Big News: Socket raises $60M Series C at a $1B valuation to secure software supply chains for AI-driven development.Announcement
Sign In

@opencode-manager/memory

Package Overview
Dependencies
Maintainers
1
Versions
31
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

@opencode-manager/memory

AI-powered memory management plugin for OpenCode - semantic search and persistent knowledge storage

Source
npmnpm
Version
0.0.21
Version published
Weekly downloads
116
96.61%
Maintainers
1
Weekly downloads
 
Created
Source

@opencode-manager/memory

Semantic memory and planning plugin for OpenCode AI agents

npm npm downloads License

Quick Start

pnpm add @opencode-manager/memory

Add to your opencode.json:

{
  "plugin": ["@opencode-manager/memory@latest"]
}

The local embedding model downloads automatically on install. For API-based embeddings (OpenAI or Voyage), see Configuration.

Features

  • Semantic Memory Search - Store and retrieve project memories using vector embeddings
  • Multiple Memory Scopes - Categorize memories as convention, decision, or context
  • Automatic Deduplication - Prevents duplicates via exact match and semantic similarity detection
  • Compaction Context Injection - Injects conventions and decisions into session compaction for seamless continuity
  • Automatic Memory Injection - Injects relevant project memories into user messages via semantic search with distance filtering and caching
  • Project KV Store - Ephemeral key-value storage with TTL management for project state
  • Bundled Agents - Ships with Code, Architect, and Librarian agents preconfigured for memory-aware workflows
  • CLI Tools - Export, import, list, stats, and cleanup commands via ocm-mem binary
  • Dimension Mismatch Detection - Detects embedding model changes and guides recovery via reindex

Agents

The plugin bundles four agents that integrate with the memory system:

AgentIDModeDescription
codeocm-codeprimaryPrimary coding agent with memory awareness. Checks memory before unfamiliar code, stores architectural decisions and conventions as it works. Delegates planning operations to @librarian subagent.
architectocm-architectprimaryRead-only planning agent. Researches the codebase, delegates to @librarian for broad knowledge retrieval, designs implementation plans, then hands off to code via memory-plan-execute.
librarianocm-librariansubagentExpert agent for managing project memory. Handles post-compaction memory extraction and contradiction resolution.
auditorocm-auditorsubagentRead-only code auditor with access to project memory for convention-aware reviews. Invoked via Task tool to review diffs, commits, branches, or PRs against stored conventions and decisions.

The auditor agent is a read-only subagent (temperature: 0.0) that can read memory but cannot write, edit, or delete memories or execute plans. It is invoked by other agents via the Task tool to review code changes against stored project conventions and decisions.

The architect agent operates in read-only mode (temperature: 0.0, all edits denied) with additional message-level read-only enforcement via the experimental.chat.messages.transform hook. After the user approves a plan, it calls memory-plan-execute which creates a new code session with the full plan as context.

Tools

Memory Tools

ToolDescription
memory-readSearch and retrieve project memories with semantic search
memory-writeStore a new project memory
memory-editUpdate an existing project memory
memory-deleteDelete a project memory by ID
memory-healthHealth check, reindex, or upgrade the plugin to latest version
memory-plan-executeCreate a new Code session and send an approved plan as the first prompt

Project KV Tools

Ephemeral key-value storage for project state with automatic TTL-based expiration.

ToolDescription
memory-kv-setStore a value with optional TTL (default 24 hours)
memory-kv-getRetrieve a value by key
memory-kv-listList all active KV entries for the project

Ralph Loop Tools

Iterative development loops with automatic auditing. Runs in an isolated git worktree by default, or in the current directory with inPlace.

