Agent run context
Inputs, prompts, retrieved sources, tool inputs and outputs, retries, final responses, model metadata, and cost signals.
Local-first optimization for production agents
Code001
An embedded quality intelligence layer for LangChain and LangGraph agents. It reads traces, diagnoses weak context and tool behavior, then generates an evidence-backed report your engineering team can act on.
No hosted backend required
Runs inside your existing agent and LLM infrastructure.
Evidence before scores
Findings include traces, likely cause, severity, and fixes.
LangGraph first
Built around Python agent teams shipping real workflows.
Product
Turn messy agent traces into engineering decisions.
Code001 is not another generic trace dashboard. It watches the prompt, retrieved context, tool calls, model response, latency, token use, errors, and outcome signals, then groups repeated failure patterns into findings.
Hybrid diagnostics
Deterministic checks catch measurable regressions while LLM review evaluates relevance, support, and instruction following.
Fix-ready findings
Every finding is written with severity, evidence, affected traces, likely cause, and recommended fix.
Workflow
One call inside your system.
Code001 starts as a local optimization layer. Teams call agent.optimize() on selected runs or trace batches. The report can be written locally or uploaded to simple object storage such as S3.
from code001 import optimize
report = optimize(
agent=my_langgraph_agent,
traces="./runs/*.jsonl",
outcomes="./evals/results.json",
llm=my_existing_model,
)
report.write_html("./code001-report.html")
report.upload_s3("s3://agent-quality/reports/")Diagnostics
Designed for the failures that slow agent teams down.
01
Missing or irrelevant context
Detects when the agent answers without enough retrieved evidence or uses sources that do not support the final response.
02
Tool failure loops
Finds repeated failures, redundant calls, retry loops, stale tool outputs, and tool choices that add latency without value.
03
Prompt and cost pressure
Flags duplicated instructions, overlarge context windows, token-heavy traces, and regressions after prompt or model changes.
04
Weak final answers
Reviews unsupported, incomplete, low-confidence, or instruction-breaking responses and links them back to trace evidence.
Report preview
A static artifact your team can inspect, share, and archive.
The first export target is HTML: easy to read, easy to store, and simple to upload to authenticated resources already approved by the team.
Early access
Built for teams already shipping agents.
Code001 is for AI platform and engineering teams that need actionable agent quality diagnosis without sending production traces to a new hosted backend.