Neuroverse

Process mixed-language input through the full NeuroVerse pipeline. Pipeline: Language Detect → Normalise → Intent Extract → Safety Check → (optional) Execute Supported languages: Tamil, Hindi, Telugu, Kannada + English (code-switched). Args: - text (string): Raw user input, possibly code-switched - user_id (string): User / agent identifier (default: "anonymous") - execute (boolean): Whether to also execute the intent (default: true) Returns: JSON with keys: language, intent, safety, execution (if execute=true) Examples: - "anna indha file ah csv convert pannu" → detects Tamil+English, extracts convert_format - "report banao sales ka" → detects Hindi+English, extracts generate_report - "drop database production" → BLOCKED by safety layer

Store a memory record in NeuroVerse's tiered memory system. Tiers: - short_term: In-process, capped at 50 per user. Lost on restart. - episodic: Persisted to JSON file. Recent actions. - semantic: Persisted to JSON file. Long-term facts. Only episodic/semantic memories with importance_score ≥ 0.4 are persisted. Args: - user_id (string): Agent / user identifier - intent (string): Canonical intent name - tier (string): short_term | episodic | semantic - language (string): Language code (default: "en") - data (object): Structured payload - importance_score (number): 0.0–1.0 Returns: JSON of the stored MemoryRecord

Retrieve memories from NeuroVerse's tiered memory system. Args: - user_id (string): Agent / user identifier - intent (string, optional): Filter by intent - tier (string, optional): Filter by tier - semantic_query (string, optional): Search constraint for vector engine - limit (number): Max results (1–100, default 10) Returns: JSON array of matching MemoryRecords

Parse, safety-check, and execute a user request end-to-end. Convenience tool that chains: Language → Intent → Safety → Execute. Args: - text (string): Raw user input - user_id (string): User / agent identifier Returns: JSON with safety verdict and execution result

Route a task to a registered downstream agent via HTTP. Args: - target_agent (string): Name of the agent - task (string): Task description - payload (object): Arbitrary payload Returns: JSON with the agent's response or a fallback error

Query the multi-model AI router. If a prompt is provided, the prompt is sent to the routed model. Otherwise, returns only the routing decision. Supported providers: OpenAI, Anthropic, Sarvam AI, Ollama. Args: - task_type (string): multilingual | reasoning | local | general - prompt (string, optional): Prompt to send Returns: JSON with routing decision and optional model response

Transcribe an audio file to text using Whisper STT. Args: - audio_path (string): Absolute path to the audio file Returns: JSON with the transcribed text

Synthesize text to speech using Coqui TTS. Args: - text (string): Text to synthesize - language (string): Language code Returns: JSON with the path to the generated audio file

Submit Reinforcement Learning from Human Feedback (RLHF) data for agent tuning.

Scan the codebase and assemble the most relevant file chunks based on a query.

Execute a complex reasoning task using specialized high-performance models (e.g. OpenRouter Reasoning). Returns the model's analytical response.