Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Long-running, serverless Node.js HTTP functions deployed onto your Neon branch, with DATABASE_URL injected automatically and compute that runs next to your data.
SkillScore100/100
Test results 100
3/3 safety checks passed
18%100%of test cases passed +82 pts
Passed 22 of 22 test cases with the skill, and 4 of 22 without it.
18 fixed4 passed either way0 still failing0 broke
This benchmark measured an earlier version of the skill.
One test taskI made changes to `neon.ts` to add a new function entry point. What CLI commands allow me to dry-run the changes against my active branch before applying them, and what command applies the configuration?
Adds every file the skill has, pinned so you can update it. No account needed.
pip install "decimalai>=0.15"
decimalai skills add sickn33-neon-functions
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"The same install from Python, for a notebook or a setup script.
pip install "decimalai>=0.15"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
print("Added. Start Claude Code with the command below.")claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.
/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai/decimalai:install sickn33-neon-functionsThe plugin needs python3 and curl on your machine.
No Python or account. Claude Code picks it up in its next session.
curl -fsSL --create-dirs -o .claude/skills/sickn33-neon-functions/SKILL.md https://app.decimal.ai/s/sickn33-neon-functions/SKILL.md.claude/skills/sickn33-neon-functions/SKILL.mdThis gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[claude-agent-sdk]"
decimalai skills add sickn33-neon-functionsimport asyncio
from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
async def main():
question = "Hello!"
async with support.run(question) as run: # one user message, one run
async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
print(message)
# In a notebook or async code: await main()
asyncio.run(main())Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.
The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[claude-agent-sdk]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
import asyncio
from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
async def main():
question = "Hello!"
async with support.run(question) as run: # one user message, one run
async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
print(message)
# In a notebook or async code: await main()
asyncio.run(main())Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.
No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdimport asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)
async def main():
async for message in query(prompt="Hello!", options=options):
print(message)
# In a notebook or async code: await main()
asyncio.run(main())This gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[openai-agents]"
decimalai skills add sickn33-neon-functionsfrom agents import Agent, Runner
from decimalai.openai_agents import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = Agent(
name=support.name,
instructions=support.instructions, # your prompt, then the skill list
tools=[*support.tools], # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.
The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[openai-agents]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
from agents import Agent, Runner
from decimalai.openai_agents import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = Agent(
name=support.name,
instructions=support.instructions, # your prompt, then the skill list
tools=[*support.tools], # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.
No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from agents import Agent, Runner
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")This gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[openai]"
decimalai skills add sickn33-neon-functionsimport json
from openai import OpenAI
from decimalai.openai import agent_config
client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
def lookup_order(order_id: str) -> str:
return f"Order {order_id} shipped yesterday."
# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
"name": "lookup_order",
"description": "Look up the status of an order.",
"parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
"required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}
question = "Where is order A1001?"
with support.run(question) as run: # one user message, one run
# run.system is your prompt, then the skill list.
messages = [{"role": "system", "content": run.system}, run.message]
while True:
reply = client.chat.completions.create(
model="gpt-5.4-mini",
messages=messages,
tools=[*TOOLS, *run.tools], # your tools, then load_skill
).choices[0].message
messages.append(reply)
if not reply.tool_calls:
break
for call in reply.tool_calls:
# load_skill is answered by the run; your own tools by FUNCTIONS.
function = FUNCTIONS.get(call.function.name)
messages.append(run.tool_result(call) or {
"role": "tool", "tool_call_id": call.id,
"content": function(**json.loads(call.function.arguments)) if function
else f"There is no tool named {call.function.name!r}.",
})
print(reply.content)
# The Responses API takes run.response_tools instead of run.tools.The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[openai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
import json
from openai import OpenAI
from decimalai.openai import agent_config
client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
def lookup_order(order_id: str) -> str:
return f"Order {order_id} shipped yesterday."
# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
"name": "lookup_order",
"description": "Look up the status of an order.",
"parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
"required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}
question = "Where is order A1001?"
with support.run(question) as run: # one user message, one run
# run.system is your prompt, then the skill list.
messages = [{"role": "system", "content": run.system}, run.message]
while True:
reply = client.chat.completions.create(
model="gpt-5.4-mini",
messages=messages,
tools=[*TOOLS, *run.tools], # your tools, then load_skill
).choices[0].message
messages.append(reply)
if not reply.tool_calls:
break
for call in reply.tool_calls:
# load_skill is answered by the run; your own tools by FUNCTIONS.
function = FUNCTIONS.get(call.function.name)
messages.append(run.tool_result(call) or {
"role": "tool", "tool_call_id": call.id,
"content": function(**json.loads(call.function.arguments)) if function
else f"There is no tool named {call.function.name!r}.",
})
print(reply.content)
# The Responses API takes run.response_tools instead of run.tools.No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from openai import OpenAI
client = OpenAI()
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
reply = client.chat.completions.create(
model="gpt-5.4-mini",
messages=[
{"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
{"role": "user", "content": "Hello!"},
],
).choices[0].messageThis gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[anthropic]"
decimalai skills add sickn33-neon-functionsimport anthropic
from decimalai.anthropic import agent_config
client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
question = "Hello!"
