Related Pages

Related topics: Architecture, Retrieval Engine & Memory Pipeline, Deployment, Operations & Known Failure Modes

Memanto Overview & Core Concepts

Memanto is a persistent, cross-agent memory layer for AI applications. It is described in the project README as an "active companion memory agent" backed by the moorcheh.ai retrieval engine, designed to keep durable, typed memories across sessions and shifting user preferences (README.md:1-40). The system is distributed as a Python package with a FastAPI-style service, a CLI, and a growing family of language SDKs and framework integrations.

1. Purpose and Scope

Memanto's role is to sit between an LLM-driven agent and a vector retrieval backend, abstracting memory operations into four primitives that any agent framework can call:

• store — persist a typed memory unit (fact, preference, event, instruction, etc.).
• recall — semantic search over prior memories, optionally filtered by type or agent.
• edit / forget — mutate or delete a specific memory by id (memanto forget).
• extract — turn raw chat-style message history into typed memories in one call.

The package exposes a memanto console script wired through memanto/__main__.py:1-20, which dispatches to subcommands defined under memanto/cli/commands/. Beyond CLI usage, the same primitives are reachable through REST endpoints under memanto/app/routes/memory.py:1-120, a Python direct API, an MCP server, and an official TypeScript SDK (@moorcheh-ai/memanto) introduced in v0.2.4 (README.md:60-90).

2. Memory Model and Lifecycle

A Memanto memory is a small structured record rather than a free-form blob. The default schema, declared in memanto/app/constants.py:1-80, defines a fixed enum of memory types that the service understands (e.g. fact, preference, instruction) plus shared fields for agent_id, source, source_ref, and provenance. The provenance field was surfaced across the recall path, CLI, MCP, and UI in v0.2.3 to make every recalled item traceable back to where it came from (memanto/app/services/conversation.py:1-60).

The typical lifecycle looks like this:

1. An agent submits content through store, or a transcript through extract (memanto/app/services/conversation.py:40-120).
2. Memanto classifies the memory type — either via an explicit field or via the rule-based fallback introduced in v0.1.2, which gained typo-tolerant fuzzy parsing in v0.1.3 (README.md:100-140).
3. The memory is embedded and written to the configured backend.
4. On later turns, recall returns the top-k items above a configurable similarity threshold (memanto/app/routes/memory.py:60-180); a chronological recall --recent path was added in v0.1.2 for time-ordered browsing.
5. Conflicting items can be reconciled through the standalone detect-conflicts job entrypoint (memanto/cli/commands/core.py:200-260).

3. Retrieval Backends: Cloud vs On-Prem

Memanto decouples the agent-facing API from the storage engine. The cloud default points at moorcheh.ai, while v0.2.0 introduced a pluggable on-prem backend so the same service can talk to a locally-run Moorcheh server (README.md:40-60). On-prem is brought up via the moorcheh up Docker quick start documented in docs/GETTING_STARTED.md:1-80, and v0.2.2 added host-mode Ollama support so embedding/LLM models can be pulled and run on the host instead of inside Docker (memanto/cli/commands/core.py:120-180).

Community reports highlight a real consequence of this split: the on-prem detect-conflicts job was found to fail on active days because the assembled conflict query exceeds the nomic-embed-text context window of 2048 tokens (issue #1329). The same path works against the cloud backend because the retrieval engine there handles longer inputs. This is a useful illustration of how deployment mode changes memory-pipeline behavior even though the agent-facing API does not.

4. Integrations and Ecosystem

Memanto ships as a library plus a set of official, separately versioned integration packages:

• LangGraph — langgraph-memanto provides persistent memory tools and, as of v0.1.1, a native BaseStore implementation (MemantoStore) plus pre-built memory nodes for LangGraph workflows.
• Claude Code — claudecode-memanto integrates Memanto as persistent engineering memory inside Claude Code and the mattpo tooling.
• MCP server and Hermes Agents — first-class integrations added in v0.1.2.
• TypeScript SDK — @moorcheh-ai/memanto, with lifecycle hooks and OpenAPI-generated types, shipping in v0.2.4.

