LangChain adapter

astropods-adapter-langchain wraps a LangChain executor — either langchain.agents.create_agent or langgraph.prebuilt.create_react_agent — and connects it to the Astro messaging sidecar. It runs astream(stream_mode="updates") under the hood, translates graph updates into the shared StreamHooks lifecycle, and auto-configures OTEL tracing.

Install

$
pip install astropods-adapter-langchain

Requires Python 3.10+.

Quick start

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from langchain_anthropic import ChatAnthropic
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from langchain.agents import create_agent
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from astropods_adapter_langchain import LangChainAdapter, serve
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llm = ChatAnthropic(model="claude-sonnet-4-6")
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system_prompt = "You are a helpful assistant."
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agent = create_agent(llm, tools=[], system_prompt=system_prompt)
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adapter = LangChainAdapter(agent, name="My Agent", system_prompt=system_prompt)
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serve(adapter)

serve(adapter) connects to the messaging sidecar at localhost:9090 (or GRPC_SERVER_ADDR) and blocks until SIGINT / SIGTERM. Under ast dev and in production the address is injected for you.

API

LangChainAdapter(executor, name, system_prompt?, tools?, voice?)

serve(adapter, options?)

Re-exported from astropods-adapter-core. Connects the adapter and blocks until shutdown. Also calls setup_observability() so OTEL tracing comes up automatically when OTEL_EXPORTER_OTLP_ENDPOINT is set.

ServeOptions

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from astropods_adapter_langchain import ServeOptions, serve
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serve(adapter, ServeOptions(server_address="astro-messaging:9090"))

How the executor maps to hooks

LangChainAdapter consumes executor.astream(..., stream_mode="updates"). Each graph update produces zero or more hook calls:

LangChain streams complete messages, not token-by-token deltas — so the user sees the reply appear all at once, not as a typing animation. (This is a LangChain limitation, not the adapter’s.) Choose Mastra if streaming-per-token UX matters.

The adapter also handles two LangChain content shapes:

• OpenAI-style — content: "string" directly.
• Anthropic-style — content: [{"type": "text", "text": "..."}, ...] content blocks.

Both shapes are flattened to text before calling on_chunk().

Memory

LangChain doesn’t have built-in conversation memory. To persist context across turns, pass options.conversation_id into your own store (Redis, a database, a LangGraph checkpointer, etc.) inside your tool or executor wiring.

Voice (STT)

Pass an optional voice provider to enable audio input:

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from astropods_adapter_langchain import LangChainAdapter, OpenAIVoice, serve
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adapter = LangChainAdapter(
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    agent,
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    name="My Agent",
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    voice=OpenAIVoice(),  # uses OPENAI_API_KEY
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)
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serve(adapter)

OpenAIVoice is bundled for convenience — it implements the VoiceProvider protocol against OpenAI’s Whisper API. You can pass any object with an async listen(data: bytes, config) -> str method.

The voice flow:

LangChain has no native TTS path, so the adapter does STT only. If you need TTS, post-process on_chunk() text and call hooks.on_audio_chunk() yourself (or use the Mastra adapter, which has TTS built in).

Tracing

When OTEL_EXPORTER_OTLP_ENDPOINT is set, serve() automatically instruments LangChain so every LLM call and tool invocation produces a trace span. No code changes needed — Astro sets the env var on deployed agents.

Example: an agent with tools

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from langchain_core.tools import tool
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from langchain_anthropic import ChatAnthropic
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from langchain.agents import create_agent
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from astropods_adapter_langchain import LangChainAdapter, serve
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@tool
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def customer_lookup(customer_id: str) -> dict:
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    """Look up a customer by ID."""
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    import requests
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    return requests.get(f"https://api.example.com/customers/{customer_id}").json()
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llm = ChatAnthropic(model="claude-sonnet-4-6")
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system_prompt = "Help the user troubleshoot. Use customer_lookup when you need account details."
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agent = create_agent(llm, tools=[customer_lookup], system_prompt=system_prompt)
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adapter = LangChainAdapter(
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    agent,
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    name="Support Agent",
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    system_prompt=system_prompt,
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    tools=[customer_lookup],  # Populates the playground tool list
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)
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serve(adapter)

The tools= argument on LangChainAdapter is for playground display only — the actual tool wiring happens when you pass tools into create_agent. Pass the same list to both so the playground reflects what the agent can actually do.

Local development

$
ast dev project start

ast dev runs the messaging sidecar on localhost:9090, sets GRPC_SERVER_ADDR, and opens the bundled playground at http://localhost:8080. The same serve(adapter) code works locally and in production.

Troubleshooting