src/llm-coding-tools-core/src/context/task.txt (1)

17-18: Consider removing redundant "back" from "return back".

Static analysis flagged the redundancy in "return a single message back" and "return back to you". While minor, cleaner phrasing improves readability.

Suggested rewording

-2. When the agent is done, it will return a single message back to you. The result returned by the agent is not visible to the user. To show the user the result, you should send a text message back to the user with a concise summary of the result. The output includes a task_id you can reuse later to continue the same subagent session.
-3. Each agent invocation starts with a fresh context unless you provide task_id to resume the same subagent session (which continues with its previous messages and tool outputs). When starting fresh, your prompt should contain a highly detailed task description for the agent to perform autonomously and you should specify exactly what information the agent should return back to you in its final and only message to you.
+2. When the agent is done, it will return a single message to you. The result returned by the agent is not visible to the user. To show the user the result, you should send a text message to the user with a concise summary of the result. The output includes a task_id you can reuse later to continue the same subagent session.
+3. Each agent invocation starts with a fresh context unless you provide task_id to resume the same subagent session (which continues with its previous messages and tool outputs). When starting fresh, your prompt should contain a highly detailed task description for the agent to perform autonomously and you should specify exactly what information the agent should return to you in its final and only message to you.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-core/src/context/task.txt` around lines 17 - 18, Edit
the sentences in src/llm-coding-tools-core/src/context/task.txt to remove the
redundant "back" in phrases like "return a single message back to you" and
"return back to you"; update them to "return a single message to you" and
"return to you" respectively, ensuring the surrounding meaning stays identical
and the references to task_id and agent behavior remain unchanged.

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

src/llm-coding-tools-serdesai/README.md (2)

148-148: Clarify the session_id rejection constraint.

The statement "session_id is rejected" is mentioned without context. Readers may not understand what session_id refers to or why it's rejected in Task calls.

📋 Suggested clarification

-Each Task call builds and runs the subagent once; `session_id` is rejected. Use [`build_agent_with_credentials_and_task`] for the lower-level helper. See [examples/serdesai-task.rs](examples/serdesai-task.rs).
+Each Task call builds and runs the subagent once (stateless execution). The Task tool does not support stateful sessions, so `session_id` parameters are rejected. Use [`build_agent_with_credentials_and_task`] for the lower-level helper. See [examples/serdesai-task.rs](examples/serdesai-task.rs).

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-serdesai/README.md` at line 148, Clarify that the Task
call API (the Task type/function) does not accept or persist a session
identifier: update the README line to state that Task builds and runs a subagent
once and any provided session_id will be rejected or ignored (i.e., Task is
single-run and does not create a reusable session), and point readers to
build_agent_with_credentials_and_task for use cases that need a persistent
session or to pass session_id (see examples/serdesai-task.rs for usage);
reference Task, session_id, and build_agent_with_credentials_and_task in the
text so readers know which symbols are affected.

---

127-127: Use a more obvious placeholder path.

The hardcoded path /path/to/your/project/examples might confuse readers as it appears to be a real system path. Consider using a more conventional placeholder pattern.

📝 Suggested improvement

-let examples_root = PathBuf::from("/path/to/your/project/examples");
+let examples_root = PathBuf::from("./examples"); // Path to your agent definitions

or

-let examples_root = PathBuf::from("/path/to/your/project/examples");
+let examples_root = PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("examples");

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-serdesai/README.md` at line 127, The placeholder path
assigned to examples_root (let examples_root =
PathBuf::from("/path/to/your/project/examples");) is ambiguous—replace it with a
clearer, conventional placeholder such as a path using angle-bracket or env-var
style (e.g. "<PROJECT_ROOT>/examples" or "${PROJECT_ROOT}/examples") and update
the PathBuf::from argument accordingly so readers understand it is not a real
filesystem path; keep the variable name examples_root and the use of
PathBuf::from unchanged.

src/llm-coding-tools-serdesai/src/agent_runtime/build.rs (1)

129-144: 🛠️ Refactor suggestion | 🟠 Major

Normalize Task gating before deriving tools and targets.

