# Manejo De Estados
Hasta ahora, hemos discutido cómo construir componentes en una aplicación multi-agente: agentes, equipos, condiciones de terminación. En muchos casos, es útil guardar el estado de estos componentes en el disco y cargarlos más tarde. Esto es particularmente útil en una aplicación web donde los endpoints sin estado responden a solicitudes y necesitan cargar el estado de la aplicación desde un almacenamiento persistente.
En este cuaderno, discutiremos cómo guardar y cargar el estado de agentes, equipos y terminación.
***
### Agentes de Ahorro y Carga
Podemos obtener el estado de un agente llamando al método `save_state()` en un `AssistantAgent`.
```python
import asyncio
from saptiva_agents import SAPTIVA_LEGACY
from saptiva_agents.agents import AssistantAgent
from saptiva_agents.conditions import MaxMessageTermination
from saptiva_agents.messages import TextMessage
from saptiva_agents.teams import RoundRobinGroupChat
from saptiva_agents.ui import Console
from saptiva_agents.core import CancellationToken
from saptiva_agents.base import SaptivaAIChatCompletionClient
model_client = SaptivaAIChatCompletionClient(
model=SAPTIVA_LEGACY,
api_key="TU_SAPTIVA_API_KEY",
)
assistant_agent = AssistantAgent(
name="assistant_agent",
system_message="You are a helpful assistant",
model_client=model_client
)
async def main() -> None:
# Utilice `asyncio.run(...)` cuando lo ejecute en un script
response = await assistant_agent.on_messages(
[TextMessage(content="Write a 3 line poem on lake tangayika", source="user")], CancellationToken()
)
print(response.chat_message.content)
await model_client.close()
asyncio.run(main())
```
```
In Tanganyika's embrace so wide and deep,
Ancient waters cradle secrets they keep,
Echoes of time where horizons sleep.
```
```python
agent_state = await assistant_agent.save_state()
print(agent_state)
```
```
{'type': 'AssistantAgentState', 'version': '1.0.0', 'llm_messages': [{'content': 'Write a 3 line poem on lake tangayika', 'source': 'user', 'type': 'UserMessage'}, {'content': "In Tanganyika's embrace so wide and deep, \nAncient waters cradle secrets they keep, \nEchoes of time where horizons sleep. ", 'source': 'assistant_agent', 'type': 'AssistantMessage'}]}
```
```python
new_assistant_agent = AssistantAgent(
name="assistant_agent",
system_message="You are a helpful assistant",
model_client=SaptivaAIChatCompletionClient(
model=SAPTIVA_LEGACY,
),
)
await new_assistant_agent.load_state(agent_state)
# Utilice `asyncio.run(...)` cuando lo ejecute en un script
response = await new_assistant_agent.on_messages(
[TextMessage(content="What was the last line of the previous poem you wrote", source="user")], CancellationToken()
)
print(response.chat_message.content)
```
```
The last line of the poem was: "Echoes of time where horizons sleep."
```
{% hint style="info" %}
Nota
Para `AssistantAgent`, su estado consiste en el modelo\_context. Si escribes tu propio agente personalizado, considera sobrescribir los métodos `save_state()` y `load_state()` para personalizar el comportamiento. Las implementaciones predeterminadas guardan y cargan un estado vacío.
{% endhint %}
***
### Equipos de Ahorro y Carga
Podemos obtener el estado de un equipo llamando al método `save_state` en el equipo y cargarlo de nuevo llamando al método `load_state` en el equipo.
Cuando llamamos a `save_state` en un equipo, guarda el estado de todos los agentes en el equipo.
Comenzaremos creando un equipo simple de `RoundRobinGroupChat` con un solo agente y le pediremos que escriba un poema.
```python
# Definir un equipo.
assistant_agent = AssistantAgent(
name="assistant_agent",
system_message="You are a helpful assistant",
model_client = SaptivaAIChatCompletionClient(
model=SAPTIVA_LEG,
api_key="TU_SAPTIVA_API_KEY",
),
)
agent_team = RoundRobinGroupChat([assistant_agent], termination_condition=MaxMessageTermination(max_messages=2))
# Ejecutar el equipo y transmitir mensajes a la consola.
stream = agent_team.run_stream(task="Write a beautiful poem 3-line about lake tangayika")
# Utilice `asyncio.run(...)` cuando lo ejecute en un script
await Console(stream)
# Guardar el estado del equipo del agente.
team_state = await agent_team.save_state()
```
```
---------- user ----------
Write a beautiful poem 3-line about lake tangayika
---------- assistant_agent ----------
In Tanganyika's gleam, beneath the azure skies,
Whispers of ancient waters, in tranquil guise,
Nature's mirror, where dreams and serenity lie.
[Prompt tokens: 29, Completion tokens: 34]
---------- Summary ----------
Number of messages: 2
Finish reason: Maximum number of messages 2 reached, current message count: 2
Total prompt tokens: 29
Total completion tokens: 34
Duration: 0.71 seconds
```
Si reiniciamos el equipo (simulando la creación del equipo) y hacemos la pregunta `¿What was the last line of the poem you wrote?`, vemos que el equipo no puede lograrlo ya que no hay referencia a la ejecución anterior.
