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Zoho-Crm

The Zoho-Crm agent connector is a Python package that equips AI agents to interact with Zoho-Crm through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.

Connector for the Zoho CRM API, providing access to CRM modules including leads, contacts, accounts, deals, campaigns, tasks, events, calls, products, quotes, and invoices. Supports OAuth 2.0 authentication with regional data center support (US, EU, AU, IN, CN, JP). Supports read operations (list and get) for all entities.

Example prompts​

The Zoho-Crm connector is optimized to handle prompts like these.

  • List all leads
  • Show me details for a specific lead
  • List all contacts
  • List all accounts
  • List all open deals
  • Show me details for a specific deal
  • List all campaigns
  • List all tasks
  • List all events
  • List recent calls
  • List all products
  • List all quotes
  • List all invoices
  • List all notes
  • Show me details for a specific note
  • Show me details for a specific call
  • Show me details for a specific campaign
  • Show me details for a specific event
  • Show me details for a specific invoice
  • Show me details for a specific product
  • Show me details for a specific quote
  • Show me details for a specific account
  • Show me details for a specific contact
  • Show me details for a specific task
  • Show me leads created in the last 30 days
  • Which deals have the highest amount?
  • List all contacts at a specific company
  • What is the total revenue across all deals by stage?
  • Show me overdue tasks
  • Which campaigns generated the most leads?
  • Summarize the deal pipeline by stage
  • Show me accounts with the highest annual revenue
  • List all events scheduled for this week
  • What are the top-performing products by unit price?
  • Show me all invoices that are past due
  • Break down leads by source and industry

Unsupported prompts​

The Zoho-Crm connector isn't currently able to handle prompts like these.

  • Delete a deal record
  • Send an email to a lead
  • Convert a lead to a contact
  • Merge duplicate contacts
  • Bulk import leads from CSV
  • Create a workflow rule

Entities and actions​

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

EntityActions
LeadsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
ContactsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
AccountsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
DealsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
CampaignsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
TasksList, Get, Context Store Search, Context Store SQL Query, Semantic Search
EventsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
CallsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
ProductsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
QuotesList, Get, Context Store Search, Context Store SQL Query
InvoicesList, Get, Context Store Search, Context Store SQL Query
NotesList, Get, Context Store Search, Context Store SQL Query, Semantic Search

Zoho-Crm API docs​

See the official Zoho-Crm API reference.

Interfaces​

Use the Zoho-Crm connector through the Airbyte Agent CLI, the Python SDK, or the API.

CLI​

Install the CLI:

curl -fsSL https://airbyte.ai/install.sh | bash

Authenticate with Airbyte:

airbyte-agent login

Create the connector. The CLI opens the hosted setup flow:

airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "zoho-crm"
}'

Describe the connector to see its supported entities and actions:

airbyte-agent connectors describe --json '{
  "workspace": "<your_workspace_name>",
  "name": "zoho-crm"
}'

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "zoho-crm",
  "entity": "leads",
  "action": "list"
}'

Python SDK​

Installation​

uv pip install airbyte-agent-sdk

Usage​

Connectors can run in hosted or open source mode.

Hosted​

In hosted mode, API credentials are stored securely in Airbyte Agents. You provide your Airbyte credentials instead. If your Airbyte client can access multiple organizations, also set organization_id.

This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.

The connect() factory returns a fully typed ZohoCrmConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector

connector = connect("zoho-crm", workspace_name="<your_workspace_name>")

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use ZohoCrmConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector

connector = connect("zoho-crm", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZohoCrmConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="zoho_crm_inspect",
    docs_tool="zoho_crm_read_docs",
)
async def zoho_crm_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@ZohoCrmConnector.agent_tool(framework="pydantic_ai")
async def zoho_crm_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@ZohoCrmConnector.agent_tool(framework="pydantic_ai")
async def zoho_crm_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector

connector = connect("zoho-crm", workspace_name="<your_workspace_name>")

@ZohoCrmConnector.agent_tool(
    inspect_tool="zoho_crm_inspect",
    docs_tool="zoho_crm_read_docs",
)
async def zoho_crm_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ZohoCrmConnector.agent_tool()
async def zoho_crm_inspect():
    return await connector.inspect_connector()

@ZohoCrmConnector.agent_tool()
async def zoho_crm_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (zoho_crm_inspect, zoho_crm_read_docs, zoho_crm_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated ZohoCrmConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or ZohoCrmConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector

connector = connect("zoho-crm", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZohoCrmConnector.tool_utils
async def zoho_crm_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZohoCrmConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Open source​

In open source mode, you provide API credentials directly to the connector.

