Logging Your AI Events (from Ollama) in Bronto

Authored by David Tracey

Many software companies are investigating the use of Large Language Models (LLMs) in their products. At Bronto we've announced — an open source tool for running LLMs locally — and show how to pipe its logs into Bronto for search and analysis.

LLMs are complex, non-deterministic systems. Beyond traditional logging use cases (performance monitoring, API usage), their unpredictable nature increases the need for logging — particularly to record and track responses to prompts. Individual log events can be large when they include a full prompt or response. Meta found this problem significant enough at their scale to build a dedicated for your OS, then start the server:

ollama serve

You'll see output including the default port it's listening on (11434).

Download and Run a Model

# Pull a model from the registry

ollama pull gemma:2b



# List downloaded models

ollama list



# Run a model interactively

ollama run gemma:2b

The run command gives you a >>> prompt where you can enter prompts or /help for commands.

Sending Ollama Logs to Bronto

Step 1: Configure Ollama Logging to File

Stop the server and restart it writing logs to a file:

ollama serve > /your_log_path/.ollama/logs/server.log 2>&1

For more detailed debug logs, add to your shell profile (.zprofile etc.):

export OLLAMA_LOG_LEVEL=DEBUG

export OLLAMA_DEBUG=true

To redirect model client logs:

# stderr only (keeps console interactive)

ollama run gemma:2b 2>>/your_log_path/.ollama/logs/gemma.log



# both stdout and stderr (API use only — disables console input)

ollama run gemma:2b > /your_log_path/.ollama/logs/gemma.log 2>&1

Verify logs are flowing:

tail -f /your_log_path/.ollama/logs/server.log

Step 2: Install OpenTelemetry Collector

Download for your platform from to send prompts against a log file and print the response. Example usage:

# Summarize 100 lines of CDN logs

python3 ollama-log-demo.py 100lines-CDN-log.csv \

  --model "gemma:2b" \

  --prompt "You have been given 100 lines from a CDN log in CSV format. Summarise the logs provided."



# Find errors and suggest fixes

python3 ollama-log-demo.py 100lines-search-log.csv \

  --model "gemma:2b" \

  --prompt "Find errors in this log and suggest how to fix them"

The final line of each Ollama response includes useful performance metadata:

Model notes from testing: gemma:2b is good for summarizing but tends to give high-level summaries even when asked for specifics. mistral takes longer but produces more detailed, data-specific responses. Defining the right prompt for your use case is key.

Searching Ollama Logs in Bronto

Ollama server logs include a mix of structured and unstructured entries:

Standard log levels:

INFO [main] HTTP server listening | hostname="127.0.0.1" port="11434"

level=INFO source=sched.go:714 msg="new model will fit in available VRAM"

level=DEBUG source=memory.go:103 msg=evaluating library=metal gpu_count=1

Model and resource logs:

llm_load_print_meta: max token length = 93

llama_model_loader: - kv 0: general.architecture str = gemma

level=INFO source=server.go:105 msg="system memory" total="8.0 GiB" free="1.2 GiB"

Even a small test with short prompts generates surprisingly large log volumes — 244 events totaling ~2MB in our test. Bronto handles these unstructured and semi-structured formats natively, and you can add a custom parser to make them more convenient to search and view.

Example searches in Bronto:

Fig.1 — Searching for log events containing "tokens"



Fig.3 — Grouping by prompt evaluation time per task_id