langchain-pollinations 0.2.6b5

LangChain provider for Pollinations unified API (OpenAI-compatible chat completions + image endpoint).

langchain-pollinations A LangChain compatible provider library for Pollinations.ai

---

langchain-pollinations provides LangChain-native wrappers for the Pollinations.ai API, designed to plug into the modern LangChain ecosystem (v1.2x) while staying strictly aligned with Pollinations.ai endpoints.

The library exposes four public entry points:

• ChatPollinations — chat model wrapper for the OpenAI-compatible POST /v1/chat/completions endpoint.
• ImagePollinations — image and video generation wrapper for GET /image/{prompt}.
• ModelInformation — utility for listing available text, image, and OpenAI-compatible models.
• AccountInformation — client for querying profile, balance, API key, and usage statistics.

Why Pollinations

Pollinations.ai provides a unified gateway for text generation, vision, tool use, and multimodal media—including images, video, and audio—behind a single OpenAI-compatible API surface. This library makes that gateway usable with idiomatic LangChain patterns (invoke, stream, bind_tools, with_structured_output) while keeping the public interface minimal and all configuration strictly typed via Pydantic.

Installation

pip install langchain-pollinations

Authentication

Copy .env.example to .env and set your key:

POLLINATIONS_API_KEY=sk-...your_key...

All four main classes also accept an explicit api_key= parameter on construction.

ChatPollinations

ChatPollinations inherits from LangChain's BaseChatModel and supports invoke, stream, ainvoke, astream, tool calling, structured output, and multimodal messages.

Available text models

Group	Models
OpenAI	openai, openai-fast, openai-large, openai-audio
Google	gemini, gemini-fast, gemini-large, gemini-legacy, gemini-search
Anthropic	claude, claude-fast, claude-large, claude-legacy
Reasoning	perplexity-reasoning, perplexity-fast, deepseek
Other	mistral, grok, kimi, qwen-coder, qwen-safety, glm, minimax, nova-fast, midijourney, chickytutor

Basic chat completion

import dotenv
from langchain_pollinations import ChatPollinations
from langchain_core.messages import HumanMessage

dotenv.load_dotenv()

llm = ChatPollinations(model="openai")
res = llm.invoke([HumanMessage(content="Write a short haiku about distributed systems.")])
print(res.content)

Streaming

import dotenv
from langchain_pollinations import ChatPollinations
from langchain_core.messages import HumanMessage

dotenv.load_dotenv()

llm = ChatPollinations(model="claude")
for chunk in llm.stream([HumanMessage(content="Explain LangGraph in three sentences.")]):
    print(chunk.content, end="", flush=True)

Vision (image URL input)

import dotenv
from langchain_pollinations import ChatPollinations
from langchain_core.messages import HumanMessage

dotenv.load_dotenv()

llm = ChatPollinations(model="openai")
msg = HumanMessage(content=[
    {"type": "text", "text": "Describe the image in one sentence."},
    {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}},
])
res = llm.invoke([msg])
print(res.content)

Audio generation

import base64
import dotenv
from langchain_pollinations import ChatPollinations
from langchain_core.messages import HumanMessage

dotenv.load_dotenv()

llm = ChatPollinations(
    model="openai-audio",
    modalities=["text", "audio"],
    audio={"voice": "coral", "format": "mp3"},
)
res = llm.invoke([HumanMessage(content="Say hello in a friendly tone.")])

audio_data = res.additional_kwargs.get("audio", {})
if audio_data.get("data"):
    with open("output.mp3", "wb") as f:
        f.write(base64.b64decode(audio_data["data"]))
    print("Saved output.mp3 | transcript:", audio_data.get("transcript"))

Thinking / Reasoning models

Enable internal reasoning with thinking parameter:

import dotenv
from langchain_pollinations import ChatPollinations
from langchain_core.messages import HumanMessage

dotenv.load_dotenv()

llm = ChatPollinations(
    model="deepseek",
    thinking={"type": "enabled", "budget_tokens": 8000},
)
res = llm.invoke([HumanMessage(content="Prove that sqrt(2) is irrational.")])
print(res.content)