The CLI is the integration glue: memanto connect rewrites agent templates to frame Memanto as the agent's memory backend (rewritten in v0.1.3), while memanto migrate (v0.2.1) imports memories from Mem0, Letta, and Supermemory — replacing the older memanto analyze benchmarking suite. Two active community bounties (issues #639 and #770) are explicitly framed around comparing Memanto against these competitors on resilience to shifting preferences and long-term context retention, which is consistent with the README positioning of Memanto as an "active companion" rather than a passive vector store.

5. Operational Notes

Security and hardening have been recurring themes across the 0.2.x line. v0.2.4 lists cross-agent authorization checks, upload path-traversal fixes, and secret-leakage fixes in the UI config endpoint as part of its release notes (README.md:90-130). Content-length and kiosk_mode defaults were unified across REST, SDK, and CLI surfaces in v0.1.2, and recall similarity thresholds were propagated end-to-end in v0.1.3, meaning configuration changes in memanto/app/constants.py are now visible at every layer without per-call overrides. For a new contributor, the recommended entry points are docs/GETTING_STARTED.md for the on-prem quick start, memanto/app/routes/memory.py for the public API contract, and memanto/cli/commands/core.py for the canonical CLI command implementations.

Related Pages

Related topics: Memanto Overview & Core Concepts, Integrations, SDKs & CLI Surface, Deployment, Operations & Known Failure Modes

Architecture, Retrieval Engine & Memory Pipeline

Purpose & Scope

Memanto is an active companion memory agent that gives AI applications durable, typed, long-term memory across sessions and agents. The system is designed around three cooperating layers: an ingestion/extraction pipeline that turns raw conversations into typed memories, a pluggable retrieval engine (moorcheh.ai cloud or a self-hosted on-prem Moorcheh server) that performs vector recall over those memories, and a service routing layer that surfaces both pipelines through REST, CLI, MCP, and SDK surfaces.

The cloud retrieval engine (moorcheh.ai) is positioned as the default, production-grade backend; the on-prem backend ships with the same API contract so that the same agent code can run fully offline or in air-gapped environments Source: memanto/app/clients/backend.py:1-80. Memory writes, conflict detection, and similarity recall all flow through this engine abstraction.

System Architecture

Memanto follows a layered architecture: a FastAPI HTTP surface (app/main.py) routes incoming requests to either memory services (app/services/) or direct Moorcheh client calls (app/clients/). A BackendClient protocol abstracts between the moorcheh cloud client and the onprem local client Source: memanto/app/clients/backend.py:1-60. Configuration is centralized in app/config.py, which determines whether the active backend is cloud or on-prem and exposes per-namespace credentials and namespace IDs.

flowchart TB
  A[HTTP / CLI / MCP / SDK] --> R[Routers in app/routes]
  R --> S[Services: extraction, recall, conflict, forget, edit]
  S --> C[BackendClient]
  C -->|cloud| M[moorcheh.ai]
  C -->|self-hosted| O[Local Moorcheh server]
  S --> V[Pydantic v2 memory models]

Memory Pipeline

Extraction

When conversation messages arrive, the extraction service normalizes turns into typed memories (e.g., preference, fact, goal) before persisting them. Rule-based classification assigns a memory type, and an LLM call (configurable per backend) extracts structured fields Source: memanto/app/services/extraction.py:1-120. Provenance metadata — source, source_ref, and provenance — is attached to every emitted memory so downstream recall can trace how a fact was learned Source: memanto/app/models/memory.py:1-80.

Recall

Recall is similarity-driven through the configured retrieval engine. Queries are embedded by the active backend (cloud: moorcheh.ai; on-prem: local Ollama + a local embedder) and the engine returns nearest memories above the configured threshold Source: memanto/app/services/recall.py:1-140. The threshold defaults are wired through REST, SDK, Direct, CLI, UI, and config layers; a --recent chronological path is also supported for time-ordered retrieval Source: memanto/app/clients/moorcheh.py:1-100.

Conflict & Edit

A separate detect-conflicts job reconciles new memories against existing ones by querying the engine for near-duplicates and running an LLM comparison. Communities have reported that this conflict query can exceed the embedding model's context window in on-prem setups (e.g., nomic-embed-text, 2048-token limit), causing failures on active days Source: community issue #1329. The v0.2.4 release adds a memanto edit path for in-place updates, and v0.2.1 shipped memanto forget for targeted single-memory deletion Source: release notes v0.2.4 / v0.2.1.

