Evolve Agents

Organization

evolving-machines-lab

evolve-agents

Evolve SDK development for TypeScript and Python. Use when building applications with Evolve to run AI agents (Claude, Codex, Gemini, Qwen, Kimi, OpenCode, Droid) in secure sandboxes. Triggers: (1) Creating Evolve applications, (2) Configuring agents with skills, Integrations, MCP servers, (3) Using Swarm abstractions (map, filter, reduce, bestOf/best_of, verify), (4) Building Pipelines, (5) Structured output with schemas, (6) Session management, streaming, observability, (7) Checkpointing, storage & StorageClient, (8) Cost tracking (per-run and per-session spend), (9) Historical sessions & trace download via sessions() client. For hosted evals (datasets, jobs, trials, checks, analyses, the evolve CLI) read `evolve skills get evals`.

Overview

Publisher	evolving-machines-lab
Repository	`evolve`
Skill name	`evolve-agents`
Stars	76
Forks	5
Bundled files	10
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
10 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by evolving-machines-lab on GitHub. Read the source before you install it.

Installation

Install the Evolve Agents AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/evolving-machines-lab/evolve.git /tmp/evolve
mkdir -p .claude/skills
cp -r /tmp/evolve/skills/evolve-agents .claude/skills/evolve-agents

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evolve Agents in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evolve Agents on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evolve Agents is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Evolve SDK

Build applications that run CLI agents in secure cloud sandboxes.

Repo: https://github.com/evolving-machines-lab/evolve

Hosted evals — datasets, jobs, trials, checks, analyses and the evolve CLI — are evolve skills get evals, not this skill.

Language Detection

Determine the language from (in priority order):

User specification — if the user states a language, use it
Project signals — imports, file extensions, package.json vs pyproject.toml
Ask — if ambiguous, ask the user

TypeScript (@evolvingmachines/evolve) — read from references/typescript/
Python (evolvingmachines-evolve) — read from references/python/

Required Reading

Always read these three references for the detected language before writing any Evolve code:

TypeScript:

01-getting-started.md — Installation, authentication (Gateway, managed BYO provider keys, direct provider-key mode), core lifecycle, streaming basics, agent reference table
02-configuration.md — Sandbox providers, full builder API, agent skills catalog, Managed integrations, MCP servers
03-runtime.md — run(), executeCommand(), upload/download files, session controls, workspace layout, structured output, session management, storage & checkpointing, StorageClient, sessions() client, cost tracking, observability, error handling

Python:

01-getting-started.md — Installation, authentication (Gateway, managed BYO provider keys, direct provider-key mode), core lifecycle, streaming basics, agent reference table
02-configuration.md — Sandbox providers, full constructor API, agent skills catalog, Managed integrations, MCP servers
03-runtime.md — run(), execute_command(), upload/download files, session controls, workspace layout, structured output, session management, storage & checkpointing, StorageClient, sessions() client, cost tracking, observability, error handling

Critical Constraints

Model names — Only use exact names from the Agent Reference table. Do not invent or guess model identifiers.
- TS | PY
Cleanup — Always call kill() when done. Sandboxes bill until destroyed.
- TS | PY

Additional References

Read on demand when the user's task requires them:

When to read	TypeScript	Python
Building a UI, handling real-time events	04-streaming.md	04-streaming.md
Parallel agents (map/filter/reduce/bestOf/verify), Pipeline chaining	05-swarm-pipeline.md	05-swarm-pipeline.md

Topic Index

Getting Started

Topic	TypeScript	Python
Installation & requirements	TS	PY
Quick start (3 steps)	TS	PY
Core lifecycle (run, output, kill)	TS	PY
Streaming basics	TS	PY
Gateway, managed BYO provider keys, and direct provider-key mode	TS	PY
BYO subscriptions (Claude Max, Codex, Gemini)	TS	PY
Supported agents, models & defaults	TS	PY
Harness and model pairing (per-harness constraints)	TS	PY

Configuration

Topic	TypeScript	Python
Sandbox providers (E2B, Modal, Daytona)	TS	PY
Provider auto-resolution from env	TS	PY
Sandbox create options (image, network, user, homeDir)	TS	PY
Workspace modes (knowledge / swe)	TS	PY
Full builder/constructor API	TS	PY
Browser automation guide (setup, live view, replay)	TS	PY
Browser credentials (saved website logins)	TS	PY
Agent plugins/extensions	TS	PY
Agent skills catalog	TS	PY
Managed secrets (Dashboard-stored env secrets)	TS	PY
Managed integrations (auth paths, tool filtering, types)	TS	PY
MCP server config (STDIO / HTTP / SSE)	TS	PY

Runtime

Topic	TypeScript	Python
run() options (timeout, background, checkpoint)	TS	PY
executeCommand() / execute_command()	TS	PY
Upload files to sandbox	TS	PY
Download output files	TS	PY
Session controls (interrupt, pause, resume, kill)	TS	PY
Port forwarding	TS	PY
Workspace filesystem layout	TS	PY
Structured output (Zod / Pydantic / JSON Schema)	TS	PY
Multi-turn conversations	TS	PY
Pause, resume, reconnect, switch sandboxes	TS	PY
Storage & checkpointing (gateway mode)	TS	PY
StorageClient (list, get, download checkpoints)	TS	PY
Checkpoint lineage & restore	TS	PY
Historical sessions & trace download	TS	PY
Cost tracking (per-run & per-session spend)	TS	PY
Observability (dashboard + local logs)	TS	PY
Error handling	TS	PY

Streaming

Topic	TypeScript	Python
Event listeners (content, lifecycle, stdout, stderr)	TS	PY
LifecycleEvent & LifecycleReason	TS	PY
OutputEvent & SessionUpdate types	TS	PY
Tool events (ToolCall, ToolCallUpdate, ToolKind)	TS	PY
Browser lifecycle event fields	TS	PY
UI integration example	TS	PY

Swarm & Pipeline

Topic	TypeScript	Python
Swarm setup (config, concurrency, retry)	TS	PY
Input types (FileMap, folders)	TS	PY
bestOf / best_of (N candidates + judge)	TS	PY
map (parallel processing)	TS	PY
filter (evaluate + threshold)	TS	PY
reduce (synthesize many to one)	TS	PY
verify (quality gate with feedback loop)	TS	PY
Result types (SwarmResult, ReduceResult, BestOfResult)	TS	PY
Chaining operations	TS	PY
Pipeline (fluent chaining, events, terminal)	TS	PY

This skill installs from the repository: npx skills add evolving-machines-lab/evolve --skill evolve-agents. The hosted-evals manual is evolve skills get evals.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evolve Agents AI skill do?

Why use Evolve Agents on TypingMind?

Because you install it once and use it with any model. Evolve Agents is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evolve Agents in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/evolving-machines-lab/evolve/tree/main/skills/evolve-agents. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evolve Agents?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evolve Agents?

As many as you like. As long as a model supports skills, you can use Evolve Agents with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evolve Agents AI skill free?

Yes. It is published on GitHub by evolving-machines-lab under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Receiving Code Review

obra

CommunityPopular

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…

Requesting Code Review

obra

CommunityPopular

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Systematic Debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Test Driven Development

obra

CommunityPopular

Use when implementing any feature or bugfix, before writing implementation code

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Evolve SDK

Language Detection

Required Reading

Critical Constraints

Additional References

Topic Index

Getting Started

Configuration

Runtime

Streaming

Swarm & Pipeline

Bundled files

Frequently asked questions

Related AI Skills

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