Agents that followthe plan.
Wire LLMs into deterministic paths — not wandering ReAct loops.
Pick the right model at every step. Cap costs by design.
Jira to Deploy Pipeline
End-to-end pipeline that takes a Jira ticket from planning to a merged, deployed PR.
Same Task. Different Outcome.
Autonomy burns money. Paths don't.
Give the same job — “summarize today's earnings reports and post to Slack” — to an autonomous agent and to a Synapse path. Here's what happens.
ReAct loop, picks its own next step
- Tool calls
- 47
- Cost / run
- $42
- Reliability
- ??
Deterministic DAG, you wire the steps
- Steps
- 6
- Cost / run
- $0.40
- Reliability
- 100%
Illustrative numbers. Real cost depends on your workflow, providers, and step count.
14+ Model Providers · 5 Messaging Apps · One Platform
Built against autonomy, on purpose
Most agent frameworks let your LLM pick its own next step. Synapse makes you wire the path — and pays you back with deterministic runs, capped costs, and a workflow you can actually debug.
Deterministic by Design
Autonomous frameworks let the agent pick its own next step — and sometimes its own next 47. Synapse runs as a strict DAG. Same input, same path, every run. Auditable. Replayable. Debuggable.
Cost-Controlled at Every Step
Cap spend per node, per run, per workflow. Use a cheap local model for routing and a frontier model only where deep reasoning actually matters. Most pipelines drop 80%+ in cost without losing quality.
If You Can Write It, Agents Can Use It
Python functions, REST APIs, webhooks, MCP servers: any tool you already have works out of the box. Zero glue code.
Runs Where You Need It
Deploy on-premises with Docker, on any cloud provider, or locally with Ollama. Your data stays in your infrastructure. No vendor lock-in, no surprise egress.
Platform Features
Everything you need to ship AI workflows
Not a framework with toy examples. A complete platform for production AI workflows.
Visual DAG Builder
Drag-and-drop orchestration canvas powered by ReactFlow. 10 step types: Agent, LLM, Tool, Evaluator, Parallel, Merge, Loop, Human, Transform, and End.
10 step types14+ LLM Providers
Mix models per step: cheap models for routing, powerful models for reasoning. Claude, GPT-4, Gemini, Grok, DeepSeek, Ollama local, AWS Bedrock, and more.
Per-step overrideReal Tool Servers
14+ built-in tool servers: Docker Python sandbox, Vault, SQL Agent (Postgres/MySQL/SQLite), Browser (Playwright), stealth Web Scraper, PDF/Excel parsers, and MCP.
Docker sandboxedAI Builder
Describe your orchestration in plain English. The AI builds the full DAG for you: agents, connections, step configs. Then you run it. No canvas required.
Chat-to-DAGHuman-in-the-Loop
Human steps pause execution mid-workflow and wait for approval. Resume from the UI, Slack, Telegram, or any connected channel. Runs survive server restarts.
Resumable executionMessaging Integrations
Deploy to Slack, Discord, Telegram, Teams, or WhatsApp. Per-channel agent binding, multi-agent chat mode, scheduling, and result auto-push.
5 platformsProduction Scale
From one user to millions of requests
The V2 API stays stable. Redis Cluster, PgBouncer, and auto-scaling workers handle the load behind it — start on one machine, grow to a Kubernetes cluster without changing a line of application code.
Redis Cluster
Horizontal sharding and automatic failover. Job queue, SSE event streams, and pub/sub signals all on one HA cluster — no single point of failure in the messaging layer.
Auto-failoverAuto-Scaling Worker Fleet
Pull-based ARQ workers scale from 1 process to 100+ independently. KEDA autoscales Kubernetes worker pods on Redis queue depth. Workers share nothing except Redis and Postgres.
KEDA autoscalePgBouncer Connection Pool
Hundreds of workers multiplexed through a small, stable Postgres connection pool in transaction mode. No max_connections exhaustion under heavy concurrent load.
100s of workersS3 Artifact Storage
File outputs, document attachments, and batch results stream directly to AWS S3, Cloudflare R2, or self-hosted MinIO — keeping Postgres lean and fast.
S3 · R2 · MinIOMulti-Tenant Quotas
Give each customer or internal team isolated concurrent run limits. Requests over quota return HTTP 429 before touching the queue — no starvation, no wasted work.
Per-tenant isolationCheckpoint Recovery
Per-step Postgres checkpoints survive worker crashes mid-run. Stale workers are reaped automatically after 90 s of silence. Failed jobs land in a DLQ with one-click retry.
Zero job lossArchitecture
How Orchestration Works
Every orchestration is a strict DAG. Execution follows your exact design. No surprises, no hallucinated detours.
Connect MCP Servers
MCPPlug in any MCP-compatible server to extend your orchestration with external tools and services. Browse the built-in registry or paste a server URL, and new capabilities appear instantly in every agent.
Add Custom Tools
ToolPaste your Python handler directly into the editor or drop in an API webhook URL. No SDK needed. Define the schema, inputs, and secrets in one form and your tool appears instantly in every agent's toolbox.
Define Your Agents
AgentCreate specialized agents in the settings panel with custom system prompts, tool access, and per-agent model overrides. Each agent has a focused role.
Wire the DAG
ParallelDrag nodes onto the canvas and connect them. Choose from 10 step types: Agent, LLM, Tool, Evaluator, Parallel, Merge, Loop, Human, Transform, or End.
Set Per-Step Models
LLMOverride the LLM at every step. Use a cheap model for routing decisions, a powerful model for complex analysis. Optimize cost without sacrificing quality.
Run the Orchestration
ToolExecution follows your exact DAG. No hallucinated detours. Each step processes input from shared state, produces output, and passes it to the next node.
