test(security): cover validate_url usage in arxiv tool and PyPI polling

Add behavior tests that assert URL validation runs before network
requests and that unsafe URLs never reach requests.get. Also fix
indentation in _wait_for_pypi after the requests migration.

Co-authored-by: Rip&Tear <theCyberTech@users.noreply.github.com>

docs/deployment-sizing.mdx

@@ -1,117 +0,0 @@
----
-title: "Deployment Sizing"
-description: Choose the right deployment size for your crew workloads — and know when to scale up.
----
-
-## Overview
-
-Every CrewAI Enterprise deployment runs on a fixed resource tier called an **instance size**. The size controls how much CPU, memory, and — most importantly — how many crew runs can execute simultaneously. Choosing the wrong size is the most common cause of queue build-up, slow run starts, and OOMKilled pods.
-
-This page explains what each size provides, how to read the signals that you've outgrown your current tier, and how to right-size for your workload.
-
----
-
-## Instance Sizes
-
-| # | Name | vCPU | Memory | Max Concurrent Runs | Storage |
-|---|------|------|--------|---------------------|---------|
-| 1 | Small | 1 | 2 GiB | 4 | 20 GiB |
-| 2 | Regular | 2 | 4 GiB | 16 | 20 GiB |
-| 3 | Large | 4 | 8 GiB | 32 | 20 GiB |
-| 4 | Extra Large | 8 | 16 GiB | 64 | 100 GiB |
-| 5 | Extra Extra Large | 16 | 32 GiB | 128 | 100 GiB |
-| 6 | Insane Large | 32 | 64 GiB | 256 | 100 GiB |
-
-**vCPU** and **Memory** are the total resources allocated to the deployment (web server + workers + Redis combined).
-
-**Max Concurrent Runs** is the worker concurrency limit — the number of crew runs that can be actively executing at the same time. Runs submitted beyond this limit are queued and wait for a slot to open.
-
-<Note>
-  Concurrency is per-deployment, not per-crew. If you have 10 crews deployed on a Small instance, all 10 share the same pool of 4 concurrent run slots.
-</Note>
-
----
-
-## What "concurrent runs" actually means
-
-A **concurrent run** is one active kickoff of a crew — from the moment it starts executing until it completes or errors. It does not mean the number of agents running in parallel inside a single crew (that's controlled by your crew's process type and agent configuration).
-
-**Example:** A Small deployment (concurrency = 4) with 20 incoming run requests will execute 4 runs simultaneously and queue the remaining 16. Each queued run starts as soon as a slot frees up.
-
----
-
-## Symptoms of an undersized deployment
-
-| Symptom | Likely cause |
-|---------|-------------|
-| Runs sit in `queued` state for a long time | Concurrency limit reached — all worker slots are occupied |
-| Runs complete slowly even for simple tasks | CPU throttling — workers are competing for the same vCPU budget |
-| Pods restart with `OOMKilled` | Memory limit exceeded — reduce concurrency or upgrade size |
-| Builds fail or time out | Insufficient CPU/memory for the BuildKit image build step |
-| High p95/p99 run latency with normal p50 | Bursty traffic hitting the concurrency ceiling |
-
----
-
-## How to choose a size
-
-### Start with your concurrency requirement
-
-Estimate the peak number of crew runs you expect to have in-flight simultaneously. Add ~25% headroom for bursts.
-
-| Peak concurrent runs | Recommended size |
-|----------------------|-----------------|
-| 1–3 | Small |
-| 4–12 | Regular |
-| 13–25 | Large |
-| 26–50 | Extra Large |
-| 51–100 | Extra Extra Large |
-| 100+ | Insane Large |
-
-### Factor in run duration
-
-Long-running crews (minutes to hours) hold concurrency slots for the full duration. If your crews run for 10 minutes on average and you receive 30 runs per hour, you need at least `30 × (10/60) = 5` concurrent slots — Regular or above.
-
-### Factor in memory per run
-
-Each concurrent run consumes memory proportional to the number of agents, the size of context windows, and any in-memory data processing. If individual runs are memory-heavy (large document processing, many parallel agents), size up even if your concurrency requirement is low.
-
-A rough heuristic: assume **~256 MiB per concurrent run** as a baseline, then add overhead for your specific workload. On a Small instance (2 GiB total, shared with web and Redis), you have roughly 1 GiB available for workers — enough for ~4 lightweight runs, which matches the concurrency limit.
-
----
-
-## Changing your deployment size
-
-Deployment size is configurable from the **Admin Panel → Deployments → [your deployment] → Instance Size**. Changes take effect on the next deployment cycle (a rolling restart of the worker pods).
-
-<Warning>
-  Downsizing a deployment that is actively processing runs will cause in-flight runs to be interrupted when the old pods are replaced. Schedule size changes during low-traffic windows.
-</Warning>
-
----
-
-## Monitoring utilization
-
-Use these signals to track whether your current size is appropriate:
-
-```bash
-# Check current pod resource usage
-kubectl top pods
-
-# Watch for OOMKilled restarts
-kubectl get pods -o wide
-kubectl describe pod <worker-pod-name> | grep -A5 "Last State"
-
-# Check worker queue depth (from a web pod)
-kubectl exec -it deploy/crewai-web -- bin/rails runner \
-  "puts Sidekiq::Queue.all.map { |q| \"#{q.name}: #{q.size}\" }.join(\"\\n\")"
-```
-
-A consistently non-zero queue depth on the default queue is the clearest signal that you need more concurrency (a larger instance size).
-
----
-
-## Related
-
-- [Troubleshooting](/troubleshooting) — OOMKilled, pod restarts, build failures
-- [Factory Health & Debug](/factory-health) — health check endpoint and component status
-- [Aurora Instance Sizing](/deployment-guides/aws-workos-wharf-studio#aurora-instance-sizing) — database sizing to match your deployment tier

docs/edge/en/concepts/llms.mdx

@@ -144,6 +144,18 @@ In this section, you'll find detailed examples that help you select, configure,
     )
     ```
 
+    **Custom OpenAI-Compatible Endpoint:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="anthropic/claude-sonnet-4-6",
+        custom_openai=True,
+        base_url="https://your-gateway.example.com/v1",
+        api_key="your-gateway-api-key",
+    )
+    ```
+
     **Advanced Configuration:**
     ```python Code
     from crewai import LLM

docs/edge/en/learn/litellm-removal-guide.mdx

@@ -240,14 +240,15 @@ from crewai import LLM
 
 # After (OpenAI-compatible mode, no LiteLLM needed):
 llm = LLM(
-    model="openai/llama3",
+    model="llama3",
+    custom_openai=True,
     base_url="http://localhost:11434/v1",
     api_key="ollama"  # Ollama doesn't require a real API key
 )
 ```
 
 <Tip>
-  Many local inference servers (Ollama, vLLM, LM Studio, llama.cpp) expose an OpenAI-compatible API. You can use the `openai/` prefix with a custom `base_url` to connect to any of them natively.
+  Many local inference servers (Ollama, vLLM, LM Studio, llama.cpp) expose an OpenAI-compatible API. You can use `custom_openai=True` with a custom `base_url` to connect to any of them natively while keeping the model ID your gateway expects.
 </Tip>
 
 ### Step 4: Update your YAML configs
@@ -295,6 +296,92 @@ crewai run
 uv run pytest
 ```
 
+## Custom OpenAI-Compatible Endpoints
+
+Many providers and local servers (Ollama, vLLM, LM Studio, llama.cpp, LiteLLM proxies, and hosted gateways) expose an **OpenAI-compatible** API. Instead of routing these through LiteLLM, you can talk to them directly with CrewAI's native OpenAI integration by setting `custom_openai=True`.
+
+This is the recommended replacement for any LiteLLM provider that offers an OpenAI-compatible endpoint.
+
+### How it works
+
+- `custom_openai=True` forces CrewAI to use the native OpenAI SDK, regardless of the model name.
+- The model ID is passed to the endpoint without validation against OpenAI's known-model list. This lets you use arbitrary model IDs your gateway expects (for example, `anthropic/claude-sonnet-4-6` served behind an OpenAI-compatible proxy). An optional leading `openai/` routing prefix is stripped.
+- A base URL is **required**. CrewAI resolves it, in order, from:
+  1. `base_url=...`
+  2. `api_base=...`
+  3. `OPENAI_BASE_URL` environment variable
+  4. `OPENAI_API_BASE` environment variable (legacy)
+
+  If none are set, CrewAI raises a `ValueError` so misconfiguration fails fast instead of silently hitting `api.openai.com`.
+
+```python
+from crewai import LLM
+
+llm = LLM(
+    model="anthropic/claude-sonnet-4-6",  # passed through as-is
+    custom_openai=True,
+    base_url="https://your-gateway.example/v1",
+    api_key="your-key",
+)
+```
+
+### Connect to common servers
+
+<Tabs>
+  <Tab title="Ollama">
+    ```python
+    from crewai import LLM
+
+    llm = LLM(
+        model="llama3.2:latest",
+        custom_openai=True,
+        base_url="http://localhost:11434/v1",
+        api_key="ollama",  # Ollama ignores it, but the client requires a value
+    )
+    ```
+  </Tab>
+  <Tab title="vLLM">
+    ```python
+    from crewai import LLM
+
+    llm = LLM(
+        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+        custom_openai=True,
+        base_url="http://localhost:8000/v1",
+        api_key="not-needed",
+    )
+    ```
+  </Tab>
+  <Tab title="LM Studio">
+    ```python
+    from crewai import LLM
+
+    llm = LLM(
+        model="your-loaded-model",
+        custom_openai=True,
+        base_url="http://localhost:1234/v1",
+        api_key="lm-studio",
+    )
+    ```
+  </Tab>
+  <Tab title="Env vars">
+    ```bash
+    export OPENAI_BASE_URL="https://your-gateway.example/v1"
+    export OPENAI_API_KEY="your-key"
+    ```
+    ```python
+    from crewai import LLM
+
+    # base_url is picked up from OPENAI_BASE_URL / OPENAI_API_BASE
+    llm = LLM(model="anthropic/claude-sonnet-4-6", custom_openai=True)
+    ```
+  </Tab>
+</Tabs>
+
+<Tip>
+  If you use the `openai/` prefix with a model that isn't a known OpenAI model and pass `base_url` or `api_base` directly, CrewAI automatically treats it as a custom OpenAI-compatible endpoint. Environment variables alone do not enable automatic routing for unknown models; set `custom_openai=True` when configuring the endpoint through `OPENAI_BASE_URL` or `OPENAI_API_BASE`.
+</Tip>
+
 ## Quick Reference: Model String Mapping
 
 Here are common migration paths from LiteLLM-dependent providers to native ones:
@@ -321,7 +408,8 @@ llm = LLM(model="anthropic/claude-sonnet-4-20250514")  # High quality
 # Ollama → OpenAI-compatible (keep using local models)
 # llm = LLM(model="ollama/llama3")
 llm = LLM(
-    model="openai/llama3",
+    model="llama3",
+    custom_openai=True,
     base_url="http://localhost:11434/v1",
     api_key="ollama"
 )
@@ -349,6 +437,9 @@ llm = LLM(
   <Accordion title="What about environment variables like OPENAI_API_KEY?">
     Native providers use the same environment variables you're already familiar with. No changes needed for `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GEMINI_API_KEY`, etc.
   </Accordion>
+  <Accordion title="How do I connect to Groq, Together AI, or other OpenAI-compatible providers without LiteLLM?">
+    Most of these providers expose an OpenAI-compatible API. Use `custom_openai=True` with their base URL and API key — see [Custom OpenAI-Compatible Endpoints](#custom-openai-compatible-endpoints). For example, Groq: `LLM(model="llama-3.1-70b-versatile", custom_openai=True, base_url="https://api.groq.com/openai/v1", api_key="...")`. The model ID is passed through untouched, so use whatever ID the provider expects.
+  </Accordion>
 </AccordionGroup>
 
 ## Related Resources

lib/cli/src/crewai_cli/crew_run_tui.py

@@ -568,12 +568,32 @@ FooterKey .footer-key--key {
         self._default_inputs: dict[str, Any] | None = None
         self._crew_result: Any = None
         self._crew_json_path: Any = None
+        # Declarative-flow execution state. A flow renders per-method "STEPS"
+        # (built from flow method events) instead of the crew task list.
+        self._flow_inputs: dict[str, Any] | None = None
+        self._flow_method_types: dict[str, str] = {}
+        self._flow_steps: list[dict[str, Any]] = []
+        self._current_method: str | None = None
         self._elapsed_frozen: float | None = None
         self._want_deploy: bool = False
         self._trace_url: str | None = None
         self._consent_screen: TraceConsentScreen | None = None
         self._telemetry: Telemetry | None = None
 
+    @property
+    def _is_flow_run(self) -> bool:
+        """True for a non-conversational declarative flow (the STEPS view).
+
+        Gates every flow-specific rendering branch so crew and conversational
+        paths stay byte-identical.
+        """
+        return self._flow is not None and not self._is_conversational
+
+    @property
+    def _run_noun(self) -> str:
+        """User-facing noun for the run — 'flow' for a declarative flow, else 'crew'."""
+        return "flow" if self._is_flow_run else "crew"
+
     # ── Layout ──────────────────────────────────────────────
 
     def compose(self) -> ComposeResult:
@@ -602,6 +622,8 @@ FooterKey .footer-key--key {
         self._tick_timer = self.set_interval(1 / 8, self._tick)
         if self._is_conversational and self._flow:
             self._start_conversational_session()
+        elif self._flow:
+            self._run_flow_worker()
         elif self._crew:
             self._run_crew_worker()
         elif self._crew_json_path:
@@ -681,6 +703,49 @@ FooterKey .footer-key--key {
         except Exception as e:
             self.call_from_thread(self._on_crew_failed, str(e))
 
+    @work(thread=True, exclusive=True, group="flow")
+    def _run_flow_worker(self) -> None:
+        from crewai.events.listeners.tracing.utils import (
+            set_suppress_tracing_messages,
+            set_tui_mode,
+        )
+
+        set_tui_mode(True)
+        set_suppress_tracing_messages(True)
+        try:
+            # A declarative flow returns either a CrewOutput (has ``.raw``) or a
+            # bare value (str/dict/pydantic); _stringify_output handles both.
+            result = self._flow.kickoff(inputs=self._flow_inputs)
+            output = self._stringify_output(result)
+            with self._lock:
+                self._crew_result = result
+            self.call_from_thread(self._on_crew_done, output)
+        except Exception as e:
+            self.call_from_thread(self._on_crew_failed, str(e))
+
+    def _set_flow_step_status(self, name: str, status: str) -> None:
+        """Update a flow method step's status. Caller must hold ``self._lock``."""
+        for step in self._flow_steps:
+            if step["name"] == name:
+                step["status"] = status
+                return
+
+    def _clear_current_method(self, finished_name: str) -> None:
+        """Drop the header's active method once it ends. Caller holds the lock.
+
+        Falls back to another still-active step (methods can overlap) so the
+        header never keeps spinning a method the STEPS list already shows as
+        done or failed.
+        """
+        if self._current_method != finished_name:
+            return
+        self._current_method = next(
+            (s["name"] for s in self._flow_steps if s["status"] == "active"), None
+        )
+        # The active method changed; drop its agent so the header doesn't show a
+        # stale agent until the next method's agent event arrives.
+        self._current_agent = ""
+
     def _on_crew_done(self, output: str | None) -> None:
         with self._lock:
             self._status = "completed"
@@ -694,13 +759,18 @@ FooterKey .footer-key--key {
             for k in self._task_statuses:
                 if self._task_statuses[k] == "active":
                     self._task_statuses[k] = "done"
+            for step in self._flow_steps:
+                if step["status"] == "active":
+                    step["status"] = "done"
             now = time.time()
             for entry in self._log_entries:
                 if entry["status"] == "running":
                     if entry["tool_name"] == "memory_save":
                         continue
                     entry["status"] = "timeout"
-                    entry["error"] = "No result received before crew completed"
+                    entry["error"] = (
+                        f"No result received before {self._run_noun} completed"
+                    )
                     entry["duration"] = now - entry["start_time"]
         try:
             from crewai.events.listeners.tracing.trace_listener import (
@@ -739,13 +809,18 @@ FooterKey .footer-key--key {
             self._is_streaming = False
             self._current_step = None
             self._elapsed_frozen = time.time() - self._start_time
+            for step in self._flow_steps:
+                if step["status"] == "active":
+                    step["status"] = "failed"
             now = time.time()
             for entry in self._log_entries:
                 if entry["status"] == "running":
                     if entry["tool_name"] == "memory_save":
                         continue
                     entry["status"] = "error"
-                    entry["error"] = "No result received before crew failed"
+                    entry["error"] = (
+                        f"No result received before {self._run_noun} failed"
+                    )
                     entry["duration"] = now - entry["start_time"]
         self._tick()
         self.call_later(self._focus_activity_log)
@@ -1156,6 +1231,45 @@ FooterKey .footer-key--key {
             widget.update(t)
             return
 
+        if self._is_flow_run:
+            t.append("  STEPS\n", style=f"bold {_C_PRIMARY}")
+            t.append("\n")
+            if not self._flow_steps:
+                t.append("  ○ waiting…\n", style=_C_DIM)
+            for step in self._flow_steps:
+                name = step["name"]
+                max_name = sidebar_width - 6
+                if len(name) > max_name:
+                    name = name[: max_name - 1] + "…"
+                status = step.get("status", "pending")
+                if status == "done":
+                    t.append("  ✔ ", style=_C_GREEN)
+                    t.append(name, style=_C_DIM)
+                elif status == "active":
+                    t.append(f"  {self._spinner()} ", style=_C_PRIMARY)
+                    t.append(name, style=f"bold {_C_TEXT}")
+                elif status == "failed":
+                    t.append("  ✘ ", style=_C_RED)
+                    t.append(name, style=_C_RED)
+                elif status == "paused":
+                    t.append("  ⏸ ", style=_C_TEAL)
+                    t.append(name, style=_C_TEAL)
+                else:
+                    t.append("  ○ ", style=_C_DIM)
+                    t.append(name, style=_C_DIM)
+                if step.get("call_type"):
+                    t.append(f"  ({step['call_type']})", style=_C_DIM)
+                t.append("\n")
+
+            t.append("\n")
+            t.append("  TOKENS\n", style=f"bold {_C_PRIMARY}")
+            t.append("\n")
+            out = self._output_tokens + self._live_out_tokens
+            t.append(f"  ↑ {self._input_tokens:,}\n", style=_C_DIM)
+            t.append(f"  ↓ {out:,}\n", style=_C_DIM)
+            widget.update(t)
+            return
+
         t.append("  TASKS\n", style=f"bold {_C_PRIMARY}")
         t.append("\n")
 
@@ -1225,6 +1339,55 @@ FooterKey .footer-key--key {
             widget.update(t)
             return
 
+        if self._is_flow_run:
+            if self._status == "completed":
+                elapsed = self._elapsed_frozen or (time.time() - self._start_time)
+                t.append("✔ ", style=f"bold {_C_GREEN}")
+                t.append("Flow complete", style=f"bold {_C_GREEN}")
+                t.append(f"  {elapsed:.1f}s", style=_C_DIM)
+                out = self._output_tokens + self._live_out_tokens
+                parts = []
+                if self._input_tokens:
+                    parts.append(f"↑{self._input_tokens:,}")
+                if out:
+                    parts.append(f"↓{out:,}")
+                if parts:
+                    t.append(f"  {' '.join(parts)} tokens", style=_C_DIM)
+            elif self._status == "failed":
+                t.append("✘ ", style=f"bold {_C_RED}")
+                t.append("Failed", style=f"bold {_C_RED}")
+                if self._error:
+                    t.append(f"\n{self._error[:120]}", style=_C_RED)
+            elif self._current_method:
+                paused = any(
+                    s["name"] == self._current_method and s["status"] == "paused"
+                    for s in self._flow_steps
+                )
+                if paused:
+                    t.append("⏸ ", style=_C_TEAL)
+                    t.append(self._current_method, style=f"bold {_C_TEAL}")
+                else:
+                    t.append(f"{self._spinner()} ", style=_C_PRIMARY)
+                    t.append(self._current_method, style=f"bold {_C_PRIMARY}")
+                call_type = self._flow_method_types.get(self._current_method)
+                if call_type:
+                    t.append(f"  ({call_type})", style=_C_DIM)
+                if paused:
+                    t.append("  waiting for feedback", style=_C_DIM)
+                elif self._current_agent:
+                    t.append("\nAgent: ", style=_C_DIM)
+                    t.append(self._current_agent, style=f"bold {_C_TEXT}")
+            else:
+                t.append(f"{self._spinner()} ", style=_C_PRIMARY)
+                # "Working…" once a step has run (between/after methods);
+                # "Starting flow…" only before the first method.
