Mining Your Own Agent: A Memory Analytics Layer for Agentic Coding Sessions

Explore a local analytics system that transforms agentic coding logs into structured memories, entities, and behavioral patterns using LLMs for insightful analysis and retrieval.

Claude Code Ollama nomic-embed-text gemma3:12b Streamlit

Overview

A local analytics system that treats your agentic coding session logs as a dataset — ingesting JSONL transcripts, extracting structured memories, entities, and behavioral patterns via LLM, embedding everything for hybrid retrieval, and surfacing it through a Streamlit UI.

Tech stack

Claude Code

Anthropic's agentic coding tool: Unleash Claude's raw power directly in your terminal or IDE to turn complex, hours-long workflows into a single command.

Claude Code is Anthropic’s powerful agentic coding assistant, designed for high-velocity development. It operates natively within your terminal, IDE (VS Code, JetBrains), or via a web interface, allowing you to delegate complex tasks like feature building, bug fixing, and codebase navigation. The agent plans, edits files, executes commands, and creates commits, maintaining awareness of your entire project structure. Internally, Anthropic engineers using Claude Code reported a 67% increase in productivity, demonstrating its capacity to deliver significant gains for Pro and Max plan users.

https://claude.com/code

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Ollama

Deploy and run open-source Large Language Models (LLMs) like Llama 3 and Mistral locally on your machine: achieve private, cost-effective AI via a simple command-line interface.

Ollama is the essential tool for running LLMs locally: consider it the Docker for AI models. It packages complex models and dependencies into a single, easy-to-use application for macOS, Linux, and Windows systems. You get immediate access to models like Gemma 2 and DeepSeek-R1 via a straightforward CLI or REST API. This local-first approach guarantees data privacy and security, eliminating cloud dependency and high API costs. Ollama also optimizes performance on consumer hardware using techniques like quantization, ensuring efficient execution even on standard desktops.

https://ollama.com

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nomic-embed-text

An open-source, high-performance text embedding model featuring an 8192-token context window and fully auditable training data.

Nomic AI developed nomic-embed-text to deliver a highly efficient, open-source alternative to proprietary models. Operating with an expansive 8192-token context window, it outperforms OpenAI's text-embedding-ada-002 and text-embedding-3-small on both short and long-context benchmarks. The model is built on a fully reproducible pipeline (offering complete transparency across model weights, training code, and data curation) and supports specialized task prefixes like search_document and search_query to optimize downstream performance in retrieval-augmented generation (RAG) pipelines.

https://huggingface.co/nomic-ai/nomic-embed-text-v1

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gemma3:12b

Google's 12-billion parameter open weights model running locally via Ollama, featuring multimodal vision capabilities and a massive 128K context window.

The gemma3:12b model brings Google's highly efficient 12-billion parameter architecture directly to local workstations via Ollama. Built on 12 trillion tokens of training data, this model excels at complex reasoning, code generation, and multilingual tasks across more than 140 languages. It natively supports multimodal inputs (processing both text and images) and leverages a 128K context window to handle massive documents or long-form conversations. Developers can run this balanced model on standard consumer hardware to achieve close to enterprise-grade AI performance without relying on external APIs.

https://ollama.com/library/gemma3:12b

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Streamlit

Streamlit is the open-source Python library that transforms data scripts into interactive, shareable web applications in minutes (not weeks).

Streamlit is purpose-built for data scientists and ML engineers: It eliminates the need for front-end development knowledge (HTML, CSS, JavaScript). The core API uses pure Python to quickly build powerful data apps, dashboards, and machine learning prototypes. Users leverage simple commands (e.g., `st.slider`, `st.dataframe`) to integrate interactive widgets and display data from libraries like Pandas or Matplotlib. The platform supports rapid iteration, automatically rerunning the script on user interaction, and offers streamlined deployment via the Streamlit Community Cloud.

https://streamlit.io

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Mining Your Own Agent: A Memory Analytics Layer for Agentic Coding Sessions

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