with support.run(question) as run: # one user message, one run
answer = client.beta.messages.tool_runner(
model="claude-haiku-5-5",
max_tokens=4096,
system=run.system, # your prompt, then the skill list
tools=[*run.tools], # your tools, then load_skill
messages=[{"role": "user", "content": question}],
).until_done()The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[anthropic]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
import anthropic
from decimalai.anthropic import agent_config
client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
question = "Hello!"
with support.run(question) as run: # one user message, one run
answer = client.beta.messages.tool_runner(
model="claude-haiku-5-5",
max_tokens=4096,
system=run.system, # your prompt, then the skill list
tools=[*run.tools], # your tools, then load_skill
messages=[{"role": "user", "content": question}],
).until_done()No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
import anthropic
client = anthropic.Anthropic()
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
reply = client.messages.create(
model="claude-haiku-5-5",
max_tokens=4096,
system=f"You are a helpful agent.\n\n{skill}",
messages=[{"role": "user", "content": "Hello!"}],
)This gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[google-genai]"
decimalai skills add sickn33-neon-functionsfrom google import genai
from google.genai import types
from decimalai.google_genai import agent_config
client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
def lookup_order(order_id: str) -> str:
return f"Order {order_id} shipped yesterday."
# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
"name": "lookup_order",
"description": "Look up the status of an order.",
"parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
"required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}
question = "Where is order A1001?"
with support.run(question) as run: # one user message, one run
config = types.GenerateContentConfig(
system_instruction=run.system, # your prompt, then the skill list
tools=[*TOOLS, *run.tool_params], # your functions, then load_skill
)
contents = [{"role": "user", "parts": [{"text": run.message}]}]
while True:
response = client.models.generate_content(
model="gemini-3.5-flash", contents=contents, config=config,
)
if not response.function_calls:
break
contents.append(response.candidates[0].content)
parts = []
for call in response.function_calls:
# load_skill is answered by the run; your own functions by FUNCTIONS.
# A loop of your own rather than automatic function calling, which
# raises KeyError when the model asks for a function you did not pass.
function = FUNCTIONS.get(call.name)
parts.append(run.tool_result(call) or {"function_response": {
"name": call.name, "id": call.id,
"response": {"result": function(**(call.args or {})) if function
else f"There is no function named {call.name!r}."},
}})
contents.append({"role": "user", "parts": parts})
print(response.text)The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[google-genai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
from google import genai
from google.genai import types
from decimalai.google_genai import agent_config
client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
def lookup_order(order_id: str) -> str:
return f"Order {order_id} shipped yesterday."
# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
"name": "lookup_order",
"description": "Look up the status of an order.",
"parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
"required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}
question = "Where is order A1001?"
with support.run(question) as run: # one user message, one run
config = types.GenerateContentConfig(
system_instruction=run.system, # your prompt, then the skill list
tools=[*TOOLS, *run.tool_params], # your functions, then load_skill
)
contents = [{"role": "user", "parts": [{"text": run.message}]}]
while True:
response = client.models.generate_content(
model="gemini-3.5-flash", contents=contents, config=config,
)
if not response.function_calls:
break
contents.append(response.candidates[0].content)
parts = []
for call in response.function_calls:
# load_skill is answered by the run; your own functions by FUNCTIONS.
# A loop of your own rather than automatic function calling, which
# raises KeyError when the model asks for a function you did not pass.
function = FUNCTIONS.get(call.name)
parts.append(run.tool_result(call) or {"function_response": {
"name": call.name, "id": call.id,
"response": {"result": function(**(call.args or {})) if function
else f"There is no function named {call.name!r}."},
}})
contents.append({"role": "user", "parts": parts})
print(response.text)No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from google import genai
from google.genai import types
client = genai.Client()
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
response = client.models.generate_content(
model="gemini-3.5-flash",
contents="Hello!",
config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)This gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[langchain]" langchain-openai
decimalai skills add sickn33-neon-functionsfrom langchain.agents import create_agent
from decimalai.langchain import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = create_agent(
"openai:gpt-5.4-mini",
tools=[], # your tools; the middleware adds load_skill
middleware=[support.middleware], # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[langchain]" langchain-openaifrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
from langchain.agents import create_agent
from decimalai.langchain import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = create_agent(
"openai:gpt-5.4-mini",
tools=[], # your tools; the middleware adds load_skill
middleware=[support.middleware], # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from langchain.agents import create_agent
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})This gets SKILL.md only. The skill's 6 other files are not included.
CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add sickn33-neon-functionsimport decimalai.crewai
from crewai import Agent, Crew, Task
agent = Agent(
role="Support", goal="Help the customer", backstory="A support agent.",
llm=llm, # your own CrewAI LLM
skills=decimalai.crewai.skills(), # the installed folder
tools=decimalai.crewai.skill_tools(), # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" "crewai>=1.15.27"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
import decimalai.crewai
from crewai import Agent, Crew, Task
agent = Agent(
role="Support", goal="Help the customer", backstory="A support agent.",
llm=llm, # your own CrewAI LLM
skills=decimalai.crewai.skills(), # the installed folder
tools=decimalai.crewai.skill_tools(), # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from crewai import Agent, Crew, Task
agent = Agent(
role="Support", goal="Help the customer", backstory="A support agent.",
llm=llm, # your own CrewAI LLM
skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()This gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[adk]"
decimalai skills add sickn33-neon-functionsfrom decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = LlmAgent(
name="support", # ADK needs a Python identifier
model="gemini-3.5-flash",
instruction=support.instruction, # your prompt, then the skill list
tools=[*support.tools], # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.
The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[adk]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = LlmAgent(
name="support", # ADK needs a Python identifier
model="gemini-3.5-flash",
instruction=support.instruction, # your prompt, then the skill list
tools=[*support.tools], # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.
No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from google.adk.agents import LlmAgent
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
agent = LlmAgent(
name="support",
model="gemini-3.5-flash",
instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)This gets SKILL.md only. The skill's 6 other files are not included.
agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[pydantic-ai]"
decimalai skills add sickn33-neon-functionsfrom pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = Agent(
"openai:gpt-4o-mini",
name=support.name,
tools=[], # your tools; the capability adds load_skill
capabilities=[support], # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.
The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.
pip install "decimalai[pydantic-ai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")
agent = Agent(
"openai:gpt-4o-mini",
name=support.name,
tools=[], # your tools; the capability adds load_skill
capabilities=[support], # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.
No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from pydantic_ai import Agent
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")This gets SKILL.md only. The skill's 6 other files are not included.
Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" deepagents
decimalai skills add sickn33-neon-functionsimport decimalai.deepagents
from deepagents import create_deep_agent
agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).
The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" deepagentsfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
import decimalai.deepagents
from deepagents import create_deep_agent
agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).
No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from deepagents import create_deep_agent
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})This gets SKILL.md only. The skill's 6 other files are not included.
Its own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" agent-framework
decimalai skills add sickn33-neon-functionsimport asyncio
from agent_framework import Agent
from decimalai.agent_framework import skills
agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))It caches skills by default, so restart the process after decimalai skills update.
The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" agent-frameworkfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
import asyncio
from agent_framework import Agent
from decimalai.agent_framework import skills
agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))It caches skills by default, so restart the process after decimalai skills update.
No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdimport asyncio
from pathlib import Path
from agent_framework import Agent
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))This gets SKILL.md only. The skill's 6 other files are not included.
Strands' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" strands-agents
decimalai skills add sickn33-neon-functionsfrom decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell
agent = Agent(
model=model, # always pass one: the default is Amazon Bedrock
system_prompt="You are a helpful agent.",
tools=[file_editor, shell],
plugins=[skills()],
)
agent("Hello!")Strands loads an agent's skills once, so build the agent again after decimalai skills update.
The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" strands-agentsfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("sickn33-neon-functions")
if result.exit_code:
raise SystemExit(result.problems)
from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell
agent = Agent(
model=model, # always pass one: the default is Amazon Bedrock
system_prompt="You are a helpful agent.",
tools=[file_editor, shell],
plugins=[skills()],
)
agent("Hello!")Strands loads an agent's skills once, so build the agent again after decimalai skills update.
No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.
.agents/skills/sickn33-neon-functions/SKILL.mdfrom pathlib import Path
from strands import Agent
skill = Path(".agents/skills/sickn33-neon-functions/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")This gets SKILL.md only. The skill's 6 other files are not included.
Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.
pip install "decimalai>=0.15"
decimalai skills add sickn33-neon-functionsNo Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.
curl -fsSL --create-dirs -o .agents/skills/sickn33-neon-functions/SKILL.md https://app.decimal.ai/s/sickn33-neon-functions/SKILL.md.agents/skills/sickn33-neon-functions/SKILL.mdThis gets SKILL.md only. The skill's 6 other files are not included.
Give its text to your model as instructions. No account or package needed.
This gets SKILL.md only. The skill's 6 other files are not included.
Raw files: /s/sickn33-neon-functions/SKILL.md · /s/sickn33-neon-functions.json
Fetch the file and give its text to your model as instructions. No account or package needed.
curl -fsSL https://app.decimal.ai/s/sickn33-neon-functions/SKILL.mdcurl -fsSL https://app.decimal.ai/s/sickn33-neon-functions.jsonThis gets SKILL.md only. The skill's 6 other files are not included.
You don't need a DecimalAI account.
Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.
Claude Code reads files from your project, so DecimalAI config does not apply there.
On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.
On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.
On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.
On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.
On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.
Editors read files from your project, so DecimalAI config does not apply there.
Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.
This document describes an upstream preview snapshot; verify current regions, access and billing against the official docs before provisioning. Neon Functions are long-running Node.js HTTP handlers deployed onto a Neon branch. Each function gets a public HTTPS URL, runs in the same region as your database, and — if the branch has Postgres — gets DATABASE_URL injected automatically. You deploy and manage them through the same Neon CLI, neon.ts, and API you already use.
