dobby-ai-sdk 0.3.0

Official Python SDK for the Dobby AI Platform — Home for your AI agents

Dobby SDK for Python

Official Python SDK for the Dobby AI Platform — Home for your AI agents.

Installation

pip install dobby-ai-sdk

Quick Start

from dobby_sdk import DobbyClient

client = DobbyClient(api_key="gk_user_...")

# LLM calls (OpenAI-compatible)
response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello from Dobby!"}],
)
print(response.choices[0].message.content)

# Streaming
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain AI agents"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

Control Points — inline policy enforcement

Ask Dobby for a policy verdict before your agent runs a risky action, and let your own code decide what to do. Unlike a gateway, Dobby returns the decision — your code is the enforcement point — and you report back what you did, which becomes an auditable adherence signal across your Org → Tenant → Process layers.

from dobby_sdk import DobbyClient, DobbyPolicyDenied

client = DobbyClient(api_key="sk_live_...", org_id="org_...", tenant_id="tenant_...")

try:
    # enforce() raises DobbyPolicyDenied if the policy blocks the action.
    client.controls.enforce("send_external_email", arguments={"to": addr})
    send_external_email(addr)              # runs only if the policy allowed it

except DobbyPolicyDenied as denied:
    # Your code halts the action — then report the honored stop.
    client.controls.report(
        denied.verdict.decision_id, honored=True, action_taken="stopped"
    )

Prefer to branch yourself instead of catching? Use check(), which returns a Verdict and never raises on a deny:

verdict = client.controls.check("delete_user", arguments={"user_id": uid})
if not verdict.allowed:
    print(f"Blocked: {verdict.reason}")    # also: verdict.action, verdict.decision_id

Task Management

# Create a task
task = client.tasks.create(
    title="Review PR #42",
    priority="high",
    agent_name="dobby-code-reviewer-agent",
)

# List pending tasks
tasks = client.tasks.list(status="pending")

# Approve a task (HITL)
client.approvals.approve(task["id"], comment="Looks good!")

Agent Fleet

# List all agents
agents = client.agents.list()

# Register an external agent
agent = client.agents.register(
    display_name="Research Agent",
    framework="crewai",
    protocol="a2a",
    endpoint_url="https://my-agent.example.com",
)

# Pause/resume
client.agents.pause("agent_abc123")

Cost Tracking

# Organization cost summary
costs = client.costs.summary(period="30d")

# Per-agent breakdown
agent_costs = client.costs.by_agent(period="7d")

Async Support

from dobby_sdk import AsyncDobbyClient

async with AsyncDobbyClient(api_key="gk_user_...") as client:
    response = await client.chat.completions.create(
        model="claude-sonnet-4-20250514",
        messages=[{"role": "user", "content": "Hello async!"}],
    )

Configuration

client = DobbyClient(
    api_key="gk_user_...",         # or DOBBY_API_KEY env var
    base_url="https://dobby-ai.com",  # or DOBBY_BASE_URL
    org_id="org_...",              # or DOBBY_ORG_ID
    tenant_id="tenant_...",        # or DOBBY_TENANT_ID
    timeout=120.0,
    max_retries=2,
)

Error Handling

from dobby_sdk import DobbyAuthError, DobbyRateLimitError, DobbyBudgetExceededError

try:
    response = client.chat.completions.create(...)
except DobbyAuthError:
    print("Invalid or expired API key")
except DobbyRateLimitError as e:
    print(f"Rate limited. Retry after: {e.retry_after}s")
except DobbyBudgetExceededError:
    print("Organization budget limit reached")