MiMo Agent (mimoagent)

mimoagent is an engineering-first agentic rollout framework. It connects agents, tools, environments, datasets and graders, and manages rollouts at scale. Four concerns stay separate:

mimoagent is a library: any agent, model protocol, dataset and backend can be composed programmatically. It faithfully records trajectories in a training schema and grades the final environment in place, ready for downstream consumption.

mimoagent started as a fork of mini-swe-agent v1.9.0 (commit b3d50788, August 2025) and has since been largely rewritten; see NOTICE.

Installation

Requires Python 3.12 and uv.

git clone https://github.com/XiaomiMiMo/mimoagent.git
cd mimoagent
uv sync                      # project + dev tools
# uv sync --extra cube       # CubeSandbox backend
# uv sync --extra modal      # Modal Sandbox backend
# uv sync --extra ray        # Ray-distributed batch runner

pip install -e . works too.

Quickstart (Docker)

The fastest way to see the whole loop is one DeepSWE task on a local Docker daemon. DeepSWE publishes its 113 tasks in Harbor layout with prebuilt images on a public registry; convert them once into a mimoagent JSONL:

git clone --depth 1 https://github.com/datacurve-ai/deep-swe.git
uv run python scripts/convert_deepswe.py --src deep-swe/tasks --out deepswe.jsonl

Check the grading path first — apply the reference solution, expect Recalculated - Pass:

uv run mimoagent -c example_configs/swe_docker.yaml \
    --dataset deepswe.jsonl --slice 0:1 \
    --recalc-input gt -o outputs/smoke-gt

Then let a model solve it:

export OPENAI_API_KEY=...                  # and OPENAI_BASE_URL for a gateway
export MIMOAGENT_MODEL_NAME=gpt-5          # any model your endpoint serves
uv run mimoagent -c example_configs/swe_docker.yaml \
    --dataset deepswe.jsonl --slice 0:1 \
    -o outputs/smoke

Each instance gets a directory under --output with instance.log, the trajectory (traj.json, agent_msgs/), and reward_extra_info.json; results.jsonl collects one record per instance. --workers N runs instances in parallel, --num-rollouts K samples several trajectories per instance, --filter / --slice / --shuffle select instances, and --redo-existing reruns finished ones.

Configuration

A config has four blocks. Values may reference environment variables as ${VAR} or ${VAR:-default}, so credentials and endpoints never need to be written into files:

use_dataset_env: true

agent:
  type: default                 # see "Agents" below
  tools: [{tool: bash, config: {timeout: 300}}, {tool: read}, {tool: write}, {tool: edit}]
  system_template: |
    You are an agent, your current working directory is {{cwd}}.
  instance_template: |
    Fix the following issue:

    {{task}}
  step_limit: 500

environment:
  environment_class: docker     # local | docker | kubernetes | cube | modal
  cwd: /testbed
  timeout: 300

model:
  model_name: "${MIMOAGENT_MODEL_NAME:-gpt-5}"
  protocol: chat                # chat | responses | anthropic
  model_kwargs:
    base_url: "${OPENAI_BASE_URL:-https://api.openai.com/v1}"
    api_key: "${OPENAI_API_KEY:-}"
    temperature: 1.0

example_configs/ has a runnable profile for every agent type and backend. --override-config '{"agent": {"step_limit": 50}}' patches any field from the command line; passing several -c files assigns one config per instance at random (for A/B comparisons).

Agents

Blackbox adapters install a pinned version of the CLI from its public source (npm registry, GitHub releases, PyPI) when the environment starts; the task container therefore needs outbound network access at setup time. For offline or mirrored setups, build a payload once with scripts/harness_payloads/build_harness_payloads.sh and point the agent at it with payload_path / payload_url; mirror URLs can be passed through install_env (NPM_REGISTRY, PIP_INDEX_URL, GITHUB_BASE, ...).

Environments

environment.image_prefix prepends a registry mirror to every task image.

Datasets

Rows can come from a Hugging Face dataset (--dataset princeton-nlp/SWE-bench_Verified --split test), a parquet file, or a JSONL file. The dataset type is taken from the row's dataset_type field or inferred from its image:

deepswe, opensource-code, arvo, and generic.

Any dataset's rows may additionally carry rubric.rubrics; with an environment.judge_agent block, an in-environment judge agent scores the result before the dataset's own verifier runs (example_configs/rubric-judge.yaml).

Development

See CONTRIBUTING.md for the workflow and AGENTS.md for a map of the codebase.

License

MIT — see LICENSE.md and NOTICE.