feat(agents): add tool-call support to LLMAgent.stream_message #68

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hurui200320 merged 1 commit from feature/m2-llm-agent-stream-tool-calls into master 2026-07-03 05:51:32 +00:00

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hurui200320 commented 2026-07-02 07:49:36 +00:00

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Summary

Fixes the capability gap where execute_stream() on a tool-declaring LLM actor yielded an empty or hallucinated response. Extracts the entire multi-turn tool-call orchestration from process_message() into a shared private _execute_tool_loop() helper that both process_message() and stream_message() now call.

Motivation

process_message() had a fully working multi-turn tool-call loop (~490 lines). stream_message() had none of this — it called astream() without passing tools, so the LLM received no tool schemas and produced no structured tool calls.

Duplicating the loop into stream_message() would create a permanent maintenance burden with two diverging implementations. The correct fix is extraction into a shared helper.

Approach

New types (module level in llm.py)

• _ToolLoopResult dataclass: carries final_response, accumulated_prompt, accumulated_completion, budget_exhausted, synthesis_was_run. Both callers read token counts from this object.
• _ToolLoopError exception: wraps any exception raised inside the helper and carries partial accumulated token counts + any_invocation_made flag. Callers catch this, set billing sentinels, then re-raise the original cause from None.

_execute_tool_loop() (new method on LLMAgent)

Contains the full tool orchestration extracted verbatim from process_message():

• Parses tool_max_rounds from config/env
• Builds invoke_kwargs with tools (+ pruning augmentation when enabled)
• Runs the multi-turn ainvoke() loop
• Dispatches each tool_call to an ephemeral ToolAgent
• Accumulates token counts across all rounds including pruning passes
• Handles token-budget exhaustion + synthesis path (§4.4.7 D-4)
• Handles stuck-model synthesis prompt (post-loop)
• On exception: wraps in _ToolLoopError carrying partial counts

process_message() refactor

Replaces ~490 lines of inline tool loop with a 15-line delegation. Observable behaviour is identical — all 63 existing llm_agent_tool_calling.feature scenarios pass unmodified.

stream_message() integration

• Tools configured: awaits _execute_tool_loop(), yields the final response as a single content chunk, sets _last_token_usage from the result's accumulated token counts.
• No tools configured: the existing astream() token-by-token path runs completely unchanged.

Single-chunk trade-off: when tools are involved, the user already waits for tool execution before the final answer begins, so yielding that answer as one chunk vs. token-by-token has minimal UX impact. The alternative (re-calling astream() after ainvoke() produced the final response) would double LLM cost for every tool-using request.

Tests

• features/llm_agent_tool_loop.feature (10 scenarios): _execute_tool_loop() internals tested independently — single round, multi-round, max_rounds exhaustion, token accumulation, budget exhaustion, stuck-model synthesis, pruning pass, tool dispatch error, exception path, pre-invocation ConfigurationError.
• features/llm_agent_stream_tool_calls.feature (7 scenarios): stream_message() integration — single-chunk output, two-round token sum, final text, no-tools astream unchanged, no-tools token counts, exception billing integrity, memory update.
• robot/llm_tool_calling.robot (2 new cases): execute_stream() on tool-configured actor exercises the tool loop path; multi-round token accumulation equals sum of all rounds.

Quality gates

Gate	Result
format	✓
lint	✓
typecheck	✓
security_scan	✓
unit_tests	✓ (1133 scenarios)
integration_tests	✓ (319 tests)
coverage_report	✓ 97.04%

Follow-up

The webapp workaround actor_config_has_tools + stream_fallback_to_non_stream in cleveragents/cleveragents-webapp#328 can now be removed.