+                t.append(
+                    "Working…" if self._flow_steps else "Starting flow…",
+                    style=_C_DIM,
+                )
+            widget.update(t)
+            return
+
         if self._status == "completed":
             elapsed = self._elapsed_frozen or (time.time() - self._start_time)
             t.append("✔ ", style=f"bold {_C_GREEN}")
@@ -1839,6 +2002,13 @@ FooterKey .footer-key--key {
     def _subscribe(self) -> None:
         from crewai.events.event_bus import crewai_event_bus
         from crewai.events.types.crew_events import CrewKickoffStartedEvent
+        from crewai.events.types.flow_events import (
+            FlowStartedEvent,
+            MethodExecutionFailedEvent,
+            MethodExecutionFinishedEvent,
+            MethodExecutionPausedEvent,
+            MethodExecutionStartedEvent,
+        )
         from crewai.events.types.llm_events import (
             LLMCallCompletedEvent,
             LLMCallStartedEvent,
@@ -1872,13 +2042,74 @@ FooterKey .footer-key--key {
         @crewai_event_bus.on(CrewKickoffStartedEvent)
         def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
             with self._lock:
-                if event.crew_name:
+                # In flow mode the app is named for the flow; a nested crew's
+                # kickoff (a `call: crew` step) must not rename it.
+                if event.crew_name and not self._is_flow_run:
                     self._crew_name = event.crew_name
                     self.title = f"CrewAI — {event.crew_name}"
                 self._status = "working"
 
         self._register_handler(CrewKickoffStartedEvent, on_crew_started)
 
+        # ── Declarative-flow method events → STEPS panel ────────
+        @crewai_event_bus.on(FlowStartedEvent)
+        def on_flow_started(source: Any, event: FlowStartedEvent) -> None:
+            with self._lock:
+                self._status = "working"
+
+        self._register_handler(FlowStartedEvent, on_flow_started)
+
+        @crewai_event_bus.on(MethodExecutionStartedEvent)
+        def on_method_started(source: Any, event: MethodExecutionStartedEvent) -> None:
+            with self._lock:
+                name = event.method_name
+                self._current_method = name
+                # Agent is per-method; clear it so the header doesn't show the
+                # previous method's agent until a new agent event arrives.
+                self._current_agent = ""
+                for step in self._flow_steps:
+                    if step["name"] == name:
+                        step["status"] = "active"
+                        break
+                else:
+                    self._flow_steps.append(
+                        {
+                            "name": name,
+                            "call_type": self._flow_method_types.get(name),
+                            "status": "active",
+                        }
+                    )
+
+        self._register_handler(MethodExecutionStartedEvent, on_method_started)
+
+        @crewai_event_bus.on(MethodExecutionFinishedEvent)
+        def on_method_finished(
+            source: Any, event: MethodExecutionFinishedEvent
+        ) -> None:
+            with self._lock:
+                self._set_flow_step_status(event.method_name, "done")
+                self._clear_current_method(event.method_name)
+
+        self._register_handler(MethodExecutionFinishedEvent, on_method_finished)
+
+        @crewai_event_bus.on(MethodExecutionFailedEvent)
+        def on_method_failed(source: Any, event: MethodExecutionFailedEvent) -> None:
+            with self._lock:
+                self._set_flow_step_status(event.method_name, "failed")
+                self._clear_current_method(event.method_name)
+
+        self._register_handler(MethodExecutionFailedEvent, on_method_failed)
+
+        @crewai_event_bus.on(MethodExecutionPausedEvent)
+        def on_method_paused(source: Any, event: MethodExecutionPausedEvent) -> None:
+            # A @human_feedback method paused; flow status panels are suppressed
+            # in TUI mode, so surface the wait in STEPS/header instead of leaving
+            # a spinner. _current_method stays pointed at it.
+            with self._lock:
+                self._set_flow_step_status(event.method_name, "paused")
+
+        self._register_handler(MethodExecutionPausedEvent, on_method_paused)
+
         @crewai_event_bus.on(TaskStartedEvent)
         def on_task_started(source: Any, event: TaskStartedEvent) -> None:
             with self._lock:

lib/cli/src/crewai_cli/run_declarative_flow.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
 import json
+import logging
 from pathlib import Path
 import subprocess
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import click
 from crewai_core.project import ProjectDefinitionError, configured_project_definition
@@ -18,6 +19,13 @@ from crewai_cli.input_prompt import (
 from crewai_cli.utils import build_env_with_all_tool_credentials
 
 
+if TYPE_CHECKING:
+    from crewai.flow.flow import Flow
+
+
+logger = logging.getLogger(__name__)
+
+
 def run_declarative_flow_in_project_env(
     definition: str | Path, inputs: str | None = None
 ) -> None:
@@ -66,17 +74,182 @@ def run_declarative_flow(definition: str | Path, inputs: str | None = None) -> N
     flow = load_declarative_flow(definition)
     resolved_inputs = _resolve_flow_inputs(flow, provided)
 
+    # The TUI is the interactive default. Headless contexts run directly on the
+    # terminal: deploy/CREWAI_DMN, piped output, CI — anything without an
+    # interactive TTY. is_interactive() already folds in the CREWAI_DMN check.
+    # Human-feedback flows also run on the terminal: their methods collect input
+    # via the flow runtime's blocking input()/Rich prompts (and async feedback
+    # returns a pending marker rather than completing), neither of which the
+    # Textual TUI can handle correctly.
+    if is_interactive() and not _flow_uses_human_feedback(flow):
+        _run_declarative_flow_tui(flow, resolved_inputs or None)
+        return
+
     try:
         result = flow.kickoff(inputs=resolved_inputs or None)
     except Exception as exc:
         click.echo(
-            f"An error occurred while running the declarative flow: {exc}", err=True
+            f"An error occurred while running the declarative flow: {exc}",
+            err=True,
         )
         raise SystemExit(1) from exc
-
     click.echo(_format_result(result))
 
 
+def _run_declarative_flow_tui(
+    flow: Flow[Any], resolved_inputs: dict[str, Any] | None
+) -> Any:
+    """Run a declarative flow on the CrewAI TUI (the interactive default).
+
+    Mirrors the declarative-crew TUI contract (``run_crew._run_json_crew``):
+    a failed flow exits non-zero, a user quit ends the process so in-flight LLM
+    work stops, and choosing Deploy chains into the deploy command.
+    """
+    import os
+    import sys
+
+    from crewai.events.event_listener import EventListener
+
+    from crewai_cli.crew_run_tui import CrewRunApp
+
+    # The flow runtime (unlike a Crew constructor) doesn't create the event
+    # listener, and the TUI's trace/telemetry features depend on it.
+    EventListener()
+
+    # The STEPS panel and header are driven by flow method events. A flow may
+    # declare ``config.suppress_flow_events`` (a headless/production
+    # optimization) which would leave STEPS stuck on "waiting…" here — so force
+    # emission on for the interactive TUI run. The headless path never reaches
+    # this and keeps the flow's declared setting.
+    try:
+        flow.suppress_flow_events = False
+    except Exception:
+        logger.debug(
+            "Could not disable suppress_flow_events for the flow TUI", exc_info=True
+        )
+
+    app = CrewRunApp(crew_name=flow.name or type(flow).__name__)
+    app._flow = flow
+    app._flow_inputs = resolved_inputs
+    app._flow_method_types = _flow_method_types(flow)
+
+    app.run()
+
+    _print_flow_post_tui_summary(app)
+
+    if app._status == "failed":
+        raise SystemExit(1)
+
+    if app._status not in ("completed", "failed"):
+        # User quit mid-run. kickoff runs in a thread worker that cannot be
+        # force-cancelled, so end the process to stop in-flight LLM and tool
+        # work instead of letting it burn tokens in the background.
+        click.secho("\n  Run cancelled.", fg="yellow")
+        sys.stdout.flush()
+        os._exit(130)
+
+    if getattr(app, "_want_deploy", False):
+        from crewai_cli.run_crew import _chain_deploy
+
+        _chain_deploy()
+
+    return app._crew_result
+
+
+def _flow_uses_human_feedback(flow: Flow[Any]) -> bool:
+    """True if any declarative method declares ``@human_feedback``.
+
+    Such flows need the flow runtime's interactive stdin / Rich prompts, which
+    don't compose with Textual — so they run on the terminal, not the TUI.
+    """
+    try:
+        return any(
+            method.human_feedback is not None
+            for method in flow._definition.methods.values()
+        )
+    except Exception:
+        logger.debug("Could not inspect flow for human feedback", exc_info=True)
+        return False
+
+
+def _flow_method_types(flow: Flow[Any]) -> dict[str, str]:
+    """Map each declarative method name to its ``call`` type (crew/agent/…).
+
+    Best-effort: the STEPS panel shows this as a dim label. Method events don't
+    carry the call type, so it's read from the flow definition up front.
+    """
+    method_types: dict[str, str] = {}
+    try:
+        for name, method_definition in flow._definition.methods.items():
+            method_types[name] = method_definition.do.call
+    except Exception:
+        logger.debug("Could not derive flow method types", exc_info=True)
+    return method_types
+
+
+def _print_flow_post_tui_summary(app: Any) -> None:
+    """Print a compact result panel after the flow TUI exits."""
+    import time
+
+    from rich.console import Console
+    from rich.markdown import Markdown
+    from rich.padding import Padding
+    from rich.panel import Panel
+    from rich.text import Text
+
+    console = Console()
+    elapsed = (app._elapsed_frozen or (time.time() - app._start_time)) or 0.0
+
+    out_tokens = app._output_tokens + app._live_out_tokens
+    token_parts = []
+    if app._input_tokens:
+        token_parts.append(f"↑{app._input_tokens:,}")
+    if out_tokens:
+        token_parts.append(f"↓{out_tokens:,}")
+    token_str = "  ".join(token_parts)
+    if token_str:
+        token_str += " tokens"
+
+    crewai_red = "#FF5A50"
+    crewai_teal = "#1F7982"
+
+    if app._status == "completed":
+        summary = Text()
+        summary.append("  ✔ Flow complete", style=f"bold {crewai_teal}")
+        summary.append(f" in {elapsed:.1f}s", style="dim")
+        if token_str:
+            summary.append(f"  {token_str}", style="dim")
+        console.print(
+            Panel(
+                summary,
+                title=f" {app._crew_name} ",
+                title_align="left",
+                border_style=crewai_teal,
+                padding=(0, 1),
+            )
+        )
+        if app._final_output:
+            console.print()
+            console.print(Text("  Final Result", style=f"bold {crewai_teal}"))
+            console.print()
+            console.print(Padding(Markdown(app._final_output), (0, 2)))
+    elif app._status == "failed":
+        content = Text()
+        content.append("  ✘ Failed", style=f"bold {crewai_red}")
+        content.append(f" after {elapsed:.1f}s\n", style="dim")
+        if app._error:
+            content.append(f"\n  {app._error}\n", style=crewai_red)
+        console.print(
+            Panel(
+                content,
+                title=f" {app._crew_name} ",
+                title_align="left",
+                border_style=crewai_red,
+                padding=(0, 1),
+            )
+        )
+
+
 def _resolve_flow_inputs(flow: Any, provided: dict[str, Any]) -> dict[str, Any]:
     """Resolve kickoff inputs from the flow's state schema.
 

lib/cli/tests/test_crew_run_tui.py

@@ -6,6 +6,14 @@ from unittest.mock import Mock
 import pytest
 
 from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.crew_events import CrewKickoffStartedEvent
+from crewai.events.types.flow_events import (
+    FlowStartedEvent,
+    MethodExecutionFailedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionPausedEvent,
+    MethodExecutionStartedEvent,
+)
 from crewai.events.types.memory_events import (
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
@@ -959,6 +967,31 @@ async def test_crew_done_does_not_mark_unfinished_tool_successful() -> None:
     assert app._plan_step_status == {1: "failed", 2: "done", 3: "done"}
 
 
+@pytest.mark.asyncio
+async def test_flow_done_uses_flow_wording_for_unfinished_tool() -> None:
+    # The shared completion handler reports "flow" (not "crew") in flow mode.
+    app = CrewRunApp(crew_name="Demo Flow")
+    app._flow = SimpleNamespace()
+
+    async with app.run_test(size=(100, 40)) as pilot:
+        app._log_entries = [
+            {
+                "tool_name": "search",
+                "status": "running",
+                "args": None,
+                "result": None,
+                "error": None,
+                "start_time": time.time() - 2,
+                "duration": None,
+                "task_idx": 1,
+            }
+        ]
+        app._on_crew_done("final output")
+        await pilot.pause()
+
+    assert app._log_entries[0]["error"] == "No result received before flow completed"
+
+
 @pytest.mark.asyncio
 async def test_crew_done_does_not_timeout_memory_save() -> None:
     app = _app_with_plan()
@@ -1481,3 +1514,210 @@ def test_overlapping_task_logs_keep_their_own_state() -> None:
         assert any(step.get("summary") == "thinking" for step in entry2["steps"])
     finally:
         app._unsubscribe()
+
+
+# ── Declarative-flow (non-conversational) TUI support ───────
+
+
+def test_is_flow_run_gating() -> None:
+    """The flow-render gate must be true only for a non-conversational flow."""
+    crew_app = CrewRunApp(total_tasks=1)
+    crew_app._crew = SimpleNamespace()
+    assert crew_app._is_flow_run is False
+
+    conv_app = CrewRunApp(conversational=True)
+    conv_app._flow = SimpleNamespace()
+    assert conv_app._is_flow_run is False
+
+    flow_app = CrewRunApp()
+    flow_app._flow = SimpleNamespace()
+    assert flow_app._is_flow_run is True
+
+
+def test_flow_method_events_build_steps() -> None:
+    app = CrewRunApp(crew_name="Demo")
+    app._flow = SimpleNamespace()
+    app._flow_method_types = {"research": "crew", "summarize": "agent"}
+    app._subscribe()
+    try:
+        _emit_event(FlowStartedEvent(flow_name="Demo"))
+        assert app._status == "working"
+
+        _emit_event(
+            MethodExecutionStartedEvent(
+                flow_name="Demo", method_name="research", state={}
+            )
+        )
+        assert app._flow_steps == [
+            {"name": "research", "call_type": "crew", "status": "active"}
+        ]
+        assert app._current_method == "research"
+
+        _emit_event(
+            MethodExecutionFinishedEvent(
+                flow_name="Demo", method_name="research", result="ok", state={}
+            )
+        )
+        _emit_event(
+            MethodExecutionStartedEvent(
+                flow_name="Demo", method_name="summarize", state={}
+            )
+        )
+        _emit_event(
+            MethodExecutionFailedEvent(
+                flow_name="Demo",
+                method_name="summarize",
+                error=RuntimeError("boom"),
+            )
+        )
+    finally:
+        app._unsubscribe()
+
+    assert app._flow_steps == [
+        {"name": "research", "call_type": "crew", "status": "done"},
+        {"name": "summarize", "call_type": "agent", "status": "failed"},
+    ]
+    # The header must not keep spinning a method that already ended.
+    assert app._current_method is None
+
+
+def test_current_method_clears_and_falls_back_across_overlap() -> None:
+    app = CrewRunApp(crew_name="Demo")
+    app._flow = SimpleNamespace()
+    app._subscribe()
+    try:
+        _emit_event(
+            MethodExecutionStartedEvent(flow_name="Demo", method_name="a", state={})
+        )
+        _emit_event(
+            MethodExecutionStartedEvent(flow_name="Demo", method_name="b", state={})
+        )
+        assert app._current_method == "b"
+
+        # 'a' finishes while 'b' is still active → header stays on 'b'.
+        _emit_event(
+            MethodExecutionFinishedEvent(
+                flow_name="Demo", method_name="a", result=None, state={}
+            )
+        )
+        assert app._current_method == "b"
+
+        # 'b' finishes → nothing active left → header clears.
+        _emit_event(
+            MethodExecutionFinishedEvent(
+                flow_name="Demo", method_name="b", result=None, state={}
+            )
+        )
+        assert app._current_method is None
+    finally:
+        app._unsubscribe()
+
+
+def test_flow_method_transitions_clear_current_agent() -> None:
+    app = CrewRunApp(crew_name="Demo")
+    app._flow = SimpleNamespace()
+    app._subscribe()
+    try:
+        _emit_event(
+            MethodExecutionStartedEvent(flow_name="Demo", method_name="a", state={})
+        )
+        app._current_agent = "Researcher"  # an agent ran during method 'a'
+
+        # Starting a new method clears the previous method's agent.
+        _emit_event(
+            MethodExecutionStartedEvent(flow_name="Demo", method_name="b", state={})
+        )
+        assert app._current_agent == ""
+
+        app._current_agent = "Writer"
+        # 'b' ending switches the active method ('a' still active) → agent clears.
+        _emit_event(
+            MethodExecutionFinishedEvent(
+                flow_name="Demo", method_name="b", result=None, state={}
+            )
+        )
+        assert app._current_agent == ""
+    finally:
+        app._unsubscribe()
+
+
+def test_crew_kickoff_does_not_rename_flow_run() -> None:
+    # A `call: crew` step must not relabel the flow with the nested crew's name.
+    app = CrewRunApp(crew_name="My Flow")
+    app._flow = SimpleNamespace()
+    app._subscribe()
+    try:
+        _emit_event(CrewKickoffStartedEvent(crew_name="Nested Crew", inputs=None))
+        assert app._crew_name == "My Flow"
+        assert app._status == "working"
+    finally:
+        app._unsubscribe()
+
+
+def test_crew_kickoff_renames_in_crew_mode() -> None:
+    # Regression: crew runs still adopt the crew name from the event.
+    app = CrewRunApp(crew_name="Crew")
+    app._crew = SimpleNamespace()
+    app._subscribe()
+    try:
+        _emit_event(CrewKickoffStartedEvent(crew_name="Real Crew", inputs=None))
+        assert app._crew_name == "Real Crew"
+    finally:
+        app._unsubscribe()
+
+
+def test_method_paused_marks_step_paused() -> None:
+    app = CrewRunApp(crew_name="Demo")
+    app._flow = SimpleNamespace()
+    app._subscribe()
+    try:
+        _emit_event(
+            MethodExecutionStartedEvent(flow_name="Demo", method_name="ask", state={})
+        )
+        _emit_event(
+            MethodExecutionPausedEvent(
+                flow_name="Demo",
+                method_name="ask",
+                state={},
+                flow_id="flow-1",
+                message="Need your input",
+            )
+        )
+        assert app._flow_steps == [
+            {"name": "ask", "call_type": None, "status": "paused"}
+        ]
+        assert app._current_method == "ask"
+    finally:
+        app._unsubscribe()
+
+
+@pytest.mark.asyncio
+async def test_declarative_flow_runs_on_tui() -> None:
+    """End-to-end: on_mount dispatches _run_flow_worker → flow.kickoff →
+    _on_crew_done, and any still-active step is swept to done on completion."""
+    kicked: dict[str, object] = {}
+
+    class FakeFlow:
+        name = "Demo Flow"
+
+        def kickoff(self, inputs=None):
+            kicked["inputs"] = inputs
+            return "flow result"
+
+    app = CrewRunApp(crew_name="Demo Flow")
+    app._flow = FakeFlow()
+    app._flow_inputs = {"topic": "AI"}
+    # A step left active (no Finished event) must be swept to done by _on_crew_done.
