ollama-telemetry 0.1.2

Privacy-first, SQLite-first observability for local Ollama agents.

ollama-telemetry

Privacy-first, SQLite-first observability for local Ollama agents.

• Stores telemetry locally in SQLite by default.
• Never stores prompts, completions, message bodies, or raw response payloads.
• No cloud account, API key, network service, or proprietary agent integration required.
• Captures Ollama token counts and latency fields when available.
• Supports explicit tracing and optional ChatOllama auto-instrumentation.

Package name availability on PyPI must be checked before publishing.

Install

pip install ollama-telemetry

For LangChain / ChatOllama:

pip install "ollama-telemetry[langchain]"

Zero-code-ish LangChain setup

from ollama_telemetry import enable

enable(auto_instrument_langchain=True)

# Existing ChatOllama code can run after this point.

Auto instrumentation is opt-in and intentionally marked as a convenience mode. The explicit API below is the recommended production approach.

Explicit tracing

telemetry.agent(...) works as either a decorator or a context manager. Both forms create one root trace.

from ollama_telemetry import telemetry
from ollama_telemetry.integrations.langchain import OllamaTelemetryCallback
from langchain_ollama import ChatOllama

telemetry.init()

@telemetry.agent(name="research_agent", workflow="summarization")
def run(document_id: str, messages):
    llm = ChatOllama(
        model="qwen3:8b",
        callbacks=[OllamaTelemetryCallback()],
    )
    with telemetry.context(entity_type="document", entity_id=document_id):
        return llm.invoke(messages)

Direct Ollama response capture

from ollama_telemetry import telemetry
from ollama import chat

telemetry.init()

with telemetry.agent("local_research_agent"):
    response = chat(
        model="qwen3:8b",
        messages=[{"role": "user", "content": "Summarize this."}],
    )
    telemetry.capture_ollama_response(response, model="qwen3:8b")

Database

Default location:

~/.ollama-telemetry/telemetry.db

Override it:

export OLLAMA_TELEMETRY_DB=/path/to/telemetry.db

The SQLite table is telemetry_events. It stores identifiers, timing, model, status, safe usage counters, and JSON metadata. It does not store content fields.

CLI

ollama-telemetry status
ollama-telemetry stats --last 7d
ollama-telemetry models --last 30d
ollama-telemetry agents --last 30d
ollama-telemetry traces --failed
ollama-telemetry prune --older-than 30d
ollama-telemetry vacuum

Privacy guarantees

The package rejects sensitive metadata keys such as prompt, completion, messages, response, content, and raw_response. Safe metadata is limited to scalar values and small lists/dictionaries after recursive filtering.

Development

python -m pip install -e ".[dev]"
pytest
python -m build

License

Apache-2.0.