Reference

agentbx

AgentBX: Crystallographic data processing and analysis framework.

class agentbx.Bundle(bundle_type, bundle_id=None)

Bases: ABC

Base class for data bundles in agentbx.

Bundles are containers that hold related data assets and metadata. Each bundle has a specific type and can contain multiple named assets.

Parameters:

• bundle_type (str) –

• bundle_id (Optional[str]) –

__init__(bundle_type, bundle_id=None)

Initialize bundle.

Parameters:

• bundle_type (str) – Type identifier for this bundle

• bundle_id (Optional[str]) – Optional custom ID

add_asset(name, asset)

Add an asset to the bundle.

Parameters:

• name (str) – Asset name

• asset (Any) – Asset data

Return type:

None

get_asset(name)

Get an asset from the bundle.

Parameters:

name (str) – Asset name

Returns:

Asset data

Raises:

KeyError – If asset not found

Return type:

Any

has_asset(name)

Check if bundle has an asset.

Parameters:

name (str) – Asset name

Returns:

True if asset exists

Return type:

bool

remove_asset(name)

Remove an asset from the bundle.

Parameters:

name (str) – Asset name

Returns:

True if asset was removed, False if not found

Return type:

bool

add_metadata(key, value)

Add metadata to the bundle.

Parameters:

• key (str) – Metadata key

• value (Any) – Metadata value

Return type:

None

get_metadata(key, default=None)

Get metadata from the bundle.

Parameters:

• key (str) – Metadata key

• default (Optional[Any]) – Default value if key not found

Returns:

Metadata value

Return type:

Any

calculate_checksum()

Calculate checksum of bundle contents.

Returns:

SHA256 checksum (first 16 characters)

Return type:

str

validate()

Validate bundle contents.

Returns:

True if bundle is valid

Raises:

ValueError – If bundle is invalid

Return type:

bool

get_size_estimate()

Estimate bundle size in bytes.

Returns:

Estimated size in bytes

Return type:

int

class agentbx.RedisConfig(host='localhost', port=6379, db=0, password=None, max_connections=10, socket_timeout=5, socket_connect_timeout=5, retry_on_timeout=True, health_check_interval=30, default_ttl=3600)

Bases: object

Redis configuration settings.

Parameters:

• host (str) –

• port (int) –

• db (int) –

• password (Optional[str]) –

• max_connections (int) –

• socket_timeout (int) –

• socket_connect_timeout (int) –

• retry_on_timeout (bool) –

• health_check_interval (int) –

• default_ttl (int) –

classmethod from_env()

Create Redis config from environment variables.

Return type:

RedisConfig

__init__(host='localhost', port=6379, db=0, password=None, max_connections=10, socket_timeout=5, socket_connect_timeout=5, retry_on_timeout=True, health_check_interval=30, default_ttl=3600)

Parameters:

• host (str) –

• port (int) –

• db (int) –

• password (Optional[str]) –

• max_connections (int) –

• socket_timeout (int) –

• socket_connect_timeout (int) –

• retry_on_timeout (bool) –

• health_check_interval (int) –

• default_ttl (int) –

Return type:

None

class agentbx.AgentConfig(agent_id, redis_config)

Bases: object

Agent configuration settings.

Parameters:

• agent_id (str) –

• redis_config (RedisConfig) –

classmethod from_env(agent_id)

Create agent config from environment variables.

Parameters:

agent_id (str) –

Return type:

AgentConfig

__init__(agent_id, redis_config)

Parameters:

• agent_id (str) –

• redis_config (RedisConfig) –

Return type:

None

class agentbx.RedisManager(host='localhost', port=6379, db=0, password=None, max_connections=10, socket_timeout=5, socket_connect_timeout=5, retry_on_timeout=True, health_check_interval=30, default_ttl=3600)

Bases: object

Manages Redis connections and provides high-level operations for agentbx.

