Part of the StudioMeyer MCP Stack — Built in Mallorca 🌴 · ⭐ if you use it

darwin

**AI agents that improve themselves.**

Build AI agent teams that learn from every run.
Self-evolving prompts. A/B tested. Safety-gated.

Quick Start · Agents · How It Works · CLI · FAQ

npm install darwin-agents better-sqlite3
export ANTHROPIC_API_KEY=sk-ant-...  # or OPENAI_API_KEY, or use Claude CLI
npx darwin run writer "Explain quantum computing simply"

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A note from us

We have been building tools and systems for ourselves for the past two years. The fact that this repo is small and has few stars is not because it is new. It is because we only just decided to share what we have built. It is not a fresh experiment, it is a long story with a recent commit.

We love building things and sharing them. We do not love social media tactics, growth hacks, or chasing stars and followers. So this repo is small. The code is real, it gets used, issues get answered. Judge for yourself.

If it helps you, sharing, testing, and feedback help us. If it could be better, an issue is more useful. If you build something with it, tell us at hello@studiomeyer.io. That genuinely makes our day.

From a small studio in Palma de Mallorca.

What is this?

Darwin is a TypeScript framework for building AI agents that automatically optimize their own prompts through experimentation, evaluation, and evolution.

Traditional AI agents use static prompts. You write them once, and they never improve. Darwin changes that:

1. Your agent runs a task
2. A Critic agent evaluates the output (quality, sources, structure)
3. After enough runs, Darwin detects patterns ("weak on technical topics")
4. It generates an improved prompt variant
5. A/B tests the new variant against the current one
6. The winner becomes the default — your agent got better, automatically

You run an agent
       │
       ▼
Darwin measures quality
       │
       ▼
Patterns emerge over time
       │
       ▼
New prompt variant generated
       │
       ▼
A/B tested against current
       │
       ▼
Winner becomes default
       │
Your agent got better.
You did nothing.

Quick Start

# Install
npm install darwin-agents better-sqlite3

# Set your API key (or use Claude CLI if installed)
export ANTHROPIC_API_KEY=sk-ant-...

# Run your first agent
npx darwin run writer "Explain the CAP theorem in simple terms"

# Enable evolution
npx darwin evolve writer --enable

# Watch it improve over time
npx darwin status writer

Define your own agent in 12 lines

import { defineAgent } from 'darwin-agents';

export default defineAgent({
  name: 'summarizer',
  role: 'Text Summarizer',
  description: 'Summarizes text into key points.',
  systemPrompt: `Summarize the given text in 3 bullet points.
Be concise. No fluff. Capture the essence.`,
  evolution: {
    enabled: true,
    evaluator: 'critic',
  },
});

Built-in Agents

Agent	What it does	Needs
writer	Content writing, explanations, copy	Nothing (zero-config)
researcher	Web research with source citations	Tavily API key
critic	Evaluates other agents' output (1-10)	Nothing
analyst	Code quality analysis	Filesystem access

Each agent ships with a dedicated multi-critic set that scores the output by the right criteria for that agent type (research = source quality + analytical depth + completeness, analyst = technical accuracy with file:line refs + pattern recognition + recommendation quality, etc.). Custom agents can register their own critic sets — see examples/custom-agent.ts and src/evolution/multi-critic.ts.

Closed-Loop & Observability (v0.4.6)

Two production patterns Darwin users commonly need but had to build themselves:

• examples/closed-loop-feedback.ts — pipe critic findings into your own memory store so the next run sees them. Symmetric (writes both successes and failures), backend-agnostic. Aligned with reflective self-improvement patterns like GEPA (ICLR 2026 Oral) and NousResearch's hermes-agent-self-evolution loop.
• examples/staleness-monitor.ts — detect agents that stopped firing, or were configured but never fired. Pure classifier + format helpers + ready-made SQL. Wire to your own cron + alert webhook.

Memory Integration (v0.4.7 — works with any MCP-compliant memory server)

Closes the loop in three lines. Defaults to zero-config local memory; one config switch points at Mem0 / Zep / Letta / Cognee / a self-hosted MCP server / your own.

