Adapter capability contract¶

Every CLI agent expresses the same orchestration concepts differently. Resume is --resume <id> for one CLI, --session-id <id> for another, and a subcommand <cli> resume <id> for a third. "Skip permission prompts" is a flag here, an environment variable there, and an always-on default for CLIs with no permission system. The event surface Bernstein observes is stream-json for some, a plain-text signal grammar for others, and upstream hooks for the rest.

To keep the orchestrator free of if adapter == "X" branches, each adapter declares its strategy on three typed axes. The orchestrator dispatches off the enum, so adding a new adapter is a contract-completion exercise rather than a hunt-and-patch across the core.

The enums and the declaration matrix live in src/bernstein/adapters/_contract.py. Strategy is declared, not probed: Bernstein never runs the CLI at start just to discover its capabilities.

The three axes¶

Resume strategy (ResumeStrategy)¶

How an adapter reattaches to a prior session for bernstein resume.

The legacy two-state view (native / fallback-fresh) consumed by bernstein resume is derived from this axis: any value other than unsupported maps to native. See resume_capability in _contract.py.

Dangerous-mode strategy (DangerousModeStrategy)¶

How an adapter is told to skip interactive permission prompts so it can run unattended.

Event channel (EventChannel)¶

The surface Bernstein reads for an adapter's lifecycle signals.

Declaring a strategy¶

The canonical declaration is a row in STRATEGY_MATRIX, keyed by registry name:

STRATEGY_MATRIX = {
    "claude": AdapterStrategy(
        resume=ResumeStrategy.FLAG,
        dangerous_mode=DangerousModeStrategy.CLI_FLAG,
        event_channel=EventChannel.STREAM_JSON,
    ),
    # ...
}

An adapter MAY instead keep the declaration next to its implementation by setting the class attribute strategy_override to an AdapterStrategy. Read the resolved strategy through CLIAdapter.strategy(), never the raw attribute: the resolver applies the inline override first, then the matrix keyed by registry name, then the conservative DEFAULT_ADAPTER_STRATEGY (no native resume, dangerous mode unsupported, text-signal channel).

Conformance¶

Every shipped adapter must declare its strategy on each axis. The conformance harness calls assert_strategies_declared(), which raises StrategyDeclarationError listing any registry adapter missing a row in STRATEGY_MATRIX. bernstein adapters check surfaces the per-adapter strategy table (strategy_conformance_table) so operators can compare adapters at a glance.

Shipped adapter declarations¶

The matrix is the source of truth; this table is regenerated from strategy_conformance_table(). Out of scope for this contract: runtime capability discovery, and removing every adapter-specific conditional (some output-formatting quirks remain as overrides).

Sampling and endpoint parameters¶

Adapters that can honour per-spawn sampling and endpoint overrides declare AdapterCapability.SUPPORTS_SAMPLING_PARAMS in their plugin_info() capabilities. The overrides travel in the per-spawn mcp_config and, for openai_agents, land in the runner manifest as optional fields:

All fields are optional; a manifest without them behaves exactly as before. When base_url is set, the runner switches the SDK to the chat-completions API (third-party OpenAI-compatible endpoints do not serve /responses) and excludes the custom client from tracing so the endpoint's key is never sent to api.openai.com.

api_key_env must match ^[A-Z][A-Z0-9_]*$ AND be a known LLM provider key (OPENAI_API_KEY, OPENROUTER_API_KEY, DEEPSEEK_API_KEY, and the rest of the built-in allowlist in openai_agents_runner.py). Any other name - including credential-shaped names of unrelated secrets such as GITHUB_TOKEN - is rejected at startup with a config_invalid error (exit code 2), the same failure path as a name whose variable is not set. To use a provider key outside the built-in set, the operator sets BERNSTEIN_ALLOWED_API_KEY_ENVS (comma-separated names) on the host; a repo-carried config cannot set host environment variables, so the override cannot be forged by the repo. The runner logs every effective value in its start event; only the env var name is ever logged, never the key itself.

The spawn path enforces the capability: requesting any of these keys for an adapter that does not declare SUPPORTS_SAMPLING_PARAMS raises SamplingParamsRefusal instead of silently dropping the parameters. See ensure_sampling_params_supported in src/bernstein/adapters/plugin_sdk.py.

Where these values come from¶

Three sources feed the per-spawn slots above, in ascending precedence:

1. Mode profile - the resolved ModeProfile for the spawn carries optional temperature, top_p, top_k, and max_tokens. They are folded in only when the target adapter declares SUPPORTS_SAMPLING_PARAMS, so a default profile never breaks a spawn on an adapter that cannot honour sampling.
2. role_model_policy[<role>] - a role may set base_url and api_key_env in bernstein.yaml to target an OpenAI-compatible endpoint other than the default. api_key_env is the NAME of an environment variable, validated at parse time against the same fail-closed credential allowlist described above; a rejected name fails bernstein.yaml parsing rather than reaching a spawn.
3. Explicit mcp_config - an operator-set value always wins over the two derived sources; the merge only fills slots the operator left unset.

A run that sets none of these behaves exactly as before.

Builtin tool source¶

By default the runner gives the agent the tools brokered by the Bernstein MCP gateway (tool_source: "gateway"). Some runs execute with no MCP gateway reachable - an isolated worktree with no bridge, a minimal offline environment - and would otherwise have no sanctioned way to act on the workdir at all.

Setting tool_source: "builtin" in the per-spawn mcp_config (which the adapter forwards to the runner manifest) opts into a small set of builtins. They split into two confinement tiers - do not conflate them:

read_file, write_file, and list_dir are workdir-confined at the builtin layer: every path argument is resolved against the run workdir, and absolute paths and .. escapes are rejected on the real (symlink-resolved) target.

run_command is not workdir-sandboxed. It runs a child process, and a child process can read and write anywhere the runner process can reach, so its TRUE filesystem confinement is the configured OS sandbox (docker/e2b/modal), not the builtin. The builtin layer applies only defense-in-depth: argv is a list run with shell=False (no shell string, no interpolation), and argv[0] must be a bare command name - absolute paths, path separators, and known shell/interpreters (sh, bash, python, env, ...) are rejected, and the survivor is resolved on PATH and run by its resolved absolute path. Because true confinement is the OS sandbox, run_command is exposed only when the run has an OS sandbox provider configured, or the operator explicitly opts in with BERNSTEIN_BUILTIN_ALLOW_RUN_COMMAND=1. Under the bare local/worktree path with no opt-in, run_command is withheld and the three file tools remain available.

The builtins never become the default; gateway stays the default and any value other than "builtin" selects it. The builtins enforce these properties, each covered by tests in tests/unit/adapters/test_openai_agents_builtins.py:

1. Path confinement (file tools). Every path argument is resolved against the run workdir. Absolute paths and .. escapes are rejected: the real (symlink-resolved) target must stay inside the real workdir.
2. Restricted exec (run_command). argv is a list run with shell=False (no shell string, no interpolation), and argv[0] is a bare command name resolved on PATH; absolute paths, path separators, and shell/interpreters are rejected. Availability is gated on an OS sandbox provider or the explicit opt-in.
3. Audited. Every call emits a tool_call and a tool_result event to the runner's line-delimited JSON event stream, tagged "tool_source": "builtin", with the tool name, arguments, and outcome - so a run with no MCP gateway stays auditable and replayable.

See build_builtin_tools in src/bernstein/adapters/openai_agents_builtins.py.