mycode-aiagent 0.4.0

Style-aware code generation — analyze any codebase and generate new code that matches its style

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Style-aware code generation. Analyze any codebase to extract its coding style, then generate new code that matches it exactly.

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What it does

MyCode learns how a developer or team writes code — naming conventions, type annotation style, import grouping, docstring format, error handling patterns — and uses that style profile to generate new code that feels like it was written by the same hand.

It is built to slot into larger agentic systems: the analyzer and generator are clean library functions, and the backend is swappable (local LLM, Claude, or OpenAI).

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Requirements

• Python 3.10+
• A running AI backend (see Backends)

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Installation

From PyPI (recommended):

pip install mycode-aiagent

# With Claude backend support
pip install "mycode-aiagent[claude]"

# With OpenAI backend support
pip install "mycode-aiagent[openai]"

# With all backends
pip install "mycode-aiagent[all]"

From source:

git clone https://github.com/RyanAbbottData/MyCode
cd MyCode
pip install -e .

The my-code CLI command is registered automatically on install.

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Backends

MyCode delegates inference to a pluggable backend. Choose one based on what you have available.

Backend	Flag	Requirement
Local LLM	--backend llama (default)	MCP server running at localhost:8000
Anthropic Claude	--backend claude	ANTHROPIC_API_KEY env var or --api-key
OpenAI	--backend openai	OPENAI_API_KEY env var or --api-key
Custom MCP server	--backend mcp	Any MCP server at --mcp-url

Setting up a local LLM

The llama backend expects an MCP server at http://localhost:8000/mcp that exposes two tools: one for code generation and one for analysis. Any MCP-compatible wrapper around a local model will work. Here is a recommended setup using llama.cpp:

1. Download a model

A code-focused model works best. Good options:

• CodeLlama-7B-Instruct — fast, runs on most hardware
• CodeLlama-13B-Instruct — better quality, needs ~10 GB VRAM
• DeepSeek-Coder-6.7B-Instruct — strong alternative

Download a .gguf quantized file (Q4_K_M is a good balance of size and quality).

2. Start the llama.cpp server

# Install llama.cpp (or use a pre-built binary)
pip install llama-cpp-python[server]

# Start the OpenAI-compatible server
python -m llama_cpp.server \
  --model ./models/codellama-7b-instruct.Q4_K_M.gguf \
  --host 0.0.0.0 \
  --port 8000 \
  --n_ctx 4096

3. Wrap it with an MCP server

The llama backend communicates over MCP, not directly with the llama.cpp HTTP API. You need a thin MCP wrapper that exposes two tools:

• A code generation tool (name must not contain "analyze")
• An analysis tool (name must contain "analyze")

Both tools accept a query string and return the model's completion. A minimal FastMCP wrapper example:

# llm_mcp_server.py
from fastmcp import FastMCP
import requests

mcp = FastMCP("local-llm")
LLM_URL = "http://localhost:8000/v1/completions"

def _complete(prompt: str) -> str:
    resp = requests.post(LLM_URL, json={
        "prompt": prompt,
        "max_tokens": 1024,
        "temperature": 0.1,
    })
    return resp.json()["choices"][0]["text"]

@mcp.tool()
def generate_code(query: str) -> str:
    return _complete(query)

@mcp.tool()
def analyze_code(query: str) -> str:
    return _complete(query)

if __name__ == "__main__":
    mcp.run(transport="streamable-http", host="0.0.0.0", port=8000)

pip install fastmcp
python llm_mcp_server.py

4. Point MyCode at it

my-code --backend llama --ricky-url http://localhost:8000/mcp analyze .

Or set a custom URL if your server runs on a different port:

my-code --backend llama --ricky-url http://localhost:9000/mcp analyze .

---

CLI Usage

Step 1 — Analyze a codebase

Point MyCode at any directory. It reads every .py file and builds a style profile.

my-code analyze ./path/to/codebase

With verbose output:

my-code analyze ./path/to/codebase --verbose

Using a different backend:

my-code --backend claude analyze ./path/to/codebase
my-code --backend openai --api-key sk-... analyze ./path/to/codebase

The profile is saved to style_profile.json by default. Specify a different path with --profile:

my-code analyze ./path/to/codebase --profile ./profiles/my_team.json

Step 2 — Generate code

my-code generate "write a function that parses a CSV file and returns a list of dicts"

MyCode loads style_profile.json and instructs the backend to produce code that matches the analyzed style — naming, annotations, docstrings, structure and all.

# Use a specific profile
my-code generate "write a retry decorator" --profile ./profiles/my_team.json

# Use Claude to generate
my-code --backend claude generate "write a binary search function"

# Override the model
my-code --backend claude --model claude-sonnet-4-6 generate "write a rate limiter"

---

CLI Reference

my-code [OPTIONS] COMMAND

Options:
  --backend {llama,claude,openai,mcp}   AI backend to use (default: llama)
  --api-key TEXT                         API key for claude/openai backends
  --model TEXT                           Override the default model
  --ricky-url TEXT                       Local LLM MCP server URL (default: http://localhost:8000/mcp)
  --mcp-url TEXT                         Custom MCP server URL (default: http://localhost:8001/mcp)
  --profile TEXT                         Path to style profile JSON (default: style_profile.json)

Commands:
  analyze PATH    Analyze a codebase and write a style profile
    --verbose     Print each file as it is analyzed

  generate TASK   Generate code matching the saved style profile

  serve              Start an MCP server (blocks until Ctrl-C)
    --host TEXT      Host to bind (default: 127.0.0.1)
    --port INT       Port to listen on (default: 8080)
    --daemon         Run as a detached background process
    --pid-file TEXT  PID file path for daemon mode (default: mycode.pid)

  stop               Stop a running daemon server
    --pid-file TEXT  PID file written by 'serve --daemon' (default: mycode.pid)

