diff --git a/.github/workflows/test-docs.yml b/.github/workflows/test-docs.yml index f99de575..277ef235 100644 --- a/.github/workflows/test-docs.yml +++ b/.github/workflows/test-docs.yml @@ -47,11 +47,14 @@ jobs: - name: Install dependencies run: npm install - - name: Increase AIO limit for Redpanda + - name: Increase AIO limit for envoy shadowing if: needs.setup.outputs.envoy-shadowing == 'true' run: | # Redpanda uses Linux AIO. With 6 brokers each needing ~10000 events, # we exceed the default limit of 65536. Increase it. + sudo sysctl -w fs.aio-max-nr=1048576 + + - name: Increase AIO limit for redpanda migrator if: needs.setup.outputs.redpanda-migrator == 'true' run: | # Redpanda uses Linux AIO. Increase limit to support multiple brokers. diff --git a/ai-agents/langchain-agent/.env.example b/ai-agents/langchain-agent/.env.example new file mode 100644 index 00000000..4982ea0d --- /dev/null +++ b/ai-agents/langchain-agent/.env.example @@ -0,0 +1,21 @@ +# Redpanda AI Gateway - OIDC credentials +REDPANDA_CLIENT_ID=your-client-id +REDPANDA_CLIENT_SECRET=your-client-secret +REDPANDA_GATEWAY_ID=d6b3mk93mouc73cortj0 + +# Gateway URL +REDPANDA_GATEWAY_URL=https://ai-gateway.d6b2mdhdvf8ruqkbl2mg.clusters.rdpa.co + +# OIDC issuer (discovery URL is {issuer}/.well-known/openid-configuration) +# REDPANDA_ISSUER=https://auth.prd.cloud.redpanda.com + +# OIDC audience for the token request +# REDPANDA_AUDIENCE=cloudv2-production.redpanda.cloud + +# LLM model (routed through gateway) +REDPANDA_MODEL=google/gemini-3-flash-preview + +# LangSmith tracing (optional) +LANGSMITH_API_KEY=your-langsmith-api-key +LANGSMITH_PROJECT=redpanda-agent +LANGSMITH_TRACING=true diff --git a/ai-agents/langchain-agent/.gitignore b/ai-agents/langchain-agent/.gitignore new file mode 100644 index 00000000..d6368489 --- /dev/null +++ b/ai-agents/langchain-agent/.gitignore @@ -0,0 +1,11 @@ +.env +.env.local +__pycache__/ +*.pyc +*.pyo +*.egg-info/ +dist/ +build/ +.venv/ +venv/ +.ruff_cache/ diff --git a/ai-agents/langchain-agent/README.adoc b/ai-agents/langchain-agent/README.adoc new file mode 100644 index 00000000..65bd9ed0 --- /dev/null +++ b/ai-agents/langchain-agent/README.adoc @@ -0,0 +1,272 @@ += Build a LangGraph Agent with the Redpanda AI Gateway +:description: Build a ReAct agent using LangGraph that connects to the Redpanda AI Gateway for unified LLM access and MCP tool calling. +:page-layout: lab +:page-categories: Agentic Data Plane +:page-cloud: true +:page-topic-type: lab +:learning-objective-1: Build and run a LangGraph ReAct agent that connects to the Redpanda AI Gateway +:learning-objective-2: Authenticate with the AI Gateway using the OIDC client_credentials flow +:learning-objective-3: Configure LangGraph to route LLM requests through the AI Gateway's OpenAI-compatible interface +// Set image path for GitHub rendering +ifndef::env-site[] +:imagesdir: ../../docs/modules/ai-agents/images/ +endif::[] + +Build a Python agent using https://langchain-ai.github.io/langgraph/[LangGraph^] that connects to the Redpanda AI Gateway for unified LLM access and MCP tool calling, with optional https://www.langchain.com/langsmith[LangSmith^] tracing. + +After completing this lab, you will be able to: + +* [ ] {learning-objective-1} +* [ ] {learning-objective-2} +* [ ] {learning-objective-3} + +== What you'll explore + +* *AI Gateway as a unified LLM interface*: The gateway provides an OpenAI-compatible API that routes to any upstream model provider (such as Google Gemini), handling provider-specific authentication and request translation. +* *OIDC authentication*: Authenticate with the gateway using a Redpanda Cloud service account and the OAuth 2.0 `client_credentials` grant. +* *Dynamic MCP tool discovery*: Use the gateway's MCP endpoint to discover and call tools at runtime, without