Release 1.4.0 (#198)

* feat(tf-policy): Added models and resources for tf-policy evaluations and set outcomes

* feat(tf-policy): Added examples and unit testcases

* removed redacted url at log

* updated format string at _http file logger

* Added tf-policy scenario doc

* docs(tfpolicy): Updated the scenario docs for tf-policy evaluation

* fix(stack): updated speculative enabled attribute alias in stack

* updated changelog

Author: isivaselvan

CHANGELOG.md

@@ -1,6 +1,49 @@
 # Unreleased
 
 # Released
+# v1.4.0
+
+## Enhancements
+
+### New resources
+
+* Added `client.tf_policy_evaluations` — read and override tf-policy evaluations
+  attached to a run.
+  * `list(run_id, options=None)` (`GET /runs/{run_id}/tf-policy-evaluations`) returns
+    an `Iterator[TfPolicyEvaluation]`.
+  * `read(tf_policy_evaluation_id, options=None)` (`GET /tf-policy-evaluations/{id}`)
+    returns a single `TfPolicyEvaluation`; pass
+    `TfPolicyEvaluationListOptions(include="tf_policy_set_outcomes")` to sideload
+    outcomes in one request.
+  * `override(tf_policy_evaluation_id, options=None)` (`POST
+    /tf-policy-evaluations/{id}/actions/override`) overrides an evaluation in
+    `awaiting_override` status and returns the updated resource.
+  * `list_set_outcomes(tf_policy_evaluation_id, options=None)` (`GET
+    /tf-policy-evaluations/{id}/tf-policy-set-outcomes`) returns an
+    `Iterator[TfPolicySetOutcome]`; supports `filter_status` and
+    `filter_enforcement_level` via `TfPolicySetOutcomeListOptions`.
+  * New models: `TfPolicyEvaluation`, `TfPolicyEvaluationStatusTimestamps`,
+    `TfPolicyResultCount`, `TfPolicyEvaluationError`, `TfPolicyEvaluationPermissions`,
+    `TfPolicyEvaluationActions`, `TfPolicyEvaluationListOptions`,
+    `TfPolicyEvaluationOverrideOptions`.
+  * New errors: `InvalidTfPolicyEvaluationIDError`.
+* Added `client.tf_policy_set_outcomes` — read a single tf-policy set outcome.
+  * `read(tf_policy_set_outcome_id)` (`GET /tf-policy-set-outcomes/{id}`) returns a
+    `TfPolicySetOutcome` with its nested `outcomes` array (snake_case inner keys, as
+    stored by atlas).
+  * New models: `TfPolicySetOutcome`, `PolicyOutcome`, `Diagnostic`, `OutcomeResource`,
+    `TraversalValue`, `PassedResource`, `TfPolicySetOutcomeListOptions`.
+  * New error: `InvalidTfPolicySetOutcomeIDError`.
+
+### New enum values
+
+* `PolicyKind.TFPOLICY = "tfpolicy"` — enables creating and reading tf-policy sets
+  with the existing `client.policy_sets` resource.
+* New enums: `TfPolicyEvaluationStatus`, `TfPolicyStage`, `TfPolicyEnforcementLevel`.
+
+## Bug Fixes
+* Updated model attributes of speculative enabled attribute with correct alias name at stack models.
+
 # v1.3.1
 