Pluggable Backends

The BackendClient interface in app/clients/backend.py defines the contract every retrieval operation must implement: add_memory, search_memory, delete_memory, and (on supported backends) update_memory. The cloud MoorchehClient wraps the hosted API and the OnPremClient translates the same calls into HTTP requests against a local Moorcheh server brought up via the moorcheh up Docker quick start Source: memanto/app/clients/onprem.py:1-90. Ollama host-mode (v0.2.2) lets users pull and run embedding/LLM models on the host instead of inside the container, removing Docker-internal Ollama dependencies Source: release notes v0.2.2.

Because the service layer only ever talks to the BackendClient, the same agents, CLI commands, and SDK clients work against either backend with no code change — only configuration in app/config.py and the matching namespace bindings need to differ.

Cross-Cutting Concerns

• Memory type classification: automatic rule-based classification was introduced in v0.1.2 with fuzzy fallback added in v0.1.3 for typo tolerance Source: release notes v0.1.2 / v0.1.3.
• Security hardening: v0.2.4 enforces cross-agent authorization, blocks upload path-traversal, and stops secret leakage through the UI config endpoint Source: release notes v0.2.4.
• Migrating from competing systems: v0.2.1 introduced memanto migrate for importing memories from Mem0, Letta, and Supermemory, replacing the earlier analyze command Source: release notes v0.2.1.

Together, these components make Memanto a configuration-driven, backend-agnostic memory pipeline whose retrieval engine and extraction services can be swapped or hardened without touching consumer integrations.

Related Pages

Related topics: Memanto Overview & Core Concepts, Architecture, Retrieval Engine & Memory Pipeline

Integrations, SDKs & CLI Surface

Overview

Memanto exposes its persistent, cross-agent memory capabilities through three concentric layers: a first-class CLI (memanto …), a pair of language SDKs (Python and TypeScript), and a set of framework integrations that ship as standalone packages. Every layer talks to the same REST surface, so a feature added at the API level is mirrored across the SDKs, the CLI, and (where applicable) the integrations on the next release.

Source: memanto/cli/main.py:1-40

The CLI is the canonical human-facing surface and is also the reference implementation used by the SDKs for local development. The Python and TypeScript SDKs target programmatic use from agents and applications, while the framework packages (langgraph-memanto, claudecode-memanto, the MCP server, the Hermes Agents adapter, and the CrewAI adapter) provide idiomatic entry points for specific agent runtimes.

CLI Surface

The CLI entry point is memanto/cli/main.py, which wires together a family of subcommand modules under memanto/cli/commands/.

Source: memanto/cli/main.py:40-120

The command tree breaks down roughly as:

Notable user-facing commands, ordered by release history:

• memanto connect — provision agent templates that frame Memanto as a companion memory layer. Source: memanto/cli/commands/agent.py:1-80
• memanto recall and memanto recall --recent — semantic plus chronological recall; the recall similarity threshold introduced in v0.1.3 is wired through this command. Source: memanto/cli/commands/memory.py:1-160
• memanto edit — in-place memory updates, added in v0.2.4. Source: memanto/cli/commands/memory_mgmt.py:1-120
• memanto forget — single-memory deletion, added in v0.2.1. Source: memanto/cli/commands/memory_mgmt.py:120-220
• memanto migrate — end-to-end import from Mem0, Letta, and Supermemory; replaces the older analyze command. Source: memanto/cli/commands/memory_mgmt.py:220-360
• memanto analyze — benchmark/compare Memanto against Mem0, Letta, and Supermemory (still available alongside migrate for evaluation workflows). Source: memanto/cli/commands/core.py:1-200
• moorcheh up — Docker quick-start for the on-prem backend, surfaced through core.py. Source: memanto/cli/commands/core.py:200-360

Cross-cutting flags such as content-length and kiosk_mode are unified across the REST, SDK, Direct, CLI, UI, and config layers so that defaults stay consistent regardless of entry point. Source: memanto/cli/commands/memory.py:160-260

SDK Layer

The Python SDK ships in the main memanto package and mirrors the CLI surface 1:1. Provenance metadata (source, source_ref, provenance) added in v0.2.3 is exposed uniformly across the recall path, SDK, CLI, MCP, and UI.

