Evaggelos Balaskas - System Engineer

coding agents are useful, but they forget. I’ve find it from time and time to either repeat my self or creating skills to reuse them. But I also use multiple agents and on different machines too which is also complicated. I was looking for a way to keep a common memory to my agents. And built my services and setup without … forgetting !

This guide shows a simple MVP setup for giving your AI coding agents one shared memory system.

What we are building

I will be using Basic Memory as the memory service and connect agents through MCP, running it with docker compose using the official image.

Model Context Protocol (MCP) is an open standard introduced by Anthropic in November 2024 that enables AI agents and large language models (LLMs) to securely connect with external tools, data sources, and services

One agent writes a note, any agent — in any later session — can retrieve it back. You can also open the same files yourself, since they are just Markdown files on disk.So basic memory becomes a shared notebook for your coding agents.

What you need

You need:

• Docker
• Docker Compose
• Git (optional)
• A terminal
• One coding agent with MCP support

No model provider API key needed here. Basic Memory’s default semantic search runs on local FastEmbed embeddings.

Official docs:

• Basic Memory repo: https://github.com/basicmachines-co/basic-memory
• Basic Memory docs: https://docs.basicmemory.com/
• Basic Memory Docker guide: https://github.com/basicmachines-co/basic-memory/blob/main/docs/Docker.md

Why no model provider is needed

Some memory systems have an LLM read your conversation and extract facts before storing anything - every save costs a model call, and you need a provider API key for that.

Basic Memory skips this. The agent (or you) writes structured Markdown directly:

---
title: Testing Conventions
permalink: testing-conventions
tags: [testing]
---

# Testing Conventions

## Observations
- [tool] Use Vitest instead of Jest
- [rule] Do not edit generated files

## Relations
- relates_to [[Project Architecture]]

Observations, Relations, done. A local SQLite index gives full-text and semantic search over these files, no cloud calls.

Step 1: Start Basic Memory with Docker Compose

Default install is uv tool install basic-memory (or uvx basic-memory mcp), a stdio MCP server per agent - no Docker needed. I wanted one server for all my agents, so Docker it is.

The pre-built image runs as UID/GID 1000, so create and own the folders first:

mkdir -p knowledge basic-memory-config
sudo chown -R 1000:1000 knowledge basic-memory-config

docker compose

docker-compose.yml

name: basic-memory

# runs the MCP server over SSE/HTTP on :8000 - no auth on that endpoint.
# keep it on a trusted network, or put a reverse proxy + auth in front of it.

services:
  basic-memory:
    image: ghcr.io/basicmachines-co/basic-memory:latest
    container_name: basic-memory-server

    volumes:
      - ./knowledge:/app/data:rw
      # config + sqlite index, container user is appuser -> /home/appuser
      - ./basic-memory-config:/home/appuser/.basic-memory:rw

    environment:
      - BASIC_MEMORY_DEFAULT_PROJECT=main
      - BASIC_MEMORY_SYNC_CHANGES=true
      - BASIC_MEMORY_LOG_LEVEL=INFO
      - BASIC_MEMORY_SYNC_DELAY=1000

    ports:
      - "8000:8000"

    command: ["basic-memory", "mcp", "--transport", "sse", "--host", "0.0.0.0", "--port", "8000"]

    restart: unless-stopped

    healthcheck:
      test: ["CMD", "basic-memory", "--version"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 30s

and start it:

docker compose -v up -d

Confirm it’s healthy:

docker compose -v ps -a

# view logs
docker compose logs -f basic-memory

a healthy container looks like this in the logs:

❯ docker compose logs -f basic-memory
basic-memory-server  | [07/06/26 09:23:04] INFO     Starting MCP server 'Basic Memory'
basic-memory-server  |                              with transport 'sse' on
basic-memory-server  |                              http://0.0.0.0:8000/mcp
basic-memory-server  | INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Step 2: Confirm the project

The default project (main) is your ./knowledge folder, mounted into the container at /app/data. To add another project pointing at a different mounted folder:

docker exec basic-memory-server basic-memory project add my-project /app/data/my-project
docker exec basic-memory-server basic-memory project list

Decide upfront whether you want one shared notebook across all your repos, or one project per repo — it’s easier to choose now than to migrate later.

If you just want to have one shared notebook, then ignore project add my-project command.

Step 3: Connect Claude Code

Claude Code connects over SSE, worked fine against the Dockerized server:

claude mcp add --transport sse basic-memory http://localhost:8000/mcp

Verify inside Claude Code:

/mcp

Expect something like:

Local MCPs (/home/<user>/.claude.json [project: <project path>])
❯ basic-memory · ✔ connected · 23 tools

To remove it:

claude mcp remove basic-memory

There’s also a Claude Code plugin for session-start briefings and /basic-memory:* commands, optional:

claude plugin marketplace add basicmachines-co/basic-memory --sparse .claude-plugin plugins/claude-code
claude plugin install basic-memory@basicmachines-co

OpenCode example

OpenCode has a native "remote" MCP type, also worked directly against the same server, no proxy. Add to ~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "basic-memory": {
      "type": "remote",
      "url": "http://localhost:8000/mcp",
      "enabled": true
    }
  }
}

Verify:

opencode mcp list

Expect:

┌  MCP Servers
│
●  ✓ basic-memory connected
│      http://localhost:8000/mcp
│
└  1 server(s)

OpenCode’s config shape for remote MCP servers might change, check their docs if it stops working: https://opencode.ai/docs/mcp-servers/

Test it

Ask your connected agent, in plain language:

"Create a note about our project architecture decisions."

