vectorvein-sdk 0.3.79

VectorVein API SDK and workflow designer

vectorvein-sdk

中文文档

Python SDK for the VectorVein platform — run workflows, build workflows programmatically, and manage AI agents.

Installation

pip install vectorvein-sdk

Requires Python 3.10+.

Quick Start

from vectorvein.api import VectorVeinClient, WorkflowInputField

with VectorVeinClient(api_key="YOUR_API_KEY") as client:
    result = client.run_workflow(
        wid="YOUR_WORKFLOW_ID",
        input_fields=[
            WorkflowInputField(node_id="node_id", field_name="input", value="Hello"),
        ],
        wait_for_completion=True,
        timeout=60,
    )
    for output in result.data:
        print(f"{output.title}: {output.value}")

CLI

After installation you can run:

vectorvein --help

Install methods that expose the vectorvein command:

pip install vectorvein-sdk
uv tool install vectorvein-sdk

CLI Design (Agent-Friendly)

• Self-descriptive help: every module and subcommand has detailed --help text and examples.
• Human-readable by default: standard text output for normal terminal usage.
• Machine mode on demand: use --format json when an Agent needs structured output.
• Predictable auth: API key resolution order is --api-key > VECTORVEIN_API_KEY.
• Stable exit codes:
  • 0: success
  • 2: invalid CLI usage / arguments
  • 3: authentication (API key) error
  • 4: API business error
  • 5: request/network error

Common CLI Examples

# Auth / user
vectorvein --api-key YOUR_API_KEY auth whoami
vectorvein user info
vectorvein --format json auth whoami

# Workflow
vectorvein workflow run \
  --wid wf_xxx \
  --input-field '{"node_id":"n1","field_name":"text","value":"Hello"}'
vectorvein workflow run \
  --wid wf_xxx \
  --upload-to n1:upload_files:./report.pdf
vectorvein workflow run \
  --wid wf_xxx \
  --upload-to n1:upload_files:./a.pdf \
  --upload-to n1:upload_files:./b.pdf \
  --upload-as list
vectorvein workflow status --rid rid_xxx
vectorvein workflow list --page 1 --page-size 10
vectorvein workflow get --wid wf_xxx
vectorvein workflow describe --wid wf_xxx
vectorvein workflow create --title 'My Workflow' --source-wid wf_xxx
vectorvein workflow update --wid wf_xxx --data @workflow_data.json --title 'Updated'
vectorvein workflow delete --wid wf_xxx
vectorvein workflow search --query 'translation'

# Workflow run records
vectorvein workflow run-record list --wid wf_xxx --status FINISHED
vectorvein workflow run-record get --rid rid_xxx
vectorvein workflow run-record stop --rid rid_xxx

# File upload
vectorvein file upload --path ./report.pdf
vectorvein file upload --path ./a.pdf --path ./b.pdf

# Task Agent — agents
vectorvein task-agent agent list --page 1 --page-size 10
vectorvein task-agent agent get --agent-id agent_xxx
vectorvein task-agent agent create --name 'My Bot' --system-prompt 'Be helpful'
vectorvein task-agent agent update --agent-id agent_xxx --name 'New Name'
vectorvein task-agent agent delete --agent-id agent_xxx
vectorvein task-agent agent search --query 'translator'

# Task Agent — tasks
vectorvein task-agent task create --agent-id agent_xxx --text "Summarize this article"
vectorvein task-agent task create --agent-id agent_xxx --text "Do it" --wait --timeout 120
vectorvein task-agent task continue --task-id task_xxx --message "Also provide a TL;DR" --wait
vectorvein task-agent task respond --task-id task_xxx --tool-call-id tc_xxx --response "Yes, proceed"
vectorvein task-agent task get --task-id task_xxx
vectorvein task-agent task list --status running --agent-id agent_xxx
vectorvein task-agent task search --query 'summary'
vectorvein task-agent task delete --task-id task_xxx

# Task Agent — cycles
vectorvein task-agent cycle list --task-id task_xxx
vectorvein task-agent cycle get --cycle-id cycle_xxx