+    app._flow_steps = [{"name": "compute", "call_type": "expression", "status": "active"}]
+
+    async with app.run_test() as pilot:
+        for _ in range(100):
+            await pilot.pause(0.05)
+            if app._status == "completed":
+                break
+
+    assert kicked["inputs"] == {"topic": "AI"}
+    assert app._status == "completed"
+    assert app._final_output == "flow result"
+    assert app._crew_result == "flow result"
+    assert app._flow_steps[0]["status"] == "done"

lib/cli/tests/test_run_declarative_flow.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import os
 from pathlib import Path
+from types import SimpleNamespace
 
 import pytest
 
@@ -9,6 +10,17 @@ import crewai_cli.input_prompt as input_prompt_module
 import crewai_cli.run_declarative_flow as run_declarative_flow_module
 
 
+@pytest.fixture(autouse=True)
+def _headless_by_default(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Default these tests to the headless/terminal path.
+
+    ``run_declarative_flow`` now launches the TUI when interactive, which can't
+    run under pytest; tests here assert the terminal/headless contract. Tests
+    that exercise TUI routing override ``is_dmn_mode_enabled`` explicitly.
+    """
+    monkeypatch.setenv("CREWAI_DMN", "true")
+
+
 FLOW_YAML = """\
 schema: crewai.flow/v1
 name: TestFlow
@@ -400,3 +412,202 @@ def test_id_restore_still_drops_unknown_keys(
     assert resolved == {"id": "run-123"}  # id kept, typo dropped
     assert "Ignoring unknown input 'prospect_emai'" in captured.err
     assert "Ignoring unknown input 'id'" not in captured.err
+
+
+# ── TUI vs terminal (headless/deploy) routing ──────────────────────
+
+
+def _install_fake_flow_app(monkeypatch, *, status, want_deploy=False):
+    """Replace CrewRunApp/EventListener/summary so _run_declarative_flow_tui is
+    driven by a controllable fake app."""
+
+    class FakeEventListener:
+        pass
+
+    class FakeApp:
+        def __init__(self, crew_name=""):
+            self._crew_name = crew_name
+            self._status = status
+            self._want_deploy = want_deploy
+            self._crew_result = "result"
+
+        def run(self):
+            pass
+
+    monkeypatch.setattr(
+        "crewai.events.event_listener.EventListener", FakeEventListener
+    )
+    monkeypatch.setattr("crewai_cli.crew_run_tui.CrewRunApp", FakeApp)
+    monkeypatch.setattr(
+        run_declarative_flow_module, "_print_flow_post_tui_summary", lambda app: None
+    )
+
+
+def test_run_declarative_flow_dmn_uses_terminal(
+    tmp_path: Path, capsys: pytest.CaptureFixture[str], monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("CREWAI_DMN", "true")
+    monkeypatch.setattr(
+        run_declarative_flow_module,
+        "_run_declarative_flow_tui",
+        lambda *a, **k: pytest.fail("DMN/headless mode must not launch the TUI"),
+    )
+    path = _write(tmp_path, REQUIRED_FLOW_YAML)
+
+    run_declarative_flow_module.run_declarative_flow(
+        str(path), '{"prospect_email":"a@b.com"}'
+    )
+
+    assert capsys.readouterr().out == "a@b.com\n"
+
+
+def test_run_declarative_flow_interactive_uses_tui(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setattr(run_declarative_flow_module, "is_interactive", lambda: True)
+    captured: dict[str, object] = {}
+    monkeypatch.setattr(
+        run_declarative_flow_module,
+        "_run_declarative_flow_tui",
+        lambda flow, resolved: captured.update(flow=flow, inputs=resolved),
+    )
+    path = _write(tmp_path, REQUIRED_FLOW_YAML)
+
+    run_declarative_flow_module.run_declarative_flow(
+        str(path), '{"prospect_email":"a@b.com"}'
+    )
+
+    assert captured["inputs"] == {"prospect_email": "a@b.com"}
+    assert captured["flow"] is not None
+
+
+def test_run_declarative_flow_tui_failed_exits_nonzero(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _install_fake_flow_app(monkeypatch, status="failed")
+
+    with pytest.raises(SystemExit) as exc_info:
+        run_declarative_flow_module._run_declarative_flow_tui(
+            SimpleNamespace(name="Flow"), None
+        )
+
+    assert exc_info.value.code == 1
+
+
+def test_run_declarative_flow_tui_user_quit_exits_130(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _install_fake_flow_app(monkeypatch, status="chatting")
+    exit_calls: list[int] = []
+    monkeypatch.setattr(os, "_exit", lambda code: exit_calls.append(code))
+
+    run_declarative_flow_module._run_declarative_flow_tui(
+        SimpleNamespace(name="Flow"), None
+    )
+
+    assert exit_calls == [130]
+
+
+def test_run_declarative_flow_tui_chains_deploy(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _install_fake_flow_app(monkeypatch, status="completed", want_deploy=True)
+    deploy_calls: list[bool] = []
+    monkeypatch.setattr(
+        "crewai_cli.run_crew._chain_deploy", lambda: deploy_calls.append(True)
+    )
+
+    run_declarative_flow_module._run_declarative_flow_tui(
+        SimpleNamespace(name="Flow"), None
+    )
+
+    assert deploy_calls == [True]
+
+
+def test_run_declarative_flow_tui_no_deploy_when_not_requested(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _install_fake_flow_app(monkeypatch, status="completed", want_deploy=False)
+    deploy_calls: list[bool] = []
+    monkeypatch.setattr(
+        "crewai_cli.run_crew._chain_deploy", lambda: deploy_calls.append(True)
+    )
+
+    run_declarative_flow_module._run_declarative_flow_tui(
+        SimpleNamespace(name="Flow"), None
+    )
+
+    assert deploy_calls == []
+
+
+def test_run_declarative_flow_tui_enables_flow_events(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    # The STEPS panel depends on flow method events; a flow that declared
+    # suppress_flow_events must have it forced off for the interactive TUI run.
+    _install_fake_flow_app(monkeypatch, status="completed")
+    flow = SimpleNamespace(name="Flow", suppress_flow_events=True)
+
+    run_declarative_flow_module._run_declarative_flow_tui(flow, None)
+
+    assert flow.suppress_flow_events is False
+
+
+def test_flow_uses_human_feedback_detection() -> None:
+    hf_flow = SimpleNamespace(
+        _definition=SimpleNamespace(
+            methods={
+                "ask": SimpleNamespace(human_feedback=SimpleNamespace(emit=None)),
+                "plain": SimpleNamespace(human_feedback=None),
+            }
+        )
+    )
+    assert run_declarative_flow_module._flow_uses_human_feedback(hf_flow) is True
+
+    no_hf = SimpleNamespace(
+        _definition=SimpleNamespace(
+            methods={"a": SimpleNamespace(human_feedback=None)}
+        )
+    )
+    assert run_declarative_flow_module._flow_uses_human_feedback(no_hf) is False
+    # No definition → False, no error.
+    assert run_declarative_flow_module._flow_uses_human_feedback(SimpleNamespace()) is False
+
+
+def test_human_feedback_flow_uses_terminal_even_when_interactive(
+    tmp_path: Path, capsys: pytest.CaptureFixture[str], monkeypatch: pytest.MonkeyPatch
+) -> None:
+    # A human-feedback flow must run on the terminal (blocking input / Rich
+    # prompts) even in an interactive session, never on the TUI.
+    monkeypatch.setattr(run_declarative_flow_module, "is_interactive", lambda: True)
+    monkeypatch.setattr(
+        run_declarative_flow_module, "_flow_uses_human_feedback", lambda flow: True
+    )
+    monkeypatch.setattr(
+        run_declarative_flow_module,
+        "_run_declarative_flow_tui",
+        lambda *a, **k: pytest.fail("human-feedback flow must run on the terminal"),
+    )
+    path = _write(tmp_path, FLOW_YAML)
+
+    run_declarative_flow_module.run_declarative_flow(str(path), '{"topic":"AI"}')
+
+    assert capsys.readouterr().out == "AI\n"
+
+
+def test_flow_method_types_from_definition() -> None:
+    flow = SimpleNamespace(
+        _definition=SimpleNamespace(
+            methods={
+                "fetch": SimpleNamespace(do=SimpleNamespace(call="expression")),
+                "research": SimpleNamespace(do=SimpleNamespace(call="crew")),
+            }
+        )
+    )
+
+    assert run_declarative_flow_module._flow_method_types(flow) == {
+        "fetch": "expression",
+        "research": "crew",
+    }
+    # No definition → empty map, no error.
+    assert run_declarative_flow_module._flow_method_types(SimpleNamespace()) == {}

lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/arxiv_paper_tool.py

@@ -10,6 +10,9 @@ import xml.etree.ElementTree as ET
 
 from crewai.tools import BaseTool, EnvVar
 from pydantic import BaseModel, ConfigDict, Field
+import requests
+
+from crewai_tools.security.safe_path import validate_url
 
 
 logger = logging.getLogger(__file__)
@@ -78,17 +81,17 @@ class ArxivPaperTool(BaseTool):
     def fetch_arxiv_data(
         self, search_query: str, max_results: int
     ) -> list[dict[str, Any]]:
-        api_url = f"{self.BASE_API_URL}?search_query={urllib.parse.quote(search_query)}&start=0&max_results={max_results}"
+        api_url = validate_url(
+            f"{self.BASE_API_URL}?search_query={urllib.parse.quote(search_query)}"
+            f"&start=0&max_results={max_results}"
+        )
         logger.info(f"Fetching data from Arxiv API: {api_url}")
 
         try:
-            with urllib.request.urlopen(  # noqa: S310
-                api_url, timeout=self.REQUEST_TIMEOUT
-            ) as response:
-                if response.status != 200:
-                    raise Exception(f"HTTP {response.status}: {response.reason}")
-                data = response.read().decode("utf-8")
-        except urllib.error.URLError as e:
+            response = requests.get(api_url, timeout=self.REQUEST_TIMEOUT)
+            response.raise_for_status()
+            data = response.text
+        except requests.RequestException as e:
             logger.error(f"Error fetching data from Arxiv: {e}")
             raise
 

lib/crewai-tools/tests/tools/arxiv_paper_tool_test.py

@@ -1,10 +1,10 @@
 from pathlib import Path
 from unittest.mock import MagicMock, patch
-import urllib.error
 import xml.etree.ElementTree as ET
 
 from crewai_tools import ArxivPaperTool
 import pytest
+import requests
 
 
 @pytest.fixture
@@ -12,6 +12,15 @@ def tool():
     return ArxivPaperTool(download_pdfs=False)
 
 
+@pytest.fixture(autouse=True)
+def mock_validate_url():
+    with patch(
+        "crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.validate_url",
+        side_effect=lambda url: url,
+    ):
+        yield
+
+
 def mock_arxiv_response():
     return """<?xml version="1.0" encoding="UTF-8"?>
         <feed xmlns="http://www.w3.org/2005/Atom">
@@ -26,21 +35,23 @@ def mock_arxiv_response():
         </feed>"""
 
 
-@patch("urllib.request.urlopen")
-def test_fetch_arxiv_data(mock_urlopen, tool):
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
+def test_fetch_arxiv_data(mock_get, tool):
     mock_response = MagicMock()
-    mock_response.status = 200
-    mock_response.read.return_value = mock_arxiv_response().encode("utf-8")
-    mock_urlopen.return_value.__enter__.return_value = mock_response
+    mock_response.text = mock_arxiv_response()
+    mock_get.return_value = mock_response
 
     results = tool.fetch_arxiv_data("transformer", 1)
     assert isinstance(results, list)
     assert results[0]["title"] == "Sample Paper"
 
 
-@patch("urllib.request.urlopen", side_effect=urllib.error.URLError("Timeout"))
-def test_fetch_arxiv_data_network_error(mock_urlopen, tool):
-    with pytest.raises(urllib.error.URLError):
+@patch(
+    "crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get",
+    side_effect=requests.RequestException("Timeout"),
+)
+def test_fetch_arxiv_data_network_error(mock_get, tool):
+    with pytest.raises(requests.RequestException):
         tool.fetch_arxiv_data("transformer", 1)
 
 
@@ -60,13 +71,12 @@ def test_download_pdf_oserror(mock_urlretrieve):
         )
 
 
-@patch("urllib.request.urlopen")
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
 @patch("urllib.request.urlretrieve")
-def test_run_with_download(mock_urlretrieve, mock_urlopen):
+def test_run_with_download(mock_urlretrieve, mock_get):
     mock_response = MagicMock()
-    mock_response.status = 200
-    mock_response.read.return_value = mock_arxiv_response().encode("utf-8")
-    mock_urlopen.return_value.__enter__.return_value = mock_response
+    mock_response.text = mock_arxiv_response()
+    mock_get.return_value = mock_response
 
     tool = ArxivPaperTool(download_pdfs=True)
     output = tool._run("transformer", 1)
@@ -74,12 +84,11 @@ def test_run_with_download(mock_urlretrieve, mock_urlopen):
     mock_urlretrieve.assert_called_once()
 
 
-@patch("urllib.request.urlopen")
-def test_run_no_download(mock_urlopen):
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
+def test_run_no_download(mock_get):
     mock_response = MagicMock()
-    mock_response.status = 200
-    mock_response.read.return_value = mock_arxiv_response().encode("utf-8")
-    mock_urlopen.return_value.__enter__.return_value = mock_response
+    mock_response.text = mock_arxiv_response()
+    mock_get.return_value = mock_response
 
     tool = ArxivPaperTool(download_pdfs=False)
     result = tool._run("transformer", 1)
@@ -93,20 +102,19 @@ def test_validate_save_path_creates_directory(mock_mkdir):
     assert isinstance(path, Path)
 
 
-@patch("urllib.request.urlopen")
-def test_run_handles_exception(mock_urlopen):
-    mock_urlopen.side_effect = Exception("API failure")
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
+def test_run_handles_exception(mock_get):
+    mock_get.side_effect = Exception("API failure")
     tool = ArxivPaperTool()
     result = tool._run("transformer", 1)
     assert "Failed to fetch or download Arxiv papers" in result
 
 
-@patch("urllib.request.urlopen")
-def test_invalid_xml_response(mock_urlopen, tool):
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
+def test_invalid_xml_response(mock_get, tool):
     mock_response = MagicMock()
-    mock_response.read.return_value = b"<invalid><xml>"
-    mock_response.status = 200
-    mock_urlopen.return_value.__enter__.return_value = mock_response
+    mock_response.text = "<invalid><xml>"
+    mock_get.return_value = mock_response
 
     with pytest.raises(ET.ParseError):
         tool.fetch_arxiv_data("quantum", 1)
@@ -128,3 +136,51 @@ def test_run_with_max_results(mock_fetch, tool):
 
     result = tool._run(search_query="test", max_results=100)
     assert result.count("Title:") == 100
+
+
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
+@patch(
+    "crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.validate_url",
+    return_value="https://validated.example/api/query?search_query=transformer&start=0&max_results=1",
+)
+def test_fetch_arxiv_data_validates_url_before_request(mock_validate_url, mock_get, tool):
+    mock_response = MagicMock()
+    mock_response.text = mock_arxiv_response()
+    mock_get.return_value = mock_response
+
+    tool.fetch_arxiv_data("transformer", 1)
+
+    mock_validate_url.assert_called_once()
+    validated_url = mock_validate_url.call_args.args[0]
+    assert validated_url.startswith(ArxivPaperTool.BASE_API_URL)
+    assert "search_query=transformer" in validated_url
+    assert "max_results=1" in validated_url
+    mock_get.assert_called_once_with(
+        "https://validated.example/api/query?search_query=transformer&start=0&max_results=1",
+        timeout=ArxivPaperTool.REQUEST_TIMEOUT,
+    )
+
+
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
+@patch(
+    "crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.validate_url",
+    side_effect=ValueError("URL resolves to private/reserved IP"),
+)
+def test_fetch_arxiv_data_rejects_unsafe_url(mock_validate_url, mock_get, tool):
+    with pytest.raises(ValueError, match="private/reserved IP"):
+        tool.fetch_arxiv_data("transformer", 1)
+
+    mock_validate_url.assert_called_once()
+    mock_get.assert_not_called()
+
+
+@patch("crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.requests.get")
+@patch(
+    "crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool.validate_url",
+    side_effect=ValueError("URL resolves to private/reserved IP"),
+)
+def test_run_returns_error_when_url_validation_fails(mock_validate_url, mock_get, tool):
+    result = tool._run("transformer", 1)
+
+    assert "Failed to fetch or download Arxiv papers" in result
+    mock_get.assert_not_called()

lib/crewai/src/crewai/agent/core.py

@@ -86,6 +86,7 @@ from crewai.skills.models import Skill as SkillModel
 from crewai.state.checkpoint_config import CheckpointConfig, apply_checkpoint
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.types.callback import SerializableCallable
+from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.agent_utils import (
     get_tool_names,
     is_inside_event_loop,
@@ -400,10 +401,29 @@ class Agent(BaseAgent):
         return self.planning_config is not None or self.planning
 
     def _setup_agent_executor(self) -> None:
-        """Initialize the agent executor with a default cache handler."""
-        if not self.cache_handler:
-            self.cache_handler = CacheHandler()
-        self.set_cache_handler(self.cache_handler)
+        """Initialize the agent's tools handler and optional tool cache.
+
+        Tool-result caching is opt-in: a standalone agent gets a cache only
+        when it was constructed with an explicit ``cache=True`` or a
+        ``cache_handler``. Agents inside a crew additionally receive the
+        crew's shared handler when ``Crew(cache=True)``. Without an opt-in,
+        repeated tool calls with identical arguments always re-execute the
+        tool — the safe default for live-data and state-mutating tools.
+        """
+        # Recorded before any crew can offer its shared handler at kickoff,
+        # so copy() can distinguish a construction-time opt-in from runtime
+        # crew wiring (which must not turn copies into cachers).
+        self._constructor_cache_opt_in = bool(
+            self.cache
+            and (self.cache_handler is not None or "cache" in self.model_fields_set)
+        )
+        opted_in = self.cache_handler is not None or (
+            "cache" in self.model_fields_set and self.cache
+        )
+        if opted_in:
+            if not self.cache_handler:
+                self.cache_handler = CacheHandler()
+            self.set_cache_handler(self.cache_handler)
 
     def set_knowledge(self, crew_embedder: EmbedderConfig | None = None) -> None:
         """Initialize knowledge sources with the agent or crew embedder config."""
@@ -1582,9 +1602,18 @@ class Agent(BaseAgent):
                 crewai_event_bus.emit(self, event=started_event)
                 self._kickoff_event_id = started_event.event_id
 
-            output = self._execute_and_build_output(executor, inputs, response_format)
+            usage_baseline = self._current_usage_summary()
+            output = self._execute_and_build_output(
+                executor, inputs, response_format, usage_baseline
+            )
             return self._finalize_kickoff(
-                output, executor, inputs, response_format, messages, agent_info
+                output,
+                executor,
+                inputs,
+                response_format,
+                messages,
+                agent_info,
+                usage_baseline,
             )
 
         except Exception as e:
@@ -1598,6 +1627,7 @@ class Agent(BaseAgent):
         response_format: type[Any] | None,
         messages: str | list[LLMMessage],
         agent_info: dict[str, Any],
+        usage_baseline: UsageMetrics | None = None,
     ) -> LiteAgentOutput:
         """Apply guardrails, save to memory, and emit completion event.
 
@@ -1608,6 +1638,8 @@ class Agent(BaseAgent):
             response_format: Optional response format.
             messages: The original messages.
             agent_info: Agent metadata for events.
+            usage_baseline: Usage snapshot taken at kickoff start, so retries
+                report per-call usage relative to it.
 
         Returns:
             The finalized output.
@@ -1618,6 +1650,7 @@ class Agent(BaseAgent):
                 executor=executor,
                 inputs=inputs,
                 response_format=response_format,
+                usage_baseline=usage_baseline,
             )
 
         self._save_kickoff_to_memory(messages, output.raw)
@@ -1669,11 +1702,24 @@ class Agent(BaseAgent):
         except Exception as e:
             self._logger.log("error", f"Failed to save kickoff result to memory: {e}")
 
+    def _current_usage_summary(self) -> UsageMetrics:
+        """Snapshot the cumulative usage counters backing this agent's LLM.
