synapseorch-ai/synapse-ai

What it solves

Synapse AI provides a way to build reliable, production-ready AI workflows without being locked into a specific framework. It solves the problem of "hallucinated detours" in AI agents by allowing users to create deterministic pipelines where agents, tools, and various LLMs are wired together in a controlled sequence.

How it works

The platform uses a Directed Acyclic Graph (DAG) approach to orchestrate AI agents. Users can define specific steps, routing, and parallelism to ensure the workflow follows a designed path. It supports a wide range of LLM providers (both local via Ollama and cloud-based) and integrates tools through Python scripts, REST APIs, webhooks, and Model Context Protocol (MCP) servers. For high-demand environments, it features a "Scale Mode" that distributes workloads across a Redis job queue and a fleet of independent workers.

Who it’s for

It is designed for developers and teams who need to move AI agents from simple demos to scalable, shipped products, particularly those requiring human-in-the-loop review, scheduled automation, or a mix of different LLMs within a single workflow.

Highlights

• Multi-Model Orchestration: Use different LLMs for different steps of a single workflow to optimize for speed or power.
• Deterministic Execution: Workflows follow a strict DAG to prevent agents from deviating from the intended process.
• Extensible Tooling: Turn Python scripts, APIs, and MCP servers into tools that agents can call.
• Human-in-the-Loop: Ability to pause workflows for human review and resume them across restarts.
• Distributed Scaling: A dedicated scale layer with Redis and worker fleets to handle millions of requests.
• AI Builder: A meta-agent that can generate orchestration DAGs from natural language descriptions.