# Agent Workspace
vectorvein agent-workspace list
vectorvein agent-workspace files --workspace-id ws_xxx
vectorvein agent-workspace read --workspace-id ws_xxx --file-path notes.txt --start-line 1 --end-line 20
vectorvein agent-workspace write --workspace-id ws_xxx --file-path output.txt --content 'done'
vectorvein agent-workspace delete --workspace-id ws_xxx --file-path old.txt
vectorvein agent-workspace download --workspace-id ws_xxx --file-path result.csv
vectorvein agent-workspace zip --workspace-id ws_xxx
vectorvein agent-workspace sync --workspace-id ws_xxx

# Raw request for advanced / not-yet-wrapped operations
vectorvein api request --method POST --endpoint workflow/list --body '{"page":1,"page_size":5}'

auth whoami returns uid, username, email, credits, and date_joined (it does not expose internal numeric user IDs). workflow list hides verbose fields by default: language, images, is_fast_access, browser_settings, chrome_settings, use_in_wechat.

JSON Input Rules

• Options like --input-field, --attachments, --body accept inline JSON.
• You can also pass @file.json, for example: --input-fields @inputs.json.
• For workflow run, input field objects must include: node_id, field_name, value.
• workflow run --upload-to format: node_id:field_name:local_file_path (repeat this option for multiple files).

Features

• Sync & Async clients — VectorVeinClient and AsyncVectorVeinClient
• Workflow execution — run workflows, poll status, create workflows via API
• Workflow management APIs — list/update workflows, records, schedules, templates, tags, trash, fast access
• Workflow builder — programmatically construct workflows with 50+ node types
• AI Agent management — create agents, run tasks, manage cycles
• File upload — upload files to the platform
• Access key management — create, list, update, delete access keys
• Agent workspace — read/write/list/zip files and trigger container sync
• User APIs — get current user info and validate API key

Workflow Execution

Synchronous

from vectorvein.api import VectorVeinClient, WorkflowInputField

with VectorVeinClient(api_key="YOUR_API_KEY") as client:
    # Fire-and-forget
    rid = client.run_workflow(
        wid="workflow_id",
        input_fields=[WorkflowInputField(node_id="n1", field_name="text", value="hello")],
        wait_for_completion=False,
    )

    # Poll for result
    result = client.check_workflow_status(rid=rid)
    print(result.status, result.data)

Asynchronous

import asyncio
from vectorvein.api import AsyncVectorVeinClient, WorkflowInputField

async def main():
    async with AsyncVectorVeinClient(api_key="YOUR_API_KEY") as client:
        result = await client.run_workflow(
            wid="workflow_id",
            input_fields=[WorkflowInputField(node_id="n1", field_name="text", value="hello")],
            wait_for_completion=True,
            timeout=120,
        )
        print(result.data)

asyncio.run(main())

Create a Workflow via API

workflow = client.create_workflow(
    title="My Workflow",
    brief="Created via SDK",
    data={"nodes": [...], "edges": [...]},
    language="en-US",
)
print(workflow.wid)

Workflow Management APIs

# Workflows
wf = client.get_workflow("workflow_id")
items = client.list_workflows(page=1, page_size=20)
client.update_workflow("workflow_id", data=wf.data, title="Updated title")

# Templates / Tags
templates = client.list_workflow_templates(page=1, page_size=20)
tags = client.list_workflow_tags()

# Run records / schedules
records = client.list_workflow_run_records(page=1, page_size=20)
schedules = client.list_workflow_run_schedules(page=1, page_size=20)

# Vector / relational data assets
vector_dbs = client.list_vector_databases(page=1, page_size=20)
objects = client.list_vector_database_objects("vid_1", page=1, page_size=20)
tables = client.list_relational_database_tables("rid_1", page=1, page_size=20)
records = client.list_relational_database_table_records("tid_1", page=1, page_size=20)

Workflow Builder

Build workflows in pure Python — no JSON editing required.

from vectorvein.workflow.graph.workflow import Workflow
from vectorvein.workflow.nodes import OpenAI, TemplateCompose, TextInOut, Text

workflow = Workflow()

