{"data":{"slug":"plurai-ai-intellagent","name":"intellagent","tagline":"A framework for comprehensive diagnosis and optimization of agents using simulated, realistic synthetic interactions","github_url":"https://github.com/plurai-ai/intellagent","owner":"plurai-ai","repo":"intellagent","owner_avatar_url":"https://avatars.githubusercontent.com/u/177747177?v=4","primary_language":"Python","stars":1255,"forks":154,"topics":["agent","evaluation","llmops","simulator","synthetic-data"],"archived":false,"github_pushed_at":"2026-07-14T06:23:01+00:00","maintenance_label":"Steady","stars_delta_30d":1,"url":"https://www.graphcanon.com/tools/plurai-ai-intellagent","markdown_url":"https://www.graphcanon.com/tools/plurai-ai-intellagent.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/plurai-ai-intellagent","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=plurai-ai-intellagent","description":"A framework for comprehensive diagnosis and optimization of agents using simulated, realistic synthetic interactions","homepage_url":"https://intellagent-doc.plurai.ai/","license":"Apache-2.0","open_issues":7,"watchers":28,"ai_summary":"IntellAgent is an advanced multi-agent framework that allows the simulation of thousands of realistic scenarios to evaluate and optimize conversational AI agents.","readme_excerpt":"
\n \"IntellAgent\n \n

Uncover Your Agent's Blind Spots

\n\n \n \n\n [Documentation](https://intellagent-doc.plurai.ai/) |\n [Quick Start](#fire-quickstart) |\n [Newsletter](https://plurai.substack.com/) |\n [Paper](https://arxiv.org/pdf/2501.11067)\n
\n\nSimulate interactions, analyze performance, and gain actionable insights for conversational agents. Test, evaluate, and optimize your agent to ensure reliable real-world deployment.\n\nIntellAgent 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.\n\n### Key Features\n\n- 🔬 **Generate Thousands of Edge-Case Scenarios:** \n Automatically generate highly realistic edge-case scenarios tailored specifically to your agent.\n\n- 🤖 **Simulate Diverse User Interactions:** \n Evaluate your agent across a wide spectrum of scenarios with varying complexity levels.\n\n- 📊 **Comprehensive Performance Evaluations:** \nAccess detailed analysis to identify performance gaps, prioritize improvements, and compare outcomes across experiments.\n- 💪 **Simple integration:** \n Simple integration to your conversational agent.\n\n## How it works\n\nIntellAgent framework consists of three steps:\n- Given the user prompt (and optional additional information such as tools and database schema)\n - The system decomposes the prompt into a policy graph.\n - It samples a subset of policies based on their concurrence in real conversation distributions.\n - It generates a scenario of user-chatbot interaction (including system databases) to address the selected subset of policies.\n- Simulating the user-chatbot interaction using a user agent.\n- Critiquing the conversation and providing feedback on the tested policies.\n> 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/)\n\n## 🔍 Demo\n\n\n\n## :fire: Quickstart\n\n> For a more detailed and comprehensive guide, see the [Start Guide](https://intellagent-doc.plurai.ai/quick_start/installation/).\n\n\n\n\n\n\n\n\n\nIntellAgent requires `python >= 3.9`\n
\n\n#### Step 1 - Download and install\n\n```bash\ngit clone git@github.com:plurai-ai/intellagent.git\ncd intellagent\n```\n\nYou can use Conda or pip to install the dependencies.\n\nUsing pip: \n```bash\npip install -r requirements.txt\n```\n\n\n#### Step 2 - Set your LLM API Key\n\nEdit the `config/llm_env.yml` file to set up your LLM configuration (OpenAI/Azure/Vertex/Anthropic):\n\n```yaml\nopenai:\n OPENAI_API_KEY: \"your-api-key-here\"\n```\n\nTo 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:\n\n\n```yaml\nllm_intellagent:\n type: 'azure'\n\nllm_chat:\n type: 'azure'\n```\n\nTo change the number of samples in the database you should modify the `num_samples` in the config file:\n```yaml\ndataset:\n num_samples: 30\n```\n\n> **Tokens Usage**\n> \n> We invest lots of effort in minimizing the total cost of running the simulator \n> - Using the default parameters, the expected cost per sample is approximately $0.10\n> - You can control expenses by modifying the `cost_limit` limit parameter in the config file\n> - We are working on leveraging user data which will significantly reduce the cost per sample\n\n#### Step 3 - Run the Simulator\nIf 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.\n\nFor fast simple environment without a database, run the following command:\n```bash\npython","github_created_at":"2024-10-28T20:53:08+00:00","created_at":"2026-07-07T17:42:57.487094+00:00","updated_at":"2026-08-20T18:01:42.785912+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"agent","name":"agent"},{"slug":"evaluation","name":"evaluation"},{"slug":"llmops","name":"llmops"},{"slug":"simulator","name":"simulator"},{"slug":"synthetic-data","name":"synthetic-data"}],"trust":{"provenance":{"is_fork":false,"github_id":879949486,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-20T18:01:42.494Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":0,"days_since_push":37,"last_release_at":"2025-01-22T06:31:41Z","stars_delta_30d":1,"open_issues_delta_30d":2},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":19,"high_count":0,"last_scan_at":"2026-07-11T11:22:25.369Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-21T06:02:12.487Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-21T06:02:12.487Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-21T06:02:12.487Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Need to uncover obscure failure points in conversational agents with realistic, complex scenarios","Seeking detailed, actionable insights for optimizing agent performance post-development"],"when_not_to_use":["Focusing primarily on real-time monitoring rather than post-hoc evaluation and optimization","Looking for tools that handle real deployment issues without synthetic testing capabilities"],"source":"enrich:decision_facts","observed_at":"2026-07-12T10:38:54.916Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"IntellAgent diagnoses and optimizes conversational AI agents using high-fidelity synthetic interactions to simulate edge cases."}]}}