davebcn87/pi-autoresearch
Pi Autoresearch
pi-autoresearch is an autonomous research extension that automates iterative code-editing and performance-measurement loops driven by large language models. It functions as an experiment lifecycle automator, executing repetitive cycles of changes and benchmarks until a specific goal is reached.
The system distinguishes itself by organizing successful experimental trials into independent git branches for review and merging. It includes a real-time research dashboard for monitoring metrics and status, and utilizes median absolute deviation to calculate confidence scores that filter benchmark noise from genuine performance gains.
The project covers a broad surface of research operations, including iterative performance benchmarking, automated regression testing via validation scripts, and persistent experiment logging. It also manages large language model state through automated summaries and context injection to prevent information loss during long sessions.
Features
- Tool-Execution Loops - Orchestrates the repetitive request-execution-return cycle of editing code and measuring performance until a goal is met.
- Agent Context Management - Maintains project state and history through automated summaries to prevent information loss in long agentic sessions.
- Autonomous Research Agents - Autonomous agent performs multi-step research by iteratively editing code and synthesizing performance findings.
- Context Injection - Injects summaries of rules and history into LLM prompts to prevent information loss during context compaction.
- Context Window Management - Manages LLM state through automated summaries and context injection to prevent information loss during long sessions.
- Experiment Execution Logs - Records every trial in a persistent append-only log to resume research across restarts.
- Experiment Tracking Tools - Implements utilities for logging and managing results from iterative development experiments.
- Performance Benchmarks - Provides tools for measuring and tracking execution performance during iterative research loops.
- Autonomous Coding Agents - Autonomous agent orchestrates iterative cycles of code editing and performance measurement to reach specific goals.
- Context Overflow Handlers - Detects LLM context window saturation and re-prompts the agent with essential state to resume research.
- Performance Benchmarking - Implements iterative performance benchmarking using custom metrics and statistical noise filtering.
- Optimization Regression Validation - Runs tests and linters after benchmarks to ensure that performance optimizations do not introduce bugs.
- Performance Measurement - Quantifies the impact of code changes by capturing wall-clock duration and custom metrics from shell commands.
- Regression Testing Suites - Employs automated regression testing through validation scripts and linters to ensure stability during iterative optimization.
- Median Absolute Deviation - Uses median absolute deviation to calculate confidence scores and filter benchmark noise from genuine gains.
- Experiment Logging - Provides persistent logging of trial statistics and model state to allow research resumption across sessions.
- Experiment Lifecycle Automators - Provides a system that executes shell scripts and hooks before and after iterations to manage research data.
- Experiment Lifecycle Hooks - Executes custom shell scripts at iteration boundaries to manage data fetching and system notifications.
- Experimental Result Branching - Groups successful experimental results into independent git branches for clean review and merging.
- Experimental Result Branching - Organizes successful experimental trials into independent git branches for clean review and merging.
- Research Automation Tools - Automates technical research workflows using shell scripts and lifecycle hooks to manage data and notifications.
- Git-Based Result Organizers - Groups successful experimental trials into independent git branches for clean review and merging.
- Lifecycle Automation - Automates the research lifecycle by running shell scripts for data fetching and notifications around iteration loops.
- Research Progress Dashboards - Ships a real-time dashboard for monitoring experiment metrics and agent status.
- Statistical Noise Filtering - Calculates confidence scores using median absolute deviation to filter benchmark noise from genuine gains.
- Regression Validation Scripts - Runs validation scripts and linters after benchmarks to ensure optimizations do not introduce regressions.
- Research Dashboards - Provides a real-time monitoring interface for tracking experiment metrics, status, and confidence scores.
- Autonomous Research Agents - Extension for persistent experiment loops and live metric tracking.
