Related Pages

Related topics: Backend Server: FastAPI, Controllers, and Data Models, Client SDKs: Python, TypeScript, and Go, MCP Server, Deployment, and Customization

Memobase Overview and System Architecture

1. Project Purpose and Design Goals

Memobase is a user profile-based memory system that brings long-term user memory to LLM applications such as virtual companions, educational tools, and personalized assistants. The system enables an AI to remember, understand, and evolve with its users by maintaining a structured user profile and a per-user event timeline (Source: readme.md:39-44).

The project is intentionally optimized against three competing metrics that are typically hard to satisfy together:

• Performance — Memobase achieves top-tier retrieval in the LOCOMO benchmark even though it is not built primarily as a RAG/search engine.
• LLM Cost — A built-in per-user buffer batches incoming chats so the per-message overhead is amortized. Workflows and prompts are hand-tuned to avoid "agent" loops. As of 0.0.40, a single flush now performs a fixed three LLM calls (down from 3–10), reducing token cost by ~40–50%.
• Latency — Because a user profile and an event timeline are always precomputed, the online read path is a handful of SQL operations that complete in under 100 ms.

Source: readme.md:44-58, readme.md:23-25

2. High-Level System Architecture

Memobase is delivered as a FastAPI backend with versioned endpoints under /api/v1, an auxiliary MCP server that exposes the same memory features to AI agents, and a Python SDK consumed by client applications (Source: src/server/api/readme.md:3-9, src/mcp/README.md:24-32).

graph TD
    Client[Client App / AI Agent] --> SDK[Memobase Python SDK]
    Client --> MCP[MCP Server]
    SDK --> API[FastAPI /api/v1]
    MCP --> API
    API --> Auth[Auth Middleware]
    API --> Ctrls[Controllers]
    Ctrls --> UserCtrl[user]
    Ctrls --> BlobCtrl[blob]
    Ctrls --> BufferCtrl[buffer]
    Ctrls --> ProfileCtrl[profile]
    Ctrls --> ModalCtrl[modal / chat]
    UserCtrl --> DB[(PostgreSQL)]
    BlobCtrl --> DB
    BufferCtrl --> DB
    ProfileCtrl --> DB
    ProfileCtrl --> Cache[(Redis)]
    ModalCtrl --> LLM[LLM Service]
    LLM --> Prompts[Prompt Templates]

The control plane routes every request through FastAPI middleware for authentication, then dispatches to one of the five controller groups. Persisted state lives in PostgreSQL while ephemeral state and caches are served by Redis. Memory-formation work is delegated to a "modal" layer that calls the LLM service using versioned prompt templates (Source: src/server/api/DEVELOPMENT.md:9-26).

3. Core Components and Data Model

The backend code is organized under memobase_server/ and is split into three layers (Source: src/server/api/DEVELOPMENT.md:28-47):

The persisted data model is intentionally compact: User, GeneralBlob, BufferZone, and UserProfile. GeneralBlob is polymorphic and can carry any of the supported content kinds listed in the table below (Source: src/server/api/readme.md:11-30).

The modal layer wraps the LLM calls that turn blobs into structured profiles. As of __version__ = "0.0.42", the modal chat pipeline consists of three LLM invocations: profile extraction, sub-topic organization, and YOLO-style profile merging (Source: src/server/api/memobase_server/__init__.py:1, src/server/api/memobase_server/controllers/modal/chat/merge_yolo.py:1-35).

4. Memory Processing Workflow

The end-to-end flow is designed to keep online reads cheap while pushing all heavy work to a batched background flush:

1. Insert — A client pushes messages into the user's BufferZone via the blob and buffer controllers. No LLM is invoked yet.
2. Flush (modal processing) — On a trigger, the modal layer runs the three-stage LLM pipeline:

• Extract — extract_profile.py emits TOPIC{tab}SUB_TOPIC{tab}MEMO rows from raw text, guided by topic guidelines and few-shot examples.
• Organize — organize_profile.py re-clusters memos under no more than max_subtopics sub-topics per topic.
• Merge (YOLO) — merge_profile_yolo.py / merge_yolo.py decide for each existing memo whether to UPDATE, APPEND, or ABORT, preserving any [mentioned on ...] time annotations.

