Jira Connector for AI Agents

Give agents tools for secure, real-time access to fetch, search, write, and sync across every system, with replication, entity mapping, and auth built-in.

20,000+
community members
6,000+
daily active companies
2PB+
synced/month
900+
contributors

About this Connector

Jira is a project management and issue tracking platform used by software teams. This connector provides access to issues, projects, users, comments, worklogs, and fields.

CRM

Sales Analytics

Customer Data

project management, issue tracking, team collaboration

Version Information

Package version

0.1.92

Connector version

1.1.6

SDK commit

64ac3a66e77864c942825e7f9491a0a46e704d21

Support Open Source

Star us on GitHub to help grow the Airbyte community

Github

Installation & Usage

Get started with the Jira connector in minutes

1

Install Package

Using uv or pip

bash

Copy
uv pip install airbyte-agent-jira

2

Import

Initialize and use

python

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from airbyte_agent_jira import JiraConnector
from airbyte_agent_jira.models import JiraAuthConfig

connector = JiraConnector(
    auth_config=JiraAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Jira API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

3

Tool

Add tools to your agent

python

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@agent.tool_plain # assumes you're using Pydantic AI
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Supported Entities & Actions

Access all your Jira data through a unified API

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| Entity | Actions |\n|--------|---------|\n| Issues | [API Search](./REFERENCE.md#issues-api_search), [Create](./REFERENCE.md#issues-create), [Get](./REFERENCE.md#issues-get), [Update](./REFERENCE.md#issues-update), [Delete](./REFERENCE.md#issues-delete), [Search](./REFERENCE.md#issues-search) |\n| Projects | [API Search](./REFERENCE.md#projects-api_search), [Get](./REFERENCE.md#projects-get), [Search](./REFERENCE.md#projects-search) |\n| Users | [Get](./REFERENCE.md#users-get), [List](./REFERENCE.md#users-list), [API Search](./REFERENCE.md#users-api_search), [Search](./REFERENCE.md#users-search) |\n| Issue Fields | [List](./REFERENCE.md#issue-fields-list), [API Search](./REFERENCE.md#issue-fields-api_search), [Search](./REFERENCE.md#issue-fields-search) |\n| Issue Comments | [List](./REFERENCE.md#issue-comments-list), [Create](./REFERENCE.md#issue-comments-create), [Get](./REFERENCE.md#issue-comments-get), [Update](./REFERENCE.md#issue-comments-update), [Delete](./REFERENCE.md#issue-comments-delete), [Search](./REFERENCE.md#issue-comments-search) |\n| Issue Worklogs | [List](./REFERENCE.md#issue-worklogs-list), [Get](./REFERENCE.md#issue-worklogs-get), [Search](./REFERENCE.md#issue-worklogs-search) |\n| Issues Assignee | [Update](./REFERENCE.md#issues-assignee-update) |

Example Prompts

The Jira connector is optimized to handle prompts like these

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Show me all open issues in my Jira instance - List recent issues created in the last 7 days - List all projects in my Jira instance - Show me details for the most recently updated issue - List all users in my Jira instance - Show me comments on the most recent issue - Show me worklogs from the last 7 days - Assign a recent issue to a teammate - Unassign a recent issue - Create a new task called 'Sample task' in a project - Create a bug with high priority - Update the summary of a recent issue to 'Updated summary' - Change the priority of a recent issue to high - Add a comment to a recent issue saying 'Please investigate' - Update my most recent comment - Delete a test issue - Remove my most recent comment - What issues are assigned to \{team_member\} this week? - Find all high priority bugs in our current sprint - Show me overdue issues across all projects - What projects have the most issues? - Search for users named \{user_name\}

Why Airbyte for AI Agents?

Built for production AI workloads with enterprise-grade reliability

Agent-Native Design

Structured, LLM-friendly schemas optimized for AI agent consumption with natural language query support.

Secure Authentication

Built-in OAuth 2.0 handling with automatic token refresh. No hard-coded credentials.

Production Ready

Battle-tested connectors with comprehensive error handling, logging, and retry logic.

Open Source

Fully open source under the MIT license. Contribute, customize, and extend freely.

Works with your favorite frameworks

Use the Jira connector with any AI agent framework

🦜

LangChain

🦙

LlamaIndex

🤖

CrewAI

AutoGen

🧠

OpenAI Agents SDK

🔮

Claude Agents SDK

Frequently Asked Questions

Didn't find your answer? Please don't hesitate to reach out.

How do I authenticate with Jira?

The Jira connector supports authentication via Atlassian account email and API token. You can generate API tokens from https://id.atlassian.com/manage-profile/security/api-tokens.

Can I use this connector with any AI agent framework?

The connector is compatible with any Python-based AI agent framework including LangChain, LlamaIndex, CrewAI, Pydantic AI, and custom implementations.

Does this connector support write operations?

Yes, the Jira connector supports write operations including creating issues, updating issues, deleting issues, creating/updating/deleting comments, and assigning/unassigning issues.

How is this different from the Airbyte data connector?

Agent connectors are specifically designed for AI agents and LLM applications. They provide natural language interfaces, optimized response formats, and seamless integration with agent frameworks, unlike traditional ETL-focused connectors.

Will there be a platform for agent connectors?

The hosted version with secure credential storage through Airbyte Cloud is already available. See the hosted usage section in the documentation for setup instructions.

20,000+
community members
6,000+
daily active companies
2PB+
synced/month
900+
contributors