anna-agent 0.1.0

Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation

AnnaAgent

The code for the paper AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation.

CLAIM

It is important to note that since this work involves data from counselling records of real patients with psychological disorders, the publicly available code can only be used to demonstrate part of the methodology. Please contact the authors of this paper if needed.

Installation

Install the project dependencies using pip:

pip install -e .

How to Run the Example

First, you need to deploy the servers with these commands:

# need vllm, the version we used is 0.6.4.
bash complaint.sh
bash counselor.sh
bash emotion.sh

The trained model will be updated here at the end of the submission progress. You can also use an untrained LLM as an alternative, it might be less effective.

There is an inner example provided through the anna-agent CLI. Install the dependencies and initialize the project before starting the demo:

python -m anna_agent.initialize
anna-agent

The command accepts an optional --config argument if you want to use a specific settings.yaml:

anna-agent --config path/to/settings.yaml

Then, you can chat with the virtual seeker.

Project Initialization

The repository offers a small helper to generate default configuration files. Run the initialization script once before starting the example. It creates a settings.yaml and .env in the target directory:

python -m anna_agent.initialize

The generated settings.yaml contains the model service settings and per-module server configuration including API keys and base URLs for the complaint, counselor and emotion modules. Environment variables are written to .env with the ANNA_ENGINE_ prefix for easy override.

Work Progress

To make it easier for readers to learn how to use it, we have added the flowchart below:

With two groups of agents (for Dynamic Evolution & Multi-session Memory, respectively), AnnaAgent simulates seekers via two main stages, i.e., the initialization stage to set the seeker's configuration (including profile, situation, symptoms, etc) and the conversation stage to interact with the counselor. The agent group for dynamic evolution contains an emotion modulator, a chief complaint chain generator and a complaint switcher. There are three agents in the agent group for multi-session memory: a situation analyzer, a status analyzer, and a memory retriever.

In addition, there are supplementary modules for speaking style analysis, scale summarization and event selection.

At the initialization stage, the seeker's basic profile and historical session conversations and reports from long-term memory are first read. The seeker's style is analyzed based on the previous session's conversations by the speaking style analysis module next. The scale summarization module summarizes historical scales based on the seeker's profile and reports. Then, the event selection module matches a suitable event based on the seeker's profile and the situation analyzer generates a situation that the seeker encounters based on the event. Meanwhile, the virtual seeker is required to complete the scales for the current session based on the current configurations and the status analyzer analyzes the change in the seeker's status based on the two groups of scales. Situations and statuses together make up short-term memory. In addition, the chief complaint chain generator generates a chief complaint chain based on the seeker's profile and long-term memory during the initialization stage.

At the conversation stage, AnnaAgent has a conversation with a counselor. For each utterance by the counselor, the memory retriever determines whether long-term memory needs to be retrieved. If it is needed, relevant information is retrieved from conversations and reports from previous sessions. In addition, the emotion modulator reasons the seeker's next emotion and performs emotion perturbation on a probability basis according to the real-time memory, i.e., the context of the conversation. After the seeker completes an utterance, the complaint switcher decides whether or not to awaken the seeker's next chief complaint stage.

Models

The training data for both the emotional inferencer and the chief complaint chain generator are derived from real data. We did not open source the labeled raw data due to ethical risk concerns. To facilitate further research and application, we set the models to be conditionally public.

Model	Link	Backbone
The Emotional Inferencer	link	Qwen2.5-7B-Instruct
Chief Complaint Chain Generator	link	Qwen2.5-7B-Instruct

In addition, we will continue to train and release emotion inferencers and chief complaint chain generators based on more models with different architectures.

Synthetic Data

We used the CPsyCounD dataset as a seed to synthesize a seeker bank that meets the requirements of the AnnaAgent format using GPT-4o-mini. It can be found at this link. We will continue to transform more data and will create more realistic seeker characters based on AnnaAgent for use in related research.

Developer Guide

For contribution guidelines refer to:

• 开发者指南 (Chinese)
• Developer Guide (English)

Citation

@article{wang2025annaagentdynamicevolutionagent,
      title={AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation}, 
      author={Ming Wang and Peidong Wang and Lin Wu and Xiaocui Yang and Daling Wang and Shi Feng and Yuxin Chen and Bixuan Wang and Yifei Zhang},
      year={2025},
      eprint={2506.00551},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.00551}, 
}