Why this is different

Existing self-evolving agent frameworks pick one memory backend and stay there. Existing MCP-memory servers (Mem0, Zep, Letta, MemPalace, agentmemory, brainctl) optimize for storage, not for closed-loop critic feedback. Darwin v0.4.7 is the first MIT-licensed, TypeScript-native, MCP-native combination of pluggable memory + symmetric self-evolution (score < 5 → mistake, score ≥ 8 → pattern, mediocre middle band → not persisted). No vendor lock-in, no cloud required by default, swap-able to Mem0/Zep/Letta with two config lines.

import { localMemory, remoteMemory } from 'darwin-agents/memory/bridge';
import { runClosedLoopTurn } from 'darwin-agents/memory/closed-loop';

// Default: spawn @studiomeyer/local-memory-mcp via npx — zero cloud, zero keys
const memory = localMemory();

// Or any remote MCP-Memory server
// const memory = remoteMemory('https://your-mcp.example.com/mcp', { authHeader: `Bearer ${KEY}` });

// Or Mem0 with the built-in preset — handles tool names + arg shape for you
// import { mem0Preset } from 'darwin-agents/memory/bridge';
// const memory = remoteMemory('https://api.mem0.ai/mcp', {
//   authHeader: `Bearer ${process.env.MEM0_KEY}`,
//   ...mem0Preset({ userId: 'darwin-agent', defaultMetadata: { project: 'darwin' } }),
// });

const result = await runClosedLoopTurn(
  { agentName: 'analyst', topic: 'Audit module X' },
  { runner: yourAgentRunner, store: memory },
);
// Run 1 sees zero lessons. Run 2 sees Run 1's findings as context.

Provider matrix

Provider	writeTool	readTool	Notes
@studiomeyer/local-memory-mcp (default)	memory_learn	memory_search	zero-config, single SQLite file, no cloud
Any self-hosted MCP-Memory server	memory_learn	memory_search	same wire, remote endpoint
Mem0 MCP (mem0ai/mem0-mcp)	add_memory	search_memories	use ...mem0Preset({ userId }) — handles tool names + arg shape + the memory field in result rows
Zep MCP	zep_add	zep_search	optional mapWriteArgs for group_id
Letta MCP	archival_insert	archival_search	optional mapReadResult for their envelope
Cognee MCP	cognee_add	cognee_search	optional mappers

Why an MCP-shaped bridge? Because the wire is the same — only tool names and arg shapes vary. One bridge, one reconnect path, one timeout policy. The pattern matches the MCP Bridge proxy paper (arXiv 2504.08999) but stays inside the Darwin process — no extra service to deploy.

v0.4.9 polish (2026-05-22)

• Spec-compliant transport. Every HTTP request now carries the MCP-Protocol-Version: 2025-11-25 header, per MCP spec 2025-11-25 §"HTTP Protocol Versioning". Strict servers MAY return 400 without it; pre-v0.4.9 only sent the version inside the initialize payload.

• Typed errors. Bridge errors are now instances of McpBridgeProtocolError (JSON-RPC errors from the server, numeric code) or McpBridgeTransportError (local timeouts, EPIPE, network resets, child exits — stable string code). Branch on instanceof to decide retry vs fail-loud without parsing message text.

  import {
    McpBridgeProtocolError,
    McpBridgeTransportError,
  } from 'darwin-agents/memory/bridge';
  
  try {
    await memory.save(record);
  } catch (err) {
    if (err instanceof McpBridgeTransportError && err.code === 'timeout') {
      // local timeout — safe to retry
    } else if (err instanceof McpBridgeProtocolError && err.code === -32602) {
      // server said our args are invalid — fail loud, don't retry
    }
  }

• Per-call timeouts. save() and fetchRelevant() accept a timeoutMs override that beats the bridge-level default, mirroring the MCP SDK's client.callTool(..., { timeout }). Useful for one-off slow embedding searches without raising requestTimeoutMs globally.

  await memory.fetchRelevant({ query: 'audit', limit: 5, timeoutMs: 30_000 });
  await memory.save(record, { timeoutMs: 5_000 });

• Mem0 preset. ...mem0Preset({ userId }) wires the right tool names (add_memory + search_memories) and arg shapes for the official mem0ai/mem0-mcp server. See the example above.

See examples/memory-darwin-integration.ts for the full closed-loop pattern: fetch relevant lessons → render them as prompt context → run the agent → persist critic findings → next run sees last run's lessons.

How Evolution Works

Real results from 300+ production runs

These are actual metrics from our development — not synthetic benchmarks.