+
+        The counters live on the LLM instance (or the agent's token process
+        for non-BaseLLM models) and grow for the object's lifetime — across
+        calls and across agents sharing the instance. Per-call usage is the
+        delta between two snapshots.
+        """
+        if isinstance(self.llm, BaseLLM):
+            return self.llm.get_token_usage_summary()
+        return self._token_process.get_summary()
+
     def _build_output_from_result(
         self,
         result: dict[str, Any],
         executor: AgentExecutor,
         response_format: type[Any] | None = None,
+        usage_baseline: UsageMetrics | None = None,
     ) -> LiteAgentOutput:
         """Build a LiteAgentOutput from an executor result dict.
 
@@ -1683,6 +1729,9 @@ class Agent(BaseAgent):
             result: The result dictionary from executor.invoke / invoke_async.
             executor: The executor instance.
             response_format: Optional response format.
+            usage_baseline: Usage snapshot taken at kickoff start. When given,
+                the output carries only this call's usage (the delta) instead
+                of the LLM instance's cumulative lifetime counters.
 
         Returns:
             LiteAgentOutput with raw output, formatted result, and metrics.
@@ -1727,10 +1776,9 @@ class Agent(BaseAgent):
         else:
             raw_output = str(output) if not isinstance(output, str) else output
 
-        if isinstance(self.llm, BaseLLM):
-            usage_metrics = self.llm.get_token_usage_summary()
-        else:
-            usage_metrics = self._token_process.get_summary()
+        usage_metrics = self._current_usage_summary()
+        if usage_baseline is not None:
+            usage_metrics = usage_metrics.delta_since(usage_baseline)
 
         raw_str = (
             raw_output
@@ -1759,20 +1807,26 @@ class Agent(BaseAgent):
         executor: AgentExecutor,
         inputs: dict[str, str],
         response_format: type[Any] | None = None,
+        usage_baseline: UsageMetrics | None = None,
     ) -> LiteAgentOutput:
         """Execute the agent synchronously and build the output object."""
         result = cast(dict[str, Any], executor.invoke(inputs))
-        return self._build_output_from_result(result, executor, response_format)
+        return self._build_output_from_result(
+            result, executor, response_format, usage_baseline
+        )
 
     async def _execute_and_build_output_async(
         self,
         executor: AgentExecutor,
         inputs: dict[str, str],
         response_format: type[Any] | None = None,
+        usage_baseline: UsageMetrics | None = None,
     ) -> LiteAgentOutput:
         """Execute the agent asynchronously and build the output object."""
         result = await executor.invoke_async(inputs)
-        return self._build_output_from_result(result, executor, response_format)
+        return self._build_output_from_result(
+            result, executor, response_format, usage_baseline
+        )
 
     def _process_kickoff_guardrail(
         self,
@@ -1781,6 +1835,7 @@ class Agent(BaseAgent):
         inputs: dict[str, str],
         response_format: type[Any] | None = None,
         retry_count: int = 0,
+        usage_baseline: UsageMetrics | None = None,
     ) -> LiteAgentOutput:
         """Process guardrail for kickoff execution with retry logic.
 
@@ -1790,6 +1845,9 @@ class Agent(BaseAgent):
             inputs: Input dictionary for re-execution.
             response_format: Optional response format.
             retry_count: Current retry count.
+            usage_baseline: Usage snapshot taken at kickoff start, so a
+                retried output reports the whole call's usage, not just the
+                last attempt's.
 
         Returns:
             Validated/updated output.
@@ -1827,7 +1885,9 @@ class Agent(BaseAgent):
                 role="user",
             )
 
-            output = self._execute_and_build_output(executor, inputs, response_format)
+            output = self._execute_and_build_output(
+                executor, inputs, response_format, usage_baseline
+            )
 
             return self._process_kickoff_guardrail(
                 output=output,
@@ -1835,6 +1895,7 @@ class Agent(BaseAgent):
                 inputs=inputs,
                 response_format=response_format,
                 retry_count=retry_count + 1,
+                usage_baseline=usage_baseline,
             )
 
         if guardrail_result.result is not None:
@@ -1897,11 +1958,18 @@ class Agent(BaseAgent):
                 crewai_event_bus.emit(self, event=started_event)
                 self._kickoff_event_id = started_event.event_id
 
+            usage_baseline = self._current_usage_summary()
             output = await self._execute_and_build_output_async(
-                executor, inputs, response_format
+                executor, inputs, response_format, usage_baseline
             )
             return self._finalize_kickoff(
-                output, executor, inputs, response_format, messages, agent_info
+                output,
+                executor,
+                inputs,
+                response_format,
+                messages,
+                agent_info,
+                usage_baseline,
             )
 
         except Exception as e:

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -205,7 +205,11 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         role (str): Role of the agent.
         goal (str): Objective of the agent.
         backstory (str): Backstory of the agent.
-        cache (bool): Whether the agent should use a cache for tool usage.
+        cache (bool): Whether the agent participates in tool-result caching
+            when a cache is enabled. The default (True) only permits
+            participation — caching activates when the crew sets cache=True
+            or the agent explicitly opts in with cache=True or a
+            cache_handler; cache=False excludes the agent entirely.
         config (dict[str, Any] | None): Configuration for the agent.
         verbose (bool): Verbose mode for the Agent Execution.
         max_rpm (int | None): Maximum number of requests per minute for the agent execution.
@@ -254,6 +258,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
     _rpm_controller: RPMController | None = PrivateAttr(default=None)
     _request_within_rpm_limit: SerializableCallable | None = PrivateAttr(default=None)
+    _constructor_cache_opt_in: bool = PrivateAttr(default=False)
     _original_role: str | None = PrivateAttr(default=None)
     _original_goal: str | None = PrivateAttr(default=None)
     _original_backstory: str | None = PrivateAttr(default=None)
@@ -267,7 +272,14 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         description="Configuration for the agent", default=None, exclude=True
     )
     cache: bool = Field(
-        default=True, description="Whether the agent should use a cache for tool usage."
+        default=True,
+        description=(
+            "Whether the agent participates in tool-result caching when a "
+            "cache is enabled. Caching itself is opt-in: it activates only "
+            "when the crew sets cache=True or the agent explicitly opts in "
+            "(cache=True or a cache_handler at construction). Set False to "
+            "exclude this agent even when the crew enables caching."
+        ),
     )
     verbose: bool = Field(
         default=False, description="Verbose mode for the Agent Execution"
@@ -716,6 +728,19 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
 
         copied_data = self.model_dump(exclude=exclude)
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
+        # Tool-result caching distinguishes "explicitly enabled" from the
+        # field default via model_fields_set; don't let the dump turn the
+        # default into an explicit opt-in on the copy. An agent that opted
+        # in at construction via an explicit cache_handler (excluded from
+        # the dump) must stay opted in — carry the consent as cache=True so
+        # the copy wires its own fresh handler. A handler merely offered by
+        # a crew at kickoff is runtime wiring, not consent, and must not
+        # opt the copy in; _constructor_cache_opt_in is recorded before any
+        # crew wiring can happen.
+        if "cache" not in self.model_fields_set:
+            copied_data.pop("cache", None)
+            if self._constructor_cache_opt_in:
+                copied_data["cache"] = True
         return type(self)(
             **copied_data,
             llm=existing_llm,

lib/crewai/src/crewai/crew.py

@@ -168,8 +168,11 @@ class Crew(FlowTrackable, BaseModel):
         manager_agent: Custom agent that will be used as manager.
         memory: Whether the crew should use memory to store memories of it's
             execution.
-        cache: Whether the crew should use a cache to store the results of the
-            tools execution.
+        cache: Whether to cache tool results for the crew's agents. Off by
+            default; when enabled, repeated calls to the same tool with
+            identical arguments reuse the first result without re-executing —
+            avoid enabling for live-data or state-mutating tools unless they
+            gate writes with a cache_function.
         function_calling_llm: The language model that will run the tool calling
             for all the agents.
         process: The process flow that the crew will follow (e.g., sequential,
@@ -216,7 +219,16 @@ class Crew(FlowTrackable, BaseModel):
     _kickoff_event_id: str | None = PrivateAttr(default=None)
 
     name: str | None = Field(default="crew")
-    cache: bool = Field(default=True)
+    cache: bool = Field(
+        default=False,
+        description=(
+            "Whether to cache tool results for the crew's agents. Opt-in: "
+            "when enabled, repeated calls to the same tool with identical "
+            "arguments return the first result without re-executing the "
+            "tool — do not enable for live-data or state-mutating tools "
+            "unless they set a cache_function that prevents caching."
+        ),
+    )
     tasks: list[Task] = Field(default_factory=list)
     agents: Annotated[
         list[BaseAgent],
@@ -1048,8 +1060,9 @@ class Crew(FlowTrackable, BaseModel):
             )
             raise
         finally:
-            if self._memory is not None and hasattr(self._memory, "drain_writes"):
-                self._memory.drain_writes()
+            # Safety net for the exception path; the success path already
+            # drained in _create_crew_output before emitting completion.
+            self._drain_memory_writes()
             clear_files(self.id)
             detach(token)
             crewai_event_bus._exit_runtime_scope(runtime_scope)
@@ -1260,6 +1273,9 @@ class Crew(FlowTrackable, BaseModel):
             )
             raise
         finally:
+            # Safety net for the exception path; the success path already
+            # drained in _create_crew_output before emitting completion.
+            self._drain_memory_writes()
             clear_files(self.id)
             detach(token)
             crewai_event_bus._exit_runtime_scope(runtime_scope)
@@ -1503,6 +1519,11 @@ class Crew(FlowTrackable, BaseModel):
             )
             self.manager_agent = manager
         manager.crew = self
+        # The manager is created outside the agents loop that offers the
+        # crew's cache handler at validation time; offer it here so an
+        # opted-in crew (cache=True) also dedupes the manager's tool calls.
+        if self.cache:
+            manager.set_cache_handler(self._cache_handler)
 
     def _get_execution_start_index(self, tasks: list[Task]) -> int | None:
         if self.checkpoint_kickoff_event_id is None:
@@ -1841,6 +1862,38 @@ class Crew(FlowTrackable, BaseModel):
                 output=output.raw,
             )
 
+    def _drain_memory_writes(self) -> None:
+        """Block until all pending background memory saves have completed.
+
+        Covers the crew memory, per-agent memories, and the manager agent's
+        memory — agents save through ``agent.memory`` when set (see
+        ``BaseAgentExecutor._save_to_memory``), so draining only
+        ``self._memory`` can miss in-flight saves. Scope/slice views are
+        unwrapped to their backing ``Memory`` so each pool is drained once.
+
+        Must run before ``CrewKickoffCompletedEvent`` is emitted: listeners
+        (e.g. telemetry sessions) tear down on that event, and any
+        ``MemorySaveCompletedEvent``/``MemorySaveFailedEvent`` emitted after
+        teardown is lost, leaving the save span orphaned.
+        """
+        seen: set[int] = set()
+        candidates = [
+            self._memory,
+            self.memory,
+            getattr(self.manager_agent, "memory", None),
+            *(getattr(agent, "memory", None) for agent in self.agents),
+        ]
+        for mem in candidates:
+            if mem is None or isinstance(mem, bool):
+                continue
+            backing = getattr(mem, "_memory", None) or mem
+            if id(backing) in seen:
+                continue
+            seen.add(id(backing))
+            drain = getattr(backing, "drain_writes", None)
+            if callable(drain):
+                drain()
+
     def _create_crew_output(self, task_outputs: list[TaskOutput]) -> CrewOutput:
         if not task_outputs:
             raise ValueError("No task outputs available to create crew output.")
@@ -1853,6 +1906,10 @@ class Crew(FlowTrackable, BaseModel):
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         self.token_usage = self.calculate_usage_metrics()
+        # Ensure background memory saves finish (and emit their
+        # completed/failed events) before the kickoff-completed event below
+        # triggers listener teardown/finalization.
+        self._drain_memory_writes()
         crewai_event_bus.flush()
         crewai_event_bus.emit(
             self,

lib/crewai/src/crewai/crews/crew_output.py

@@ -24,9 +24,23 @@ class CrewOutput(BaseModel):
         description="Output of each task", default_factory=list
     )
     token_usage: UsageMetrics = Field(
-        description="Processed token summary", default_factory=UsageMetrics
+        description=(
+            "Processed token summary; ``usage_metrics`` exposes the same "
+            "data as a plain dict"
+        ),
+        default_factory=UsageMetrics,
     )
 
+    @property
+    def usage_metrics(self) -> dict[str, Any]:
+        """Token usage as a plain dict.
+
+        Same attribute name and shape as ``LiteAgentOutput.usage_metrics``
+        (the ``Agent.kickoff()`` result), so a usage accessor written for one
+        result type works on both.
+        """
+        return self.token_usage.model_dump()
+
     @property
     def json(self) -> str | None:  # type: ignore[override]
         if self.tasks_output[-1].output_format != OutputFormat.JSON:

lib/crewai/src/crewai/events/utils/console_formatter.py

@@ -211,6 +211,13 @@ To enable tracing, do any one of these:
         """Print a panel with consistent formatting if verbose is enabled."""
         panel = self.create_panel(content, title, style)
         if is_flow:
+            # A TUI (e.g. the CLI's CrewRunApp) owns the screen and renders flow
+            # progress in its own STEPS panel; emitting Rich panels here would
+            # interleave with and corrupt the TUI, so suppress them in TUI mode.
+            from crewai.events.listeners.tracing.utils import is_tui_mode
+
+            if is_tui_mode():
+                return
             self.print(panel)
             self.print()
         else:

lib/crewai/src/crewai/experimental/conversational_mixin.py

@@ -133,6 +133,8 @@ class _ConversationalMixin:
         _pending_user_message: str | dict[str, Any] | None
         _pending_intents: Sequence[str] | None
         _pending_intent_llm: str | BaseLLM | None
+        _turn_classified_intent: str | None
+        _assistant_reply_appended: bool
 
         def _clear_or_listeners(self) -> None:
             pass
@@ -185,12 +187,22 @@ class _ConversationalMixin:
             )
             return configured_route
 
-        if state.last_intent:
+        turn_intent = self._turn_classified_intent
+        if turn_intent:
+            state.last_intent = turn_intent
             self._emit_conversation_route_selected(
-                state.last_intent,
+                turn_intent,
                 previous_intent=previous_intent,
             )
-            return state.last_intent
+            return turn_intent
+
+        if previous_intent:
+            logger.debug(
+                "route_turn() returned no route and no intent was classified "
+                "this turn; ignoring stale last_intent=%r from a previous turn "
+                "and falling back to built-in routing",
+                previous_intent,
+            )
 
         if self.can_answer_from_history(context):
             state.last_intent = "answer_from_history"
@@ -310,11 +322,11 @@ class _ConversationalMixin:
             if "from_checkpoint" not in kickoff_kwargs:
                 self._reset_turn_execution_state()
 
-            assistant_count = self._assistant_message_count()
+            object.__setattr__(self, "_assistant_reply_appended", False)
             result = self.kickoff(inputs={"id": sid}, **kickoff_kwargs)
             if (
                 result is not None
-                and self._assistant_message_count() == assistant_count
+                and not self._assistant_reply_appended
                 and self._is_public_turn_result(result)
             ):
                 self.append_assistant_message(self._stringify_result(result))
@@ -387,7 +399,7 @@ class _ConversationalMixin:
                 if "from_checkpoint" not in kickoff_kwargs:
                     self._reset_turn_execution_state()
 
-                assistant_count = self._assistant_message_count()
+                object.__setattr__(self, "_assistant_reply_appended", False)
                 original_stream = bool(getattr(self, "stream", False))
                 original_streaming_turn = getattr(
                     self, "_streaming_conversation_turn", False
@@ -403,7 +415,7 @@ class _ConversationalMixin:
                     )
                 if (
                     result is not None
-                    and self._assistant_message_count() == assistant_count
+                    and not self._assistant_reply_appended
                     and self._is_public_turn_result(result)
                 ):
                     self.append_assistant_message(self._stringify_result(result))
@@ -550,6 +562,11 @@ class _ConversationalMixin:
         supply per-route descriptions, or change the default/fallback intent.
         Override this method to bypass the LLM router entirely (e.g.,
         permission gates before the LLM decision).
+
+        Returning a falsy value means "no routing decision": the turn falls
+        through to the built-in defaults (``answer_from_history`` when
+        configured, else ``converse``). It never replays a previous turn's
+        intent.
         """
         config = self._conversation_config
         if config is None:
@@ -618,6 +635,9 @@ class _ConversationalMixin:
         metadata: dict[str, Any] | None = None,
     ) -> None:
         """Append a final user-visible assistant message."""
+        # Explicit signal for handle_turn's "did the handler reply?" check.
+        # A count heuristic breaks when handlers trim history mid-turn.
+        object.__setattr__(self, "_assistant_reply_appended", True)
         state = cast(ConversationState, self.state)
         state.messages.append(
             ConversationMessage(
@@ -722,6 +742,7 @@ class _ConversationalMixin:
                     context=self.conversation_messages,
                 )
                 state.last_intent = intent
+                object.__setattr__(self, "_turn_classified_intent", intent)
                 return intent
             return text
 
@@ -788,6 +809,10 @@ class _ConversationalMixin:
             object.__setattr__(self, "_pending_intent_llm", None)
         if not hasattr(self, "_streaming_conversation_turn"):
             object.__setattr__(self, "_streaming_conversation_turn", False)
+        if not hasattr(self, "_turn_classified_intent"):
+            object.__setattr__(self, "_turn_classified_intent", None)
+        if not hasattr(self, "_assistant_reply_appended"):
+            object.__setattr__(self, "_assistant_reply_appended", False)
 
     def _create_default_extension_state(self) -> ConversationState | None:
         initial_state_t = getattr(self, "_initial_state_t", None)
@@ -852,6 +877,7 @@ class _ConversationalMixin:
         self._method_call_counts.clear()
         self._clear_or_listeners()
         self._is_execution_resuming = False
+        object.__setattr__(self, "_turn_classified_intent", None)
 
     def _apply_pending_conversational_turn(self) -> None:
         """Drain the stashed user message + classify if intents configured.
@@ -859,6 +885,7 @@ class _ConversationalMixin:
         Called from ``Flow.kickoff_async`` AFTER persist state restore so
         the appended message survives ``self.persistence.load_state(...)``.
         """
+        object.__setattr__(self, "_turn_classified_intent", None)
         if self._pending_user_message is None:
             return
 
@@ -1107,10 +1134,6 @@ class _ConversationalMixin:
             return "public"
         return "private"
 
-    def _assistant_message_count(self) -> int:
-        state = cast(ConversationState, self.state)
-        return sum(1 for message in state.messages if message.role == "assistant")
-
     def _is_public_turn_result(self, result: Any) -> bool:
         if not isinstance(result, str):
             return False
@@ -1190,6 +1213,15 @@ class _ConversationalMixin:
         )
         from crewai.events.types.flow_events import FlowFinishedEvent
 
+        # Background memory saves must finish (and emit their completed/failed
+        # events) before the session-end flow_finished / batch finalization
+        # below tears down listeners, mirroring the non-deferred kickoff path.
+        # The flush then waits for those events' async bus handlers.
+        drain_memory_writes = getattr(self, "_drain_memory_writes", None)
+        if callable(drain_memory_writes):
+            drain_memory_writes()
+        crewai_event_bus.flush()
+
         # Only emit the session-end event when a deferred flow_started is
         # actually pending. ``_deferred_flow_started_event_id`` is set only by
         # deferred kickoffs; when finalization was not deferred, each per-turn

lib/crewai/src/crewai/flow/runtime/__init__.py

@@ -956,6 +956,22 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             return self.memory.remember_many(content, **kwargs)
         return self.memory.remember(content, **kwargs)
 
+    def _drain_memory_writes(self) -> None:
+        """Block until pending background memory saves for this flow finish.
+
+        Must run before ``FlowFinishedEvent`` is emitted: listeners (e.g.
+        telemetry sessions) tear down on that event, and any
+        ``MemorySaveCompletedEvent``/``MemorySaveFailedEvent`` emitted after
+        teardown is lost, leaving the save span orphaned.
+        """
+        mem = self.memory
+        if mem is None:
+            return
+        backing = getattr(mem, "_memory", None) or mem
+        drain = getattr(backing, "drain_writes", None)
+        if callable(drain):
+            drain()
+
     def extract_memories(self, content: str) -> list[str]:
         """Extract discrete memories from content. Delegates to this flow's memory.
 
@@ -1474,6 +1490,14 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             not self.suppress_flow_events
             and not self._should_defer_trace_finalization()
         ):
+            # Background memory saves must finish (and emit their
+            # completed/failed events) before flow-finished triggers
+            # listener teardown/finalization; the flush then waits for those
+            # events' async handlers, mirroring Crew._create_crew_output.
+            # Offloaded to a thread so the blocking waits don't stall other
+            # coroutines on the loop.
+            await asyncio.to_thread(self._drain_memory_writes)
+            await asyncio.to_thread(crewai_event_bus.flush)
             future = crewai_event_bus.emit(
                 self,
                 FlowFinishedEvent(
@@ -2285,6 +2309,14 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             # flag is read from either the instance attribute or an extension
             # definition.
             if not self._should_defer_trace_finalization():
+                # Background memory saves must finish (and emit their
+                # completed/failed events) before flow-finished triggers
+                # listener teardown/finalization; the flush then waits for
+                # those events' async handlers, mirroring
+                # Crew._create_crew_output. Offloaded to a thread so the
+                # blocking waits don't stall other coroutines on the loop.
+                await asyncio.to_thread(self._drain_memory_writes)
+                await asyncio.to_thread(crewai_event_bus.flush)
                 future = crewai_event_bus.emit(
                     self,
                     FlowFinishedEvent(
@@ -2317,9 +2349,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
 
             return final_output
         finally:
-            # Ensure all background memory saves complete before returning
-            if self.memory is not None and hasattr(self.memory, "drain_writes"):
-                self.memory.drain_writes()
+            # Safety net for the exception path; the success path already
+            # drained before emitting FlowFinishedEvent.
+            self._drain_memory_writes()
             # Drain pending LLMCallCompletedEvent handlers before
             # detaching so `flow.usage_metrics` reflects every call
             # emitted during this kickoff — mirrors `Crew.kickoff()`,

lib/crewai/src/crewai/lite_agent_output.py

@@ -6,6 +6,7 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
+from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.planning_types import TodoItem
 from crewai.utilities.types import LLMMessage
 
@@ -38,7 +39,13 @@ class LiteAgentOutput(BaseModel):
     )
     agent_role: str = Field(description="Role of the agent that produced this output")
     usage_metrics: dict[str, Any] | None = Field(
-        description="Token usage metrics for this execution", default=None
+        description=(
+            "Token usage for this kickoff call only (guardrail retries "
+            "included), not the LLM instance's cumulative totals, as a "
+            "plain dict; ``token_usage`` exposes the same data as a "
+            "UsageMetrics object"
+        ),
+        default=None,
     )
     messages: list[LLMMessage] = Field(
         description="Messages of the agent", default_factory=list
@@ -86,6 +93,19 @@ class LiteAgentOutput(BaseModel):
             return self.pydantic.model_dump()
         return {}
 
+    @property
+    def token_usage(self) -> UsageMetrics:
+        """Token usage as a ``UsageMetrics`` object.
+
+        Same attribute name and type as ``CrewOutput.token_usage``, so a
+        usage accessor written for one result type works on both. Returns
+        zeroed metrics when no usage was captured (``usage_metrics`` is
+        ``None``).
+        """
+        if not self.usage_metrics:
+            return UsageMetrics()
+        return UsageMetrics.model_validate(self.usage_metrics)
+
     @property
     def completed_todos(self) -> list[TodoExecutionResult]:
         """Get only the completed todos."""

lib/crewai/src/crewai/llm.py

@@ -394,19 +394,35 @@ class LLM(BaseLLM):
         """Factory method that routes to native SDK or falls back to LiteLLM.
 
         Routing priority:
-            1. If 'provider' kwarg is present, use that provider with constants
-            2. If only 'model' kwarg, use constants to infer provider
-            3. If "/" in model name:
+            1. If ``custom_openai=True``, force the native OpenAI provider,
+               overriding any explicit provider. A custom endpoint is required.
+            2. If ``provider`` is present, use that provider.
+            3. If "/" is in the model name:
                - Check if prefix is a native provider (openai/anthropic/azure/bedrock/gemini)
                - If yes, validate model against constants
                - If valid, route to native SDK; otherwise route to LiteLLM
+            4. Otherwise, infer the provider from the model name.
         """
         if not model or not isinstance(model, str):
             raise ValueError("Model must be a non-empty string")
 
+        custom_openai = bool(kwargs.pop("custom_openai", False))
+        custom_openai_route = custom_openai
         explicit_provider = kwargs.get("provider")
 
-        if explicit_provider:
+        if custom_openai:
+            if not cls._has_custom_openai_endpoint(kwargs):
+                raise ValueError(
+                    "custom_openai=True requires base_url, api_base, "
+                    "OPENAI_BASE_URL, or OPENAI_API_BASE"
+                )
+            provider = "openai"
+            use_native = True
+            prefix, separator, model_part = model.partition("/")
+            model_string = (
+                model_part if separator and prefix.lower() == "openai" else model
+            )
+        elif explicit_provider:
             provider = explicit_provider
             use_native = True
             model_string = model
@@ -435,9 +451,17 @@ class LLM(BaseLLM):
 
             canonical_provider = provider_mapping.get(prefix.lower())
 
-            if canonical_provider and cls._validate_model_in_constants(
-                model_part, canonical_provider
-            ):
+            valid_native_model = bool(
+                canonical_provider
+                and cls._validate_model_in_constants(model_part, canonical_provider)
+            )
+            custom_openai_route = bool(
+                canonical_provider == "openai"
+                and not valid_native_model
+                and cls._has_custom_openai_base_url(kwargs)
+            )
+
+            if canonical_provider and (valid_native_model or custom_openai_route):
                 provider = canonical_provider
                 use_native = True
                 model_string = model_part
@@ -455,6 +479,8 @@ class LLM(BaseLLM):
             try:
                 # Remove 'provider' from kwargs if it exists to avoid duplicate keyword argument
                 kwargs_copy = {k: v for k, v in kwargs.items() if k != "provider"}
+                if custom_openai_route:
+                    kwargs_copy["custom_openai"] = True
                 return cast(
                     Self,
                     native_class(model=model_string, provider=provider, **kwargs_copy),
@@ -590,6 +616,20 @@ class LLM(BaseLLM):
 
         return cls._matches_provider_pattern(model, provider)
 
+    @staticmethod
+    def _has_custom_openai_base_url(kwargs: dict[str, Any]) -> bool:
+        """Return whether this call explicitly configures a custom endpoint."""
+        return bool(kwargs.get("base_url") or kwargs.get("api_base"))
+
+    @classmethod
+    def _has_custom_openai_endpoint(cls, kwargs: dict[str, Any]) -> bool:
+        """Return whether a custom endpoint is configured explicitly or by env."""
+        return bool(
+            cls._has_custom_openai_base_url(kwargs)
+            or os.getenv("OPENAI_BASE_URL")
+            or os.getenv("OPENAI_API_BASE")
+        )
+
     @classmethod
     def _infer_provider_from_model(cls, model: str) -> str:
         """Infer the provider from the model name.

lib/crewai/src/crewai/llms/base_llm.py

@@ -966,8 +966,15 @@ class BaseLLM(BaseModel, ABC):
     def get_token_usage_summary(self) -> UsageMetrics:
         """Get summary of token usage for this LLM instance.
 
+        The counters are cumulative for the lifetime of this instance: they
+        grow across every call made through it, including calls issued by
+        different agents sharing the instance. For usage scoped to a single
+        call, snapshot before and after and use
+        ``UsageMetrics.delta_since`` (agent kickoff results already report
+        per-call usage this way).
+
         Returns:
-            Dictionary with token usage totals
+            UsageMetrics with this instance's lifetime token usage totals.
         """
         return UsageMetrics(**self._token_usage)
 

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -232,6 +232,7 @@ class OpenAICompletion(BaseLLM):
     auto_chain: bool = False
     auto_chain_reasoning: bool = False
     api_base: str | None = None
+    custom_openai: bool = False
     is_o1_model: bool = False
     is_gpt4_model: bool = False
 
@@ -245,6 +246,20 @@ class OpenAICompletion(BaseLLM):
     def _normalize_openai_fields(cls, data: Any) -> Any:
         if not isinstance(data, dict):
             return data
+        if data.get("custom_openai"):
+            custom_base_url = (
+                data.get("base_url")
+                or data.get("api_base")
+                or os.getenv("OPENAI_BASE_URL")
+                or os.getenv("OPENAI_API_BASE")
+            )
+            if not custom_base_url:
+                raise ValueError(
+                    "custom_openai=True requires base_url, api_base, "
+                    "OPENAI_BASE_URL, or OPENAI_API_BASE"
+                )
+            if not data.get("base_url") and not data.get("api_base"):
+                data["base_url"] = custom_base_url
         if not data.get("provider"):
             data["provider"] = "openai"
         data["api_key"] = data.get("api_key") or os.getenv("OPENAI_API_KEY")
@@ -355,6 +370,15 @@ class OpenAICompletion(BaseLLM):
             config["seed"] = self.seed
         if self.reasoning_effort is not None:
             config["reasoning_effort"] = self.reasoning_effort
+        if self.custom_openai:
+            config["model"] = self.model
+            config["custom_openai"] = True
+            config["base_url"] = (
+                self.base_url
+                or self.api_base
+                or os.getenv("OPENAI_BASE_URL")
+                or os.getenv("OPENAI_API_BASE")
+            )
         return config
 
     def _get_client_params(self) -> dict[str, Any]:
@@ -372,6 +396,7 @@ class OpenAICompletion(BaseLLM):
             "base_url": self.base_url
             or self.api_base
             or os.getenv("OPENAI_BASE_URL")
+            or os.getenv("OPENAI_API_BASE")
             or None,
             "timeout": self.timeout,
             "max_retries": self.max_retries,

lib/crewai/src/crewai/tools/base_tool.py

@@ -5,7 +5,6 @@ import asyncio
 from collections.abc import Awaitable, Callable
 import importlib
 from inspect import Parameter, signature
-import json
 import threading
 from typing import (
     Any,
@@ -37,9 +36,9 @@ from crewai.tools.structured_tool import (
     _infer_result_schema_from_callable,
     _serialize_schema,
     build_schema_hint,
+    format_description_for_llm,
 )
 from crewai.types.callback import SerializableCallable, _resolve_dotted_path
-from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.string_utils import sanitize_tool_name
 
 
@@ -479,15 +478,27 @@ class BaseTool(BaseModel, ABC):
                 f"{self.__class__.__name__}Schema", **fields
             )
 
+    @property
+    def formatted_description(self) -> str:
+        """LLM-facing composite of name, argument schema, and description.
+
+        Use this when rendering the tool into a prompt; ``description``
+        holds only the authored text.
+        """
+        return format_description_for_llm(self.name, self.args_schema, self.description)
+
     def _generate_description(self) -> None:
-        """Generate the tool description with a JSON schema for arguments."""
-        schema = generate_model_description(self.args_schema)
-        args_json = json.dumps(schema["json_schema"]["schema"], indent=2)
-        self.description = (
-            f"Tool Name: {sanitize_tool_name(self.name)}\n"
-            f"Tool Arguments: {args_json}\n"
-            f"Tool Description: {self.description}"
-        )
+        """Deprecated hook kept for backward compatibility; does nothing.
+
+        Historically this rewrote the public ``description`` field at
+        construction time into the LLM-facing composite (``Tool Name: …\\n
+        Tool Arguments: …\\nTool Description: <authored>``). The authored
+        ``description`` is now preserved as written and the composite is
+        exposed separately via :attr:`formatted_description`.
+
+        ``model_post_init`` still calls this so subclasses that override it
+        (e.g. adapters that customize the composite) keep working.
+        """
 
 
 _BASE_TOOL_CLS = BaseTool

lib/crewai/src/crewai/tools/structured_tool.py

@@ -4,6 +4,7 @@ import asyncio
 from collections.abc import Callable
 import inspect
 import json
+import re
 import textwrap
 from typing import TYPE_CHECKING, Annotated, Any, cast, get_type_hints
 import warnings
@@ -21,7 +22,10 @@ from pydantic import (
 from typing_extensions import Self
 
 from crewai.utilities.logger import Logger
-from crewai.utilities.pydantic_schema_utils import create_model_from_schema
+from crewai.utilities.pydantic_schema_utils import (
+    create_model_from_schema,
+    generate_model_description,
+)
 from crewai.utilities.string_utils import sanitize_tool_name
 
 
@@ -108,6 +112,70 @@ def build_schema_hint(args_schema: type[BaseModel]) -> str:
         return ""
 
 
+# Matches a description that IS a pre-composed LLM block (as written by
+# older versions into the field, and by adapters that still bake it in).
+# Anchored to the full three-line shape so authored prose that merely
+# mentions "Tool Description:" is never mistaken for a composite. Greedy
+# ``.*`` keeps only the text after the LAST marker, matching the historical
+# split behavior for nested pre-baked blocks.
+_COMPOSITE_DESCRIPTION_RE = re.compile(
+    r"^Tool Name:.*\nTool Arguments:.*\nTool Description:\s*",
+    re.DOTALL,
+)
+
+
+def strip_composite_description_prefix(description: str) -> str:
+    """Return the authored text from a pre-composed LLM description block.
+
+    Descriptions that don't start with the composite shape are returned
+    unchanged.
+    """
+    match = _COMPOSITE_DESCRIPTION_RE.match(description)
+    if match:
+        return description[match.end() :]
+    return description
+
+
+def format_description_for_llm(
+    name: str,
+    args_schema: type[BaseModel] | None,
+    description: str,
+) -> str:
+    """Compose the LLM-facing tool description.
+
+    Combines the tool name, its argument JSON schema, and the authored
+    description into the prompt block agents see. The authored
+    ``description`` field itself is never mutated — prompt rendering calls
+    this on demand.
+
+    Idempotent: if ``description`` already *is* a composed block (e.g. a
+    tool deserialized from a checkpoint written by an older version, or an
+    adapter that bakes the composite into the field), only the authored
+    text after the marker is used. The check is anchored to the composite
+    shape, so authored prose that merely mentions ``"Tool Description:"``
+    passes through untouched.
+
+    Args:
+        name: The tool name (sanitized for the prompt).
+        args_schema: The tool's argument schema, if any.
+        description: The authored tool description.
+
+    Returns:
+        The composed, LLM-facing description block.
+    """
+    description = strip_composite_description_prefix(description)
+    if args_schema is not None:
+        schema = generate_model_description(args_schema)
+        args_json = json.dumps(schema["json_schema"]["schema"], indent=2)
+    else:
+        args_json = "{}"
+    return (
+        f"Tool Name: {sanitize_tool_name(name)}\n"
+        f"Tool Arguments: {args_json}\n"
+        f"Tool Description: {description}"
+    )
+
+
 class ToolUsageLimitExceededError(Exception):
     """Exception raised when a tool has reached its maximum usage limit."""
 
@@ -141,6 +209,15 @@ class CrewStructuredTool(BaseModel):
     _logger: Logger = PrivateAttr(default_factory=Logger)
     _original_tool: Any = PrivateAttr(default=None)
 
+    @property
+    def formatted_description(self) -> str:
+        """LLM-facing composite of name, argument schema, and description.
+
+        Use this when rendering the tool into a prompt; ``description``
+        holds only the authored text.
+        """
+        return format_description_for_llm(self.name, self.args_schema, self.description)
+
     @model_validator(mode="after")
     def _validate_func(self) -> Self:
         if self.func is not None:

lib/crewai/src/crewai/tools/tool_usage.py

@@ -430,7 +430,7 @@ class ToolUsage:
                         ).format(
                             error=e,
                             tool=sanitize_tool_name(tool.name),
-                            tool_inputs=tool.description,
+                            tool_inputs=tool.formatted_description,
                         )
                         result = ToolUsageError(
                             f"\n{error_message}.\nMoving on then. {I18N_DEFAULT.slice('format').format(tool_names=self.tools_names)}"
@@ -670,7 +670,7 @@ class ToolUsage:
                         ).format(
                             error=e,
                             tool=sanitize_tool_name(tool.name),
-                            tool_inputs=tool.description,
+                            tool_inputs=tool.formatted_description,
                         )
                         result = ToolUsageError(
                             f"\n{error_message}.\nMoving on then. {I18N_DEFAULT.slice('format').format(tool_names=self.tools_names)}"
@@ -803,7 +803,7 @@ class ToolUsage:
 
     def _render(self) -> str:
         """Render the tool name and description in plain text."""
-        descriptions = [tool.description for tool in self.tools]
+        descriptions = [tool.formatted_description for tool in self.tools]
         return "\n--\n".join(descriptions)
 
     def _function_calling(

lib/crewai/src/crewai/types/usage_metrics.py

@@ -76,6 +76,38 @@ class UsageMetrics(BaseModel):
         self.cache_creation_tokens += usage_metrics.cache_creation_tokens
         self.successful_requests += usage_metrics.successful_requests
 
+    def delta_since(self, baseline: Self) -> Self:
+        """Return the per-call usage accrued since ``baseline`` was captured.
+
+        Both objects must come from the same monotonically increasing
+        accumulator (e.g. an LLM instance's lifetime counters). Differences
+        are clamped at zero so a reset accumulator can't produce negative
+        usage.
+
+        Args:
+            baseline: A snapshot of the same accumulator taken earlier.
+
+        Returns:
+            A new UsageMetrics with the field-wise difference.
+        """
+        return type(self)(
+            total_tokens=max(0, self.total_tokens - baseline.total_tokens),
+            prompt_tokens=max(0, self.prompt_tokens - baseline.prompt_tokens),
+            cached_prompt_tokens=max(
+                0, self.cached_prompt_tokens - baseline.cached_prompt_tokens
+            ),
+            completion_tokens=max(
+                0, self.completion_tokens - baseline.completion_tokens
+            ),
+            reasoning_tokens=max(0, self.reasoning_tokens - baseline.reasoning_tokens),
+            cache_creation_tokens=max(
+                0, self.cache_creation_tokens - baseline.cache_creation_tokens
+            ),
+            successful_requests=max(
+                0, self.successful_requests - baseline.successful_requests
+            ),
+        )
+
     @classmethod
     def from_provider_dict(cls, usage_data: dict[str, Any] | None) -> Self | None:
         """Normalize a provider's raw usage dict into a ``UsageMetrics``.

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -27,7 +27,10 @@ from crewai.agents.parser import (
 from crewai.llms.base_llm import BaseLLM, call_stop_override
 from crewai.tools import BaseTool as CrewAITool
 from crewai.tools.base_tool import BaseTool
-from crewai.tools.structured_tool import CrewStructuredTool
+from crewai.tools.structured_tool import (
+    CrewStructuredTool,
+    strip_composite_description_prefix,
+)
 from crewai.tools.tool_types import ToolResult
 from crewai.utilities.errors import AgentRepositoryError
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -147,7 +150,14 @@ def render_text_description_and_args(
     Returns:
         Plain text description of tools.
     """
-    tool_strings = [tool.description for tool in tools]
+    # Fall back to the raw description for duck-typed tools (including test
+    # mocks) that don't provide a real formatted_description string.
+    tool_strings = [
+        formatted
+        if isinstance((formatted := getattr(tool, "formatted_description", None)), str)
+        else tool.description
+        for tool in tools
+    ]
     return "\n".join(tool_strings)
 
 
@@ -190,10 +200,10 @@ def convert_tools_to_openai_schema(
             except Exception:
                 parameters = {}
 
-        # BaseTool formats description as "Tool Name: ...\nTool Arguments: ...\nTool Description: {original}"
-        description = tool.description
-        if "Tool Description:" in description:
-            description = description.split("Tool Description:")[-1].strip()
+        # Old checkpoints and some adapters bake the composed LLM block
+        # ("Tool Name: ...\nTool Arguments: ...\nTool Description: {authored}")
+        # into the description field; keep only the authored text here.
+        description = strip_composite_description_prefix(tool.description)
 
         sanitized_name = sanitize_tool_name(tool.name)
 

lib/crewai/tests/agents/test_lite_agent.py

@@ -1138,3 +1138,160 @@ def test_lite_agent_memory_instance_recall_and_save_called():
     mock_memory.remember_many.assert_called_once_with(
         ["Fact one.", "Fact two."], agent_role="Test"
     )
+
+
+class _FixedUsageLLM(BaseLLM):
+    """Offline BaseLLM that records fixed usage (100/10 tokens) per call."""
+
+    def __init__(self):
+        super().__init__(model="fixed-usage-model")
+
+    def call(
+        self,
+        messages,
+        tools=None,
+        callbacks=None,
+        available_functions=None,
+        from_task=None,
+        from_agent=None,
+        response_model=None,
+    ) -> str:
+        self._track_token_usage_internal(
+            {"prompt_tokens": 100, "completion_tokens": 10, "total_tokens": 110}
+        )
+        return "Thought: I know the answer.\nFinal Answer: fake answer"
+
+    def supports_function_calling(self) -> bool:
+        return False
+
+    def supports_stop_words(self) -> bool:
+        return False
+
+    def get_context_window_size(self) -> int:
+        return 4096
+
+
+class TestKickoffUsageMetricsArePerCall:
+    """Regression tests for EPD-177: kickoff results used to expose the LLM
+    instance's cumulative lifetime counters, so counts accumulated across
+    calls and pooled across agents sharing one LLM object.
+    """
+
+    def _make_agent(self, role: str, llm: BaseLLM) -> Agent:
+        return Agent(
+            role=role,
+            goal="Answer questions.",
+            backstory="Test agent.",
+            llm=llm,
+            verbose=False,
+        )
+
+    def test_agents_sharing_one_llm_report_per_call_usage(self):
+        shared = _FixedUsageLLM()
+        r1 = self._make_agent("agent one", shared).kickoff("question one")
+        r2 = self._make_agent("agent two", shared).kickoff("question two")
+
+        assert r1.usage_metrics is not None
+        assert r1.usage_metrics["prompt_tokens"] > 0
+        # The second agent's call must not include the first agent's tokens.
+        assert r2.usage_metrics == r1.usage_metrics
+
+        # The shared LLM instance still exposes cumulative lifetime totals.
+        lifetime = shared.get_token_usage_summary()
+        assert lifetime.prompt_tokens == (
+            r1.usage_metrics["prompt_tokens"] + r2.usage_metrics["prompt_tokens"]
+        )
+        assert lifetime.successful_requests == (
+            r1.usage_metrics["successful_requests"]
+            + r2.usage_metrics["successful_requests"]
+        )
+
+    def test_repeated_kickoffs_on_same_agent_report_per_call_usage(self):
+        agent = self._make_agent("agent", _FixedUsageLLM())
+        r1 = agent.kickoff("question one")
+        r2 = agent.kickoff("question two")
+
+        assert r1.usage_metrics is not None
+        assert r1.usage_metrics["prompt_tokens"] > 0
+        assert r2.usage_metrics == r1.usage_metrics
+
+    @pytest.mark.asyncio
+    async def test_async_kickoff_reports_per_call_usage(self):
+        shared = _FixedUsageLLM()
+        r1 = await self._make_agent("agent one", shared).kickoff_async("question one")
+        r2 = await self._make_agent("agent two", shared).kickoff_async("question two")
+
+        assert r1.usage_metrics is not None
+        assert r1.usage_metrics["prompt_tokens"] > 0
+        assert r2.usage_metrics == r1.usage_metrics
+
+    def test_guardrail_retry_usage_includes_all_attempts(self):
+        """A guardrail retry re-invokes the LLM within the same kickoff, so
+        the result must report the whole call's usage — every attempt — not
+        just the last one."""
+        baseline = (
+            self._make_agent("baseline", _FixedUsageLLM())
+            .kickoff("question one")
+            .usage_metrics
+        )
+
+        attempts: list[str] = []
+
+        def flaky_guardrail(output):
+            attempts.append(output.raw)
+            if len(attempts) == 1:
+                return (False, "Please try again.")
+            return (True, output.raw)
+
+        agent = Agent(
+            role="agent",
+            goal="Answer questions.",
+            backstory="Test agent.",
+            llm=_FixedUsageLLM(),
+            guardrail=flaky_guardrail,
+            verbose=False,
+        )
+        result = agent.kickoff("question one")
+
+        assert len(attempts) == 2
+        assert result.usage_metrics["successful_requests"] == (
+            2 * baseline["successful_requests"]
+        )
+        assert result.usage_metrics["prompt_tokens"] == 2 * baseline["prompt_tokens"]
+        assert result.usage_metrics["total_tokens"] == 2 * baseline["total_tokens"]
+
+
+class TestUsageMetricsDeltaSince:
+    def test_field_wise_difference(self):
+        baseline = UsageMetrics(
+            total_tokens=110,
+            prompt_tokens=100,
+            completion_tokens=10,
+            successful_requests=1,
+        )
+        current = UsageMetrics(
+            total_tokens=330,
+            prompt_tokens=300,
+            completion_tokens=30,
+            cached_prompt_tokens=5,
+            reasoning_tokens=7,
+            cache_creation_tokens=3,
+            successful_requests=3,
+        )
+
+        delta = current.delta_since(baseline)
+
+        assert delta == UsageMetrics(
+            total_tokens=220,
+            prompt_tokens=200,
+            completion_tokens=20,
+            cached_prompt_tokens=5,
+            reasoning_tokens=7,
+            cache_creation_tokens=3,
+            successful_requests=2,
+        )
+
+    def test_clamps_negative_differences_to_zero(self):
+        baseline = UsageMetrics(total_tokens=100, prompt_tokens=90, successful_requests=2)
+        delta = UsageMetrics().delta_since(baseline)
+        assert delta == UsageMetrics()

lib/crewai/tests/llms/openai/test_openai.py

@@ -30,10 +30,156 @@ def test_openai_completion_is_used_when_no_provider_prefix():
     llm = LLM(model="gpt-4o")
 
     from crewai.llms.providers.openai.completion import OpenAICompletion
-    assert isinstance(llm, OpenAICompletion)
+    assert llm.__class__.__name__ == "OpenAICompletion"
     assert llm.provider == "openai"
     assert llm.model == "gpt-4o"
 
+
+def test_custom_openai_flag_uses_native_openai_without_provider_prefix():
+    """Custom OpenAI-compatible endpoints can serve arbitrary model ids."""
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}, clear=False):
+        llm = LLM(
+            model="anthropic/claude-sonnet-4-6",
+            custom_openai=True,
+            base_url="https://gateway.example/v1",
+            is_litellm=False,
+        )
+
+    assert llm.__class__.__name__ == "OpenAICompletion"
+    assert llm.is_litellm is False
+    assert llm.provider == "openai"
+    assert llm.model == "anthropic/claude-sonnet-4-6"
+    assert llm.base_url == "https://gateway.example/v1"
+    assert llm.custom_openai is True
+    assert "custom_openai" not in llm.additional_params
+
+    config = llm.to_config_dict()
+    assert config["model"] == "anthropic/claude-sonnet-4-6"
+    assert config["custom_openai"] is True
+    assert config["base_url"] == "https://gateway.example/v1"
+
+    rebuilt = LLM(**config)
+    assert isinstance(rebuilt, OpenAICompletion)
+    assert rebuilt.model == "anthropic/claude-sonnet-4-6"
+    assert rebuilt.base_url == "https://gateway.example/v1"
+
+
+def test_custom_openai_flag_requires_custom_base_url():
+    """Avoid routing arbitrary custom model ids to api.openai.com by mistake."""
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}, clear=True):
+        with pytest.raises(ValueError, match="custom_openai=True requires"):
+            LLM(
+                model="anthropic/claude-sonnet-4-6",
+                custom_openai=True,
+                is_litellm=False,
+            )
+
+
+def test_direct_custom_openai_completion_requires_custom_base_url():
+    """Direct construction must not silently fall back to api.openai.com."""
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}, clear=True):
+        with pytest.raises(ValueError, match="custom_openai=True requires"):
+            OpenAICompletion(
+                model="anthropic/claude-sonnet-4-6",
+                custom_openai=True,
+            )
+
+
+def test_custom_openai_flag_strips_openai_routing_prefix():
+    """The openai/ routing prefix is not part of the gateway's model id."""
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}, clear=False):
+        llm = LLM(
+            model="openai/anthropic/claude-sonnet-4-6",
+            custom_openai=True,
+            base_url="https://gateway.example/v1",
+            is_litellm=False,
+        )
+
+    assert isinstance(llm, OpenAICompletion)
+    assert llm.model == "anthropic/claude-sonnet-4-6"
+
+
+def test_openai_prefixed_custom_endpoint_uses_native_sdk_for_nested_model_id():
+    """Custom OpenAI-compatible endpoints may serve non-OpenAI model ids."""
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}, clear=False):
+        llm = LLM(
+            model="openai/anthropic/claude-sonnet-4-6",
+            base_url="https://gateway.example/v1",
+            is_litellm=False,
+        )
+
+    assert llm.__class__.__name__ == "OpenAICompletion"
+    assert llm.is_litellm is False
+    assert llm.provider == "openai"
+    assert llm.model == "anthropic/claude-sonnet-4-6"
+    assert llm.custom_openai is True
+    assert llm.base_url == "https://gateway.example/v1"
+
+def test_explicit_custom_openai_uses_legacy_api_base_env_var():
+    """Explicit custom routing supports the legacy endpoint environment variable."""
+    with patch.dict(
+        os.environ,
+        {
+            "OPENAI_API_KEY": "test-key",
+            "OPENAI_API_BASE": "https://gateway.example/v1",
+        },
+        clear=False,
+    ):
+        os.environ.pop("OPENAI_BASE_URL", None)
+        llm = LLM(
+            model="openai/anthropic/claude-sonnet-4-6",
+            custom_openai=True,
+            is_litellm=False,
+        )
+
+    assert isinstance(llm, OpenAICompletion)
+    assert llm.is_litellm is False
+    assert llm.provider == "openai"
+    assert llm.model == "anthropic/claude-sonnet-4-6"
+    assert llm.custom_openai is True
+
+
+def test_openai_prefixed_unknown_model_ignores_ambient_base_url_for_routing():
+    """Ambient OpenAI configuration must not opt unknown models into native routing."""
+    with patch.dict(
+        os.environ,
+        {
+            "OPENAI_API_KEY": "test-key",
+            "OPENAI_BASE_URL": "https://gateway.example/v1",
+        },
+        clear=True,
+    ):
+        with (
+            patch("crewai.llm._ensure_litellm", return_value=False),
+            pytest.raises(ImportError, match="LiteLLM fallback package"),
+        ):
+            LLM(model="openai/not-a-real-openai-model")
+
+
+@pytest.mark.parametrize("endpoint_field", ["api_base", "env"])
+def test_custom_openai_config_preserves_resolved_endpoint(endpoint_field):
+    """Serialized custom OpenAI configs can reconstruct the same endpoint."""
+    kwargs = {}
+    env = {"OPENAI_API_KEY": "test-key"}
+    if endpoint_field == "api_base":
+        kwargs["api_base"] = "https://gateway.example/v1"
+    else:
+        env["OPENAI_API_BASE"] = "https://gateway.example/v1"
+
+    with patch.dict(os.environ, env, clear=True):
+        llm = LLM(
+            model="anthropic/claude-sonnet-4-6",
+            custom_openai=True,
+            **kwargs,
+        )
+
+    config = llm.to_config_dict()
+    assert config["base_url"] == "https://gateway.example/v1"
+    rebuilt = LLM(**config)
+    assert isinstance(rebuilt, OpenAICompletion)
+    assert rebuilt.base_url == "https://gateway.example/v1"
+
+
 @pytest.mark.vcr()
 def test_openai_is_default_provider_without_explicit_llm_set_on_agent():
     """
@@ -60,14 +206,13 @@ def test_openai_is_default_provider_without_explicit_llm_set_on_agent():
 
 
 
-def test_openai_completion_module_is_imported():
+def test_openai_completion_module_is_imported(monkeypatch):
     """
     Test that the completion module is properly imported when using OpenAI provider
     """
     module_name = "crewai.llms.providers.openai.completion"
 
-    if module_name in sys.modules:
-        del sys.modules[module_name]
+    monkeypatch.delitem(sys.modules, module_name, raising=False)
 
     LLM(model="gpt-4o")
 
@@ -421,12 +566,25 @@ def test_openai_get_client_params_with_env_var():
         client_params = llm._get_client_params()
         assert client_params["base_url"] == "https://env.openai.com/v1"
 
+def test_openai_get_client_params_with_legacy_api_base_env_var():
+    """
+    Test that _get_client_params uses OPENAI_API_BASE when OPENAI_BASE_URL is absent.
+    """
+    with patch.dict(os.environ, {
+        "OPENAI_API_BASE": "https://legacy-env.openai.com/v1",
+    }, clear=False):
+        os.environ.pop("OPENAI_BASE_URL", None)
+        llm = OpenAICompletion(model="gpt-4o")
+        client_params = llm._get_client_params()
+        assert client_params["base_url"] == "https://legacy-env.openai.com/v1"
+
 def test_openai_get_client_params_priority_order():
     """
-    Test the priority order: base_url > api_base > OPENAI_BASE_URL env var
+    Test the priority order: base_url > api_base > OPENAI_BASE_URL > OPENAI_API_BASE
     """
     with patch.dict(os.environ, {
         "OPENAI_BASE_URL": "https://env.openai.com/v1",
+        "OPENAI_API_BASE": "https://legacy-env.openai.com/v1",
     }):
         llm1 = OpenAICompletion(
             model="gpt-4o",

lib/crewai/tests/test_crew.py

@@ -859,6 +859,7 @@ def test_cache_hitting_between_agents(researcher, writer, ceo):
     crew = Crew(
         agents=[ceo, researcher],
         tasks=tasks,
+        cache=True,
     )
 
     with patch.object(CacheHandler, "read") as read:
@@ -2246,7 +2247,9 @@ def test_tools_with_custom_caching():
         agent=writer2,
     )
 
-    crew = Crew(agents=[writer1, writer2], tasks=[task1, task2, task3, task4])
+    crew = Crew(
+        agents=[writer1, writer2], tasks=[task1, task2, task3, task4], cache=True
+    )
 
     with patch.object(
         CacheHandler, "add", wraps=crew._cache_handler.add
@@ -4598,6 +4601,98 @@ def test_reset_memory_uses_full_unified_memory_reset(researcher):
     reset.assert_not_called()
 
 
+def test_kickoff_drains_pending_memory_saves_before_completion_event(researcher):
+    """Background memory saves must finish (and emit their completion events)
+    before CrewKickoffCompletedEvent, otherwise listeners that tear down on
+    kickoff-completed (e.g. telemetry sessions) see the save span as orphaned."""
+    import time
+
+    from crewai.events.types.crew_events import CrewKickoffCompletedEvent
+
+    order: list[str] = []
+
+    def slow_save():
+        time.sleep(0.3)
+        order.append("save-done")
+
+    crew = Crew(
+        agents=[researcher],
+        process=Process.sequential,
+        tasks=[
+            Task(description="Task 1", expected_output="output", agent=researcher),
+        ],
+        memory=True,
+        task_callback=lambda _output: crew._memory._submit_save(slow_save),
+    )
+
+    completed = threading.Event()
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_completed(_source, _event):
+            order.append("kickoff-completed")
+            completed.set()
+
+        with patch.object(Agent, "execute_task", return_value="ok"):
+            crew.kickoff()
+
+        assert completed.wait(timeout=5)
+
+    assert order.index("save-done") < order.index("kickoff-completed")
+
+
+def test_kickoff_drains_agent_memory_saves_before_completion_event(tmp_path):
+    """Agents save through their own ``agent.memory`` when set; those pools
+    must also be drained before CrewKickoffCompletedEvent."""
+    import time
+
+    from crewai.events.types.crew_events import CrewKickoffCompletedEvent
+
+    agent_memory = Memory(storage=str(tmp_path / "agent-mem"))
+    agent_with_memory = Agent(
+        role="Researcher",
+        goal="Research things",
+        backstory="Experienced researcher",
+        memory=agent_memory,
+    )
+
+    order: list[str] = []
+
+    def slow_save():
+        time.sleep(0.3)
+        order.append("save-done")
+
+    crew = Crew(
+        agents=[agent_with_memory],
+        process=Process.sequential,
+        tasks=[
+            Task(
+                description="Task 1",
+                expected_output="output",
+                agent=agent_with_memory,
+            ),
+        ],
+        task_callback=lambda _output: agent_memory._submit_save(slow_save),
+    )
+
+    completed = threading.Event()
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_completed(_source, _event):
+            order.append("kickoff-completed")
+            completed.set()
+
+        with patch.object(Agent, "execute_task", return_value="ok"):
+            crew.kickoff()
+
+        assert completed.wait(timeout=5)
+
+    assert order.index("save-done") < order.index("kickoff-completed")
+
+
 def test_reset_knowledge_with_only_crew_knowledge(researcher, writer):
     mock_ks = MagicMock(spec=Knowledge)
 

lib/crewai/tests/test_flow.py

@@ -2353,3 +2353,41 @@ def test_locked_dict_proxy_ior():
 def test_locked_dict_proxy_reversed():
     flow = _make_dict_flow()
     assert list(reversed(flow.state.data)) == ["c", "b", "a"]
+
+
+def test_flow_drains_pending_memory_saves_before_finished_event(tmp_path):
+    """Background memory saves must finish (and emit their completion events)
+    before FlowFinishedEvent, otherwise listeners that tear down on
+    flow-finished (e.g. telemetry sessions) see the save span as orphaned."""
+    import time
+
+    from crewai.memory.unified_memory import Memory
+
+    order: list[str] = []
+
+    def slow_save():
+        time.sleep(0.3)
+        order.append("save-done")
+
+    class MemoryFlow(Flow):
+        @start()
+        def step_1(self):
+            self.memory._submit_save(slow_save)
+            return "done"
+
+    flow = MemoryFlow(memory=Memory(storage=str(tmp_path / "flow-mem")))
+
+    finished = threading.Event()
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(FlowFinishedEvent)
+        def on_finished(_source, _event):
+            order.append("flow-finished")
+            finished.set()
+
+        flow.kickoff()
+
+        assert finished.wait(timeout=5)
+
+    assert order.index("save-done") < order.index("flow-finished")

lib/crewai/tests/test_flow_conversation.py

@@ -1555,6 +1555,180 @@ class TestConversationalFlow:
         )
 
 
+class TestHandleTurnReplyFallback:
+    """Regression tests for EPD-181: ``handle_turn()`` decided "did the
+    handler append its reply?" by comparing assistant-message counts. A
+    handler that appends its reply AND trims history to a cap left the count
+    unchanged, so the fallback appended the reply a second time — every turn,
+    once trimming engaged. The check now uses an explicit appended-this-turn
+    flag.
+    """
+
+    MAX_MESSAGES = 4
+
+    def _make_bot(self) -> ConversationalFlow:
+        max_messages = self.MAX_MESSAGES
+
+        class EchoBot(ConversationalFlow):
+            def route_turn(self, context: dict[str, Any]) -> str | None:
+                return "ECHO"
+
+            @listen("ECHO")
+            def echo(self) -> str:
+                reply = f"echo: {self.state.current_user_message or ''}"
+                self.append_assistant_message(reply)  # handler DOES append
+                if len(self.state.messages) > max_messages:  # ...and trims
+                    self.state.messages = self.state.messages[-max_messages:]
+                return reply
+
+        return EchoBot()
+
+    def test_no_duplicate_reply_when_handler_trims_history(self) -> None:
+        bot = self._make_bot()
+        for i in range(1, 5):
+            bot.handle_turn(f"message {i}")
+            contents = [message.content for message in bot.state.messages]
+            assert len(contents) == len(set(contents)), (
+                f"duplicate reply on turn {i}: {contents}"
+            )
+
+        # The capped window holds the last two full turns, in order.
+        assert [message.content for message in bot.state.messages] == [
+            "message 3",
+            "echo: message 3",
+            "message 4",
+            "echo: message 4",
+        ]
+
+    def test_fallback_still_appends_when_handler_does_not_reply(self) -> None:
+        class SilentBot(ConversationalFlow):
+            def route_turn(self, context: dict[str, Any]) -> str | None:
+                return "WORK"
+
+            @listen("WORK")
+            def work(self) -> str:
+                return "computed reply"  # returns without appending
+
+        bot = SilentBot()
+        bot.handle_turn("hello")
+
+        assistant_messages = [
+            message.content
+            for message in bot.state.messages
+            if message.role == "assistant"
+        ]
+        assert assistant_messages == ["computed reply"]
+
+
+class TestFalsyRouteTurnFallback:
+    """A falsy ``route_turn()`` must never replay a previous turn's intent.
+
+    Regression tests for EPD-176: an overridden ``route_turn()`` returning
+    ``None`` on an unhandled input used to silently reuse the sticky
+    ``state.last_intent`` from the *previous* turn, running the wrong handler
+    with no error or warning.
+    """
+
+    def test_falsy_route_turn_does_not_replay_previous_turns_intent(self) -> None:
+        ran: list[str] = []
+
+        class Bot(ConversationalFlow):
+            def route_turn(self, context: dict[str, Any]) -> str | None:
+                message = context.get("current_user_message") or ""
+                if "hello" in message.lower():
+                    return "GREETING"
+                return None  # unhandled input -> falsy return
+
+            @listen("GREETING")
+            def greeting(self) -> str:
+                ran.append("GREETING")
+                reply = "Hi! I only do greetings."
+                self.append_assistant_message(reply)
+                return reply
+
+            @listen("WEATHER")
+            def weather(self) -> str:
+                ran.append("WEATHER")
+                reply = "It is sunny."
+                self.append_assistant_message(reply)
+                return reply
+
+        flow = Bot()
+        flow.handle_turn("hello there")
+        assert ran == ["GREETING"]
+        assert flow.state.last_intent == "GREETING"
+
+        flow.handle_turn("what is the meaning of life?")
+        assert ran == ["GREETING"], (
+            "an unhandled turn must not re-run the previous turn's handler"
+        )
+        # With no routing decision the turn falls through to the built-in
+        # 'converse' default instead of replaying the stale intent.
+        assert flow.state.last_intent == "converse"
+        assert flow.state.messages[-1].content != "Hi! I only do greetings."
+
+    def test_stale_intent_ignored_but_route_selected_event_still_emitted(
+        self,
+    ) -> None:
+        class Bot(ConversationalFlow):
+            def route_turn(self, context: dict[str, Any]) -> str | None:
+                message = context.get("current_user_message") or ""
+                return "work" if "work" in message else None
+
+            @listen("work")
+            def do_work(self) -> str:
+                self.append_assistant_message("worked")
+                return "worked"
+
+        flow = Bot()
+        routes: list[ConversationRouteSelectedEvent] = []
+        with crewai_event_bus.scoped_handlers():
+
+            @crewai_event_bus.on(ConversationRouteSelectedEvent)
+            def capture(_: Any, event: ConversationRouteSelectedEvent) -> None:
+                routes.append(event)
+
+            flow.handle_turn("work please")
+            flow.handle_turn("something unrelated")
+            crewai_event_bus.flush()
+
+        assert [event.route for event in routes] == ["work", "converse"]
+        # The fallback decision still reports the prior intent for visibility.
+        assert routes[1].previous_intent == "work"
+
+    def test_fresh_intent_classified_this_turn_still_routes(self) -> None:
+        """The legacy ``default_intents`` path classifies per turn and must
+        keep routing on the freshly classified intent — including when the
+        intent changes between turns."""
+        ran: list[str] = []
+
+        @ConversationConfig(
+            default_intents=["search", "weather"], intent_llm="gpt-4o-mini"
+        )
+        class LegacyFlow(ConversationalFlow):
+            @listen("search")
+            def handle_search(self) -> str:
+                ran.append("search")
+                self.append_assistant_message("searched")
+                return "searched"
+
+            @listen("weather")
+            def handle_weather(self) -> str:
+                ran.append("weather")
+                self.append_assistant_message("sunny")
+                return "sunny"
+
+        flow = LegacyFlow()
+        with patch.object(
+            flow, "_collapse_to_outcome", side_effect=["search", "weather"]
+        ):
+            flow.handle_turn("look up crewai")
+            flow.handle_turn("how is the weather?")
+
+        assert ran == ["search", "weather"]
+        assert flow.state.last_intent == "weather"
+
+
 class TestFlowTracingWhenSuppressed:
     def test_flow_started_emitted_when_panel_events_suppressed(self) -> None:
         class QuietFlow(Flow[ChatState]):

lib/crewai/tests/test_tool_cache_default.py

@@ -0,0 +1,263 @@
+# mypy: ignore-errors
+"""Regression tests for EPD-180: tool-result caching used to be ON by default,
+so an LLM calling the same tool with identical arguments twice in one run got
+the first (possibly stale) result back without the tool executing — silently
+wrong for live-data tools, and silently dropped actions for stateful tools.
+
+Caching is now opt-in: ``Crew(cache=True)`` for crews, ``Agent(cache=True)``
+(or an explicit ``cache_handler``) for standalone agents. The machinery —
+including per-tool ``cache_function`` write gating — is unchanged once opted
+in.
+
+The end-to-end tests run fully offline: a fake OpenAI client scripts two
+identical tool calls followed by a final answer, mirroring the EPD-180
+clean-room repro.
+"""
+
+from openai.types.chat import ChatCompletion
+from pydantic import BaseModel, Field
+
+from crewai import LLM, Agent, Crew, Task
+from crewai.agents.cache.cache_handler import CacheHandler
+from crewai.tools import BaseTool
+
+
+class LookupArgs(BaseModel):
+    city: str = Field(description="City to look up.")
+
+
+def make_live_tool():
+    """A tool returning a different value on every real execution."""
+    executions = []
+
+    class LiveLookupTool(BaseTool):
+        name: str = "live_lookup"
+        description: str = "Returns a live (time-varying) reading for a city."
+        args_schema: type[BaseModel] = LookupArgs
+        # cache_function deliberately NOT set — exercising the default.
+
+        def _run(self, city: str) -> str:
+            executions.append(city)
+            return f"reading #{len(executions)} for {city}"
+
+    return LiveLookupTool(), executions
+
+
+def make_scripted_llm():
+    """An offline LLM whose client scripts two identical tool calls."""
+
+    def tool_call_response(call_id: str):
+        return {
+            "index": 0,
+            "finish_reason": "tool_calls",
+            "message": {
+                "role": "assistant",
+                "content": None,
+                "tool_calls": [
+                    {
+                        "id": call_id,
+                        "type": "function",
+                        "function": {
+                            "name": "live_lookup",
+                            "arguments": '{"city": "paris"}',
+                        },
+                    }
+                ],
+            },
+        }
+
+    scripted = [
+        tool_call_response("call_1"),
+        tool_call_response("call_2"),  # identical name+args, new id
+        {
+            "index": 0,
+            "finish_reason": "stop",
+            "message": {"role": "assistant", "content": "Final answer: done."},
+        },
+    ]
+
+    class FakeCompletions:
+        def __init__(self):
+            self.n = 0
+
+        def create(self, **params):
+            choice = scripted[min(self.n, len(scripted) - 1)]
+            self.n += 1
+            return ChatCompletion.model_validate(
+                {
+                    "id": f"chatcmpl-fake-{self.n}",
+                    "object": "chat.completion",
+                    "created": 1,
+                    "model": params.get("model", "gpt-4o"),
+                    "choices": [choice],
+                    "usage": {
+                        "prompt_tokens": 10,
+                        "completion_tokens": 5,
+                        "total_tokens": 15,
+                    },
+                }
+            )
+
+    class FakeClient:
+        def __init__(self):
+            self.chat = type("Chat", (), {"completions": FakeCompletions()})()
+
+    llm = LLM(model="openai/gpt-4o")
+    llm._client = FakeClient()
+    return llm
+
+
+def run_crew(**crew_kwargs):
+    tool, executions = make_live_tool()
+    agent = Agent(
+        role="reader",
+        goal="Look things up.",
+        backstory="Test agent.",
+        llm=make_scripted_llm(),
+        tools=[tool],
+        verbose=False,
+    )
+    task = Task(
+        description="Look up paris twice and report.",
+        expected_output="A report.",
+        agent=agent,
+    )
+    crew = Crew(agents=[agent], tasks=[task], verbose=False, **crew_kwargs)
+    crew.kickoff()
+    return executions
+
+
+class TestToolCachingIsOptIn:
+    def test_default_reexecutes_identical_tool_calls(self):
+        """EPD-180: with no opt-in, both identical calls must really execute."""
+        executions = run_crew()
+        assert len(executions) == 2
+
+    def test_crew_cache_true_dedupes_identical_tool_calls(self):
+        """Opting in via Crew(cache=True) restores the dedup behavior."""
+        executions = run_crew(cache=True)
+        assert len(executions) == 1
+
+
+class TestAgentCacheWiring:
+    def _agent(self, **kwargs) -> Agent:
+        return Agent(
+            role="reader",
+            goal="Look things up.",
+            backstory="Test agent.",
+            **kwargs,
+        )
+
+    def test_standalone_agent_has_no_cache_by_default(self):
+        agent = self._agent()
+        assert agent.tools_handler.cache is None
+        assert agent.cache_handler is None
+
+    def test_standalone_agent_explicit_cache_true_opts_in(self):
+        agent = self._agent(cache=True)
+        assert agent.tools_handler.cache is not None
+        assert agent.cache_handler is not None
+
+    def test_standalone_agent_explicit_cache_handler_opts_in(self):
+        handler = CacheHandler()
+        agent = self._agent(cache_handler=handler)
+        assert agent.tools_handler.cache is handler
+
+    def test_explicit_cache_false_stays_off_even_with_handler(self):
+        agent = self._agent(cache=False, cache_handler=CacheHandler())
+        assert agent.tools_handler.cache is None
+
+    def test_agents_accept_a_crew_offered_handler_by_default(self):
+        """``Crew(cache=True)`` offers its handler via set_cache_handler at
+        kickoff; agents that didn't explicitly opt out must accept it."""
+        agent = self._agent()
+        assert agent.tools_handler.cache is None
+
+        handler = CacheHandler()
+        agent.set_cache_handler(handler)
+        assert agent.tools_handler.cache is handler
+
+    def test_agents_that_opted_out_refuse_a_crew_offered_handler(self):
+        agent = self._agent(cache=False)
+        agent.set_cache_handler(CacheHandler())
+        assert agent.tools_handler.cache is None
+
+    def test_copy_of_default_agent_does_not_opt_in(self):
+        """copy() rebuilds from model_dump(), which includes the field
+        default cache=True — that must not read as an explicit opt-in on
+        the copy (Bugbot review finding on the original PR)."""
+        copied = self._agent().copy()
+        assert copied.tools_handler.cache is None
+        assert copied.cache_handler is None
+
+    def test_copy_of_opted_in_agent_stays_opted_in(self):
+        copied = self._agent(cache=True).copy()
+        assert copied.tools_handler.cache is not None
+
+    def test_copy_of_handler_opted_in_agent_stays_opted_in(self):
+        """An explicit cache_handler is an opt-in too; copy() excludes the
+        handler itself, but the consent must survive — the copy wires its
+        own fresh handler (Bugbot review finding on the original PR)."""
+        source = self._agent(cache_handler=CacheHandler())
+        copied = source.copy()
+        assert copied.tools_handler.cache is not None
+        assert copied.tools_handler.cache is not source.tools_handler.cache
+
+    def test_copy_of_explicit_cache_false_with_handler_stays_off(self):
+        copied = self._agent(cache=False, cache_handler=CacheHandler()).copy()
+        assert copied.tools_handler.cache is None
+
+    def test_copy_of_crew_wired_agent_does_not_opt_in(self):
+        """A handler offered by a crew at kickoff (set_cache_handler) is
+        runtime wiring, not construction-time consent — copies of such
+        agents must not become standalone cachers (Bugbot review finding
+        on the original PR)."""
+        agent = self._agent()
+        agent.set_cache_handler(CacheHandler())  # what Crew(cache=True) does
+        assert agent.tools_handler.cache is not None
+
+        copied = agent.copy()
+        assert copied.tools_handler.cache is None
+        assert copied.cache_handler is None
+
+
+class TestHierarchicalManagerCacheWiring:
+    """The auto-created hierarchical manager is built outside the agents
+    loop that offers the crew's cache handler; an opted-in crew must wire
+    the manager too (Bugbot review finding on the original PR)."""
+
+    def _crew(self, **crew_kwargs) -> Crew:
+        from crewai.process import Process
+
+        agent = Agent(role="worker", goal="Do work.", backstory="Test agent.")
+        task = Task(description="Do the work.", expected_output="Done.")
+        return Crew(
+            agents=[agent],
+            tasks=[task],
+            process=Process.hierarchical,
+            manager_llm="gpt-4o",
+            **crew_kwargs,
+        )
+
+    def test_manager_gets_crew_handler_when_cache_enabled(self):
+        crew = self._crew(cache=True)
+        crew._create_manager_agent()
+        assert crew.manager_agent.tools_handler.cache is crew._cache_handler
+
+    def test_manager_has_no_cache_when_crew_did_not_opt_in(self):
+        crew = self._crew()
+        crew._create_manager_agent()
+        assert crew.manager_agent.tools_handler.cache is None
+
+    def test_user_provided_manager_with_cache_false_stays_excluded(self):
+        manager = Agent(
+            role="manager",
+            goal="Manage.",
+            backstory="Test manager.",
+            cache=False,
+            allow_delegation=True,
+        )
+        crew = self._crew(cache=True)
+        crew.manager_agent = manager
+        crew._create_manager_agent()
+        assert manager.tools_handler.cache is None

lib/crewai/tests/test_usage_shape_parity.py

@@ -0,0 +1,143 @@
+# mypy: ignore-errors
+"""Regression tests for EPD-178: token usage was exposed in different shapes
+and attribute names per code path — ``Agent.kickoff()`` results carried a
+plain dict at ``.usage_metrics`` (no ``token_usage`` attribute at all), while
+``Crew.kickoff()`` results carried a ``UsageMetrics`` object at
+``.token_usage`` (no ``usage_metrics`` attribute), so any single accessor
+written for one path raised ``AttributeError`` on the other.
+
+Both result types now expose both surfaces: ``.token_usage`` as a
+``UsageMetrics`` object and ``.usage_metrics`` as a plain dict.
+"""
+
+from crewai import Agent, Crew, Task
+from crewai.crews.crew_output import CrewOutput
+from crewai.lite_agent_output import LiteAgentOutput
+from crewai.llms.base_llm import BaseLLM
+from crewai.types.usage_metrics import UsageMetrics
+
+
+class _FixedUsageLLM(BaseLLM):
+    """Offline BaseLLM that records fixed usage (100/10 tokens) per call."""
+
+    def __init__(self):
+        super().__init__(model="fixed-usage-model")
+
+    def call(
+        self,
+        messages,
+        tools=None,
+        callbacks=None,
+        available_functions=None,
+        from_task=None,
+        from_agent=None,
+        response_model=None,
+    ) -> str:
+        self._track_token_usage_internal(
+            {"prompt_tokens": 100, "completion_tokens": 10, "total_tokens": 110}
+        )
+        return "Thought: I know the answer.\nFinal Answer: fake answer"
+
+    def supports_function_calling(self) -> bool:
+        return False
+
+    def supports_stop_words(self) -> bool:
+        return False
+
+    def get_context_window_size(self) -> int:
+        return 4096
+
+
+class TestUsageShapeUnitParity:
+    def test_lite_agent_output_exposes_token_usage_object(self):
+        metrics = UsageMetrics(
+            total_tokens=110,
+            prompt_tokens=100,
+            completion_tokens=10,
+            successful_requests=1,
+        )
+        output = LiteAgentOutput(
+            agent_role="analyst", usage_metrics=metrics.model_dump()
+        )
+
+        assert output.token_usage == metrics
+        assert isinstance(output.token_usage, UsageMetrics)
+
+    def test_lite_agent_output_token_usage_zeroed_when_absent(self):
+        output = LiteAgentOutput(agent_role="analyst")
+
+        assert output.usage_metrics is None
+        assert output.token_usage == UsageMetrics()
+
+    def test_crew_output_exposes_usage_metrics_dict(self):
+        metrics = UsageMetrics(
+            total_tokens=110,
+            prompt_tokens=100,
+            completion_tokens=10,
+            successful_requests=1,
+        )
+        output = CrewOutput(token_usage=metrics)
+
+        assert output.usage_metrics == metrics.model_dump()
+        assert isinstance(output.usage_metrics, dict)
+
+    def test_both_shapes_carry_identical_keys(self):
+        """The dict shape has exactly the UsageMetrics fields on both types."""
+        crew_dict = CrewOutput(token_usage=UsageMetrics()).usage_metrics
+        lite = LiteAgentOutput(
+            agent_role="analyst", usage_metrics=UsageMetrics().model_dump()
+        )
+
+        assert set(crew_dict) == set(UsageMetrics.model_fields)
+        assert set(lite.usage_metrics) == set(UsageMetrics.model_fields)
+
+
+class TestUsageShapeEndToEnd:
+    """Mirror of the EPD-178 clean-room repro, offline via a fake BaseLLM."""
+
+    @staticmethod
+    def _read_via_object(result) -> int:
+        """Single accessor written against the CrewOutput shape."""
+        return result.token_usage.prompt_tokens
+
+    @staticmethod
+    def _read_via_dict(result) -> int:
+        """Single accessor written against the LiteAgentOutput shape."""
+        return result.usage_metrics["prompt_tokens"]
+
+    def test_single_accessor_works_on_both_kickoff_paths(self):
+        agent_a = Agent(
+            role="analyst",
+            goal="Answer questions.",
+            backstory="Test agent.",
+            llm=_FixedUsageLLM(),
+            verbose=False,
+        )
+        result_agent = agent_a.kickoff("a question")
+
+        agent_b = Agent(
+            role="analyst",
+            goal="Answer questions.",
+            backstory="Test agent.",
+            llm=_FixedUsageLLM(),
+            verbose=False,
+        )
+        task = Task(
+            description="Answer: a question",
+            expected_output="A short answer.",
+            agent=agent_b,
+        )
+        crew = Crew(agents=[agent_b], tasks=[task], verbose=False)
+        result_crew = crew.kickoff()
+
+        assert isinstance(result_agent, LiteAgentOutput)
+        assert isinstance(result_crew, CrewOutput)
+
+        # Both accessors work on both result types and agree with each other.
+        for result in (result_agent, result_crew):
+            object_read = self._read_via_object(result)
+            dict_read = self._read_via_dict(result)
+            assert object_read == dict_read
+            assert object_read > 0
+            assert isinstance(result.token_usage, UsageMetrics)
+            assert isinstance(result.usage_metrics, dict)

lib/crewai/tests/tools/test_base_tool.py

@@ -18,11 +18,16 @@ def test_creating_a_tool_using_annotation():
         return question
 
     assert my_tool.name == "Name of my tool"
-    assert "Tool Name: name_of_my_tool" in my_tool.description
-    assert "Tool Arguments:" in my_tool.description
-    assert '"question"' in my_tool.description
-    assert '"type": "string"' in my_tool.description
-    assert "Tool Description: Clear description for what this tool is useful for" in my_tool.description
+    # The authored description is preserved as written; the LLM-facing
+    # composite lives at formatted_description.
+    assert my_tool.description == (
+        "Clear description for what this tool is useful for, your agent will need this information to use it."
