neatlogs 1.3.6

A Python package for extracting and managing LLM logs to build a collaborative workspace

neatlogs

LLM observability for AI agents.
Instrument once. Inspect everything.

Website  ·  Docs  ·  Get API key  ·  AI Skill

Agent failures don't throw exceptions — they produce wrong outputs, miss tool calls, or hallucinate.
Neatlogs captures every trace so you can see exactly what the model was given, what it decided, and what each step returned.

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Installation

pip install neatlogs

Optional extras install the underlying LLM / framework libraries:

pip install "neatlogs[openai]"
pip install "neatlogs[crewai]"
pip install "neatlogs[langchain,langgraph]"
pip install "neatlogs[google-genai]"

Requires Python >= 3.10, < 3.14.

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Quickstart

import neatlogs
from neatlogs import span

neatlogs.init(
    api_key="your-api-key",       # or NEATLOGS_API_KEY env var
    workflow_name="my-agent",
    instrumentations=["openai"],
)

# Import instrumented libraries AFTER init()
from openai import OpenAI


@span(kind="WORKFLOW", name="quickstart")
def main():
    client = OpenAI()
    return client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "What is AI?"}],
    )


main()
neatlogs.flush()
neatlogs.shutdown()

Auto-instrumentation captures LLM calls, tools, and retrievals as child spans. Use @span(kind="WORKFLOW") on your main function or request handler so each run shows up as a top-level trace in the dashboard.

Call neatlogs.init() before importing any instrumented library.

For long-running servers (FastAPI, Celery workers), call init() once at startup and decorate each request handler with @span(kind="WORKFLOW"). Do not call flush() / shutdown() per request.

Full walkthrough: Your First Trace.

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Integrate into your codebase (recommended)

The fastest way to add NeatLogs to an existing project is the official Agent Skill — it encodes import order, @span kinds, CrewAI prompt binding, and troubleshooting so your coding agent gets it right.

Repo: github.com/neatlogs/skills

npx skills add neatlogs/skills --skill "neatlogs-py"

For Cursor:

npx skills add neatlogs/skills --skill "neatlogs-py" --agent "cursor"

No Node.js? Ask in chat: "Install the NeatLogs AI skill from github.com/neatlogs/skills"

Example prompts once installed:

• "Add neatlogs tracing to my OpenAI calls"
• "Instrument my CrewAI agents with neatlogs"
• "Wrap my FastAPI handler so each request is a top-level trace"

Full install options: skills README

API reference: docs.neatlogs.com

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Platform features

• Traces: Full span trees — LLM calls, tools, retrievals, reranking, guardrails — with inputs, outputs, tokens, cost, and latency.

• Timeline view: See which steps ran in parallel, where latency concentrated, and where the process was idle.

• AI assistant: Ask questions grounded in the actual span data for a trace.

• AI Search: Query traces in plain English without writing SQL.

• Detections: Rules that flag matching spans — regex, numeric conditions, PII, or model classifiers.

• Prompt management: Version prompts, promote labels, test in the Playground.

• Evals: Human review campaigns — select traces or spans (or auto-collect future ones via filters), send custom rating forms to assigned reviewers, and track batch progress and scores.

• Comments & voting: Pin notes to spans, @mention teammates, and thumbs-up/down vote outputs while debugging a trace.

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Supported libraries

Pass keys to instrumentations in neatlogs.init(). Install extras when noted.

LLM providers

Provider	Key	Install
OpenAI	openai	pip install "neatlogs[openai]"
Anthropic	anthropic	pip install "neatlogs[anthropic]"
Google Gemini	google_genai	pip install "neatlogs[google-genai]"
Azure OpenAI	azure_ai_inference	pip install "neatlogs[azure-ai-inference]"
AWS Bedrock	bedrock	pip install "neatlogs[bedrock]"
LiteLLM	litellm	pip install "neatlogs[litellm]"
Groq	groq	pip install "neatlogs[groq]"
Vertex AI	vertexai	pip install "neatlogs[vertexai]"
Mistral	mistralai	pip install "neatlogs[mistralai]"

Agent frameworks

Framework	Key	Install
LangChain	langchain	pip install "neatlogs[langchain]"
LangGraph	langgraph	pip install "neatlogs[langgraph]"
CrewAI	crewai	pip install "neatlogs[crewai]"
LlamaIndex	llamaindex	pip install "neatlogs[llama-index]"
Haystack	haystack	pip install "neatlogs[haystack]"
AutoGen	autogen	pip install "neatlogs[autogen-agentchat]"
DSPy	dspy	pip install "neatlogs[dspy]"
MCP	mcp	pip install "neatlogs[mcp]"

Vector stores & HTTP

Library	Key	Notes
ChromaDB	chromadb	Auto-instrumented when installed
Pinecone	pinecone	Auto-instrumented when installed
Qdrant	qdrant	Auto-instrumented when installed
Weaviate	weaviate	Auto-instrumented when installed
Milvus	milvus	pip install "neatlogs[milvus]"
Redis	redis	Auto-instrumented when installed
OpenSearch	opensearch	Auto-instrumented when installed
Elasticsearch	elasticsearch	Auto-instrumented when installed
Marqo	marqo	Auto-instrumented when installed
HTTP clients	requests, httpx, urllib3, aiohttp	Auto-instrumented when installed

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Configuration

NEATLOGS_API_KEY=your-api-key
NEATLOGS_ENDPOINT=https://staging-cloud.neatlogs.com   # optional — this is the default

Get your API key from the NeatLogs dashboard. Full init() options: reference.

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Examples

Runnable reference apps live in examples/sdk_examples/. Each folder has a requirements.txt (PyPI install) and .env.example.

Example	Framework	Run
anthropic_multiagent/	Anthropic + Bedrock	python main.py
openai_multiagent/	OpenAI via Azure	python main.py
google_genai_multiagent/	Google GenAI	python main.py
langchain_react/	LangChain ReAct	python react_agent.py
langgraph_multiagent/	LangGraph	python main.py
langgraph_research_assistant/	LangGraph	python main.py
marketing_strategy_demo/	CrewAI + Gemini search	python main.py
neatlogs_support_bot/	CrewAI RAG bot	python main.py
reasoning_model_workflow/	Multi-provider reasoning	python main.py
support_copilot_demo/	Support agent demo traces	RUN=A python support_copilot.py
support_copilot_demo_triaged/	Post-Triage support demo	SENDGRID_FAKE_SUCCESS=1 RUN=B python support_copilot.py

Adding NeatLogs to your own code? Use the AI skill above — not copy-paste from this README.

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Best practices

1. init() before LLM imports — auto-instrumentation patches libraries at import time.
2. Wrap script/server entry points in @span(kind="WORKFLOW") — each run gets a clear top-level trace in the dashboard.
3. Use auto-instrumentation first — only add more @span decorators for custom orchestration.
4. trace() for prompts and sessions — not as a wrapper around @span(kind="WORKFLOW").
5. workflow_name = feature name — put env/version/tech stack in tags=.
6. Scripts: flush() then shutdown() at exit. Servers: init() once, no per-request shutdown.

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License

MIT — see LICENSE.