ai-diagnos-lsp 1.0

An AI powered LSP providing AI highly configurable AI diagnostics.

AI Diagnos LSP

A Python‑based Language Server that provides AI‑powered diagnostics using LangChain. It integrates with multiple LLM providers (Gemini, OpenRouter, Groq, Cerebras, HuggingFace) and features semantic location matching, cross‑file analysis, and persistent diagnostic storage. Designed to be used with the ai_diagnos.nvim plugin, but can be configured for any editor that supports the Language Server Protocol.

Features

• Pull‑based diagnostics – diagnostics are requested on file open, save, or via custom commands.
• Multiple LLM providers – Gemini, OpenRouter, Groq, Cerebras, HuggingFace, or an “Omniprovider” that falls back through them.
• Semantic location matching – locates issues by exact code snippet, reducing halucinations.
• Cross‑file analysis – recursively resolves imports to provide additional context (Limited to supported languages).
• Diagnostics storage – SQLite database caches results; configurable TTLs for invalidation and deletion.
• Configurable analysis triggers – which analysers run on open, save, change, or manual command.
• Debouncing and file size limits – prevents excessive API calls on large files or rapid edits.
• Progress notifications – optional periodic updates while the LLM is processing.
• Custom LSP commands – trigger analysis, clear AI diagnostics.
• omni language support - LLM based analysis is omnilingual, the only changes that need to be done per language are the parsers, wich there is a nice plugin system to write yourself. Theres also a repo for publishing parsers here.
• prompt overridability - prompts are overridable, allowing fine tuned functionality per language.

[!NOTE] The core will only include a parser for python. Parsers for every other language must be be added via plugins. See here Prompt overrides per language for finer performance can also be found on here

Requirements

• Python ≥ 3.9
• pip and venv (or uv)
• API keys for the chosen provider(s)

Installation

The LSP is a Python package. You can install it from its repository:

# Clone the repository
git clone https://github.com/VectorZeroAI/ai-diagnos-lsp.git
cd ai-diagnos-lsp

# Install with pip
pip install .

# Or with uv (faster)
uv sync
uv pip install .

The server executable ai-diagnos-lsp will be placed in your Python environment’s bin directory.

Configuration

The LSP accepts configuration via the initializationOptions field in the LSP initialize request. The options mirror those of the Neovim plugin. Below is the complete set of options with their types and defaults.

Option	Type	Default	Description
api_key_openrouter	string	–	OpenRouter API key.
api_key_gemini	string	–	Gemini API key.
api_key_groq	string	–	Groq API key.
api_key_cerebras	string	–	Cerebras API key.
api_key_huggingface	string	–	HuggingFace API key.
api_key_openai	string	-	OpenAI API key. (For openai usage)
api_key_claude	string	-	Claude API key. (For claude provider usage)
use	string	Omniprovider	Wich provider to use. Omniprovider jumps through all the providers one after the other
model_openrouter	string	"tngtech/tng-r1t-chimera:free"	Model name for OpenRouter.
model_gemini	string	"gemini-2.5-flash-lite"	Model name for Gemini.
model_groq	string	"openai/gpt-oss-120b"	Model name for Groq.
model_cerebras	string	"openai/gpt-oss-120b"	Model name for Cerebras.
model_huggingface	string	"Qwen2.5-Coder-7B-Instruct"	Model name for HuggingFace.
model_openai	string	"gpt-5.2"	Model name for openai
model_claude	string	"claude-sonnet-4-6"	Model name for claude provider
fallback_models_gemini	array of string	["gemini-2.5-flash", "gemini-3-flash-preview"]	Fallback models for Gemini.
fallback_models_groq	array of string	["openai/gpt-oss-20b", "openai/gpt-oss-safeguard-20b", "qwen/qwen3-32b", "llama-3.3-70b-versatile"]	Fallback models for Groq.
fallback_models_cerebras	array of string	[]	Fallback models for Cerebras.
debounce_ms	integer	3000	Minimum time (ms) between analyses for the same file.
max_file_size	integer	10000	Maximum number of lines to analyse. Larger files are skipped.
timeout	integer	99999	Maximum wait time (seconds) for the LLM to respond.
show_progress	boolean	true	Show periodic progress notifications.
show_progress_every_ms	integer	5000	Interval between progress notifications.
ai_diagnostics_symbol	string	"AI"	(Not used by LSP, but passed through.)
AnalysisSubsystem	object	See below	Controls which analyses run on which events.
CrossFileAnalysis	object	See below	Cross‑file analysis specific settings.
DiagnosticsSubsystem	object	See below	Database and TTL settings.
plugins	object	See below	Add your own parsers per language, to enable omnilang cross file diagnostics.