hard-coding tool definitions. + +== Prerequisites + +* Python 3.12 or later +* https://python-poetry.org/[Poetry^] for dependency management +* A Redpanda Cloud account with: +** A cluster that has the AI Gateway enabled +** A service account with permissions to access the cluster +* (Optional) A https://www.langchain.com/langsmith[LangSmith^] API key for tracing + +== Get the lab files + +Clone the repository and navigate to the lab directory: + +[,bash] +---- +git clone https://github.com/redpanda-data/redpanda-labs.git +cd redpanda-labs/ai-agents/langchain-agent +---- + +== Set up the project + +. Install the project dependencies: ++ +[,bash] +---- +poetry install +---- + +. Copy the example environment file and fill in your credentials: ++ +[,bash] +---- +cp .env.example .env.local +---- + +. Edit `.env.local` with your Redpanda Cloud service account credentials: ++ +[source,env] +---- +REDPANDA_CLIENT_ID= +REDPANDA_CLIENT_SECRET= +REDPANDA_GATEWAY_ID= +REDPANDA_GATEWAY_URL= +---- ++ +You can find these values in the https://cloud.redpanda.com[Redpanda Cloud console^]. + +== Run the agent + +Start the agent: + +[,bash] +---- +poetry run redpanda-agent +---- + +The agent opens a terminal UI where you can interact with it. The agent authenticates with the AI Gateway using your service account credentials, discovers available MCP tools, and displays a chat prompt. From there, your requests are routed through the gateway to the configured LLM provider. + +== Explore the lab + +Key technical components include the agent architecture, authentication flow, and dynamic tool discovery. + +=== Architecture + +The agent uses the following architecture: + +[,text] +---- +Python Agent (LangGraph) + | + |-- OIDC client_credentials flow --> Redpanda Cloud IdP --> Bearer token + | + |-- ChatOpenAI (base_url=/v1) + | | + | +-- OpenAI-compatible API via gateway + | + |-- MCP tools via gateway (/mcp/) + | | + | +-- tool_search --> discovers available tools dynamically + | +-- AgentMiddleware --> injects and executes discovered tools + | + |-- LangSmith tracing (optional) +---- + +=== How the AI Gateway works + +The AI Gateway is a multi-tenant platform where each user configures their own gateway instance. The gateway translates upstream provider responses into the OpenAI chat completions format, so clients interact with a standard interface regardless of the underlying model provider. + +Every request to the gateway requires two headers: + +[cols="1,2,2"] +|=== +| Header | Value | Purpose + +| `Authorization` +| `Bearer ` +| OIDC authentication + +| `rp-aigw-id` +| `` +| Identifies the gateway instance +|=== + +Because the gateway speaks the OpenAI format, you use `ChatOpenAI` from `langchain-openai`: + +[,python] +---- +from langchain_openai import ChatOpenAI + +llm = ChatOpenAI( + base_url=f"{gateway_url}/v1", + api_key="not-used", # Auth is via OIDC Bearer token + model="google/gemini-3-flash-preview", + default_headers={ + "Authorization": f"Bearer {token}", + "rp-aigw-id": gateway_id, + }, +) +---- + +=== OIDC authentication + +Authentication is against the *Redpanda Cloud OIDC identity provider*, not the gateway itself. The gateway validates the resulting tokens. + +The `GatewayAuth` class uses OIDC discovery to resolve the token endpoint automatically from the issuer: + +[,text] +---- +https://auth.prd.cloud.redpanda.com/.well-known/openid-configuration +---- + +It fetches this discovery document on the first token request, then uses a `client_credentials` grant with the audience `cloudv2-production.redpanda.cloud`: + +[,python] +---- +auth = GatewayAuth() # uses REDPANDA_ISSUER env var or default +token = await auth.get_token() +---- + +NOTE: The AI agent is responsible for refreshing tokens before they expire. `GatewayAuth` handles this automatically