Features: - Connection pooling and health checks - Automatic serialization/deserialization of complex objects - Bundle storage and retrieval with metadata - Caching with TTL support - Error handling and retry logic

Parameters:

• host (str) –

• port (int) –

• db (int) –

• password (Optional[str]) –

• max_connections (int) –

• socket_timeout (int) –

• socket_connect_timeout (int) –

• retry_on_timeout (bool) –

• health_check_interval (int) –

• default_ttl (int) –

__init__(host='localhost', port=6379, db=0, password=None, max_connections=10, socket_timeout=5, socket_connect_timeout=5, retry_on_timeout=True, health_check_interval=30, default_ttl=3600)

Initialize Redis manager with connection parameters.

Parameters:

• host (str) – Redis server hostname

• port (int) – Redis server port

• db (int) – Redis database number

• password (Optional[str]) – Redis password (if required)

• max_connections (int) – Maximum connections in pool

• socket_timeout (int) – Socket timeout in seconds

• socket_connect_timeout (int) – Connection timeout in seconds

• retry_on_timeout (bool) – Whether to retry on timeout

• health_check_interval (int) – Health check interval in seconds

• default_ttl (int) – Default TTL for cached items in seconds

is_healthy()

Check if Redis connection is healthy.

Return type:

bool

store_bundle(bundle, bundle_id=None)

Store a bundle in Redis.

Parameters:

• bundle (Any) – Bundle object to store

• bundle_id (Optional[str]) – Optional custom ID, auto-generated if None

Returns:

Bundle ID

Return type:

str

Raises:

ConnectionError – If Redis connection is not healthy.

get_bundle(bundle_id)

Retrieve a bundle from Redis.

Parameters:

bundle_id (str) – Bundle ID to retrieve

Returns:

Deserialized bundle object

Return type:

Any

Raises:

• ConnectionError – If Redis connection is not healthy.

• KeyError – If bundle is not found in Redis.

delete_bundle(bundle_id)

Delete a bundle from Redis.

Parameters:

bundle_id (str) – ID of bundle to delete

Returns:

True if deleted, False if not found

Return type:

bool

Raises:

ConnectionError – If Redis connection is not healthy.

list_bundles(bundle_type=None)

List all bundle IDs, optionally filtered by type.

Parameters:

bundle_type (Optional[str]) – Optional bundle type filter

Returns:

List of bundle IDs

Return type:

list[str]

Raises:

ConnectionError – If Redis connection is not healthy.

cache_get(key)

Get value from cache.

Parameters:

key (str) – Cache key

Returns:

Cached value or None if not found

Return type:

Any

cache_set(key, value, ttl=None)

Set value in cache.

Parameters:

• key (str) – Cache key

• value (Any) – Value to cache

• ttl (Optional[int]) – Time to live in seconds (uses default if None)

Returns:

True if successful

Return type:

bool

close()

Close Redis connection.

Return type:

None

get_bundle_metadata(bundle_id)

Get metadata for a specific bundle.

Parameters:

bundle_id (str) – Bundle ID to retrieve metadata for

Returns:

Bundle metadata

Return type:

dict

Raises:

• ConnectionError – If Redis connection is not healthy.

• KeyError – If bundle metadata is not found in Redis.

list_bundles_with_metadata(bundle_type=None)

List all bundles with their metadata, optionally filtered by type.

Parameters:

bundle_type (Optional[str]) – Optional bundle type filter

Returns:

List of bundle metadata dictionaries

Return type:

list[dict]

Raises:

ConnectionError – If Redis connection is not healthy.

inspect_bundle(bundle_id)

Get comprehensive information about a bundle including metadata and content summary.

Parameters:

bundle_id (str) – Bundle ID to inspect

Returns:

Comprehensive bundle information

Return type:

dict

Raises:

ConnectionError – If Redis connection is not healthy.

agentbx.main()

Main entry point for the CLI.

Return type:

None

Core Modules

Redis Manager

Redis manager for agentbx - handles connections, serialization, and caching.

class agentbx.core.redis_manager.RedisManager(host='localhost', port=6379, db=0, password=None, max_connections=10, socket_timeout=5, socket_connect_timeout=5, retry_on_timeout=True, health_check_interval=30, default_ttl=3600)

Bases: object

Manages Redis connections and provides high-level operations for agentbx.

