BoundedCode v0.1.0-alpha.1 (Public Alpha, pre-release)

Bounded context. Bounded cost. Unbounded codebases.

BoundedCode is a local-first AI software-engineering platform for working
on large repositories. It keeps model context bounded, persists task state,
verifies changes with behavioural evidence and escalates to a frontier model
only when needed (optional).

This is the first public alpha: usable, validated experimentally on a
small sample, tested on one machine with one model. Commands and
configuration may change. It is not production-ready.

Highlights

• Local inference: llama.cpp (llama-server, supervised), with model
  profiles measured on the reference machine. Validated with
  Qwen3.6-35B-A3B (UD-Q4_K_M).
• Long-running tasks: a persistent task ledger and audit log. A task
  resumes after Ctrl-C, a crash or a reboot.
• Compact context: small task-specific context packs. Retrieval seeds are
  ranked so that quoted error messages lead to their origin. On a 1.8 M-token
  repository the model saw 1.6 % of the source.
• Repository intelligence:
  • codebase-memory-mcp for breadth (graph, impact);
  • optional Serena v1.7.0 (MIT, pinned) for semantic depth;
  • cross-service contract analysis (HTTP, topics, env, Terraform).
• Behavioural verification: a task is TASK_VERIFIED only when a test it
  adds fails on the base commit and passes with the change. Otherwise it is
  reported as tests_green / UNVERIFIED.
• Isolation: each task works on a git worktree branch agent/<id>.
  Nothing is pushed or merged.
• Sandbox: agent tools run in a network-less container, with secret
  masking, protected paths, a deterministic command policy and hardened git
  handling.
• Runaway control:
  • per-response caps on thinking and visible output;
  • a progress-aware strategy budget;
  • stopped strategies are recorded and not repeated.
• Task contract: required behaviour, allowed alternatives and material
  ambiguity are surfaced before implementation (task run --clarify).
• Optional frontier escalation: a Z1-Z4 policy via the Codex CLI with a
  ChatGPT sign-in, or manual packets. API keys are refused, and packets are
  sanitized.

Validation

Initial validation: 0/8, then 1/8 after the first defect fixes
(report).
Those failures drove the engineering work. Development reruns are not
counted as validation.

Second independent validation: 6 previously unseen public tasks from
SWE-bench Multilingual and Multi-SWE-bench. They were screened before
execution for consistency between each issue and its acceptance test, frozen,
then run once each:

• 5/6 strict TASK_VERIFIED successes with hidden acceptance passing
• 6/6 hidden acceptance tests passed
• all five strict successes local-only (0 frontier calls)
• 0 false verification passes; 0 human code intervention

The sixth task (Prometheus) was implemented correctly, but the evidence
checker did not link its data-driven test file to the test that reads it.
It is therefore scored as a strict failure.

Small validation sample; not a statistically comprehensive benchmark.
Full report.

Tested configuration

Component	Tested configuration
Machine	Lenovo LOQ 15IRH8
CPU / GPU	i7-13620H, RTX 4060 Laptop 8 GB
RAM / OS	64 GB, Debian 13
Model	Qwen3.6-35B-A3B UD-Q4_K_M
llama.cpp	v0.5.0

Not a minimum requirement.

Known limitations

1. Small validation sample (8 + 6 tasks, one run each). It covers one
   machine and one model.
2. Ambiguity detection is model-derived and has false positives. Under the
   default ask policy, a false positive costs a clarification question.
3. Behavioural evidence misses data-driven test files consumed by a test
   elsewhere.
4. Frontier escalation was enabled but not exercised in the second
   validation.
5. The strategy governor has not yet stopped a live runaway; it is
   calibrated on development runs.

Install

Source release. Follow the
Quick start. Model
weights are not distributed: download them from their publisher and review
their license.