Getting Started¶

This guide walks you from a clean environment to a working agent, then through the miminions CLI and a development setup. Every snippet runs against the real API.

Installation¶

pipuv

# Core framework
pip install miminions

# Core + SQLite vector memory (fastembed + sqlite-vec + pysqlite3)
pip install miminions[sqlite]

# Everything (same extras as [sqlite])
pip install miminions[all]

uv add miminions
# with vector memory:
uv add "miminions[sqlite]"

What's in core vs. [sqlite]

The core install gives you the agent runtime (pydantic_ai-backed), the tools system, workspaces, the markdown memory store, the data store, and the full CLI. It does not bundle fastembed or sqlite-vec.

The [sqlite] extra adds SQLiteMemory — a local vector store that embeds text with fastembed and runs KNN search through sqlite-vec. Install it if you want semantic recall or the three-tier global memory. The first time you construct SQLiteMemory, the embedding model is downloaded.

Requirements¶

Python 3.12+ (the package declares requires-python = ">=3.12").
An LLM backend. By default MiMinions talks to OpenRouter, so set an API key:

export OPENROUTER_API_KEY="sk-or-..."

The default model is the free openai/gpt-oss-20b:free. You can pick a different provider or model instead — see Choosing a model / provider below. For offline experiments, provider="test" needs no key at all.

Your First Agent¶

Create a Minion, register a plain Python function as a tool, and await run(...). Tool schemas are inferred from the function signature.

import asyncio
from miminions.agent import create_minion


async def main():
    agent = create_minion("MyAgent", description="A small demo agent")

    def calculator(operation: str, a: int, b: int) -> int:
        """Perform a basic arithmetic operation."""
        if operation == "add":
            return a + b
        if operation == "multiply":
            return a * b
        return 0

    agent.register_tool("calculator", "Perform arithmetic", calculator)

    reply = await agent.run("What is 6 multiplied by 7?")
    print(reply)


asyncio.run(main())

Import from subpackages, never the top level

import miminions exposes nothing — from miminions import Minion will fail. Always import from the subpackage, e.g. from miminions.agent import create_minion, from miminions.tools import GenericTool, from miminions.data import LocalDataManager.

Choosing a model / provider¶

create_minion(provider=...) selects an LLM backend through the internal ModelFactory. Supported provider strings:

Provider	Default model	Key / notes
`openrouter`	`openai/gpt-oss-20b:free`	default; needs `OPENROUTER_API_KEY`
`openai`	`gpt-4o`	needs an OpenAI key in the environment
`anthropic`	`claude-3-5-sonnet-latest`	needs an Anthropic key in the environment
`gemini`	`gemini-1.5-flash`	needs a Google API key in the environment
`test`	`TestModel` (offline)	no key; deterministic, great for tests

# Pick a provider by name:
agent = create_minion("MyAgent", provider="anthropic")

# Offline / no network — ideal for unit tests:
agent = create_minion("MyAgent", provider="test")

# Or pass a fully constructed pydantic_ai model directly:
from pydantic_ai.models.openai import OpenAIModel
agent = create_minion("MyAgent", model=OpenAIModel("gpt-4o"))

When missing keys surface

With the default openrouter provider, a missing OPENROUTER_API_KEY fails fast: create_minion(...) raises ValueError at construction. The other real providers build fine without a key and instead raise an auth/network error at await agent.run(...) time. Use provider="test" to exercise your tools and wiring without any credentials.

Next steps from here: attach Memory, wire in Workspaces and Context, or expose external tools over MCP. See the Tools and Tasks pages for more.

Using the CLI¶

Installing the package registers a miminions console command (you can also run python -m miminions). On first run it bootstraps a default workspace and a default agent under ~/.miminions/, where all CLI state persists (set MIMINIONS_HOME to relocate it).

miminions --help

Registered command groups: auth, agent, task, knowledge, workspace, execution, chat, gateway, prompt.

Chat¶

Start an interactive async chat session. It runs against the default workspace unless you pass --workspace, and each reply streams to the terminal as it is generated.

# Interactive session against the default workspace
miminions chat start

# Use a specific workspace (id or name)
miminions chat start --workspace "Demo"

# Resume a prior session — its history is reloaded into LLM context
miminions chat start --session <session_id>

# Show tool calls and per-turn token usage / latency
miminions chat start --verbose

Type /exit or /quit to end the session.

