What security features does Forgemax offer for AI agent interactions?

Forgemax runs LLM-generated JavaScript in a secure V8 isolate (powered by `deno_core`) with no host access, fresh runtimes, strict resource limits, code validation, API boundary enforcement, and error redaction to protect sensitive data.

How do AI agents interact with tools using Forgemax?

AI agents first use the `search` function to query a hierarchical capability manifest and discover available tools. They then write JavaScript code to `execute` specific tool calls against real MCP servers through secure, sandboxed proxy objects.

Forgemax is an AI agent gateway that streamlines interaction with diverse tool ecosystems by collapsing numerous server-side tools into two core functions: `search` and `execute`. This significantly optimizes LLM context window usage.

How does Forgemax reduce LLM context window token usage?

Instead of dumping every tool schema into the LLM's context window, Forgemax exposes just two tools (`search` and `execute`). AI agents write JavaScript to interact, cutting token usage from potentially thousands to around 1,000 tokens.

Can Forgemax connect to and manage multiple tool servers?

Yes, Forgemax excels at multi-server orchestration. It connects to and manages multiple downstream MCP servers (e.g., GitHub, Cloudflare, Stripe) via various transports, routing tool calls and applying per-server resilience policies.

Forgemax: AI Agent Gateway for Streamlined Tool Interaction

About

Forgemax implements a 'Code Mode' approach for Large Language Model (LLM) tool orchestration, drastically reducing the token footprint by exposing only two universal tools: `search` for capability discovery and `execute` for sandboxed JavaScript execution against tool APIs. This method allows LLMs to leverage their strong coding abilities to write JavaScript that interacts with complex tool sets through typed proxy objects, eliminating the need to embed extensive JSON schemas into the LLM's context. It centralizes and secures access to various MCP servers, enabling efficient, chained tool calls within a single execution cycle while protecting sensitive credentials and internal states from the sandbox.

Key Features

Sandboxed JavaScript Execution: Runs LLM-generated JavaScript in a secure V8 isolate (powered by `deno_core`), providing fresh runtimes, strict resource limits, and no access to host resources.
3 GitHub stars
Comprehensive Security Model: Implements multi-layered security measures including code validation, process isolation, API boundary enforcement, and error redaction to protect sensitive data and prevent vulnerabilities.
Code Mode Gateway: Collapses N servers x M tools into two LLM-facing tools (`search` and `execute`), significantly reducing context window token usage.
Hierarchical Capability Manifest: Dynamically builds a queryable index of tools across connected MCP servers, supporting progressive discovery at different levels of detail.
Multi-Server Orchestration: Connects to and manages multiple downstream MCP servers via various transports (stdio, HTTP/SSE), routing tool calls and applying per-server resilience policies.

Use Cases

Building AI agents that interact with a wide array of backend services and APIs without overwhelming the LLM's context window.
Developing complex AI workflows that chain multiple tool calls efficiently within a single `execute` command.
Securely exposing internal enterprise tools to AI models while maintaining strict control over access and execution environments.

About

Key Features

Sandboxed JavaScript Execution: Runs LLM-generated JavaScript in a secure V8 isolate (powered by `deno_core`), providing fresh runtimes, strict resource limits, and no access to host resources.
3 GitHub stars
Comprehensive Security Model: Implements multi-layered security measures including code validation, process isolation, API boundary enforcement, and error redaction to protect sensitive data and prevent vulnerabilities.
Code Mode Gateway: Collapses N servers x M tools into two LLM-facing tools (`search` and `execute`), significantly reducing context window token usage.
Hierarchical Capability Manifest: Dynamically builds a queryable index of tools across connected MCP servers, supporting progressive discovery at different levels of detail.
Multi-Server Orchestration: Connects to and manages multiple downstream MCP servers via various transports (stdio, HTTP/SSE), routing tool calls and applying per-server resilience policies.

Use Cases

Building AI agents that interact with a wide array of backend services and APIs without overwhelming the LLM's context window.
Developing complex AI workflows that chain multiple tool calls efficiently within a single `execute` command.
Securely exposing internal enterprise tools to AI models while maintaining strict control over access and execution environments.