with a 30-second buffer before expiry. + +=== Dynamic MCP tool discovery + +The gateway's MCP endpoint uses a two-level tool discovery pattern: + +. `list_tools()` returns a small set of static tools, including `tool_search`. +. Calling `tool_search` discovers additional tools available on the gateway (such as `redpanda-docs:ask_redpanda_question`). +. The set of discovered tools can change at any time because they are not static. + +The agent uses LangChain's `AgentMiddleware` to inject dynamically discovered tools at runtime: + +[,python] +---- +from langchain.agents import create_agent + +graph = create_agent( + model=llm, + tools=static_tools, # For example, [tool_search] + middleware=[middleware], # MCPGatewayMiddleware +) +---- + +The `MCPGatewayMiddleware` provides two hooks: + +* `awrap_model_call`: Injects discovered tools into the model's tool list before each LLM call. +* `awrap_tool_call`: Intercepts calls to discovered tools and executes them through the MCP `ClientSession.call_tool()` method. + +NOTE: MCP tool names like `redpanda-docs:ask_redpanda_question` contain colons, which are not valid in OpenAI tool names. The middleware sanitizes names by replacing invalid characters with hyphens and maintains a mapping to the original MCP name. + +=== MCP transport + +Use `streamable_http` as the transport: + +[,python] +---- +client = MultiServerMCPClient({ + "gateway": { + "transport": "streamable_http", + "url": f"{gateway_url}/mcp/", + "headers": { ... }, + }, +}) +---- + +=== Enable LangSmith tracing (optional) + +To enable tracing, set these environment variables in your `.env.local` file: + +[source,env] +---- +LANGSMITH_API_KEY= +LANGSMITH_PROJECT=redpanda-agent +LANGSMITH_TRACING=true +---- + +LangGraph auto-detects these variables and traces all LLM and tool calls. + +=== Project structure + +[cols="1,2"] +|=== +| File | Purpose + +| `src/agent/auth.py` +| OIDC token management using `authlib` + +| `src/agent/gateway.py` +| `ChatOpenAI` configured for the AI Gateway + +| `src/agent/tools.py` +| MCP tool loading and `AgentMiddleware` for dynamic discovery + +| `src/agent/graph.py` +| LangGraph 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"src"}] + +[tool.poetry.dependencies] +python = ">=3.12,<3.14" +langchain = ">=1.0,<2.0" +langchain-core = ">=1.0,<2.0" +langchain-anthropic = ">=0.3,<1.0" +langchain-openai = ">=0.3" +langgraph = ">=1.0,<2.0" +langsmith = ">=0.3.0" +langchain-mcp-adapters = ">=0.1" +mcp = ">=1.9" +authlib = ">=1.6" +httpx = ">=0.27" +python-dotenv = ">=1.0" +textual = ">=3.0" + +[tool.poetry.group.dev.dependencies] +ruff = ">=0.9" +vulture = ">=2.14" + +[tool.poetry.scripts] +redpanda-agent = "agent.main:main" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.ruff] +target-version = "py312" +line-length = 100 +src = ["src"] + +[tool.ruff.lint] +select = ["C901", "E", "F", "I", "UP", "ANN", "ARG", "RUF"] +ignore = [] + +[tool.ruff.lint.mccabe] +max-complexity = 10 diff --git a/ai-agents/langchain-agent/src/agent/__init__.py b/ai-agents/langchain-agent/src/agent/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ai-agents/langchain-agent/src/agent/auth.py b/ai-agents/langchain-agent/src/agent/auth.py new file mode 100644 index 00000000..0bb08ddd --- /dev/null +++ b/ai-agents/langchain-agent/src/agent/auth.py @@ -0,0 +1,93 @@ +"""OIDC token management for Redpanda AI Gateway.""" + +import os +import time + +import httpx +from authlib.integrations.httpx_client import AsyncOAuth2Client + + +class GatewayAuth: + """Manages OIDC client_credentials tokens for the Redpanda AI Gateway.""" + + DEFAULT_ISSUER = "https://auth.prd.cloud.redpanda.com" + DEFAULT_AUDIENCE = "cloudv2-production.redpanda.cloud" + + def __init__( + self, + client_id: str | None = None, + client_secret: str | None = None, + gateway_url: str | None = None, + gateway_id: str | None = None, + issuer: str | None = None, + audience: str | None = None, + ) -> None: + self._gateway_url = ( + gateway_url + or os.environ.get( + "REDPANDA_GATEWAY_URL", + "https://ai-gateway.d6b2mdhdvf8ruqkbl2mg.clusters.rdpa.co", + ) + ) + self._gateway_id = ( + gateway_id or os.environ.get("REDPANDA_GATEWAY_ID", "d6b3mk93mouc73cortj0") + ) + self._issuer = ( + issuer + or os.environ.get("REDPANDA_ISSUER", self.DEFAULT_ISSUER) + ).rstrip("/") + self._audience = ( + audience + or os.environ.get("REDPANDA_AUDIENCE", self.DEFAULT_AUDIENCE) + ) + self._client = AsyncOAuth2Client( + client_id=client_id or os.environ["REDPANDA_CLIENT_ID"], + client_secret=client_secret or os.environ["REDPANDA_CLIENT_SECRET"], + ) + self._token: str = "" + self._expires_at: float = 0.0 + self._token_endpoint: str | None = None + + @property + def gateway_url(self) -> str: + """The resolved gateway base URL.""" + return self._gateway_url + + async def _discover_token_endpoint(self) -> str: + """Fetch the token endpoint from the OIDC discovery document.""" + if self._token_endpoint: + return self._token_endpoint + + discovery_url = f"{self._issuer}/.well-known/openid-configuration" + async with httpx.AsyncClient() as http: + resp = await http.get(discovery_url) + resp.raise_for_status() + self._token_endpoint = resp.json()["token_endpoint"] + return self._token_endpoint + + async def get_token(self) -> str: + """Return a valid Bearer token, refreshing if expired.""" + if self._token and time.time() < self._expires_at - 30: + return self._token + + token_endpoint = await self._discover_token_endpoint() + token_response = await self._client.fetch_token( + url=token_endpoint, + grant_type="client_credentials", + audience=self._audience, + ) + self._token = token_response["access_token"] + self._expires_at = time.time() + token_response.get("expires_in", 3600) + return self._token + + async def get_headers(self) -> dict[str, str]: + """Return auth + gateway ID headers for requests.""" + token = await self.get_token() + return { + "Authorization": f"Bearer {token}", + "rp-aigw-id": self._gateway_id, + } + + async def close(self) -> None: + """Close the underlying HTTP client.""" + await self._client.aclose() diff --git a/ai-agents/langchain-agent/src/agent/gateway.py b/ai-agents/langchain-agent/src/agent/gateway.py new file mode 100644 index 00000000..0de80b9d --- /dev/null +++ b/ai-agents/langchain-agent/src/agent/gateway.py @@ -0,0 +1,23 @@ +"""LLM client configured for the Redpanda AI Gateway.""" + +import os + +from langchain_openai import ChatOpenAI + +from agent.auth import GatewayAuth + + +async def create_llm(auth: GatewayAuth) -> ChatOpenAI: + """Create a ChatOpenAI instance routed through the Redpanda AI Gateway. + + The gateway returns OpenAI-compatible responses regardless of upstream provider. + """ + headers = await auth.get_headers() + model = os.environ.get("REDPANDA_MODEL", "google/gemini-3-flash-preview") + + return ChatOpenAI( + base_url=f"{auth.gateway_url}/v1", + api_key="not-used", + model=model, + default_headers=headers, + ) diff --git a/ai-agents/langchain-agent/src/agent/graph.py b/ai-agents/langchain-agent/src/agent/graph.py new file mode 100644 index 00000000..3d0051ad --- /dev/null +++ b/ai-agents/langchain-agent/src/agent/graph.py @@ -0,0 +1,32 @@ +"""LangGraph agent definition using create_agent with middleware.""" + +from collections.abc import Sequence + +from langchain.agents import create_agent +from langchain.agents.middleware import AgentMiddleware +from langchain_core.language_models import BaseChatModel +from langchain_core.tools import BaseTool +from langgraph.graph.state import CompiledStateGraph + +SYSTEM_PROMPT = ( + "You are a helpful Redpanda assistant. You answer questions about Redpanda — its " + "architecture, configuration, topic management, connectors, and more.