A Markdown file should appear under ./knowledge on your host in real time. Open it — you’ll see the frontmatter, an Observations list, and a Relations list. That’s the entire format.

Then start a fresh session (or switch agents) and ask:

Retrieve all notes from basic-memory

If the agent surfaces what you saved earlier, memory is working end to end. In practice it looks like this:

What to put in memory

Good:

• Project conventions
• Folder structure
• Testing framework
• Architecture decisions
• Things the agent should avoid

here is an example from my homepc:

❯ tree knowledge/
knowledge/
└── basic-memory
    ├── conventions
    │   └── Docker Healthcheck Convention.md
    ├── projects
    │   ├── Changelog Conventions.md
    │   ├── Deployment Preferences.md
    │   ├── Git Commit Conventions.md
    │   └── README Conventions.md
    └── services
        ├── Hermes Agent.md
        ├── Port Registry.md
        ├── Port Suggestion Registry.md
        └── Service Registry.md

5 directories, 9 files

Bad:

do not put

• Passwords
• Private keys
• Production tokens
• Customer private data

See the Security notes near the top — treat ./knowledge like any other sensitive project directory, and don’t commit it to a public repo unless that’s actually intended.

Troubleshooting

Agent can’t connect to the MCP server? run the proxy bridge manually and see what breaks:

uvx mcp-proxy http://localhost:8000/mcp

Your coding agent cannot connect to an MCP server that isn’t reachable, obviously.

Notes not showing up, or search feels stale:

docker exec basic-memory-server basic-memory status
docker exec basic-memory-server basic-memory doctor

doctor checks file-vs-database consistency and rebuilds the local search index if it’s out of sync.

Running multiple projects and not sure which one is active:

docker exec basic-memory-server basic-memory project list

I hope you find the article useful.

That’s it !
-Evaggelos

Run OpenCode, an AI coding agent on your own machine — no cloud, no API, no data ever leaving your computer privacy first and no costs!

Introduction

If you’ve been curious about running AI coding agents entirely on your own machine then this blog post is for you. We will walk through setting up OpenCode, a terminal-based AI coding agent, and connecting it to LM Studio so it uses our local language models (LLMs) that you control.

What You’ll Need

Before we begin, make sure you have the following:

• A reasonably modern computer (macbook M series Pro with Apple Silicon work great, for this blog post I am using Macbook M4 Pro)
• LM Studio installed — download it from lmstudio.ai
• Additional you can install/use Visual Studio Code!

What is a AI coding Agent ?

so OpenCode is an open source AI coding agent that

• Turn Ideas into Real Tools
• Automate Boring Repetitive Tasks
• Fix Broken Things
• Connect Different Apps Together
• Explain Technical Jargon

eg.

I need a simple website for my dog-walking business where people can book a time and see my prices.

and opencode starts working on that

and the result is something like that, without writing a single line of code !

and yes, this example was made entirely on my macbook with opencode and lmstudio.

Install opencode

Open your terminal and run the official install script:

curl -fsSL https://opencode.ai/install | bash

or via brew (my preferable way)

brew install anomalyco/tap/opencode

This downloads and installs the opencode CLI tool. Once it’s done, close and reopen your terminal (or run source ~/.bashrc / source ~/.zshrc) so the command is available.

Verify it worked:

opencode --version

eg.

❯ opencode --version
1.2.27

Download a Model in LM Studio

Open LM Studio and use the built-in model browser to download a model. For this guide, we’ll use two good options that run well on consumer hardware:

• Ministral 3B — fast and lightweight, great for quick tasks
• Qwen 3.5 9B — more capable, needs more RAM/VRAM

Search for either model in LM Studio’s Discover tab and download it. Once downloaded, you’ll see it listed in your local models.

you can also use the CLI to get the models

eg. lms get mistralai/ministral-3-3b

❯ lms get mistralai/ministral-3-3b
   ✓ Satisfied mistralai/ministral-3-3b
   └─ ✓ Satisfied Ministral 3 3B Instruct 2512 Q4_K_M [GGUF]

⠋ Resolving download plan...

and list them lms ls

You have 3 models, taking up 9.62 GB of disk space.

LLM                                     PARAMS    ARCH        SIZE       DEVICE
mistralai/ministral-3-3b (1 variant)    3B        mistral3    2.99 GB    Local
qwen/qwen3.5-9b (1 variant)             9B        qwen35      6.55 GB    Local     

EMBEDDING                               PARAMS    ARCH          SIZE        DEVICE
text-embedding-nomic-embed-text-v1.5              Nomic BERT    84.11 MB    Local     

I am not going to analyse the models but in short, Qwen3.5-9B is best for a local, open, multimodal assistant that can handle:

• coding
• tool calling / agents
• long documents
• multilingual tasks
• document and image understanding

and fits in a a MacBook M4 Pro with 48GB RAM.

