Hands-on studio to design, test and deploy declaratively configured multi-agent systems built on the Neuro SAN framework. Ships ready examples, an Agent Network Designer UI (nsflow), CLI tooling, and integrations with major LLMs and external tools for rapid prototyping.
Lets you build, generate, and run multi-agent LLM workflows from natural-language prompts with no coding. Automatically profiles agents, creates tools/workflows, and supports multiple LLM providers plus CLI/Docker deployment.
Structures AI-assisted development as deterministic YAML workflows—planning, implementation, validation, review, and PR creation—so agent runs are repeatable and isolated. Mixes deterministic nodes with AI nodes and runs from CLI, Web UI, or chat integrations.
Performs fast static type checking and provides a language server with code navigation, semantic highlighting, and completions for Python. Processes ~1.85M lines/sec and completes IDE rechecks typically under 10ms — intended for responsive editor workflows and large codebases.
Provides a local model gateway and control plane for coding agents — route Claude Code, Codex, Grok CLI, ZCode and compatible clients to multiple providers through one stable local endpoint while managing routing, failover, tools, credentials, and observability.
Lets AI agents drive GitHub in natural language via MCP: browse repos, triage issues, review pull requests, and trigger Actions runs. Runs as a GitHub-hosted remote OAuth server or a local Go binary, with per-toolset scoping and a read-only mode.
Simulates adversarial attacks against LLMs and AI agents to surface vulnerabilities (e.g., jailbreaks, prompt injection, PII leakage) and ships guardrails to block risky inputs/outputs; runs locally and can be driven from CLI or Python.
Lets AI agents like Claude Desktop and Cursor explore schemas and run SQL across Postgres, MySQL, MariaDB, SQL Server, and SQLite through one MCP server. A read-only mode stops the agent mutating data; no per-database drivers to wire up.
Bridges IDA Pro with language-model MCP clients so LLMs can query, inspect, modify and annotate disassemblies through a typed MCP API. Provides batch-first analysis tools (decompile, xrefs, int_convert), headless idalib support, and integrations for many MCP clients; requires commercial IDA Pro and Python 3.11+.
Combines static code analysis with LLM reasoning to produce interactive architecture diagrams, component-level documentation, and navigable outputs for IDEs, CI, and docs. Emits Mermaid diagrams and incremental updates with CLI and editor integrations.
Run large-language and multimodal models locally on edge devices (Android, iOS, desktop, web, Raspberry Pi) with hardware acceleration, function-calling, and multi-language SDKs—designed for low-latency, privacy-sensitive on-device inference.
Measures generative AI inference performance with token-level metrics (TTFT, inter-token latency), latency, and throughput under realistic traffic patterns. Provides a multiprocess engine, real-time TUI dashboard, extensible plugins, and integrations for telemetry and result uploads, aimed at inference benchmarking and capacity planning.