One-shot prompt¶

Send a single prompt and print the reply (no interactive loop). Workspace context is injected automatically via ContextBuilder.

miminions prompt ask "Summarize the project facts in this workspace"

# Target a workspace and/or session explicitly
miminions prompt ask --workspace "Demo" "What rules are active here?"

Common Use Cases¶

1. Writing and editing content¶

Fit: Strong

Use prompt ask for one-off drafts and chat start for iterative editing.

# One-off draft
miminions prompt ask "Draft a launch update for our weekly team newsletter."

# Iterative editing session
miminions chat start

2. Coding support¶

Fit: Strong

Use chat/prompt for code generation, bug explanation, and refactor suggestions. Optionally create a coding-focused agent for a reusable setup.

# Ask for code help directly
miminions prompt ask "Explain this stack trace and suggest a fix strategy."

# Interactive coding loop
miminions chat start

# Optional: create a dedicated coding agent
miminions agent add

3. Research and summarization¶

Fit: Strong

Use chat/prompt to summarize text, compare options, and extract key points.

miminions prompt ask "Summarize the key tradeoffs between option A and option B."
miminions chat start

4. Customer support automation¶

Fit: Partial

MiMinions is strong for drafting replies and FAQ content. Full ticket routing and automation typically needs extra workflow/tool integration.

# Draft support replies or FAQ content
miminions prompt ask "Draft a friendly reply for a delayed order complaint."

# Add workflow/tool integration for deeper automation
miminions execution --help

5. Personal productivity¶

Fit: Strong

Use task commands for planning/prioritization, knowledge for notes, and chat for ongoing planning.

miminions task --help
miminions knowledge --help
miminions chat start

6. Data analysis help¶

Fit: Partial to strong

MiMinions is strong for explanation, query drafting, and insight summaries. Deeper automated analysis depends on connecting data/tools through execution.

# Ask for analysis framing or query drafting
miminions prompt ask "Draft SQL to compare weekly active users month over month."

# Extend with custom tooling/data integration
miminions execution --help

Agents¶

# List persisted CLI agents
miminions agent list

# Add an agent (prompts for name/description/type)
miminions agent add

# Give it a goal, then run it
miminions agent set-goal default --goal "Add 2 and 3"
miminions agent run default

# Inspect an agent's tools
miminions agent tool-list default
miminions agent ask default --prompt "echo hello"

Note

On agent run, the --async flag is not yet functional — it just prints a placeholder. Run agents synchronously for now.

Workspaces¶

# List all workspaces
miminions workspace list

# Create a workspace populated with example nodes and rules
miminions workspace add --name "Demo" --description "Demo workspace" --sample

# Create a workspace and scaffold its prompt/memory/skills/sessions/data files
miminions workspace add --name "Demo2" --init-files

# Show details for one workspace (by id prefix or name)
miminions workspace show Demo

# Update, set state, manage rules
miminions workspace update Demo --description "Updated description"
miminions workspace set-state Demo --key phase --value "review"
miminions workspace add-rule Demo --name "gate" --priority HIGH \
    --condition '{"type":"state_equals","key":"phase","value":"review"}'
miminions workspace remove-rule Demo gate

# Scaffold files for an existing workspace
miminions workspace init-files Demo

# Remove a workspace
miminions workspace remove Demo

The full workspace subcommand set is list / add / show / update / remove / set-state / add-rule / remove-rule / init-files. See Workspaces for the underlying model and the CLI reference for every command group.

Development Setup¶

git clone https://github.com/MiMinions-ai/MiMinions.git
cd MiMinions
pip install -e ".[dev]"
pytest tests/

The [dev] extra pulls in pytest, pytest-asyncio, pytest-cov, flake8, and build. Async tests run automatically (asyncio_mode = "auto").

Running tests by layer¶

pytest tests/unit/          # fast, isolated unit tests
pytest tests/integration/   # integration tests
pytest tests/e2e/           # end-to-end tests

All three layers run under pytest; there is no separate runner script.

Publishing¶

Build and publish the distribution with uv:

uv build
uv publish

Build a standalone CLI binary with PyInstaller (install the [cli-build] extra first):

pip install miminions[cli-build]
bash deploy/build_cli.sh

Ready to go deeper? Explore the module guides: Agent · Memory · Context · Tools · Workspaces · Tasks · Data · Gateway · CLI.

Practice use cases¶

Real-estate deal desk — underwrite tool + three-tier memory starter