---

Python API

MyCode is a first-class library. All CLI functionality is available programmatically.

from my_code import StyleAnalyzer, generate_code, make_backend
from pathlib import Path

# Create a backend
backend = make_backend()                           # local LLM (default)
backend = make_backend("claude")                   # Claude (reads ANTHROPIC_API_KEY)
backend = make_backend("openai", api_key="sk-...") # OpenAI

# Analyze a codebase
analyzer = StyleAnalyzer(backend)
profile = analyzer.analyze_codebase(Path("./my_project"), verbose=True)

# Save and reload the profile
StyleAnalyzer.save_profile(profile, Path("style.json"))
profile = StyleAnalyzer.load_profile(Path("style.json"))

# Generate code
code = generate_code(
    task="write a function that validates an email address",
    backend=backend,
    profile=profile,
)
print(code)

Bring your own backend

Implement AIBackend to connect any model:

from my_code import AIBackend, StyleAnalyzer, generate_code

class MyBackend(AIBackend):
    max_file_chars = 4000  # how much of each file to send for analysis

    def ask_for_code(self, prompt: str) -> str:
        # call your model, return the generated code as a string
        ...

    def ask_to_analyze(self, prompt: str) -> str:
        # call your model, return a JSON string describing the style
        ...

backend = MyBackend()
analyzer = StyleAnalyzer(backend)
profile = analyzer.analyze_codebase(Path("."))
code = generate_code("write a logging helper", backend, profile)

---

Running as an MCP Server

MyCode can expose itself as an MCP server so any MCP-compatible agent or orchestrator can call its tools directly — no MCP knowledge required.

Quick start

# Local LLM backend (default) — foreground, blocks until Ctrl-C
my-code serve

# Claude backend
my-code --backend claude serve --port 8080

# OpenAI backend
my-code --backend openai serve --port 8080

# Bind on all interfaces
my-code --backend claude serve --host 0.0.0.0 --port 8080

On startup the server prints the URL and the config snippet to paste:

MyCode MCP server running at http://127.0.0.1:8080/mcp
Add to your MCP config:  {"mycode": {"url": "http://127.0.0.1:8080/mcp"}}

Running as a daemon

Add --daemon to run the server as a detached background process. The terminal returns immediately and the server keeps running.

my-code --backend claude serve --daemon
# → MyCode MCP server started as daemon (PID 12345) at http://127.0.0.1:8080/mcp
# → Stop with: my-code stop

The PID is written to mycode.pid by default. Stop the server with:

my-code stop

When running multiple instances on different ports, use --pid-file to keep them separate:

my-code --backend claude serve --port 8080 --daemon --pid-file mycode-8080.pid
my-code --backend openai serve --port 8081 --daemon --pid-file mycode-8081.pid

my-code stop --pid-file mycode-8080.pid
my-code stop --pid-file mycode-8081.pid

Connecting from an MCP consumer

Add the printed snippet to your consumer's MCP config file (e.g. .mcp.json):

{
  "mycode": { "url": "http://127.0.0.1:8080/mcp" }
}

Tools exposed

Tool	Required args	Optional args
analyze_codebase	path — directory to analyze	save_to — path to save the profile JSON
generate_code	task — what to write	profile — inline profile object; profile_path — path to a saved profile (default: style_profile.json)

Both tools return plain text. analyze_codebase returns the style profile as a JSON string. generate_code returns the generated source code.

Programmatic usage

from my_code import run_server, make_backend

# Blocking — call from a background thread if needed
run_server(backend=make_backend("claude"), host="127.0.0.1", port=8080)

Or use MCPServer directly for more control:

from my_code import MCPServer, make_backend
import threading

server = MCPServer(make_backend("claude"), host="127.0.0.1", port=8080)
httpd = server.start()                          # binds immediately
port = httpd.server_address[1]                  # actual port (useful when port=0)
t = threading.Thread(target=httpd.serve_forever, daemon=True)
t.start()
# ... httpd.shutdown() to stop

---

Deep Analysis

For a richer style profile, scripts/deep_analyze.py runs six focused queries (naming, error handling, string formatting, module structure, docstrings, and representative snippets) and synthesizes them into a single detailed profile.

# Run from the project root; writes style_profile.json
python scripts/deep_analyze.py

This is slower than the standard analyze command but produces a more detailed profile, which leads to better code generation.

---

Running Tests

# Library smoke tests (analyze → generate pipeline)
python tests/test_library.py

# MCP server protocol tests
python -m pytest tests/test_server.py -v
# or
python -m unittest tests/test_server.py

Both test suites use a MockBackend — no live AI backend required.

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Project Structure

my_code/
├── analyzer.py          # StyleAnalyzer — scans files, builds style profile
├── generator.py         # generate_code() — formats prompt and calls backend
├── mcp_client.py        # Generic MCP client (Streamable HTTP transport)
├── server.py            # MCPServer — exposes analyze/generate as MCP tools
├── cli.py               # CLI entry point (my-code command)
├── backends/
│   ├── base.py          # AIBackend abstract base class
│   ├── ricky_backend.py # Local LLM backend (connects via MCP)
│   ├── claude_backend.py
│   ├── openai_backend.py
│   └── mcp_backend.py   # Generic MCP server backend
└── utils/
    └── prompts.py       # Prompt templates for extraction, summary, generation
scripts/
└── deep_analyze.py      # Multi-query deep style analysis
tests/
├── test_library.py      # Smoke tests for analyze/generate (no live backend)
└── test_server.py       # MCP server protocol tests (no live backend)