Blazing Agents Python SDK
Build production agents in Python with a typed client for the Blazing Agents API.
The official Python SDK is a client for the Blazing Agents /v1 API that
your backend uses to manage Agents and run them for your users. It supports
CPython 3.11 and newer.
Features
- A typed method for every
/v1operation an API key can call: Agents, Skills, Sessions, Memories, Prompts, Tasks, Workspaces, Artifacts, Providers, MCP Connections, Chat Connections, usage, spending limits, Tenant settings, and merchant monetization. - Chat streams in the AI SDK UI message stream format, with steering, Tool approvals, and backend functions that run in your process.
- Text and structured-object generation.
- End-user scoped clients through
for_user. - Lazy pagination, uploads, and downloads.
- Request IDs and typed errors.
- Matching synchronous and asynchronous clients with Pydantic models.
Installation
pip install blazing-agents
Quick start
Create a Full API key in the Blazing Agents dashboard and set
BLAZING_AGENTS_API_KEY in your server environment. The key acts for your
whole Tenant, so keep it on your backend and never send it to a browser.
from blazing_agents import BlazingAgents
client = BlazingAgents()
answer = client.completion(
agent_id="ag_...",
prompt="Write a friendly welcome message.",
)
print(answer)
Replace ag_... with an Agent ID from the dashboard.
For requests from your users, scope the client to the user ID that your backend verified at sign-in, never to an ID from the request body. A scoped client reaches only that End-user's Agents, Sessions, and Workspace. An unscoped Agent shares one Workspace across all your users, so give each user an Agent of their own:
from uuid import uuid4
from blazing_agents import ChatStream
def create_agent_for_user(user_id: str) -> str:
agent = client.for_user(user_id).agents.create(
name="Assistant",
provider_id="prv_...",
model="openai/gpt-6-luna",
)
return agent.id
def chat_as_user(user_id: str, agent_id: str, text: str) -> ChatStream:
return client.for_user(user_id).chat(
agent_id=agent_id,
message={
"id": str(uuid4()),
"role": "user",
"parts": [{"type": "text", "text": text}],
},
)
Call create_agent_for_user once per user and store the Agent ID with the
user. A scoped chat with an Agent created without the scope fails with 404.
chat_as_user returns the chat as a stream of server-sent event bytes.
Send it to the browser as a text/event-stream response, and AI SDK
useChat reads it. For asyncio applications, AsyncBlazingAgents has the
same methods.
Examples
The examples repository has complete TypeScript and Python apps built on the Blazing Agents SDKs.
Documentation
Read the Python SDK documentation for client setup, End-user scoping, chat and generation, a guide to each resource, and error handling.
Development
uv sync --locked
uv run ruff check .
uv run ruff format --check .
uv run python scripts/run_typechecks.py
uv run python scripts/run_tests.py
See CONTRIBUTING.md for the changelog convention and the release process.
License
1 maintainer
Metadata
1 maintainer
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