ToolDescription
ralph-cancelCancel an active Ralph loop and clean up the worktree
ralph-statusCheck status of active Ralph loops
memory-plan-ralphExecute an architect plan using a Ralph iterative loop

Slash Commands

CommandDescriptionAgent
/reviewRun a code review on current changesauditor (subtask)
/ralph-loopStart a Ralph loop (delegates to memory-plan-ralph)code
/cancel-ralphCancel the active Ralph loopcode

CLI

Manage memories using the ocm-mem CLI. The CLI auto-detects the project ID from git and resolves the database path automatically.

ocm-mem <command> [options]

Global options (apply to all commands):

FlagDescription
--db-path <path>Path to memory database
--project, -p <name>Project name or SHA (auto-detected from git)
--dir, -d <path>Git repo path for project detection
--help, -hShow help

Commands

export

Export memories to file (JSON or Markdown).

ocm-mem export --format markdown --output memories.md
ocm-mem export --project my-project --scope convention
ocm-mem export --limit 50 --offset 100
FlagDescription
--format, -fOutput format: json or markdown (default: json)
--output, -oOutput file path (prints to stdout if omitted)
--scope, -sFilter by scope: convention, decision, or context
--limit, -lMax number of memories (default: 1000)
--offsetPagination offset (default: 0)

import

Import memories from file.

ocm-mem import memories.json --project my-project
ocm-mem import memories.md --project my-project --force
FlagDescription
--format, -fInput format: json or markdown (auto-detected from extension)
--forceSkip duplicate detection and import all

list

List all projects with memory counts.

ocm-mem list

stats

Show memory statistics for a project (scope breakdown).

ocm-mem stats
ocm-mem stats --project my-project

cleanup

Delete memories by criteria.

ocm-mem cleanup --older-than 90
ocm-mem cleanup --ids 1,2,3 --force
ocm-mem cleanup --scope context --dry-run
ocm-mem cleanup --all --project my-project
FlagDescription
--older-than <days>Delete memories older than N days
--ids <id,id,...>Delete specific memory IDs
--scope <scope>Filter by scope: convention, decision, or context
--allDelete all memories for the project
--dry-runPreview what would be deleted without deleting
--forceSkip confirmation prompt

Configuration

On first run, the plugin automatically copies the bundled config to your config directory:

  • Path: ~/.config/opencode/memory-config.jsonc
  • Falls back to: $XDG_CONFIG_HOME/opencode/memory-config.jsonc

The plugin supports JSONC format, allowing comments with // and /* */.

You can edit this file to customize settings. The file is created only if it doesn't already exist. If a config exists at the old location (~/.local/share/opencode/memory/config.json), it will be automatically migrated to the new location.

{
  "embedding": {
    "provider": "local",
    "model": "all-MiniLM-L6-v2",
    "dimensions": 384,
    "baseUrl": "",
    "apiKey": ""
  },
  "dedupThreshold": 0.25,
  "logging": {
    "enabled": false,
    "debug": false,
    "file": ""
  },
  "compaction": {
    "customPrompt": true,
    "maxContextTokens": 4000
  },
  "memoryInjection": {
    "enabled": true,
    "debug": false,
    "maxTokens": 2000,
    "cacheTtlMs": 30000
  },
  "messagesTransform": {
    "enabled": true,
    "debug": false
  },
  "executionModel": "",
  "auditorModel": "",
  "ralph": {
    "enabled": true,
    "defaultMaxIterations": 15,
    "cleanupWorktree": false,
    "defaultAudit": true,
    "model": "",
    "minAudits": 1
  }
}

For API-based embeddings:

{
  "embedding": {
    "provider": "openai",
    "model": "text-embedding-3-small",
    "apiKey": "sk-..."
  }
}

Options

Embedding

  • embedding.provider - Embedding provider: "local", "openai", or "voyage"
  • embedding.model - Model name
    • local: "all-MiniLM-L6-v2" (384d)
    • openai: "text-embedding-3-small" (1536d), "text-embedding-3-large" (3072d), or "text-embedding-ada-002" (1536d)
    • voyage: "voyage-code-3" (1024d) or "voyage-2" (1536d)
  • embedding.dimensions - Vector dimensions (optional, auto-detected for known models)
  • embedding.apiKey - API key for openai/voyage providers
  • embedding.baseUrl - Custom endpoint (optional, defaults to provider's official API)

Storage

  • dataDir - Directory for SQLite database storage (default: "~/.local/share/opencode/memory")
  • dedupThreshold - Similarity threshold for deduplication (0–1, default: 0.25, clamped to 0.05–0.40)

Config Location

  • Config file: ~/.config/opencode/memory-config.jsonc (or $XDG_CONFIG_HOME/opencode/memory-config.jsonc)
  • Old config location (~/.local/share/opencode/memory/config.json) is automatically migrated on first load

Logging

  • logging.enabled - Enable file logging (default: false)
  • logging.debug - Enable debug-level log output (default: false)
  • logging.file - Log file path. When empty, resolves to ~/.local/share/opencode/memory/logs/memory.log (default: ""). Logs remain in the data directory, only config has moved.

