DeterminFlow

What does this agent do, and when should you use it?

DeterminFlow is a production-oriented AI workflow runtime that organizes LLM calls, scripts, APIs, database operations, and human approvals into versioned, validatable, retryable, recoverable, and auditable workflows. Its core components include four Core Node types (Agent, Script, Approval, Subprocess), a visual Workflow Editor, a FastAPI service, a React console, Cron Automation, WebSocket events, and a Plugin mechanism for packaging complete business capabilities. Each Agent node runs in an isolated session that reads only its local context and uses only whitelisted tools; outputs pass through JSON detection, parsing, repair, and targeted retries. Workflow definitions and inputs are frozen at Task start, and every attempt, token spend, artifact, and checkpoint is persisted so execution can resume from the failed node even after a process restart. The runtime is production-proven in the real AI novel pipeline of Bishu Writing; the open-source bishu-novel plugin contains 7 production workflows, 84 orchestration nodes, 33 Agent/Prompt combinations, and 15 reusable script modules, with node-level context isolation measured to save roughly 70%–89% of tokens. The project is licensed under AGPL-3.0 and maintained by alikon-art.

The execution flow: users define versioned workflows (with variables, conditions, parallelism, loops, human approvals, and subprocesses) via the visual Workflow Editor or API; Task start freezes the workflow definition and inputs; each Agent Node runs in its own session with per-node tool whitelist/blacklist, Workspace, and max-turn settings, producing structured JSON; invalid outputs trigger repair, retry, skip, or human escalation, and downstream nodes can reject results to force targeted upstream rework; Script Nodes handle deterministic work like file conversion, API calls, and database writes; every attempt, error, token count, artifact, and checkpoint is saved, so execution continues past process restarts. Delivery surfaces include FastAPI (http://localhost:8020, docs at /docs), the GET /api/plugins endpoint, a React console, Cron Automation, WebSocket events, and health checks; MCP, Agent/Prompt templates, Skills, and Rules serve as reusable assets, and the Core runs independently of any business Plugin.

1. Teams with well-defined AI pipelines (e.g., multi-writer + validation + persistence) that want to migrate from a single long-context agent to a node-based, resumable runtime
2. Cost-sensitive product teams that want each model to read only its local context, saving 70%–89% of tokens on real production tasks
3. Compliance or cost-control scenarios needing per-node tool permissions and per-model-call token accounting and auditing
4. Teams packaging mature AI workflows as Workflow + Agent + Prompt + Skill + Rule bundles for installation by other teams or the community (Plugin mechanism)
5. Situations requiring workflows exposed as APIs, managed background processes, Cron Automation, or WebSocket event services

What are this agent's strengths and limitations?

How do you install or deploy this agent?

Option 1 — Desktop installers: download from GitHub Releases (https://github.com/alikon-art/DeterminFlow/releases/latest) or https://determinflow.com/download; Core builds are DeterminFlow_1.1.0_x64-setup.exe (Windows) and DeterminFlow_1.1.0_aarch64.dmg (macOS Apple Silicon); Full builds pre-install bishu-novel 0.2.2 and public-api 0.1.37; the desktop bundles include the runtime, so no Python, Node.js, or Git is needed. Option 2 — From source (macOS/Linux):

git clone https://github.com/alikon-art/DeterminFlow.git
cd DeterminFlow
python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.lock
cp .env.example .env

cp config/models_config.example. config/models_config.

python run.py

On Windows PowerShell, use .\.venv\Scripts\python.exe -m pip install -r requirements.lock and copy .env and models_config. before running run.py. Alternatively, run docker compose up --build. Prerequisites: Python 3.11+, Node.js 22.12+, npm.

How do you use this agent?

After startup, the Web UI is at http://localhost:8020, API docs at http://localhost:8020/docs, and plugin status at GET /api/plugins. On first launch, configure your model API key in the settings page or set DEEPSEEK_API_KEY in .env (model config reads it via ${DEEPSEEK_API_KEY}); you may also opt into the community-provided free models. In the console, use the Workflow Editor to define nodes, variables, and branches and publish a versioned workflow, then trigger Tasks manually, via API/event, or via Cron Automation. During execution you can inspect status and usage at Workflow, Task, Node, attempt, and model-call granularity, and retry, skip, or resume from the failed node on errors. New configuration uses the DETERMINFLOW_* prefix; the community edition runs tasks with 4 local Workflow Executor processes by default—set DETERMINFLOW_WORKFLOW_EXECUTOR_MODE=inline and DETERMINFLOW_WORKFLOW_EXECUTOR_COUNT=1 to fall back to a single process. For Docker, use docker compose up --build. Verification: python -m pytest -q, plus npm run lint, npm run test:extensions, and npm run build in the web directory.

How does this agent compare with similar options?

FAQ