Invoke Claude, GPT-4o, and Gemini through LangChain 1.0 without tripping on
the content-block, token-accounting, and structured-output quirks that silently
break production code. Use when initializing chat models, routing across providers,
iterating AIMessage content, or choosing a structured-output method.
Trigger with "langchain model inference", "ChatAnthropic", "ChatOpenAI",
"with_structured_output", "AIMessage content blocks", "langchain routing".
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Invoke Claude, GPT-4o, and Gemini through LangChain 1.0 without tripping on
the content-block, token-accounting, and structured-output quirks that silently
break production code. Use when initializing chat models, routing across providers,
iterating AIMessage content, or choosing a structured-output method.
Trigger with "langchain model inference", "ChatAnthropic", "ChatOpenAI",
"with_structured_output", "AIMessage content blocks", "langchain routing".
Designed for Claude Code, also compatible with Codex
LangChain Model Inference (Python)
Overview
AIMessage.content is a str on simple OpenAI calls and a list[dict] on Claude
the instant any tool_use, thinking, or image block enters the response.
Code that does message.content.lower() crashes with
AttributeError: 'list' object has no attribute 'lower' — the #1 first-production-call
LangChain 1.0 bug on Anthropic. And that is one of four separate "content shape"
pitfalls in this skill:
P02 — AIMessage.content list-vs-string divergence
P03 — with_structured_output(method="function_calling") silently drops
Optional[list[X]] fields on ~40% of real schemas
P05 — temperature=0 is not deterministic on Anthropic even though it is on OpenAI
P58 — Claude expects the system message at position 0; middleware that reorders
messages makes it silently ignored
This skill walks through ChatAnthropic, ChatOpenAI, and ChatGoogleGenerativeAI
initialization; model routing; token counting that is actually correct during
streaming; content-block iteration; and a decision tree for with_structured_output
methods that holds up on real schemas. Pin: langchain-core 1.0.x,
langchain-anthropic 1.0.x, langchain-openai 1.0.x, langchain-google-genai 1.0.x.
Pain-catalog anchors: P01, P02, P03, P04, P05, P53, P54, P58, P63, P64, P65.
Prerequisites
Python 3.10+
langchain-core >= 1.0, < 2.0
At least one provider package: pip install langchain-anthropic langchain-openai
Provider API key(s): ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY
Instructions
Step 1 — Initialize a chat model with explicit, version-safe defaults
from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI
claude = ChatAnthropic(
model="claude-sonnet-4-6",
temperature=,
max_tokens=,
timeout=,
max_retries=,
)
gpt4o = ChatOpenAI(
model=,
temperature=,
timeout=,
max_retries=,
)
0
4096
30
# seconds. Default is None — hangs forever on provider stall.
2
# Retries, not attempts. See P30 in pain catalog.
"gpt-4o"
0
30
2
Explicit timeout and max_retries are not optional in production — the defaults
are wrong for every workload we have measured. max_retries=6 (the default on
ChatOpenAI) means a single logical call can bill as 7 requests on flaky
networks.
Step 2 — Iterate AIMessage.content as typed blocks, not strings
from langchain_core.messages import AIMessage
defextract_text(msg: AIMessage) -> str:
"""Safe on both provider shapes. Works for streaming deltas too.
Handles both dict blocks (provider-native) and typed block objects
(LangChain 1.0 wrappers) — which Gemini, OpenAI tools, and future
SDK versions may return.
"""ifisinstance(msg.content, str):
return msg.content
parts = []
for block in msg.content:
# Block may be a dict (provider-native) or a typed object (1.0 wrapper)
block_type = block.get("type") ifisinstance(block, dict) elsegetattr(block, "type", None)
if block_type == "text":
parts.append(block["text"] ifisinstance(block, dict) else block.text)
return"".join(parts)
AIMessage.text() (1.0+) does this for you in most cases — prefer it. Roll your
own only when you need to filter by block type (tool_use, image, thinking).
See Content Blocks for the full block-type
reference and streaming-delta shape.