AnalysisSubsystem

{
    "write": ["CrossFile", "Basic", "BasicLogic", "CrossFileLogic", "BasicStyle", "CrossFileStyle"],
    "open": ["Basic", "CrossFile", "BasicLogic", "CrossFileLogic", "BasicStyle", "CrossFileStyle"],
    "change": [],
    "command": ["CrossFile"],
    "max_threads": 5
}

• write, open, change, command: lists of analysis types to run on each event. Supported types:

  • "Basic" – single‑file general analysis
  • "CrossFile" – cross‑file general analysis
  • "BasicLogic" – single‑file logic‑focused analysis
  • "CrossFileLogic" – cross‑file logic‑focused analysis
  • "BasicStyle" – single‑file style/convention analysis
  • "CrossFileStyle" – cross‑file style/convention analysis
  • "Deep" – (planned) more thorough analysis
  • "Workspace" – (planned) whole‑workspace analysis

  Currently implemented: Basic, CrossFile, BasicLogic, CrossFileLogic, BasicStyle, CrossFileStyle. The others are placeholders for future expansion.

• max_threads: maximum concurrent analysis threads.

CrossFileAnalysis

{
    "scope": ["/path/to/project/src"],
    "max_analysis_depth": null,
    "max_string_size_char": 1000000
}

• scope: list of directory paths that limit which imported files are included.
• max_analysis_depth: how deep to recursively follow imports (null = unlimited).
• max_string_size_char: maximum character length of the aggregated context.

DiagnosticsSubsystem

{
    "check_ttl_for_deletion": 360,
    "sqlite_db_name": "diagnostics.db",
    "check_ttl_for_invalidation": 5,
    "ttl_until_deletion": 2592000,
    "ttl_until_invalidation": 15
}

• check_ttl_for_deletion: seconds between checks for old files to delete.
• sqlite_db_name: database file name.
• check_ttl_for_invalidation: seconds between checks for stale diagnostics.
• ttl_until_deletion: seconds after last write before a file’s records are purged (default 30 days).
• ttl_until_invalidation: seconds after a file write before old diagnostics are considered stale and deleted.

plugins

In the current plugin system, there are 2 plugin types:

1. Parsers
2. Prompts

Any plugins can be found in the plugin repo. TODO: ADD THE LINK HERE

To make the LSP have first class support for a language, you must find first class parser for the language and a first class Prompt for the language. You can write these yourself as well, making them specific to your needs.

Prompts

These are the prompt_overrides, wich are overrides over the normal prompt parts, wich are also activated per filetype, wich enables dedicated per langage support, wich can greatly increase the performance in that specific language.

The prompt overrides are python files, containing any of the following constant strings:

• TASK
• NOTE
• CROSS_FILE_NOTE
• LOGIC_ERRORS_DESC
• CONSISTENCY_ERROR_DESC
• FORMAT_DESC
• FOOTER
• GOOD_EXAMPLES
• COT_EXAMPLES
• BAD_EXAMPLES

The plugin system will automatically load the python file and get all the overrides that exist.

EXAMPLE:

TASK = """Your task is to find memory leaks"""
NOTE = """Focus on memory leaks"""  
FOOTER= """Focus on memory leaks. Dont forget memory leaks."""

Plugins

These are the plugins that allow the cross file analysis to work with filetypes other then python. You can find them in the plugin repo, for their respective language. The parsers must follow the protocol described in the Parser Protocol section below.

