---
title: "intellagent"
type: "tool"
slug: "plurai-ai-intellagent"
canonical_url: "https://www.graphcanon.com/tools/plurai-ai-intellagent"
github_url: "https://github.com/plurai-ai/intellagent"
homepage_url: "https://intellagent-doc.plurai.ai/"
stars: 1251
forks: 154
primary_language: "Python"
license: "Apache-2.0"
categories: ["ai-agents", "evaluation-observability"]
tags: ["llmops", "evaluation", "synthetic-data", "simulator", "agent"]
updated_at: "2026-07-07T18:47:29.025868+00:00"
---

# intellagent

> Framework for comprehensive diagnosis and optimization of agents using simulated, realistic synthetic interactions

A multi-agent framework that simulates realistic scenarios to stress-test and optimize conversational agents.

## Facts

- Repository: https://github.com/plurai-ai/intellagent
- Homepage: https://intellagent-doc.plurai.ai/
- Stars: 1,251 · Forks: 154 · Open issues: 5 · Watchers: 28
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-07T17:11:39+00:00

## Categories

- [AI Agents](/categories/ai-agents.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)

## Tags

llmops, evaluation, synthetic-data, simulator, agent

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## README (excerpt)

```text
<div align="center">
 <img src="./docs/figures/intellagent_logo.png" alt="IntellAgent Logo" width="600">
 
 <p><i>Uncover Your Agent's Blind Spots</i></p>

 
 

 [Documentation](https://intellagent-doc.plurai.ai/) |
 [Quick Start](#fire-quickstart) |
 [Newsletter](https://plurai.substack.com/) |
 [Paper](https://arxiv.org/pdf/2501.11067)
</div>

Simulate interactions, analyze performance, and gain actionable insights for conversational agents. Test, evaluate, and optimize your agent to ensure reliable real-world deployment.

IntellAgent is an advanced multi-agent framework that transforms the evaluation and optimization of conversational agents. By simulating thousands of realistic, challenging interactions, IntellAgent stress-tests agents to uncover hidden failure points. These insights enhance agent performance, reliability, and user experience.

### Key Features

- 🔬 **Generate Thousands of Edge-Case Scenarios:**  
  Automatically generate highly realistic edge-case scenarios tailored specifically to your agent.

- 🤖 **Simulate Diverse User Interactions:**  
  Evaluate your agent across a wide spectrum of scenarios with varying complexity levels.

- 📊 **Comprehensive Performance Evaluations:**  
Access detailed analysis to identify performance gaps, prioritize improvements, and compare outcomes across experiments.
- 💪 **Simple integration:**  
  Simple integration to your conversational agent.

## How it works

IntellAgent framework consists of three steps:
- Given the user prompt (and optional additional information such as tools and database schema)
  - The system decomposes the prompt into a policy graph.
  - It samples a subset of policies based on their concurrence in real conversation distributions.
  - It generates a scenario of user-chatbot interaction (including system databases) to address the selected subset of policies.
- Simulating the user-chatbot interaction using a user agent.
- Critiquing the conversation and providing feedback on the tested policies.
> To better understand the key concepts and how the IntellAgent system operates, refer to the [system overview guide](https://intellagent-doc.plurai.ai/How_it_Works/how-it-works/)

## 🔍 Demo



## :fire: Quickstart

> For a more detailed and comprehensive guide, see the [Start Guide](https://intellagent-doc.plurai.ai/quick_start/installation/).









IntellAgent requires `python >= 3.9`
<br />

#### Step 1 - Download and install

```bash
git clone git@github.com:plurai-ai/intellagent.git
cd intellagent
```

You can use Conda or pip to install the dependencies.

Using pip: 
```bash
pip install -r requirements.txt
```


#### Step 2 - Set your LLM API Key

Edit the `config/llm_env.yml` file to set up your LLM configuration (OpenAI/Azure/Vertex/Anthropic):

```yaml
openai:
  OPENAI_API_KEY: "your-api-key-here"
```

To change the default LLM provider or model for either the IntellAgent system or the chatbot, you can easily update the configuration file. For instance, modify the `config/config_education.yml` file:


```yaml
llm_intellagent:
    type: 'azure'

llm_chat:
    type: 'azure'
```

To change the number of samples in the database you should modify the `num_samples` in the config file:
```yaml
dataset:
    num_samples: 30
```

> **Tokens Usage**
> 
> We invest lots of effort in minimizing the total cost of running the simulator 
> - Using the default parameters, the expected cost per sample is approximately $0.10
> - You can control expenses by modifying the `cost_limit` limit parameter in the config file
> - We are working on leveraging user data which will significantly reduce the cost per sample

####  Step 3 - Run the Simulator
If you're utilizing Azure OpenAI services for the `llm_intellagent`, ensure you [disable](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/content-filters) the default `jailbreak` filter before running the simulator.

For fast simple environment without a database, run the following command:
```bash
python
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/plurai-ai-intellagent`](/api/graphcanon/tools/plurai-ai-intellagent)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_