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Autonomous LLM build orchestrator: plans a goal into tasks, edits code with anchored SEARCH/REPLACE, and verifies every change through build/test/lint/typecheck gates before reporting done. Runs as a CLI or MCP server.

MCP ServersOfficial Registry0 stars0 forksPythonAGPL-3.0Updated today
Install in Claude Code / Claude Desktop
Method: pip / Python · misterdev
Claude Code CLI
claude mcp add misterdev -- python -m misterdev
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "misterdev": {
      "command": "python",
      "args": ["-m", "misterdev.core.evolution.tool_promotion"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Install first: pip install misterdev
Use cases

MCP Servers overview

<!-- mcp-name: io.github.dcondrey/misterdev -->
<p align="center">
  <img src="https://raw.githubusercontent.com/dcondrey/misterdev/main/assets/logo.gif" alt="misterdev" width="220">
</p>

<h1 align="center">misterdev</h1>

<p align="center">
  <strong>An autonomous LLM build orchestrator that plans a goal into tasks, edits your code with surgical precision, and verifies every change through correctness gates before it ever reports done.</strong>
</p>

<p align="center">
  <a href="https://pypi.org/project/misterdev/"><img src="https://img.shields.io/pypi/v/misterdev" alt="PyPI version"></a>
  <a href="https://pypi.org/project/misterdev/"><img src="https://img.shields.io/pypi/pyversions/misterdev" alt="Python versions"></a>
  <a href="LICENSE"><img src="https://img.shields.io/badge/license-AGPL--3.0-blue" alt="License: AGPL-3.0"></a>
  <a href="https://github.com/dcondrey/misterdev/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/dcondrey/misterdev/ci.yml?branch=main" alt="CI"></a>
  <a href="https://glama.ai/mcp/servers/dcondrey/misterdev"><img src="https://glama.ai/mcp/servers/dcondrey/misterdev/badges/score.svg" alt="misterdev MCP server"></a>
</p>

<p align="center">
  <a href="#install">Install</a> ·
  <a href="#what-it-does">What it does</a> ·
  <a href="#cli-reference">CLI</a> ·
  <a href="#extending-misterdev">Extending</a> ·
  <a href="#configuration">Configuration</a> ·
  <a href="#development">Development</a> ·
  <a href="#license">License</a>
</p>

---

Point misterdev at a repository and a goal. It reads the codebase as a symbol graph, decomposes the goal into concrete tasks, and works each one in a try-edit-verify loop: it emits an anchored SEARCH/REPLACE edit, applies it against the file on disk, and runs the change through a sequence of correctness gates — build, tests, lint, typecheck, and any optional gates you enable. A gate that fails RED blocks the change; a gate that has nothing to check SKIPs and never blocks. When a change regresses the suite, misterdev reverts it through git. Nothing merges unless it stays green.

```console
$ misterdev build . "add rate limiting to the public API"

  planning   goal → 3 tasks  (model: anthropic/claude-sonnet-4-6, budget $100.00)
  task 1/3   middleware: token-bucket limiter          api/limiter.py
    edit     1 hunk applied · syntax ok
    gates    build GREEN · tests GREEN (142 passed) · lint GREEN · typecheck GREEN
  task 2/3   wire limiter into request pipeline         api/app.py
    edit     2 hunks applied
    gates    build GREEN · tests RED (1 failed) → rolling back, regenerating
    edit     2 hunks applied (attempt 2)
    gates    build GREEN · tests GREEN (145 passed) · lint GREEN · typecheck GREEN
  task 3/3   docs + config surface                      README.md, config.py
    gates    all GREEN

  done       3/3 tasks · 145 tests green · $0.38 over 11 calls
```

Because misterdev only trusts its gates, the loop is honest: "the model said it's done" is never the finish line — the build, the tests, and the diff are.

## Install

```bash
pip install misterdev
# or
uv pip install misterdev
```

Python 3.10 – 3.13. Optional extras add capability without bloating the core install:

```bash
pip install 'misterdev[local-embeddings]'   # offline semantic context ranking (fastembed, no API key)
pip install 'misterdev[lsp]'                 # LSP semantic-diagnostics gate
pip install 'misterdev[web]'                 # headless-browser web verification gate (+ playwright install chromium)
pip install 'misterdev[mcp]'                 # Model Context Protocol tool-host substrate
```

Extras are all opt-in and timeout-bounded. When an extra's runtime dependency is absent, the gate it powers SKIPs rather than failing.

## What it does

### Autonomous build loop

Give misterdev a goal and it drives the whole cycle: analyze the project, plan tasks, edit, and validate — repeating until the goal is met or the budget is spent. Edits are **anchored SEARCH/REPLACE** hunks: the model emits only the changed regions, which are applied against the on-disk file, so a 5,000-line module is edited without reprinting it and without hitting the output-token ceiling. Matching tries exact first, then tolerates whitespace and indentation drift, always requiring a single unique anchor so a partial file is never written.

### Polyglot symbol-graph context

A tree-sitter symbol graph gives misterdev structural understanding of **Python, Rust, TypeScript/JavaScript, Go, Java, C/C++, C#, Swift, and Kotlin**. Per-file outlines plus a whole-project structural map feed planning and editing, and large files are sent as a symbol outline plus verbatim windows of the task-relevant symbols — so context and cost scale with the edit, not with the file.