\n\n" + "You have a `tool_search` tool that discovers documentation tools on the gateway. " + "Call it first to find tools by keyword (e.g. 'redpanda', 'docs'), then call the " + "discovered tools directly by name.\n\n" + "When you use a tool, cite the relevant documentation in your answer." +) + + +def build_graph( + llm: BaseChatModel, + tools: list[BaseTool], + middleware: Sequence[AgentMiddleware[object, None, object]] | None = None, +) -> CompiledStateGraph: # type: ignore[type-arg] + """Build and compile the agent graph with optional middleware.""" + return create_agent( + model=llm, + tools=tools, + system_prompt=SYSTEM_PROMPT, + middleware=middleware or [], + ) diff --git a/ai-agents/langchain-agent/src/agent/main.py b/ai-agents/langchain-agent/src/agent/main.py new file mode 100644 index 00000000..b07b10c1 --- /dev/null +++ b/ai-agents/langchain-agent/src/agent/main.py @@ -0,0 +1,231 @@ +"""TUI entry point for the Redpanda agent.""" + +import asyncio +from typing import ClassVar + +from dotenv import load_dotenv +from langchain_core.messages import AnyMessage, HumanMessage +from textual import work +from textual.app import App, ComposeResult +from textual.binding import Binding +from textual.containers import VerticalScroll +from textual.timer import Timer +from textual.widgets import Input, Markdown, Static + +from agent.auth import GatewayAuth +from agent.gateway import create_llm +from agent.graph import build_graph +from agent.sprite import SPRITE +from agent.tools import load_mcp_tools + +SPINNER_FRAMES = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"] +STATUS_ID = "status-msg" + + +class ChatMessage(Markdown): + """A single chat message.""" + + def __init__(self, role: str, content: str) -> None: + super().__init__(content) + self.add_class(role) + + +class RedpandaAgentApp(App[None]): + """Redpanda Agent TUI.""" + + CSS = """ + Screen { + background: #111111; + } + #banner { + width: 100%; + content-align: center middle; + text-align: center; + padding: 2 0 0 0; + } + #title-text { + width: 100%; + content-align: center middle; + text-align: center; + color: #666666; + padding: 0 0 1 0; + } + #chat-scroll { + height: 1fr; + padding: 0 4; + } + .user { + margin: 1 0 0 12; + padding: 1 2; + background: #1a1a2e; + color: #e0e0e0; + } + .assistant { + margin: 0 12 1 0; + padding: 1 2; + background: #1a1a1a; + color: #cccccc; + } + .status { + margin: 0 12 0 0; + padding: 0 2; + color: #cc2200; + } + #input { + dock: bottom; + margin: 1 4; + background: #1a1a1a; + border: tall #333333; + color: #e0e0e0; + } + #input:focus { + border: tall #cc0000; + } + """ + + TITLE = "Redpanda Agent" + BINDINGS: ClassVar[list[Binding]] = [ + Binding("ctrl+c", "quit", "Quit", show=False), + ] + + def __init__(self) -> None: + super().__init__() + self._auth: GatewayAuth | None = None + self._graph = None + self._messages: list[AnyMessage] = [] + self._agent_ready = False + self._spinner_timer: Timer | None = None + self._spinner_frame = 0 + self._status_text = "" + + def compose(self) -> ComposeResult: + """Build the UI layout.""" + yield Static(SPRITE, id="banner", markup=True) + yield Static("redpanda agent", id="title-text") + yield VerticalScroll(id="chat-scroll") + yield Input(placeholder="", disabled=True, id="input") + + def _tick_spinner(self) -> None: + """Advance the spinner animation one frame.""" + scroll = self.query_one("#chat-scroll", VerticalScroll) + existing = scroll.query(f"#{STATUS_ID}") + if existing: + frame = SPINNER_FRAMES[self._spinner_frame % len(SPINNER_FRAMES)] + existing.first().update(f"{frame} {self._status_text}") + self._spinner_frame += 1 + + async def _set_status(self, text: str) -> None: + """Show an animated spinner with a status