Release v4.7.1

fix: repair async MongoDB state store, async DB hook names, and safe_format drift

.github/workflows/test-optimized.yml

@@ -98,6 +98,20 @@ jobs:
           --ignore=tests/unit/cli/test_message_queue.py \
           --maxfail=5
 
+    - name: Run praisonai-agents subscription-auth smoke tests
+      env:
+        PRAISONAI_TEST_TIER: smoke
+        PRAISONAI_ALLOW_NETWORK: '0'
+        OPENAI_MODEL_NAME: gpt-4o-mini
+        OPENAI_API_KEY: 'test-key'
+      shell: bash
+      run: |
+        set -euo pipefail
+        cd src/praisonai-agents
+        python -m pytest tests/smoke/test_subscription_auth.py \
+          tests/unit/auth/test_agent_auth_wiring.py \
+          -q --tb=line --timeout=60 --maxfail=5
+
     - name: Standalone praisonai-code import smoke
       shell: bash
       run: |

README.md

@@ -130,7 +130,7 @@ Each layer wraps the one inside it. When an agent misbehaves, the layer tells yo
 | **3 · Harness** | Can it act, and be checked? | `tools=`, `MCP()`, `guardrails=`, `approval=`, `hooks=`, `sandbox=` |
 | **4 · Loop** | When do we stop? | `execution=ExecutionConfig(...)`, `reflection=`, `autonomy=`, doom-loop detection |
 | **5 · Graph** | Who runs when, and who checks whom? | `AgentFlow`, `route()`, `parallel()`, `loop()`, `repeat()` |
-| **⬡ Managed** | *Where does it actually run?* | `run_on="docker"` — one shared remote sandbox, or a fully hosted loop |
+| **⬡ Managed** | *Where does it actually run?* | `tools_run_on="docker"` — one shared sandbox for the tools, or `run_on="anthropic"` for the whole agent |
 
 ### Layer 1 · Prompt — *Did I say it clearly?*
 
@@ -235,7 +235,7 @@ The harness is commoditising; **where** the agent executes is the next multiplie
 pip install praisonai
 ```
 
-The simplest way in is `run_on=` — one whole team or workflow shares **one** sandbox, so a file written by step 1 is there for step 2:
+The simplest way in is `tools_run_on=` — one whole team or workflow shares **one** sandbox, so a file written by step 1 is there for step 2. Thinking stays on your machine:
 
 ```python
 from praisonaiagents import Agent, AgentFlow
@@ -243,7 +243,7 @@ from praisonaiagents import Agent, AgentFlow
 writer = Agent(name="Writer", instructions="You write files.")
 reader = Agent(name="Reader", instructions="You read files.")
 
-flow = AgentFlow(run_on="docker", steps=[writer, reader])   # or e2b | modal | daytona | flyio
+flow = AgentFlow(tools_run_on="docker", steps=[writer, reader])  # or e2b | modal | daytona | flyio
 flow.run("Write 'hello' to /workspace/note.txt, then read it back")
 ```
 
@@ -251,7 +251,7 @@ Same thing with no Python at all:
 
 ```yaml
 name: remote-demo
-run_on: docker            # every step shares one sandbox
+tools_run_on: docker      # every step shares one sandbox
 agents:
   writer: {role: Writer, goal: Write files}
   reader: {role: Reader, goal: Read files}
@@ -262,22 +262,51 @@ steps:
     action: "Read /workspace/note.txt"
 ```
 
-For a single agent, pick the axis you need — remote **tools** or a remote **loop**:
+For a single agent, two words cover it — and they answer different questions:
 
 ```python
-from praisonai import Agent, LocalAgent, LocalAgentConfig, HostedAgent
+from praisonaiagents import Agent
 
-# A. Tools run in a remote sandbox; the agent loop stays local
-agent = Agent(name="builder", backend=LocalAgent(
-    compute="e2b",                     # or modal | daytona | flyio | docker | tenki
-    config=LocalAgentConfig(model="gpt-4o-mini", name="RemoteTools"),
-))
+# A. Only the TOOLS move. Thinking stays on your machine.
+agent = Agent(name="builder", instructions="You build things.",
+              tools_run_on="docker")   # docker | e2b | modal | daytona | flyio
+                                       # tenki | sandlock | ssh | novita | subprocess
 
-# B. The entire agent loop runs in the cloud (needs ANTHROPIC_API_KEY)
-agent = Agent(name="teacher", backend=HostedAgent(provider="anthropic"))
+# B. The WHOLE agent moves — model calls, loop and tools
+agent = Agent(name="teacher", instructions="You teach.", run_on="anthropic")  # hosted
+agent = Agent(name="builder", instructions="You build.", run_on="docker")     # self-hosted
 agent.start("Write a Python script that prints the first 10 primes, then run it")
 ```
 
+Ask any object where it runs, and it will tell you:
+
+```python
+>>> Agent(name="builder", instructions="x", tools_run_on="docker")
+Agent(name='builder', thinks_on='this machine', tools_run_on='a Docker container')
+
+>>> agent.where_does_it_run()
+Thinking (the AI model calls) happens on this machine.
+Tools run on a Docker container.
+Your own tools (check_db) still run on this machine -- only shell, file and
+code tools move. They read and write this machine's files.
+```
+
+Naming a place that cannot do the job is a typo, not a preference, so it says so:
+
+```python
+>>> Agent(name="x", instructions="i", run_on="e2b")
+TypeError: Agent(run_on='e2b') is not valid: run_on= places the whole agent
+-- model calls, loop and tools -- on a managed runtime, and 'e2b' runs
+commands but cannot host an agent loop.
+  To run only the tools there:  Agent(tools_run_on='e2b')
+```
+
+To run one block of code somewhere else, name the place on that call:
+
+```python
+agent.execute_code_sync("print(6 * 7)", run_in="sandlock")   # kernel-enforced
+```
+
 See what is running and reclaim strays:
 
 ```bash

SECURITY.md

@@ -8,11 +8,18 @@ PraisonAI takes security seriously. We appreciate responsible disclosure from re
 |---------|----------|-------------|
 | `praisonaiagents` | [PyPI](https://pypi.org/project/praisonaiagents/) | `src/praisonai-agents/` |
 | `praisonai` | [PyPI](https://pypi.org/project/praisonai/) | `src/praisonai/` |
-| `praisonai-platform` | [PyPI](https://pypi.org/project/praisonai-platform/) | `src/praisonai-platform/` |
 | `praisonai` | [npm](https://www.npmjs.com/package/praisonai) | `src/praisonai-ts/` |
 
 Report issues against the **specific package** where the vulnerability exists. A platform-only issue does not necessarily affect the core SDK, and vice versa.
 
+### Packages maintained in their own repositories
+
+`praisonai-platform` now lives at
+[MervinPraison/PraisonAI-Platform](https://github.com/MervinPraison/PraisonAI-Platform).
+Report platform vulnerabilities through
+[that repository's advisories](https://github.com/MervinPraison/PraisonAI-Platform/security/advisories/new),
+not here — it is versioned and released independently of the SDK.
+
 ## In scope
 
 - Authentication, authorisation, and multi-tenant isolation (e.g. workspace boundaries)

api.md

@@ -30,6 +30,7 @@ Methods:
 
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">aclose</a>() -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">add_mcp_server</a>(name: str, mcp: Any) -> Any</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">add_verification</a>(hook: Any) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">agent_id</a>() -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">analyze_prompt</a>(prompt: str) -> set</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>() -> Optional[bool]</code>
@@ -70,6 +71,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">redo</a>() -> bool</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">refresh_tools</a>() -> List[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">remove_mcp_server</a>(name: str) -> bool</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">remove_verification</a>(name: str) -> bool</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieval_config</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieve</a>(query: str, **kwargs) -> 'ContextPack'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rules_manager</a>() -> Optional[Any]</code>
@@ -238,10 +240,12 @@ from praisonaiagents import Memory, MemoryBackend, MemoryConfig
 
 Methods:
 
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">acommit_memory_batch</a>(writes: List[tuple]) -> None</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_cache_optimized_context</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_cache_boundary: bool = True, include_in_output: Optional[bool] = None) -> Dict[str, str]</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_context_for_task</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_in_output: Optional[bool] = None) -> str</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">calculate_quality_metrics</a>(output: str, expected_output: str, llm: Optional[str] = None, custom_prompt: Optional[str] = None) -> Dict[str, float]</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">close_connections</a>()</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">commit_memory_batch</a>(writes: List[tuple]) -> None</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">compute_quality_score</a>(completeness: float, relevance: float, clarity: float, accuracy: float, weights: Dict[str, float] = None) -> float</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">delete_long_term</a>(memory_id: str) -> bool</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">delete_memories</a>(memory_ids: List[str]) -> int</code>
@@ -251,6 +255,7 @@ Methods:
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">finalize_task_output</a>(content: str, agent_name: str, quality_score: float, threshold: float = 0.7, metrics: Dict[str, Any] = None, task_id: str = None)</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">forget</a>(**kwargs) -> int</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">get_all_memories</a>() -> List[Dict[str, Any]]</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">get_context</a>(query: Optional[str] = None, **kwargs) -> str</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">get_learn_context</a>() -> str</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">learn</a>()</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">recall</a>(query: str, **kwargs) -> List[Dict[str, Any]]</code>
@@ -744,7 +749,7 @@ Methods:
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_token_usage_summary</a>() -> Dict[str, Any]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">has_state</a>(key: str) -> bool</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">increment_state</a>(key: str, amount: float = 1, default: float = 0) -> float</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">launch</a>(path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">launch</a>(path: str = '/agents', port: int = 8000, host: str = '127.0.0.1', debug: bool = False, protocol: str = 'http')</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">restore_session_state</a>(session_id: str) -> bool</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run</a>(content = None, return_dict = False, **kwargs)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_all_tasks</a>()</code>
@@ -878,6 +883,7 @@ Methods:
 * <code title="cli">praisonai recipe list <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai recipe optimize <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai recipe run <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
+* <code title="cli">praisonai recipe runs <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai recipe serve <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai replay cleanup <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
 * <code title="cli">praisonai replay context <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
@@ -894,7 +900,11 @@ Methods:
 * <code title="cli">praisonai schedule describe <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule list <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule logs <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule pause <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule remove <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule restart <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule resume <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule runs <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule schedule-callback <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule start <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule stats <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
@@ -903,6 +913,7 @@ Methods:
 * <code title="cli">praisonai schedule suggestion-dismiss <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule suggestion-propose <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule suggestions <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule update <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai validate check <a href="./src/praisonai/praisonai/cli/commands/validate.py">--help</a></code>
 * <code title="cli">praisonai validate schema <a href="./src/praisonai/praisonai/cli/commands/validate.py">--help</a></code>
 * <code title="cli">praisonai validate validate <a href="./src/praisonai/praisonai/cli/commands/validate.py">--help</a></code>

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.164" \
+    "praisonai>=4.7.1" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.164" \
+    "praisonai>=4.7.1" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.164" \
+    "praisonai>=4.7.1" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/middleware/hol_guard_tool_policy.py

@@ -0,0 +1,110 @@
+"""
+HOL Guard Tool Policy Example - PraisonAI Agents
+
+Demonstrates using HOL Guard (an optional, external runtime security engine)
+at the existing `wrap_tool_call` middleware boundary to inspect command-bearing
+tool calls *before* they execute.
+
+HOL Guard is installed and versioned separately. Its side-effect-free CLI can
+classify a shell command before execution:
+
+    hol-guard command test '<command>' --json
+
+Fail-closed policy: the wrapped tool only runs when Guard returns an explicit
+allow/benign classification. Review/block/unknown verdicts, malformed output,
+timeouts, or CLI failures all short-circuit the call so the tool never runs.
+
+    pip install hol-guard   # optional, external dependency
+"""
+
+import json
+import shutil
+import subprocess
+
+from praisonaiagents import Agent, tool
+from praisonaiagents.hooks import wrap_tool_call, ToolRequest, ToolResponse
+
+
+COMMAND_BEARING_TOOLS = {"run_shell"}
+GUARD_TIMEOUT_SECONDS = 10
+
+
+@tool
+def run_shell(command: str) -> str:
+    """Run a shell command and return its output."""
+    result = subprocess.run(
+        command, shell=True, capture_output=True, text=True
+    )
+    return result.stdout or result.stderr
+
+
+def _inspect_with_hol_guard(command: str) -> bool:
+    """Return True only if HOL Guard explicitly allows the command.
+
+    Fails closed (returns False) on any ambiguity: missing CLI, non-zero exit,
+    timeout, malformed JSON, or a non-allow verdict.
+    """
+    if not shutil.which("hol-guard"):
+        return False
+
+    try:
+        completed = subprocess.run(
+            ["hol-guard", "command", "test", command, "--json"],
+            capture_output=True,
+            text=True,
+            timeout=GUARD_TIMEOUT_SECONDS,
+        )
+    except (subprocess.TimeoutExpired, OSError):
+        return False
+
+    if completed.returncode != 0:
+        return False
+
+    try:
+        verdict = json.loads(completed.stdout)
+    except (json.JSONDecodeError, ValueError):
+        return False
+
+    if not isinstance(verdict, dict):
+        return False
+
+    decision = str(verdict.get("decision", "")).lower()
+    return decision in ("allow", "benign")
+
+
+@wrap_tool_call
+def hol_guard_tool_policy(request: ToolRequest, call_next):
+    """Gate command-bearing tools through HOL Guard before execution."""
+    if request.tool_name not in COMMAND_BEARING_TOOLS:
+        return call_next(request)
+
+    command = request.arguments.get("command", "")
+
+    if _inspect_with_hol_guard(command):
+        return call_next(request)
+
+    blocked_message = "Blocked by HOL Guard: command was not explicitly allowed."
+    # Surface the denial in `result` too: the agent tool executor returns
+    # `ToolResponse.result` to the model and discards `error`, so a denial with
+    # only `error` set would reach the model as a null result. Keeping `error`
+    # populated preserves the signal for after_tool hooks and logging.
+    return ToolResponse(
+        tool_name=request.tool_name,
+        result=blocked_message,
+        error=blocked_message,
+        context=request.context,
+    )
+
+
+agent = Agent(
+    name="GuardedShellBot",
+    instructions="You run shell commands only when they are safe.",
+    tools=[run_shell],
+    hooks=[hol_guard_tool_policy],
+)
+
+
+if __name__ == "__main__":
+    print("HOL Guard tool policy registered on 'run_shell'.")
+    print("Commands are only executed when Guard returns an explicit allow.")
+    print("\n✓ HOL Guard example complete")

examples/persistence/state_redis.py

@@ -14,7 +14,7 @@ Expected Output:
     Agent responds with state persisted to Redis
 """
 
-from praisonaiagents import Agent
+from praisonaiagents import Agent, MemoryConfig
 from praisonaiagents.db import db
 
 print("=== Redis State Store (Agent-First) ===")
@@ -28,8 +28,7 @@ my_db = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant.",
-    memory=my_db,
-    session_id="redis-state-example"
+    memory=MemoryConfig(db=my_db, session_id="redis-state-example"),
 )
 
 # Chat - state is automatically managed via Redis

examples/python/autonomy/03_verification_hooks.py

@@ -23,6 +23,7 @@ from praisonaiagents.hooks.verification import (
     VerificationResult,
     BaseVerificationHook,
     CommandVerificationHook,
+    FileCheckHook,
 )
 
 
@@ -100,7 +101,19 @@ def main():
     print(f"   Name: {cmd_hook.name}")
     print(f"   Command: {cmd_hook.command}")
     print(f"   Timeout: {cmd_hook.timeout_seconds}s")
-    
+
+    # 6. FileCheckHook: declarative, no-shell completion gate
+    print("\n6. FileCheckHook (declarative completion gate):")
+    file_hook = FileCheckHook(
+        name="changelog", path="CHANGELOG.md", non_empty=True
+    )
+    file_result = file_hook.run()
+    print(f"   {file_hook.name}: success={file_result.success} — {file_result.output}")
+    print(
+        "   Gate wiring: when verification_hooks are configured, the autonomous "
+        "loop cannot finalise with a success outcome while a blocking hook fails."
+    )
+
     print("\n" + "=" * 60)
     print("✓ Verification hooks example completed successfully!")
     print("=" * 60)

examples/serve/README.md

@@ -24,7 +24,7 @@ Examples for all PraisonAI server types and the unified endpoints CLI.
 | `praisonai serve unified --port 8765` | Start unified server |
 | `praisonai serve agents --file agents.yaml --port 8000` | Start agents API |
 | `praisonai serve recipe --port 8765` | Start recipe server |
-| `praisonai serve mcp --transport http --port 8080` | Start MCP server |
+| `praisonai mcp serve --transport http-stream --port 8080` | Start MCP server |
 | `praisonai serve tools --port 8081` | Start tools MCP server |
 | `praisonai serve a2a --port 8082` | Start A2A server |
 | `praisonai serve a2u --port 8083` | Start A2U server |

examples/serve/mcp_http_server.py

@@ -13,7 +13,7 @@ Usage:
     python mcp_http_server.py
     
     # Or use CLI:
-    praisonai serve mcp --transport http --port 8080
+    praisonai mcp serve --transport http-stream --port 8080
     