# Create nodes
text_input = TextInOut("input")
text_input.ports["text"].value = "Tell me a joke"

template = TemplateCompose("tpl")
template.ports["template"].value = "User says: {{user_input}}\nRespond with humor."
template.add_port("user_input", "text", value="", is_output=False)

llm = OpenAI("llm")
llm.ports["llm_model"].value = "gpt-4"
llm.ports["temperature"].value = 0.9

output = Text("out")

# Assemble
workflow.add_nodes([text_input, template, llm, output])
workflow.connect(text_input, "output", template, "user_input")
workflow.connect(template, "output", llm, "prompt")
workflow.connect(llm, "output", output, "text")

# Validate & layout
print(workflow.check())   # {"no_cycle": True, "no_isolated_nodes": True, ...}
workflow.layout({"direction": "LR"})

# Export
json_str = workflow.to_json()
mermaid_str = workflow.to_mermaid()

# Push to platform
client.create_workflow(title="Joke Bot", data=workflow.to_dict())

Available Node Types (50+)

Category	Nodes
LLMs	OpenAI, Claude, Gemini, Deepseek, AliyunQwen, BaiduWenxin, ChatGLM, MiniMax, Moonshot, LingYiWanWu, XAi, CustomModel
Text Processing	TextInOut, TemplateCompose, TextSplitters, TextReplace, TextTruncation, RegexExtract, ListRender, MarkdownToHtml
Output	Text, Table, Audio, Document, Html, Echarts, Email, Mermaid, Mindmap, PictureRender
Image Generation	DallE, StableDiffusion, Flux1, Kolors, Recraft, Pulid, Inpainting, BackgroundGeneration
Media Processing	GptVision, ClaudeVision, GeminiVision, QwenVision, DeepseekVl, GlmVision, InternVision, Ocr, SpeechRecognition
Media Editing	ImageEditing, ImageBackgroundRemoval, ImageSegmentation, ImageWatermark, AudioEditing, VideoEditing, VideoScreenshot
Video Generation	KlingVideo, CogVideoX
Audio	Tts, SoundEffects, MinimaxMusicGeneration
Web Crawlers	TextCrawler, BilibiliCrawler, DouyinCrawler, YoutubeCrawler
Tools	ProgrammingFunction, TextSearch, ImageSearch, TextTranslation, CodebaseAnalysis, WorkflowInvoke
Control Flow	Conditional, JsonProcess, RandomChoice, HumanFeedback, Empty
File Processing	FileUpload, FileLoader
Database	RunSql, GetTableInfo, SmartQuery
Vector DB	AddData, DeleteData, Search
Triggers	ButtonTrigger

Workflow Utilities

from vectorvein.workflow.utils.json_to_code import generate_python_code
from vectorvein.workflow.utils.analyse import analyse_workflow_record, format_workflow_analysis_for_llm

# Convert workflow JSON to Python code
code = generate_python_code(json_file="workflow.json")

# Analyse workflow structure
result = analyse_workflow_record(json_str, connected_only=True)
summary = format_workflow_analysis_for_llm(result, max_length=200)

AI Agent

Create and Run an Agent Task

from vectorvein.api import AgentDefinition, AgentSettings, TaskInfo, VectorVeinClient

with VectorVeinClient(api_key="YOUR_API_KEY") as client:
    # Create an agent
    agent = client.create_agent(
        name="Research Assistant",
        system_prompt="You are a helpful research assistant.",
        default_model_name="gpt-5.4",
        default_load_user_memory=True,
        default_compress_memory_after_tokens=64000,
        default_cloud_storage_paths=["/documents/reports"],
    )

    # Run a task
    task = client.create_agent_task(
        task_info=TaskInfo(text="Summarize the latest AI news"),
        agent_id_to_start=agent.agent_id,
    )

    # Check status
    task = client.get_agent_task(task.task_id)
    print(task.status, task.result)

    # List cycles (reasoning steps)
    cycles = client.list_agent_cycles(task_id=task.task_id)
    for cycle in cycles.cycles:
        print(f"Cycle {cycle.cycle_index}: {cycle.title}")