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector
from airbyte_agent_sdk.connectors.zoho_crm.models import ZohoCrmAuthConfig

connector = ZohoCrmConnector(
    auth_config=ZohoCrmAuthConfig(
        client_id="<OAuth 2.0 Client ID from Zoho Developer Console>",
        client_secret="<OAuth 2.0 Client Secret from Zoho Developer Console>",
        refresh_token="<OAuth 2.0 Refresh Token (does not expire)>"
    ),
    dc_region="<The Zoho data center region domain suffix: - com (US) - com.au (AU) - eu (EU) - in (IN) - com.cn (CN) - jp (JP)
>"
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use ZohoCrmConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector
from airbyte_agent_sdk.connectors.zoho_crm.models import ZohoCrmAuthConfig

connector = ZohoCrmConnector(
    auth_config=ZohoCrmAuthConfig(
        client_id="<OAuth 2.0 Client ID from Zoho Developer Console>",
        client_secret="<OAuth 2.0 Client Secret from Zoho Developer Console>",
        refresh_token="<OAuth 2.0 Refresh Token (does not expire)>"
    ),
    dc_region="<The Zoho data center region domain suffix: - com (US) - com.au (AU) - eu (EU) - in (IN) - com.cn (CN) - jp (JP)
>"
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZohoCrmConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="zoho_crm_inspect",
    docs_tool="zoho_crm_read_docs",
)
async def zoho_crm_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@ZohoCrmConnector.agent_tool(framework="pydantic_ai")
async def zoho_crm_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@ZohoCrmConnector.agent_tool(framework="pydantic_ai")
async def zoho_crm_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector
from airbyte_agent_sdk.connectors.zoho_crm.models import ZohoCrmAuthConfig

connector = ZohoCrmConnector(
    auth_config=ZohoCrmAuthConfig(
        client_id="<OAuth 2.0 Client ID from Zoho Developer Console>",
        client_secret="<OAuth 2.0 Client Secret from Zoho Developer Console>",
        refresh_token="<OAuth 2.0 Refresh Token (does not expire)>"
    ),
    dc_region="<The Zoho data center region domain suffix: - com (US) - com.au (AU) - eu (EU) - in (IN) - com.cn (CN) - jp (JP)
>"
)

@ZohoCrmConnector.agent_tool(
    inspect_tool="zoho_crm_inspect",
    docs_tool="zoho_crm_read_docs",
)
async def zoho_crm_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ZohoCrmConnector.agent_tool()
async def zoho_crm_inspect():
    return await connector.inspect_connector()

@ZohoCrmConnector.agent_tool()
async def zoho_crm_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (zoho_crm_inspect, zoho_crm_read_docs, zoho_crm_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated ZohoCrmConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or ZohoCrmConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zoho_crm import ZohoCrmConnector
from airbyte_agent_sdk.connectors.zoho_crm.models import ZohoCrmAuthConfig

connector = ZohoCrmConnector(
    auth_config=ZohoCrmAuthConfig(
        client_id="<OAuth 2.0 Client ID from Zoho Developer Console>",
        client_secret="<OAuth 2.0 Client Secret from Zoho Developer Console>",
        refresh_token="<OAuth 2.0 Refresh Token (does not expire)>"
    ),
    dc_region="<The Zoho data center region domain suffix: - com (US) - com.au (AU) - eu (EU) - in (IN) - com.cn (CN) - jp (JP)
>"
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZohoCrmConnector.tool_utils
async def zoho_crm_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Authentication​

For all authentication options, see the connector's authentication documentation.

IP allow list​

If your organization restricts access to specific IPs, add the Airbyte Agents IP addresses to your allow list.

Version information​

Connector version: 1.1.0

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