Use this skill to help the user define, run locally, deploy, and manage functions next to their database. Deliver a deployed function with its invocation URL, a working local neon dev loop, or a precise answer from the official Neon docs.
Reach for Neon Functions when the workload is a request/response handler that benefits from staying alive and staying close to the data:
pg pool, an in-memory counter) persists across requests on the same isolate.DATABASE_URL is injected for you.waitUntil (analytics, audit logs) all fit.If the workload is a pure static site, a cron/background job that needs its own lifecycle and cancellation, or something that must run outside us-east-2 today, this isn't the right tool yet (see Timeouts and Availability below).
fetch(request) method returning a Response (Workers/WinterTC-compatible). A Hono app exports exactly that shape, so export default app just works. Runs on Node.js 24, so all Node APIs are available.DATABASE_URL injected automatically when the branch has Postgres.neon, neon.ts, or the Neon API.Neon (Functions included) is backend primitives, not full-stack app hosting. Host your app on Vercel (or Netlify, or another frontend/app host); Functions are the long-running, stateful slice of your backend that lives next to your data. They compose with that platform in two ways:
Either way, secure a Function like any standalone REST API: verify a JWT or API key at the top of the handler (see the WARNING under Functions as an agent backend). Because a Function is just your backend, you can move pieces between your host and Neon — relocate an agent or a stateful WebSocket server onto a Function when it needs more runtime, and back if needed.
Functions are declared in neon.ts (see the neon skill for the branch-first workflow and neon.ts basics). Add @neon/config and declare functions under preview.functions, keyed by slug:
// neon.ts
import { defineConfig } from "@neon/config/v1";
export default defineConfig({
preview: {
functions: {
todos: {
// slug: ^[a-z0-9]{1,20}$ — lowercase letters/digits, no hyphens
name: "todo api", // display label only
source: "src/index.ts", // entry file, relative to neon.ts
},
},
},
});
The slug is the function's permanent identity (it appears in the invocation URL and CLI commands) and can't be changed after the first deploy. Use name for a human-readable label.
A minimal function — a Hono app that queries the branch's Postgres via the injected DATABASE_URL:
// src/index.ts
import { Hono } from "hono";
import { drizzle } from "drizzle-orm/node-postgres";
import { Pool } from "pg";
import { parseEnv } from "@neon/env";
import config from "../neon";
import { todos } from "./db/schema";
const env = parseEnv(config);
const pool = new Pool({ connectionString: env.postgres.databaseUrl, max: 5 });
const db = drizzle(pool);
const app = new Hono();
app.get("/", (c) => c.text("Neon + Hono + Drizzle"));
app.post("/todos", async (c) => {
const { text } = await c.req.json<{ text: string }>();
const [row] = await db.insert(todos).values({ text }).returning();
return c.json(row, 201);
});
app.get("/todos", async (c) => c.json(await db.select().from(todos)));
export default app;
Create the pg pool at module scope (reused across requests on the same isolate) and keep max small (e.g. 5), since each isolate keeps its own pool.
parseEnv(config) requires every variable the config implies. A function that only talks to Postgres over the pooled URL can scope it to just that key — parseEnv then validates and returns only what you asked for (the keys autocomplete from your neon.ts):
const { postgres } = parseEnv(config, ["DATABASE_URL"]); // not the unpooled URL, auth, etc.
const pool = new Pool({ connectionString: postgres.databaseUrl, max: 5 });
neon dev # serves every function in neon.ts with hot reload; injects DATABASE_URL & friends
neon deploy # bundles with esbuild, uploads, and applies neon.ts to the linked branch
To deploy a single function without neon.ts: neon functions deploy <slug> --path . --entry src/index.ts. Retrieve the public URL with neon functions get <slug> (the invocation_url field, of the form https://<branch_id>-<slug>.compute.c-1.us-east-2.aws.neon.tech). Manage with neon functions list|get|delete.
When neon checkout creates a new branch and a neon.ts is present, it applies the policy automatically — deploying the function to the fresh branch. Checking out an existing branch does not re-deploy; run neon deploy explicitly.
neon.ts)The preview.functions block from Setup is part of neon.ts, Neon's infrastructure-as-code file — one TypeScript file declares every function (its source, display name, and env) alongside any other branch services, in version control (see the neon skill for the full reference). Treat it like Terraform for your branch:
neon config status # print the branch's live config (deployed functions)
neon config plan # dry-run diff of what apply would change
neon config apply # bundle + deploy the declared functions (neon deploy is an alias)
Functions are branch-scoped: each branch runs its own deployment at its own URL. When a neon.ts is present, neon checkout applies the policy as it creates a branch, so a fresh preview/CI branch comes up with the function already deployed. Checking out an existing branch doesn't redeploy — run neon deploy to apply changes.
