lema

Run local LLMs as a real coding agent — on your machine, with your code, no cloud.

lema wraps your local model (LM Studio, Ollama, llama.cpp) in an agent loop that actually works: it reads files, writes code, runs commands, searches the web, checks its own output, and remembers what it's already solved. Designed for small models — 4–14B runs well.

npm install -g @iivgll4/lema
lema "fix the failing tests in this repo"

The idea

Small local models aren't dumb — they just have no harness. They don't verify their own output. They forget what worked last time. They lose track on anything longer than a single file.

lema is the harness:

• Verification loop — after every change, it runs your tests. If they fail, it keeps going.
• Skill memory — when a task completes cleanly, lema stores the solution. Next time it searches its own library before starting from scratch.
• Auto-compaction — when context fills up mid-task, it summarizes and continues instead of crashing or hallucinating.
• Effort dial — low / medium / high / ultra. Tell it how hard to think.

Quick start

You need LM Studio running with a model loaded.

npm install -g @iivgll4/lema
lema ping              # check connection
lema models            # see what's loaded
lema "add a /health endpoint and write a test for it"

Or drop into the TUI for an interactive session:

lema

Project rules

Put an AGENTS.md (or CLAUDE.md, or .lema/rules.md) in your project root and lema will read it automatically before every task — coding style, architecture decisions, what to avoid, anything you want the model to always know.

# AGENTS.md

- All code must pass `npm test` before finishing
- Use TypeScript strict mode, no `any` in public signatures
- Commit messages follow Conventional Commits

lema injects the rules at the start of context and re-injects a condensed version every few turns so they don't get lost on long tasks.

What it can do

Agent tasks — give it a goal, it figures out the steps:

lema "refactor auth.ts to use the new UserService interface"
lema "find what's causing the memory leak and fix it"
lema "write tests for every exported function in utils/"

Web search — built in, no setup:

lema "what changed in React 19 and do we need to update anything"

Effort control — faster or deeper depending on the task:

lema --effort low   "summarize this file"
lema --effort high  "find the race condition in the session handler"

MCP server — Claude Code and other MCP clients can control lema programmatically:

lema-mcp  # starts the MCP server

TUI commands

Type / in the interactive session to see all commands:

| Command | What it does | |---|---| | /compact [hint] | Summarize and compress the conversation to free up context window | | /effort <level> | Switch reasoning depth: low medium high ultra | | /remember <text> | Save anything to memory — lema will recall it on relevant future tasks | | /memory [query] | Search memory, or list everything if no query given | | /clear | Clear the screen | | /models | List loaded models, pick one interactively | | /skills | Browse the skill library | | /skill new "<prompt>" | Author a new reusable skill | | /settings web on\|off | Toggle built-in web search | | /ping | Check LM Studio connection | | /help | Show all commands | | /exit | Quit |

Works best with

LM Studio + qwen/qwen3.5-9b (8-bit) — this is the primary tested setup. Fast, handles multi-step tasks, good at tool use.

Any OpenAI-compatible server works. The agent loop compensates a lot for model quality — a well-prompted 9B with verification often beats a raw 30B without it.

Config

lema.config.json in your project root — all optional:

{
  "model": "qwen/qwen3.5-9b",
  "effort": "medium",
  "baseUrl": "http://localhost:1234/v1"
}

Environment: LEMA_BASE_URL, LEMA_MODEL, LEMA_EMBED_MODEL.

Skills

lema builds a skill library as it works. After a verified task it stores what it did:

lema skills          # browse the library

On future tasks it retrieves relevant skills before starting — so it doesn't reinvent the same solution twice.

Development

git clone https://github.com/iivgll/lema
npm install
npm run dev -- "your task here"
npm run build

Open source · MIT · made for local models