IntellAgent: Simulate, Analyze, and Optimize Conversational Agents
Uncover your agent's blind spots through realistic synthetic interactions, diagnose performance gaps, and optimize for reliable deployment.
- Source repo
- plurai-ai/intellagent
- Stars
- ★ 1.3k
- Last updated
- 1mo ago
- License
- Apache-2.0
- Primary language
- Python
- FA score
- 33/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API · Claude API
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An agent developer wants to identify edge-case failures before deploying a new customer support chatbot; they use IntellAgent to simulate thousands of challenging interactions.
- Not a fit if
- Users needing ready-made CrewAI or AutoGen support; those integrations are not yet available
- Teams that cannot accept default usage-metrics collection or data leaving the machine
- Source review
- 33/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
IntellAgent is an advanced multi-agent framework for evaluating and optimizing conversational agents. It automatically generates thousands of realistic edge-case scenarios, simulates diverse user interactions, and provides comprehensive performance evaluations. The framework decomposes a user prompt into a policy graph, samples a subset of policies based on real conversation distributions, and generates a simulated interaction (including system databases) to stress-test the agent. It then uses a user agent to simulate the conversation and a critic to assess the policies. IntellAgent offers simple integration, requiring only configuration of LLM API keys and environment settings. It supports multiple LLM providers including OpenAI, Azure, Vertex, and Anthropic, and includes a Streamlit dashboard for visualizing results. The framework is designed to be cost-effective, with expected costs around $0.10 per sample, and provides detailed analytics to guide improvements. It is open-sourced under Apache-2.0 and includes documentation and examples for customization.
IntellAgent operates through a three-step process. First, given a user prompt (and optional additional information like tools and database schema), it decomposes the prompt into a policy graph, samples a subset of policies based on their concurrence in real conversation distributions, and generates a scenario of user-chatbot interaction (including system databases) to address that subset. Second, it simulates the user-chatbot interaction using a user agent. Third, it critiques the conversation and provides feedback on the tested policies. The framework provides a visualization tool: running streamlit run simulator/visualization/Simulator_Visualizer.py launches a dashboard with detailed analytics. It uses configuration files like config/llm_env.yml for API keys and config/config_education.yml for environment settings, including the number of samples (dataset.num_samples). It supports different LLM providers by setting the llm_intellagent and llm_chat types in the configuration.
- An agent developer wants to identify edge-case failures before deploying a new customer support chatbot; they use IntellAgent to simulate thousands of challenging interactions.
- An LLMOps team needs to compare multiple versions of an agent to decide whether to promote a release; they use IntellAgent to run controlled experiments and compare performance metrics.
- A researcher evaluating a novel agent architecture uses IntellAgent to generate synthetic conversations and obtain structured evaluation results for their paper.
- A company wants to improve its virtual assistant without exposing real user data; they use IntellAgent's synthetic data generation to test and refine behavior.
- A platform integrator wants to automate evaluation in their CI pipeline; they use IntellAgent's CLI and configuration files to run tests locally and integrate with LangGraph.
How do you install or deploy this agent?
To install IntellAgent, ensure Python >= 3.9 is available. Clone the repository: git clone [email protected]:plurai-ai/intellagent.git, then cd intellagent. Install dependencies with pip: pip install -r requirements.txt. After installation, configure your LLM API key by editing config/llm_env.yml, for example: `openai:
OPENAI_API_KEY: "your-api-key-here"`.
How do you use this agent?
To use IntellAgent, first set up your LLM configuration in config/llm_env.yml and adjust environment settings in files like config/config_education.yml. For instance, change the LLM provider by modifying llm_intellagent and llm_chat types, and set the number of database samples via dataset.num_samples. Then run the simulator: for a simple environment without a database, execute python run.py --output_path results/education --config_path ./config/config_education.yml; for a more complex environment with a database, use python run.py --output_path results/airline --config_path ./config/config_airline.yml. If using Azure OpenAI, disable the default jailbreak filter. After simulation, launch the visualizer with streamlit run simulator/visualization/Simulator_Visualizer.py. For troubleshooting, reduce num_workers if rate-limited, or increase timeout if timeouts occur.