     # Test with curl:
     curl http://localhost:8080/mcp/tools

examples/yaml/workflows/remote_sandbox_workflow.yaml

@@ -2,20 +2,25 @@
 #
 #   praisonai examples/yaml/workflows/remote_sandbox_workflow.yaml
 #
-# `run_on:` is the only line that differs from a normal local workflow. Every
-# step shares the same sandbox and /workspace, so the file the writer creates
-# is there for the reader. Orchestration still runs on your machine, and the
-# sandbox is torn down when the run finishes.
+# `tools_run_on:` is the only line that differs from a normal local workflow.
+# Every step shares the same sandbox and working directory, so the file the
+# writer creates is there for the reader. Thinking and orchestration still run
+# on your machine, and the sandbox is torn down when the run finishes.
 #
-# Providers: docker | e2b | modal | daytona | flyio | tenki | local
+# Places: docker | e2b | modal | daytona | flyio | tenki | local
+#         subprocess | sandlock | ssh | novita
 #   - docker needs a local Docker daemon and nothing else
 #   - e2b / modal / daytona / flyio need that provider's API key
+#   - sandlock is a kernel-enforced local boundary (Linux 6.12+)
+#   - subprocess is process separation only, NOT a security boundary
+#
+# `run_on:` is still accepted here as the old spelling for this key.
 #
 # See what is still running:  praisonai managed ps
 # Reclaim strays:             praisonai managed stop --all
 
 name: remote-sandbox-demo
-run_on: docker
+tools_run_on: docker
 
 agents:
   writer:

src/praisonai-agents/.agent/workflows/security-audit.md

@@ -99,13 +99,12 @@ pip index versions praisonaiagents | head -1
 pip index versions praisonai | head -1
 ```
 
-13. Publish **praisonai-platform** (only when the advisory targets platform code):
-```bash
-cd /Users/praison/praisonai-package/src/praisonai-platform
-praisonai publish pypi
-# or: uv lock && uv build && uv publish --token $PYPI_TOKEN
-```
-Platform releases are **independent** of `praisonai` / `praisonaiagents` version numbers. A platform-only fix (e.g. `0.1.8`) does **not** require bumping the main SDK.
+13. **praisonai-platform** advisories are handled in a different repository:
+[MervinPraison/PraisonAI-Platform](https://github.com/MervinPraison/PraisonAI-Platform).
+It is not part of this monorepo and is not published from here. Triage, patch,
+release and advisory publication all happen in that repo, on its own semver line.
+Nothing in this repository imports it, so a platform advisory never requires an
+SDK release.
 
 14. Publish **praisonai (npm / TypeScript)** (only when the advisory targets `src/praisonai-ts`):
 ```bash
@@ -113,9 +112,8 @@ cd /Users/praison/praisonai-package/src/praisonai-ts
 npm version patch && npm publish
 ```
 
-15. Verify platform / npm when applicable:
+15. Verify npm when applicable:
 ```bash
-pip index versions praisonai-platform | head -1
 npm view praisonai version
 ```
 
@@ -139,7 +137,7 @@ gh api repos/MervinPraison/PraisonAI/security-advisories/<GHSA_ID> \
 }
 EOF
 ```
-- `<PACKAGE_NAME>`: `praisonaiagents` (core SDK), `praisonai` (Python wrapper), `praisonai-platform` (platform layer), or `praisonai` (npm ecosystem for TypeScript)
+- `<PACKAGE_NAME>`: `praisonaiagents` (core SDK), `praisonai` (Python wrapper), or `praisonai` (npm ecosystem for TypeScript)
 - `<LAST_VULNERABLE_VERSION>`: version before the fix
 - `<FIRST_PATCHED_VERSION>`: the version just published with the fix
 - Always include credits for the reporter
@@ -190,8 +188,8 @@ After validating each advisory in source code, assign **one** label:
 | Package | Version line | Notes |
 |---|---|---|
 | `praisonaiagents` + `praisonai` | Aligned patch (e.g. `1.6.62` / `4.6.62`) | Main SDK + wrapper; bump together via `bump_and_release.py` |
-| `praisonai-platform` | Independent semver (e.g. `0.1.8`) | Platform-only fixes; users need **not** upgrade the main SDK |
 | `praisonai` (npm) | Independent semver (e.g. `1.7.2`) | TypeScript SDK; publish from `src/praisonai-ts` |
+| `praisonai-platform` | Independent semver, **separate repository** | Not released from this repo — see [PraisonAI-Platform](https://github.com/MervinPraison/PraisonAI-Platform) |
 
 Always set the advisory `patched_versions` to the **package that actually contains the fix**. Do not imply users must upgrade all packages when only one changed.
 
@@ -217,9 +215,11 @@ Close (or publish advisory with no code change only if already mitigated) when:
 |---|---|---|---|
 | praisonaiagents | pip | src/praisonai-agents/praisonaiagents/ | tools/, mcp/, memory/, agent/, sandbox/ |
 | praisonai | pip | src/praisonai/praisonai/ | cli/, capabilities/, mcp_server/, sandbox/, recipe/ |
-| praisonai-platform | pip | src/praisonai-platform/praisonai_platform/ | api/routes/, services/, auth |
 | praisonai | npm | src/praisonai-ts/src/ | agent/, mcp/, tools/, code-mode/ |
 
+`praisonai-platform` is **not audited from this repository** — it lives in
+[MervinPraison/PraisonAI-Platform](https://github.com/MervinPraison/PraisonAI-Platform).
+
 ## RESOURCES & DOCUMENTATION
 
 - **Official Repository**: https://github.com/MervinPraison/PraisonAI — Source code, issue tracking, and contribution guidelines
@@ -234,7 +234,7 @@ Close (or publish advisory with no code change only if already mitigated) when:
 - ALWAYS set patched_versions before publishing advisory
 - ALWAYS publish to PyPI/npm BEFORE publishing advisories (so Dependabot can point users to a fix)
 - Python SDK: `praisonai publish pypi` from `src/praisonai-agents`, then `bump_and_release.py` for the wrapper
-- Platform-only: `praisonai publish pypi` from `src/praisonai-platform` — no SDK bump required
+- Platform-only: released from the separate [PraisonAI-Platform](https://github.com/MervinPraison/PraisonAI-Platform) repo, never from here
 - npm: `npm publish` from `src/praisonai-ts` when the TypeScript SDK is affected
 - ALWAYS request CVE AFTER publishing advisory
 - Use generic commit messages: "refactor: harden X" not "fix: SQL injection in Y"

src/praisonai-agents/AGENTS.md

@@ -1074,7 +1074,7 @@ PraisonAI's reliability engineering rests on three pillars — **C**ontext, **H*
 > **Evaluator status.** The `ContextEvaluator` / `HarnessEvaluator` / `LoopEvaluator` classes named in the rubric are **planned follow-ups** (PA-CHL-001–004), not yet in the repo. Until they land, the equivalent checks are covered by existing modules:
 >
 > - **Context** — `context/tokens.py` and `eval/tokens.py` (`estimate_tokens` / `count_tokens`) for budget; `compaction/compactor.py` (`ContextCompactor`) for compaction/retention.
-> - **Harness** — `runtime/turn_context.py` for turn-context parity; `runtime/example_harness.py` and `tests/` for harness/artifact checks.
+> - **Harness** — `runtime/turn_context.py` for turn-context parity; `examples/runtime/example_harness.py` and `tests/` for harness/artifact checks.
 > - **Loop** — `eval/loop.py` (`EvaluationLoop`) and `agent/autonomy.py` for convergence and doom-loop guards.
 >
 > Keep terminology consistent with the `agent/context_agent.py` (`ContextAgent`) PRP methodology.

src/praisonai-agents/DOCS_PARITY.md

@@ -18,76 +18,76 @@ This report compares **Python SDK feature categories** against **Python document
 | Category | Features | Docs | Lines |
 |----------|----------|------|-------|
 | ✅ AGUI | 1 | 2 | 420 |
-| ✅ Agent | 22 | 50 | 15813 |
+| ✅ Agent | 22 | 53 | 17053 |
 | ✅ Agent-to-Agent (A2A) | 1 | 7 | 2996 |
 | ✅ Agent-to-User (A2U) | 1 | 3 | 689 |
-| ✅ Approval | 1 | 5 | 2537 |
+| ✅ Approval | 1 | 5 | 2658 |
 | ✅ Audio | 2 | 12 | 1000 |
-| ✅ Auto Generation | 5 | 11 | 3274 |
-| ✅ Autonomy | 3 | 7 | 2756 |
-| ✅ Bots | 7 | 31 | 13366 |
+| ✅ Auto Generation | 5 | 11 | 3542 |
+| ✅ Autonomy | 3 | 7 | 2790 |
+| ✅ Bots | 7 | 31 | 13411 |
 | ✅ Budget | 1 | 1 | 287 |
-| ✅ CLI | 5 | 121 | 36059 |
-| ✅ Chunking | 2 | 2 | 385 |
+| ✅ CLI | 5 | 122 | 36734 |
+| ✅ Chunking | 2 | 2 | 420 |
 | ✅ Citations | 2 | 1 | 202 |
-| ✅ Code Execution | 2 | 14 | 4583 |
-| ✅ Conditions | 1 | 3 | 1179 |
-| ✅ Configuration | 3 | 8 | 3047 |
-| ✅ Context Management | 16 | 34 | 13396 |
+| ✅ Code Execution | 2 | 14 | 4855 |
+| ✅ Conditions | 1 | 3 | 1347 |
+| ✅ Configuration | 3 | 8 | 3139 |
+| ✅ Context Management | 16 | 36 | 13988 |
 | ✅ Deep Research | 8 | 2 | 587 |
 | ✅ Display | 6 | 3 | 741 |
 | ✅ Embeddings | 6 | 23 | 2049 |
-| ✅ Evaluation | 1 | 7 | 2905 |
+| ✅ Evaluation | 1 | 7 | 2920 |
 | ✅ Events | 1 | 2 | 749 |
-| ✅ Execution | 3 | 4 | 1562 |
-| ✅ Failover | 2 | 1 | 432 |
+| ✅ Execution | 3 | 4 | 1606 |
+| ✅ Failover | 2 | 1 | 452 |
 | ✅ Files | 2 | 7 | 2275 |
 | ✅ Flow | 1 | 3 | 788 |
-| ✅ Gateway | 7 | 74 | 27180 |
-| ✅ Guardrails | 4 | 4 | 2025 |
-| ✅ Handoffs | 11 | 6 | 2563 |
-| ✅ Hooks | 2 | 9 | 4434 |
+| ✅ Gateway | 7 | 81 | 29955 |
+| ✅ Guardrails | 4 | 4 | 2205 |
+| ✅ Handoffs | 11 | 6 | 2626 |
+| ✅ Hooks | 2 | 9 | 4509 |
 | ✅ Image | 1 | 11 | 1255 |
-| ✅ Knowledge | 4 | 13 | 4333 |
-| ✅ LLM | 3 | 15 | 5192 |
-| ✅ Loops | 4 | 5 | 1422 |
-| ✅ MCP | 1 | 61 | 13993 |
-| ✅ Memory | 6 | 18 | 7070 |
+| ✅ Knowledge | 4 | 16 | 4942 |
+| ✅ LLM | 3 | 15 | 5372 |
+| ✅ Loops | 4 | 5 | 1475 |
+| ✅ MCP | 1 | 61 | 14135 |
+| ✅ Memory | 6 | 18 | 7187 |
 | ✅ OCR | 2 | 1 | 237 |
-| ✅ Observability | 2 | 23 | 2814 |
+| ✅ Observability | 2 | 23 | 2811 |
 | ✅ Optimizer | 1 | 2 | 792 |
-| ✅ Output | 3 | 5 | 1242 |
+| ✅ Output | 3 | 5 | 1265 |
 | ✅ Parallel Execution | 3 | 2 | 497 |
-| ✅ Planning | 6 | 6 | 1549 |
-| ✅ Plugins | 8 | 4 | 2858 |
-| ✅ Prompts | 2 | 9 | 1965 |
-| ✅ Providers | 1 | 56 | 7743 |
+| ✅ Planning | 6 | 6 | 1605 |
+| ✅ Plugins | 8 | 5 | 3120 |
+| ✅ Prompts | 2 | 9 | 1971 |
+| ✅ Providers | 1 | 56 | 7781 |
 | ✅ Query | 1 | 2 | 736 |
-| ✅ RAG | 5 | 15 | 3003 |
+| ✅ RAG | 5 | 15 | 3118 |
 | ✅ Realtime | 2 | 5 | 668 |
-| ✅ Reflection | 3 | 3 | 680 |
+| ✅ Reflection | 3 | 3 | 730 |
 | ✅ Retrieval | 2 | 5 | 1054 |
 | ✅ Routing | 1 | 2 | 430 |
-| ✅ Sandbox | 5 | 7 | 3202 |
-| ✅ Security | 1 | 3 | 3332 |
-| ✅ Sessions | 4 | 18 | 7158 |
+| ✅ Sandbox | 5 | 7 | 3483 |
+| ✅ Security | 1 | 3 | 3482 |
+| ✅ Sessions | 4 | 19 | 7631 |
 | ✅ Skills | 6 | 15 | 5638 |
-| ✅ Tasks | 2 | 6 | 2819 |
-| ✅ Telemetry | 1 | 2 | 604 |
+| ✅ Tasks | 2 | 6 | 2897 |
+| ✅ Telemetry | 1 | 2 | 648 |
 | ✅ Templates | 1 | 8 | 1663 |
-| ✅ Tools | 12 | 141 | 41246 |
+| ✅ Tools | 12 | 144 | 43113 |
 | ✅ Tracing | 3 | 2 | 139 |
-| ✅ Vector Store | 1 | 12 | 1211 |
+| ✅ Vector Store | 1 | 12 | 1215 |
 | ✅ Video | 2 | 6 | 649 |
 | ✅ Vision | 2 | 1 | 329 |
-| ✅ Web | 3 | 9 | 2467 |
-| ✅ Workflows | 5 | 16 | 6593 |
+| ✅ Web | 3 | 9 | 2487 |
+| ✅ Workflows | 5 | 18 | 7406 |
 
 ## Documentation Without Features
 
 These docs exist but don't match any implemented feature category:
 
-- ℹ️ Database (42 docs, 6713 lines)
+- ℹ️ Database (42 docs, 6959 lines)
 - ℹ️ Documents (1 docs, 787 lines)
 
 ---

src/praisonai-agents/examples/runtime/example_harness.py

@@ -14,16 +14,16 @@ import logging
 import time
 from typing import Any, Dict, List, Optional, TYPE_CHECKING
 
-from .turn_context import (
+from praisonaiagents.runtime.turn_context import (
     PreparedTurnContext,
-    TurnRuntimeProtocol,
     RuntimeMode,
 )
-from .context_builder import default_context_builder
-from ..streaming.events import StreamEvent, StreamEventType
+from praisonaiagents.runtime.protocols import TurnRuntimeProtocol
+from praisonaiagents.runtime.context_builder import default_context_builder
+from praisonaiagents.streaming.events import StreamEvent, StreamEventType
 
 if TYPE_CHECKING:
-    from ..agent.protocols import AgentProtocol
+    from praisonaiagents.agent.protocols import AgentProtocol
 
 logger = logging.getLogger(__name__)
 
@@ -363,4 +363,4 @@ async def demonstrate_harness_integration():
 
 if __name__ == "__main__":
     # Run the demonstration
-    asyncio.run(demonstrate_harness_integration())
+    asyncio.run(demonstrate_harness_integration())

src/praisonai-agents/praisonaiagents/__init__.py

@@ -92,20 +92,6 @@ from ._lazy import lazy_import, create_lazy_getattr_with_fallback
 # Thread-safe cache for lazy-loaded values
 _lazy_cache = {}
 