Features: - Connection pooling and health checks - Automatic serialization/deserialization of complex objects - Bundle storage and retrieval with metadata - Caching with TTL support - Error handling and retry logic

Parameters:

• host (str) –

• port (int) –

• db (int) –

• password (Optional[str]) –

• max_connections (int) –

• socket_timeout (int) –

• socket_connect_timeout (int) –

• retry_on_timeout (bool) –

• health_check_interval (int) –

• default_ttl (int) –

__init__(host='localhost', port=6379, db=0, password=None, max_connections=10, socket_timeout=5, socket_connect_timeout=5, retry_on_timeout=True, health_check_interval=30, default_ttl=3600)

Initialize Redis manager with connection parameters.

Parameters:

• host (str) – Redis server hostname

• port (int) – Redis server port

• db (int) – Redis database number

• password (Optional[str]) – Redis password (if required)

• max_connections (int) – Maximum connections in pool

• socket_timeout (int) – Socket timeout in seconds

• socket_connect_timeout (int) – Connection timeout in seconds

• retry_on_timeout (bool) – Whether to retry on timeout

• health_check_interval (int) – Health check interval in seconds

• default_ttl (int) – Default TTL for cached items in seconds

is_healthy()

Check if Redis connection is healthy.

Return type:

bool

store_bundle(bundle, bundle_id=None)

Store a bundle in Redis.

Parameters:

• bundle (Any) – Bundle object to store

• bundle_id (Optional[str]) – Optional custom ID, auto-generated if None

Returns:

Bundle ID

Return type:

str

Raises:

ConnectionError – If Redis connection is not healthy.

get_bundle(bundle_id)

Retrieve a bundle from Redis.

Parameters:

bundle_id (str) – Bundle ID to retrieve

Returns:

Deserialized bundle object

Return type:

Any

Raises:

• ConnectionError – If Redis connection is not healthy.

• KeyError – If bundle is not found in Redis.

delete_bundle(bundle_id)

Delete a bundle from Redis.

Parameters:

bundle_id (str) – ID of bundle to delete

Returns:

True if deleted, False if not found

Return type:

bool

Raises:

ConnectionError – If Redis connection is not healthy.

list_bundles(bundle_type=None)

List all bundle IDs, optionally filtered by type.

Parameters:

bundle_type (Optional[str]) – Optional bundle type filter

Returns:

List of bundle IDs

Return type:

list[str]

Raises:

ConnectionError – If Redis connection is not healthy.

cache_get(key)

Get value from cache.

Parameters:

key (str) – Cache key

Returns:

Cached value or None if not found

Return type:

Any

cache_set(key, value, ttl=None)

Set value in cache.

Parameters:

• key (str) – Cache key

• value (Any) – Value to cache

• ttl (Optional[int]) – Time to live in seconds (uses default if None)

Returns:

True if successful

Return type:

bool

close()

Close Redis connection.

Return type:

None

get_bundle_metadata(bundle_id)

Get metadata for a specific bundle.

Parameters:

bundle_id (str) – Bundle ID to retrieve metadata for

Returns:

Bundle metadata

Return type:

dict

Raises:

• ConnectionError – If Redis connection is not healthy.

• KeyError – If bundle metadata is not found in Redis.

list_bundles_with_metadata(bundle_type=None)

List all bundles with their metadata, optionally filtered by type.

Parameters:

bundle_type (Optional[str]) – Optional bundle type filter

Returns:

List of bundle metadata dictionaries

Return type:

list[dict]

Raises:

ConnectionError – If Redis connection is not healthy.

inspect_bundle(bundle_id)

Get comprehensive information about a bundle including metadata and content summary.

Parameters:

bundle_id (str) – Bundle ID to inspect

Returns:

Comprehensive bundle information

Return type:

dict

Raises:

ConnectionError – If Redis connection is not healthy.

Base Client

Base client class for Redis operations.

class agentbx.core.base_client.BaseClient(redis_manager, client_id)

Bases: ABC

Base class for clients that interact with Redis.

Provides common Redis operations and connection management.

