start the OpenViking server first
pip install openviking
openviking-server

# Then configure Hermes
hermes memory setup    # select "openviking"
# Or manually:
hermes config set memory.provider openviking
echo "OPENVIKING_ENDPOINT=http://localhost:1933" >> ~/.hermes/.env
```

**Key features:**
- Tiered context loading: L0 (~100 tokens) → L1 (~2k) → L2 (full)
- Automatic memory extraction on session commit (profile, preferences, entities, events, cases, patterns)
- `viking://` URI scheme for hierarchical knowledge browsing

---

### Mem0

Server-side LLM fact extraction with semantic search, reranking, and automatic deduplication.

| | |
|---|---|
| **Best for** | Hands-off memory management — Mem0 handles extraction automatically |
| **Requires** | `pip install mem0ai` + API key |
| **Data storage** | Mem0 Cloud |
| **Cost** | Mem0 pricing |

**Tools:** `mem0_profile` (all stored memories), `mem0_search` (semantic search + reranking), `mem0_conclude` (store verbatim facts)

**Setup:**
```bash
hermes memory setup    # select "mem0"
# Or manually:
hermes config set memory.provider mem0
echo "MEM0_API_KEY=your-key" >> ~/.hermes/.env
```

**Config:** `$HERMES_HOME/mem0.json`

| Key | Default | Description |
|-----|---------|-------------|
| `user_id` | `hermes-user` | User identifier |
| `agent_id` | `hermes` | Agent identifier |

---

### Hindsight

Long-term memory with knowledge graph, entity resolution, and multi-strategy retrieval. The `hindsight_reflect` tool provides cross-memory synthesis that no other provider offers. Automatically retains full conversation turns (including tool calls) with session-level document tracking.

| | |
|---|---|
| **Best for** | Knowledge graph-based recall with entity relationships |
| **Requires** | Cloud: API key from [ui.hindsight.vectorize.io](https://ui.hindsight.vectorize.io). Local: LLM API key (OpenAI, Groq, OpenRouter, etc.) |
| **Data storage** | Hindsight Cloud or local embedded PostgreSQL |
| **Cost** | Hindsight pricing (cloud) or free (local) |

**Tools:** `hindsight_retain` (store with entity extraction), `hindsight_recall` (multi-strategy search), `hindsight_reflect` (cross-memory synthesis)

**Setup:**
```bash
hermes memory setup    # select "hindsight"
# Or manually:
hermes config set memory.provider hindsight
echo "HINDSIGHT_API_KEY=your-key" >> ~/.hermes/.env
```

The setup wizard installs dependencies automatically and only installs what's needed for the selected mode (`hindsight-client` for cloud, `hindsight-all` for local). Requires `hindsight-client >= 0.4.22` (auto-upgraded on session start if outdated).

**Local mode UI:** `hindsight-embed -p hermes ui start`

**Config:** `$HERMES_HOME/hindsight/config.json`

| Key | Default | Description |
|-----|---------|-------------|
| `mode` | `cloud` | `cloud` or `local` |
| `bank_id` | `hermes` | Memory bank identifier |
| `recall_budget` | `mid` | Recall thoroughness: `low` / `mid` / `high` |
| `memory_mode` | `hybrid` | `hybrid` (context + tools), `context` (auto-inject only), `tools` (tools only) |
| `auto_retain` | `true` | Automatically retain conversation turns |
| `auto_recall` | `true` | Automatically recall memories before each turn |
| `retain_async` | `true` | Process retain asynchronously on the server |
| `tags` | — | Tags applied when storing memories |
| `recall_tags` | — | Tags to filter on recall |

See [plugin README](https://github.com/NousResearch/hermes-agent/blob/main/plugins/memory/hindsight/README.md) for the full configuration reference.

---

### Holographic

Local SQLite fact store with FTS5 full-text search, trust scoring, and HRR (Holographic Reduced Representations) for compositional algebraic queries.

| | |
|---|---|
| **Best for** | Local-only memory with advanced retrieval, no external dependencies |
| **Requires** | Nothing (SQLite is always available). NumPy optional for HRR algebra. |
| **Data storage** | Local SQLite |
| **Cost** | Free |

**Tools:** `fact_store` (9 actions: add, search, probe, related, reason, contradict, update, remove, list), `fact_feedback` (helpful/unhelpful rating that trains trust scores)

**Setup:**
```bash
hermes memory setup    # select "holographic"
# Or manually:
hermes config set memory.provider holographic
```

**Config:** `config.yaml` under `plugins.hermes-memory-store`

| Key | Default | Description |
|-----|---------|-------------|
| `db_path` | `$HERMES_HOME/memory_store.db` | SQLite database path |
| `auto_extract` | `false` | Auto-extract facts at session end |
| `default_trust` | `0.5` | Default trust score (0.0–1.0) |

**Unique capabilities:**
- `probe` — entity-specific algebraic recall (all facts about a person/thing)
- `reason` — compositional AND queries across multiple entities
- `contradict` — automated detection of conflicting facts
- Trust scoring with asymmetric feedback (+0.05 helpful / -0.10 unhelpful)

---

### RetainDB

Cloud memory API with hybrid search (Vector + BM25 + Reranking), 7 memory types, and delta compression.

| | |
|---|---|
| **Best for** | Teams already using RetainDB's infrastructure |
| **Requires** | RetainDB account + API key |
| **Data storage** | RetainDB Cloud |
| **Cost** | $20/month |

**Tools:** `retaindb_profile` (user profile), `retaindb_search` (semantic search), `retaindb_context` (task-relevant context), `retaindb_remember` (store with type + importance), `retaindb_forget` (delete memories)

**Setup:**
```bash
hermes memory setup    # select "retaindb"
# Or manually:
hermes config set memory.provider retaindb
echo "RETAINDB_API_KEY=your-key" >> ~/.hermes/.env
```

---

### ByteRover

Persistent memory via the `brv` CLI — hierarchical knowledge tree with tiered retrieval (fuzzy text → LLM-driven search). Local-first with optional cloud sync.

| | |
|---|---|
| **Best for** | Developers who want portable, local-first memory with a CLI |
| **Requires** | ByteRover CLI (`npm install -g byterover-cli` or [install script](https://byterover.dev)) |
| **Data storage** | Local (default) or ByteRover Cloud (optional sync) |
| **Cost** | Free (local) or ByteRover pricing (cloud) |

**Tools:** `brv_query` (search knowledge tree), `brv_curate` (store facts/decisions/patterns), `brv_status` (CLI version + tree stats)

**Setup:**
```bash
# Install the CLI first
curl -fsSL https://byterover.dev/install.sh | sh

