We Open-Sourced Our Agent Checkpoint Module
Problem: Your 12-step agent crashes at Step 11 → you restart from Step 1, burning API calls and time.
Solution: 500 lines of Python, zero dependencies.
The Core Implementation
from nb_checkpoint import Checkpoint
cp = Checkpoint("research-agent")
result = cp.pipeline([
("search", lambda ctx: client.chat("Search quantum papers").text),
("extract", lambda ctx: client.chat(f"Extract from: {ctx[x27searchx27]}").text),
("summarize", lambda ctx: client.chat(f"Summarize: {ctx[x27extractx27]}").text),
])
Crash at "extract"? Next run auto-resumes from Step 2. Zero wasted calls.
How It Saves Money
Assuming ¥0.01/DeepSeek call, 10-step agent:
| Scenario | Calls wasted/day | Monthly cost |
|---|---|---|
| Without Checkpoint (1 crash/day) | 10 extra calls | ¥300/month |
| With Checkpoint | ~0 | ¥0 extra |
For teams running 100+ agents daily, this is real money.
Three APIs
Step API (most flexible):
cp = Checkpoint("research-agent")
papers = cp.step("search", lambda: client.chat("Search quantum").text)
analysis = cp.step("analyze", lambda: client.chat(f"Analyze: {papers}").text)
Pipeline API (simplest):
result = cp.pipeline([
("search", lambda ctx: client.chat("Search quantum").text),
("analyze", lambda ctx: client.chat(f"Analyze: {ctx[x27searchx27]}").text),
("report", lambda ctx: client.chat(f"Report: {ctx[x27analyzex27]}").text),
])
AgentSession (ready to use):
from nb_checkpoint import AgentSession
session = AgentSession(
"research-agent",
llm_call=lambda prompt: openai.ChatCompletion.create(
model="gpt-4", messages=[{"role": "user", "content": prompt}]
)["choices"][0]["message"]["content"]
)
vs LangChain Checkpoint
| NB Checkpoint | LangChain | |
|---|---|---|
| Dependencies | Zero | LangChain required |
| API | Step / Pipeline / AgentSession | StateGraph only |
| Lines of code | ~500 | ~5000+ |
Install Now
pip install nb-checkpoint