 ## Security Fixes

docs/scenarios/tf-policy-evaluation.md

@@ -0,0 +1,241 @@
+# Scenario: tf-policy evaluation and override
+
+This scenario covers reading tf-policy compliance results for a run, filtering
+policy-set outcomes, and overriding a `mandatory_overridable` failure. tf-policy
+is HCP Terraform's native policy-as-code engine (distinct from Sentinel and
+OPA) — evaluations are attached to a run's stages (Init/Plan/Apply) rather
+than created directly, so this scenario reads and reacts to results rather
+than authoring them.
+
+> tf-policy is HCP Terraform only and gated behind an organization-level
+> feature flag while in private beta. If policy-set creation with
+> `kind=PolicyKind.TFPOLICY` fails validation, confirm the flag is enabled for
+> your organization before assuming a client-side issue.
+
+## Prerequisites
+
+```bash
+export TFE_TOKEN="your-api-token"
+export TFE_ADDRESS="https://app.terraform.io"
+```
+
+The workspace whose runs you're inspecting must be running a Terraform
+version tf-policy supports (`>= 1.16.0-alpha20260626` at the time of writing —
+check with your organization admin, since this is a fast-moving minimum on a
+beta feature). Evaluations on an older version come back `errored` with an
+`incompatible_terraform_version_error`, not a client-side exception.
+
+## Step 1: List a run's tf-policy evaluations
+
+A run has one evaluation per applicable stage, and each stage evaluates a
+different scope (see [Evaluation and enforcement](https://developer.hashicorp.com/terraform/policy#evaluation-and-enforcement)):
+
+- **Init** — provider and module policies, evaluated during workspace
+  initialization, before Terraform installs providers or modules.
+- **Plan** — resource policies whose referenced attributes are known at plan
+  time, evaluated against the proposed plan before any infrastructure
+  changes.
+- **Apply** — resource policies that reference computed values (ARNs, IDs,
+  and similar) that stay unknown until Terraform actually applies; these
+  necessarily run after infrastructure changes are made.
+
+An Init-stage evaluation only has an empty result if your policy set has no
+provider/module policies — don't assume `list()[0]` is the interesting one
+for a resource-policy scenario; find the evaluation whose stage matches what
+your policy actually targets.
+
+```python
+from pytfe import TFEClient
+
+client = TFEClient()
+run_id = "run-abc123"
+
+evaluations = list(client.tf_policy_evaluations.list(run_id))
+for e in evaluations:
+    print(e.id, e.stage_type, e.status, e.result_count)
+```
+
+## Step 2: Read one evaluation, with outcomes sideloaded
+
+```python
+from pytfe.models import TfPolicyEvaluationListOptions
+
+opts = TfPolicyEvaluationListOptions(include="tf_policy_set_outcomes")
+evaluation = client.tf_policy_evaluations.read(evaluations[0].id, options=opts)
+
+print(evaluation.status, evaluation.actions, evaluation.permissions)
+```
+
+`evaluation.actions.is_overridable` and `evaluation.permissions.can_override`
+both need to be `True` before an override call will succeed — check them
+before attempting one rather than relying on the error path.
+
+## Step 3: Inspect policy-set outcomes and diagnostics
+
+```python
+for outcome in client.tf_policy_evaluations.list_set_outcomes(evaluation.id):
+    print(outcome.policy_set_name, outcome.result_count)
+    for policy in outcome.outcomes:
+        print(" ", policy.policy_name, policy.status, policy.enforcement_level)
+        for diag in policy.diagnostics:
+            print("    diag:", diag.summary, [r.resource_name for r in diag.resources])
+```
+
+Filter to just the failures, or just one enforcement level:
+
+```python
+from pytfe.models import TfPolicySetOutcomeListOptions
+
+failed = client.tf_policy_evaluations.list_set_outcomes(
+    evaluation.id,
+    options=TfPolicySetOutcomeListOptions(filter_status="failed"),
+)
+
+mandatory_overridable = client.tf_policy_evaluations.list_set_outcomes(
+    evaluation.id,
+    options=TfPolicySetOutcomeListOptions(
+        filter_enforcement_level="mandatory_overridable"
+    ),
+)
+```
+
+You can also read a single outcome directly if you already have its ID (e.g.
+from a webhook payload) without listing through the evaluation:
+
+```python
+outcome = client.tf_policy_set_outcomes.read("tfpsout-abc123")
+```
+
+## Step 4: Override a `mandatory_overridable` failure
+
+```python
+from pytfe.models import TfPolicyEvaluationOverrideOptions
+
+result = client.tf_policy_evaluations.override(
+    evaluation.id,
+    TfPolicyEvaluationOverrideOptions(comment="Approved by platform-team — ticket OPS-123"),
+)
+print(result.status)  # "overridden"
+```
+
+`comment` is optional — omit `options` entirely to override with no comment.
+The override only succeeds while the evaluation is in `awaiting_override`
+status; calling it again on an already-overridden evaluation raises `TFEError`
+rather than silently no-op'ing, so guard on `status` first if you're looping
+over a batch.
+
+Override is only available for **Plan-stage** evaluations. Init and Apply
+stage evaluations are never overridable, regardless of enforcement level or
+`AWAITING_OVERRIDE` status — check `stage_type == TfPolicyStage.PLAN` (in
+addition to `actions.is_overridable`) before attempting one:
+
+```python
+from pytfe.models import TfPolicyEvaluationStatus, TfPolicyStage
+
+overridable = [
+    e for e in evaluations
+    if e.status == TfPolicyEvaluationStatus.AWAITING_OVERRIDE
+    and e.stage_type == TfPolicyStage.PLAN
+    and e.actions and e.actions.is_overridable
+]
+for e in overridable:
+    client.tf_policy_evaluations.override(e.id)
+```
+
+## Step 5: Gate a downstream workflow on compliance
+
+The read-only surface above is enough to build a pre-flight compliance gate —
+this is the pattern the `hashicorp.terraform` Ansible collection's
+`tf_policy_evaluation_info` module wraps:
+
+```python
+evaluations = list(client.tf_policy_evaluations.list(run_id))
+non_compliant = [
+    e for e in evaluations
+    if e.status in (
+        TfPolicyEvaluationStatus.FAILED,
+        TfPolicyEvaluationStatus.ERRORED,
+        TfPolicyEvaluationStatus.AWAITING_OVERRIDE,
+    )
+]
+if non_compliant:
+    raise SystemExit(f"Run {run_id} is not tf-policy compliant: {non_compliant}")
+```
+
+## Creating a `kind=tfpolicy` policy set
+
+tf-policy policy sets are primarily supported as **VCS-connected** policy
+sets — commit your `.policy.hcl` files to a repository and connect it via
+`vcs_repo` on `PolicySetCreateOptions`, the same pattern as Sentinel/OPA
+policy sets. That's the supported path for anything beyond local iteration.
+
+For quick local testing without setting up a VCS/OAuth connection, the
+direct-upload path below also works and is what this scenario uses:
+
+```python
+from pytfe.models import PolicyKind, PolicySetCreateOptions
+
+policy_set = client.policy_sets.create(
+    "my-organization",
+    PolicySetCreateOptions(
+        name="tfpolicy-guardrails",
+        kind=PolicyKind.TFPOLICY,
+        policy_tool_version="0.1.0",
+        agent_enabled=True,
+        overridable=True,
+    ),
+)
+
+version = client.policy_set_versions.create(policy_set.id)
+client.policy_set_versions.upload(version, "./policies")  # directory, not a tarball
+```
+
+`client.policy_set_versions.upload()` takes the `PolicySetVersion` object
+itself (it reads the upload link off it), not a URL string — this differs
+from `client.configuration_versions.upload()`, which does take the upload URL
+directly. Easy to transpose the two if you're working with both in the same
+script.
+
+If your `.policy.hcl` files live in a subdirectory of the uploaded archive
+rather than at its root, set `policies_path` on the policy set to point at
+that subdirectory:
+
+```python
+from pytfe.models import PolicySetUpdateOptions
+
+client.policy_sets.update(
+    policy_set.id,
+    PolicySetUpdateOptions(policies_path="policies"),
+)
+```
+
+Without `policies_path` set, the engine looks for policy files at the
+archive root. A nested layout with `policies_path` left unset is accepted
+without error and silently evaluates zero policies — every evaluation
+"passes" with `result_count` at all zeros, indistinguishable from a
+genuinely compliant run until you notice nothing was actually checked.
+
+## Cleanup
+
+```python
+client.policy_sets.delete(policy_set.id)
+```
+
+Evaluations themselves aren't deletable - they're immutable records tied to
+the run that produced them and are cleaned up when the run/workspace is.
+
+## Wire-format notes
+
+- `TfPolicyEnforcementLevel.MANDATORY_OVERRIDABLE` serializes as
+  `"mandatory_overridable"` - underscore, unlike the hyphenated style used
+  elsewhere in the JSON:API surface.
+- The `outcomes` array on `TfPolicySetOutcome`, and everything nested inside
+  it (`PolicyOutcome`, `Diagnostic`, `OutcomeResource`, `TraversalValue`,
+  `PassedResource`), is **snake_case on the wire** rather than dash-cased.
+  The backend serializes that column verbatim from a stored value rather than
+  passing it through the usual attribute-name transform, so the SDK models
+  read it as-is — this is intentional, not a bug, if you're ever comparing
+  raw JSON against the rest of the API's dash-case convention.
+- `override()`'s request body is a bare `{"comment": "..."}`, not a JSON:API
+  `{"data": {"attributes": {...}}}` envelope - the SDK handles this for you,
+  but it's worth knowing if you're debugging against raw HTTP logs.