1. Store — The resulting structured rows are written into UserProfile and the latest events into the user timeline.
2. Read — A context(max_token_size=…, prefer_topics=[…]) call renders a prompt-ready string in milliseconds, optionally filtering by topic or running pick_related_profiles.py to select only relevant memos for a given conversation.

Sources: src/server/api/memobase_server/prompts/extract_profile.py:3-58, src/server/api/memobase_server/prompts/organize_profile.py:30-72, src/server/api/memobase_server/prompts/merge_profile_yolo.py:1-26, src/server/api/memobase_server/prompts/pick_related_profiles.py:60-88

The same features are exposed to MCP-compatible agents through three tools — save_memory, get_user_profiles, and search_memories — so a tool-using LLM can persist and recall context against a Memobase backend in the same way a developer would via the SDK (Source: src/mcp/README.md:38-43).

See Also

• Memobase Profile Customization
• Memobase-Playground (full-stack chatbot template)
• Memobase-Inspector (web UI for project inspection)
• LOCOMO Benchmark Results

Related Pages

Related topics: Memobase Overview and System Architecture, Client SDKs: Python, TypeScript, and Go, MCP Server, Deployment, and Customization

Backend Server: FastAPI, Controllers, and Data Models

1. Purpose and High-Level Role

The Memobase backend is a FastAPI server that provides persistent user-memory services for LLM applications. According to src/server/api/readme.md, it exposes versioned endpoints under /api/v1 and centralizes four concerns: user identity, raw data blobs, a transient processing buffer, and the extracted user profile that downstream clients retrieve to enrich conversations.

The server is structured into four cooperating layers:

• API layer — FastAPI route handlers that parse requests and return BaseResponse objects.
• Controllers — Business logic that orchestrates database, Redis, and LLM operations.
• Models — SQLAlchemy ORM entities and Pydantic-style response/dataclass types.
• Connectors — Connection pools for PostgreSQL and Redis, plus LLM/embedding clients.

Source: src/server/api/readme.md, src/server/api/DEVELOPMENT.md

2. Request Lifecycle and Middleware

Every incoming HTTP request flows through global_wrapper_middleware before reaching a route handler. The middleware (src/server/api/memobase_server/api_layer/middleware.py) performs several cross-cutting tasks:

1. Generates or reads the X-Request-ID header and binds it to structlog context.
2. Resolves project_id from the bearer token via parse_project_id and check_project_secret from auth/token.py.
3. Records a per-project counter and latency histogram through telemetry_manager.
4. Maps the matched path against PATH_MAPPINGS to decide which business counters to increment.
5. Catches unhandled exceptions and returns a JSONResponse with the framework CODE enum.

Health and admin endpoints live alongside business routes in chore.py (src/server/api/memobase_server/api_layer/chore.py). healthcheck() verifies PostgreSQL via db_health_check, Redis via redis_health_check, embedding sanity via check_embedding_sanity, and LLM reachability via llm_sanity_check. root_running_status_check() is restricted to the DEFAULT_PROJECT_ID and rejects other callers with HTTP 405.

sequenceDiagram
    participant Client
    participant Middleware
    participant API Route
    participant Controller
    participant DB/Redis/LLM

    Client->>Middleware: HTTP request + bearer token
    Middleware->>Middleware: parse_project_id, structlog bind, telemetry
    Middleware->>API Route: Forward with request.state.memobase_project_id
    API Route->>Controller: controllers.user/blob/buffer/profile
    Controller->>DB/Redis/LLM: CRUD + LLM calls
    Controller-->>API Route: Promise[result]
    API Route-->>Middleware: BaseResponse / IdResponse / typed payload
    Middleware-->>Client: JSON + X-Request-ID

3. API Layer: Route Handlers

Route handlers are thin adapters that read project_id from request.state, call into controllers.full, and convert the resulting Promise to a typed response via .to_response(...). The user routes (src/server/api/memobase_server/api_layer/user.py) cover the full CRUD lifecycle:

The roleplay surface (src/server/api/memobase_server/api_layer/roleplay.py) extends profile reads with parameters such as prefer_topics, only_topics, max_subtopic_size, and a topic_limits_json override that are forwarded to proactive_topics inside controllers.modal.roleplay. The Go SDK mirrors these surfaces through User.Delete, User.Flush, and User.GetAll (src/client/memobase-go/core/user.go).