### Correctness gates

Every change runs through an ordered gate sequence: **build → lint → tests → typecheck**, with optional gates layered on top — an **adversarial critic** (an independent second model that reviews each diff before it is applied), **goal-check**, **claim-verifier**, **mutation** scoring, **runtime-smoke**, **web**, and **vision** verification. A gate that fails **RED** blocks the change; a gate with nothing to check **SKIPs** and never blocks. Regressions are reverted via git, so a working tree only ever moves forward.

### Dynamic model selection

misterdev keeps a per-model **performance ledger** and pairs it with a **cost-aware selector** that picks for quality-per-dollar across the **full breadth of OpenRouter** — routing each task up a **capability ladder** (harvested free / cheap → a strong mid-tier → a frontier tier) and **escalating to a stronger model only when a cheaper one can't clear the gates**. The strongest tier is reserved for the final attempt, so frontier spend is the rare safety net, not the default; a hard task that a mid model stalls on is finished by a frontier model, while easy tasks resolve on free/cheap ones. Quality never drops because a weak model that writes bad code fails the gate and the policy climbs. It runs against **OpenRouter or Anthropic** with automatic failover, caches responses to avoid paying twice, and token budgeting keeps spend inside the ceiling you set.

### Parallel worktrees

Disjoint tasks run concurrently, each in its own **isolated git worktree**, so independent work doesn't contend for the tree. An **integration gate** re-checks each wave against the full suite and reverts any task that regresses it — parallelism without cross-contamination.

### Self-improving

misterdev keeps a durable, fingerprinted stream of its own real failures and runs an **AlphaEvolve-style keep-if-better loop** over its own source: it attributes what breaks, classifies *why* (harness artifact vs observation gap vs capability), proposes a targeted structural self-edit, and promotes it only when it beats the champion on a **held-out task set it never optimized against** — with zero regressions. A reward-hacking guardrail walls off the tests and benchmark. The result is a loop that removes whole failure classes over time **without overfitting** to any one benchmark. See [docs/path-to-100.md](docs/path-to-100.md).

On the correctness side, misterdev works **reproduction-first**: for an issue-driven task it synthesizes a failing test from the acceptance criteria, **validates that the test actually fails on the clean tree** (a test that reproduces nothing is discarded rather than trusted), then drives the fix to turn it green — so the model edits toward a concrete, verified target instead of a description.

**Two-timescale evolution** *(built; opt-in via `orchestrator.runtime_tooling`; see [docs/two-timescale-evolution.md](docs/two-timescale-evolution.md))* takes the self-improvement further than a memoryless runtime agent can. At **runtime**, the model may author a small task-specific helper tool that runs **sandboxed** (a hardened, network-less container — untrusted code never touches the host or the repo, and with no container engine the capability degrades off); its output feeds the edit. Every invented tool is then captured with the task's outcome into a **tool corpus** — a free byproduct of normal runs — and a deliberate promotion pass admits the tools whose success **generalizes** on a held-out task split (baseline drawn from the reproduction corpus) into a **persistent, best-per-capability tool library that future runs start from**. Fast loop invents; slow loop keeps only the winners; the same held-out gate that guards scaffold self-edits keeps the library general rather than benchmark-overfit. Capability **compounds across runs** instead of being reinvented each task — the memory the current top open-source scaffolds lack. Run the promotion pass with `python -m misterdev.core.evolution.tool_promotion <project>`.

### Extensibility

Tools, gates, and targets **self-register through Python entry points**. `pip install misterdev-plugin-x` adds a capability with **zero edits to the core** — misterdev discovers the entry point at runtime and wires it in. A working example lives at [`examples/misterdev-plugin-hello`](examples/misterdev-plugin-hello). See [Extending misterdev](#extending-misterdev).

### Agentic MCP

misterdev can connect to **Model Context Protocol** servers and let the model call their discovered tools mid-build — bounded, opt-in, and constrained by a tool allowlist. Transports include stdio and **remote streamable-http with auth**, so you can point it at a hosted MCP gateway like **Glama** and give the build access to a whole catalog of tools without running any of them locally.

## Benchmarks

Gate-verified pass@1 on [Aider's polyglot benchmark](https://github.com/Aider-AI/polyglot-benchmark) (Exercism exercises with hidden test suites), `anthropic/claude-sonnet-4-6`:

| Language | Solved | Rate |
| --- | --- | --- |
| JavaScript | 9 / 10 | **90%** |
| Python | 8 / 10 | **80%** |
| Rust | 7 / 10 | **70%** |

A continuous stress run has s
agentic-ai-developmentagentic-codingai-coding-assistantsanthropicautonomous-agentautonomous-coding-agentclaudecode-generation-agentcoding-agentllmllm-agentsllm-orchestrationllmopsmcpmcp-serversmodel-context-protocolopenrouterswe-benchtree-sitter

What people ask about misterdev

What is dcondrey/misterdev?

+

dcondrey/misterdev is mcp servers for the Claude AI ecosystem. Autonomous LLM build orchestrator: plans a goal into tasks, edits code with anchored SEARCH/REPLACE, and verifies every change through build/test/lint/typecheck gates before reporting done. Runs as a CLI or MCP server. It has 0 GitHub stars and was last updated today.

How do I install misterdev?

+

You can install misterdev by cloning the repository (https://github.com/dcondrey/misterdev) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is dcondrey/misterdev safe to use?

+

dcondrey/misterdev has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.

Who maintains dcondrey/misterdev?

+

dcondrey/misterdev is maintained by dcondrey. The last recorded GitHub activity is from today, with 5 open issues.

Are there alternatives to misterdev?

+

Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.

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