1flowbase
Give local AI agents one model endpoint that can run your own observable multi-model workflow behind the scenes.
Claude Code, Codex, OpenCode, Cline, Continue, and SDKs call one normal model name. 1flowbase can run a workflow behind that name: mount a vision model for screenshots, call several models as a Fusion-style review panel, verify or format the result, and show the full trace of model calls, tool callbacks, tokens, latency, and failures.
Agent client -> one virtual model endpoint -> your workflow -> trace / tokens / cost -> final answer
| If you need to... | 1flowbase helps you... |
|---|---|
| make a text coding model understand screenshots | mount GLM-5V-Turbo, Gemini, GPT vision, OCR, or another visual model as a tool |
| run a Fusion-style model panel | fan out to several branch models, synthesize the result, and publish it as one endpoint |
| debug why an agent answer was slow, expensive, or wrong | inspect workflow nodes, model calls, tool callbacks, token usage, latency, and errors |
| reuse a better model chain from existing clients | publish the workflow as OpenAI-compatible or Claude-compatible model APIs |
What You Can Build
Add vision to text-first coding models
Keep GLM-5.2, DeepSeek, or another strong text coding model as the main planner, then let 1flowbase route screenshots, UI images, charts, and PDF pages to a mounted vision model.
Claude Code
-> 1flowbase virtual model endpoint
-> GLM-5.2 / DeepSeek / other main coding model
-> mounted vision tool
-> GLM-5V-Turbo / Gemini / GPT vision / OCR model
-> structured visual result
-> final coding answer
Guide: Make GLM-5.2 See Images in Claude Code with 1flowbase
Publish a Fusion-style multi-model reviewer
1flowbase includes a fusion template. Your client calls one model name; 1flowbase asks several branch models, runs a synthesis model, returns the final answer, and keeps every branch visible.
User request
-> Main LLM
-> fusion tool
-> Branch LLM A
-> Branch LLM B
-> Branch LLM C
-> Synthesis LLM
-> final answer
Guide: Fusion-Style Workflows: Publish a Multi-Model Panel as an Observable Virtual Model
Publish workflow-backed model APIs
Build the workflow once, then expose it through common model APIs:
| Protocol | API path | Typical usage |
|---|---|---|
| OpenAI Responses API | /v1/responses |
newer OpenAI-style clients and application code |
| OpenAI Chat Completions API | /v1/chat/completions |
SDKs, coding tools, chat clients, application frameworks |
| Claude-compatible Messages API | /v1/messages |
Claude-compatible clients that support custom endpoints |
Installation or Upgrade
Linux/macOS:
curl -fsSL https://raw.githubusercontent.com/taichuy/1flowbase/main/scripts/shell/docker-deploy.sh | sh
Windows PowerShell:
irm https://raw.githubusercontent.com/taichuy/1flowbase/main/scripts/powershell/docker-deploy.ps1 | iex
Windows CMD:
powershell -NoProfile -ExecutionPolicy Bypass -Command "irm https://raw.githubusercontent.com/taichuy/1flowbase/main/scripts/powershell/docker-deploy.ps1 | iex"
Run From Source
Use this path when you want to develop 1flowbase itself.
Requirements: Node.js >= 24.0.0, pnpm, latest stable Rust, and Docker for local middleware.
git clone https://github.com/taichuy/1flowbase.git
cd 1flowbase
docker compose -f docker/docker-compose.middleware.yaml up -d
cd web
pnpm install
pnpm dev
Frontend:
http://127.0.0.1:3100
Start backend services:
cd api
# Copy api/apps/api-server/.env.example to .env before the first run.
cargo run -p api-server --bin api-server
cargo run -p plugin-runner --bin plugin-runner
Default backend endpoints:
API Server: http://127.0.0.1:7800
Plugin Runner: http://127.0.0.1:7801
Script-assisted startup:
node scripts/node/dev-up.js
node scripts/node/dev-up.js status
node scripts/node/dev-up.js stop
node scripts/node/dev-up.js restart
See scripts/README.md for more options.
Where 1flowbase Fits
1flowbase is not just a model proxy and not just a generic workflow canvas.
| Tool category | What it usually does | How 1flowbase is different |
|---|---|---|
| LLM gateway / model router | routes one request to one provider or model | composes multiple model and tool nodes into one workflow-backed virtual model |
| AI workflow builder | builds an AI app or workflow | exposes the workflow as OpenAI / Claude-compatible model APIs |
| Agent framework | helps developers code agent graphs | provides a visual runtime, protocol publishing, and execution logs |
| Observability / cost tracker | shows token or spend totals | connects cost to workflow nodes, model calls, tool callbacks, and trace logs |
Model routers choose a model.
1flowbase builds a new virtual model from a workflow.
Feature Preview
Publish as OpenAI-compatible API
Publish as Claude-compatible Messages API
Customize exposed model information
Use in local AI agent clients
Call a published workflow from compatible clients that support custom model endpoints.
Inspect execution logs
Trace model requests, node inputs and outputs, tool callbacks, response content, latency, and errors.
View tool callback traces
Track token consumption
Common Use Cases
Make a text coding model understand screenshots
Screenshot / UI mockup / chart
-> vision tool
-> structured visual context
-> strong coding model
-> patch, plan, or explanation
Useful for UI reconstruction, frontend debugging, visual regression analysis, chart reading, PDF page understanding, and design-to-code workflows.
Build a Fusion-style reviewer
Architecture proposal
-> cheap broad reviewer
-> strong reasoning reviewer
-> provider-diverse reviewer
-> synthesis model
-> final recommendation
Useful for architecture review, research synthesis, code review, document review, and high-stakes agent decisions.
Control cost with model cascading
Simple classification -> small model
Formatting -> small model
Complex reasoning -> strong model
Final verification -> verifier node
Guarantee output structure
Use verifiers, JSON Schema validation, and formatter nodes before returning the final result. This is useful for JSON outputs, API responses, tool call parameters, code patches, document generation, and automated task results.
Build a programmable upstream model for agents
Code generation -> test / lint check -> reviewer node -> fix node -> final patch
The client calls one model name while 1flowbase runs your workflow behind it.
Current Status
Implemented
- visual workflow editor
- multi-node workflow orchestration
- virtual model endpoint publishing
- OpenAI Responses protocol support
- OpenAI Chat Completions protocol support
- Claude-compatible Messages protocol support
- streaming response support
- mounted LLM tools for multimodal and branch-model workflows
-
fusionworkflow template - execution logs
- tool callback traces inside 1flowbase workflows
- application-level token consumption statistics
- prompt and model configuration version history
Enhancing
- deeper local agent conversation collection
- session search and playback
- Token Bill of Materials by prompt, history, tool definitions, command outputs, media inputs, and nodes
- abnormal cost detection and optimization suggestions
- session export and Recall Pack generation
- more Claude Code / Codex / OpenCode / Cline / Continue templates
- MCP-aware plugin nodes and tool-call source attribution
Planned
- low-code application building platform for AI organizations
- team workspace and multi-tenant management
- permissions, approval, audit, and cost governance
- support for more local AI agent clients
- template market and workflow recipe ecosystem
Note: 1flowbase is not currently positioned as an MCP server or