-# Backward compatibility: _get_lazy_cache function for tests
-import threading
-_lazy_cache_local = threading.local()
-
-def _get_lazy_cache():
-    """Get thread-local lazy cache dict. Thread-safe for concurrent access.
-    
-    Note: This is kept for backward compatibility with tests.
-    The main lazy loading now uses the centralized _lazy.py utility.
-    """
-    if not hasattr(_lazy_cache_local, 'cache'):
-        _lazy_cache_local.cache = {}
-    return _lazy_cache_local.cache
-
 # ============================================================================
 # LAZY IMPORT MAPPING
 # ============================================================================

src/praisonai-agents/praisonaiagents/_warning_patch.py

@@ -27,7 +27,7 @@ SUPPRESSED_PATTERNS = [
     "The `dict` method is deprecated; use `model_dump` instead",
     "Pydantic serializer warnings",
     "PydanticSerializationUnexpectedValue",
-    "Expected",  # Catches all "Expected N fields but got M" patterns
+    "fields but got",  # litellm/pydantic "Expected N fields but got M"
     "Expected `StreamingChoices` but got `Choices`",
     "serialized value may not be as expected",
     "Mixing V1 models and V2 models",

src/praisonai-agents/praisonaiagents/agent/audio_agent.py

@@ -136,9 +136,8 @@ class AudioAgent:
                 - AudioConfig: Full configuration object
             verbose: Verbosity level for output
         """
-        # Handle model= alias
-        if llm is None and model is not None:
-            llm = model
+        from ..utils.model_alias import resolve_model_name
+        llm = resolve_model_name(llm, model, "AudioAgent")
         
         self.name = name or "AudioAgent"
         self.instructions = instructions

src/praisonai-agents/praisonaiagents/agent/autonomy.py

@@ -217,6 +217,13 @@ class AutonomyConfig:
                 f"Invalid autonomy level: {level!r}. "
                 f"Must be one of {sorted(VALID_AUTONOMY_LEVELS)}"
             )
+        verification_hooks = data.get("verification_hooks")
+        # Config parity: accept declarative `verification:` entries and map
+        # `command:`/`file:` shapes onto the two hook classes.
+        declarative = data.get("verification")
+        if declarative:
+            built = _build_verification_hooks(declarative)
+            verification_hooks = (verification_hooks or []) + built
         return cls(
             enabled=data.get("enabled", True),
             level=level,
@@ -227,7 +234,7 @@ class AutonomyConfig:
             observe=data.get("observe", False),
             completion_promise=data.get("completion_promise"),
             clear_context=data.get("clear_context", False),
-            verification_hooks=data.get("verification_hooks"),
+            verification_hooks=verification_hooks,
             track_changes=data.get("track_changes"),
             sandbox=data.get("sandbox"),
             snapshot_dir=data.get("snapshot_dir"),
@@ -237,6 +244,49 @@ class AutonomyConfig:
             budget_action=data.get("budget_action", "pause"),
         )
 
+
+def _build_verification_hooks(entries: List[Any]) -> List[Any]:
+    """Map declarative ``verification:`` config entries onto hook instances.
+
+    Each entry is a dict with either a ``command`` key (→
+    :class:`CommandVerificationHook`) or a ``file`` key (→
+    :class:`FileCheckHook`). Unknown shapes are skipped.
+
+    Example::
+
+        verification:
+          - {name: tests, command: "python -m pytest -q"}
+          - {name: changelog, file: CHANGELOG.md, non_empty: true}
+    """
+    from ..hooks.verification import CommandVerificationHook, FileCheckHook
+
+    hooks: List[Any] = []
+    for entry in entries or []:
+        if not isinstance(entry, dict):
+            continue
+        blocking = entry.get("blocking", True)
+        if "command" in entry:
+            hooks.append(CommandVerificationHook(
+                name=entry.get("name", "command"),
+                command=entry["command"],
+                cwd=entry.get("cwd"),
+                timeout=entry.get("timeout", 60.0),
+                blocking=blocking,
+            ))
+        elif "file" in entry:
+            hooks.append(FileCheckHook(
+                name=entry.get("name", "file"),
+                path=entry["file"],
+                exists=entry.get("exists", True),
+                non_empty=entry.get("non_empty", False),
+                contains=entry.get("contains"),
+                json_field=entry.get("json_field"),
+                equals=entry.get("equals"),
+                blocking=blocking,
+            ))
+    return hooks
+
+
 class AutonomySignal(str, Enum):
     """Signals detected from prompts for autonomy decisions.
     

src/praisonai-agents/praisonaiagents/agent/chat_mixin.py

@@ -1211,7 +1211,15 @@ Your Goal: {self.goal}"""
         return content
 
     def _extract_llm_response_content(self, response) -> Optional[str]:
-        """Return assistant message text, a tool-call summary, or str(response) as fallback."""
+        """Return assistant message text, or a tool-call summary.
+
+        A well-formed response that simply carries no content -- a content filter,
+        a refusal, or ``finish_reason="length"`` with zero output tokens -- yields
+        the empty string.  ``str(response)`` is reserved for responses this method
+        could not parse at all, where the repr is a diagnostic rather than an
+        answer; returning it for a merely empty message put the raw SDK object's
+        repr in front of the user and into ``chat_history``.
+        """
         if not response:
             return None
         try:
@@ -1226,6 +1234,17 @@ Your Goal: {self.goal}"""
                     if tool_calls:
                         names = [getattr(tc.function, "name", "?") for tc in tool_calls]
                         return f"[tool_calls: {', '.join(names)}]"
+                    # Parsed cleanly; the model returned no content.  A refusal
+                    # (safety) is carried in ``message.refusal`` independently of
+                    # ``finish_reason`` -- surface it so an empty answer isn't silent.
+                    finish_reason = getattr(choice, "finish_reason", None)
+                    refusal = getattr(msg, "refusal", None)
+                    if refusal or finish_reason not in (None, "stop"):
+                        logging.warning(
+                            f"Agent {self.name}: model returned no content "
+                            f"(finish_reason={finish_reason!r}, refused={bool(refusal)})"
+                        )
+                    return ""
         except (AttributeError, IndexError, TypeError) as e:
             logging.warning(
                 f"Failed to extract LLM response content (falling back to str): {e}"
@@ -1725,7 +1744,7 @@ Your Goal: {self.goal}"""
             return max_retries
         return 2
 
-    def _chat_completion(self, messages, temperature=1.0, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, _retry_depth=0, _fallback_index=0):
+    def _chat_completion(self, messages, temperature=None, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, _retry_depth=0, _fallback_index=0):
         start_time = time.time()
 
         # --- Proactive Context Budget Management (default-on) ---
@@ -1849,6 +1868,11 @@ Your Goal: {self.goal}"""
                     stream = False  # Set for the main execution below
                 else:
                     raise  # Re-raise if it's a different ValueError
+            except ToolExecutionError:
+                # A tool failed during the streaming attempt. Re-raise so it is
+                # not relabelled as an LLM error and so the tool is not executed
+                # a second time by the non-streaming fallback below.
+                raise
             except Exception as e:
                 from ..errors import LLMError
                 # Don't retry if it's an LLMError that has exhausted retries
@@ -1953,6 +1977,8 @@ Your Goal: {self.goal}"""
 
         except BudgetExceededError:
             raise
+        except ToolExecutionError:
+            raise
         except Exception as e:
             from ..errors import LLMError
             
@@ -2119,7 +2145,7 @@ Your Goal: {self.goal}"""
         self, 
         exc: Exception, 
         messages, 
-        temperature=1.0, 
+        temperature=None, 
         tools=None, 
         stream=True,
         reasoning_steps=False,
@@ -2309,7 +2335,7 @@ Your Goal: {self.goal}"""
     def _execute_unified_chat_completion(
         self, 
         messages, 
-        temperature=1.0, 
+        temperature=None, 
         tools=None, 
         stream=None,
         reasoning_steps=False,
@@ -2443,7 +2469,7 @@ Your Goal: {self.goal}"""
     async def _execute_unified_achat_completion(
         self, 
         messages, 
-        temperature=1.0, 
+        temperature=None, 
         tools=None, 
         stream=True,  # Async methods keep stream=True default (async adapters support streaming vs sync smart fallback)
         reasoning_steps=False,
@@ -2781,7 +2807,11 @@ Your Goal: {self.goal}"""
                 
                 model_name = self.llm if isinstance(self.llm, str) else "gpt-4o-mini"
                 model_limit = get_model_limit(model_name)
-                current_tokens = estimate_messages_tokens(optimized)
+                # Count the whole request (history + system prompt + tool schemas),
+                # otherwise a large prompt/tool block can push the call over the
+                # window while the history alone stays under the threshold.
+                overhead = self._estimate_request_overhead_tokens(system_prompt, tools)
+                current_tokens = estimate_messages_tokens(optimized) + overhead
                 
                 # If over 95% of limit, apply emergency truncation
                 if current_tokens > model_limit * 0.95:
@@ -2789,20 +2819,67 @@ Your Goal: {self.goal}"""
                         f"[{self.name}] Context at {current_tokens} tokens (limit: {model_limit}), "
                         f"applying emergency truncation"
                     )
-                    target = int(model_limit * 0.8)  # Target 80% of limit
+                    # Leave room for the fixed overhead so the full request lands
+                    # near 80% of the window rather than the history alone.
+                    target = max(1, int(model_limit * 0.8) - overhead)
                     optimized = self.context_manager.emergency_truncate(optimized, target)
                     result["emergency_truncated"] = True
-                    result["tokens_after"] = estimate_messages_tokens(optimized)
+                    result["tokens_after"] = estimate_messages_tokens(optimized) + overhead
             except Exception as e:
                 logging.debug(f"Hard limit check skipped: {e}")
             
             return optimized, result
             
         except Exception as e:
-            # Context management should never break the chat flow
-            logging.warning(f"Context management error (continuing without): {e}")
+            # Context management should never break the chat flow, but silently
+            # returning the unchanged history disables the hard-limit truncation
+            # too and sends an over-budget request. Fall back to emergency
+            # truncation so we still fit within the model window.
+            logging.error(f"Context management failed: {e}")
+            try:
+                from ..context.budgeter import get_model_limit
+                from ..context.tokens import estimate_messages_tokens
+
+                model_name = self.llm if isinstance(self.llm, str) else "gpt-4o-mini"
+                model_limit = get_model_limit(model_name)
+                # Include the system prompt and tool schemas in the budget: the
+                # request can exceed the window on those alone even when the
+                # history is under the threshold.
+                overhead = self._estimate_request_overhead_tokens(system_prompt, tools)
+                if estimate_messages_tokens(messages) + overhead > model_limit * 0.95:
+                    target = max(1, int(model_limit * 0.8) - overhead)
+                    truncated = self.context_manager.emergency_truncate(messages, target)
+                    return truncated, {"emergency_truncated": True}
+            except Exception as inner:
+                logging.debug(f"Emergency truncation fallback skipped: {inner}")
             return messages, None
 
+    def _estimate_request_overhead_tokens(
+        self,
+        system_prompt: str = "",
+        tools: Optional[list] = None,
+    ) -> int:
+        """Estimate non-history request tokens (system prompt + tool schemas).
+
+        Emergency-truncation thresholds must budget the full request, not just
+        the message history, or a large system prompt / tool block can push the
+        call over the model window undetected.
+        """
+        try:
+            from ..context.tokens import (
+                estimate_tokens_heuristic,
+                estimate_tool_schema_tokens,
+            )
+        except Exception:
+            return 0
+
+        overhead = 0
+        if system_prompt:
+            overhead += estimate_tokens_heuristic(system_prompt)
+        if tools:
+            overhead += estimate_tool_schema_tokens(tools)
+        return overhead
+
     def _truncate_tool_output(self, tool_name: str, output: str, tool_call_id: str | None = None) -> str:
         """
         Truncate tool output according to configured budget.
@@ -2888,7 +2965,7 @@ Your Goal: {self.goal}"""
             )
         return rendered
 
-    def chat(self, prompt: str, temperature: float = 1.0, tools: Optional[List[Any]] = None, output_json: Optional[Any] = None, output_pydantic: Optional[Any] = None, reasoning_steps: bool = False, stream: Optional[bool] = None, task_name: Optional[str] = None, task_description: Optional[str] = None, task_id: Optional[str] = None, config: Optional[Dict[str, Any]] = None, force_retrieval: bool = False, skip_retrieval: bool = False, attachments: Optional[List[str]] = None, tool_choice: Optional[str] = None, seed: Optional[int] = None, cancel_token: Optional[Any] = None) -> Optional[str]:
+    def chat(self, prompt: str, temperature: Optional[float] = None, tools: Optional[List[Any]] = None, output_json: Optional[Any] = None, output_pydantic: Optional[Any] = None, reasoning_steps: bool = False, stream: Optional[bool] = None, task_name: Optional[str] = None, task_description: Optional[str] = None, task_id: Optional[str] = None, config: Optional[Dict[str, Any]] = None, force_retrieval: bool = False, skip_retrieval: bool = False, attachments: Optional[List[str]] = None, tool_choice: Optional[str] = None, seed: Optional[int] = None, cancel_token: Optional[Any] = None) -> Optional[str]:
         """
         Chat with the agent.
         
@@ -2901,6 +2978,14 @@ Your Goal: {self.goal}"""
                         'required' forces the LLM to call a tool before responding.
             ...other args...
         """
+        # Tools go to their sandbox before the first model call, once per
+        # agent. Placed on chat/achat/_start_stream rather than run/start
+        # because AgentTeam and AgentFlow call these three directly -- an
+        # agent used inside a team would otherwise run its tools locally.
+        if getattr(self, 'tools_run_on', None) is not None:
+            from .tools_placement import ensure_tools_placed
+            ensure_tools_placed(self)
+
         # Slash-command invocation: /skill-name [args] renders the skill
         # body before any backend/LLM call.
         prompt = self._resolve_skill_invocation(prompt)
@@ -3490,11 +3575,14 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 reflection_output = _get_display_functions()['ReflectionOutput'](**reflection_data)
                             else:
                                 # Use OpenAI's structured output for OpenAI models
+                                _reflection_kwargs = {}
+                                if temperature is not None:
+                                    _reflection_kwargs['temperature'] = temperature
                                 reflection_response = self._openai_client.sync_client.beta.chat.completions.parse(
                                     model=self.reflect_llm if self.reflect_llm else self.llm,
                                     messages=messages,
-                                    temperature=temperature,
-                                    response_format=_get_display_functions()['ReflectionOutput']
+                                    response_format=_get_display_functions()['ReflectionOutput'],
+                                    **_reflection_kwargs
                                 )
 
                                 reflection_output = reflection_response.choices[0].message.parsed
@@ -3559,6 +3647,22 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 logging.error("Reflection parsing failed.", exc_info=True)
                                 messages.append({"role": "assistant", "content": "Self Reflection failed."})
                                 reflection_count += 1
+                                if reflection_count >= self.max_reflect:
+                                    # Bound the retry, as the async path does. Without this
+                                    # a persistently unparseable reflection (e.g. a
+                                    # structured-output refusal, where message.parsed is
+                                    # None) loops forever, two LLM calls at a time.
+                                    if self.verbose:
+                                        _get_display_functions()['display_self_reflection']("Maximum reflection count reached after repeated parse errors, returning current response", console=self.console)
+                                    self._append_to_chat_history({"role": "assistant", "content": response_text})
+                                    try:
+                                        validated_response = self._apply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                        self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                        return self._trigger_after_agent_hook(original_prompt, validated_response, start_time)
+                                    except Exception as guard_e:
+                                        logging.error(f"Agent {self.name}: Guardrail validation failed after reflection error: {guard_e}")
+                                        self._rollback_chat_history_to(chat_history_length)
+                                        return None
                                 continue  # Continue even after error to try again
                     except Exception:
                         # Catch any exception from the inner try block and re-raise to outer handler
@@ -3578,7 +3682,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
         return clean_triple_backticks(output)
 
-    async def achat(self, prompt: str, temperature: float = 1.0, tools: Optional[List[Any]] = None, output_json: Optional[Any] = None, output_pydantic: Optional[Any] = None, reasoning_steps: bool = False, stream: Optional[bool] = None, task_name: Optional[str] = None, task_description: Optional[str] = None, task_id: Optional[str] = None, config: Optional[Dict[str, Any]] = None, force_retrieval: bool = False, skip_retrieval: bool = False, attachments: Optional[List[str]] = None, tool_choice: Optional[str] = None, seed: Optional[int] = None, cancel_token: Optional[Any] = None):
+    async def achat(self, prompt: str, temperature: Optional[float] = None, tools: Optional[List[Any]] = None, output_json: Optional[Any] = None, output_pydantic: Optional[Any] = None, reasoning_steps: bool = False, stream: Optional[bool] = None, task_name: Optional[str] = None, task_description: Optional[str] = None, task_id: Optional[str] = None, config: Optional[Dict[str, Any]] = None, force_retrieval: bool = False, skip_retrieval: bool = False, attachments: Optional[List[str]] = None, tool_choice: Optional[str] = None, seed: Optional[int] = None, cancel_token: Optional[Any] = None):
         """Async version of chat method with self-reflection support.
         
