Alem Tuzlak f55edad5c1 fix(showcase/langgraph-python): chain reasoning-chain demo pills end-to-end
The tool-rendering-reasoning-chain demo previously promised chained tool
calls in its pill titles but the agent and fixtures only delivered single
tools — clicking "Weather + flights to Tokyo" produced just a WeatherCard,
"Compare two stocks" only fetched AAPL, "Find flights from SFO to JFK"
showed flights but no destination weather. Three changes close the gap.

Agent: replace the soft "call 2+ tools when relevant" system prompt with
concrete per-pill chain examples mirroring the pattern already used by the
langgraph-typescript `tool-rendering` agent (weather→flights, ticker→peer,
roll→contrast die, flights→destination weather).

Pills: drop the redundant Tokyo pill (it was the SFO/JFK chain in reverse)
and reword each remaining pill message to PRE-DISCLOSE the chain so the
model commits to the follow-up call:
  - "Compare AAPL and MSFT stocks for me."
  - "Roll a 20-sided die for me and compare it to a smaller one."
  - "Find flights from SFO to JFK and show me the weather there."

Fixtures: 9 fixtures (3 per pill: final-content → second-leg → first-leg,
ordered by toolCallId specificity for first-match-wins). Each fixture is
scoped by a langgraph-python-UNIQUE userMessage tail ("Compare AAPL and
MSFT stocks", "compare it to a smaller one", "show me the weather there").
Those substrings appear nowhere else across the 14+ integrations sharing
showcase-aimock on Railway, so the new fixtures cannot cross-contaminate
the other reasoning-chain demos that still ship the older prompt set.
A toolName-based gate was considered and rejected because most fleet
agents register `roll_dice` and aimock's `toolName` matcher is a tool-LIST
gate, not a tool-CALL gate — it would NOT have isolated this demo.

Probe: collapse the two-turn flow (Tokyo + SFO/JFK) into one chained turn
(SFO→JFK + JFK weather) that asserts BOTH per-tool renderers
(FlightListCard + WeatherCard) mount in a single response. Same coverage
at half the wall-clock and exercises the actual chained-tool path.
2026-05-12 17:18:46 +02:00
2026-05-07 19:08:48 +00:00
2026-04-07 12:29:28 +02:00
2023-06-30 16:46:26 -07:00

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CopilotKit

Build agent-native applications with generative UI, shared state, and human-in-the-loop workflows.

CopilotKit



What is CopilotKit

CopilotKit is a best-in-class SDK for building full-stack agentic applications, Generative UI, and chat applications.

We are the company behind the AG-UI Protocol - adopted by Google, LangChain, AWS, Microsoft, Mastra, PydanticAI, and more!

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Add AI to your app in 1 minute

Features:

  • Chat UI – A React-based chat interface that supports message streaming, tool calls, and agent responses.
  • Backend Tool Rendering – Enables agents to call backend tools that return UI components rendered directly in the client.
  • Generative UI – Allows agents to generate and update UI components dynamically at runtime based on user intent and agent state.
  • Shared State – A synchronized state layer that both agents and UI components can read from and write to in real time.
  • Human-in-the-Loop – Lets agents pause execution to request user input, confirmation, or edits before continuing.

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Quick Start

New projects:

npx copilotkit@latest create -f <framework>

Existing projects:

npx copilotkit@latest init

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What this gives you:

  • CopilotKit installed – Core packages are fully set up in your app
  • Provider configured – Context, state, and hooks ready to use
  • Agent <> UI connected – Agents can stream actions and render UI immediately
  • Deployment-ready – Your app is ready to deploy

Complete getting started guide →

How it works:

CopilotKit connects your UI, agents, and tools into a single interaction loop.

CopilotKit Diagram — Motion x2 6 sec version

This enables:

  • Agents that ask users for input
  • Tools that render UI
  • Stateful workflows across steps and sessions

⭐️ useAgent Hook

The useAgent hook is a proper superset of useCoAgent and sits directly on AG-UI, giving more control over the agent connection.

// Programmatically access and control your agents
const { agent } = useAgent({ agentId: "my_agent" });

// Render and update your agent's state
return <div>
  <h1>{agent.state.city}</h1>
  <button onClick={() => agent.setState({ city: "NYC" })}>
    Set City
  </button>
</div>

Check out the useAgent docs to learn more.

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Generative UI

Generative UI is a core CopilotKit pattern that allows agents to dynamically render UI as part of their workflow.

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Compare the Three Types

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Explore:

Generative UI educational repo →

🖥️ AG-UI: The Agent–User Interaction Protocol

Connect agent workflow to user-facing apps, with deep partnerships and 1st-party integrations across the agentic stack—including LangGraph, CrewAI, and more.

AG-UI


npx create-ag-ui-app my-agent-app
Learn more in the AG-UI README →

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🙋🏽‍♂️ Contributing

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📄 License

This repository's source code is available under the MIT License.

S
Description
copilotkit-debug: Use when diagnosing CopilotKit issues -- runtime connectivity failures, agent not responding, streaming errors, tool execution problems, transcription…; copilotkit-develop: Use when building AI-powered features with CopilotKit v2 -- adding chat interfaces, registering frontend tools, sharing application context with agents,…; copilotkit-agui: Use when building custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents commun…
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