Important: Context Length

In simple words, context length is the AI’s short-term memory limit. Depending on the model and use, you need to adjust it on LM Studio. It is measured by tokens. Tokens are a chunk of a words. When using cloud AI models via API, the cost is measured on how many tokens you are using in a specific amount of time.

• Use Small Context Lenght (4096 - 8192) when you have a quick question, review/reply to a short email or debug a small snippet of code. It will produce a quick reply.

• Use Medium Context Length (32k) when you want to analyze a report, write a short story or working with a few coding files. It may take a couple minutes.

• Use Large Context Length (128+) when you want to upload a big document, or you want to analyze a project at once. It will be slow, slower on local machines.

See below details about LM Studio and LLM.

Start the LM Studio Local Server

LM Studio includes a built-in local API server that speaks the OpenAI API format — which means tools like opencode can talk to it directly.

In LM Studio, go to the Local Server tab (the <-> icon on the left sidebar) and click Start Server. By default it runs at http://localhost:1234.

Tweak Settings

to get the best from LM Studio

You can leave the server running in the background while you use opencode.

or you can use CLI to start LM Studio server:

❯ lms server start -p 1234 --bind 127.0.0.1
Waking up LM Studio service...
Success! Server is now running on port 1234

verify which models are available

by running in CLI a simple curl command curl -s http://localhost:1234/v1/models | jq .

{
  "data": [
    {
      "id": "qwen/qwen3.5-9b",
      "object": "model",
      "owned_by": "organization_owner"
    },
    {
      "id": "mistralai/ministral-3-3b",
      "object": "model",
      "owned_by": "organization_owner"
    },
    {
      "id": "text-embedding-nomic-embed-text-v1.5",
      "object": "model",
      "owned_by": "organization_owner"
    }
  ],
  "object": "list"
}

Configure opencode

opencode uses a config file called opencode.json stored in ~/.config/opencode/. You’ll need to create or edit this file to tell opencode about your LM Studio models.

Create the directory if it doesn’t exist:

mkdir -p ~/.config/opencode

Then create (or edit) the config file:

vim ~/.config/opencode/opencode.json

Paste in the following configuration:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "lmstudio": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "lmstudio",
      "options": {
        "baseURL": "http://127.0.0.1:1234/v1",
        "apiKey": "lmstudio"
      },
      "models": {
        "qwen/qwen3.5-9b": {
          "name": "qwen3.5"
        },
        "mistralai/ministral-3-3b": {
          "name": "ministral3"
        }
      }
    }
  }
}

A few things to note:

• The baseURL points to LM Studio’s local server — keep this as-is unless you’ve changed LM Studio’s port.
• The apiKey value "lmstudio" is a placeholder — LM Studio doesn’t actually require a real API key, but the field needs to be present.
• The model IDs (e.g. mistralai/ministral-3-3b) must match exactly what LM Studio uses. You can check the model identifier in LM Studio’s model list.

Save and close the file.

Load a Model via the CLI (Optional but Useful)

LM Studio comes with a CLI tool called lms that lets you load and unload models from the terminal without opening the GUI. This is handy for scripting or keeping things lightweight.

First, unload any currently loaded model (to free memory):

lms unload "mistralai/ministral-3-3b"

Then load it fresh with a specific context window size:

lms load "mistralai/ministral-3-3b" --context-length 16384

The --context-length flag controls how much text the model can hold in memory at once. 16384 (16K tokens) is a good balance of capability and memory use. If you have more RAM to spare, try 32768.

full example with ministral

❯ lms unload "mistralai/ministral-3-3b"
Model "mistralai/ministral-3-3b" unloaded.

~
❯ lms load "mistralai/ministral-3-3b" --context-length 16384

Model loaded successfully in 2.67s.
(2.78 GiB)
To use the model in the API/SDK, use the identifier "mistralai/ministral-3-3b".

Test opencode with Your Local Model

opencode run --model lmstudio/mistralai/ministral-3-3b "capital of greece?"

The --model flag follows the format lmstudio/<model-id>, where the model ID matches what you put in the config file.

You should see the model respond directly in your terminal. If everything is connected correctly, the response comes from your local machine — no internet required.

output:

> build · mistralai/ministral-3-3b

Athens.

Run opencode with Your Local Model

Now you’re ready to use opencode on your project.

Change to the code directory cd project

and to start an interactive session in your current project directory, just run:

opencode

opencode will open its TUI (terminal user interface) where you can have a longer back-and-forth conversation, ask it to read files, write code, and more.

Verify opencode is using the correct model and type

/init

To initial your project. It will create an AGENTS.md file for your project.

or you can use VS code with the opencode extension and use it from there !

That’s it!

Happy coding my friends.