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To summarize an AI agent run with Gemma 4 locally, give the model a structured record of the run—not just the agent’s final reply. Include the user’s goal, timestamped messages, tool calls and observed results, errors, retries, and outputs or file changes. Ask Gemma to separate recorded facts from interpretation and identify what the record cannot establish, then verify its account against the original events.
What Gemma 4 can—and cannot—tell you
Gemma 4 can turn recorded evidence into a readable account of an agent run. It cannot reliably establish actions that were never logged. An agent’s final natural-language answer may omit intermediate tool calls, failed attempts, or other activity, so it is not necessarily a complete run history.
For a faithful summary, preserve the distinction between what the agent requested and what a tool actually returned. A tool call records an attempted action; a corresponding result or artifact is stronger evidence of what happened. If the trace has a gap, the summary should say the outcome is unknown rather than filling it in.
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Google’s Ollama setup guide describes a basic local workflow: install Ollama for your operating system, download a Gemma 4 model, then confirm it is available. The model registry changes over time, so check its current Gemma 4 tags and listings before choosing a variant.
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Install Ollama using the instructions for your operating system in Google’s guide.
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Download a model with
ollama pull gemma4. The guide lists tags includinggemma4:e2b,gemma4:e4b,gemma4:26b, andgemma4:31b; the Ollama registry also listsgemma4:12band MLX variants. Tags and storage requirements can change. -
Run
ollama listto check that the model is available locally.Free tools Windows power users keep installed
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For a one-off interactive session, start
ollama run gemma4and provide the run record and prompt.
For an application, Ollama documents the local generation endpoint http://localhost:11434/api/generate; its registry also shows the /api/chat endpoint. Send the trace as a prompt or as messages, and save the returned summary with the original run ID so it remains associated with its evidence. See Google’s Ollama guide and the Ollama registry for runtime details.
Choose a model that fits the run and your hardware
Model size, available memory, trace length, and runtime support all affect the choice. Google’s Gemma 4 model card specifies a 128K-token context window for small models and 256K tokens for medium models. These are model specifications, not a guarantee that a particular device or runtime will process that many tokens efficiently.
Google’s Ollama guide explains that Ollama and llama.cpp variants use quantized GGUF models to reduce compute requirements, with a possible quality tradeoff compared with non-quantized weights. Google’s Gemma 4 12B developer guide describes a local laptop setup with 16 GB of dedicated GPU VRAM or unified memory; that is a setup-specific reference, not a guarantee that every laptop with 16 GB will perform well. Neither source establishes which variant is most accurate for summarizing agent traces.
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Compare the trace length with the model’s context window, leaving room for the prompt and generated summary.
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Check the current registry for the model tag, storage needs, and availability in your runtime.
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Consider whether quantization’s lower compute demands suit your device and whether its potential quality cost is acceptable.
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Choose a trace source that records tool results, not only the agent’s messages.
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Build an auditable run record
Start with the best event record your agent framework can export. If it supports OpenTelemetry, its trace can provide a parent agent span with child model-call and tool-execution spans. The OpenTelemetry walkthrough illustrates fields such as model identifiers, token counts, finish reason, call duration, and optional structured prompt, response, and tool content. Those details help distinguish what an agent asked a tool to do from the result the tool observed. See OpenTelemetry’s GenAI observability walkthrough.
A runtime-neutral JSON record can be organized like this. It is a practical format, not a required standard schema:
{
"run_id": "stable session or trace identifier",
"goal": "the user's requested outcome",
"events": [
{
"event_id": "event reference",
"timestamp": "UTC timestamp if available",
"kind": "assistant_message | tool_call | tool_result | error | artifact",
"tool": "tool name, when applicable",
"input": "redacted input or short description",
"output": "observed result or short description",
"status": "success | error | unknown"
}
],
"final_artifacts": ["file names, links, or output identifiers"],
"known_gaps": ["events unavailable or content intentionally omitted"]
}Keep event IDs and timestamps attached to events, and retain stable identifiers so readers can check summary claims against the source run. OpenTelemetry’s GenAI agent span conventions describe conversation IDs for correlating work. They also note that content capture is opt-in; the conventions are evolving, so check the current guidance for your instrumentation.
Ask for a summary that shows its evidence
Use a prompt that tells Gemma what counts as evidence and how to handle missing information. The following is a practical prompt based on trace structure and Gemma’s text-generation interface; it is not a prompt published or performance-tested by Google.
Summarize this agent run for a person who did not watch it.
Use only the supplied run record. For each claim about an action or result,
include its event ID (and timestamp if available).
Report, in order:
1. The user's goal.
2. Actions the agent actually took and the tools it called.
3. What each tool returned, distinguishing request from observed result.
4. Files or other outputs changed or produced.
5. Errors, retries, unresolved work, and anything the record cannot establish.
Separate logged facts from interpretation. Do not claim success unless an event
or artifact supports it. If evidence is missing, say so.
RUN RECORD:
[paste a redacted JSON trace or export]Use the prompt with the same local runtime you used to prepare the model—for example, paste it into ollama run gemma4, or send the record and instructions to Ollama’s local API. Preserve the original trace alongside the generated report.
Handle long traces without hiding important events
If a trace is too large for the chosen setup, first remove repetitive, low-value events while retaining failures, tool results, and state changes. If it still does not fit, summarize consecutive chunks and preserve their event IDs and timestamps. Then request a final synthesis from the chunk summaries together with the important original events. This workflow can make a long run manageable, but it does not guarantee that the final account captures every detail; check it against the source events.
Protect privacy and verify the report
Local inference alone does not show that the entire agent workflow was offline. An agent may call remote tools, and an application or telemetry collector may export run data. OpenTelemetry’s walkthrough describes configurable collection of message and tool content; its agent span conventions say instrumentation should not capture content by default and should offer opt-in capture. Review the settings for the runtime, agent, telemetry collector, and connected tools before making privacy claims. Redact credentials, personal data, and sensitive tool outputs before summarization.
Keep the event record as the authoritative account and treat Gemma’s prose as a derived report. Check names, timestamps, tool outcomes, file changes, and claims of success against the captured events. When the record does not show an action or result, keep it marked as unknown.
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