Human Review & Resume
HumanHuman steps pause execution and notify you via Slack, Telegram, or the UI. Approve, reject, or provide feedback. Execution continues exactly where it stopped.
All step types
Vs. Everything Else
Agents that wander vs. agents that follow the plan.
Most frameworks let your LLM pick its own next step. Synapse makes you wire the path — and rewards you with deterministic runs, capped costs, and a workflow you can debug.
| Feature | Synapse AI | LangChain | CrewAI | SuperAGI | AutoGen | n8n / Zapier | Langflow | Flowise | Dify |
|---|---|---|---|---|---|---|---|---|---|
Visual DAG Canvas Drag-and-drop nodes with real-time per-step status, no YAML files or code-first setup | |||||||||
Local LLM Support 14+ providers including Ollama; run fully air-gapped or mix local and cloud models in the same workflow | |||||||||
Human-in-the-Loop Gates Pause mid-run for human approval; resume from browser or connected Slack/Telegram/Teams. Survives server restarts. | |||||||||
Docker Code Sandbox Executes AI-generated Python in isolated containers with data-science libraries and a persistent cross-session file vault | |||||||||
Stealth Web Scraping Headless Chromium with anti-bot evasion. Scrapes LinkedIn, JS-heavy, and financial sites straight out of the box. | |||||||||
AI-Generated DAGs Describe a workflow in plain English; an AI builder drafts the full DAG, creates sub-agents, and asks for approval before saving | |||||||||
Per-Step Model Override Assign a different LLM to each node: fast local model for routing, Claude Opus only where deep reasoning actually matters. | |||||||||
Nested Orchestrations Promote any workflow to a callable agent and stack pipelines on top of each other like composable, reusable functions | |||||||||
Full Observability Per-step token counts, live USD cost enforcement, p95/p99 latency profiles, and complete execution history per run | |||||||||
Messaging Integration Bind Telegram, Slack, Discord, Teams, or WhatsApp to any workflow; human gates push approval prompts directly to your chat | |||||||||
Pipelines as Callable Agents Promote any orchestration into a reusable agent tool — call a full pipeline from inside another pipeline like a composable function | |||||||||
Typed Shared State All steps read/write a single typed schema dict — no edge wiring between nodes. State persists across agents, transforms, and human pauses in one run. | |||||||||
Deterministic Control-Flow Steps IF/Else, Switch, Transform (Python), Extract JSON — native zero-LLM step types that branch, reshape, and route data without spending tokens |
Use Cases
What people build with Synapse
Real workflows, real production use cases. Not toys.
Content Research & Writing
Research any topic online, synthesize findings, draft long-form content, create a Google Doc, and share the link. End to end, no manual steps.
Code Review & Autonomous PR
Review pull requests, write fixes, run tests in a sandbox, and merge when ready. Human gate at each stage ensures you stay in control.
Market Intelligence Report
Scrape financial sites with stealth mode, aggregate and analyze data with a Python sandbox, then auto-push a summary to Slack on a schedule.
Customer Support Bot
Multi-agent Slack or Telegram bot that classifies, routes, and answers. Users switch agents mid-conversation. Escalates to human when confidence is low.
Database Q&A Agent
Natural-language SQL: describe what you want, get structured results back. Works with PostgreSQL, MySQL, and SQLite. No SQL knowledge required.
Deep Web Research Agent
Navigate websites, extract data from PDFs and JS-heavy pages with stealth mode, store findings in Vault, and produce a fully cited research report.
If you can wrap it in an API, Python function, or MCP server? Synapse can automate it.
Agents and orchestrations are not limited to what's built in. Any tool you already have becomes a first-class step in your workflow.
See It In Action
Watch Synapse AI work
Full UI walkthrough, from creating agents to running a multi-step orchestration.
Synapse AI: Platform Demo
Multi-agent orchestration in action
AI Builder in Action
Describe your workflow in chat. Watch the full DAG appear in seconds.
Human Review Gate
Execution pauses at the Human step. You review, approve, and it continues.
Real-Time Step Status
Watch each node light up as it completes. Live cost and turn tracking.
Examples
Don't start from scratch
Start with a working example and make it yours. Every example is a fully configured bundle of agents, orchestrations, and MCP servers, ready to customize.
Starter Example
Get up and running fast. Includes a Personal Assistant with full tool access and a Web Research Agent, plus a ready-to-use Content Research & Writing Pipeline.
- Personal Assistant with full tool access
- Web Research Agent
- Content Research & Writing Pipeline
- Browser + Vault + Sandbox tools included
Developer Example
Built for developers. Includes a Code Review Agent, Software Engineer Agent, QA Engineer, Deployment Monitor, and a complete Dev and Strict Pipeline orchestration.
- Code Review & Software Engineer Agents
- QA Engineer + Deployment Monitor
- Dev Pipeline + Strict Pipeline orchestrations
- GitHub + Filesystem MCP servers
Productivity Example
For business and content power users. Includes a Data Analyst, Content Writer, Jira Analyst, Slack Notifier, and Market Intelligence orchestration.
- Data Analyst + Content Writer Agents
- Jira Analyst + Slack Notifier
- Market Intelligence orchestration
- Jira + Slack + Notion MCP servers
OneCommandtoInstall.
macOS, Linux, Windows, or Docker. Deploy on your infrastructure — on-premises, cloud, or local.
synapse startStart backend + frontend, open browsersynapse stopStop background processessynapse upgradePull latest and rebuildsynapse uninstallRemove everythingCLI requires Python 3.11+ and Node.js 22+. Docker image is self-contained — no prerequisites needed.