Step 3 — Route across providers with a factory, not a conditional
from langchain_core.language_models import BaseChatModel
# Version-safe defaults applied to every model the factory builds.# Callers can override via **kwargs.
_SAFE_DEFAULTS = {"timeout": 30, "max_retries": 2}
defchat_model(provider: str, **kwargs) -> BaseChatModel:
defaults = {**_SAFE_DEFAULTS, **kwargs} # caller's kwargs winif provider == "anthropic":
return ChatAnthropic(model="claude-sonnet-4-6", **defaults)
if provider == "openai":
return ChatOpenAI(model="gpt-4o", **defaults)
if provider == "gemini":
from langchain_google_genai import ChatGoogleGenerativeAI
return ChatGoogleGenerativeAI(model="gemini-2.5-pro", **defaults)
raise ValueError(f"Unknown provider: {provider!r}")
A factory centralizes the version-safe defaults from Step 1 (timeout=30,
max_retries=2) and the structured-output method pick from Step 5. Chains depend
on the BaseChatModel protocol, not the concrete class. Callers override with
chat_model("openai", timeout=60) when they need it.
Step 4 — Count tokens correctly during streaming
ChatAnthropic.stream() does not populate response_metadata["token_usage"]
until the stream closes (P01). If your cost dashboard reads on_llm_end, it
lags by the stream duration. Use astream_events(version="v2"):
asyncfor event in claude.astream_events({"input": "..."}, version="v2"):
if event["event"] == "on_chat_model_stream":
chunk = event["data"]["chunk"]
ifhasattr(chunk, "usage_metadata") and chunk.usage_metadata:
meter.record(chunk.usage_metadata["input_tokens"],
chunk.usage_metadata["output_tokens"])
See Token Accounting for per-provider differences
(Anthropic reports input/output/cache separately; OpenAI aggregates; Gemini
reports completion-only on stream start).
Step 5 — Pick the right with_structured_output method
from pydantic import BaseModel, ConfigDict
classPlan(BaseModel):
model_config = ConfigDict(extra="ignore") # P53 — models add helpful extra fields
steps: list[str]
estimated_minutes: int
structured = claude.with_structured_output(Plan, method="json_schema")
plan = structured.invoke("Plan a 3-step deploy")
Avoid Optional[list[X]] fields — they silently return None on some providers (P03).
See Structured Output Methods for a
concrete comparison matrix and fallback pattern.
Output
Chat models initialized with explicit timeouts (30s) and max_retries=2
Content-safe extractor that handles both str and list[dict] shapes
Factory-based routing with a single BaseChatModel return type
Streaming token counter that reports incrementally, not at stream end
with_structured_output chosen per provider capability, with Pydantic validation
Error Handling
Error
Cause
Fix
AttributeError: 'list' object has no attribute 'lower'
Treating Claude AIMessage.content as str (P02)
Use msg.text() or the Step 2 extractor
ValidationError: extra fields not permitted
Pydantic v2 strict default; model added fields (P53)
Set model_config = ConfigDict(extra="ignore")
ValidationError: Field required on Optional[list[X]]
ImportError: cannot import name 'ChatOpenAI' from 'langchain.chat_models'
Legacy 0.2 import path (P38)
from langchain_openai import ChatOpenAI
Examples
Routing: cheap draft, expensive final
A common pattern — draft with gpt-4o-mini, finalize with claude-sonnet-4-6.
The factory in Step 3 makes this trivial; combined with with_structured_output
the finalize step returns a typed object.
See Provider Quirks for the full
draft-then-finalize example including the token budget calculation.
Extracting tool calls from a single-shot response
A classification task that should return one tool call with a typed argument.
Use bind_tools([...], tool_choice={"type": "tool", "name": "Classify"}) for
a single forced call — but never loop on a forced choice (P63).
See Structured Output Methods for the
worked example and the decision tree for tool vs structured-output for extraction.
Multi-modal: screenshot plus prompt
Images are passed as content blocks, but the block shape differs between providers
(P64). LangChain 1.0 abstracts this into a universal image content block.
See Content Blocks for the universal shape and
per-provider adapter examples.