Per-branch deploy tuning (e.g. runtime) lives in the branch closure, keyed by slug, so it can vary by branch without changing which functions exist:
export default defineConfig({
preview: {
functions: { todos: { name: "todo api", source: "src/index.ts" } },
},
branch: (branch) => ({
preview: { functions: { todos: { runtime: "nodejs24" } } },
}),
});
Neon injects branch-scoped connection strings and service URLs at runtime — you don't declare these or pass them at deploy time:
| Variable | Notes |
|---|---|
NEON_BRANCH | The branch name (e.g. main, preview/foo). Injected on every branch, including the default. |
DATABASE_URL | Pooled connection string. Use for most queries. Present only if the branch has Postgres. |
DATABASE_URL_UNPOOLED | Direct connection. Use for migrations, LISTEN/NOTIFY, multi-round-trip transactions. |
NEON_AUTH_BASE_URL | Present when Neon Auth is enabled on the branch. |
NEON_DATA_API_URL | Present when the Data API is enabled on the branch. |
Object storage (AWS_*) and AI Gateway (OPENAI_*, NEON_AI_GATEWAY_*) vars are also injected when those services are declared — see the neon-object-storage and neon-ai-gateway skills.
neon env pull / neon-env run / neon dev emit NEON_BRANCH (and the connection strings) into your local dev environment too, so local runs mirror the deployed runtime.
Your own secrets are per-deployment. Set them with --env KEY=VALUE on neon functions deploy (repeatable; --env KEY= deletes a key, unmentioned keys carry over), or declare them in neon.ts under the function's env (resolved at deploy time, so read from process.env to avoid hardcoding):
functions: {
todos: {
name: "todo api",
source: "src/index.ts",
env: { OPENAI_API_KEY: process.env.OPENAI_API_KEY! },
},
}
Load a .env before deploy with neon deploy --env .env.production. Pull the branch's Neon-managed vars onto disk for local dev with neon env pull (link/checkout do this automatically; pass --no-env-pull to skip and use neon-env run -- <cmd> for runtime injection). Limits: ≤1,000 vars, ≤64 KiB total, and the NEON_ prefix is reserved.
When the branch has Postgres, Neon injects the connection strings at runtime — you don't declare them, pass them at deploy time, or hardcode anything. The two you'll use:
DATABASE_URL — pooled connection string (routed through Neon's connection pooler). Use it for normal request/response query traffic. Kept un-prefixed because every Postgres ORM (Drizzle, Prisma, Knex, …) reads DATABASE_URL by default.DATABASE_URL_UNPOOLED — direct connection string to the same database. Use it for migrations, LISTEN/NOTIFY, and long multi-statement transactions.Use Drizzle (or another ORM) on top of node-postgres (pg) for queries and schema management — not Neon's serverless driver. Functions are long-running and reuse an isolate across many requests, so a persistent pg pool is the right fit; the serverless driver's HTTP transport is meant for fully isolated, lambda-style runtimes.
Create the connection pool once at module scope and reuse it across requests — don't open a connection per request:
import { drizzle } from "drizzle-orm/node-postgres";
import { Pool } from "pg";
// Created once per isolate; reused by every request that isolate handles.
const pool = new Pool({ connectionString: process.env.DATABASE_URL, max: 5 });
const db = drizzle(pool);
Pooling is recommended because an isolate is reused across many requests (and several requests can be in flight on the same isolate at once — see Timeouts and runtime limits). A module-scope pool is opened once on cold start and then shared by every subsequent request that isolate serves, so you amortize connection setup instead of paying it on every request and you avoid exhausting Postgres connections under load.
Keep max small (e.g. 5): each isolate keeps its own pool, so total connections to Postgres scale with the number of live isolates. You don't need to close the pool on shutdown — when the runtime evicts an isolate it sends SIGINT/SIGTERM, and Neon's pooler reclaims those connections for you, so an explicit drain handler is redundant.
Reading
process.env.DATABASE_URLdirectly works everywhere. The function in Setup instead uses@neon/env'sparseEnv(config)to read the same value in a typed, validated way — either is fine.
A WebSocket server is the canonical Functions workload: a long-running handler holds connections open in-process, with no external state store needed to keep a stream coherent. Because a function is a real Node.js process (not a lambda), the WebSocket handshake works the way it does in any Node server — the ws library upgrades the socket, and the connection stays alive as long as bytes flow (15-minute heartbeat, see Timeouts).
The return signature is the whole trick. A function's default export is normally { fetch }. To also accept WebSockets, export an upgrade method alongside it — the runtime routes plain HTTP to fetch and the WebSocket handshake to upgrade:
export default {
fetch(request: Request): Response | Promise<Response> { /* HTTP */ },
async upgrade(req: IncomingMessage, socket: Duplex, head: Buffer) { /* WS handshake */ },
};
Simple example — raw ws, no framework, with auth. Browsers can't set headers on a WebSocket, so authenticate with a ?token= query param (verify it the same way as the agent backend: jwtVerify against your JWKS) before accepting the connection:
// src/index.ts
import type { IncomingMessage } from "node:http";
import type { Duplex } from "node:stream";
import { WebSocketServer, type WebSocket } from "ws";
const clients = new Set<WebSocket>();
const wss = new WebSocketServer({ noServer: true });
export default {
// Plain HTTP (health checks, REST) is handled by fetch.
fetch: () => new Response("WebSocket endpoint — connect with ?token=<jwt>"),
// The runtime hands the WebSocket handshake to upgrade().