What are this agent's strengths and limitations?
- Automatic generation of thousands of edge-case scenarios, significantly expanding test coverage compared to handcrafted tests.
- Policy graph decomposition and sampling based on real conversation distributions ensures realistic and relevant stress-testing.
- Simple integration: just configure LLM keys and adjust YAML files; supports multiple LLM providers.
- Built-in visualization with Streamlit dashboard provides actionable insights and comparison across experiments.
- Active roadmap and community involvement, with plans for integration with LangGraph, CrewAI, AutoGen, and more.
- Requires an LLM API key and incurs token costs; default cost per sample is about $0.10, which may add up for large-scale testing.
- Dependent on external LLM services; using Azure requires additional configuration to disable content filters.
- Beta stage: some integrations (e.g., CrewAI, AutoGen) are not yet supported, limiting immediate usability.
- Simulation can be slow for complex environments with databases, requiring careful tuning of workers and timeouts.
- Open analytics feature collects basic metrics by default, though it can be disabled via
PLURAI_DO_NOT_TRACK.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| IntellAgent: Simulate, Analyze, and Optimize Conversational Agents This agent | 33 · Major gaps | CLIFree + model costs | ★ 1.3k | 1mo ago | Python | OpenAI API · Claude API |
| Agent Learning Kit | 38 · Major gaps | CLIFree + model costs | ★ 128 | 3d ago | Python | — |
| Oumi | 58 · Major gaps | CLIFree + model costs | ★ 9.4k | 3d ago | Python | Claude Code · OpenAI API |
| AI Eval Skills | 52 · Major gaps | Agent plugin / skill | ★ 1.5k | 24d ago | — | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README mentions collecting basic usage metrics and provides a PLURAI_DO_NOT_TRACK flag, but no details on data flow, permissions, or user confirmation. No mention of least privilege, sensitive data handling, rollback, or external effects. Publisher identity is unverified, but no malicious behavior found. Deductions: lack of security mechanisms and transparency.
Evidence shows: Test files (test_dialog_graph.py, test_event_graph.py, test_langgraph_tool.py) cover core graph logic, and test names align with behavior, indicating good internal consistency. Dependencies are pinned in pyproject.toml and requirements.txt, but no availability guarantees or detailed failure messages. Deductions: insufficient evidence for dependency availability and failure message handling.
Evidence shows: README describes multiple scenarios (education, airline) and customization options, indicating a broad audience and scenarios. However, details on capability boundaries, trigger precision, and environment fit are limited. Deductions: unclear capability boundaries and trigger precision.
Evidence shows: README provides installation steps, configuration instructions, and examples, with clear information architecture. License is Apache-2.0, version 0.1.0, but no changelog or maintenance responsibility details. Deductions: missing version history and maintenance responsibility.
Evidence shows: README claims comprehensive performance evaluation and actionable insights, with output as a visualization dashboard, indicating usability. Cost-benefit only mentions ~$0.10 per sample, lacking detailed analysis. Deductions: insufficient cost-benefit evidence.
Evidence shows: README references a paper and documentation, but no specific evidence links. Test files provide partial verification but do not cover all claims. Deductions: insufficient claim traceability and cross-source corroboration.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- Publisher identity is unverified; proceed with caution.
- README mentions collecting usage metrics but lacks details on data flow and privacy; review healthcare_analytics.py code.
- Dependencies are pinned but no security audit or vulnerability remediation information is provided.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
FAQ
What is the cost of running simulations?
cost_limit parameter in the config file.Can I use my own LLM provider?
config/llm_env.yml and set the provider type in the environment config file.How do I handle rate limits or timeouts?
num_workers value in config_default. For timeouts, increase the timeout values in the same file.Is there integration with popular agent frameworks?
Does IntellAgent collect any data?
PLURAI_DO_NOT_TRACK to true.