Closes #67

## Summary Fixes the capability gap where `execute_stream()` on a tool-declaring LLM actor yielded an empty or hallucinated response. Extracts the entire multi-turn tool-call orchestration from `process_message()` into a shared private `_execute_tool_loop()` helper that both `process_message()` and `stream_message()` now call. ## Motivation `process_message()` had a fully working multi-turn tool-call loop (~490 lines). `stream_message()` had none of this — it called `astream()` without passing `tools`, so the LLM received no tool schemas and produced no structured tool calls. Duplicating the loop into `stream_message()` would create a permanent maintenance burden with two diverging implementations. The correct fix is extraction into a shared helper. ## Approach ### New types (module level in `llm.py`) - **`_ToolLoopResult`** dataclass: carries `final_response`, `accumulated_prompt`, `accumulated_completion`, `budget_exhausted`, `synthesis_was_run`. Both callers read token counts from this object. - **`_ToolLoopError`** exception: wraps any exception raised inside the helper and carries partial accumulated token counts + `any_invocation_made` flag. Callers catch this, set billing sentinels, then re-raise the original `cause from None`. ### `_execute_tool_loop()` (new method on `LLMAgent`) Contains the full tool orchestration extracted verbatim from `process_message()`: - Parses `tool_max_rounds` from config/env - Builds `invoke_kwargs` with tools (+ pruning augmentation when enabled) - Runs the multi-turn `ainvoke()` loop - Dispatches each `tool_call` to an ephemeral `ToolAgent` - Accumulates token counts across all rounds including pruning passes - Handles token-budget exhaustion + synthesis path (§4.4.7 D-4) - Handles stuck-model synthesis prompt (post-loop) - On exception: wraps in `_ToolLoopError` carrying partial counts ### `process_message()` refactor Replaces ~490 lines of inline tool loop with a 15-line delegation. Observable behaviour is identical — all 63 existing `llm_agent_tool_calling.feature` scenarios pass unmodified. ### `stream_message()` integration - **Tools configured:** awaits `_execute_tool_loop()`, yields the final response as a single content chunk, sets `_last_token_usage` from the result's accumulated token counts. - **No tools configured:** the existing `astream()` token-by-token path runs completely unchanged. **Single-chunk trade-off:** when tools are involved, the user already waits for tool execution before the final answer begins, so yielding that answer as one chunk vs. token-by-token has minimal UX impact. The alternative (re-calling `astream()` after `ainvoke()` produced the final response) would double LLM cost for every tool-using request. ## Tests - `features/llm_agent_tool_loop.feature` (10 scenarios): `_execute_tool_loop()` internals tested independently — single round, multi-round, max_rounds exhaustion, token accumulation, budget exhaustion, stuck-model synthesis, pruning pass, tool dispatch error, exception path, pre-invocation ConfigurationError. - `features/llm_agent_stream_tool_calls.feature` (7 scenarios): `stream_message()` integration — single-chunk output, two-round token sum, final text, no-tools astream unchanged, no-tools token counts, exception billing integrity, memory update. - `robot/llm_tool_calling.robot` (2 new cases): `execute_stream()` on tool-configured actor exercises the tool loop path; multi-round token accumulation equals sum of all rounds. ## Quality gates | Gate | Result | |------|--------| | format | ✓ | | lint | ✓ | | typecheck | ✓ | | security_scan | ✓ | | unit_tests | ✓ (1133 scenarios) | | integration_tests | ✓ (319 tests) | | coverage_report | ✓ 97.04% | ## Follow-up The webapp workaround `actor_config_has_tools` + `stream_fallback_to_non_stream` in `cleveragents/cleveragents-webapp#328` can now be removed. Closes #67

hurui200320 added 1 commit 2026-07-02 07:49:36 +00:00

feat(agents): add tool-call support to LLMAgent.stream_message ... 306f114b8a

Extract the entire multi-turn tool-call orchestration from the inline
loop inside process_message() into a new shared private method
_execute_tool_loop().  Both process_message() and stream_message() now
delegate to this single implementation, eliminating the previous
capability gap where stream_message() had no tool-calling support.

Design:

- _ToolLoopResult dataclass: carries final_response, accumulated_prompt,
  accumulated_completion, budget_exhausted, and synthesis_was_run.  Both
  callers read token counts from this object instead of local sentinels.

- _ToolLoopError exception: wraps any exception raised inside the helper
  and carries partial accumulated token counts + any_invocation_made flag.
  Callers catch this, set their _captured_prompt/_captured_completion
  sentinels (billing integrity), then re-raise the original cause.

- _execute_tool_loop(): extracted verbatim from process_message().
  Contains the full ainvoke() loop, per-tool ToolAgent dispatch, token
  accumulation across rounds, token-budget exhaustion + synthesis path
  (§4.4.7 D-4), stuck-model synthesis prompt, and tool-output pruning
  pass (§4.4.8).  On exception, wraps in _ToolLoopError for the caller.

process_message() refactor:

- Replaces ~490 lines of inline tool loop with a 15-line delegation.
  For tool-configured actors: awaits _execute_tool_loop(), extracts
  response text and token counts from the result.
  For no-tool actors: single plain ainvoke() (unchanged behaviour).
  Billing-integrity sentinels (_captured_prompt/_captured_completion)
  are set from _ToolLoopError on exception and from the result on
  success.  All existing Behave scenarios continue to pass unmodified.

stream_message() integration:

- When tools are configured: awaits _execute_tool_loop() and yields the
  final response as a single content chunk.  Token counts come from the
  result's accumulated_prompt/accumulated_completion fields (spanning
  all tool rounds + pruning passes).  Memory update is performed before
  the generator returns.  Billing sentinels are updated from
  _ToolLoopError on exception.
- When no tools are configured: the existing astream() token-by-token
  path runs completely unchanged.

Single-chunk trade-off: when tools are involved, the user already waits
for tool execution before the final answer begins, so yielding that
answer as one chunk vs. token-by-token has minimal UX impact.  The
alternative (re-calling astream() after ainvoke() produced the final
response) would double LLM cost for every tool-using request.

Webapp workaround: the actor_config_has_tools fallback in
cleveragents/cleveragents-webapp#328 (stream_fallback_to_non_stream in
_execute_via_cleveractors_stream / _generate_actor_sse) can now be
removed.