message in the chat.""" + self._status_text = text + self._spinner_frame = 0 + frame = SPINNER_FRAMES[0] + + scroll = self.query_one("#chat-scroll", VerticalScroll) + existing = scroll.query(f"#{STATUS_ID}") + if existing: + existing.first().update(f"{frame} {text}") + else: + widget = Static(f"{frame} {text}", id=STATUS_ID) + widget.add_class("status") + await scroll.mount(widget) + scroll.scroll_end(animate=False) + + if not self._spinner_timer: + self._spinner_timer = self.set_interval(0.08, self._tick_spinner) + + async def _clear_status(self) -> None: + """Stop the spinner and remove the status message.""" + if self._spinner_timer: + self._spinner_timer.stop() + self._spinner_timer = None + + scroll = self.query_one("#chat-scroll", VerticalScroll) + existing = scroll.query(f"#{STATUS_ID}") + if existing: + await existing.first().remove() + + async def on_mount(self) -> None: + """Initialize the agent after the app mounts.""" + self._init_agent() + + @work(thread=False) + async def _init_agent(self) -> None: + """Connect to the gateway and build the agent graph.""" + load_dotenv(".env.local") + load_dotenv() # fallback to .env + input_w = self.query_one("#input", Input) + + await self._set_status("connecting...") + self._auth = GatewayAuth() + + llm, (tools, _mcp_client, middleware) = await asyncio.gather( + create_llm(self._auth), + load_mcp_tools(self._auth), + ) + + self._graph = build_graph(llm, tools, middleware=[middleware]) + self._agent_ready = True + + await self._clear_status() + tool_names = ", ".join(t.name for t in tools) + scroll = self.query_one("#chat-scroll", VerticalScroll) + await scroll.mount( + ChatMessage("assistant", f"Ready. {len(tools)} tools loaded: {tool_names}") + ) + scroll.scroll_end(animate=False) + + input_w.placeholder = "ask anything about redpanda..." + input_w.disabled = False + input_w.focus() + + async def on_input_submitted(self, event: Input.Submitted) -> None: + """Handle user message submission.""" + text = event.value.strip() + if not text or not self._agent_ready: + return + + event.input.value = "" + event.input.disabled = True + + scroll = self.query_one("#chat-scroll", VerticalScroll) + await scroll.mount(ChatMessage("user", text)) + scroll.scroll_end(animate=False) + + await self._set_status("thinking...") + self._send_message(text) + + @work(thread=False) + async def _send_message(self, text: str) -> None: + """Send a message to the agent and display the response.""" + scroll = self.query_one("#chat-scroll", VerticalScroll) + input_w = self.query_one("#input", Input) + + self._messages.append(HumanMessage(content=text)) + result = await self._graph.ainvoke({"messages": self._messages}) + self._messages = result["messages"] + + await self._clear_status() + + assistant_msg = self._messages[-1] + await scroll.mount( + ChatMessage("assistant", str(assistant_msg.content)) + ) + scroll.scroll_end(animate=False) + + input_w.disabled = False + input_w.focus() + + async def action_quit(self) -> None: + """Clean up and quit.""" + if self._auth: + await self._auth.close() + self.exit() + + +def main() -> None: + """Launch the TUI.""" + RedpandaAgentApp().run() + + +if __name__ == "__main__": + main() diff --git a/ai-agents/langchain-agent/src/agent/sprite.py b/ai-agents/langchain-agent/src/agent/sprite.py new file mode 100644 index 00000000..c20d8f4f --- /dev/null +++ b/ai-agents/langchain-agent/src/agent/sprite.py @@ -0,0 +1,23 @@ +"""8-bit red panda — block-character pixel art for Rich markup.""" + + +def make_sprite() -> str: + """Build a red panda sprite: head, body with raised tail, legs.""" + R = "[#cc2200]\u2588[/]" + D = "[#222222]\u2588[/]" + W = "[#eeeeee]\u2588[/]" + T = "[#882200]\u2588[/]" + S = " " + rows = [ + f"{R}{R}{S}{S}{S}{S}{S}{S}{R}{R}{S}{S}{S}{R}{T}", + f"{R}{R}{R}{R}{R}{R}{R}{R}{R}{R}{S}{R}{T}{R}", + f"{R}{W}{D}{R}{R}{R}{R}{D}{W}{R}{S}{T}{R}{T}", + f"{R}{W}{W}{W}{D}{D}{W}{W}{W}{R}{S}{R}{T}", + f"{S}{R}{R}{R}{R}{R}{R}{R}{R}", + f"{S}{S}{R}{R}{R}{R}{R}{R}", + f"{S}{S}{D}{D}{S}{S}{D}{D}", + ] + return "\n".join(rows) + + +SPRITE = make_sprite() diff --git a/ai-agents/langchain-agent/src/agent/tools.py b/ai-agents/langchain-agent/src/agent/tools.py new file mode 100644 index 00000000..f390eb1e --- /dev/null +++ b/ai-agents/langchain-agent/src/agent/tools.py @@ -0,0 +1,184 @@ +"""MCP tool loading and dynamic discovery middleware for the Redpanda AI Gateway. + +The gateway uses a two-level discovery pattern: +1. `list_tools()` returns a `tool_search` meta-tool +2. Calling `tool_search` discovers actual tools (e.g. `redpanda-docs:ask_redpanda_question`) +3. Discovered tools can change at any time — they are NOT static + +We use `AgentMiddleware` with `awrap_model_call` and `awrap_tool_call` to dynamically +inject tools discovered via `tool_search` into the agent's tool set at runtime. +""" + +import json +import re +from collections.abc import Awaitable, Callable + +from langchain.agents.middleware import ( + AgentMiddleware, + ModelRequest, + ModelResponse, + ToolCallRequest, +) +from langchain_core.messages import ToolMessage +from langchain_core.tools import BaseTool, StructuredTool +from langchain_mcp_adapters.client import MultiServerMCPClient +from langgraph.types import Command +from pydantic import create_model + +from agent.auth import GatewayAuth + +# Type aliases for middleware handler signatures +ModelHandler = Callable[[ModelRequest[None]], Awaitable[ModelResponse[None]]] +ToolHandler = Callable[[ToolCallRequest], Awaitable[ToolMessage | Command[str]]] + +MCP_SESSION_KEY = "gateway" +TOOL_SEARCH_NAME = "tool_search" + + +class MCPGatewayMiddleware(AgentMiddleware): # type: ignore[type-arg] + """Middleware that discovers MCP tools at runtime and injects them into the agent.""" + + def __init__(self, client: MultiServerMCPClient) -> None: + self._client = client + self._discovered: dict[str, StructuredTool] = {} + self._name_map: dict[str, str] = {} # sanitized name -> original MCP name + + async def awrap_model_call( + self, + request: ModelRequest[None], + handler: ModelHandler, + ) -> ModelResponse[None]: + """Inject dynamically discovered tools into the model's tool list.""" + if self._discovered: + extra = list(self._discovered.values()) + return await handler( + request.override(tools=[*(request.tools or []), *extra]) + ) + return await handler(request) + + async def awrap_tool_call( + self, + request: ToolCallRequest, + handler: ToolHandler, + ) -> ToolMessage | Command[str]: + """Handle calls to dynamically discovered tools via MCP session.""" + tool_name = request.tool_call["name"] + + # If it's a statically registered tool (e.g. tool_search), let it through. + # When tool_search returns results, cache the discovered tools. + if tool_name not in self._discovered: + result = await handler(request) + if tool_name == TOOL_SEARCH_NAME and isinstance(result, ToolMessage): + self._register_discovered_tools(result.content) + return result + + # Execute dynamically discovered tool via raw MCP session + mcp_name = self._name_map.get(tool_name, tool_name) + args = request.tool_call.get("args", {}) + async with self._client.session(MCP_SESSION_KEY) as session: + mcp_result = await session.call_tool(mcp_name, args) + + parts = [] + for block in mcp_result.content: + if hasattr(block, "text"): + parts.append(block.text) + else: + parts.append(json.dumps(block.model_dump(), default=str)) + + content = "\n".join(parts) + if mcp_result.isError: + content = f"Error: {content}" + + return ToolMessage( + content=content, + tool_call_id=request.tool_call["id"], + ) + + def _register_discovered_tools(self, content: str | list[dict[str, str]]) -> None: + """Parse tool_search output and register discovered tools.""" + if isinstance(content, list): + text = next( + ( + b["text"] if isinstance(b, dict) else getattr(b, "text", "") + for b in content + if (isinstance(b, dict) and b.get("type") == "text") + or getattr(b, "type", None) == "text" + ), + "", + ) + else: + text = content + + try: + data = json.loads(text) + except (json.JSONDecodeError, TypeError): + return + + for tool_def in data.get("tools", []): + original_name = tool_def.get("name", "") + if not original_name: + continue + safe_name = _sanitize_tool_name(original_name) + if safe_name in self._discovered: + continue + self._name_map[safe_name] = original_name + schema = tool_def.get("inputSchema", {}) + fields = _schema_to_fields(schema) + input_model = create_model(f"{safe_name}_Input", **fields) + self._discovered[safe_name] = StructuredTool.from_function( + func=lambda **_kw: "", + coroutine=lambda **_kw: "", + name=safe_name, + description=tool_def.get("description", original_name), + args_schema=input_model, + ) + + +def _sanitize_tool_name(name: str) -> str: + """Sanitize an MCP tool name to match OpenAI's ``^[a-zA-Z0-9_-]{1,128}$`` pattern.""" + safe = re.sub(r"[^a-zA-Z0-9_-]", "-", name) + return safe[:128] + + +def _schema_to_fields( + schema: dict[str, dict[str, str | dict[str, str]]], +) -> dict[str, tuple[type, str | None | type[...]]]: + """Convert a JSON Schema properties dict to pydantic create_model fields.""" + type_map: dict[str, type] = { + "string": str, + "integer": int, + "number": float, + "boolean": bool, + } + fields: dict[str, tuple[type, str | None | type[...]]] = {} + required = set(schema.get("required", [])) + for prop_name, prop_def in schema.get("properties", {}).items(): + py_type = type_map.get(str(prop_def.get("type", "string")), str) + default: str | None | type[...] = ... if prop_name in required else prop_def.get("default") + fields[prop_name] = (py_type, default) + return fields + + +async def load_mcp_tools( + auth: GatewayAuth, +) -> tuple[list[BaseTool], MultiServerMCPClient, MCPGatewayMiddleware]: + """Connect to the gateway MCP endpoint and return tools + middleware. + + Returns the static tools (e.g. tool_search), the MCP client, and the + middleware that handles dynamic tool discovery at runtime. + """ + headers = await auth.get_headers() + + client = MultiServerMCPClient( + { + MCP_SESSION_KEY: { + "transport": "streamable_http", + "url": f"{auth.gateway_url}/mcp/", + "headers": headers, + }, + } + ) + static_tools = await client.get_tools() + middleware = MCPGatewayMiddleware(client) + + return static_tools, client, middleware diff --git a/ai-agents/langchain-agent/vulture_whitelist.py b/ai-agents/langchain-agent/vulture_whitelist.py new file mode 100644 index 00000000..e9843bfe --- /dev/null +++ b/ai-agents/langchain-agent/vulture_whitelist.py @@ -0,0 +1,18 @@ +# Vulture whitelist — framework-invoked methods and class attributes. +# These are called by Textual / LangChain at runtime, not directly. + +from agent.main import RedpandaAgentApp # noqa: F401 +from agent.tools import MCPGatewayMiddleware # noqa: F401 + +# Textual framework hooks +RedpandaAgentApp.CSS +RedpandaAgentApp.TITLE +RedpandaAgentApp.BINDINGS +RedpandaAgentApp.compose +RedpandaAgentApp.on_mount +RedpandaAgentApp.on_input_submitted +RedpandaAgentApp.action_quit + +# LangChain AgentMiddleware overrides +MCPGatewayMiddleware.awrap_model_call +MCPGatewayMiddleware.awrap_tool_call diff --git a/docs/modules/ai-agents/pages/langchain-agent.adoc b/docs/modules/ai-agents/pages/langchain-agent.adoc new file mode 120000 index 00000000..57d91c9c --- /dev/null +++ b/docs/modules/ai-agents/pages/langchain-agent.adoc @@ -0,0 +1 @@ +../../../../ai-agents/langchain-agent/README.adoc \ No newline at end of file