autocontext

autocontext is an iterative strategy generation and evaluation system that uses LLM-based judging to score and improve agent outputs.

Available Tools

• autocontext_judge — Evaluate agent output against a rubric. Returns a 0–1

score with reasoning and per-dimension breakdowns.

• autocontext_improve — Run a multi-round improvement loop. The agent output

is judged, revised based on feedback, and re-evaluated until the quality threshold is met or max rounds are exhausted.

• autocontext_queue — Enqueue a task for background evaluation by the task

runner daemon.

• autocontext_status — Check the status of runs and queued tasks.
• autocontext_scenarios — List available evaluation scenarios and their

families.

• autocontext_runtime_snapshot — Inspect run artifacts, package provenance,

branchable session lineage, and recent event-stream entries.

Quick Start

1. Evaluate output quality

Use autocontext_judge with a task prompt, the agent's output, and a rubric:

autocontext_judge(
  task_prompt="Write a Python function to parse CSV files",
  agent_output="def parse_csv(path): ...",
  rubric="Correctness, error handling, edge cases, documentation"
)

2. Improve output iteratively

Use autocontext_improve to automatically revise output through judge-guided feedback loops:

autocontext_improve(
  task_prompt="Write a Python function to parse CSV files",
  initial_output="def parse_csv(path): ...",
  rubric="Correctness, error handling, edge cases, documentation",
  max_rounds=5,
  quality_threshold=0.85
)

3. Queue background tasks

Use autocontext_queue with a scenario name to enqueue evaluation tasks for asynchronous processing:

autocontext_queue(spec_name="my_scenario")

Check results later with autocontext_status.

For deeper context, use autocontext_runtime_snapshot with the run ID. Add session_id when you need the active branch path before continuing work:

autocontext_runtime_snapshot(run_id="run_123", session_id="sess_123")

4. Discover scenarios

Use autocontext_scenarios to see what evaluation scenarios are available:

autocontext_scenarios()
autocontext_scenarios(family="agent_task")

Configuration

The extension auto-detects configuration from these sources:

1. Project config — .autoctx.json in the working directory (created via autoctx init)
2. Environment variables:

• AUTOCONTEXT_AGENT_PROVIDER or AUTOCONTEXT_PROVIDER — Provider type
• AUTOCONTEXT_AGENT_API_KEY or AUTOCONTEXT_API_KEY — Provider API key
• AUTOCONTEXT_AGENT_DEFAULT_MODEL or AUTOCONTEXT_MODEL — Model override
• AUTOCONTEXT_DB_PATH — SQLite database path override

1. Pi provider — Falls back to Pi's configured LLM provider

CLI Companion

For standalone usage outside Pi, install the autoctx CLI:

bash
npm install -g autoctx
autoctx init
autoctx solve --description "your problem" --gens 5
autoctx simulate --description "your simulation" --runs 3