Agents:      writer, researcher, marketing, blog-writer
Total Runs:  300+
Success Rate: 100%

Writer:       7.2/10  (120 runs across tech, webdesign, market)
Marketing:    7.8/10  (70 runs across LinkedIn, Instagram)
Researcher:   7.6/10  (50+ runs, web research with citations)

Multi-Model Critics in action

platform-compliance  ████████░░  8/10
scroll-stopping      ████████░░  8/10
conversion-intent    ████████░░  8/10

Feature Comparison

Feature	Darwin	EvoAgentX	DSPy	CrewAI	AutoGen
Self-evolving prompts	Yes	Yes	Yes (compiler)	No	No
A/B testing	Yes	No	No	No	No
Safety gate + rollback	Yes	No	No	No	No
TypeScript native	Yes	No (Python)	No (Python)	No (Python)	No (Python)
Zero-config first agent	Yes	No	No	No	Partial
MCP native	Yes	No	No	No	No
File-based (no DB required)	Yes	No	No	No	No
Built-in Critic agent	Yes	No	No	No	No

Architecture

darwin/
├── src/
│   ├── core/           # Agent runner, config, MCP handling
│   ├── memory/         # SQLite storage (experiments, prompts, learnings)
│   ├── evolution/      # Darwin loop, A/B testing, safety gate, patterns
│   ├── agents/         # Built-in agents (writer, researcher, critic, analyst)
│   └── cli/            # CLI commands (run, status, evolve, create)

Memory System

Darwin uses SQLite by default — zero config, single file, no database to install.

.darwin/
├── darwin.db           # All experiments, prompts, learnings
└── reports/            # Markdown reports per run
    ├── exp-writer-2026-03-08-001.md
    └── exp-researcher-2026-03-08-002.md

Want semantic search, cross-agent learnings, and analytics? Upgrade to Darwin Pro for PostgreSQL + pgvector support.

CLI Reference

darwin run <agent> "task"          # Run an agent
darwin run writer "Hello" --task-type tech   # With task categorization
darwin run analyst --path ./src    # Analyze a codebase

darwin status                      # Overview of all agents
darwin status writer               # Detailed agent stats + evolution history

darwin evolve writer --enable      # Enable self-evolution
darwin evolve writer --reset       # Reset to v1

darwin create my-agent             # Scaffold a new agent

Darwin Pro

The free version uses SQLite — great for getting started, handles thousands of experiments.

For teams and production use, Darwin Pro adds:

Feature	Free (SQLite)	Pro (PostgreSQL)
Experiment tracking	✓	✓
Prompt versioning	✓	✓
A/B testing	✓	✓
Safety gate	✓	✓
Keyword search	✓	✓
Semantic search (pgvector)	—	✓
Cross-agent learnings	—	✓
Analytics & time series	—	✓
Contradiction detection	—	✓
Team support (multi-user)	—	✓
Data export (CSV/JSON)	—	✓
Learning decay	—	✓

Coming soon. Follow the repo for updates.

FAQ

What do I need to run Darwin? Node.js 20+ and one of: Claude CLI (default provider), ANTHROPIC_API_KEY, OPENAI_API_KEY, or a local Ollama instance. For storage, install better-sqlite3 (default) or use PostgreSQL via DARWIN_POSTGRES_URL.

Does Darwin work with models other than Claude? Yes! Darwin supports multiple providers: Claude CLI (default), Anthropic API, OpenAI/compatible APIs, and Ollama (local). Set provider in your config or use DARWIN_PROVIDER env var.

How many runs until I see improvement? Around 10 runs. First 5 establish a baseline, then Darwin generates a variant and A/B tests it over the next 5 runs.

Is my data safe? Everything stays local. SQLite file on your disk. No telemetry, no cloud, no data leaves your machine.

Can I use this for non-English tasks? Yes. Agents detect language automatically. Darwin's evaluation is language-agnostic.

What if Darwin makes my agent worse? The safety gate prevents regressions. If a new variant scores >20% lower, Darwin automatically rolls back to the last known-good version.

Known Limitations

• LLM-as-Judge bias: Critics use LLMs to evaluate LLM outputs. We mitigate this with multi-model critics (GPT + Claude), but inherent self-preference bias exists. Research context.
• Statistical simplicity: A/B tests use mean comparison with a 5% threshold, not formal significance tests (t-test, Mann-Whitney U). computeDynamicMinRuns() adjusts sample sizes based on variance, but p-values are on the roadmap.
• No human-in-the-loop approval: Prompt mutations go directly to A/B testing. Telegram notifications inform you, but there's no approval gate before testing starts.

Contributing

PRs welcome. See CONTRIBUTING.md.

About StudioMeyer

StudioMeyer is an AI and design studio based in Palma de Mallorca, working with clients worldwide. We build custom websites and AI infrastructure for small and medium businesses. Production stack on Claude Agent SDK, MCP and n8n, with Sentry, Langfuse and LangGraph for observability and an in-house guard layer.

License

MIT — use freely, commercially or personally.

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