```python
await agent_team.reset()
stream = agent_team.run_stream(task="What was the last line of the poem you wrote?")
await Console(stream)
```
```
---------- user ----------
What was the last line of the poem you wrote?
---------- assistant_agent ----------
I'm sorry, but I am unable to recall or access previous interactions, including any specific poem I may have composed in our past conversations. If you like, I can write a new poem for you.
[Prompt tokens: 28, Completion tokens: 40]
---------- Summary ----------
Number of messages: 2
Finish reason: Maximum number of messages 2 reached, current message count: 2
Total prompt tokens: 28
Total completion tokens: 40
Duration: 0.70 seconds
```
```
TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=28, completion_tokens=40), content="I'm sorry, but I am unable to recall or access previous interactions, including any specific poem I may have composed in our past conversations. If you like, I can write a new poem for you.", type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')
```
A continuación, cargamos el estado del equipo y hacemos la misma pregunta. Vemos que el equipo es capaz de devolver con precisión la última línea del poema que escribió.
```python
print(team_state)
# Cargar estado del equipo
await agent_team.load_state(team_state)
stream = agent_team.run_stream(task="What was the last line of the poem you wrote?")
await Console(stream)
```
```
{'type': 'TeamState', 'version': '1.0.0', 'agent_states': {'group_chat_manager/a55364ad-86fd-46ab-9449-dcb5260b1e06': {'type': 'RoundRobinManagerState', 'version': '1.0.0', 'message_thread': [{'source': 'user', 'models_usage': None, 'content': 'Write a beautiful poem 3-line about lake tangayika', 'type': 'TextMessage'}, {'source': 'assistant_agent', 'models_usage': {'prompt_tokens': 29, 'completion_tokens': 34}, 'content': "In Tanganyika's gleam, beneath the azure skies, \nWhispers of ancient waters, in tranquil guise, \nNature's mirror, where dreams and serenity lie.", 'type': 'TextMessage'}], 'current_turn': 0, 'next_speaker_index': 0}, 'collect_output_messages/a55364ad-86fd-46ab-9449-dcb5260b1e06': {}, 'assistant_agent/a55364ad-86fd-46ab-9449-dcb5260b1e06': {'type': 'ChatAgentContainerState', 'version': '1.0.0', 'agent_state': {'type': 'AssistantAgentState', 'version': '1.0.0', 'llm_messages': [{'content': 'Write a beautiful poem 3-line about lake tangayika', 'source': 'user', 'type': 'UserMessage'}, {'content': "In Tanganyika's gleam, beneath the azure skies, \nWhispers of ancient waters, in tranquil guise, \nNature's mirror, where dreams and serenity lie.", 'source': 'assistant_agent', 'type': 'AssistantMessage'}]}, 'message_buffer': []}}, 'team_id': 'a55364ad-86fd-46ab-9449-dcb5260b1e06'}
---------- user ----------
What was the last line of the poem you wrote?
---------- assistant_agent ----------
The last line of the poem I wrote is:
"Nature's mirror, where dreams and serenity lie."
[Prompt tokens: 86, Completion tokens: 22]
---------- Summary ----------
Number of messages: 2
Finish reason: Maximum number of messages 2 reached, current message count: 2
Total prompt tokens: 86
Total completion tokens: 22
Duration: 0.96 seconds
```
```
TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=22), content='The last line of the poem I wrote is: \n"Nature\'s mirror, where dreams and serenity lie."', type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')
```
***
### Estado Persistente (Archivo o Base de Datos)
En muchos casos, es posible que deseemos persistir el estado del equipo en disco (o en una base de datos) y cargarlo posteriormente. El estado es un diccionario que puede ser serializado a un archivo o escrito en una base de datos.
```python
import json
## Guardar el estado en el disco.
with open("coding/team_state.json", "w") as f:
json.dump(team_state, f)
## Cargar el estado desde el disco.
with open("coding/team_state.json", "r") as f:
team_state = json.load(f)
new_agent_team = RoundRobinGroupChat([assistant_agent], termination_condition=MaxMessageTermination(max_messages=2))
await new_agent_team.load_state(team_state)
stream = new_agent_team.run_stream(task="What was the last line of the poem you wrote?")
await Console(stream)
await model_client.close()
```
---------- user ----------
What was the last line of the poem you wrote?
---------- assistant_agent ----------
The last line of the poem I wrote is:
"Nature's mirror, where dreams and serenity lie."
[Prompt tokens: 86, Completion tokens: 22]
---------- Summary ----------
Number of messages: 2
Finish reason: Maximum number of messages 2 reached, current message count: 2
Total prompt tokens: 86
Total completion tokens: 22
Duration: 0.72 seconds
```
TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=22), content='The last line of the poem I wrote is: \n"Nature\'s mirror, where dreams and serenity lie."', type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')
```
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://saptiva.gitbook.io/saptiva-docs/saptiva-agents/tutorial/manejo-de-estados.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.