Or use reasoning_effort for models that support it:

import dotenv
from langchain_pollinations import ChatPollinations
from langchain_core.messages import HumanMessage

dotenv.load_dotenv()

llm = ChatPollinations(
    model="perplexity-reasoning",
    thinking={"type": "enabled", "budget_tokens": 8000},
    reasoning_effort="high"
)
res = llm.invoke([HumanMessage(content="Prove that sqrt(2) is irrational.")])
print(res.content)

Tool calling

import dotenv

from langchain.tools import tool
from langchain.agents import create_agent
from langchain_pollinations import ChatPollinations

dotenv.load_dotenv()

@tool
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"It is sunny in {city}."

llm = ChatPollinations(model="openai")

agent = create_agent(
    model=llm,
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

res = agent.invoke(
    {"messages": [{"role": "user", "content": "What is the weather in Tokyo?"}]},
)

for msg in res["messages"]:
    print(f"{msg.type}: {msg.content}")
    print("*" * 100)

Tool binding

import dotenv, pprint
from langchain_pollinations import ChatPollinations
from langchain_core.tools import tool

dotenv.load_dotenv()

@tool
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"It is sunny in {city}."

llm = ChatPollinations(model="openai").bind_tools([get_weather])
res = llm.invoke("What is the weather in Caracas?")

print("Response type:", type(res), "\n")
pprint.pprint(res.model_dump())

print("\nTool call:")
pprint.pprint(res.tool_calls)

bind_tools also accepts Pollinations built-in tools by type string:

llm = ChatPollinations(model="gemini").bind_tools([
    {"type": "google_search"},
    {"type": "code_execution"},
])

Structured output

import dotenv
from pydantic import BaseModel
from langchain_pollinations import ChatPollinations

dotenv.load_dotenv()

class MovieReview(BaseModel):
    title: str
    rating: int
    summary: str

llm = ChatPollinations(model="openai").with_structured_output(MovieReview)
review = llm.invoke("Review the movie Interstellar.")
print(review)

Async usage

All blocking methods have async counterparts: ainvoke, astream, abatch.

import asyncio
import dotenv
from langchain_pollinations import ChatPollinations
from langchain_core.messages import HumanMessage

dotenv.load_dotenv()

async def main():
    llm = ChatPollinations(model="gemini-fast")
    async for chunk in llm.astream([HumanMessage(content="List 3 Python tips.")]):
        print(chunk.content, end="", flush=True)

asyncio.run(main())

ImagePollinations

ImagePollinations targets GET /image/{prompt} and supports synchronous and asynchronous generation of images and videos with full LangChain invoke/ainvoke compatibility.

Available image / video models

Type	Models
Image	flux, zimage, klein, klein-large, nanobanana, nanobanana-pro, seedream, seedream-pro, kontext
Image (quality)	gptimage, gptimage-large, imagen-4
Video	veo, grok-video, seedance, seedance-pro, wan, ltx-2

Basic image generation

import dotenv
from langchain_pollinations import ImagePollinations

dotenv.load_dotenv()

img = ImagePollinations(model="flux", width=1024, height=1024, seed=42)
data = img.generate("a cyberpunk city at night, neon lights")
with open("city.jpg", "wb") as f:
    f.write(data)

Fluent interface with with_params()

with_params() returns a new pre-configured instance without mutating the original, making it easy to create specialized generators from a shared base:

import dotenv
from langchain_pollinations import ImagePollinations

dotenv.load_dotenv()

base = ImagePollinations(model="flux", width=1024, height=1024)

pixel_art = base.with_params(model="klein", enhance=True)
portrait  = base.with_params(width=768, height=1024, safe=True)

data1 = pixel_art.generate("a pixel art knight standing on a cliff")
with open("knight.jpg", "wb") as f:
    f.write(data1)
data2 = portrait.generate("a watercolor portrait of a scientist")
with open("scientist.jpg", "wb") as f:
    f.write(data2)