When enabled, logs are written to the specified file with timestamps. The log file has a 10MB size limit with automatic rotation.

Compaction

  • compaction.customPrompt - Use a custom compaction prompt optimized for session continuity (default: true)
  • compaction.maxContextTokens - Token budget for injected memory context with priority-based trimming (default: 4000)

Memory Injection

  • memoryInjection.enabled - Inject relevant project memories into user messages via semantic search (default: true)
  • memoryInjection.debug - Enable debug logging for memory injection (default: false)
  • memoryInjection.maxResults - Maximum number of vector search results to retrieve (default: 5)
  • memoryInjection.distanceThreshold - Maximum vector distance for a memory to be considered relevant; lower values are stricter (default: 0.5)
  • memoryInjection.maxTokens - Token budget for the injected <project-memory> block (default: 2000)
  • memoryInjection.cacheTtlMs - How long (ms) to cache results for identical queries (default: 30000)

Messages Transform

  • messagesTransform.enabled - Enable the messages transform hook that handles memory injection and Architect read-only enforcement (default: true)
  • messagesTransform.debug - Enable debug logging for messages transform (default: false)

Execution

  • executionModel - Model override for plan execution sessions, format: provider/model (e.g. anthropic/claude-haiku-3-5-20241022). When set, memory-plan-execute uses this model for the new Code session. When empty or omitted, OpenCode's default model is used (typically the model field from opencode.json). Recommended: Set this to a fast, cheap model (e.g. Haiku or MiniMax) and use a smart model (e.g. Opus) for the Architect session — planning needs reasoning, execution needs speed.

Ralph

  • ralph.enabled - Enable Ralph iterative development loops (default: true)
  • ralph.defaultMaxIterations - Default max iterations for loops, 0 = unlimited (default: 15)
  • ralph.cleanupWorktree - Auto-remove worktree on cancel (default: false)
  • ralph.defaultAudit - Run auditor after each coding iteration by default (default: true)
  • ralph.model - Model override for Ralph sessions (provider/model), falls back to executionModel (default: "")
  • ralph.minAudits - Minimum audit iterations required before completion (default: 1)

Auditor

  • auditorModel - Model override for the auditor agent (provider/model). When set, overrides the auditor agent's default model. When not set, uses platform default (default: "")

architect → code Workflow

Plan with a smart model, execute with a fast model. The architect agent researches and designs; the code agent implements.

After the architect presents a plan, the user approves via one of three execution modes:

  • Approve plan — Creates a new Code session via memory-plan-execute
  • Execute with Ralph loop — Runs the plan in an isolated worktree with iterative coding/auditing via memory-plan-ralph
  • Ralph in place — Same as Ralph loop but runs in the current directory (no worktree isolation)

Set executionModel in your config to a fast model (e.g., Haiku) and use a smart model (e.g., Opus) for the architect session.

See the full workflow guide for setup details.

Ralph Loop

The Ralph loop is an iterative development system that alternates between coding and auditing phases:

  • Coding phase — The Code agent works on the task
  • Auditing phase — The Auditor agent reviews changes against project conventions
  • Repeat — Findings from the audit feed back into the next coding iteration

The loop completes when the Code agent outputs the completion promise. It auto-terminates after maxIterations (if set) or after 3 consecutive errors.

By default, Ralph loops run in an isolated git worktree. Set inPlace: true to run in the current directory instead (skips worktree creation, auto-commit, and cleanup).

See the full documentation for details on the Ralph loop system.

Documentation

Full documentation available at chriswritescode-dev.github.io/opencode-manager/features/memory

Development

pnpm build      # Compile TypeScript to dist/
pnpm test       # Run tests
pnpm typecheck  # Type check without emitting

License

MIT

Keywords

opencode

FAQs

Package last updated on 21 Mar 2026

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts