davebcn87/pi-autoresearch

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.

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