examples/stack.py

@@ -30,7 +30,7 @@ def _print_stack(item):
     print(f"- description: {item.description}")
     print(f"- created_at: {item.created_at}")
     print(f"- updated_at: {item.updated_at}")
-    print(f"- speculation_enabled: {item.speculation_enabled}")
+    print(f"- speculative_enabled: {item.speculative_enabled}")
     print(f"- project_id: {item.project.id if item.project else None}")
     print(f"- agent_pool_id: {item.agent_pool.id if item.agent_pool else None}")
 
@@ -77,7 +77,7 @@ def main():
     parser.add_argument("--name", help="Stack name (required for create)")
     parser.add_argument("--description", help="Stack description")
     parser.add_argument(
-        "--speculation-enabled",
+        "--speculative-enabled",
         type=lambda v: str(v).lower() in ("1", "true", "yes", "y"),
         default=None,
         help="Enable speculation (true/false)",
@@ -130,7 +130,7 @@ def main():
         options = StackCreateOptions(
             name=args.name,
             description=args.description,
-            speculation_enabled=args.speculation_enabled,
+            speculative_enabled=args.speculative_enabled,
             vcs_repo=_build_vcs_repo_options(args),
             project=Project(id=args.project_id),
             agent_pool=AgentPool(id=args.agent_pool_id) if args.agent_pool_id else None,
@@ -157,7 +157,7 @@ def main():
             [
                 args.name,
                 args.description,
-                args.speculation_enabled is not None,
+                args.speculative_enabled is not None,
                 args.agent_pool_id,
                 args.vcs_identifier,
                 args.vcs_branch,
@@ -172,7 +172,7 @@ def main():
         options = StackUpdateOptions(
             name=args.name,
             description=args.description,
-            speculation_enabled=args.speculation_enabled,
+            speculative_enabled=args.speculative_enabled,
             vcs_repo=_build_vcs_repo_options(args),
             agent_pool=AgentPool(id=args.agent_pool_id) if args.agent_pool_id else None,
             project=Project(id=args.project_id) if args.project_id else None,