CAMP Python SDK
Official Python client for CAMP — Career AI Model Platform — 7 career-native model families behind a standard chat-completions/embeddings request shape, plus live demo and media-generation endpoints.
Install
pip install careerstudiomax-camp
Quickstart
from camp import CampClient
client = CampClient(api_key="sk_live_your_key_here")
resp = client.chat_completions(
model="careerlm-base",
messages=[{"role": "user", "content": "Write a cover letter opener for a Senior PM role"}],
)
print(resp["choices"][0]["message"]["content"])
What you can do
| Method | Description |
|---|---|
client.chat_completions(messages) |
Standard-format chat completions |
client.chat_completions_stream(on_event, messages) |
Same, streamed |
client.embeddings(input) |
Standard-format embeddings |
client.models() |
List available models |
client.score(content, type="cv") |
Structured scoring (CV, content) |
client.embed(texts) |
Text embeddings (simplified) |
client.agent_stream(on_event, task) |
Streaming agentic task execution |
Deep reasoning
Pass reasoning=True to chat_completions() to raise the internal model's effort/complexity tier for that one call — useful for a genuinely hard question where the default fast path isn't giving you enough depth. Off by default, real added latency per call, no extra cost (it's still the same self-hosted model, just told to think harder — never escalates to an external provider).
resp = client.chat_completions(
model="careerlm-base",
messages=[{"role": "user", "content": "Compare 3 career paths for a mid-level backend engineer, with tradeoffs"}],
reasoning=True,
)
Streaming
def on_chunk(event):
print(event.get("text", ""), end="", flush=True)
client.chat_completions_stream(on_chunk, messages=[{"role": "user", "content": "Draft a resignation letter"}])
Errors
from camp import CampClient, CampError
try:
client.score(cv_text)
except CampError as e:
print(e.status, str(e))
Context manager
with CampClient(api_key="...") as client:
...
Full endpoint reference: camp.careerstudiomax.com/docs
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