aicoolies logo
GraphBit logo

GraphBit

Rust-native multi-agent orchestration for production

Share
open-sourceOpen Source
Visit Website →

GraphBit is a Rust-native, multi-agent orchestration framework built for production. It targets the gap between Python-first frameworks like LangGraph and the operational expectations of enterprise systems — predictable memory, low latency, deterministic concurrency, and the ability to embed an agent runtime in services that already run Rust without dragging in a Python interpreter.

We have a review for this tool

A detailed review by the aicoolies team — click to read

GraphBit is an agent framework written from scratch in Rust by InfinitiBit, designed for teams who want multi-agent orchestration with the operational profile of a real production service rather than a notebook. The wager is that 2026 production agent systems are starting to outgrow the Python-first frameworks not because Python is wrong, but because the runtime characteristics — GIL contention, memory profile under sustained load, deployment friction in non-Python environments — become the bottleneck once agents move from prototypes to high-throughput services.

Architecturally, GraphBit defines agents and tools as graph nodes with explicit edges, similar in spirit to LangGraph but with Rust's type system and Tokio's async runtime doing the heavy lifting. Workflows are deterministic and observable by default, multi-agent topologies (supervisor, swarm, hierarchical) are first-class patterns, and the framework integrates with the major LLM providers as well as local inference endpoints like Ollama and vLLM. Memory and tool calling are explicit graph operations, not implicit framework magic.

GraphBit is Apache-2.0 licensed and ships with Python bindings, so teams can prototype in Python and deploy the same workflow inside a Rust service when latency and predictability start to matter. The repository is past 500 stars on GitHub and active in 2026, with InfinitiBit positioning the project for enterprise customers who need on-premises, deterministic agent execution rather than another hosted orchestration cloud.

Pricing

Free open-source (Apache-2.0) / enterprise support available

Platforms

Rust crate, Python bindings, self-hosted, Docker, Kubernetes

Categories

Tags

Use Cases

Related Tools

Hermes Agent logo

Hermes Agent

Top Pick

Open-source AI agent framework with persistent memory, reusable skills, tools, and messaging gateways

Hermes Agent is an open-source AI agent framework with persistent memory, reusable skills, 40+ tools, cron jobs, and messaging gateways.

open-sourceOpen Source
eve vercel

eve by Vercel

Filesystem-first framework for durable AI agents

Eve is Vercel's filesystem-first TypeScript framework for building durable AI agents as ordinary project files. It combines Markdown instructions and skills, typed tools, channels, connections, subagents, schedules, sandboxes, and evals with Vercel's agent runtime so teams can ship deployable agents without hand-rolling orchestration. The current beta fits Vercel-native backend agent projects.

open-sourceOpen Source
Windows-MCP logo

Windows-MCP

MCP server for controlling Windows desktops through UIAutomation

Windows-MCP is an open-source MCP server for giving AI agents structured access to Windows desktop automation. It focuses on UIAutomation, snapshots, input control, and Windows-specific app workflows, making it different from general filesystem or shell MCP servers.

open-sourceOpen Source
BrowserOS logo

BrowserOS

Open-source agentic browser that runs local AI agents in your browsing workflow.

BrowserOS is a privacy-first, open-source agentic browser for running AI assistants locally inside real browsing sessions instead of handing every task to a remote cloud browser.

open-sourceOpen Source
Agent Governance Toolkit logo

Agent Governance Toolkit

Microsoft’s open-source toolkit for adding policy enforcement, identity, sandboxing, and audit controls to production AI agents.

Agent Governance Toolkit is an open-source Microsoft project for teams moving AI agents from demos into controlled production workflows. It focuses on runtime policy enforcement, zero-trust identity, sandboxed execution, and reliability patterns around autonomous agents, giving security and platform teams a governance layer around tool calls and agent actions rather than another prompt-only guardrail.

open-sourceOpen SourceTelemetry
rampart

Rampart

Microsoft’s pytest-native red teaming framework for turning AI agent safety findings into CI tests.

RAMPART is an open-source Microsoft framework for safety and security testing of agentic AI applications. It brings red-team findings into a pytest-native workflow so teams can turn prompt injection, unsafe tool use, and behavioral boundary failures into repeatable regression tests. The strongest aicoolies angle is developer workflow: RAMPART makes agent safety part of CI/CD instead of a one-off security review.

open-sourceOpen Source

Comparisons