         Args:
@@ -3586,6 +3690,14 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             attachments: Optional list of image/file paths that are ephemeral
                         (used for THIS turn only, NEVER stored in history).
         """
+        # Tools go to their sandbox before the first model call, once per
+        # agent. Placed on chat/achat/_start_stream rather than run/start
+        # because AgentTeam and AgentFlow call these three directly -- an
+        # agent used inside a team would otherwise run its tools locally.
+        if getattr(self, 'tools_run_on', None) is not None:
+            from .tools_placement import ensure_tools_placed
+            ensure_tools_placed(self)
+
         # Slash-command invocation: /skill-name [args] renders the skill body.
         prompt = self._resolve_skill_invocation(prompt)
 
@@ -4131,11 +4243,14 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                             self._rollback_chat_history_to(chat_history_length)
                                             return None
                                     
+                                    _areflection_kwargs = {}
+                                    if temperature is not None:
+                                        _areflection_kwargs['temperature'] = temperature
                                     reflection_response = await self._openai_client.async_client.beta.chat.completions.parse(
                                         model=self.reflect_llm if self.reflect_llm else self.llm,
                                         messages=reflection_messages,
-                                        temperature=temperature,
-                                        response_format=_get_display_functions()['ReflectionOutput']
+                                        response_format=_get_display_functions()['ReflectionOutput'],
+                                        **_areflection_kwargs
                                     )
                                     
                                     reflection_output = reflection_response.choices[0].message.parsed
@@ -4249,9 +4364,10 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             tool_call_kwargs = dict(
                                 model=self.llm,
                                 messages=messages,
-                                temperature=temperature,
                                 tools=formatted_tools,
                             )
+                            if temperature is not None:
+                                tool_call_kwargs['temperature'] = temperature
                             if effective_tool_choice:
                                 tool_call_kwargs['tool_choice'] = effective_tool_choice
                             response = await self._openai_client.async_client.chat.completions.create(
@@ -4265,11 +4381,14 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             self._execute_callback_and_display(original_prompt, result, time.time() - start_time, task_name, task_description, task_id)
                             return await self._atrigger_after_agent_hook(original_prompt, result, start_time)
                         elif output_json or output_pydantic:
+                            _json_kwargs = {}
+                            if temperature is not None:
+                                _json_kwargs['temperature'] = temperature
                             response = await self._openai_client.async_client.chat.completions.create(
                                 model=self.llm,
                                 messages=messages,
-                                temperature=temperature,
-                                response_format={"type": "json_object"}
+                                response_format={"type": "json_object"},
+                                **_json_kwargs
                             )
                             response_text = response.choices[0].message.content
                             if get_logger().getEffectiveLevel() == logging.DEBUG:
@@ -4279,10 +4398,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             self._execute_callback_and_display(original_prompt, response_text, time.time() - start_time, task_name, task_description, task_id)
                             return await self._atrigger_after_agent_hook(original_prompt, response_text, start_time)
                         else:
+                            _plain_kwargs = {}
+                            if temperature is not None:
+                                _plain_kwargs['temperature'] = temperature
                             response = await self._openai_client.async_client.chat.completions.create(
                                 model=self.llm,
                                 messages=messages,
-                                temperature=temperature
+                                **_plain_kwargs
                             )
                         
                         response_text = response.choices[0].message.content
@@ -4319,11 +4441,14 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                             logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                                         return await self._atrigger_after_agent_hook(original_prompt, response_text, start_time)
                                     
+                                    _areflection_kwargs = {}
+                                    if temperature is not None:
+                                        _areflection_kwargs['temperature'] = temperature
                                     reflection_response = await self._openai_client.async_client.beta.chat.completions.parse(
                                         model=self.reflect_llm if self.reflect_llm else self.llm,
                                         messages=reflection_messages,
-                                        temperature=temperature,
-                                        response_format=_get_display_functions()['ReflectionOutput']
+                                        response_format=_get_display_functions()['ReflectionOutput'],
+                                        **_areflection_kwargs
                                     )
                                     
                                     reflection_output = reflection_response.choices[0].message.parsed
@@ -4349,10 +4474,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                         {"role": "user", "content": "Now regenerate your response using the reflection you made"}
                                     ]
                                     
+                                    _regen_kwargs = {}
+                                    if temperature is not None:
+                                        _regen_kwargs['temperature'] = temperature
                                     new_response = await self._openai_client.async_client.chat.completions.create(
                                         model=self.llm,
                                         messages=regenerate_messages,
-                                        temperature=temperature
+                                        **_regen_kwargs
                                     )
                                     response_text = new_response.choices[0].message.content
                                     reflection_count += 1
@@ -4439,7 +4567,12 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     # after-context aggregation) are fired once, guarded, inside
                     # execute_tool_async — no inline duplicate dispatch here.
                     # Pass the tools list to honor task-scoped tools
-                    result = await self.execute_tool_async(function_name, arguments, tools_override=tools)
+                    result = await self.execute_tool_async(
+                        function_name,
+                        arguments,
+                        tool_call_id=getattr(tool_call, "id", None),
+                        tools_override=tools,
+                    )
 
                     results.append(result)
                 except Exception as e:
@@ -4540,6 +4673,14 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
     def _start_stream(self, prompt: str, **kwargs) -> Generator[str, None, None]:
         """Own the durable context for the complete streaming generator."""
+        # Tools go to their sandbox before the first model call, once per
+        # agent. Placed on chat/achat/_start_stream rather than run/start
+        # because AgentTeam and AgentFlow call these three directly -- an
+        # agent used inside a team would otherwise run its tools locally.
+        if getattr(self, 'tools_run_on', None) is not None:
+            from .tools_placement import ensure_tools_placed
+            ensure_tools_placed(self)
+
         durable_context = None
         durable_token = None
         try:
@@ -5227,7 +5368,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             agent_id=getattr(self, 'name', None),
         )
 
-    def _chat_completion_with_retry(self, messages, temperature=1.0, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, stream_callback=None, emit_events=True):
+    def _chat_completion_with_retry(self, messages, temperature=None, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, stream_callback=None, emit_events=True):
         """
         Wrapper for _execute_unified_chat_completion that adds jittered exponential backoff retry logic.
         
@@ -5314,7 +5455,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         # This should never be reached, but just in case
         raise RuntimeError("Retry loop completed without returning or raising an exception")
 
-    async def _achat_completion_with_retry(self, messages, temperature=1.0, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, stream_callback=None, emit_events=True):
+    async def _achat_completion_with_retry(self, messages, temperature=None, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, stream_callback=None, emit_events=True):
         """
         Async wrapper for _execute_unified_achat_completion that adds jittered exponential backoff retry logic.
         

src/praisonai-agents/praisonaiagents/agent/code_agent.py

@@ -59,7 +59,8 @@ class CodeAgent:
     def __init__(
         self,
         name: str = "CodeAgent",
-        llm: Optional[str] = None,
+        llm: Optional[str] = None,  # Deprecated alias for model=
+        model: Optional[str] = None,
         code: Optional[Union[bool, Dict, CodeConfig]] = None,
         instructions: Optional[str] = None,
         verbose: bool = True,
@@ -69,14 +70,16 @@ class CodeAgent:
         
         Args:
             name: Agent name
-            llm: LLM model (default: gpt-4o-mini)
+            llm: Deprecated alias for model=. Passing both raises TypeError.
+            model: Model to use (default: gpt-4o-mini)
             code: Code configuration (bool, dict, or CodeConfig)
             instructions: System instructions
             verbose: Enable verbose output
             **kwargs: Additional arguments
         """
+        from ..utils.model_alias import resolve_model_name
         self.name = name
-        self.llm = llm or "gpt-4o-mini"
+        self.llm = resolve_model_name(llm, model, "CodeAgent") or "gpt-4o-mini"
         self.instructions = instructions
         self.verbose = verbose
         

src/praisonai-agents/praisonaiagents/agent/durable.py

@@ -17,6 +17,7 @@ from ..runtime.journal import (
     KIND_MODEL_DECISION,
     KIND_TOOL_CALL,
     KIND_TOOL_RESULT,
+    KIND_VERIFICATION,
     RunJournal,
 )
 
@@ -58,6 +59,85 @@ def _accepts_idempotency_key(execute_tool_fn: Callable) -> bool:
     )
 
 
+def _normalize_restart_safe(value: Any) -> Optional[bool]:
+    """Accept only genuine booleans as a restart-safety declaration.
+
+    ``None`` means undeclared. Any other type (e.g. the string ``"False"``) is a
+    malformed declaration and is treated as unsafe so we fail closed rather than
+    coercing a truthy value into an unintended replay.
+    """
+    if value is None:
+        return None
+    if isinstance(value, bool):
+        return value
+    return False
+
+
+def _canonical_tool_name(name: Any) -> str:
+    """Normalise a tool name for tolerant comparison across repair variants.
+
+    ``execute_tool`` may repair case/separator variants (e.g. ``Send-Email`` →
+    ``send_email``); the declaration lookup mirrors that so an explicit
+    ``restart_safe`` declaration is not missed for a repaired name.
+    """
+    return "".join(ch for ch in str(name).lower() if ch.isalnum())
+
+
+def _declared_restart_safe(
+    function_name: str, executor: Optional[Callable]
+) -> Optional[bool]:
+    """Return an explicit ``restart_safe`` declaration for ``function_name``.
+
+    Resolves the tool object first from the executor's owning agent (its live
+    ``tools``), then from the global tool registry, and reads its
+    ``restart_safe`` attribute. Returns ``None`` when the tool is undeclared or
+    cannot be resolved.
+    """
+    if not function_name:
+        return False
+    target = _canonical_tool_name(function_name)
+    agent = getattr(executor, "__self__", None)
+    tools = getattr(agent, "tools", None)
+    if isinstance(tools, (list, tuple)):
+        for tool in tools:
+            name = getattr(tool, "name", None) or getattr(tool, "__name__", None)
+            if (
+                name is not None
+                and _canonical_tool_name(name) == target
+                and getattr(tool, "restart_safe", None) is not None
+            ):
+                return _normalize_restart_safe(tool.restart_safe)
+    try:
+        from ..tools import get_registry as _get_tool_registry
+
+        tool = _get_tool_registry().get(function_name)
+    except Exception:  # pragma: no cover - registry lookup is best-effort
+        tool = None
+    declared = getattr(tool, "restart_safe", None) if tool is not None else None
+    return _normalize_restart_safe(declared)
+
+
+def _is_restart_safe(function_name: str, executor: Optional[Callable] = None) -> bool:
+    """Resolve whether a tool is safe to re-run for an in-flight durable resume.
+
+    An explicit ``restart_safe`` declaration on the tool contract wins. When the
+    tool is undeclared (or cannot be resolved), fall back to the read-only name
+    heuristic and, ultimately, fail closed — an effectful/unknown tool is *not*
+    replayed so a missing declaration never causes a duplicated side effect.
+    """
+    if not function_name:
+        return False
+    declared = _declared_restart_safe(function_name, executor)
+    if declared is not None:
+        return declared
+    try:
+        from .tool_execution import ToolExecutionMixin
+
+        return bool(ToolExecutionMixin._is_read_only_tool(function_name))
+    except Exception:  # pragma: no cover - heuristic import is best-effort
+        return False
+
+
 class DurableRunContext:
     """Per-turn journal state; safe for parallel tool-call wrappers."""
 
@@ -80,6 +160,10 @@ class DurableRunContext:
         self._results_by_tool_call_id: Dict[str, Any] = {}
         self._pending_by_call_id: Dict[str, Tuple[int, str, Dict[str, Any], str]] = {}
         self._pending_by_fingerprint: Dict[str, Tuple[int, str, Dict[str, Any], str]] = {}
+        # Names of in-flight tool calls (journalled tool_call with no tool_result
+        # at the crash). Used only when replaying to gate replay by restart-safety.
+        self._inflight_names_by_call_id: Dict[str, str] = {}
+        self._inflight_names_by_fingerprint: Dict[str, str] = {}
         self._recorded_iterations = set()
         for event in events:
             if event.kind == KIND_TOOL_RESULT:
@@ -106,8 +190,11 @@ class DurableRunContext:
                 or f"{self.run_id}:{event.seq}:{name}"
             )
             pending = (event.seq, call_id, args, key)
+            fingerprint = self._fingerprint(name, args)
             self._pending_by_call_id[call_id] = pending
-            self._pending_by_fingerprint[self._fingerprint(name, args)] = pending
+            self._pending_by_fingerprint[fingerprint] = pending
+            self._inflight_names_by_call_id[call_id] = name
+            self._inflight_names_by_fingerprint[fingerprint] = name
 
         self._resume_completed_steps = len(self._recorded_iterations)
 
@@ -119,6 +206,44 @@ class DurableRunContext:
     def _fingerprint(function_name: str, arguments: Dict[str, Any]) -> str:
         return f"{function_name}:{json.dumps(_json_safe(arguments), sort_keys=True)}"
 
+    def _is_inflight_call(
+        self,
+        function_name: str,
+        arguments: Dict[str, Any],
+        tool_call_id: Optional[str],
+    ) -> bool:
+        """Whether this call was in-flight (journalled, no result) at the crash.
+
+        Only meaningful while replaying: such calls have a recorded ``tool_call``
+        but no ``tool_result``, so re-driving the loop would re-execute their
+        side effect. Completed calls (already have a result) are replayed
+        upstream and never reach this check.
+        """
+        if not self.replaying:
+            return False
+        call_id = str(tool_call_id) if tool_call_id is not None else None
+        if call_id and call_id in self._inflight_names_by_call_id:
+            return True
+        return self._fingerprint(function_name, arguments) in (
+            self._inflight_names_by_fingerprint
+        )
+
+    @staticmethod
+    def _not_safely_resumable_result(function_name: str) -> Dict[str, Any]:
+        """Typed, operator-visible outcome for a gated in-flight effectful tool."""
+        return {
+            "error": (
+                f"Tool '{function_name}' was in-flight when the durable run "
+                "crashed and is not declared restart_safe; its outcome could not "
+                "be confirmed. It was not re-executed on resume to avoid a "
+                "duplicate side effect. Reconcile the external action manually "
+                "or mark the tool @tool(restart_safe=True) if it is idempotent."
+            ),
+            "error_type": "NotSafelyResumable",
+            "not_safely_resumable": True,
+            "restart_safe": False,
+        }
+
     def restore_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         """Append completed decision/result pairs once, before the next model call."""
         if not self.replaying or self._restored:
@@ -277,6 +402,26 @@ class DurableRunContext:
         )
         self.finalize("failed")
 
+    def record_verification(self, payloads: List[Dict[str, Any]]) -> None:
+        """Append verification-gate results as durable ``verification`` events.
+
+        Each payload gets its own fresh ``seq`` so the events append cleanly
+        alongside the tool-call cursor without colliding on ``(run_id, seq,
+        kind)``. Best-effort: journalling never raises into the gate.
+        """
+        if self._closed:
+            return
+        with self._lock:
+            for payload in payloads:
+                seq = self._next_seq
+                self._next_seq += 1
+                try:
+                    self.journal.append(
+                        JournalEvent(self.run_id, seq, KIND_VERIFICATION, payload)
+                    )
+                except Exception:  # pragma: no cover - journalling is best-effort
+                    pass
+
     def wrap_sync(self, execute_tool_fn: Optional[Callable]) -> Optional[Callable]:
         if execute_tool_fn is None:
             return None
@@ -286,9 +431,19 @@ class DurableRunContext:
             call_id = str(tool_call_id) if tool_call_id is not None else None
             if call_id and call_id in self._results_by_tool_call_id:
                 return self._results_by_tool_call_id[call_id]
+            gate_inflight = self._is_inflight_call(
+                function_name, arguments, tool_call_id
+            ) and not _is_restart_safe(function_name, execute_tool_fn)
             seq, call_id, idempotency_key = self._allocate_step(
                 function_name, arguments, tool_call_id
             )
+            if gate_inflight:
+                return self._record_result(
+                    seq,
+                    call_id,
+                    self._not_safely_resumable_result(function_name),
+                    iteration_index,
+                )
             token = _active_idempotency_key.set(idempotency_key)
             try:
                 call_kwargs = dict(kwargs)
@@ -319,9 +474,20 @@ class DurableRunContext:
             call_id = str(tool_call_id) if tool_call_id is not None else None
             if call_id and call_id in self._results_by_tool_call_id:
                 return self._results_by_tool_call_id[call_id]
+            gate_inflight = self._is_inflight_call(
+                function_name, arguments, tool_call_id
+            ) and not _is_restart_safe(function_name, execute_tool_fn)
             seq, call_id, idempotency_key = await asyncio.to_thread(
                 self._allocate_step, function_name, arguments, tool_call_id
             )
+            if gate_inflight:
+                return await asyncio.to_thread(
+                    self._record_result,
+                    seq,
+                    call_id,
+                    self._not_safely_resumable_result(function_name),
+                    iteration_index,
+                )
             token = _active_idempotency_key.set(idempotency_key)
             try:
                 call_kwargs = dict(kwargs)

src/praisonai-agents/praisonaiagents/agent/embedding_agent.py

@@ -110,8 +110,8 @@ class EmbeddingAgent:
                 - EmbeddingConfig: Full configuration object
             verbose: Verbosity level for output
         """
-        if llm is None and model is not None:
-            llm = model
+        from ..utils.model_alias import resolve_model_name
+        llm = resolve_model_name(llm, model, "EmbeddingAgent")
         
         self.name = name or "EmbeddingAgent"
         self.instructions = instructions

src/praisonai-agents/praisonaiagents/agent/execution_location.py

@@ -43,24 +43,67 @@ HERE = "this machine"
 # a place that never executes.
 _ALIASES = {"native": "sandlock", "local": "subprocess"}
 
