Running Claude Opus 4.8 coding agents through claude opus 4.8 n4n.ai gives you one OpenAI-compatible surface for agentic workflows without writing bespoke Anthropic client code. This guide walks through standing up a coding agent that calls the model via that endpoint, with automatic fallback and cache control wired in from the start.
Step 1: Point the OpenAI client at the gateway
Install the official SDK:
pip install openaiCreate a client that targets the unified endpoint. The base URL is the only change from vanilla OpenAI usage.
from openai import OpenAI
client = OpenAI(
base_url="https://api.n4n.ai/v1",
api_key="sk-your-key", # from your dashboard
)
# Quick sanity check
resp = client.chat.completions.create(
model="claude-opus-4-8",
messages=[{"role": "user", "content": "Reply with the word OK"}],
)
print(resp.choices[0].message.content)If you see OK, the connection works. The endpoint fronts 240+ models, so the same client can later swap to other backends without code changes.
Step 2: Define the coding agent loop
A coding agent needs tool calls and iterative refinement. Use the standard tools parameter; Claude Opus 4.8 handles JSON schema natively through the compatibility layer.
import json
tools = [
{
"type": "function",
"function": {
"name": "run_shell",
"description": "Execute a bash command and return stdout/stderr",
"parameters": {
"type": "object",
"properties": {
"cmd": {"type": "string"}
},
"required": ["cmd"]
}
}
}
]
def run_shell(cmd: str) -> str:
import subprocess
try:
return subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=30).stdout
except Exception as e:
return str(e)
def run_agent(task: str) -> str:
messages = [
{"role": "system", "content": "You are a coding agent. Use tools to solve tasks."},
{"role": "user", "content": task}
]
while True:
resp = client.chat.completions.create(
model="claude-opus-4-8",
messages=messages,
tools=tools,
tool_choice="auto",
)
msg = resp.choices[0].message
if msg.tool_calls:
messages.append(msg) # assistant turn with tool_call
for tc in msg.tool_calls:
args = json.loads(tc.function.arguments)
result = run_shell(args["cmd"])
messages.append({
"role": "tool",
"tool_call_id": tc.id,
"content": result
})
else:
return msg.contentParse tool_calls, execute locally, append the result as a tool message, then call again until the model returns final text. Keep the loop bounded—max 10 iterations—to avoid runaway agents.
Step 3: Send routing directives and enable fallback
The gateway honors client routing directives and forwards provider cache-control hints. To pin a provider or allow fallback when a provider is degraded, pass headers via extra_headers:
resp = client.chat.completions.create(
model="claude-opus-4-8",
messages=messages,
tools=tools,
extra_headers={
"X-Route-Preference": "anthropic,openai", # try anthropic first, fall back
"X-Fallback": "on"
},
)If Anthropic is rate-limited, the gateway automatically retries against the next eligible provider that serves Claude Opus 4.8. Your code sees a single response; you don’t need to implement retry logic. For background tasks, set X-Route-Preference to allow cheaper secondary providers; for latency-sensitive interactive agents, keep the order tight.
Step 4: Wire prompt caching for long system prompts
Coding agents often reuse a large system prompt (repo map, style guide). Forward cache-control hints to avoid re-paying input tokens each turn. The compatibility layer accepts Anthropic-style cache_control inside message metadata:
messages = [
{
"role": "system",
"content": "You are a coding agent. Repo layout: ... (long text)",
"extra": {"cache_control": {"type": "ephemeral"}}
},
{"role": "user", "content": "Implement quicksort in Rust."}
]
resp = client.chat.completions.create(
model="claude-opus-4-8",
messages=messages,
)Subsequent turns with the same prefix hit the cache. Per-token usage metering shows prompt_tokens drop after the first call. Put volatile content—current file contents, recent errors—after the cached prefix so the static portion stays hot.
Step 5: Read usage and enforce budgets
Every response includes usage with prompt_tokens, completion_tokens, and total_tokens. Log it to cap agent spend per step:
usage = resp.usage
if usage.total_tokens > 5000:
raise RuntimeError("Agent exceeded token budget for this step")
print(f"prompt={usage.prompt_tokens} completion={usage.completion_tokens}")Because metering is per-token at the gateway, you can reconcile across agents by correlating usage with your own request IDs. Emit these metrics to your observability stack; a sudden prompt_tokens spike means a cache miss or a forgotten system prefix.
Step 6: Stream tokens for interactive agents
For a responsive CLI, enable streaming:
stream = client.chat.completions.create(
model="claude-opus-4-8",
messages=messages,
stream=True,
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")Streaming works identically for tool-call deltas; accumulate tool_calls fragments before execution. Use stream=True only when a human is waiting—batch non-interactive refactors to reduce connection overhead.
Step 7: Verify end-to-end success
Reuse the client from Step 1. Create test_agent.py:
from openai import OpenAI
client = OpenAI(api_key="sk-test") # base_url already set via env or previous config
msgs = [{"role": "user", "content": "Write a Python function add(a,b) and call it with 2,3. Return only the result."}]
r = client.chat.completions.create(model="claude-opus-4-8", messages=msgs)
out = r.choices[0].message.content.strip()
assert out == "5", f"Expected 5 got {out}"
print("PASS", r.usage)Execute:
python test_agent.pyA passing run prints PASS and a usage object. If you get a 401, check the key. A 404 on model name means the model string differs in your tenant—list available models via GET /v1/models. To confirm fallback, send X-Force-Provider: nonexistent with X-Fallback: on; the call should still succeed against a real provider.
Operational notes
- Keep system prompts cache-friendly: static repo context first, volatile state last.
- Set
X-Route-Preferenceper task criticality; indexing jobs can tolerate more fallback latency than chat. - Monitor
prompt_tokensacross turns; a steady drop after turn one confirms caching. - The same client works for embeddings or vision models on the gateway—no second SDK.
- Bound agent loops with a max-iteration counter and a token budget checked in Step 5.
Following these steps gives you a production-shaped Claude Opus 4.8 coding agent that degrades gracefully and bills precisely.