Parameters:

• redis_manager (RedisManager) –

• client_id (str) –

__init__(redis_manager, client_id)

Initialize base client.

Parameters:

• redis_manager (RedisManager) – Redis manager instance

• client_id (str) – Unique identifier for this client

store_bundle(bundle, bundle_id=None)

Store a bundle in Redis.

Parameters:

• bundle (Any) –

• bundle_id (Optional[str]) –

Return type:

str

get_bundle(bundle_id)

Retrieve a bundle from Redis.

Parameters:

bundle_id (str) –

Return type:

Any

delete_bundle(bundle_id)

Delete a bundle from Redis.

Parameters:

bundle_id (str) –

Return type:

bool

list_bundles(bundle_type=None)

List all bundles, optionally filtered by type.

Parameters:

bundle_type (Optional[str]) –

Return type:

list[str]

cache_get(key)

Get value from cache.

Parameters:

key (str) –

Return type:

Any

cache_set(key, value, ttl=None)

Set value in cache.

Parameters:

• key (str) –

• value (Any) –

• ttl (Optional[int]) –

Return type:

bool

get_client_info()

Get information about this client.

Return type:

Dict[str, Any]

Bundle Base

Base bundle class for agentbx data containers.

class agentbx.core.bundle_base.Bundle(bundle_type, bundle_id=None)

Bases: ABC

Base class for data bundles in agentbx.

Bundles are containers that hold related data assets and metadata. Each bundle has a specific type and can contain multiple named assets.

Parameters:

• bundle_type (str) –

• bundle_id (Optional[str]) –

__init__(bundle_type, bundle_id=None)

Initialize bundle.

Parameters:

• bundle_type (str) – Type identifier for this bundle

• bundle_id (Optional[str]) – Optional custom ID

add_asset(name, asset)

Add an asset to the bundle.

Parameters:

• name (str) – Asset name

• asset (Any) – Asset data

Return type:

None

get_asset(name)

Get an asset from the bundle.

Parameters:

name (str) – Asset name

Returns:

Asset data

Raises:

KeyError – If asset not found

Return type:

Any

has_asset(name)

Check if bundle has an asset.

Parameters:

name (str) – Asset name

Returns:

True if asset exists

Return type:

bool

remove_asset(name)

Remove an asset from the bundle.

Parameters:

name (str) – Asset name

Returns:

True if asset was removed, False if not found

Return type:

bool

add_metadata(key, value)

Add metadata to the bundle.

Parameters:

• key (str) – Metadata key

• value (Any) – Metadata value

Return type:

None

get_metadata(key, default=None)

Get metadata from the bundle.

Parameters:

• key (str) – Metadata key

• default (Optional[Any]) – Default value if key not found

Returns:

Metadata value

Return type:

Any

calculate_checksum()

Calculate checksum of bundle contents.

Returns:

SHA256 checksum (first 16 characters)

Return type:

str

validate()

Validate bundle contents.

Returns:

True if bundle is valid

Raises:

ValueError – If bundle is invalid

Return type:

bool

get_size_estimate()

Estimate bundle size in bytes.

Returns:

Estimated size in bytes

Return type:

int

Agents

Base Agent

Structure Factor Agent

Target Agent

Gradient Agent

Experimental Data Agent

Utilities

Crystallographic Utils

Data Analysis Utils

Utilities for analyzing crystallographic data and structure factors.

agentbx.utils.data_analysis_utils.analyze_complex_data(data, name='data')

Analyze complex data and return comprehensive statistics.

Parameters:

• data (Any) – Complex data array (CCTBX flex array or numpy array)

• name (str) – Name of the data for logging

Returns:

Dictionary with analysis results

Return type:

Dict[str, Any]

agentbx.utils.data_analysis_utils.analyze_miller_array(miller_array, name='miller_array')

Analyze a CCTBX miller array and return comprehensive statistics.

Parameters:

• miller_array (Any) – CCTBX miller array

• name (str) – Name of the array for logging

Returns:

Dictionary with analysis results

Return type:

Dict[str, Any]

agentbx.utils.data_analysis_utils.analyze_bundle(bundle)

Analyze a bundle and return comprehensive information about its contents.