+# ...but only for the SANDBOX registry. "local" resolves to two different
+# backends depending on which parameter carried it:
+#   run_in="local"        -> SandboxManager collapses it to subprocess:
+#                            scrubbed environment, blocked commands and paths
+#   tools_run_on="local"  -> the wrapper's LocalCompute: a plain shell in the
+#                            current directory, full environment inherited,
+#                            no security policy at all
+# Describing the second in the first's words overstates it, which is the exact
+# failure this module exists to prevent. One word, two backends -- so the
+# phrase has to depend on where the word came from.
+_COMPUTE_ONLY_WORDS = {"local": "a plain shell on this machine (no policy applied)"}
+
 # Tiers that separate a process but do NOT contain it. Verified: under the
 # subprocess backend, outbound network still works and ~/.ssh is readable even
 # though SecurityPolicy declares them blocked.
-_NOT_A_BOUNDARY = {"subprocess", "local", "native"}
+# `native` is deliberately absent: it aliases sandlock, which applies Landlock,
+# seccomp, a scrubbed environment and deny-all networking. Listing it here made
+# tools_run_on="native" and tools_run_on="sandlock" -- the same backend --
+# disagree about whether they contain anything.
+_NOT_A_BOUNDARY = {"subprocess", "local"}
 
 
-def say_place(name: Any) -> str:
-    """Turn a provider name into a phrase a non-developer can read."""
+def say_place(name: Any, *, via: str = "sandbox") -> str:
+    """Turn a place name into a phrase a non-developer can read.
+
+    ``via`` says which registry resolved the name -- ``"compute"`` for
+    ``tools_run_on=``, ``"sandbox"`` for ``run_in=``. It only matters for names
+    that mean different backends in each (see ``_COMPUTE_ONLY_WORDS``).
+    """
     if name is None:
         return HERE
     if name is True:
         return _PLACE_WORDS["subprocess"]
     if not isinstance(name, str):
-        # A configured provider instance: use its own declared name if it has one.
+        # A configured provider instance: use its own declared name if it has
+        # one. Normalise BEFORE the compute-only check, or a backend holding a
+        # LocalCompute object (provider_name="local") skips the branch and gets
+        # described in the subprocess backend's stronger words -- the exact
+        # overstatement this module exists to prevent.
         name = getattr(name, "provider_name", None) or type(name).__name__
+    if via == "compute" and name.lower() in _COMPUTE_ONLY_WORDS:
+        return _COMPUTE_ONLY_WORDS[name.lower()]
     key = str(name).lower()
     key = _ALIASES.get(key, key)
-    return _PLACE_WORDS.get(key, str(name))
+
+    known = _PLACE_WORDS.get(key)
+    if known:
+        return known
+
+    # A place contributed by another package can name itself; the bare name is
+    # the last resort rather than the only option, because the phrase table is
+    # a literal that cannot know about packages installed later.
+    try:
+        from ..managed._compute_bridge import contributed_display_name
+
+        declared = contributed_display_name(key)
+        if declared:
+            return declared
+    except Exception:
+        pass
+    return str(name)
 
 
 def _backend_places(backend: Any) -> Optional[Dict[str, str]]:
@@ -68,6 +111,21 @@ def _backend_places(backend: Any) -> Optional[Dict[str, str]]:
     if backend is None:
         return None
 
+    # Ask the backend what it is, rather than guessing from its class name.
+    # Name-matching missed any backend that did not happen to contain "Hosted"
+    # or "Anthropic" -- a self-hosted docker backend reported that it was
+    # thinking on this machine while the whole loop ran in a container.
+    declared = getattr(backend, "provider_name", None)
+    if isinstance(declared, str):
+        try:
+            from .placement import managed_runtimes
+
+            if declared.lower() in managed_runtimes():
+                where = say_place(declared)
+                return {"thinks_on": where, "tools_run_on": where}
+        except Exception:
+            pass
+
     cls = type(backend).__name__
     # Hosted runtimes move the entire turn off this machine.
     if "Hosted" in cls or "Anthropic" in cls:
@@ -75,10 +133,13 @@ def _backend_places(backend: Any) -> Optional[Dict[str, str]]:
         where = say_place(provider)
         return {"thinks_on": where, "tools_run_on": where}
 
-    # Local/sandboxed agents keep the loop here and may push tools out.
+    # Local/sandboxed agents keep the loop here and may push tools out. A
+    # backend's compute object is a COMPUTE-registry provider, so it is read
+    # with the compute vocabulary: a LocalCompute here is the unrestricted
+    # shell, not the subprocess backend "local" collapses to under run_in=.
     compute = getattr(backend, "_compute", None) or getattr(backend, "compute", None)
     if compute is not None:
-        return {"thinks_on": HERE, "tools_run_on": say_place(compute)}
+        return {"thinks_on": HERE, "tools_run_on": say_place(compute, via="compute")}
     return {"thinks_on": HERE, "tools_run_on": HERE}
 
 
@@ -94,11 +155,21 @@ def describe(obj: Any, *, shared: bool = False) -> Dict[str, str]:
         if from_backend:
             places.update(from_backend)
 
-        run_on = getattr(obj, "run_on", None)
-        if run_on is not None:
-            where = say_place(run_on)
+        where_tools = getattr(obj, "tools_run_on", None)
+        if where_tools is not None:
+            where = say_place(where_tools, via="compute")
             places["tools_run_on"] = f"{where} (shared)" if shared else where
 
+        # run_on= puts the WHOLE agent on a managed runtime, so it moves the
+        # thinking as well. _backend_places() already reports that (run_on= is
+        # turned into a backend at construction); this only covers an object
+        # that carries the name without a built backend.
+        whole = getattr(obj, "run_on", None)
+        if whole is not None and not from_backend:
+            everywhere = say_place(whole)
+            places["thinks_on"] = everywhere
+            places["tools_run_on"] = everywhere
+
         sandbox_config = getattr(obj, "sandbox_config", None)
         if sandbox_config is not None:
             places["code_runs_on"] = say_place(
@@ -109,6 +180,42 @@ def describe(obj: Any, *, shared: bool = False) -> Dict[str, str]:
     return places
 
 
+def _tools_staying_here(obj: Any) -> list:
+    """Names of the object's own tools, which do NOT move to the sandbox.
+
+    Only four tool names are bridged (``execute_command``, ``read_file``,
+    ``write_file``, ``list_files``); everything else the user passed keeps
+    running in this process, against this filesystem. The capability prompt
+    tells the model the sandbox is where things happen, so anything staying
+    behind has to be said out loud.
+
+    Reads the pre-attach tool list when a sandbox is already attached: by then
+    ``agent.tools`` has been rewritten and the originals live in the sandbox's
+    restore record.
+    """
+    try:
+        from ..managed.shared_compute import BRIDGED_TOOL_NAMES
+    except Exception:
+        BRIDGED_TOOL_NAMES = ("execute_command", "read_file", "write_file", "list_files")
+
+    tools = None
+    shared = getattr(obj, "_tools_sandbox", None)
+    if shared is not None:
+        for patched, original_tools, _ in getattr(shared, "_patched", []):
+            if patched is obj:
+                tools = original_tools
+                break
+    if tools is None:
+        tools = getattr(obj, "tools", None) or []
+
+    names = []
+    for tool in tools:
+        name = getattr(tool, "__name__", None) or getattr(tool, "name", None)
+        if isinstance(name, str) and name not in BRIDGED_TOOL_NAMES:
+            names.append(name)
+    return names
+
+
 def repr_fields(obj: Any, *, shared: bool = False) -> str:
     """Render the places as ``key='value'`` pairs for a ``__repr__``."""
     return ", ".join(f"{k}={v!r}" for k, v in describe(obj, shared=shared).items())
@@ -126,12 +233,20 @@ def explain(obj: Any, *, shared: bool = False) -> str:
         f"Tools run on {places['tools_run_on']}.",
     ]
 
-    if shared and getattr(obj, "run_on", None) is not None:
+    if shared and getattr(obj, "tools_run_on", None) is not None:
         lines.append(
             "Every step shares that same sandbox, so a file written by one step "
             "is visible to the next."
         )
 
+    staying = _tools_staying_here(obj)
+    if staying and places["tools_run_on"] != places["thinks_on"]:
+        listed = ", ".join(staying[:6]) + ("..." if len(staying) > 6 else "")
+        lines.append(
+            f"Your own tools ({listed}) still run on {HERE} -- only shell, file "
+            f"and code tools move. They read and write this machine's files."
+        )
+
     if "code_runs_on" in places:
         lines.append(
             f"Code you run yourself with execute_code() runs on {places['code_runs_on']}."
@@ -139,6 +254,7 @@ def explain(obj: Any, *, shared: bool = False) -> str:
 
     # Say plainly when the chosen tier is not a security boundary.
     raw = [
+        getattr(obj, "tools_run_on", None),
         getattr(obj, "run_on", None),
         getattr(getattr(obj, "sandbox_config", None), "sandbox_type", None),
     ]
@@ -147,7 +263,8 @@ def explain(obj: Any, *, shared: bool = False) -> str:
     ):
         lines.append(
             "Note: a separate process is not a security boundary -- it can still "
-            "reach the network and read your files. Use docker or a cloud "
+            "reach the network and read your files, and its blocked-command "
+            "list can be worked around by ordinary shell syntax. Use docker or a cloud "
             "provider for untrusted code."
         )
 

src/praisonai-agents/praisonaiagents/agent/execution_mixin.py

@@ -1519,6 +1519,7 @@ Write the complete compiled report:"""
                 run_id=getattr(self, '_current_run_id', 'unknown'),
                 session_id=getattr(self, '_session_id', None) or 'default',
                 tool_name=function_name,
+                metadata={"tool_call_id": tool_call_id},
             ),
         )
 
@@ -1564,18 +1565,32 @@ Write the complete compiled report:"""
                         result.get("approval_error") or
                         result.get("circuit_open") or
                         result.get("loop_blocked") or
-                        result.get("retryable") is False):
+                        result.get("_praison_retryable") is False):
+                        result.pop("_praison_retryable", None)
                         return result
                     
                     # Determine error type for retry policy
                     error_type = self._classify_error_type(result, last_exception)
                     
-                    # Check if we should retry
-                    if not retry_policy.should_retry(error_type, attempt):
+                    # Check if we should retry. A tool that *raised* is flattened
+                    # into an error dict before it can be classified, so it always
+                    # arrives here as "unknown" — which no default retry_on
+                    # contains. The sync path escalates exactly those error types
+                    # to its own retry loop (tool_execution.py:853-866); honouring
+                    # the impl's explicit `retryable` flag is how the async path
+                    # reaches the same decision instead of never retrying at all.
+                    # `_praison_retryable` is a private control-plane key (see
+                    # _execute_tool_async_impl): a tool's own result may legitimately
+                    # contain "retryable" (common for HTTP-API wrappers) and must
+                    # never steer our retry loop.
+                    if (not retry_policy.should_retry(error_type, attempt)
+                            and result.get("_praison_retryable") is not True):
+                        result.pop("_praison_retryable", None)
                         return result
                     
                     # Don't retry on last attempt
                     if attempt == retry_policy.max_attempts - 1:
+                        result.pop("_praison_retryable", None)
                         return result
                     
                     # Emit retry hook event  
@@ -1774,7 +1789,7 @@ Write the complete compiled report:"""
                         result = {
                             "error": f"Tool timed out after {tool_timeout}s",
                             "timeout": True,
-                            "retryable": False,
+                            "_praison_retryable": False,
                         }
                 else:
                     result = await _invoke()
@@ -1814,7 +1829,26 @@ Write the complete compiled report:"""
                 # sync path. The next pre-execution check will then BLOCK/HALT.
                 if loop_guard is not None:
                     loop_guard.record(function_name, arguments, False, result=None)
-                return {"error": f"Error executing {function_name}: {str(e)}"}
+                # Carry the retry verdict with the flattened exception. Without it
+                # the caller only sees an opaque error string, classifies it as
+                # "unknown" (never in RetryPolicy.retry_on) and gives up — while
+                # the sync path, which still has the exception object, retries.
+                # Mirror the sync path exactly: a ToolExecutionError already
+                # carries an explicit is_retryable verdict (e.g. a terminal
+                # non-retryable failure) and must be honoured verbatim; for any
+                # other exception apply the same terminal rule — programming
+                # errors are terminal, everything else may be retried.
+                if isinstance(e, ToolExecutionError):
+                    retryable = e.is_retryable
+                else:
+                    retryable = not isinstance(e, (ValueError, TypeError, AttributeError))
+                # Private control-plane key: a tool's own result may legitimately
+                # contain "retryable" (common for HTTP-API wrappers) and must not
+                # collide with the framework's verdict nor leak to the model.
+                return {
+                    "error": f"Error executing {function_name}: {str(e)}",
+                    "_praison_retryable": retryable,
+                }
 
         except ToolExecutionError:
             # A loop-guard HALT must propagate to stop the run, mirroring the sync
@@ -1824,18 +1858,26 @@ Write the complete compiled report:"""
             logging.error(f"Error in execute_tool_async: {str(e)}", exc_info=True)
             return {"error": f"Error in execute_tool_async: {str(e)}"}
 
-    def launch(self, path: str = '/', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = "http"):
+    def launch(self, path: str = '/', port: int = 8000, host: str = '127.0.0.1', debug: bool = False, protocol: str = "http"):
         """
         Launch the agent as an HTTP API endpoint or MCP server.
         
         This method now delegates to protocol-based launchers to follow
         the protocol-driven architecture. Heavy implementations (FastAPI/uvicorn)
         are only imported when needed through lazy loading.
-        
+
+        Request authorisation matches ``PraisonAIAgents.launch``: when
+        ``PRAISONAI_LAUNCH_AUTH_TOKEN`` is set, every HTTP route requires that
+        bearer token (401 otherwise). When it is unset and ``host`` is
+        non-loopback, a one-time token is generated and printed rather than
+        serving an unauthenticated endpoint on all interfaces.
+
         Args:
             path: API endpoint path (default: '/') for HTTP, or base path for MCP.
             port: Server port (default: 8000)
-            host: Server host (default: '0.0.0.0')
+            host: Server host (default: '127.0.0.1'; changed from '0.0.0.0' to
+                avoid binding all interfaces by default — pass '0.0.0.0'
+                explicitly to expose externally).
             debug: Enable debug mode (default: False)
             protocol: "http" to launch as FastAPI, "mcp" to launch as MCP server.
             
