
EchoVault Memory
io.github.go-portsPublished by GitHub account go-portsv0.3.3Updated Oct 24, 2026
Local-first memory for coding agents; stores decisions, bugs, and context across sessions.
The publisher's own server: Memory.fit
Overview
A local-first memory server for coding agents that saves, searches and reloads decisions, bugs and context across sessions, stored as Markdown on your machine.
- What you can do
- Save memories with titles, rationale, impact, tags and categories, plus structured detail templates or detail files, and delete them by ID or prefix later on. Search memories with hybrid keyword and optional semantic vector search, and fetch full details for a result. Load prior context at session start, list memories for the current project and list session files. Keep one shared vault across Claude Code, Cursor, Codex and OpenCode, and manage the memory home and embedding configuration from the command line.
- When to use it
- Worth adding when you want a coding agent to carry decisions, bug history and project context from one session to the next without a cloud service. Useful if you already work across several agents and want them to share one memory vault you can browse as Markdown.
- Requirements
- Runs as a local process over stdio; the Go build is a single static binary, and building from source needs a C compiler because it links SQLite libraries. Initialize a vault and install the agent config first. The MEMORY_HOME environment variable sets the storage directory and should point at persistent storage. Semantic search optionally needs Ollama locally or an OpenAI or OpenRouter API key in the config file.
Installation
In SourceWeft
- Open EchoVault Memory in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
EchoVault — Go
Local memory for coding agents. Your agent remembers decisions, bugs, and context across sessions — no cloud, no API keys, no cost.
Install · Features · How it works · Commands
This is the Go port of EchoVault. It is a single static binary with no Python runtime dependency. The vault format and MCP interface are fully compatible with the Python version — you can switch between them without losing any memories.
Features
Works with 4 agents — Claude Code, Cursor, Codex, OpenCode. One command sets up MCP config for your agent.
MCP native — Runs as an MCP server exposing memory_save, memory_search, and memory_context as tools. Agents call them directly — no shell hooks needed.
Local-first — Everything stays on your machine. Memories are stored as Markdown in ~/.memory/vault/, readable in Obsidian or any editor.
Zero idle cost — No background processes, no daemon, no RAM overhead. The MCP server only runs when the agent starts it.
Hybrid search — FTS5 keyword search works out of the box. Add Ollama or OpenAI for semantic vector search.
Secret redaction — 3-layer redaction strips API keys, passwords, and credentials before anything hits disk. Supports explicit <redacted> tags, pattern detection, and custom .memoryignore rules.
Cross-agent — Memories saved by Claude Code are searchable in Cursor, Codex, and OpenCode. One vault, many agents.
Obsidian-compatible — Session files are valid Markdown with YAML frontmatter. Point Obsidian at ~/.memory/vault/ and browse your agent's memory visually.
Install
Pre-built binary
Download the latest release for your platform from the releases page and place the binary somewhere on your $PATH.
Build from source
CGO required. The binary links against
go-sqlite3andsqlite-vec, so a C compiler (gcc/clang) must be present. On macOS:xcode-select --install. On Debian/Ubuntu:apt install build-essential.
First run
That's it. memory setup installs the MCP server config automatically.
By default the config is installed globally. To install for a specific project:
Configure embeddings (optional)
Embeddings enable semantic search. Without them, you still get fast keyword search via FTS5.
Generate a starter config:
This creates ~/.memory/config.yaml with sensible defaults:
What each section does:
embedding— How memories get turned into vectors for semantic search.ollamaruns locally;openaiandopenroutercall cloud APIs.nomic-embed-textis a good local model for Ollama.context— Controls how memories are retrieved at session start.autouses vector search when embeddings are available, falls back to keywords.topup_recentalso includes recent memories so the agent has fresh context.
For cloud providers, add api_key under the provider section. API keys are redacted in memory config output.
Configure memory location
By default, EchoVault stores data in ~/.memory.
You can change that in two ways:
MEMORY_HOME=/path/to/memory(highest priority, per-shell/per-process)memory config set-home /path/to/memory(persistent default)
Useful commands:
memory config shows both memory_home and memory_home_source (env, config, or default).
The --memory-home global flag overrides everything for a single invocation:
Usage
Once set up, your agent uses memory via MCP tools:
- Session start — agent calls
memory_contextto load prior decisions and context - During work — agent calls
memory_searchto find relevant memories - Session end — agent calls
memory_saveto persist decisions, bugs, and learnings
The MCP tool descriptions instruct agents to save and retrieve automatically. No manual prompting needed in most cases.
You can also use the CLI directly:
For long details, use --details-file notes.md. To scaffold structured details automatically, use --details-template.
How it works
- Markdown vault — one file per session per project, with YAML frontmatter
- SQLite index — FTS5 for keywords, sqlite-vec for semantic vectors
- Compact pointers — search returns ~50-token summaries; full details fetched on demand
- 3-layer redaction — explicit tags, pattern matching, and
.memoryignorerules
Supported agents
All agents share the same memory vault at your effective memory_home path (default ~/.memory/). A memory saved by Claude Code is searchable from Cursor, Codex, or OpenCode.
Commands
Global flags
Uninstall
To also remove all stored memories: rm -rf ~/.memory/
Privacy
Everything stays local by default. If you configure OpenAI or OpenRouter for embeddings, those API calls go to their servers. Use Ollama for fully local operation.
License
MIT — see LICENSE.
Source: README.md at commit cc0802b
Tools
0Version history
1- v0.3.3LatestSep 16, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts
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