4. Controllers and LLM-Driven Modal Logic

Controllers in memobase_server/controllers/ host the business rules. The modal sub-package coordinates LLM work: controllers/modal/chat/merge_yolo.py merges incoming memos into existing profile slots by issuing a low-temperature llm_complete call with the merge_yolo prompt and then dispatching per-memo actions returned by the model. The prompt module (prompts/extract_profile.py, prompts/pick_related_profiles.py, and the Chinese variant prompts/zh_summary_entry_chats.py) supplies the extraction, selection, and summarization templates consumed by the chat modal. Source: src/server/api/memobase_server/controllers/modal/chat/merge_yolo.py, src/server/api/memobase_server/prompts/extract_profile.py

5. Data Models and Persistence

The persistence layer uses SQLAlchemy ORM with four core entities described in src/server/api/readme.md:

• User — Core identity and metadata; mutated through create_user, update_user, delete_user in api_layer/user.py.
• GeneralBlob — Polymorphic storage for chat, document, image, code, and transcript blob types, typed via BlobType (api_layer/user.py).
• BufferZone — Per-user staging area where newly inserted blobs wait to be flushed into profiles by the LLM pipeline.
• UserProfile — Topic / sub-topic / memo triples that are produced by modal chat processing and exposed through users/profile and users/context.

Response shaping is centralized in models/response.py (BaseResponse, IdResponse, UserDataResponse) and the Promise monad in models/utils.py so that controllers can return either success data or a CODE error without throwing across async boundaries.

6. Common Failure Modes

When integrating against this backend, watch for the following:

• Missing bearer token — Middleware leaves request.state.memobase_project_id unset; handlers that depend on it will raise AttributeError.
• Health-check failures — healthcheck returns HTTP 500 with Database not available or Redis not available if the corresponding connector fails (chore.py).
• LLM extraction gaps — merge_yolo logs No Corresponding Merge Action warnings and skips memos when the model omits an action id; callers should expect occasional missing profile updates rather than a transactional guarantee.
• Non-default project status — root_running_status_check rejects all projects other than DEFAULT_PROJECT_ID, so admin probes must use the bootstrap credentials.

See Also

• Backend Architecture & Data Flow
• LLM Prompts and Modal Processing
• Authentication, Telemetry, and Middleware
• MCP Server Integration

Related Pages

Related topics: Memobase Overview and System Architecture, Backend Server: FastAPI, Controllers, and Data Models, MCP Server, Deployment, and Customization

Client SDKs: Python, TypeScript, and Go

Overview and Scope

Memobase exposes a unified HTTP API for long-term user memory and ships official client SDKs that wrap the REST endpoints in idiomatic language bindings. The repository organizes these SDKs under src/client/memobase/ (Python) and src/client/memobase-go/ (Go); both target the same backend hosted by memobase_server Source: src/client/memobase/core/async_entry.py. The Python package offers a synchronous entry point (core/entry.py) and an asynchronous counterpart (core/async_entry.py) for asyncio-based applications, while the Go package provides a User-centric API in core/user.go. The author of the Python package states explicit design rules in core/__init__.py, including "Flat is better than nested", "Secretly handling errors for users is arrogant", and "Any single type should be fully functional on its own" Source: src/client/memobase/core/__init__.py — guiding the SDK toward transparent wrappers rather than opinionated abstractions.

Python SDK

Package Layout

The Python package at src/client/memobase/ is split into focused modules:

• core/entry.py — synchronous high-level client.
• core/async_entry.py — asynchronous high-level client.
• core/user.py — per-user object representing a single Memobase user.
• core/blob.py — typed models for the various blob payloads (chat, summary, doc, code, image, transcript).
• network.py — shared HTTP transport, response unpacking, and error helpers.

Context Assembly

The most prominent API is User.context(...). It accepts a rich set of options for shaping the memory string that gets injected into a prompt Source: src/client/memobase/core/async_entry.py:

These options are encoded into query parameters against /users/profile/{user_id}; the response is unpacked into UserProfile objects and (optionally) re-rendered as JSON. The readme.md quickstart demonstrates the typical call Source: readme.md:

print(u.context(max_token_size=500, prefer_topics=["basic_info"]))

On the server side, the same call triggers parallel retrieval of profile data and event gists, bounded by a profile_event_ratio and a max_profile_token_size Source: src/server/api/memobase_server/controllers/context.py.

Profile Updates and Errors

User.update_profile(profile_id, content, topic, sub_topic) issues PUT /users/profile/... to overwrite a single memo Source: src/client/memobase/core/async_entry.py. The client intentionally surfaces server errors rather than swallowing them, consistent with the "no silent error handling" guideline.

Blob Models

core/blob.py defines a tagged union of blob types using Literal discriminators Source: src/client/memobase/core/blob.py: ChatBlob (OpenAI-style messages), DocBlob (free-form text), SummaryBlob, CodeBlob (with optional language), ImageBlob (URL or base64), and TranscriptBlob (list of TranscriptStamp). A BlobData envelope carries the type, the underlying payload, optional fields, and timestamps, and to_blob() rehydrates the appropriate subclass — except for ImageBlob and TranscriptBlob, which currently raise NotImplementedError. The same schema is mirrored on the server in src/server/api/memobase_server/models/blob.py, ensuring a round-trippable contract Source: src/server/api/memobase_server/models/blob.py.

Go SDK

The Go client (src/client/memobase-go/core/user.go) exposes a User type bound to a project-scoped HTTP client. Three representative methods illustrate the pattern Source: src/client/memobase-go/core/user.go:

• GetAll(blobType, page, pageSize) ([]string, error) — paginates /users/{id}/blobs/{blobType} and returns blob IDs. Response shapes are validated with explicit type assertions on the unpacked JSON, returning descriptive errors when the server deviates from the expected schema.
• Delete(blobID) error — issues DELETE /blobs/{user_id}/{blob_id}.
• Flush(blobType BlobType, sync bool) error — triggers buffer flushing for a user, with a synchronous flag that lets callers ensure profile extraction completes before continuing.

The Go client follows the same "transparent error" philosophy: it returns errors directly and delegates decoding to network.UnpackResponse for a consistent envelope.

Common Architectural Flow

sequenceDiagram
    participant App as Application
    participant SDK as Client SDK
    participant API as Memobase Server
    participant LLM as LLM Provider

    App->>SDK: u.context(max_token_size, prefer_topics, chats)
    SDK->>API: GET /users/profile/{user_id}?...
    API->>LLM: pick_related_profiles / summary prompts
    LLM-->>API: ranked memos + event gists
    API-->>SDK: profiles + events JSON
    SDK-->>App: rendered context string or UserProfile list
    App->>App: inject into LLM system prompt

TypeScript SDK Status

The retrieved source fragments do not include a TypeScript SDK under src/client/. The top-level readme.md links to "Memobase-Playground" and "Memobase-Inspector" as application examples rather than client libraries Source: readme.md. Until TS sources are added, applications targeting JavaScript runtimes should either call the HTTP API directly or wrap the Python SDK.

MCP Integration

src/mcp/README.md documents a Model Context Protocol server built on top of the Memobase Python SDK. It exposes three tools — save_memory, get_user_profiles, and search_memories — and demonstrates the canonical integration path: the SDK does the network work, the MCP server translates each tool into SDK calls, and any MCP-compatible client can drive it Source: src/mcp/README.md.

Common Failure Modes

• NotImplementedError on image / transcript blobs — BlobData.to_blob() does not yet support ImageBlob or TranscriptBlob; the server-side models in src/server/api/memobase_server/models/blob.py carry the same limitation Source: src/client/memobase/core/blob.py.
• Invalid chats payloads — the async client validates each chat against OpenAICompatibleMessage and raises ValueError with the underlying ValidationError if it fails Source: src/client/memobase/core/async_entry.py.
• Profile / event ratio misconfiguration — the server asserts 0 < profile_event_ratio <= 1; passing an out-of-range value raises AssertionError Source: src/server/api/memobase_server/controllers/context.py.