# Then configure Hermes
hermes memory setup    # select "byterover"
# Or manually:
hermes config set memory.provider byterover
```

**Key features:**
- Automatic pre-compression extraction (saves insights before context compression discards them)
- Knowledge tree stored at `$HERMES_HOME/byterover/` (profile-scoped)
- SOC2 Type II certified cloud sync (optional)

---

### Supermemory

Semantic long-term memory with profile recall, semantic search, explicit memory tools, and session-end conversation ingest via the Supermemory graph API.

| | |
|---|---|
| **Best for** | Semantic recall with user profiling and session-level graph building |
| **Requires** | `pip install supermemory` + [API key](https://supermemory.ai) |
| **Data storage** | Supermemory Cloud |
| **Cost** | Supermemory pricing |

**Tools:** `supermemory_store` (save explicit memories), `supermemory_search` (semantic similarity search), `supermemory_forget` (forget by ID or best-match query), `supermemory_profile` (persistent profile + recent context)

**Setup:**
```bash
hermes memory setup    # select "supermemory"
# Or manually:
hermes config set memory.provider supermemory
echo 'SUPERMEMORY_API_KEY=***' >> ~/.hermes/.env
```

**Config:** `$HERMES_HOME/supermemory.json`

| Key | Default | Description |
|-----|---------|-------------|
| `container_tag` | `hermes` | Container tag used for search and writes. Supports `{identity}` template for profile-scoped tags. |
| `auto_recall` | `true` | Inject relevant memory context before turns |
| `auto_capture` | `true` | Store cleaned user-assistant turns after each response |
| `max_recall_results` | `10` | Max recalled items to format into context |
| `profile_frequency` | `50` | Include profile facts on first turn and every N turns |
| `capture_mode` | `all` | Skip tiny or trivial turns by default |
| `search_mode` | `hybrid` | Search mode: `hybrid`, `memories`, or `documents` |
| `api_timeout` | `5.0` | Timeout for SDK and ingest requests |

**Environment variables:** `SUPERMEMORY_API_KEY` (required), `SUPERMEMORY_CONTAINER_TAG` (overrides config).

**Key features:**
- Automatic context fencing — strips recalled memories from captured turns to prevent recursive memory pollution
- Session-end conversation ingest for richer graph-level knowledge building
- Profile facts injected on first turn and at configurable intervals
- Trivial message filtering (skips "ok", "thanks", etc.)
- **Profile-scoped containers** — use `{identity}` in `container_tag` (e.g. `hermes-{identity}` → `hermes-coder`) to isolate memories per Hermes profile
- **Multi-container mode** — enable `enable_custom_container_tags` with a `custom_containers` list to let the agent read/write across named containers. Automatic operations (sync, prefetch) stay on the primary container.

<details>
<summary>Multi-container example</summary>

```json
{
  "container_tag": "hermes",
  "enable_custom_container_tags": true,
  "custom_containers": ["project-alpha", "shared-knowledge"],
  "custom_container_instructions": "Use project-alpha for coding context."
}
```

</details>

**Support:** [Discord](https://supermemory.link/discord) · [support@supermemory.com](mailto:support@supermemory.com)

---

## Provider Comparison

| Provider | Storage | Cost | Tools | Dependencies | Unique Feature |
|----------|---------|------|-------|-------------|----------------|
| **Honcho** | Cloud | Paid | 5 | `honcho-ai` | Dialectic user modeling + session-scoped context |
| **OpenViking** | Self-hosted | Free | 5 | `openviking` + server | Filesystem hierarchy + tiered loading |
| **Mem0** | Cloud | Paid | 3 | `mem0ai` | Server-side LLM extraction |
| **Hindsight** | Cloud/Local | Free/Paid | 3 | `hindsight-client` | Knowledge graph + reflect synthesis |
| **Holographic** | Local | Free | 2 | None | HRR algebra + trust scoring |
| **RetainDB** | Cloud | $20/mo | 5 | `requests` | Delta compression |
| **ByteRover** | Local/Cloud | Free/Paid | 3 | `brv` CLI | Pre-compression extraction |
| **Supermemory** | Cloud | Paid | 4 | `supermemory` | Context fencing + session graph ingest + multi-container |

## Profile Isolation

Each provider's data is isolated per [profile](/docs/user-guide/profiles):

- **Local storage providers** (Holographic, ByteRover) use `$HERMES_HOME/` paths which differ per profile
- **Config file providers** (Honcho, Mem0, Hindsight, Supermemory) store config in `$HERMES_HOME/` so each profile has its own credentials
- **Cloud providers** (RetainDB) auto-derive profile-scoped project names
- **Env var providers** (OpenViking) are configured via each profile's `.env` file

## Building a Memory Provider

See the [Developer Guide: Memory Provider Plugins](/docs/developer-guide/memory-provider-plugin) for how to create your own.