Parameters:

bundle (Any) – Bundle object

Returns:

Dictionary with bundle analysis

Return type:

Dict[str, Any]

agentbx.utils.data_analysis_utils.print_analysis_summary(analysis, indent=0)

Print a formatted summary of analysis results.

Parameters:

• analysis (Dict[str, Any]) – Analysis results dictionary

• indent (int) – Indentation level for formatting

Return type:

None

agentbx.utils.data_analysis_utils.compare_structure_factors(f_calc, f_obs, name_prefix='')

Compare calculated and observed structure factors.

Parameters:

• f_calc (Any) – Calculated structure factors (miller array)

• f_obs (Any) – Observed structure factors (miller array)

• name_prefix (str) – Prefix for naming in output

Returns:

Dictionary with comparison results

Return type:

Dict[str, Any]

Redis Utils

Redis utility functions for agentbx.

agentbx.utils.redis_utils.inspect_bundles_cli()

CLI tool to inspect bundles in Redis.

CLI Utils

Command-line interface for agentbx utilities.

agentbx.utils.cli.main()

Main entry point for the CLI.

Return type:

None

Schemas

Schema Generator

Schema generator for agentbx.

This module generates Pydantic schemas from YAML definitions.

class agentbx.schemas.generator.AssetDefinition(*args, **kwargs)

Bases: BaseModel

Pydantic model for individual asset definitions from YAML.

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

class agentbx.schemas.generator.ValidationRule(*args, **kwargs)

Bases: BaseModel

Pydantic model for validation rules from YAML.

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

class agentbx.schemas.generator.WorkflowPattern(*args, **kwargs)

Bases: BaseModel

Pydantic model for workflow patterns from YAML.

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

class agentbx.schemas.generator.SchemaDefinition(*args, **kwargs)

Bases: BaseModel

Complete schema definition parsed from YAML.

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

class agentbx.schemas.generator.SchemaGenerator(schema_dir)

Bases: object

Generates Pydantic models from YAML schema definitions.

Parameters:

schema_dir (Path) –

__init__(schema_dir)

Parameters:

schema_dir (Path) –

load_schema(schema_file)

Load and parse a single YAML schema file.

Parameters:

schema_file (Path) –

Return type:

SchemaDefinition

load_all_schemas()

Load all YAML schema files from the schema directory.

Return type:

None

generate_asset_model(asset_name, asset_def)

Generate Pydantic field definition for an asset.

Parameters:

• asset_name (str) –

• asset_def (AssetDefinition) –

Return type:

str

generate_validators(schema)

Generate custom validators for CCTBX-specific validation.

Parameters:

schema (SchemaDefinition) –

Return type:

List[str]

generate_bundle_model(schema)

Generate a complete Pydantic model for a bundle type.

Parameters:

schema (SchemaDefinition) –

Return type:

str

generate_all_models()

Generate Pydantic models for all loaded schemas.

Return type:

str

write_generated_models(output_file)

Write generated models to a Python file.

Parameters:

output_file (Path) –

Return type:

None

agentbx.schemas.generator.main()

Main function to auto-generate models from default directories.

Return type:

int

agentbx.schemas.generator.watch_for_changes(generator, schemas_dir, output_file, verbose=False)

Watch for changes in schema files and auto-regenerate.

Parameters:

• generator (Any) –

• schemas_dir (Path) –

• output_file (Path) –

• verbose (bool) –

Return type:

None

agentbx.schemas.generator.quick_generate()

Quick generation using default paths.