See Also

• Server context assembly and profile_event_ratio
• Prompt system: topic / sub-topic configuration
• Profile extraction and merge_yolo controller
• Event gisting and tagging pipeline
• Memobase MCP server (src/mcp/)

Related Pages

Related topics: Memobase Overview and System Architecture, Backend Server: FastAPI, Controllers, and Data Models, Client SDKs: Python, TypeScript, and Go

MCP Server, Deployment, and Customization

1. Purpose and Scope

The Memobase MCP Server is a template implementation of the Model Context Protocol (MCP) that connects AI agents to Memobase's long-term, persistent memory. It is forked from coleam00/mcp-mem0 and acts as a practical reference for engineers who want to expose Memobase storage, retrieval, and semantic search behind an MCP-compatible interface. Source: src/mcp/README.md.

The server itself does not host the memory backend. To function, it requires a reachable Memobase backend with a project URL and a project token. The two supported backends are:

• A self-hosted local instance exposed at http://localhost:8019 with the default token secret. Source: src/mcp/README.md.
• Memobase Cloud, reachable at https://api.memobase.dev with a sk-proj-… token obtained from the cloud console. Source: src/mcp/README.md.

The wider Memobase project is described as a *user memory system for LLM applications that maintains persistent user context and memory*; the MCP server is the thin agent-facing adapter that lets any MCP client tap into that system. Source: src/server/api/DEVELOPMENT.md.

2. Exposed Tools and Runtime Architecture

The server exposes three essential memory-management tools to MCP clients. Source: src/mcp/README.md.

The server implements the best practices described by Anthropic for MCP servers, so it is compatible with any MCP-aware client (Cursor, Windsurf, Claude Desktop, n8n, or custom Python clients). Source: src/mcp/README.md.

Internally, the broader Memobase backend that the MCP server calls into follows a layered architecture: a FastAPI entry point routes requests through controllers (User, Blob, Buffer, Profile, Modal) backed by PostgreSQL, Redis, and an LLM service. The MCP server is the lightweight client-side layer that surfaces only the user-relevant operations to the agent. Source: src/server/api/DEVELOPMENT.md.

graph LR
    A[MCP Client<br/>Cursor / Claude / n8n] -- MCP/JSON-RPC --> B[Memobase MCP Server]
    B -- save / get / search --> C[Memobase Backend<br/>FastAPI + Postgres + Redis]
    C -- LLM calls --> D[LLM Provider]
    B -. context string .-> A

3. Deployment Options

3.1 Local install with `uv` (Python 3.11+)

The recommended path for development is uv. The flow is: install uv, clone the repository, cd memobase/src/mcp, run uv pip install -e . to install in editable mode, then copy .env.example to .env and fill in the project URL and token. Source: src/mcp/README.md.

3.2 Docker (recommended for production-like use)

A Docker image can be built and run on port 8050 (the default MCP SSE port). The MCP server then runs as a long-lived SSE endpoint that any MCP client can connect to. Source: src/mcp/README.md.

3.3 Client wiring

The server can be reached in two transport modes:

• SSE transport (HTTP-based, used by Cursor and Windsurf). For Windsurf, the configuration key is serverUrl instead of url. For n8n, the host must be host.docker.internal because n8n reaches outside its own container. Source: src/mcp/README.md.
• Stdio transport, used by Claude Desktop and other local clients. The configuration points at the Python executable inside the project's .venv. Source: src/mcp/README.md.

3.4 Required environment variables

The .env file must contain the project URL and the project token that identify which Memobase backend the MCP server should talk to. Without them, the three tools will fail at request time. Source: src/mcp/README.md.

4. Customization

The MCP server is deliberately a *template*. The README describes four extension points. Source: src/mcp/README.md:

1. Add new tools by defining methods with the @mcp.tool() decorator on the mcp instance.
2. Add a custom lifespan function to inject additional dependencies (database connections, custom clients, telemetry, etc.) into each request.
3. Add helper functions in utils.py to keep tool bodies small and readable.
4. Add prompts and resources using the @mcp.prompt() and @mcp.resource() decorators.

4.1 Customizing user-profile schemas

The memory model is not hard-coded. The server-side prompt module profile_init_utils.py exposes two configuration knobs sourced from CONFIG:

• overwrite_user_profiles — when set, *replaces* the default topic/sub-topic schema with the supplied list. Source: src/server/api/memobase_server/prompts/profile_init_utils.py.
• additional_user_profiles — when set, *extends* the default schema with extra topics. Source: src/server/api/memobase_server/prompts/profile_init_utils.py.