Agent Schema Notes

# Custom agent definitions / settings follow backend ai_agents field names.
# Use *_after_tokens; legacy *_after_characters is removed from the SDK.
definition = AgentDefinition(
    model_name="glm-5.1",
    backend_type="zhipuai",
    compress_memory_after_tokens=64000,
    agent_type="computer",
    workspace_files=[],
    sub_agent_ids=[],
)

settings = AgentSettings(
    model_name="glm-5.1",
    backend_type="zhipuai",
    compress_memory_after_tokens=96000,
    agent_type="tool",
)

Agent Task Control

client.pause_agent_task(task_id=task.task_id)
client.resume_agent_task(task_id=task.task_id)
client.continue_agent_task(task_id=task.task_id, message="Also check arxiv")

# Pending input and task preferences
client.add_pending_message(task_id=task.task_id, message="Prioritize security checks")
client.toggle_agent_task_favorite(task_id=task.task_id, is_favorited=True)

# Prompt optimization helpers
client.start_prompt_optimization(task_id=task.task_id, optimization_direction="Improve instruction clarity")
optimizer = client.get_prompt_optimizer_config()

# Agent favorites and system-prompt updates
favorites = client.list_favorite_agents(page=1, page_size=20)
client.update_agent_system_prompt(agent_id=agent.agent_id, system_prompt="You are concise and accurate.")

Agent Ecosystem APIs

# Collections / tags
collections = client.list_agent_collections(page=1, page_size=20)
tags = client.list_agent_tags()

# Skills / user memory
skills = client.list_skills(page=1, page_size=20)
memories = client.list_user_memories(page=1, page_size=20)

# MCP / workflow tools / schedules
servers = client.list_mcp_servers(page=1, page_size=20)
tools = client.list_my_workflow_tools()
schedules = client.list_task_schedules(page=1, page_size=20)

File Upload

result = client.upload_file("report.pdf")
print(result.oss_path, result.file_size)

Access Key Management

# Create a long-term access key
keys = client.create_access_keys(access_key_type="L", app_id="app_id", description="prod key")
print(keys[0].access_key)

# List keys
response = client.list_access_keys(page=1, page_size=20)
for key in response.access_keys:
    print(key.access_key, key.status, key.use_count)

# Delete
client.delete_access_keys(app_id="app_id", access_keys=["key_to_delete"])

Agent Workspace

# List files in workspace
files = client.list_workspace_files(workspace_id="ws_id")
for f in files.files:
    print(f.key, f.size)

# Read / Write
content = client.read_workspace_file(workspace_id="ws_id", file_path="notes.txt")
client.write_workspace_file(workspace_id="ws_id", file_path="output.txt", content="done")

# Download
url = client.download_workspace_file(workspace_id="ws_id", file_path="result.csv")

# Zip all files in workspace
zip_info = client.zip_workspace_files(workspace_id="ws_id")
print(zip_info["download_url"])

# Trigger async container-to-OSS sync (Computer Agent workspace)
client.sync_workspace_container_to_oss(workspace_id="ws_id")

User APIs

identity = client.validate_api_key()
print(identity.user_id, identity.username)

profile = client.get_user_info()
print(profile["username"], profile["credits"])

Exceptions

All exceptions inherit from VectorVeinAPIError:

Exception	Description
APIKeyError	Invalid or expired API key
WorkflowError	Workflow execution failure
AccessKeyError	Access key operation failure
RequestError	HTTP request failure
TimeoutError	Operation timed out

from vectorvein.api import VectorVeinClient, APIKeyError, WorkflowError, TimeoutError

try:
    result = client.run_workflow(wid="wf_id", input_fields=[], wait_for_completion=True, timeout=30)
except TimeoutError:
    print("Workflow took too long")
except WorkflowError as e:
    print(f"Workflow failed: {e}")
except APIKeyError:
    print("Check your API key")

Development

git clone <repo-url>
cd vectorvein-sdk
pip install -e ".[dev]"

# Run unit tests (no API key needed)
python -m pytest tests/ -v

# Run all tests including live API tests
VECTORVEIN_RUN_LIVE_TESTS=1 python -m pytest tests/ -v

For live tests, copy tests/dev_settings.example.py to tests/dev_settings.py and fill in your credentials.

License

MIT