Tests added:
- features/llm_agent_tool_loop.feature + steps: 10 Behave scenarios
  covering _execute_tool_loop() internals independently (single round,
  multi-round, max_rounds exhaustion, token accumulation, budget
  exhaustion, stuck-model synthesis, pruning pass, tool dispatch error,
  exception path with partial counts, pre-invocation ConfigurationError).
- features/llm_agent_stream_tool_calls.feature + steps: 7 Behave
  scenarios for stream_message() with tool-call support (single chunk
  output, two-round token sum, final text, no-tools astream unchanged,
  no-tools token counts, exception billing integrity, memory update).
- robot/llm_tool_calling.robot: two new Robot integration tests:
  'Streaming Path Exercises Tool Loop Via Execute Stream' and
  'Streaming Path Accumulates Tokens Across Two Tool Rounds'.
- robot/ToolCallingTestLib.py: new keywords and
  create_executor_with_multi_round_tool_calling_agent factory.

Quality gates: format ✓  lint ✓  typecheck ✓  security_scan ✓
unit_tests ✓  integration_tests ✓  coverage_report ✓ (97.0%)

ISSUES CLOSED: #67

hurui200320 added a new dependency 2026-07-02 07:49:41 +00:00

#67 Add tool-call support to LLMAgent.stream_message

hurui200320 added the

Type

Feature

label 2026-07-02 07:49:43 +00:00

hurui200320 added this to the v2.1.0 milestone 2026-07-02 07:49:43 +00:00

hurui200320 commented 2026-07-02 08:41:37 +00:00

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Self-QA Review: Approved ✅

PR reviewed in 1 cycle.

Verdict: Approve — ready to merge.

Issues found: 0 Critical / 0 Major / 3 Minor / 5 Nits

Minor findings (non-blocking)

1. Weak assertion in max_rounds exhaustion test — step only asserts final_response is not None (tautological); should verify synthesis_was_run=True.
2. Docstring/code mismatch in _ToolLoopError — docstring shows raise tle.cause, callers use raise _tle.cause from None.
3. Pre-existing break vs continue inconsistency — budget-exhaust synthesis path uses break on undeclared tool names while other paths use continue; preserved verbatim from original code, safe to defer to cleanup.

Nits

• # type: ignore in new test files violates project policy.
• Unused imports in new test step files.
• Inconsistent local variable naming inside _execute_tool_loop().
• _ToolLoopError.__init__ accepts BaseException but wrapper only catches Exception.
• _tool_token_str variable name in stream_message() is misleading.

All 11 acceptance criteria from #67 are satisfied. None of the above are blockers; they can be addressed in a follow-up cleanup commit if desired.

## Self-QA Review: Approved ✅ PR reviewed in 1 cycle. **Verdict:** Approve — ready to merge. **Issues found:** 0 Critical / 0 Major / 3 Minor / 5 Nits ### Minor findings (non-blocking) 1. **Weak assertion in max_rounds exhaustion test** — step only asserts `final_response is not None` (tautological); should verify `synthesis_was_run=True`. 2. **Docstring/code mismatch in `_ToolLoopError`** — docstring shows `raise tle.cause`, callers use `raise _tle.cause from None`. 3. **Pre-existing `break` vs `continue` inconsistency** — budget-exhaust synthesis path uses `break` on undeclared tool names while other paths use `continue`; preserved verbatim from original code, safe to defer to cleanup. ### Nits - `# type: ignore` in new test files violates project policy. - Unused imports in new test step files. - Inconsistent local variable naming inside `_execute_tool_loop()`. - `_ToolLoopError.__init__` accepts `BaseException` but wrapper only catches `Exception`. - `_tool_token_str` variable name in `stream_message()` is misleading. All 11 acceptance criteria from #67 are satisfied. None of the above are blockers; they can be addressed in a follow-up cleanup commit if desired.

hurui200320 force-pushed feature/m2-llm-agent-stream-tool-calls from 306f114b8a to 85b429cfb0 2026-07-03 05:11:02 +00:00 Compare

hurui200320 force-pushed feature/m2-llm-agent-stream-tool-calls from 85b429cfb0 to 9bd766c383 2026-07-03 05:19:52 +00:00 Compare

hurui200320 force-pushed feature/m2-llm-agent-stream-tool-calls from 9bd766c383 to 4141469ac9 2026-07-03 05:39:51 +00:00 Compare

hurui200320 force-pushed feature/m2-llm-agent-stream-tool-calls from 4141469ac9 to 306f114b8a 2026-07-03 05:48:51 +00:00 Compare

hurui200320 merged commit 306f114b8a into master 2026-07-03 05:51:32 +00:00

hurui200320 deleted branch feature/m2-llm-agent-stream-tool-calls 2026-07-03 05:51:32 +00:00

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#67 Add tool-call support to LLMAgent.stream_message

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