These two modes map to the example configurations shipped with the repository:

• example_config/profile_for_assistant/config.yaml — a productivity-oriented schema with topics such as task_management (with priorities, task_categories, deadline_buffer, recurring_tasks, task_format) and productivity_settings (with focus_mode, notification_prefs, automation_rules, report_frequency, tracking_metrics, tool_integrations). Source: src/server/api/example_config/profile_for_assistant/config.yaml.
• example_config/profile_for_companion/config.yaml — a companion-oriented schema including personalization (with humor_pref, response_len, tech_depth, learn_style) and a session-tracking topic that records convo_count, favorite_topics, active_projects, saved_convos, and feedback_hist. Source: src/server/api/example_config/profile_for_companion/config.yaml.

The extraction prompt is parameterized over the supplied schema: it asks the LLM to "consider using the same topic/subtopic if it's mentioned in the conversation again" and to fall back to creating new topics only when necessary, which keeps memories stable across runs. Source: src/server/api/memobase_server/prompts/extract_profile.py.

5. Common Failure Modes

• Missing or wrong MEMOBASE_PROJECT_URL / token — the three MCP tools will return auth or connection errors because the server cannot reach the backend. Source: src/mcp/README.md.
• Wrong port in client config — the default is 8050; clients such as Cursor, Windsurf, and n8n must match the port the server was actually launched on. Source: src/mcp/README.md.
• localhost inside n8n — n8n cannot reach the host's loopback; use host.docker.internal instead. Source: src/mcp/README.md.
• Conflicting Windsurf config key — Windsurf expects serverUrl; using url will silently fail. Source: src/mcp/README.md.
• Schema drift — if overwrite_user_profiles is changed, previously stored profile entries under removed topics will be ignored by extraction until they are re-ingested. Source: src/server/api/memobase_server/prompts/profile_init_utils.py.

See Also

• Memobase-Playground — full-stack reference chatbot that demonstrates end-to-end memory usage.
• Memobase-Inspector — web UI for inspecting users, profiles, and test prompts.
• Profile customization docs — official guide for designing the profile schema.
• get_context API reference — the backend call that produces the context string the MCP server eventually returns to the agent.

Doramagic Pitfall Log

Found 12 structured pitfall item(s), including 1 high/blocking item(s). Top priority: Installation risk - Installation risk requires verification.

1. Installation risk: Installation risk requires verification

• Severity: high
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/memodb-io/memobase/issues/155

2. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/memodb-io/memobase/issues/158

3. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/memodb-io/memobase/issues/76

4. Configuration risk: Configuration risk requires verification

• Severity: medium
• Finding: Project evidence flags a configuration risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/memodb-io/memobase/issues/159

5. Capability evidence risk: Capability evidence risk requires verification

• Severity: medium
• Finding: README/documentation is current enough for a first validation pass.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: capability.assumptions | https://github.com/memodb-io/memobase

6. Runtime risk: Runtime risk requires verification

• Severity: medium
• Finding: Project evidence flags a runtime risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/memodb-io/memobase/issues/137

7. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/memodb-io/memobase

8. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: downstream_validation.risk_items | https://github.com/memodb-io/memobase

9. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: risks.scoring_risks | https://github.com/memodb-io/memobase

10. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: Project evidence flags a security or permission risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/memodb-io/memobase/issues/153

11. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: issue_or_pr_quality=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/memodb-io/memobase

12. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: release_recency=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/memodb-io/memobase

Community Discussion Evidence

Doramagic exposes project-level community discussion separately from official documentation. Review these links before using memobase with real data or production workflows.

• Your AI needs an identity — here's how to fix that - github / github_issue
• Community source 2 - github / github_issue
• Community source 3 - github / github_issue
• “add_user” and “update_user” handle additional data differently - github / github_issue
• When will RESTful API access be supported? - github / github_issue
• How to use it in Java? - github / github_issue
• Error in get_embedding: Failed to embed texts: 404 page not found Traceb - github / github_issue
• i need java sdk - github / github_issue
• Community source 9 - github / github_issue
• Capability evidence risk requires verification - GitHub / issue
• Runtime risk requires verification - GitHub / issue