@@ -1859,6 +1901,8 @@ Write the complete compiled report:"""
         """
         global _server_started, _registered_agents, _shared_apps, _server_lock
 
+        from .launch_security import authorise_launch_request, resolve_launch_host
+
         # Try to import FastAPI dependencies - lazy loading
         try:
             import uvicorn
@@ -1883,7 +1927,15 @@ Write the complete compiled report:"""
             print("\nOr install all API dependencies with:")
             print("pip install 'praisonaiagents[api]'")
             return None
-                
+
+        # Fail-closed bind guard: refuse to serve keyless on a non-loopback host
+        # (auto-generates and prints a one-time token instead). Matches the
+        # serve/jobs precedent and keeps this path in lock-step with
+        # PraisonAIAgents.launch. Applied only after the dependency guard above
+        # so a launch that bails out (missing deps) cannot mutate the
+        # process-wide launch token and retroactively 401 a running endpoint.
+        host = resolve_launch_host(host)
+
         should_start = False
         with _server_lock:
             # Initialize port-specific collections if needed (once per port)
@@ -1936,6 +1988,8 @@ Write the complete compiled report:"""
             # Define the endpoint handler
             @_shared_apps[port].post(path)
             async def handle_agent_query(request: Request, query_data: Optional[AgentQuery] = None):
+                if not authorise_launch_request(request):
+                    raise HTTPException(status_code=401, detail="Unauthorized")
                 # Handle both direct JSON with query field and form data
                 if query_data is None:
                     try:

src/praisonai-agents/praisonaiagents/agent/handoff.py

@@ -14,6 +14,7 @@ from typing import Optional, Any, Callable, Dict, List, Union, TYPE_CHECKING, Li
 from dataclasses import dataclass, field
 from enum import Enum
 import inspect
+from contextlib import contextmanager
 from praisonaiagents._logging import get_logger
 import asyncio
 import contextvars
@@ -469,12 +470,46 @@ class Handoff:
                     return value
         return 'gpt-4o-mini'
 
+    @contextmanager
+    def _seed_target_history(self, prior_messages: Optional[List[Any]]):
+        """Temporarily seed the filtered handoff context onto the target agent.
+
+        The configured ``context_policy``/``input_filter`` produce the messages the
+        target should see; without seeding them the target only ever received the
+        literal handoff prompt string and the configured policy had no effect.
+
+        The seeding is *invocation-scoped*: the prior messages are prepended for the
+        duration of this handoff and the target's original ``chat_history`` is
+        restored on exit. This keeps handoff context from leaking into later
+        handoffs or ordinary chats that reuse the same target agent, and stops the
+        target prompt from growing across sequential handoffs.
+        No shared mutable state is retained between agents.
+        """
+        existing = getattr(self.agent, 'chat_history', None)
+        if not prior_messages or not isinstance(existing, list):
+            yield
+            return
+        try:
+            prior = list(prior_messages)
+        except TypeError:
+            yield
+            return
+        original = existing
+        try:
+            # Avoid re-seeding if the context is already at the head.
+            if existing[:len(prior)] != prior:
+                self.agent.chat_history = prior + existing
+            yield
+        finally:
+            self.agent.chat_history = original
+
     def _execute_with_runtime_resolution(
         self, 
         source_agent: 'Agent', 
         prompt: str, 
         effective_tools: Optional[List[Any]], 
-        context: Dict[str, Any]
+        context: Dict[str, Any],
+        prior_messages: Optional[List[Any]] = None,
     ) -> str:
         """Execute handoff through the target agent's full chat pipeline."""
         target_model_ref = self._extract_model_ref(self.agent)
@@ -484,14 +519,16 @@ class Handoff:
             target_model_ref,
             _get_handoff_depth(),
         )
-        return self.agent.chat(prompt, tools=effective_tools)
+        with self._seed_target_history(prior_messages):
+            return self.agent.chat(prompt, tools=effective_tools)
     
     async def _execute_with_runtime_resolution_async(
         self, 
         source_agent: 'Agent', 
         prompt: str, 
         effective_tools: Optional[List[Any]], 
-        context: Dict[str, Any]
+        context: Dict[str, Any],
+        prior_messages: Optional[List[Any]] = None,
     ) -> str:
         """Execute handoff through the target agent's full async chat pipeline."""
         target_model_ref = self._extract_model_ref(self.agent)
@@ -501,13 +538,14 @@ class Handoff:
             target_model_ref,
             _get_handoff_depth(),
         )
-        async_chat = getattr(self.agent, 'achat', None)
-        if callable(async_chat) and inspect.iscoroutinefunction(async_chat):
-            return await async_chat(prompt, tools=effective_tools)
-        loop = asyncio.get_running_loop()
-        return await loop.run_in_executor(
-            None, lambda: self.agent.chat(prompt, tools=effective_tools)
-        )
+        with self._seed_target_history(prior_messages):
+            async_chat = getattr(self.agent, 'achat', None)
+            if callable(async_chat) and inspect.iscoroutinefunction(async_chat):
+                return await async_chat(prompt, tools=effective_tools)
+            loop = asyncio.get_running_loop()
+            return await loop.run_in_executor(
+                None, lambda: self.agent.chat(prompt, tools=effective_tools)
+            )
     
     def _check_safety(self, source_agent: 'Agent') -> None:
         """
@@ -661,8 +699,9 @@ class Handoff:
             # Execute on_handoff callback
             self._execute_callback(self.config.on_handoff or self.on_handoff, source_agent, kwargs)
             
-            # Prepare context (for future extensibility - context data available for hooks)
-            _ = self._prepare_context(source_agent, kwargs)
+            # Prepare context per the configured context_policy/input_filter and
+            # thread the filtered history through to the target agent.
+            handoff_data = self._prepare_context(source_agent, kwargs)
             
             # Build prompt with context
             context_prefix = f"[Handoff from {source_agent.name}] "
@@ -677,7 +716,8 @@ class Handoff:
             
             # Resolve runtime at turn-time instead of using construction-time pin
             response = self._execute_with_runtime_resolution(
-                source_agent, full_prompt, effective_tools, kwargs
+                source_agent, full_prompt, effective_tools, kwargs,
+                prior_messages=handoff_data.messages,
             )
             
             result = HandoffResult(
@@ -769,8 +809,9 @@ class Handoff:
                 # Execute callback
                 self._execute_callback(self.config.on_handoff or self.on_handoff, source_agent, kwargs)
                 
-                # Prepare context (for future extensibility)
-                _ = self._prepare_context(source_agent, kwargs)
+                # Prepare context per the configured context_policy/input_filter
+                # and thread the filtered history through to the target agent.
+                handoff_data = self._prepare_context(source_agent, kwargs)
                 
                 # Build prompt
                 context_prefix = f"[Handoff from {source_agent.name}] "
@@ -785,7 +826,8 @@ class Handoff:
                 
                 # Resolve runtime at turn-time for async execution
                 response = await self._execute_with_runtime_resolution_async(
-                    source_agent, full_prompt, effective_tools, kwargs
+                    source_agent, full_prompt, effective_tools, kwargs,
+                    prior_messages=handoff_data.messages,
                 )
                 
                 result = HandoffResult(
@@ -898,20 +940,39 @@ class Handoff:
                 # Prepare handoff data with context policy
                 handoff_data = self._prepare_context(source_agent, kwargs)
                 
-                # Get the last user message or context to pass to target agent
+                # Get the last user message or context to pass to target agent.
+                # Track the index of the message chosen for ``prompt`` so exactly
+                # that item is excluded from the seeded history (it may not be the
+                # final list item if an assistant/tool message follows it).
                 last_message = None
-                for msg in reversed(handoff_data.messages):
+                selected_index = None
+                for i in range(len(handoff_data.messages) - 1, -1, -1):
+                    msg = handoff_data.messages[i]
                     if isinstance(msg, dict) and msg.get('role') == 'user':
                         last_message = msg.get('content', '')
+                        selected_index = i
                         break
                 
                 if not last_message and handoff_data.messages:
-                    last_msg = handoff_data.messages[-1]
+                    selected_index = len(handoff_data.messages) - 1
+                    last_msg = handoff_data.messages[selected_index]
                     if isinstance(last_msg, dict):
                         last_message = last_msg.get('content', '')
                     else:
                         last_message = str(last_msg)
-                
+
+                # Tool-style handoff (as_tool): ContextPolicy.NONE discards the
+                # history, so the prompt must come from the generated ``task``/
+                # ``prompt`` argument instead. Without this the sub-agent would
+                # never run and the tool would report a hollow "completed". The
+                # task is the sub-agent's whole instruction, so it is passed
+                # verbatim rather than wrapped in a ``[Handoff from …]`` prefix.
+                task_prompt = None
+                if not last_message and not self.input_type:
+                    task_prompt = kwargs.get('task') or kwargs.get('prompt')
+                    if task_prompt:
+                        last_message = str(task_prompt)
+
                 # Prepare context information
                 context_info = f"[Handoff from {source_agent.name}] "
                 if kwargs and self.input_type:
@@ -919,15 +980,26 @@ class Handoff:
                 
                 # Execute the target agent with tool boundary enforcement
                 if last_message:
-                    prompt = context_info + last_message
+                    prompt = last_message if task_prompt else context_info + last_message
                     logger.info(f"Handing off to {self.agent.name} with prompt: {prompt}")
                     
                     # Compute effective tools based on tool policy
                     effective_tools = self._compute_effective_tools(source_agent)
                     
-                    # Resolve runtime at turn-time for tool function execution
+                    # Resolve runtime at turn-time for tool function execution.
+                    # The selected message is already folded into ``prompt`` above,
+                    # so exclude exactly that item from the seeded history to avoid
+                    # duplicating it while preserving any messages that follow it.
+                    if selected_index is None:
+                        prior_messages = list(handoff_data.messages)
+                    else:
+                        prior_messages = (
+                            handoff_data.messages[:selected_index]
+                            + handoff_data.messages[selected_index + 1:]
+                        )
                     response = self._execute_with_runtime_resolution(
-                        source_agent, prompt, effective_tools, kwargs
+                        source_agent, prompt, effective_tools, kwargs,
+                        prior_messages=prior_messages,
                     )
                     
                     result = HandoffResult(
@@ -995,6 +1067,17 @@ class Handoff:
                     )
                 )
             handoff_tool.__signature__ = inspect.Signature(sig_params)
+        elif self.config.context_policy == ContextPolicy.NONE:
+            # Tool-style handoff (as_tool): no conversation context is passed, so
+            # advertise an explicit ``task`` parameter the caller must fill. This
+            # is what ``handoff_tool`` reads as the sub-agent's prompt above.
+            handoff_tool.__signature__ = inspect.Signature([
+                inspect.Parameter(
+                    'task',
+                    inspect.Parameter.KEYWORD_ONLY,
+                    annotation=str,
+                )
+            ])
         
         return handoff_tool
 
@@ -1468,8 +1551,9 @@ class TypedHandoff(Handoff, Generic[T]):
             # Execute on_handoff callback
             self._execute_callback(self.config.on_handoff or self.on_handoff, source_agent, kwargs)
             
-            # Apply input filter if provided (fixes Greptile P1 issue)
-            _ = self._prepare_context(source_agent, kwargs)
+            # Apply context policy / input filter; the resulting messages are
+            # seeded onto the target only for the duration of its chat below.
+            handoff_data = self._prepare_context(source_agent, kwargs)
             
             # Validate payload against schema
             try:
@@ -1496,7 +1580,8 @@ class TypedHandoff(Handoff, Generic[T]):
             logger.info(f"Typed handoff to {self.agent.name} with validated payload")
             
             # Execute with timeout if sync
-            response = self.agent.chat(full_prompt)
+            with self._seed_target_history(handoff_data.messages):
+                response = self.agent.chat(full_prompt)
             
             result = HandoffResult(
                 success=True,
@@ -1581,8 +1666,9 @@ class TypedHandoff(Handoff, Generic[T]):
                 # Execute callback
                 self._execute_callback(self.config.on_handoff or self.on_handoff, source_agent, kwargs)
                 
-                # Apply input filter if provided (fixes Greptile P1 issue)
-                _ = self._prepare_context(source_agent, kwargs)
+                # Apply context policy / input filter; the resulting messages are
+                # seeded onto the target only for the duration of its chat below.
+                handoff_data = self._prepare_context(source_agent, kwargs)
                 
                 # Validate payload against schema
                 try:
@@ -1608,12 +1694,13 @@ class TypedHandoff(Handoff, Generic[T]):
                 logger.info(f"Async typed handoff to {self.agent.name}")
                 
                 # Execute - check for async chat method
-                if hasattr(self.agent, 'achat'):
-                    response = await self.agent.achat(full_prompt)
-                else:
-                    # Run sync chat in executor
-                    loop = asyncio.get_event_loop()
-                    response = await loop.run_in_executor(None, self.agent.chat, full_prompt)
+                with self._seed_target_history(handoff_data.messages):
+                    if hasattr(self.agent, 'achat'):
+                        response = await self.agent.achat(full_prompt)
+                    else:
+                        # Run sync chat in executor
+                        loop = asyncio.get_event_loop()
+                        response = await loop.run_in_executor(None, self.agent.chat, full_prompt)
                 
                 return HandoffResult(
                     success=True,

src/praisonai-agents/praisonaiagents/agent/launch_security.py

@@ -0,0 +1,88 @@
+"""Shared authorisation + bind guard for the ``launch()`` serving paths.
+
+Both ``Agent.launch()`` (``agent/execution_mixin.py``) and
+``PraisonAIAgents.launch()`` (``agents/agents.py``) expose a prompt-execution
+HTTP endpoint. They must enforce identical request authorisation and must not
+silently bind all interfaces without a token. Factoring the two helpers here
+keeps that policy in one place so the two launch paths cannot drift apart.
+
+Stdlib only — no heavy imports, no new public params.
+"""
+
+from __future__ import annotations
+
+import os
+import secrets as _secrets
+from typing import Optional
+
+__all__ = [
+    "launch_auth_token",
+    "authorise_launch_request",
+    "resolve_launch_host",
+]
+
+# Hosts that are safe to serve keyless on: loopback only.
+_LOOPBACK_HOSTS = frozenset({"127.0.0.1", "::1", "localhost"})
+
+# One auto-generated token per process, reused across launches so a later
+# non-loopback launch cannot mint a fresh token and lock out an endpoint that
+# is already serving with the previously generated one.
+_GENERATED_TOKEN: Optional[str] = None
+
+
+def launch_auth_token() -> Optional[str]:
+    """Return the configured launch bearer token, if any."""
+    return os.environ.get("PRAISONAI_LAUNCH_AUTH_TOKEN")
+
+
+def authorise_launch_request(request) -> bool:
+    """Return True if the request carries the configured bearer token.
+
+    When no token is configured this returns True (local-dev flow preserved).
+    Accepts either ``Authorization: Bearer <token>`` or ``X-Auth-Token``.
+    """
+    token = launch_auth_token()
+    if not token:
+        return True
+    auth = request.headers.get("Authorization", "")
+    if auth.startswith("Bearer ") and _secrets.compare_digest(auth[7:], token):
+        return True
+    supplied = request.headers.get("X-Auth-Token", "")
+    return bool(supplied) and _secrets.compare_digest(supplied, token)
+
+
+def _is_loopback(host: str) -> bool:
+    return host in _LOOPBACK_HOSTS
+
+
+def resolve_launch_host(host: str) -> str:
+    """Fail-closed bind guard, mirroring the ``serve``/jobs precedent.
+
+    * Token set → bind ``host`` unchanged (auth protects the endpoint).
+    * Token unset + loopback host → bind unchanged (local dev unchanged).
+    * Token unset + non-loopback host → auto-generate a token, export it via
+      ``PRAISONAI_LAUNCH_AUTH_TOKEN`` so every route enforces it, and print it
+      once. This closes the unauthenticated-exposure hole without refusing to
+      start.
+
+    Returns the host to bind (unchanged; the guard acts on the token, not the
+    host, so an explicit ``0.0.0.0`` is still honoured — just never keyless).
+
+    A generated token is reused for the lifetime of the process: repeated
+    non-loopback launches share the one token rather than each minting a fresh
+    one, so a later launch cannot invalidate an already-running endpoint's
+    clients.
+    """
+    if launch_auth_token() or _is_loopback(host):
+        return host
+
+    global _GENERATED_TOKEN
+    if _GENERATED_TOKEN is None:
+        _GENERATED_TOKEN = _secrets.token_urlsafe(32)
+        print(
+            f"🔐 launch() bound to non-loopback host {host!r} without "
+            f"PRAISONAI_LAUNCH_AUTH_TOKEN set. Generated a one-time bearer token "
+            f"(set PRAISONAI_LAUNCH_AUTH_TOKEN to override): {_GENERATED_TOKEN}"
+        )
+    os.environ["PRAISONAI_LAUNCH_AUTH_TOKEN"] = _GENERATED_TOKEN
+    return host

src/praisonai-agents/praisonaiagents/agent/ocr_agent.py

@@ -89,8 +89,8 @@ class OCRAgent:
             ocr: OCR configuration
             verbose: Verbosity level for output
         """
-        if llm is None and model is not None:
-            llm = model
+        from ..utils.model_alias import resolve_model_name
+        llm = resolve_model_name(llm, model, "OCRAgent")
         
