This guide provides a quick overview for getting started with Taiga tooling in langchain_taiga. For more details on each tool and configuration, see the docstrings in your repository or relevant doc pages.

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Overview

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Integration details

ClassPackageSerializableJS supportPackage latest
create_entity_tool, search_entities_tool, get_entity_by_ref_tool, update_entity_by_ref_tool , add_comment_by_ref_tool, add_attachment_by_ref_toollangchain-taigaN/ATBDPyPI - Version

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Tool features

  • create_entity_tool: Creates user stories, tasks and issues in Taiga.
  • search_entities_tool: Searches for user stories, tasks and issues in Taiga.
  • get_entity_by_ref_tool: Gets a user story, task or issue by reference.
  • update_entity_by_ref_tool: Updates a user story, task or issue by reference.
  • add_comment_by_ref_tool: Adds a comment to a user story, task or issue.
  • add_attachment_by_ref_tool: Adds an attachment to a user story, task or issue.

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Setup

The integration lives in the langchain-taiga package. 
%pip install --quiet -U langchain-taiga

/home/henlein/Workspace/PyCharm/langchain/.venv/bin/python: No module named pip
Note: you may need to restart the kernel to use updated packages.

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Credentials

This integration requires you to set TAIGA_URL, TAIGA_API_URL, TAIGA_USERNAME, TAIGA_PASSWORD and OPENAI_API_KEY as environment variables to authenticate with Taiga. 
export TAIGA_URL="https://taiga.xyz.org/"
export TAIGA_API_URL="https://taiga.xyz.org/"
export TAIGA_USERNAME="username"
export TAIGA_PASSWORD="pw"
export OPENAI_API_KEY="OPENAI_API_KEY"
It’s also helpful (but not needed) to set up LangSmith for best-in-class observability: 
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

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Instantiation

Below is an example showing how to instantiate the Taiga tools in langchain_taiga. Adjust as needed for your specific usage. 
from langchain_taiga.tools.discord_read_messages import create_entity_tool
from langchain_taiga.tools.discord_send_messages import search_entities_tool

create_tool = create_entity_tool
search_tool = search_entities_tool

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Invocation

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Direct invocation with args

Below is a simple example of calling the tool with keyword arguments in a dictionary. 
from langchain_taiga.tools.taiga_tools import (
    add_attachment_by_ref_tool,
    add_comment_by_ref_tool,
    create_entity_tool,
    get_entity_by_ref_tool,
    search_entities_tool,
    update_entity_by_ref_tool,
)

response = create_entity_tool.invoke(
    {
        "project_slug": "slug",
        "entity_type": "us",
        "subject": "subject",
        "status": "new",
        "description": "desc",
        "parent_ref": 5,
        "assign_to": "user",
        "due_date": "2022-01-01",
        "tags": ["tag1", "tag2"],
    }
)

response = search_entities_tool.invoke(
    {"project_slug": "slug", "query": "query", "entity_type": "task"}
)

response = get_entity_by_ref_tool.invoke(
    {"entity_type": "user_story", "project_id": 1, "ref": "1"}
)

response = update_entity_by_ref_tool.invoke(
    {"project_slug": "slug", "entity_ref": 555, "entity_type": "us"}
)


response = add_comment_by_ref_tool.invoke(
    {"project_slug": "slug", "entity_ref": 3, "entity_type": "us", "comment": "new"}
)

response = add_attachment_by_ref_tool.invoke(
    {
        "project_slug": "slug",
        "entity_ref": 3,
        "entity_type": "us",
        "attachment_url": "url",
        "content_type": "png",
        "description": "desc",
    }
)

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Invocation with ToolCall

If you have a model-generated ToolCall, pass it to tool.invoke() in the format shown below. 
# This is usually generated by a model, but we'll create a tool call directly for demo purposes.
model_generated_tool_call = {
    "args": {"project_slug": "slug", "query": "query", "entity_type": "task"},
    "id": "1",
    "name": search_entities_tool.name,
    "type": "tool_call",
}
tool.invoke(model_generated_tool_call)

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Chaining

Below is a more complete example showing how you might integrate the create_entity_tool and search_entities_tool tools in a chain or agent with an LLM. This example assumes you have a function (like create_react_agent) that sets up a LangChain-style agent capable of calling tools when appropriate. 
# Example: Using Taiga Tools in an Agent

from langgraph.prebuilt import create_react_agent
from langchain_taiga.tools.taiga_tools import create_entity_tool, search_entities_tool

# 1. Instantiate or configure your language model
# (Replace with your actual LLM, e.g., ChatOpenAI(temperature=0))
llm = ...

# 2. Build an agent that has access to these tools
agent_executor = create_react_agent(llm, [create_entity_tool, search_entities_tool])

# 4. Formulate a user query that may invoke one or both tools
example_query = "Please create a new user story with the subject 'subject' in slug project: 'slug'"

# 5. Execute the agent in streaming mode (or however your code is structured)
events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)

# 6. Print out the model's responses (and any tool outputs) as they arrive
for event in events:
    event["messages"][-1].pretty_print()

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API reference

See the docstrings in: for usage details, parameters, and advanced configurations.