cognee_add_developer_rules
Ingest core developer rule files into Cognee's memory layer.
This function loads a predefined set of developer-related configuration,
rule, and documentation files from the base repository and assigns them
to the special 'developer_rules' node set in Cognee. It ensures these
foundational files are always part of the structured memory graph.
Parameters
----------
base_path : str
Root path to resolve relative file paths. Defaults to current directory.
graph_model_file : str, optional
Optional path to a custom schema file for knowledge graph generation.
graph_model_name : str, optional
Optional class name to use from the graph_model_file schema.
Returns
-------
list
A message indicating how many rule files were scheduled for ingestion,
and how to check their processing status.
Notes
-----
- Each file is processed asynchronously in the background.
- Files are attached to the 'developer_rules' node set.
- Missing files are skipped with a logged warning.
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cognify
Transform data into a structured knowledge graph in Cognee's memory layer.
This function launches a background task that processes the provided text/file location and
generates a knowledge graph representation. The function returns immediately while
the processing continues in the background due to MCP timeout constraints.
Parameters
----------
data : str
The data to be processed and transformed into structured knowledge.
This can include natural language, file location, or any text-based information
that should become part of the agent's memory.
graph_model_file : str, optional
Path to a custom schema file that defines the structure of the generated knowledge graph.
If provided, this file will be loaded using importlib to create a custom graph model.
Default is None, which uses Cognee's built-in KnowledgeGraph model.
graph_model_name : str, optional
Name of the class within the graph_model_file to instantiate as the graph model.
Required if graph_model_file is specified.
Default is None, which uses the default KnowledgeGraph class.
Returns
-------
list
A list containing a single TextContent object with information about the
background task launch and how to check its status.
Notes
-----
- The function launches a background task and returns immediately
- The actual cognify process may take significant time depending on text length
- Use the cognify_status tool to check the progress of the operation
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save_interaction
Logs user-agent interactions and query-answer pairs
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codify
Analyze and generate a code-specific knowledge graph from a software repository.
This function launches a background task that processes the provided repository
and builds a code knowledge graph. The function returns immediately while
the processing continues in the background due to MCP timeout constraints.
Parameters
----------
repo_path : str
Path to the code repository to analyze. This can be a local file path or a
relative path to a repository. The path should point to the root of the
repository or a specific directory within it.
Returns
-------
list
A list containing a single TextContent object with information about the
background task launch and how to check its status.
Notes
-----
- The function launches a background task and returns immediately
- The code graph generation may take significant time for larger repositories
- Use the codify_status tool to check the progress of the operation
- Process results are logged to the standard Cognee log file
- All stdout is redirected to stderr to maintain MCP communication integrity
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search
Search the Cognee knowledge graph for information relevant to the query.
This function executes a search against the Cognee knowledge graph using the
specified query and search type. It returns formatted results based on the
search type selected.
Parameters
----------
search_query : str
The search query in natural language. This can be a question, instruction, or
any text that expresses what information is needed from the knowledge graph.
search_type : str
The type of search to perform. Valid options include:
- "GRAPH_COMPLETION": Returns an LLM response based on the search query and Cognee's memory
- "RAG_COMPLETION": Returns an LLM response based on the search query and standard RAG data
- "CODE": Returns code-related knowledge in JSON format
- "CHUNKS": Returns raw text chunks from the knowledge graph
- "INSIGHTS": Returns relationships between nodes in readable format
The search_type is case-insensitive and will be converted to uppercase.
Returns
-------
list
A list containing a single TextContent object with the search results.
The format of the result depends on the search_type:
- For CODE: JSON-formatted search results
- For GRAPH_COMPLETION/RAG_COMPLETION: A single text completion
- For CHUNKS: String representation of the raw chunks
- For INSIGHTS: Formatted string showing node relationships
- For other types: String representation of the search results
Notes
-----
- Different search types produce different output formats
- The function handles the conversion between Cognee's internal result format and MCP's output format
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get_developer_rules
Retrieve all developer rules that were generated based on previous interactions.
This tool queries the Cognee knowledge graph and returns a list of developer
rules.
Parameters
----------
None
Returns
-------
list
A list containing a single TextContent object with the retrieved developer rules.
The format is plain text containing the developer rules in bulletpoints.
Notes
-----
- The specific logic for fetching rules is handled internally.
- This tool does not accept any parameters and is intended for simple rule inspection use cases.
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list_data
List all datasets and their data items with IDs for deletion operations.
This function helps users identify data IDs and dataset IDs that can be used
with the delete tool. It provides a comprehensive view of available data.
Parameters
----------
dataset_id : str, optional
If provided, only list data items from this specific dataset.
If None, lists all datasets and their data items.
Should be a valid UUID string.
Returns
-------
list
A list containing a single TextContent object with formatted information
about datasets and data items, including their IDs for deletion.
Notes
-----
- Use this tool to identify data_id and dataset_id values for the delete tool
- The output includes both dataset information and individual data items
- UUIDs are displayed in a format ready for use with other tools
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delete
Delete specific data from a dataset in the Cognee knowledge graph.
This function removes a specific data item from a dataset while keeping the
dataset itself intact. It supports both soft and hard deletion modes.
Parameters
----------
data_id : str
The UUID of the data item to delete from the knowledge graph.
This should be a valid UUID string identifying the specific data item.
dataset_id : str
The UUID of the dataset containing the data to be deleted.
This should be a valid UUID string identifying the dataset.
mode : str, optional
The deletion mode to use. Options are:
- "soft" (default): Removes the data but keeps related entities that might be shared
- "hard": Also removes degree-one entity nodes that become orphaned after deletion
Default is "soft" for safer deletion that preserves shared knowledge.
Returns
-------
list
A list containing a single TextContent object with the deletion results,
including status, deleted node counts, and confirmation details.
Notes
-----
- This operation cannot be undone. The specified data will be permanently removed.
- Hard mode may remove additional entity nodes that become orphaned
- The function provides detailed feedback about what was deleted
- Use this for targeted deletion instead of the prune tool which removes everything
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prune
Reset the Cognee knowledge graph by removing all stored information.
This function performs a complete reset of both the data layer and system layer
of the Cognee knowledge graph, removing all nodes, edges, and associated metadata.
It is typically used during development or when needing to start fresh with a new
knowledge base.
Returns
-------
list
A list containing a single TextContent object with confirmation of the prune operation.
Notes
-----
- This operation cannot be undone. All memory data will be permanently deleted.
- The function prunes both data content (using prune_data) and system metadata (using prune_system)
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cognify_status
Get the current status of the cognify pipeline.
This function retrieves information about current and recently completed cognify operations
in the main_dataset. It provides details on progress, success/failure status, and statistics
about the processed data.
Returns
-------
list
A list containing a single TextContent object with the status information as a string.
The status includes information about active and completed jobs for the cognify_pipeline.
Notes
-----
- The function retrieves pipeline status specifically for the "cognify_pipeline" on the "main_dataset"
- Status information includes job progress, execution time, and completion status
- The status is returned in string format for easy reading
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codify_status
Get the current status of the codify pipeline.
This function retrieves information about current and recently completed codify operations
in the codebase dataset. It provides details on progress, success/failure status, and statistics
about the processed code repositories.
Returns
-------
list
A list containing a single TextContent object with the status information as a string.
The status includes information about active and completed jobs for the cognify_code_pipeline.
Notes
-----
- The function retrieves pipeline status specifically for the "cognify_code_pipeline" on the "codebase" dataset
- Status information includes job progress, execution time, and completion status
- The status is returned in string format for easy reading
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