Python SDK

pip install tenzro
# or
poetry add tenzro
# or
uv add tenzro

from tenzro import Tenzro
import os

tenzro = Tenzro(api_key=os.environ["TENZRO_API_KEY"])

# AI chat completion
response = tenzro.ai.chat(
    model="gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Hello!"},
    ],
)
print(response.choices[0].message.content)

from tenzro import AsyncTenzro
import asyncio

async def main():
    tenzro = AsyncTenzro(api_key=os.environ["TENZRO_API_KEY"])

    response = await tenzro.ai.chat(
        model="gemini-2.5-flash",
        messages=[{"role": "user", "content": "Hello!"}],
    )
    print(response.choices[0].message.content)

    await tenzro.close()

asyncio.run(main())

# Create vector database
db = tenzro.vec.create(
    project_id="project-id",
    db_name="embeddings",
    dimension=1536,
)

# Insert vectors
tenzro.vec.upsert(
    db_id=db.vec_db_id,
    vectors=[
        {
            "id": "doc-1",
            "vector": embedding,
            "metadata": {"title": "Document 1"},
        },
    ],
)

# Search vectors
results = tenzro.vec.search(
    db_id=db.vec_db_id,
    vector=query_embedding,
    top_k=10,
)

# Set value with TTL
tenzro.kev.set(
    db_id=kev_db_id,
    key="session:123",
    value=user_data,
    ttl=3600,  # 1 hour
)

# Get value
session = tenzro.kev.get_value(db_id=kev_db_id, key="session:123")

# Delete value
tenzro.kev.delete_key(db_id=kev_db_id, key="session:123")

# Create agent
agent = tenzro.agents.create(
    project_id="project-id",
    agent_name="assistant",
    system_prompt="You are a helpful assistant.",
    ai_model="gemini-2.5-flash",
)

# Chat with agent
response = tenzro.agents.chat(
    agent_id=agent.agent_id,
    message="Hello!",
    conversation_id="conv-123",
)

print(response.response)

# Stream AI responses
stream = tenzro.ai.stream(
    model="gemini-2.5-flash",
    messages=[{"role": "user", "content": "Write a story"}],
)

for chunk in stream:
    print(chunk.text, end="", flush=True)

from tenzro import APIError, AuthenticationError, RateLimitError

try:
    tenzro.ai.chat(model="gemini-2.5-flash", messages=[])
except AuthenticationError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limited, retry after: {e.retry_after}")
except APIError as e:
    print(f"API error {e.status_code}: {e.message}")

# Automatically close client
with Tenzro(api_key=api_key) as tenzro:
    response = tenzro.ai.chat(
        model="gemini-2.5-flash",
        messages=[{"role": "user", "content": "Hello!"}],
    )

# Async context manager
async with AsyncTenzro(api_key=api_key) as tenzro:
    response = await tenzro.ai.chat(
        model="gemini-2.5-flash",
        messages=[{"role": "user", "content": "Hello!"}],
    )