Architecture

The server is built with pygls and organised into several subsystems:

• AIDiagnosLSP – main server class, inherits from LanguageServer. Holds configuration, diagnostics cache, and references to subsystems.
• AnalysisSubsystem – manages analysis requests. Uses a ThreadPoolExecutor to run analysers concurrently, respects debounce and file size limits, and dispatches to the appropriate analyser based on the event.
• Analysers – currently BasicDiagnoseFunctionWorker (single‑file analysis) and CrossFileAnalyserWorkerThread (cross‑file), along with their logic‑ and style‑focused variants. Each builds a LangChain (prompt + LLM + output parser) and invokes it in a separate thread.
• DiagnosticsHandlingSubsystem – persistent storage via SQLite. Stores diagnostics per file per analysis type, with timestamps. Provides methods to save new diagnostics, load them for a file, and background threads for TTL‑based cleanup. (Doc here)
• Utility modules – grep.py, parser.py, langchain invoker and other utils.

Data flow

1. Client sends a text document event (didOpen, didSave, didChange) or a custom workspace/executeCommand.
2. Server calls AnalysisSubsystem.submit_document_for_analysis(doc, event).
3. Subsystem checks debounce, file size, and launches the required analysers in a thread pool.
4. Analyser builds the appropriate chain and invokes the LLM.
5. LLM returns a JSON‑formatted list of diagnostics (using the Pydantic model GeneralDiagnosticsPydanticObjekt).
6. The JSON formatted list of diagnostics gets json fixed if broken, and stripped of the chain of thoughts the model may have produced.
7. Analyser calls DiagnosticsHandlingSubsystem.save_new_diagnostic() to store the result.
8. It then calls load_diagnostics_for_file() which retrieves all stored diagnostics for that file, converts them to LSP Diagnostic objects (using snippet matching via grep), and updates the server’s in‑memory cache.
9. Finally, the server sends a workspace/diagnostic/refresh request to notify the client to pull updated diagnostics.

[!NOTE] Due to the fact that neovims support for the pull LSprotocol is incomplete, I also made the language server push the diagnostics once they are loaded. Also note that there is a load_all_diagnostics method that is called on startup, wich may slow the servers startup down, but also loads all the diagnostics for all the files, making the worspace diagnostics navigation directly possible through all of them. (I should make that an optional feature.)

LSP Methods

The server implements the following LSP methods:

• initialize – stores the configuration from initializationOptions.
• initialized – initialises subsystems and loads all stored diagnostics.
• textDocument/didOpen – attempts to load cached diagnostics; if none, triggers analysis.
• textDocument/didChange – reloads diagnostics from cache (can be configured to trigger analysis).
• textDocument/didSave – registers a file write and triggers analysis.
• textDocument/diagnostic – returns diagnostics for a specific document (pull model).
• workspace/diagnostic – returns diagnostics for the whole workspace.
• workspace/executeCommand – supports custom commands:
  • Analyse.Document – expects a URI parameter; forces analysis.
  • Clear.AIDiagnostics – clears diagnostics for a given URI.
  • Clear.AIDiagnostics.All – clears all diagnostics.

Troubleshooting

Set the environment variable AI_DIAGNOS_LOG to anything to enable detailed logging to ai_diagnos_lsp.log in the working directory. (DEBUG USE ONLY)

BE AWARE THAT THE LOGS CONTAIN API KEYS PLAINTEXT

Common issues:

• Server fails to start: Ensure all required API keys are provided and the Python environment has the necessary packages. (They dont need to be valid, but empty may crash the server)
• No diagnostics appear: Check that the file type is supported (the client must request diagnostics; the LSP only responds to pull requests). Verify that the file size is below max_file_size and that debounce is not suppressing the request.
• LLM errors: Inspect the log for API errors or timeouts. Verify that the model names are correct and the API keys have access.
• Parsing errors: Get a better LLM. Those mean that the LLM returned absolutely invalid content

Contributions

Contributions are welcome - especially new parsers. Plugin repo is here TODO: ADD IT HERE

Parser Protocol

A plugin parser is a single executable that acts as a parser function, that gets the file in, and returns the list of files that the file imports back, limited to the project scope defined in the request. Communication happens over stdin/stdout.

Input:

{
    "project_scope": ["dir_name", "another_dir_name", "..."],
    "file_content": ["line_content", "another_line_content", "..."],
    "solid_file_content": "The whole file content as one string",
    "file_path": "/file/path"
}

Output:

[
    "file/imported/as/filepath", "file/imported/as/filepath", "file/imported/as/filepath"
]

[!NOTE] stdin is used, not arguments

License

MIT