Export Agent Serialized

1 curl https://api.letta.com/v1/agents/agent_id/export \ 2 -H "Authorization: Bearer <token>"

1 { 2 "agent_type": "agent_type", 3 "core_memory": [ 4 { 5 "created_at": "created_at", 6 "is_template": true, 7 "label": "label", 8 "limit": 1, 9 "updated_at": "updated_at", 10 "value": "value", 11 "description": "description", 12 "metadata_": { 13 "key": "value" 14 }, 15 "template_name": "template_name" 16 } 17 ], 18 "created_at": "created_at", 19 "embedding_config": { 20 "embedding_endpoint_type": "openai", 21 "embedding_model": "embedding_model", 22 "embedding_dim": 1, 23 "embedding_endpoint": "embedding_endpoint", 24 "embedding_chunk_size": 1, 25 "handle": "handle", 26 "azure_endpoint": "azure_endpoint", 27 "azure_version": "azure_version", 28 "azure_deployment": "azure_deployment" 29 }, 30 "llm_config": { 31 "model": "model", 32 "model_endpoint_type": "openai", 33 "context_window": 1, 34 "model_endpoint": "model_endpoint", 35 "model_wrapper": "model_wrapper", 36 "put_inner_thoughts_in_kwargs": true, 37 "handle": "handle", 38 "temperature": 1.1, 39 "max_tokens": 1, 40 "enable_reasoner": true, 41 "max_reasoning_tokens": 1 42 }, 43 "message_buffer_autoclear": true, 44 "messages": [ 45 { 46 "created_at": "created_at", 47 "in_context": true, 48 "role": "role", 49 "content": [ 50 { 51 "type": "text", 52 "text": "text" 53 } 54 ], 55 "tool_calls": [], 56 "tool_returns": [], 57 "updated_at": "updated_at", 58 "group_id": "group_id", 59 "model": "model", 60 "name": "name", 61 "tool_call_id": "tool_call_id" 62 } 63 ], 64 "multi_agent_group": { 65 "key": "value" 66 }, 67 "name": "name", 68 "system": "system", 69 "tags": [ 70 { 71 "tag": "tag" 72 } 73 ], 74 "tool_exec_environment_variables": [ 75 { 76 "created_at": "created_at", 77 "key": "key", 78 "updated_at": "updated_at", 79 "value": "value", 80 "description": "description" 81 } 82 ], 83 "tool_rules": [ 84 { 85 "tool_name": "tool_name", 86 "type": "type" 87 } 88 ], 89 "tools": [ 90 { 91 "args_json_schema": { 92 "key": "value" 93 }, 94 "created_at": "created_at", 95 "description": "description", 96 "json_schema": { 97 "name": "name", 98 "description": "description", 99 "parameters": { 100 "properties": { 101 "key": { 102 "type": "type" 103 } 104 } 105 } 106 }, 107 "name": "name", 108 "return_char_limit": 1, 109 "source_type": "source_type", 110 "tags": [ 111 "tags" 112 ], 113 "tool_type": "tool_type", 114 "updated_at": "updated_at", 115 "source_code": "source_code", 116 "metadata_": { 117 "key": "value" 118 } 119 } 120 ], 121 "updated_at": "updated_at", 122 "version": "version", 123 "description": "description", 124 "metadata_": { 125 "key": "value" 126 } 127 }

Export the serialized JSON representation of an agent.

Path parameters

agent_idstringRequired

Response

Successful Response

agent_typestring

core_memorylist of objects

created_atstring

embedding_configobject

Embedding model configuration. This object specifies all the information necessary to access an embedding model to usage with Letta, except for secret keys.

Attributes: embedding_endpoint_type (str): The endpoint type for the model. embedding_endpoint (str): The endpoint for the model. embedding_model (str): The model for the embedding. embedding_dim (int): The dimension of the embedding. embedding_chunk_size (int): The chunk size of the embedding. azure_endpoint (:obj:str, optional): The Azure endpoint for the model (Azure only). azure_version (str): The Azure version for the model (Azure only). azure_deployment (str): The Azure deployment for the model (Azure only).

llm_configobject

Configuration for a Language Model (LLM) model. This object specifies all the information necessary to access an LLM model to usage with Letta, except for secret keys.

Attributes: model (str): The name of the LLM model. model_endpoint_type (str): The endpoint type for the model. model_endpoint (str): The endpoint for the model. model_wrapper (str): The wrapper for the model. This is used to wrap additional text around the input/output of the model. This is useful for text-to-text completions, such as the Completions API in OpenAI. context_window (int): The context window size for the model. put_inner_thoughts_in_kwargs (bool): Puts inner_thoughts as a kwarg in the function call if this is set to True. This helps with function calling performance and also the generation of inner thoughts. temperature (float): The temperature to use when generating text with the model. A higher temperature will result in more random text. max_tokens (int): The maximum number of tokens to generate.

message_buffer_autoclearboolean

messageslist of objects

multi_agent_groupany

namestring

systemstring

tagslist of objects

tool_exec_environment_variableslist of objects

tool_ruleslist of objects

toolslist of objects

updated_atstring

versionstring

descriptionstringOptional

metadata_map from strings to anyOptional

Export Agent Serialized

Path parameters

Headers

Response

Errors