The TypeScript SDK, published as @moorcheh-ai/memanto, was introduced in v0.2.4 alongside the v2 memory route response models. It ships with lifecycle hooks and OpenAPI-generated types so that the client and server stay in lockstep.

Source: memanto/cli/commands/core.py:360-440

The SDK clients are thin: they serialize to the v2 response models, handle auth, and delegate retrieval to either the cloud endpoint or the pluggable on-prem backend (added in v0.2.0) backed by a local Moorcheh server.

Framework Integrations

Framework-specific packages sit on top of the SDKs and give each runtime a native feel.

• langgraph-memanto — released alongside LangGraph v0.1.0 and expanded in v0.1.1 to include a custom BaseStore implementation (MemantoStore) and pre-built memory nodes for LangGraph workflows.
• claudecode-memanto — Claude Code Skills integration, v0.1.0, with 8 composable skill templates added in v0.2.1 to give Claude Code persistent engineering memory.
• MCP server — first-class Model Context Protocol server added in v0.1.2, exposing memory tools to any MCP-compatible client.
• Hermes Agents adapter — first-class integration shipped in v0.1.2.
• CrewAI adapter — renamed on PyPI in v0.1.2 to consolidate the integration namespace.

A simple data-flow view of how these surfaces relate:

flowchart LR
    Agent["Agent runtime<br/>(LangGraph / Claude Code / CrewAI / Hermes)"]
    MCP["MCP server"]
    SDK["Python or TypeScript SDK"]
    CLI["memanto CLI"]
    API["Memanto REST API<br/>(v2 memory routes)"]
    Backend["Cloud or on-prem<br/>Moorcheh backend"]

    Agent --> SDK
    Agent --> MCP
    MCP --> SDK
    CLI --> API
    SDK --> API
    API --> Backend

Known Gaps and Community Notes

The on-prem detect-conflicts job fails on active days because the generated conflict query exceeds the nomic-embed-text 2048-token context window; this is a known issue tracked at #1329 and motivates chunking work in the conflict pipeline. The CLI command surface is also being progressively hardened — v0.2.4 shipped fixes for cross-agent authorization, upload path-traversal, and secret leakage in the UI config endpoint, all of which are reflected in the CLI's request signing path. Source: memanto/cli/commands/core.py:440-520

Related Pages

Related topics: Memanto Overview & Core Concepts, Architecture, Retrieval Engine & Memory Pipeline, Integrations, SDKs & CLI Surface

Deployment, Operations & Known Failure Modes

This page covers how Memanto is packaged and operated across cloud and on-prem topologies, the configuration knobs operators most often tune, and the recurring failure modes that have surfaced in community reporting through v0.2.4.

Deployment Topologies

Memanto ships two interchangeable backends behind a single REST surface, selected at boot via configuration.

Cloud backend (default)

The default backend targets the managed moorcheh.ai retrieval engine. Operators only need to provide API credentials and the upstream service handles vector storage, indexing, and recall. This is the path documented in the bulk of the SDK and CLI examples.

On-prem backend (Docker quick start)

For self-hosted deployments, Memanto composes an Ollama embedding/LLM runtime with a local Moorcheh vector server behind a single docker-compose.yml orchestration. The Dockerfile image exposes the FastAPI app and CLI, while docker-compose.yml wires the app container together with the Moorcheh and Ollama sidecars so the stack comes up with zero config. Source: docker-compose.yml:1-80 Source: Dockerfile:1-60

A dedicated client module, memanto/app/clients/onprem.py, isolates the on-prem transport from the rest of the application. The application boot path branches on configuration and instantiates this client instead of the cloud client, keeping the route handlers, services, and CLI commands identical across topologies. Source: memanto/app/clients/onprem.py:1-120

Configuration Surface

The operational surface is intentionally narrow. .env.example enumerates the variables an operator is expected to set before first boot, including:

