XiaomiMiMo/mimoagent

XiaomiMiMo/mimoagent

Description: The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!

License: MIT

Stars: 4

Forks: 1

Open issues: 0

Created: 2026-09-21T07:37:31Z

Pushed: 2026-09-21T21:03:16Z

Default branch: mimo-oss

Fork: yes

Parent repository: SWE-agent/mini-swe-agent

Archived: no

README:

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:

| Layer | What it covers | |---|---| | Agent | custom white-box agents and 10+ black-box coding CLIs (Claude Code, Codex, OpenCode, ...); agent loops run in or out of the pod, with different levels of intrusiveness into the environment | | Model | three native protocols — OpenAI Chat, OpenAI Responses, Anthropic Messages — each through its official SDK | | Environment | pluggable backends: local, Docker, Kubernetes pods, CubeSandbox, Modal Sandboxes | | Dataset | adapters for datasets and benchmarks together with their graders: DeepSWE, opensource-code, ARVO, and generic |

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](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

| agent.type | What runs | Notes | |---|---|---| | default | native tool-calling loop with bash, read, write, edit, agent (subagents) | parallel tool calls, optional anti-reward-hacking guard | | bashonly-agent | the default loop with only bash | | | cc-agent | native loop with the Claude Code tool catalogue (Bash, Read, Write, Edit, Grep, Glob, Agent, Compact) | model-decided context compaction | | codex-agent | native loop with the Codex catalogue; ptc: true exposes exec + wait and runs JavaScript in the Codex code-mode host | requires protocol: responses | | mimocode-agent | native loop with the MiMo-Code catalogue | | | blackbox: claude-code, codex, mimocode, opencode, pi, grok, kimi-code, kimi-cli, kilocode, openclaw, omp, hermes, dsh, mini-swe-agent | the upstream coding CLI itself, installed and run inside the task environment | mimoagent supplies the task and grades the repository |

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_class | Backend | Notes | |---|---|---| | local | subprocesses on the host | development only | | docker | one container per instance | forward_env passes proxies through | | kubernetes | one pod per instance | kubeconfig (default $KUBECONFIG / ~/.kube/config, in-cluster when running as a pod), namespace ($K8S_NAMESPACE), node_selector, tolerations, image_pull_secrets, resource requests/limits, labels /...