Return type:

None

Generated Schemas

class agentbx.schemas.generated.TargetDataBundle(*args, **kwargs)

Bases: BaseModel

Target function values computed from structure factors and experimental data

Generated from target_data.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_target_value()

Validate Scalar target function value

validate_r_factors()

Validate Crystallographic R-factors

validate_likelihood_parameters()

Validate Maximum likelihood alpha and beta parameters

validate_target_gradients_wrt_sf()

Validate Gradients of target w.r.t structure factors

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.GradientDataBundle(*args, **kwargs)

Bases: BaseModel

Gradients of target function w.r.t. atomic parameters via chain rule

Generated from gradient_data.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_coordinate_gradients()

Validate Gradients w.r.t. atomic coordinates: dT/d(xyz)

validate_bfactor_gradients()

Validate Gradients w.r.t. B-factors: dT/d(B)

validate_occupancy_gradients()

Validate Gradients w.r.t. occupancies: dT/d(occ)

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.GeometryGradientDataBundle(*args, **kwargs)

Bases: BaseModel

Geometry gradients computed from CCTBX geometry restraints

Generated from geometry_gradient_data.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_coordinates()

Validate Atomic coordinates in Cartesian space

validate_geometric_gradients()

Validate Gradients of geometry restraints w.r.t. coordinates

validate_restraint_energies()

Validate Individual restraint energies by type

validate_restraint_counts()

Validate Number of restraints by type

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.AgentConfigurationBundle(*args, **kwargs)

Bases: BaseModel

Agent configuration and capability definitions

Generated from agent_configuration.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_agent_definition()

Validate Basic agent definition

validate_capabilities()

Validate Agent capabilities and their configurations

validate_security_policies()

Validate Security policies for the agent

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.MacromoleculeDataBundle(*args, **kwargs)

Bases: BaseModel

Central macromolecule representation with PDB hierarchy

Generated from macromolecule_data.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_pdb_hierarchy()

Validate CCTBX PDB hierarchy with full atomic model information

validate_crystal_symmetry()

Validate Crystal symmetry (unit cell and space group)

validate_model_manager()

Validate MMTBX model manager with geometry restraints

validate_xray_structure()

Validate X-ray structure derived from PDB hierarchy

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.XrayAtomicModelDataBundle(*args, **kwargs)

Bases: BaseModel

Atomic model data for structure factor calculations

Generated from xray_atomic_model_data.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_xray_structure()

Validate CCTBX xray.structure object with atomic model

validate_miller_indices()

Validate Miller indices for structure factor calculation

validate_bulk_solvent_params()

Validate Bulk solvent correction parameters

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.ExperimentalDataBundle(*args, **kwargs)

Bases: BaseModel

Experimental crystallographic data for refinement and validation

Generated from experimental_data.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_f_obs()

Validate Observed structure factor amplitudes

validate_r_free_flags()

Validate Free R flags for cross-validation

validate_sigmas()

Validate Uncertainties in observed structure factors

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.AgentSecurityBundle(*args, **kwargs)

Bases: BaseModel

Agent security and authorization configuration

Generated from agent_security.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_agent_registration()

Validate Agent registration and validation information

validate_permissions()

Validate List of granted permissions for the agent

validate_capabilities()

Validate Agent capabilities and their schemas

validate_whitelisted_modules()

Validate Modules the agent is allowed to import and use

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.RedisStreamsBundle(*args, **kwargs)

Bases: BaseModel

Redis stream configuration for agent communication

Generated from redis_streams.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_stream_configuration()

Validate Configuration for Redis streams

validate_consumer_groups()

Validate Consumer group configurations

validate_message_schemas()

Validate JSON schemas for message validation

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.StructureFactorDataBundle(*args, **kwargs)

Bases: BaseModel

Computed structure factors from atomic models

Generated from structure_factor_data.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

validate_f_calc()

Validate Calculated structure factors from atomic model

validate_f_mask()

Validate Structure factors from bulk solvent mask

validate_f_model()

Validate Combined structure factors: scale * (f_calc + k_sol * f_mask)

validate_scale_factors()

Validate Scaling parameters used in structure factor calculation

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool

class agentbx.schemas.generated.CoordinateUpdateBundle(*args, **kwargs)

Bases: BaseModel

Coordinate update bundle for geometry minimization and agent communication.

Generated from coordinate_update.yaml

Parameters:

• args (Any) –

• kwargs (Any) –

Return type:

Any

calculate_checksum()

Calculate checksum of bundle contents.

Return type:

str

validate_dependencies(available_bundles)

Validate that all dependencies are satisfied.

Parameters:

available_bundles (Dict[str, pydantic.BaseModel]) –

Return type:

bool