async upgrade(req: IncomingMessage, socket: Duplex, head: Buffer) {
const url = new URL(req.url ?? "/", "http://localhost");
const identity = await verifyToken(url.searchParams.get("token")); // reject if invalid
if (!identity) {
socket.write("HTTP/1.1 401 Unauthorized\r\n\r\n");
socket.destroy();
return;
}
wss.handleUpgrade(req, socket, head, (ws) => {
clients.add(ws);
ws.on("close", () => clients.delete(ws));
ws.on("message", (data) => broadcast(data.toString())); // see fan-out below
});
},
};
Hono variant. If you only need Hono for the HTTP side and are happy driving ws yourself, just swap fetch in the simple example for app.fetch and keep the raw upgrade — Hono serves routing/middleware, ws serves the socket.
To instead declare WebSocket routes inside the Hono app — app.get("/ws", upgradeWebSocket(...)) with the standard onOpen/onMessage/onClose lifecycle — you need an adapter that bridges Hono's upgradeWebSocket() helper to Neon's upgrade(req, socket, head). Hono ships adapters for Cloudflare/Deno/Bun/Node, but none for Neon, and the Node one (@hono/node-ws) is deprecated and assumes it owns the HTTP server. references/hono-websockets.md has a small self-contained createNeonWebSocket(app) adapter to copy in — it depends only on hono and ws (no deprecated package; adapted from @hono/node-ws, MIT) and returns a ready-to-export { fetch, upgrade } handler. Usage is idiomatic Hono, and because the handshake routes through app.request, auth is just normal route middleware:
// src/index.ts
import { Hono } from "hono";
import { createNeonWebSocket } from "./hono-ws";
const app = new Hono();
const { upgradeWebSocket, handler } = createNeonWebSocket(app);
app.get(
"/ws",
async (c, next) => {
if (!(await verifyToken(c.req.query("token")))) return c.text("Unauthorized", 401);
await next();
},
upgradeWebSocket(() => ({
onOpen: (_evt, ws) => ws.send("welcome"),
onMessage: (evt, ws) => ws.send(`echo: ${evt.data}`),
onClose: () => console.log("disconnected"),
})),
);
export default handler; // Neon's { fetch, upgrade } contract
Don't put header-modifying middleware (e.g. CORS) on an
upgradeWebSocketroute — the helper rewrites headers internally and will throw. The fan-out and reconnect guidance below applies unchanged.
A connection stays open only while bytes flow: Neon evicts a silent stream after 15 minutes (Timeouts and runtime limits), and intermediary proxies / load balancers are usually far stricter (often tens of seconds). Don't rely on the app being chatty enough — send a periodic ping from the server so the socket never goes quiet. ws.ping() sends a WebSocket ping frame and the browser answers with a pong automatically, so there's no client code to write:
const HEARTBEAT_MS = 25_000; // comfortably under proxy idle timeouts
const beat = setInterval(() => {
for (const ws of clients) if (ws.readyState === ws.OPEN) ws.ping();
}, HEARTBEAT_MS);
beat.unref?.();
(With the Hono upgradeWebSocket helper you don't hold the raw socket, so send an application-level keepalive instead — e.g. ws.send("ping") on the same interval, ignored by the client.)
Under load the runtime runs several isolates in parallel, each with its own copy of module state — so each isolate has its own clients set. Broadcasting only to the local set means a client connected to isolate A never sees a message sent by a client on isolate B. The chat would silently fracture.
Fan out across every isolate with Postgres LISTEN/NOTIFY: each isolate LISTENs on a channel over a dedicated unpooled connection, and broadcasting means NOTIFY (so every isolate, including the sender's, re-broadcasts to its own sockets). This is also why message state must live in Postgres, not module memory — module state doesn't survive eviction.
import { Pool, Client } from "pg";
const pool = new Pool({ connectionString: process.env.DATABASE_URL, max: 5 });
const CHANNEL = "chat_events";
// One dedicated DIRECT connection per isolate, just to receive events.
// Use DATABASE_URL_UNPOOLED — LISTEN needs a real session, not a pooled one.
const listener = new Client({ connectionString: process.env.DATABASE_URL_UNPOOLED });
listener.connect().then(() => listener.query(`LISTEN ${CHANNEL}`));
listener.on("notification", (msg) => {
if (!msg.payload) return;
for (const ws of clients) if (ws.readyState === ws.OPEN) ws.send(msg.payload);
});
// Broadcast by NOTIFYing through the pool — every isolate's listener fires.
function broadcast(event: unknown) {
return pool.query("SELECT pg_notify($1, $2)", [CHANNEL, JSON.stringify(event)]);
}
Idle functions are evicted (and isolates restart for operational reasons), so a client's socket will drop — treat reconnection as normal, not exceptional. Reconnect with exponential backoff, capped, and re-mint a fresh token on every attempt (tokens are short-lived, so a stale one fails the upgrade auth check):
let closed = false, retry = 0, timer: ReturnType<typeof setTimeout>;
async function connect() {
if (closed) return;
const token = await getToken(); // re-mint each attempt; short-lived
const ws = new WebSocket(`${WS_URL}?token=${encodeURIComponent(token)}`);
ws.onopen = () => { retry = 0; }; // reset backoff on success
ws.onmessage = (e) => { /* apply the event */ };
ws.onclose = () => {
if (!closed) timer = setTimeout(connect, Math.min(1000 * 2 ** retry++, 15000));
};
ws.onerror = () => ws.close(); // let onclose drive the retry
}
connect();
Together — Hono fetch + ws upgrade, JWT auth over ?token=, LISTEN/NOTIFY fan-out, and client backoff — these compose into a complete realtime chat backend on a single function.
When you only need server → client streaming (live counters, notifications, progress, token streams), SSE is simpler than a WebSocket and needs no upgrade method or extra library: a plain fetch handler returns a Response whose body is a ReadableStream with Content-Type: text/event-stream, and the runtime holds it open as long as bytes flow. The browser consumes it with EventSource, which reconnects on its own — so there's no client backoff to write.
// src/index.ts — minimal SSE endpoint
const encoder = new TextEncoder();
export default {
fetch: () => {
let timer: ReturnType<typeof setInterval>;
return new Response(new ReadableStream<Uint8Array>({
start(controller) {
controller.enqueue(encoder.encode("data: hello\n\n"));
timer = setInterval(() => {
if ((controller.desiredSize ?? 0) > 0) controller.enqueue(encoder.encode(": ping\n\n"));
}, 25_000);
},
cancel() { clearInterval(timer); },
}), {headers: {"Content-Type": "text/event-stream", "Cache-Control": "no-cache, no-transform"}});
},
};
The same rules as WebSockets apply. Heartbeat: a stream stays open only while bytes flow — Neon's window is 15 minutes (Timeouts and runtime limits) but proxies are usually far stricter, so emit a : ping\n\n comment every ~25–30s (shown above) to keep idle streams from being dropped. Keep state in Postgres, and fan out across isolates with LISTEN/NOTIFY (hold a Set of stream controllers and enqueue to each). EventSource is GET-only and can't set headers, so authenticate with a ?token= query param or cookie, exactly like the WebSocket case. references/sse.md has the full pattern — Hono variant, cross-isolate fan-out, wire format, client, and caveats.
An MCP server is a natural Functions workload: a long-running HTTP handler that exposes tools to AI clients (Cursor, Claude, ChatGPT, agents), with those tools reading and writing the branch's Postgres right next to the compute. MCP's streamable HTTP transport is a plain POST/GET on a single endpoint (conventionally /mcp), so it maps onto a function's fetch handler with no upgrade method or extra protocol — a Hono app using the official @modelcontextprotocol/sdk plus @hono/mcp (which bridges the transport to a route) is the simplest host. Build the server, register its tools, and create the transport once at module scope, then hand every /mcp request to it:
const transport = new StreamableHTTPTransport();
app.all("/mcp", async (c) => {
if (!mcpServer.isConnected()) await mcpServer.connect(transport);
return transport.handleRequest(c);
});
Because the function's URL is public, authenticate before connecting the transport — Better Auth covers both OAuth (its MCP plugin makes your app the authorization server so third-party clients self-authorize per the MCP spec) and a simpler API-key / session-JWT check for your own callers. references/mcp.md has the full pattern — server with Postgres-backed tools via Drizzle, both Better Auth auth options, and testing with mcporter / add-mcp.
A function is a long-lived Node.js process running a web-standard request/response handler, so standard Node integration SDKs work unchanged — initialize them once at module load, gated on an env var so local dev and unconfigured branches stay a no-op, and pass secrets via --env or neon.ts env. For wiring up Sentry error monitoring across the HTTP framework, the function runtime, and an agent's own caught/fallback failures (the long-running case Functions target), see references/sentry.md. For running a Mastra agent on a function and shipping its traces to a Mastra Studio (Mastra Cloud) project for observability, see references/mastra-studio.md.
Functions are long-running but still serverless — they are a request/response runtime, not a background job runner. The hard limits:
Time to first byte: 15 minutes. Your handler must begin returning a response within 15 minutes of receiving a request. Most handlers finish in seconds; the 15-minute ceiling exists so agent workloads like image/video generation have room.
Heartbeat: 15 minutes. Open WebSocket/SSE connections stay alive as long as data flows. The timeout only fires when a connection goes silent — send at least one byte every 15 minutes to keep a quiet stream alive.
waitUntil: 15 minutes. Work registered with waitUntil keeps the invocation alive after the response is sent, up to 15 minutes — for cleanup like analytics writes and audit logs, not a background job runner. (Current runtime tracks waitUntil; outside Neon it is a no-op for lifecycle tracking.)
Idle eviction. With no active connections the platform shuts the function down; it may also evict/restart for operational reasons — e.g. maintenance, or moving the function to a different compute node (active functions can run for hours first). Treat eviction like a process restart — WebSocket/SSE clients must reconnect. The platform sends SIGINT before evicting, so a process.on("SIGINT", ...) handler lets you detect that the function is about to be evicted and run any last-minute cleanup. You don't need one just to close Postgres connections — Neon's pooler reclaims those on its own.