prepare_build still decides Task availability in two places: Ruleset::from_permission_config(...).filter_allowed_tools(...) for the tool itself, and summarize_callable_targets(...) for the callable subagents. Keeping those paths separate makes the feature easy to desynchronize, and downstream at Lines 209-219 attach_standard_tools will quietly skip Task if only one side says “yes”. Please drive both decisions from the same normalized Task-allowance result.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-serdesai/src/agent_runtime/build.rs` around lines 129 -
144, prepare_build currently computes Task availability in two places
(Ruleset::from_permission_config(...).filter_allowed_tools(...) and
summarize_callable_targets(...)), risking desynchronization; refactor
prepare_build to first compute a single normalized Task-allowance result (e.g.,
let task_allowance = Ruleset::from_permission_config(&agent.permission) or a
normalized structure derived from it) and pass that same result into both
filter_allowed_tools(runtime.tools()) and
summarize_callable_targets(runtime.catalog(), name) (or modify those functions
to accept the normalized allowance), so both tools and callable_target_summaries
are derived from the identical gating decision used later by
attach_standard_tools.

src/llm-coding-tools-core/src/context/task.txt (1)

15-22: Document delegation depth limits in Usage Notes.

Given runtime depth limiting, this section should mention that Task delegation can be rejected after max depth is reached, so callers know how to handle/avoid it.

Proposed addition

 6. If the agent description mentions that it should be used proactively, then you should try your best to use it without the user having to ask for it first. Use your judgement.
+7. Task delegation depth may be capped by runtime settings. If a Task call is rejected due to depth limits, complete the work directly or return a clear fallback summary.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-core/src/context/task.txt` around lines 15 - 22, Update
the "Usage Notes" section in task.txt to document the runtime task delegation
depth limit: explicitly state that delegation/starting subagents can be rejected
once the configured max delegation depth is reached, explain callers may receive
a rejection or error and should handle retrying with reduced delegation or
continuing the task locally, and reference the task_id/resume behavior in point
3 so callers know to prefer resuming existing subagent sessions rather than
creating deeper nested delegations.

src/llm-coding-tools-serdesai/src/agent_runtime/task.rs (1)

164-192: Extract the shared task fixtures.

catalog() and credentials() are effectively duplicated here and in src/llm-coding-tools-serdesai/src/task/handle.rs:208-236. A small #[cfg(test)] helper would keep the provider/model setup from drifting between the two suites.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-serdesai/src/agent_runtime/task.rs` around lines 164 -
192, The catalog() and credentials() functions are duplicated across test files;
extract them into a single #[cfg(test)] test-helper module and reuse it from
both places. Create a test-only module (e.g., mod test_fixtures) that defines
pub(crate) fn catalog() -> ModelCatalog and pub(crate) fn credentials() ->
Arc<CredentialResolver<false>> (preserving the ProviderSource, ModelInfo,
ProviderModelSource and resolver setup), then replace the local definitions in
both task.rs and the other file with calls to these shared helpers; ensure
visibility/imports so tests can call test_fixtures::catalog() and
test_fixtures::credentials().

src/llm-coding-tools-core/src/tools/task.rs (1)

121-132: Add a chain-based regression for the documented depth contract.

The new docs promise self-delegation and diamond-shaped call graphs remain valid until max_depth, but this test only locks in the raw < boundary. An A -> B -> A style case in the runtime path would better protect that guarantee.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-core/src/tools/task.rs` around lines 121 - 132, Add a
chain-based regression test to ensure the delegation depth contract treats
revisits in a call chain (e.g., A -> B -> A) correctly: create a test (e.g.,
task_settings_chain_delegation_allows_self_delegation_until_max_depth) that uses
TaskSettings::with_max_depth(3) and asserts allows_delegation returns true for
chain depths representing A (0), A->B (1) and A->B->A (2) but false at depth 3;
reference TaskSettings::with_max_depth and TaskSettings::allows_delegation to
locate where to add the test and lock in the documented behavior for
self-delegation/diamond graphs.

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

src/llm-coding-tools-serdesai/src/agent_runtime/task.rs (1)

219-220: Variable shadows imported module name.

The variable tool_names shadows the imported tool_names module. While Rust resolves tool_names::TASK correctly to the module, this naming collision reduces readability. Consider renaming the variable to names or attached_tools.

This pattern repeats at lines 253, 279, 316, and 349.