         self.name = name or "OCRAgent"
         self.instructions = instructions

src/praisonai-agents/praisonaiagents/agent/placement.py

@@ -0,0 +1,211 @@
+"""One vocabulary for "where does this run?", shared by Agent, AgentFlow and AgentTeam.
+
+Two questions get asked about placement and they have different answers:
+
+``run_on=``
+    Where the **whole agent** runs -- the model calls, the loop, the tools, all
+    of it on someone else's machine. Only a *managed runtime* can answer this,
+    because running the loop remotely means something has to host the loop.
+
+``tools_run_on=``
+    Where **the tools** run. Thinking stays on this machine; only the shell,
+    the file writes and the code execution move. Any sandbox or compute place
+    can answer this, because all it has to do is run a command.
+
+Keeping the two words apart is the entire point. The old surface used one word
+(``run_on=``) on ``AgentFlow``/``AgentTeam`` for the tools-only meaning, another
+(``compute=``) on ``LocalAgent`` for exactly the same meaning, and a third
+(``backend=``) for the whole-agent meaning -- so the same concept had two names
+and one name had two concepts.
+
+Because the two questions have disjoint answer sets, a wrong pairing is a typo
+worth catching rather than a preference worth honouring. ``run_on="docker"``
+cannot mean anything: Docker does not host agent loops. It gets a ``TypeError``
+naming the parameter the user actually wanted, not a silent fallback.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Literal, NamedTuple, Optional, Union
+
+# Runtimes that can host an entire agent loop. Resolved from the wrapper's
+# entry-point registry when it is installed so third-party backends work with
+# `run_on=` too; the literal is the floor, not the ceiling.
+_FALLBACK_MANAGED = ("anthropic",)
+
+# Static spellings so editors can autocomplete and type-checkers can reject a
+# typo at the call site rather than at the first model turn. The runtime
+# functions below stay the authority for validation -- these exist for tooling,
+# which cannot call a function to learn a set.
+# Keep in sync with _compute_bridge._PROVIDERS | _sandbox_adapter.SANDBOX_ONLY.
+ManagedRuntime = Literal["anthropic"]
+
+ToolPlace = Literal[
+    "local", "subprocess", "sandlock", "docker", "e2b",
+    "modal", "daytona", "flyio", "tenki", "novita", "ssh",
+]
+
+
+def managed_runtimes() -> tuple:
+    """Names that ``run_on=`` accepts -- places that host a whole agent."""
+    try:
+        from praisonai.integrations.backend_registry import get_backend_registry
+
+        names = tuple(get_backend_registry().list_all_names())
+        if names:
+            return tuple(sorted(set(names) | set(_FALLBACK_MANAGED)))
+    except Exception:
+        pass
+    return _FALLBACK_MANAGED
+
+
+def tool_places() -> list:
+    """Names that ``tools_run_on=`` accepts -- places that can run a command.
+
+    Includes the resolver's aliases. Validation runs before resolution, so
+    leaving them out rejected spellings the resolver handles happily:
+    ``tools_run_on="native"`` raised "not a known place" while
+    ``resolve_compute("native")`` returned a working sandlock adapter.
+    """
+    try:
+        from ..managed._compute_bridge import _ALIASES, available_providers
+
+        return sorted(set(available_providers()) | set(_ALIASES))
+    except Exception:
+        return ["local", "docker"]
+
+
+class Placement(NamedTuple):
+    """The resolved answer to "where does this run?"."""
+
+    whole: Optional[Any] = None   # managed runtime name/object, or None
+    tools: Optional[Any] = None   # tools place name/object, or None
+
+
+def _name_of(value: Any) -> Optional[str]:
+    """The comparable name of a placement value, or None if it is an object.
+
+    Objects (a built provider, a live ``SSHSandbox``) carry their own config and
+    are never second-guessed -- only bare strings get routed by name.
+    """
+    return value.lower() if isinstance(value, str) else None
+
+
+def _quote_list(names) -> str:
+    return ", ".join(repr(n) for n in names)
+
+
+def resolve_placement(
+    owner: str,
+    *,
+    run_on: Any = None,
+    tools_run_on: Any = None,
+    backend: Any = None,
+    supports_run_on: bool = True,
+) -> Placement:
+    """Validate placement arguments and return where things should run.
+
+    ``owner`` is the class name, used only to make errors read naturally.
+    ``supports_run_on`` is False for AgentFlow/AgentTeam, which can place tools
+    but cannot (yet) hand a whole multi-agent run to a managed runtime.
+
+    Raises:
+        TypeError: on any combination that names two different places for the
+            same thing, or a place that cannot do the job asked of it.
+    """
+    managed = managed_runtimes()
+    places = tool_places()
+
+    # ── a place that cannot do the job asked of it ───────────────────────────
+    run_on_name = _name_of(run_on)
+    if run_on_name is not None and run_on_name not in managed:
+        if run_on_name in places:
+            raise TypeError(
+                f"{owner}(run_on={run_on!r}) is not valid: run_on= places the "
+                f"whole agent -- model calls, loop and tools -- on a managed "
+                f"runtime, and {run_on_name!r} runs commands but cannot host an "
+                f"agent loop.\n"
+                f"  To run only the tools there:  {owner}(tools_run_on={run_on!r})\n"
+                f"  Runtimes that host a whole agent: {_quote_list(managed)}"
+            )
+        raise TypeError(
+            f"{owner}(run_on={run_on!r}) is not a known managed runtime.\n"
+            f"  Runtimes that host a whole agent: {_quote_list(managed)}\n"
+            f"  Places that can run tools:        {_quote_list(places)} "
+            f"(pass those as tools_run_on=)"
+        )
+
+    tools_name = _name_of(tools_run_on)
+    if tools_name is not None and tools_name in managed and tools_name not in places:
+        raise TypeError(
+            f"{owner}(tools_run_on={tools_run_on!r}) is not valid: {tools_name!r} "
+            f"hosts an entire agent, not individual tools.\n"
+            f"  Did you mean:  {owner}(run_on={tools_run_on!r})"
+        )
+
+    # A typo here used to survive construction and only surface on the first
+    # model turn -- after the prompt was built and, for a real run, after the
+    # API spend. run_on= validates at the call site and YAML validates at load
+    # time; this is the same standard for the third spelling.
+    if tools_name is not None and tools_name not in places:
+        raise TypeError(
+            f"{owner}(tools_run_on={tools_run_on!r}) is not a known place.\n"
+            f"  Places that can run tools: {_quote_list(places)}\n"
+            f"  Runtimes that host a whole agent: {_quote_list(managed)} "
+            f"(pass those as run_on=)"
+        )
+
+    # Names that cannot work as a bare string, because the connection details
+    # live in the object. Catch it now rather than at first use.
+    if tools_name is not None:
+        try:
+            from ..managed._sandbox_adapter import NEEDS_INSTANCE
+        except Exception:
+            NEEDS_INSTANCE = {}
+        if tools_name in NEEDS_INSTANCE:
+            raise TypeError(
+                f"{owner}(tools_run_on={tools_run_on!r}) needs more than a name: "
+                f"{NEEDS_INSTANCE[tools_name]}"
+            )
+
+    # ── two names for the same thing ─────────────────────────────────────────
+    if run_on is not None and backend is not None:
+        raise TypeError(
+            f"{owner}(run_on=..., backend=...) sets the agent's runtime twice. "
+            f"run_on={run_on!r} is the short form of backend=HostedAgent("
+            f"provider={run_on!r}); pass one or the other.\n"
+            f"  Use backend= only when you need to configure the runtime "
+            f"(model, system prompt, tools)."
+        )
+
+    if run_on is not None and tools_run_on is not None:
+        raise TypeError(
+            f"{owner}(run_on={run_on!r}, tools_run_on={tools_run_on!r}) points "
+            f"the tools at two machines. run_on= already runs everything -- "
+            f"including tools -- on {run_on!r}.\n"
+            f"  Whole agent remote:  {owner}(run_on={run_on!r})\n"
+            f"  Only tools remote:   {owner}(tools_run_on={tools_run_on!r})"
+        )
+
+    if backend is not None and tools_run_on is not None:
+        raise TypeError(
+            f"{owner}(backend=..., tools_run_on={tools_run_on!r}) points the "
+            f"tools at two machines: backend= runs the whole agent (tools "
+            f"included) on its managed runtime, so the tools cannot also run on "
+            f"{tools_run_on!r}.\n"
+            f"  Drop tools_run_on= to keep the hosted runtime, or drop backend= "
+            f"to keep local thinking with remote tools."
+        )
+
+    # ── a class that cannot answer this question ─────────────────────────────
+    if run_on is not None and not supports_run_on:
+        raise TypeError(
+            f"{owner}(run_on={run_on!r}) is not supported: run_on= hands one "
+            f"agent's whole loop to a managed runtime, and a {owner} "
+            f"orchestrates several agents locally.\n"
+            f"  To run every step's tools in one shared sandbox:\n"
+            f"      {owner}(tools_run_on={run_on!r})\n"
+            f"  To host a single agent's loop, put run_on= on that Agent."
+        )
+
+    return Placement(whole=run_on, tools=tools_run_on)

src/praisonai-agents/praisonaiagents/agent/realtime_agent.py

@@ -57,7 +57,8 @@ class RealtimeAgent:
     def __init__(
         self,
         name: str = "RealtimeAgent",
-        llm: Optional[str] = None,
+        llm: Optional[str] = None,  # Deprecated alias for model=
+        model: Optional[str] = None,
         realtime: Optional[Union[bool, Dict, RealtimeConfig]] = None,
         instructions: Optional[str] = None,
         verbose: bool = True,
@@ -67,14 +68,16 @@ class RealtimeAgent:
         
         Args:
             name: Agent name
-            llm: LLM model (default: gpt-4o-realtime-preview)
+            llm: Deprecated alias for model=. Passing both raises TypeError.
+            model: Model to use (default: gpt-4o-realtime-preview)
             realtime: Realtime configuration (bool, dict, or RealtimeConfig)
             instructions: System instructions
             verbose: Enable verbose output
             **kwargs: Additional arguments
         """
+        from ..utils.model_alias import resolve_model_name
         self.name = name
-        self.llm = llm or "gpt-4o-realtime-preview"
+        self.llm = resolve_model_name(llm, model, "RealtimeAgent") or "gpt-4o-realtime-preview"
         self.instructions = instructions
         self.verbose = verbose
         

src/praisonai-agents/praisonaiagents/agent/sandbox_mixin.py

@@ -52,19 +52,61 @@ class SandboxMixin:
         
         return self._sandbox_manager
     
+
+    def _manager_for(self, run_in: Optional[Union[bool, str, 'SandboxConfig']]):
+        """Return the sandbox manager for a ``run_in=`` argument.
+
+        ``run_in=`` names where a single ``execute_code()`` call runs, so the
+        place lives on the call that uses it rather than on the constructor.
+        ``Agent(sandbox=...)`` still sets the default for every call.
+
+        Managers are cached per place: a fresh ``SandboxManager`` per call would
+        start and tear down a container each time, which for Docker is seconds
+        of latency and for any backend means the previous call's files are gone.
+        """
+        if run_in is None:
+            if not self.has_sandbox:
+                raise RuntimeError(
+                    "No sandbox configured. Either name a place on the call --\n"
+                    "    agent.execute_code_sync(code, run_in='docker')\n"
+                    "or set a default for every call with Agent(sandbox='docker')."
+                )
+            return self.get_sandbox_manager()
+
+        from ..sandbox import SandboxConfig, SandboxManager
+
+        if run_in is True:
+            config = SandboxConfig.subprocess()
+        elif isinstance(run_in, str):
+            config = SandboxConfig(sandbox_type=run_in)
+        else:
+            config = run_in
+
+        key = getattr(config, "sandbox_type", None) or repr(config)
+        cache = getattr(self, "_sandbox_managers", None)
+        if cache is None:
+            cache = self._sandbox_managers = {}
+        if key not in cache:
+            cache[key] = SandboxManager(config)
+        return cache[key]
+
     async def execute_code(
         self,
         code: str,
         language: str = "python",
         check_security: bool = True,
+        run_in: Optional[Union[bool, str, 'SandboxConfig']] = None,
         **kwargs
     ) -> 'SandboxResult':
-        """Execute code safely in configured sandbox.
+        """Execute code safely in a sandbox.
         
         Args:
             code: Code to execute
             language: Programming language (python, bash, etc.)
             check_security: Whether to run security pre-checks
+            run_in: Where to run this call -- "docker", "sandlock",
+                "subprocess", a SandboxConfig, or True for the default local
+                sandbox. Overrides Agent(sandbox=...) for this call only.
             **kwargs: Additional arguments passed to sandbox.execute()
             
         Returns:
@@ -73,10 +115,7 @@ class SandboxMixin:
         Raises:
             RuntimeError: If no sandbox is configured
         """
-        if not self.has_sandbox:
-            raise RuntimeError(
-                "No sandbox configured. Set sandbox=True or provide SandboxConfig to Agent()"
-            )
+        manager = self._manager_for(run_in)
         
         # Security pre-check if enabled
         security_warnings = None
@@ -88,7 +127,6 @@ class SandboxMixin:
                 if self.verbose:
                     logger.warning(f"Security warnings for code execution:\n{warning_text}")
 
-        manager = self.get_sandbox_manager()
         result = await manager.run_code(code, language=language, **kwargs)
 
         # Attach warnings to the result rather than forwarding a `metadata`
@@ -109,6 +147,7 @@ class SandboxMixin:
         code: str,
         language: str = "python", 
         check_security: bool = True,
+        run_in: Optional[Union[bool, str, 'SandboxConfig']] = None,
         **kwargs
     ) -> 'SandboxResult':
         """Synchronous wrapper for execute_code.
@@ -123,12 +162,15 @@ class SandboxMixin:
             SandboxResult with execution details
         """
         from ..approval.utils import run_coroutine_safely
-        return run_coroutine_safely(self.execute_code(code, language, check_security, **kwargs))
+        return run_coroutine_safely(
+            self.execute_code(code, language, check_security, run_in=run_in, **kwargs)
+        )
     
     async def run_shell_command(
         self,
         command: Union[str, list],
         check_security: bool = True,
+        run_in: Optional[Union[bool, str, 'SandboxConfig']] = None,
         **kwargs
     ) -> 'SandboxResult':
         """Run a shell command in the sandbox.
@@ -141,8 +183,7 @@ class SandboxMixin:
         Returns:
             SandboxResult with execution details
         """
-        if not self.has_sandbox:
-            raise RuntimeError("No sandbox configured")
+        manager = self._manager_for(run_in)
         
         # Convert command to string for security checking
         if isinstance(command, list):
@@ -159,7 +200,7 @@ class SandboxMixin:
                 if self.verbose:
                     logger.warning(f"Security warnings for command:\n{warning_text}")
         
-        async with self.get_sandbox_manager() as sandbox:
+        async with manager as sandbox:
             return await sandbox.run_command(command, **kwargs)
     
     def get_sandbox_status(self) -> Dict[str, Any]:

src/praisonai-agents/praisonaiagents/agent/tool_execution.py

@@ -374,6 +374,12 @@ class ToolExecutionMixin:
 
         return resolve_tool_names(tool_names)
 
+    def _resolve_tools_list(self, tools):
+        """Resolve any tool-name strings inside a mixed ``tools=`` list."""
+        from ..tools.resolver import resolve_tools_list
+
+        return resolve_tools_list(tools)
+
     def _cast_arguments(self, func, arguments):
         """Cast arguments to their expected types based on function signature."""
         if not callable(func) or not arguments:
@@ -489,6 +495,7 @@ class ToolExecutionMixin:
                 run_id=getattr(self, '_current_run_id', 'unknown'),
                 session_id=getattr(self, '_session_id', None) or 'default',
                 tool_name=function_name,
+                metadata={"tool_call_id": tool_call_id},
             ),
         )
 
@@ -746,26 +753,21 @@ class ToolExecutionMixin:
             if blocked_result is not None:
                 result = blocked_result
             else:
-                # Apply tool retry logic with exponential backoff
-                execution_config = getattr(self, '_execution_config', None)
-                if execution_config is None:
-                    # Fall back to reading individual config attributes for backward compatibility
-                    max_retry_limit = getattr(self, 'max_retry_limit', 2)
-                    retry_initial_delay = 1.0
-                    retry_backoff_factor = 2.0
-                    retry_jitter = 0.1
-                else:
-                    max_retry_limit = execution_config.max_retry_limit
-                    retry_initial_delay = execution_config.retry_initial_delay
-                    retry_backoff_factor = execution_config.retry_backoff_factor
-                    retry_jitter = execution_config.retry_jitter
-                
+                # Apply tool retry logic with exponential backoff.
+                # ONE vocabulary: the effective RetryPolicy caps how many times
+                # the tool body may run, whichever of the two loops does the
+                # retrying, and owns the backoff. ExecutionConfig.max_retry_limit
+                # / retry_* are translated into it (see
+                # _effective_tool_retry_policy) rather than run as a second,
+                # independent budget in different units.
+                retry_policy = self._effective_tool_retry_policy(function_name)
+                max_attempts = retry_policy.max_attempts
+
                 result = None
                 last_exception = None
-                
-                # max_retry_limit is the number of retries (not total attempts)
-                # So total attempts = 1 (initial) + max_retry_limit (retries)
-                for attempt in range(1, max_retry_limit + 2):
+
+                # max_attempts is the total number of tool-body runs.
+                for attempt in range(1, max_attempts + 1):
                     try:
                         # P8/G11: Apply tool timeout if configured
                         tool_timeout = getattr(self, '_tool_timeout', None)
@@ -807,7 +809,12 @@ class ToolExecutionMixin:
                                         _progress_active.clear()
                                     future.cancel()
                                     logging.warning(f"Tool {function_name} timed out after {tool_timeout}s")
-                                    result = {"error": f"Tool timed out after {tool_timeout}s", "timeout": True}
+                                    # Tag as an OUTER wall-clock timeout: unlike an
+                                    # inner-loop timeout (already retried to
+                                    # exhaustion), the body ran once here and the
+                                    # inner budget never saw it, so this one still
+                                    # earns an outer retry pass under the single budget.
+                                    result = {"error": f"Tool timed out after {tool_timeout}s", "timeout": True, "_outer_timeout": True}
                                     # The timed-out thread cannot be reclaimed. Retire this
                                     # executor so the next call gets a fresh worker instead of
                                     # queuing behind a permanently stuck one — but bound how many
@@ -826,59 +833,80 @@ class ToolExecutionMixin:
                         
