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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow.
| Date | Stars |
|---|---|
| 2026-07-31 | 745 |
| 2026-08-05 | 748 |
| 2026-08-06 | 748 |
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# Temporal AI Agent This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The purpose of the agent is to collect information towards a goal, running tools along the way. The agent supports both native tools and Model Context Protocol (MCP) tools, allowing it to interact with external services. The agent operates in single-agent mode by default, focusing on one specific goal. It also supports experimental multi-agent/multi-goal mode where users can choose between different agent types and switch between them during conversations. Goals are organized in the `/goals/` directory by category (finance, HR, travel, ecommerce, etc.) and can leverage both native and MCP tools. The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use any LLM supported by [LiteLLM](https://docs.litellm.ai/docs/providers), including: - OpenAI models (GPT-4, GPT-3.5) - Anthropic Claude models - Google Gemini models - Deepseek models - Ollama models (local) - And many more! It's really helpful to [watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ) to understand how interaction works. [](https://www.youtube.com/watch?v=GEXllEH2XiQ) ### Multi-Agent Demo Video See multi-agent execution in action [here](https://www.youtube.com/watch?v=8Dc_0dC14yY). ## Why Temporal? There are a lot of AI and Agentic AI tools out there, and more on the way. But why Temporal? Temporal gives this system reliability, state management, a code-first approach that we really like, built-in observability and easy error handling. For more, check out [architecture-decisions](docs/architecture-decisions.md). ## What is "Agentic AI"? These are the key elements of an agentic framework: 1. Goals that a system can accomplish, made up of tools that can execute individual steps 2. Agent loops - executing an LLM, executing tools, and eliciting input from an external source such as a human: repeat until goal(s) are done 3. Support for tool calls that require input and approval 4. Use of an LLM to check human input for relevance before calling the 'real' LLM 5. Use of an LLM to summarize and compact the conversation history 6. Prompt construction made of system prompts, conversation history, and tool metadata - sent to the LLM to create user questions and confirmations 7. Ideally high durability (done in this system with Temporal Workflow and Activities) For a deeper dive into this, check out the [architecture guide](docs/architecture.md). ## 🔧 MCP Tool Calling Support This agent acts as an **MCP (Model Context Protocol) client**, enabling seamless integration with external services and tools. The system supports two types of tools: - **Native Tools**: Custom tools implemented directly in the codebase (in `/tools/`) - **MCP Tools**: External tools accessed via Model Context Protocol (MCP) servers like Stripe, databases, or APIs. Configuration is covered in [the Setup guide](docs/setup.md) - Set `AGENT_GOAL=goal_food_ordering` with `SHOW_CONFIRM=False` in `.env` for an example of a goal that calls MCP Tools (Stripe). ## Setup and Configuration See [the Setup guide](docs/setup.md) for detailed instructions. The basic configuration requires just two environment variables: ```bash LLM_MODEL=openai/gpt-4o # or any other model supported by LiteLLM LLM_KEY=your-api-key-here ``` ## Customizing Interaction & Tools See [the guide to adding goals and tools](docs/adding-goals-and-tools.md). The system supports MCP (Model Context Protocol) for easy integration with external services. MCP server configurations are managed in `shared/mcp_config.py`, and goals are organized by category in the `/goals/` directory. ## Architecture See [the architecture guide](docs/architecture.md). ## Testing The project includes comprehensive tests for workflows and activities using Temporal's testing framework: ```bash
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Rob Holland · @temporalio · United Kingdom
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Dan Davison · Temporal
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Simon Emms · United Kingdom
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cc2a7dfd69792a06, name:ai agent, desc:ai agent