Video generation

import dotenv
from langchain_pollinations import ImagePollinations

dotenv.load_dotenv()

vid = ImagePollinations(
    model="seedance", 
    duration=4, 
    aspect_ratio="16:9", 
    audio=True
)
resp = vid.generate_response("two medieval horse-knights fighting with spades at sunset, cinematic")

content_type = resp.headers.get("content-type", "")
ext = ".mp4" if "video" in content_type else ".bin"
with open(f"fighting_knights{ext}", "wb") as f:
    f.write(resp.content)
print(f"Saved fighting_knights{ext} ({len(resp.content)} bytes)")

Async generation

import asyncio
import dotenv
from langchain_pollinations import ImagePollinations

dotenv.load_dotenv()

async def main():
    img = ImagePollinations(model="flux")
    data = await img.agenerate("a misty forest at dawn, soft light")
    with open("forest.jpg", "wb") as f:
        f.write(data)

asyncio.run(main())

ModelInformation

import dotenv
from langchain_pollinations import ModelInformation

dotenv.load_dotenv()

info = ModelInformation()

# Text models
for m in info.list_text_models():
    print(
        m.get("name"), 
        "- input_modalities: ", m.get("input_modalities"),
        "- output_modalities: ", m.get("output_modalities"),
        "- tools: ", m.get("tools"),
    )
print()

# Image models
for m in info.list_image_models():
    print(
        m.get("name"), 
        "- input_modalities: ", m.get("input_modalities"),
        "- output_modalities: ", m.get("output_modalities"),
        "- tools: ", m.get("tools"),
    )
print()

# All model IDs at once
available = info.get_available_models()
print("Text models:", available["text"], "\n")
print("Image models:", available["image"], "\n")

# OpenAI-compatible /v1/models
compat = info.list_compatible_models()
print(compat)

Async equivalents: alist_text_models, alist_image_models, alist_compatible_models, aget_available_models.

AccountInformation

import dotenv
from langchain_pollinations import AccountInformation
from langchain_pollinations.account import AccountUsageDailyParams, AccountUsageParams

dotenv.load_dotenv()

account = AccountInformation()

balance = account.get_balance()
print(f"Balance: {balance['balance']} credits")

# Retrieve API key metadata
key_info = account.get_key()
print(key_info, "\n")

# Paginated usage logs
usage = account.get_usage(params=AccountUsageParams(limit=50, format="json"))
print(usage, "\n")

# Daily aggregated usage
daily = account.get_usage_daily(params=AccountUsageDailyParams(format="json"))
print(daily, "\n")

Async equivalents: aget_profile, aget_balance, aget_key, aget_usage, aget_usage_daily.

Error handling

All errors surface as PollinationsAPIError, which carries structured fields parsed directly from the API error envelope:

from langchain_pollinations import ChatPollinations, PollinationsAPIError
from langchain_core.messages import HumanMessage

try:
    llm = ChatPollinations(model="gemini", api_key="anyway")
    res = llm.invoke([HumanMessage(content="Hello")])
    print(res.content)
except PollinationsAPIError as e:
    if e.is_auth_error:
        print("Check your POLLINATIONS_API_KEY.")
    elif e.is_payment_required:
        print("Insufficient balance. Some models are for paid-only use.")
    elif e.is_validation_error:
        print(f"Bad request: {e.details}")
    elif e.is_server_error:
        print(f"Server error {e.status_code} – consider retrying.")
    else:
        print(e.to_dict())

PollinationsAPIError exposes: status_code, message, error_code, request_id, timestamp, details, cause, and convenience properties is_auth_error, is_payment_required, is_validation_error, is_client_error, is_server_error.

Debug logging

Set POLLINATIONS_HTTP_DEBUG=true to log every outgoing request and incoming response. Authorization headers are automatically redacted in all log output.

POLLINATIONS_HTTP_DEBUG=true python my_script.py

Contributing

Issues and pull requests are welcome—especially around edge-case compatibility with LangChain agent and tool flows, LangGraph integration, and improved ergonomics for saving generated media.

License

Released under the MIT License.