♻️ Suggested fix

-        let tool_names: Vec<_> = agent.tools().iter().map(|t| t.name()).collect();
-        assert!(!tool_names.contains(&tool_names::TASK));
+        let names: Vec<_> = agent.tools().iter().map(|t| t.name()).collect();
+        assert!(!names.contains(&tool_names::TASK));

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-serdesai/src/agent_runtime/task.rs` around lines 219 -
220, The local variable named tool_names shadows the imported module tool_names,
which harms readability; rename the variable returned from
agent.tools().iter().map(|t| t.name()).collect() (and the other occurrences at
the same pattern) to a non-conflicting identifier such as names or
attached_tools and update subsequent uses and assertions (e.g., change
assert!(!tool_names.contains(&tool_names::TASK)) to
assert!(!names.contains(&tool_names::TASK))) so the module reference
tool_names::TASK remains unambiguous; ensure you update all repeated instances
at the same code pattern in this file.

src/llm-coding-tools-serdesai/README.md (1)

112-147: ⚠️ Potential issue | 🟡 Minor

Mention the extra llm-coding-tools-models-dev dependency in this section.

This example imports ModelsDevCatalog and uses load_result.catalog, but the surrounding text only calls out llm-coding-tools-agents and a generic ModelCatalog. Copy-pasting it still requires llm-coding-tools-models-dev in Cargo.toml.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-serdesai/README.md` around lines 112 - 147, The README
example uses ModelsDevCatalog and load_result.catalog but doesn't mention the
required crate; update the Task Tool section to explicitly note the extra
dependency llm-coding-tools-models-dev and show adding it to Cargo.toml (or list
it in the prerequisites), and adjust the imports/examples to reference
ModelsDevCatalog so readers know to include llm-coding-tools-models-dev when
using ModelsDevCatalog and load_result.catalog.

src/llm-coding-tools-serdesai/src/agent_runtime/build.rs (1)

157-160: Keep PreparedBuild depth-aware in one place.

When delegation is blocked at current_depth, this clears only callable_target_summaries. prepared.tools can still contain ToolCatalogKind::Task, so the same build artifact says “Task exists” and “Task cannot be attached” at once. attach_standard_tools() masks it today, but that invariant is easy to break later. Filter the Task entry here too.

♻️ Suggested adjustment

     let mut prepared = prepare_build(runtime, name, model_catalog, credentials)?;
     if !runtime.task_settings().allows_delegation(current_depth) {
+        prepared.tools.retain(|entry| entry.kind != ToolCatalogKind::Task);
         prepared.callable_target_summaries.clear();
     }
     Ok(prepared)

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-serdesai/src/agent_runtime/build.rs` around lines 157 -
160, The prepared build is depth-aware only for callable_target_summaries but
not for prepared.tools, causing inconsistent state when delegation is
disallowed; after calling prepare_build (symbol: prepare_build) and checking
runtime.task_settings().allows_delegation(current_depth), filter out any
ToolCatalogKind::Task entries from prepared.tools in the same branch where
prepared.callable_target_summaries.clear() is invoked (reference symbols:
prepared, PreparedBuild, prepared.tools, callable_target_summaries,
ToolCatalogKind::Task) so the PreparedBuild consistently reflects that tasks
cannot be attached; keep this filtering colocated with the existing delegation
check to preserve the invariant and avoid relying on attach_standard_tools to
mask the inconsistency.

src/llm-coding-tools-agents/src/runtime/task.rs (1)

109-119: Skip the sort in resolve_allowed_tools().

This branch only needs to know whether any callable task target exists, but sorted_agents() allocates and sorts the whole catalog before a scan that may break on the first match. Iterating runtime.catalog().iter() here keeps behavior unchanged and removes extra work from every allowed_tools() call.

♻️ Suggested simplification

-    let agents = sorted_agents(runtime.catalog());
     let task_rules = Ruleset::from_permission_config(&caller.permission);
     let has_explicit_task_permission = caller.permission.contains_key(tool_names::TASK);
     let mut task_is_callable = false;

     // Expose `task` only when at least one delegated target remains callable.
-    for target in agents {
+    for target in runtime.catalog().iter() {
         if target_is_callable(target, &task_rules, has_explicit_task_permission) {
             task_is_callable = true;
             break;
         }
     }

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/llm-coding-tools-agents/src/runtime/task.rs` around lines 109 - 119, The
code currently calls sorted_agents(runtime.catalog()) in
resolve_allowed_tools()/allowed_tools(), which allocates and sorts the entire
catalog even though we only need to know if any callable target exists; change
this to iterate runtime.catalog() directly (e.g., for target in
runtime.catalog().iter()) so you avoid the allocation/sort, keeping the existing
logic that builds task_rules via
Ruleset::from_permission_config(&caller.permission) and checking targets with
target_is_callable(...); ensure the loop variable types match the catalog
iterator (no reliance on sorted_agents return type) and preserve the
has_explicit_task_permission check and early break when a callable target is
found.

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes