datacurve-pier 0.3.0

A framework for running coding agents in sandboxed environments and keeping full trajectories for analysis.

pier

Pier is a Harbor-compatible framework for evaluating coding agents in sandboxed environments. It reads Harbor's task format and runs trials against it.

pier run -p path/to/task --agent claude-code --env modal

Why pier

Pier is a fork. We wanted a smaller, more opinionated base to build on. On top of Harbor, Pier adds:

• Installed agents in air-gapped tasks (allow_internet = false). When the agent runs inside the sandbox (Claude Code, Codex, etc.), both the install step and the inference call need the network. Pier lets agents declare their install scripts and a network allowlist, which docker and modal environments honor when setting up the sandbox.
• Augmented ATIF v1.7. Strict one step per API turn, strict reasoning vs agent message separation, no fabricated assistant text, peak_context_tokens, summarization_count, llm_call_count, real upstream timestamps.
• A chat-style trajectory viewer (pier view).
• pier critique run for inspecting completed trials with a fresh agent in a fresh sandbox.

What works today

• Task format: Harbor-compatible.
• Environments: docker, modal. Per-agent install specs and network allowlists are honored on both, so installed agents work under allow_internet = false.
• Agents: nop, oracle, claude-code, codex, cursor-cli, gemini-cli, opencode, mini-swe-agent. All emit augmented ATIF v1.7.
• Datasets: local Harbor-format task directories via -p / --path.
• CLI: pier run, pier job, pier view, pier critique run, pier check / pier analyze (vendored from Harbor)

Pier does not currently resolve or download Harbor registry datasets directly.

Install

uv tool install datacurve-pier
# or
pip install datacurve-pier

Run

export ANTHROPIC_API_KEY=...
pier run -p path/to/task --agent claude-code --env modal --env-file .env

Run a local dataset, optionally a deterministic random subset:

pier run -p path/to/dataset --agent claude-code --env modal
pier run -p path/to/dataset --n-tasks 10 --sample-seed 0

To use a Harbor registry dataset, download it with Harbor first, then point Pier at it:

uv run --directory ~/code/harbor harbor download swebenchpro -o ~/code/pier/datasets
uv run pier run -p datasets/swebenchpro --n-tasks 10 --sample-seed 0

Trials land under jobs/<timestamp_or_name>/<trial_id>/. See pier run --help, pier job --help, pier critique --help, and pier view --help for everything else.

Agent runtime configuration

Use agent.model_name for trial metadata, agent.env for runtime env vars, and agent-specific kwargs for tool config. Pier's network allowlist also reads URLs out of those configs (Codex config_toml, OpenCode opencode_config, mini-swe config_yaml), so any base URL you set is allowlisted without code changes.

A few things we've learned plumbing this through Respan and OpenRouter:

Claude Code routes through the Anthropic face from Respan. Plan mode is disabled by default (--disallowedTools EnterPlanMode).

- name: claude-code
  model_name: claude-opus-4-7
  env:
    ANTHROPIC_AUTH_TOKEN: ${RESPAN_API_KEY}
    ANTHROPIC_BASE_URL: https://endpoint.respan.ai/api/anthropic
    ANTHROPIC_CUSTOM_HEADERS: "X-Respan-Route-Provider: vertex_ai"
  kwargs:
    reasoning_effort: max

Codex needs a [model_providers.<name>] block with wire_api = "responses" (not WebSockets, which Codex defaults to and Respan doesn't speak).

- name: codex
  model_name: openai/gpt-5.5
  env: { RESPAN_API_KEY: ${RESPAN_API_KEY} }
  kwargs:
    config_toml: |
      model_provider = "respan"
      [model_providers.respan]
      name = "Respan Gateway"
      base_url = "https://endpoint.respan.ai/api/"
      wire_api = "responses"
      env_key = "RESPAN_API_KEY"
    reasoning_effort: xhigh

Gemini CLI:

- name: gemini-cli
  model_name: gemini/gemini-3.1-pro-preview
  env:
    GEMINI_API_KEY: ${RESPAN_API_KEY}
    GOOGLE_GENERATIVE_AI_API_KEY: ${RESPAN_API_KEY}
    GEMINI_API_BASE: https://endpoint.respan.ai/api/google/vertexai/v1beta
    GOOGLE_GEMINI_BASE_URL: https://endpoint.respan.ai/api/google/vertexai/

Cursor CLI uses the installed cursor-agent binary, so it fits the same inside-the-sandbox path as Claude Code, Codex, Gemini CLI, and OpenCode. Use cursor/composer-2.5 for Composer 2.5 trial metadata and pass CURSOR_API_KEY through your env file.

- name: cursor-cli
  model_name: cursor/composer-2.5
  env:
    CURSOR_API_KEY: ${CURSOR_API_KEY}

OpenCode uses opencode_config to add unknown providers or override known ones. To redirect Google to Respan, override just options.baseURL; to add a fully custom provider, use opencode_config.provider.<name> with the npm package, options, and models.

mini-swe-agent picks a native adapter from the model-name prefix: openai/... → litellm_response (OpenAI Responses end-to-end), openrouter/... → openrouter (BYOK costs from cost_details.upstream_inference_cost), everything else → LiteLLM auto.

For Gemini 3 via mini-swe-agent/LiteLLM, omitting reasoning_effort uses the Gemini API default high/dynamic thinking level, but it does not request readable thought summaries. Set kwargs.reasoning_effort: high explicitly when you want LiteLLM to send includeThoughts and preserve returned summaries as reasoning content.

- name: mini-swe-agent
  model_name: openrouter/qwen/qwen3.6-plus
  env: { OPENROUTER_API_KEY: ${OPENROUTER_API_KEY} }
  kwargs:
    set_cache_control: default_end