Runtime: Node.js 24, memory fixed at 2048 MiB during the preview. Slugs must match ^[a-z0-9]{1,20}$. An isolate is reused across many requests — multiple requests can be in flight on the same isolate at once (interleaved on Node's single-threaded event loop), and under load the runtime runs several isolates in parallel, each with its own copy of module state. State held in module scope is therefore per-isolate (shared by every request that isolate handles) and in-memory only — persist anything that must survive eviction in Postgres. This reuse is exactly why you create a connection pool once at module scope rather than per request (see Connecting to Postgres).
A Neon Function is a great home for an AI agent precisely because it doesn't time out the way lambda-style serverless does (15-minute budget, see above). But that advantage disappears the moment you proxy the agent stream through your web app's backend — a Next.js route handler, Remix/SvelteKit/Nuxt action, etc. hosted on Vercel, Netlify, Cloudflare, and the like. Those platforms cap serverless/edge execution at short windows (often ~10–60s, sometimes up to ~300s), so a long agent or image/video generation stream gets cut off mid-response even though the Neon Function would happily keep going.
Building the agent itself. The Vercel AI SDK and Mastra are the recommended ways to build the agent — point either at the Neon AI Gateway (see the neon-ai-gateway skill) for one credential across every model, with no extra provider keys. For a complete AI SDK agent running as a Function (streaming toUIMessageStreamResponse, multi-step tool calling next to Postgres, and persisting generated images to Object Storage), see references/ai-sdk.md; for the Mastra equivalent with built-in tracing, see references/mastra-studio.md.
The fix: call the function directly from the client. Don't route the long request through your app server.
Browser ──(Authorization: Bearer <JWT>)──▶ Neon Function (agent) ✅ no host timeout
Browser ──▶ your app backend ──▶ Neon Function ❌ host cuts the stream
jwt plugin, NextAuth, or your own signer) — that call is fast and well within host limits.new DefaultChatTransport({ api: NEON_FUNCTION_URL, fetch }) where fetch attaches Authorization: Bearer <token>. Your app server is never in the path of the long stream.OPTIONS, set Access-Control-Allow-Origin/-Headers).[!WARNING] A Neon Function has a public HTTPS URL — it is reachable by anyone. A direct client→function call means there is no app backend in front of it to gate access, so you must authenticate the function yourself. Verify a JWT (e.g. against your app's JWKS), check a shared secret / API key, or validate a session token at the top of the handler and reject anything else. Never deploy an unauthenticated agent.
// src/index.ts — verify the caller before doing any work
import { createRemoteJWKSet, jwtVerify } from "jose";
const jwks = createRemoteJWKSet(new URL(`${process.env.AUTH_BASE_URL}/api/auth/jwks`));
export default {
async fetch(request: Request) {
if (request.method === "OPTIONS") return new Response(null, { status: 204, headers: cors(request) });
const auth = request.headers.get("authorization");
if (!auth?.toLowerCase().startsWith("bearer ")) {
return new Response("Unauthorized", { status: 401, headers: cors(request) });
}
try {
const { payload } = await jwtVerify(auth.slice(7), jwks, {
issuer: process.env.AUTH_BASE_URL,
audience: process.env.AUTH_BASE_URL,
});
const userId = payload.sub; // scope the agent to this user
// ... run the agent, return result.toUIMessageStreamResponse({ headers: cors(request) })
} catch {
return new Response("Unauthorized", { status: 401, headers: cors(request) });
}
},
};
Pass the JWKS/issuer URL to the function via its env (see Environment variables). Persist anything you need to keep (generated images, history) in Postgres — module state doesn't survive eviction.
Neon Functions is a preview (early access) feature available only on new projects in the us-east-2 region. Confirm the user's Neon project is a new project in us-east-2; it can't be enabled on existing projects. Preview access and billing can change; verify the current project/account terms before provisioning. If the user does not yet have access, point them to the private beta sign-up: https://neon.com/blog/were-building-backends#access
The Neon documentation is the source of truth and Functions is evolving rapidly, so always verify against the official docs. Any doc page can be fetched as markdown by appending .md to the URL or by requesting Accept: text/markdown. Find the right page from the docs index (https://neon.com/docs/llms.txt) and the changelog announcements.
The snippets are integration sketches, not production-complete servers. Before public deployment, implement the JWT/auth helpers, bounded validated request bodies, tenant-scoped database filters, allowed origins and rate limits. Confirm exact branch/project and plan effects before deploy/apply/delete; installation or CLI execution can load project code. Query tokens can leak through logs: use short-lived connection tickets, redact URLs, and prefer secure session cookies where feasible. LISTEN/NOTIFY is transient with payload limits, not durable delivery; add reconnect/re-LISTEN handling and persistent event IDs/replay when loss matters.
Automatically, when a task matches. Use when a user wants to host an API, an AI agent with long streaming responses, a WebSocket or server-sent-events (SSE)...
Skills that do similar work, with their headline benchmark lift.
From the same source and nearby in the catalogue.