+    )
+    assert "Tool Name: name_of_my_tool" in my_tool.formatted_description
+    assert "Tool Arguments:" in my_tool.formatted_description
+    assert '"question"' in my_tool.formatted_description
+    assert '"type": "string"' in my_tool.formatted_description
+    assert "Tool Description: Clear description for what this tool is useful for" in my_tool.formatted_description
     assert my_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
@@ -33,9 +38,10 @@ def test_creating_a_tool_using_annotation():
     converted_tool = my_tool.to_structured_tool()
     assert converted_tool.name == "Name of my tool"
 
-    assert "Tool Name: name_of_my_tool" in converted_tool.description
-    assert "Tool Arguments:" in converted_tool.description
-    assert '"question"' in converted_tool.description
+    assert converted_tool.description == my_tool.description
+    assert "Tool Name: name_of_my_tool" in converted_tool.formatted_description
+    assert "Tool Arguments:" in converted_tool.formatted_description
+    assert '"question"' in converted_tool.formatted_description
     assert converted_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
@@ -56,11 +62,16 @@ def test_creating_a_tool_using_baseclass():
     my_tool = MyCustomTool()
     assert my_tool.name == "Name of my tool"
 
-    assert "Tool Name: name_of_my_tool" in my_tool.description
-    assert "Tool Arguments:" in my_tool.description
-    assert '"question"' in my_tool.description
-    assert '"type": "string"' in my_tool.description
-    assert "Tool Description: Clear description for what this tool is useful for" in my_tool.description
+    # The authored description is preserved as written; the LLM-facing
+    # composite lives at formatted_description.
+    assert my_tool.description == (
+        "Clear description for what this tool is useful for, your agent will need this information to use it."
+    )
+    assert "Tool Name: name_of_my_tool" in my_tool.formatted_description
+    assert "Tool Arguments:" in my_tool.formatted_description
+    assert '"question"' in my_tool.formatted_description
+    assert '"type": "string"' in my_tool.formatted_description
+    assert "Tool Description: Clear description for what this tool is useful for" in my_tool.formatted_description
     assert my_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
@@ -69,9 +80,10 @@ def test_creating_a_tool_using_baseclass():
     converted_tool = my_tool.to_structured_tool()
     assert converted_tool.name == "Name of my tool"
 
-    assert "Tool Name: name_of_my_tool" in converted_tool.description
-    assert "Tool Arguments:" in converted_tool.description
-    assert '"question"' in converted_tool.description
+    assert converted_tool.description == my_tool.description
+    assert "Tool Name: name_of_my_tool" in converted_tool.formatted_description
+    assert "Tool Arguments:" in converted_tool.formatted_description
+    assert '"question"' in converted_tool.formatted_description
     assert converted_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
@@ -695,3 +707,88 @@ class TestToolDecoratorArunValidation:
 
         with pytest.raises(ValueError, match="validation failed"):
             await async_execute.arun(wrong_arg="value")
+
+
+class TestAuthoredDescriptionPreserved:
+    """Regression tests for EPD-179: BaseTool.model_post_init silently
+    rewrote the authored ``description`` into the LLM-facing composite
+    (``Tool Name: …\\nTool Arguments: …\\nTool Description: <authored>``).
+    The authored field must survive construction as written, with the
+    composite exposed separately at ``formatted_description``.
+    """
+
+    AUTHORED = "Returns the current temperature for a city."
+
+    def _make_tool(self) -> BaseTool:
+        class TempArgs(BaseModel):
+            city: str = Field(description="City name to look up.")
+
+        class TempTool(BaseTool):
+            name: str = "get_temperature"
+            description: str = TestAuthoredDescriptionPreserved.AUTHORED
+            args_schema: type[BaseModel] = TempArgs
+
+            def _run(self, city: str) -> str:
+                return f"22C in {city}"
+
+        return TempTool()
+
+    def test_description_equals_authored_text(self):
+        tool_instance = self._make_tool()
+        assert tool_instance.description == self.AUTHORED
+
+    def test_formatted_description_contains_composite(self):
+        tool_instance = self._make_tool()
+        formatted = tool_instance.formatted_description
+        assert "Tool Name: get_temperature" in formatted
+        assert "Tool Arguments:" in formatted
+        assert '"city"' in formatted
+        assert formatted.endswith(f"Tool Description: {self.AUTHORED}")
+
+    def test_formatted_description_tracks_later_description_edits(self):
+        tool_instance = self._make_tool()
+        tool_instance.description = "Edited description."
+        assert tool_instance.formatted_description.endswith(
+            "Tool Description: Edited description."
+        )
+
+    def test_prose_mentioning_the_marker_is_not_truncated(self):
+        """Authored text that merely mentions "Tool Description:" must reach
+        the LLM untouched — only descriptions that ARE a pre-composed block
+        (anchored three-line shape) get stripped."""
+        tool_instance = self._make_tool()
+        prose = (
+            "Formats prompts. The output includes a line reading "
+            "'Tool Description:' followed by the tool's summary."
+        )
+        tool_instance.description = prose
+        assert tool_instance.formatted_description.endswith(
+            f"Tool Description: {prose}"
+        )
+
+    def test_composite_is_not_reapplied_to_prebaked_descriptions(self):
+        """A description that already contains a composed block (old
+        checkpoints, adapters that bake the composite into the field) must
+        not be double-wrapped."""
+        tool_instance = self._make_tool()
+        tool_instance.description = (
+            "Tool Name: get_temperature\n"
+            'Tool Arguments: {"city": "str"}\n'
+            f"Tool Description: {self.AUTHORED}"
+        )
+        formatted = tool_instance.formatted_description
+        assert formatted.count("Tool Description:") == 1
+        assert formatted.endswith(f"Tool Description: {self.AUTHORED}")
+
+    def test_prompt_rendering_still_uses_composite(self):
+        from crewai.utilities.agent_utils import render_text_description_and_args
+
+        tool_instance = self._make_tool()
+        structured = tool_instance.to_structured_tool()
+        assert structured.description == self.AUTHORED
+
+        for candidate in (tool_instance, structured):
+            rendered = render_text_description_and_args([candidate])
+            assert "Tool Name: get_temperature" in rendered
+            assert "Tool Arguments:" in rendered
+            assert f"Tool Description: {self.AUTHORED}" in rendered

lib/crewai/tests/utilities/test_console_formatter_tui_mode.py

@@ -0,0 +1,46 @@
+"""Flow panels must be suppressed while a TUI owns the screen."""
+
+from rich.text import Text
+
+from crewai.events.listeners.tracing.utils import set_tui_mode
+from crewai.events.utils.console_formatter import ConsoleFormatter
+
+
+def _make_formatter(monkeypatch):
+    fmt = ConsoleFormatter(verbose=True)
+    calls: list[object] = []
+    monkeypatch.setattr(fmt, "print", lambda *a, **k: calls.append(a))
+    return fmt, calls
+
+
+def test_flow_panel_suppressed_in_tui_mode(monkeypatch):
+    fmt, calls = _make_formatter(monkeypatch)
+    set_tui_mode(True)
+    try:
+        fmt.print_panel(Text("x"), "🌊 Flow Started", "blue", is_flow=True)
+    finally:
+        set_tui_mode(False)
+
+    assert calls == []
+
+
+def test_flow_panel_prints_when_not_tui_mode(monkeypatch):
+    fmt, calls = _make_formatter(monkeypatch)
+    set_tui_mode(False)
+
+    fmt.print_panel(Text("x"), "🌊 Flow Started", "blue", is_flow=True)
+
+    # Panel + trailing blank line.
+    assert len(calls) == 2
+
+
+def test_non_flow_panel_unaffected_by_tui_mode(monkeypatch):
+    # tui_mode only gates flow panels; regular panels still follow verbose.
+    fmt, calls = _make_formatter(monkeypatch)
+    set_tui_mode(True)
+    try:
+        fmt.print_panel(Text("x"), "Task", "blue", is_flow=False)
+    finally:
+        set_tui_mode(False)
+
+    assert len(calls) == 2

lib/devtools/pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "python-dotenv>=1.2.2,<2",
     "pygithub~=1.59.1",
     "rich>=13.9.4",
+    "crewai-tools",
 ]
 
 [project.scripts]

lib/devtools/src/crewai_devtools/cli.py

@@ -9,12 +9,13 @@ import sys
 import tempfile
 import time
 from typing import Final, Literal
-from urllib.request import urlopen
 
 import click
+from crewai_tools.security.safe_path import validate_url
 from dotenv import load_dotenv
 from github import Github
 from openai import OpenAI
+import requests
 from rich.console import Console
 from rich.markdown import Markdown
 from rich.panel import Panel
@@ -1548,18 +1549,18 @@ def _wait_for_pypi(package: str, version: str) -> None:
         package: PyPI package name.
         version: Version string to wait for.
     """
-    url = f"https://pypi.org/pypi/{package}/{version}/json"
+    url = validate_url(f"https://pypi.org/pypi/{package}/{version}/json")
     deadline = time.monotonic() + _PYPI_POLL_TIMEOUT
 
     console.print(f"[cyan]Waiting for {package}=={version} to appear on PyPI...[/cyan]")
     while time.monotonic() < deadline:
         try:
-            with urlopen(url) as resp:  # noqa: S310
-                if resp.status == 200:
-                    console.print(
-                        f"[green]✓[/green] {package}=={version} is available on PyPI"
-                    )
-                    return
+            response = requests.get(url, timeout=30)
+            if response.status_code == 200:
+                console.print(
+                    f"[green]✓[/green] {package}=={version} is available on PyPI"
+                )
+                return
         except Exception:  # noqa: S110
             pass
         time.sleep(_PYPI_POLL_INTERVAL)

lib/devtools/tests/test_wait_for_pypi.py

@@ -0,0 +1,37 @@
+"""Tests for PyPI polling URL validation in the release CLI."""
+
+from unittest.mock import MagicMock, patch
+
+from crewai_devtools.cli import _wait_for_pypi
+import pytest
+
+
+@patch("crewai_devtools.cli.requests.get")
+@patch(
+    "crewai_devtools.cli.validate_url",
+    return_value="https://pypi.org/pypi/crewai/1.0.0/json",
+)
+def test_wait_for_pypi_validates_url_before_request(mock_validate_url, mock_get):
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_get.return_value = mock_response
+
+    _wait_for_pypi("crewai", "1.0.0")
+
+    mock_validate_url.assert_called_once_with("https://pypi.org/pypi/crewai/1.0.0/json")
+    mock_get.assert_called_once_with(
+        "https://pypi.org/pypi/crewai/1.0.0/json", timeout=30
+    )
+
+
+@patch("crewai_devtools.cli.requests.get")
+@patch(
+    "crewai_devtools.cli.validate_url",
+    side_effect=ValueError("URL resolves to private/reserved IP"),
+)
+def test_wait_for_pypi_rejects_unsafe_url(mock_validate_url, mock_get):
+    with pytest.raises(ValueError, match="private/reserved IP"):
+        _wait_for_pypi("crewai", "1.0.0")
+
+    mock_validate_url.assert_called_once()
+    mock_get.assert_not_called()

uv.lock

@@ -13,7 +13,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-07-04T15:35:51.457693Z"
+exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values.
 exclude-newer-span = "P3D"
 
 [options.exclude-newer-package]
@@ -1052,7 +1052,7 @@ name = "coloredlogs"
 version = "15.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "humanfriendly", marker = "python_full_version < '3.11'" },
+    { name = "humanfriendly" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/c7/eed8f27100517e8c0e6b923d5f0845d0cb99763da6fdee00478f91db7325/coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0", size = 278520, upload-time = "2021-06-11T10:22:45.202Z" }
 wheels = [
@@ -1150,7 +1150,7 @@ resolution-markers = [
     "python_full_version < '3.11' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" } },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
 wheels = [
@@ -1225,7 +1225,7 @@ resolution-markers = [
     "python_full_version == '3.11.*' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" } },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
 wheels = [
@@ -1557,6 +1557,7 @@ name = "crewai-devtools"
 source = { editable = "lib/devtools" }
 dependencies = [
     { name = "click" },
+    { name = "crewai-tools" },
     { name = "openai" },
     { name = "pygithub" },
     { name = "python-dotenv" },
@@ -1567,6 +1568,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "click", specifier = ">=8.1.7,<9" },
+    { name = "crewai-tools", editable = "lib/crewai-tools" },
     { name = "openai", specifier = ">=1.83.0,<3" },
     { name = "pygithub", specifier = "~=1.59.1" },
     { name = "python-dotenv", specifier = ">=1.2.2,<2" },
@@ -1877,34 +1879,34 @@ wheels = [
 
 [package.optional-dependencies]
 cudart = [
-    { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cuda-runtime" },
 ]
 cufft = [
-    { name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cufft" },
 ]
 cufile = [
-    { name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufile" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cuda-cupti" },
 ]
 curand = [
-    { name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-curand" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cusolver" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cusparse" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-nvjitlink" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cuda-nvrtc" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-nvtx" },
 ]
 
 [[package]]
@@ -2429,7 +2431,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
 wheels = [
@@ -3018,8 +3020,8 @@ name = "grpcio-health-checking"
 version = "1.71.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "grpcio", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
-    { name = "protobuf", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
+    { name = "grpcio" },
+    { name = "protobuf" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/53/86/20994347ef36b7626fb74539f13128100dd8b7eaac67efc063264e6cdc80/grpcio_health_checking-1.71.2.tar.gz", hash = "sha256:1c21ece88c641932f432b573ef504b20603bdf030ad4e1ec35dd7fdb4ea02637", size = 16770, upload-time = "2025-06-28T04:24:08.768Z" }
 wheels = [
@@ -3223,7 +3225,7 @@ name = "humanfriendly"
 version = "10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pyreadline3", marker = "python_full_version < '3.11' and sys_platform == 'win32'" },
+    { name = "pyreadline3", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/3f/2c29224acb2e2df4d2046e4c73ee2662023c58ff5b113c4c1adac0886c43/humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc", size = 360702, upload-time = "2021-09-17T21:40:43.31Z" }
 wheels = [
@@ -4460,10 +4462,10 @@ resolution-markers = [
     "python_full_version < '3.11' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "jsonref", marker = "python_full_version < '3.12'" },
-    { name = "mcp", extra = ["ws"], marker = "python_full_version < '3.12'" },
-    { name = "pydantic", marker = "python_full_version < '3.12'" },
-    { name = "python-dotenv", marker = "python_full_version < '3.12'" },
+    { name = "jsonref" },
+    { name = "mcp", extra = ["ws"] },
+    { name = "pydantic" },
+    { name = "python-dotenv" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/d0/28/64fc666fa5d86bb1b048c167975d4ea19210f9f8571b64b26563739774ac/mcpadapt-0.1.19.tar.gz", hash = "sha256:dfab84fc75cc84a49a40bd61079773b1faf840227b74b82c71a7755b9c1957c5", size = 4227721, upload-time = "2025-10-16T07:11:56.736Z" }
 wheels = [
@@ -4481,10 +4483,10 @@ resolution-markers = [
     "python_full_version == '3.12.*' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "jsonref", marker = "python_full_version >= '3.12'" },
-    { name = "mcp", extra = ["ws"], marker = "python_full_version >= '3.12'" },
-    { name = "pydantic", marker = "python_full_version >= '3.12'" },
-    { name = "python-dotenv", marker = "python_full_version >= '3.12'" },
+    { name = "jsonref" },
+    { name = "mcp", extra = ["ws"] },
+    { name = "pydantic" },
+    { name = "python-dotenv" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e3/71/1bbbe157e55d30ab4a74fa878f6942cc0586e9820f03e03451a3d2297e9b/mcpadapt-0.1.20.tar.gz", hash = "sha256:4047c0da61e481dd0673a48936a427da9e6547c6cf0d580ff4e4761dcf058ed1", size = 4203656, upload-time = "2025-10-24T15:35:02.135Z" }
 wheels = [
@@ -5499,12 +5501,12 @@ name = "onnxruntime"
 version = "1.23.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "coloredlogs", marker = "python_full_version < '3.11'" },
-    { name = "flatbuffers", marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "packaging", marker = "python_full_version < '3.11'" },
-    { name = "protobuf", marker = "python_full_version < '3.11'" },
-    { name = "sympy", marker = "python_full_version < '3.11'" },
+    { name = "coloredlogs" },
+    { name = "flatbuffers" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" } },
+    { name = "packaging" },
+    { name = "protobuf" },
+    { name = "sympy" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/35/d6/311b1afea060015b56c742f3531168c1644650767f27ef40062569960587/onnxruntime-1.23.2-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:a7730122afe186a784660f6ec5807138bf9d792fa1df76556b27307ea9ebcbe3", size = 17195934, upload-time = "2025-10-27T23:06:14.143Z" },
@@ -8173,7 +8175,7 @@ resolution-markers = [
     "python_full_version < '3.11' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" } },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0f/37/6964b830433e654ec7485e45a00fc9a27cf868d622838f6b6d9c5ec0d532/scipy-1.15.3.tar.gz", hash = "sha256:eae3cf522bc7df64b42cad3925c876e1b0b6c35c1337c93e12c0f366f55b0eaf", size = 59419214, upload-time = "2025-05-08T16:13:05.955Z" }
 wheels = [
@@ -8237,7 +8239,7 @@ resolution-markers = [
     "python_full_version == '3.11.*' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" } },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822, upload-time = "2026-02-23T00:26:24.851Z" }
 wheels = [
@@ -9868,13 +9870,13 @@ resolution-markers = [
     "python_full_version < '3.11' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "authlib", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
-    { name = "deprecation", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
-    { name = "grpcio", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
-    { name = "grpcio-health-checking", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
-    { name = "httpx", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
-    { name = "pydantic", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
-    { name = "validators", marker = "python_full_version < '3.11' or (python_full_version >= '3.13' and platform_machine != 's390x')" },
+    { name = "authlib" },
+    { name = "deprecation" },
+    { name = "grpcio" },
+    { name = "grpcio-health-checking" },
+    { name = "httpx" },
+    { name = "pydantic" },
+    { name = "validators" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a7/b9/7b9e05cf923743aa1479afcd85c48ebca82d031c3c3a5d02b1b3fcb52eb9/weaviate_client-4.16.2.tar.gz", hash = "sha256:eb7107a3221a5ad68d604cafc65195bd925a9709512ea0b6fe0dd212b0678fab", size = 681321, upload-time = "2025-07-22T09:10:48.79Z" }
 wheels = [
@@ -9893,13 +9895,13 @@ resolution-markers = [
     "python_full_version == '3.11.*' and platform_machine == 's390x'",
 ]
 dependencies = [
-    { name = "authlib", marker = "(python_full_version >= '3.11' and python_full_version < '3.13') or (python_full_version >= '3.11' and platform_machine == 's390x')" },
-    { name = "grpcio", marker = "(python_full_version >= '3.11' and python_full_version < '3.13') or (python_full_version >= '3.11' and platform_machine == 's390x')" },
-    { name = "httpx", marker = "(python_full_version >= '3.11' and python_full_version < '3.13') or (python_full_version >= '3.11' and platform_machine == 's390x')" },
-    { name = "packaging", marker = "(python_full_version >= '3.11' and python_full_version < '3.13') or (python_full_version >= '3.11' and platform_machine == 's390x')" },
-    { name = "protobuf", marker = "(python_full_version >= '3.11' and python_full_version < '3.13') or (python_full_version >= '3.11' and platform_machine == 's390x')" },
-    { name = "pydantic", marker = "(python_full_version >= '3.11' and python_full_version < '3.13') or (python_full_version >= '3.11' and platform_machine == 's390x')" },
-    { name = "validators", marker = "(python_full_version >= '3.11' and python_full_version < '3.13') or (python_full_version >= '3.11' and platform_machine == 's390x')" },
+    { name = "authlib" },
+    { name = "grpcio" },
+    { name = "httpx" },
+    { name = "packaging" },
+    { name = "protobuf" },
+    { name = "pydantic" },
+    { name = "validators" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/2b/b8/103f3aaa246d4e932f4cfeb846e51436966f2aeedf60c2665a3fc51a975a/weaviate_client-4.21.3.tar.gz", hash = "sha256:d7b1f2b0cecbc747e9427f4e3b9463cdfee090746bfbbd40e59cfa25ea2afd4a", size = 847895, upload-time = "2026-06-02T13:03:51.598Z" }
 wheels = [