• Backend selector – the flag that toggles between cloud and on-prem clients.
• Model identifiers – the embedding and chat model names consumed by Ollama on-prem. The Docker quick start pins nomic-embed-text for embeddings (context length 2048) and qwen2.5 for chat completions.
• Auth and secret material – service tokens used by memanto/app/routes/auth_deps.py to authorize cross-agent requests. Source: .env.example:1-60

memanto/app/routes/auth_deps.py is the central authorization dependency injected into REST routes. It validates caller identity, enforces the cross-agent boundary, and resolves the active agent scope for the request. Because this dependency is shared by all v2 memory routes, any misconfiguration manifests as blanket 401/403 responses rather than route-specific errors. Source: memanto/app/routes/auth_deps.py:1-140

Ollama host-mode (v0.2.2+)

Since v0.2.2 the on-prem installer can pull Ollama models onto the host machine instead of into a Docker-internal Ollama container. This is exposed in memanto/cli/commands/core.py and matters for operators whose host has GPU passthrough but whose containers cannot share it. Source: community release notes for v0.2.2.

Security Hardening (v0.2.4)

The v0.2.4 release consolidated three operational security fixes that are particularly relevant when Memanto is exposed beyond a single trusted host:

Before v0.2.4, an authenticated caller could sometimes act on behalf of another agent, uploaded filenames were not normalized against ../ traversal, and the UI's config endpoint echoed raw secret values to the browser. Operators upgrading from ≤ v0.2.3 should rotate any credentials that may have been exposed via the UI and audit agent-scoped logs for cross-agent writes. Source: release notes for v0.2.4.

Known Failure Modes

`detect-conflicts` context window overflow (issue #1329)

The most prominent operational bug in the current series is that the on-prem detect-conflicts job fails on any day where the conflict query exceeds the embedding model's context window. Reported environment: memanto 0.2.4 brought up via the Docker quick start, nomic-embed-text (2048 tokens), qwen2.5 chat model, all defaults. The job is implemented as a long-running entrypoint that drives memanto/app/services/daily_analysis_service.py; once the accumulated prompt exceeds 2048 tokens the embed call returns an empty or truncated vector and the conflict pass aborts. Source: memanto/app/services/daily_analysis_service.py:1-200 Source: memanto/app/clients/onprem.py:120-260

Practical mitigations reported by the community:

• Switch the embedding model to one with a larger context window (e.g. mxbai-embed-large at 512 tokens is *not* a fix; pick a model whose documented context length comfortably exceeds your worst-case daily prompt).
• Run detect-conflicts on smaller time windows so the per-invocation query stays bounded.
• Pin the embed call to fail loud rather than silently truncate, which at least surfaces the failure in logs instead of producing empty conflict results.

Empty-result silent success

Because the same client module serves both cloud and on-prem, a misconfigured Ollama endpoint can produce successful HTTP responses whose underlying embedding is empty. Operators should treat 0 vector results from any recall path as an infrastructure signal, not a "no memories found" signal. Source: memanto/app/clients/onprem.py:200-320

Auth dependency as single point of failure

Because auth_deps.py is the gatekeeper for all v2 memory routes, a stale or rotated token presents as uniform 401s across remember, recall, forget, and edit. When triaging, verify the token against this dependency before suspecting the route handler itself. Source: memanto/app/routes/auth_deps.py:60-140

Operational Checklist

1. Confirm .env.example variables are set; in particular the backend selector and model identifiers must match what docker-compose.yml actually starts.
2. After upgrading to v0.2.4, rotate any credential that may have been reflected by the UI config endpoint.
3. For on-prem, validate that the chosen embedding model's context length exceeds the worst-case prompt size of daily_analysis_service.py before scheduling detect-conflicts as a recurring job.
4. Monitor recall and detect-conflicts for empty result sets — they are the canonical signal of an Ollama transport failure, not a missing-memory condition.

Doramagic Pitfall Log

Found 23 structured pitfall item(s), including 3 high/blocking item(s). Top priority: Security or permission risk - Security or permission risk requires verification.

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

• Severity: high
• Finding: Developers should check this security_permissions risk before relying on the project: [BOUNTY $100] 🐜The Memanto Bug & Exploit Challenge
• User impact: Developers may expose sensitive permissions or credentials: [BOUNTY $100] 🐜The Memanto Bug & Exploit Challenge
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: [BOUNTY $100] 🐜The Memanto Bug & Exploit Challenge. Context: Observed when using python, docker
• Evidence: failure_mode_cluster:github_issue | https://github.com/moorcheh-ai/memanto/issues/770

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

• Severity: high
• 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/moorcheh-ai/memanto/issues/639

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

• Severity: high
• 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/moorcheh-ai/memanto/issues/770

4. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: Claude Code Skills v0.1.0
• User impact: Upgrade or migration may change expected behavior: Claude Code Skills v0.1.0
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: Claude Code Skills v0.1.0. Context: Observed during installation or first-run setup.
• Evidence: failure_mode_cluster:github_release | https://github.com/moorcheh-ai/memanto/releases/tag/integrations/claudecode/v0.1.0

5. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: V0.2.0
• User impact: Upgrade or migration may change expected behavior: V0.2.0
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: V0.2.0. Context: Observed when using docker
• Evidence: failure_mode_cluster:github_release | https://github.com/moorcheh-ai/memanto/releases/tag/v0.2.0

6. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.1.2
• User impact: Upgrade or migration may change expected behavior: v0.1.2
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.1.2. Context: Observed when using docker
• Evidence: failure_mode_cluster:github_release | https://github.com/moorcheh-ai/memanto/releases/tag/v0.1.2

7. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.1.3
• User impact: Upgrade or migration may change expected behavior: v0.1.3
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.1.3. Context: Observed during version upgrade or migration.
• Evidence: failure_mode_cluster:github_release | https://github.com/moorcheh-ai/memanto/releases/tag/v0.1.3

8. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.2.1
• User impact: Upgrade or migration may change expected behavior: v0.2.1
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.2.1. Context: Observed when using python
• Evidence: failure_mode_cluster:github_release | https://github.com/moorcheh-ai/memanto/releases/tag/v0.2.1

9. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.2.4
• User impact: Upgrade or migration may change expected behavior: v0.2.4
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.2.4. Context: Observed when using node
• Evidence: failure_mode_cluster:github_release | https://github.com/moorcheh-ai/memanto/releases/tag/v0.2.4

10. 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: capability.host_targets | https://github.com/moorcheh-ai/memanto

11. Configuration risk: Configuration risk requires verification

• Severity: medium
• Finding: Developers should check this configuration risk before relying on the project: detect-conflicts (on-prem) fails on any active day because the conflict query exceeds the embedding model's context window
• User impact: Developers may misconfigure credentials, environment, or host setup: detect-conflicts (on-prem) fails on any active day because the conflict query exceeds the embedding model's context window
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: detect-conflicts (on-prem) fails on any active day because the conflict query exceeds the embedding model's context window. Context: Observed when using docker
• Evidence: failure_mode_cluster:github_issue | https://github.com/moorcheh-ai/memanto/issues/1329

12. Configuration risk: Configuration risk requires verification

• Severity: medium
• Finding: Developers should check this configuration risk before relying on the project: v0.2.3
• User impact: Upgrade or migration may change expected behavior: v0.2.3
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.2.3. Context: Source discussion did not expose a precise runtime context.
• Evidence: failure_mode_cluster:github_release | https://github.com/moorcheh-ai/memanto/releases/tag/v0.2.3

Community Discussion Evidence

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

• [[BOUNTY $100] 🐜The Memanto Bug & Exploit Challenge](https://github.com/moorcheh-ai/memanto/issues/770) - github / github_issue
• detect-conflicts (on-prem) fails on any active day because the conflic - github / github_issue
• [[BOUNTY $100] 🐜 The Great Agentic Memory Showdown: Memanto Benchmarking](https://github.com/moorcheh-ai/memanto/issues/639) - github / github_issue
• v0.2.4 - github / github_release
• v0.2.3 - github / github_release
• v0.2.2 - github / github_release
• Claude Code Skills v0.1.0 - github / github_release
• v0.2.1 - github / github_release
• V0.2.0 - github / github_release
• LangGraph v0.1.1 - github / github_release
• LangGraph v0.1.0 - github / github_release
• v0.1.3 - github / github_release