                         # Check if the result indicates a retryable error
                         if isinstance(result, dict) and result.get("error"):
-                            # Check if this is a circuit breaker error (always retryable)
-                            if result.get("circuit_open"):
-                                raise ToolExecutionError(
-                                    result["error"],
-                                    tool_name=function_name,
-                                    agent_id=self.name,
-                                    is_retryable=True,
-                                )
-                            # Check if this is a timeout error (retryable)
-                            elif result.get("timeout"):
-                                raise ToolExecutionError(
-                                    result["error"],
-                                    tool_name=function_name,
-                                    agent_id=self.name,
-                                    is_retryable=True,
-                                )
-                            # For other error dicts: approval/permission denials are legitimate
-                            # non-retryable outcomes; everything else represents a tool failure
-                            # that should engage the outer retry/backoff loop.
-                            elif result.get("approval_denied") or result.get("permission_denied") or result.get("approval_error") or result.get("policy_denied") or result.get("guardrail_denied"):
+                            # Approval/permission/policy/guardrail denials are
+                            # legitimate non-retryable outcomes: stop immediately.
+                            if result.get("approval_denied") or result.get("permission_denied") or result.get("approval_error") or result.get("policy_denied") or result.get("guardrail_denied"):
                                 break
-                            else:
-                                # Avoid compounding with the inner retry loop in
-                                # _execute_tool_with_circuit_breaker: error types it already
-                                # retries (e.g. timeout/rate_limit/connection_error) are
-                                # exhausted by the time they reach here, so escalating them as
-                                # retryable would re-run the entire inner loop from scratch on
-                                # every outer attempt. Only escalate error types the inner loop
-                                # does NOT retry (e.g. "unknown") so they get one outer pass.
-                                error_type = self._classify_error_type(result, None)
-                                inner_policy = self._get_tool_retry_policy(function_name)
-                                is_retryable = error_type not in inner_policy.retry_on
-                                raise ToolExecutionError(
-                                    result.get("error", f"Tool '{function_name}' failed"),
-                                    tool_name=function_name,
-                                    agent_id=self.name,
-                                    is_retryable=is_retryable,
-                                )
+                            # ONE budget: the inner loop
+                            # (_execute_tool_with_circuit_breaker) already retries
+                            # its retry_on error types (timeout/rate_limit/
+                            # connection_error) to exhaustion before returning here,
+                            # so re-escalating them as retryable would restart the
+                            # whole inner budget on every outer attempt and run the
+                            # tool up to max_attempts² times.
+                            error_type = self._classify_error_type(result, None)
+                            inner_policy = self._get_tool_retry_policy(function_name)
+                            # Only escalate to an outer retry pass when the failure is
+                            # a transient the inner loop did NOT already re-drive:
+                            #   • _outer_timeout — this loop's own wall-clock guard
+                            #     tripped; the body ran once and the inner budget
+                            #     never saw it. BUT the timed-out body is still
+                            #     running in an orphaned thread (future.cancel() is
+                            #     a no-op once started), so retrying a mutating tool
+                            #     would duplicate its side effects. Only re-drive it
+                            #     when the tool is known idempotent/read-only.
+                            #   • circuit_open — returned by the inner loop WITHOUT a
+                            #     body run, so retrying costs no duplicate side effect.
+                            #   • _praison_retryable — a raised exception was flattened
+                            #     into this error dict (see _execute_tool_impl); honour
+                            #     that verdict, but ONLY for error types the inner loop
+                            #     does not itself retry, or inner×outer would run the
+                            #     body max_attempts² times.
+                            # A plain error dict the tool body *returned* carries no tag
+                            # — it is the tool's own reported outcome, not a transient
+                            # the framework must re-drive. The body already ran to
+                            # completion, so re-running it would duplicate its side
+                            # effects (charge money, send a message, write a row) for a
+                            # decision the tool already made. If a user genuinely wants
+                            # such an outcome retried, its error type belongs in the
+                            # RetryPolicy.retry_on the inner loop honours — not a word
+                            # in the tool's own payload. Surface it once.
+                            is_retryable = bool(
+                                (result.get("_outer_timeout")
+                                    and self._is_tool_idempotent(function_name))
+                                or result.get("circuit_open")
+                                or (result.get("_praison_retryable") is True
+                                    and error_type not in inner_policy.retry_on
+                                    # The body already ran and may have completed
+                                    # its side effect before raising. Honour an
+                                    # explicit non-idempotent declaration here, as
+                                    # the _outer_timeout clause above already does.
+                                    and not self._tool_declares_not_idempotent(function_name))
+                            )
+                            # Strip the private control-plane tag before it can reach
+                            # the model or be re-surfaced as the tool's payload.
+                            result.pop("_praison_retryable", None)
+                            raise ToolExecutionError(
+                                result.get("error", f"Tool '{function_name}' failed"),
+                                tool_name=function_name,
+                                agent_id=self.name,
+                                is_retryable=is_retryable,
+                            )
                         else:
                             # Success path - return the result
                             break
                             
                     except ToolExecutionError as e:
                         last_exception = e
-                        # Only retry if the error is marked as retryable and we have retries left
-                        # attempt starts at 1, so (attempt - 1) gives us the retry count
-                        if not e.is_retryable or (attempt - 1) >= max_retry_limit:
+                        # Only retry if the error is marked as retryable and the
+                        # policy still has attempts left.
+                        if not e.is_retryable or attempt >= max_attempts:
                             raise e
-                        
-                        # Calculate delay for exponential backoff
-                        delay = BackoffPolicy.delay(attempt, retry_initial_delay, retry_backoff_factor, retry_jitter)
+
+                        # Calculate delay for exponential backoff (policy owns it)
+                        delay = retry_policy.get_delay_ms(attempt - 1) / 1000.0
                         logging.warning(
-                            f"Tool {function_name} failed (attempt {attempt}/{max_retry_limit + 1}): {e}. "
+                            f"Tool {function_name} failed (attempt {attempt}/{max_attempts}): {e}. "
                             f"Retrying in {delay:.2f}s..."
                         )
                         time.sleep(delay)
@@ -894,15 +922,14 @@ class ToolExecutionMixin:
                             is_retryable=is_retryable,
                         )
                         last_exception = tool_error
-                        
-                        # attempt starts at 1, so (attempt - 1) gives us the retry count
-                        if not is_retryable or (attempt - 1) >= max_retry_limit:
+
+                        if not is_retryable or attempt >= max_attempts:
                             raise tool_error from e
-                        
-                        # Calculate delay for exponential backoff
-                        delay = BackoffPolicy.delay(attempt, retry_initial_delay, retry_backoff_factor, retry_jitter)
+
+                        # Calculate delay for exponential backoff (policy owns it)
+                        delay = retry_policy.get_delay_ms(attempt - 1) / 1000.0
                         logging.warning(
-                            f"Tool {function_name} failed (attempt {attempt}/{max_retry_limit + 1}): {e}. "
+                            f"Tool {function_name} failed (attempt {attempt}/{max_attempts}): {e}. "
                             f"Retrying in {delay:.2f}s..."
                         )
                         time.sleep(delay)
@@ -1994,6 +2021,12 @@ class ToolExecutionMixin:
         if not decision.approved:
             error_msg = f"Tool execution denied: {decision.reason}"
             logging.warning(error_msg)
+            feedback = getattr(decision, "feedback", None)
+            if feedback:
+                error_msg = (
+                    f"Tool '{function_name}' was denied by the user. "
+                    f"Do not retry it as-is. User guidance: {feedback}"
+                )
             return {"error": error_msg, "approval_denied": True}
         
         if decision.modified_args:
@@ -2027,6 +2060,12 @@ class ToolExecutionMixin:
         if not decision.approved:
             error_msg = f"Tool execution denied: {decision.reason}"
             logging.warning(error_msg)
+            feedback = getattr(decision, "feedback", None)
+            if feedback:
+                error_msg = (
+                    f"Tool '{function_name}' was denied by the user. "
+                    f"Do not retry it as-is. User guidance: {feedback}"
+                )
             return {"error": error_msg, "approval_denied": True}
         
         if decision.modified_args:
@@ -2069,7 +2108,13 @@ class ToolExecutionMixin:
                         result.get("guardrail_denied") or
                         result.get("circuit_open")):
                         return result
-                    
+
+                    # The body already ran and may have completed its side effect
+                    # before failing. Honour an explicit non-idempotent declaration
+                    # rather than re-driving it (send a second email, charge twice).
+                    if self._tool_declares_not_idempotent(function_name):
+                        return result
+
                     # Determine error type for retry policy
                     error_type = self._classify_error_type(result, last_exception)
                     
@@ -2551,8 +2596,20 @@ class ToolExecutionMixin:
                             f"{error_msg} Expected parameters for '{function_name}' — "
                             f"required: {schema['required']}, optional: {schema['optional']}."
                         )
-                        return {"error": error_msg, "expected_parameters": schema}
-                return {"error": error_msg}
+                        # An argument-binding error is a programming error — the same
+                        # call will always fail — so mark it terminal and never retry.
+                        return {"error": error_msg, "expected_parameters": schema, "_praison_retryable": False}
+                # A tool that *raised* is flattened into an error dict here, which
+                # erases the exception type the retry policy needs. Carry the same
+                # verdict the exception paths use so the outer loop can retry a
+                # transient failure without re-running a tool that merely *returned*
+                # an error dict of its own. Programming errors are terminal;
+                # everything else may be retried. Private key: never leaks to the
+                # model and cannot collide with a tool's own "retryable" field.
+                return {
+                    "error": error_msg,
+                    "_praison_retryable": not isinstance(e, (ValueError, TypeError, AttributeError)),
+                }
 
         # Unresolved: return a corrective, model-readable message so the model can
         # retry with a valid name instead of repeating the same mistake.
@@ -2939,17 +2996,124 @@ class ToolExecutionMixin:
         else:
             return f"Unknown bridge tool: {function_name}"
 
+    def _tool_declares_not_idempotent(self, tool_name):
+        """True only when the tool *explicitly* declares itself unsafe to re-run.
+
+        Deliberately narrower than ``_is_tool_idempotent``, which answers False
+        for unknown tools as a safe default: gating a retry on that would stop
+        retrying every unregistered user tool.
+
+        This keys on an explicit declaration and nothing else. In particular it
+        must NOT consult the shared ``MUTATING_TOOLS`` name registry -- that is a
+        73-name list maintained for the escalation loop guard, and using it here
+        silently removed the retry from any *undeclared* tool that happened to
+        share a name with an entry (``write_file``, ``store_memory``, ``mkdir``...).
+        """
+        tools = getattr(self, 'tools', [])
+        if not isinstance(tools, (list, tuple)):
+            tools = []
+        for tool in tools:
+            name_attr = getattr(tool, '__name__', None) or getattr(tool, 'name', None)
+            if name_attr == tool_name:
+                explicit = getattr(tool, 'idempotent', None)
+                if not isinstance(explicit, bool):
+                    # ``restart_safe`` is the public replay-safety contract on
+                    # ``@tool`` (FunctionTool) and ``BaseTool``: ``False`` marks an
+                    # effectful tool that must never be silently re-executed.
+                    explicit = getattr(tool, 'restart_safe', None)
+                if isinstance(explicit, bool):
+                    return not explicit
+                return False
+        # A tool from the process-global registry (only reachable under
+        # ToolConfig(allow_global_tools=True)) can carry its own declaration;
+        # ``_is_tool_idempotent`` consults it, so this must too.
+        if getattr(self, '_allow_global_tools', False):
+            try:
+                from ..tools.registry import get_registry
+                global_tool = get_registry().get(tool_name)
+                for attr in ('idempotent', 'restart_safe'):
+                    explicit = getattr(global_tool, attr, None)
+                    if isinstance(explicit, bool):
+                        return not explicit
+            except Exception:
+                pass
+        return False
+
+    def _is_tool_idempotent(self, tool_name):
+        """Whether re-running ``tool_name`` is safe (no duplicated side effects).
+
+        Used to decide if an OUTER wall-clock timeout may be retried: the timed-out
+        tool body is still running in an orphaned thread (``future.cancel()`` is a
+        no-op once started), so auto-retrying a mutating tool would duplicate its
+        side effects (send a second email, charge twice). Only tools classified as
+        read-only/idempotent are safe to re-drive.
+
+        Precedence: explicit ``idempotent`` attribute on the tool > the shared
+        read-only/mutating name registry (reused from the escalation loop guard).
+        Unknown tools default to NOT idempotent (safe: surface the timeout once).
+        """
+        tools = getattr(self, 'tools', [])
+        if not isinstance(tools, (list, tuple)):
+            tools = []
+        for tool in tools:
+            tool_name_attr = getattr(tool, '__name__', None) or getattr(tool, 'name', None)
+            if tool_name_attr == tool_name:
+                explicit = getattr(tool, 'idempotent', None)
+                if isinstance(explicit, bool):
+                    return explicit
+                break
+        # A tool from the process-global registry (only reachable when
+        # ToolConfig(allow_global_tools=True)) can carry its own explicit
+        # ``idempotent`` flag even when its name is absent from the shared
+        # read-only/mutating registries below.
+        if getattr(self, '_allow_global_tools', False):
+            try:
+                from ..tools.registry import get_registry
+                global_tool = get_registry().get(tool_name)
+                explicit = getattr(global_tool, 'idempotent', None)
+                if isinstance(explicit, bool):
+                    return explicit
+            except Exception:
+                pass
+        try:
+            from ..escalation.loop_guard import IDEMPOTENT_TOOLS, MUTATING_TOOLS
+        except Exception:
+            return False
+        if tool_name in IDEMPOTENT_TOOLS:
+            return True
+        if tool_name in MUTATING_TOOLS:
+            return False
+        return False
+
     def _get_tool_retry_policy(self, tool_name):
         """Get retry policy for a tool (tool-level > agent-level > default).
-        
+
+        Kept as the historical name; both retry loops now go through
+        ``_effective_tool_retry_policy`` so they share one policy object.
+
         Args:
             tool_name: Name of the tool to get retry policy for
             
         Returns:
             RetryPolicy instance
         """
+        return self._effective_tool_retry_policy(tool_name)
+
+    def _effective_tool_retry_policy(self, tool_name):
+        """The one RetryPolicy that governs every retry of ``tool_name``.
+
+        Precedence: per-tool ``.retry_policy`` > agent-level
+        ``ToolConfig(retry_policy=...)`` > a policy translated from the
+        ExecutionConfig spelling of the same knobs.
+
+        ``ExecutionConfig`` speaks a second vocabulary for the same thing --
+        ``max_retry_limit`` counts *retries* (so total runs = limit + 1),
+        ``retry_initial_delay`` is in *seconds*, and ``retry_jitter`` is a
+        *fraction*. Translating it here, once, is what keeps the two retry loops
+        from running two independent budgets in two different units.
+        """
         from ..tools.retry import RetryPolicy
-        
+
         # Check for tool-level retry policy first
         tools = getattr(self, 'tools', [])
         # Handle non-iterable tools (e.g., single MCP instance)
@@ -2960,16 +3124,43 @@ class ToolExecutionMixin:
             tool_policy = getattr(tool, 'retry_policy', None) if hasattr(tool, 'retry_policy') else None
             if tool_name_attr == tool_name and tool_policy is not None:
                 return tool_policy
-        
+
         # Check for agent-level retry policy
         agent_policy = getattr(self, '_tool_retry_policy', None)
         if agent_policy is not None:
             return agent_policy
-        
-        # Return default retry policy (cached class-level instance)
-        if not hasattr(ToolExecutionMixin, '_default_retry_policy'):
-            ToolExecutionMixin._default_retry_policy = RetryPolicy()
-        return ToolExecutionMixin._default_retry_policy
+
+        # Translate the ExecutionConfig spelling into the RetryPolicy vocabulary.
+        execution_config = getattr(self, '_execution_config', None)
+        if execution_config is None:
+            max_retry_limit = getattr(self, 'max_retry_limit', 2)
+            initial_delay_s, backoff_factor, jitter_fraction = 1.0, 2.0, 0.1
+        else:
+            max_retry_limit = execution_config.max_retry_limit
+            initial_delay_s = execution_config.retry_initial_delay
+            backoff_factor = execution_config.retry_backoff_factor
+            jitter_fraction = execution_config.retry_jitter
+
+        cached = getattr(self, '_translated_retry_policy', None)
+        key = (max_retry_limit, initial_delay_s, backoff_factor, jitter_fraction)
+        if cached is not None and cached[0] == key:
+            return cached[1]
+        # Reproduce BackoffPolicy.delay's 60s base cap so ExecutionConfig users
+        # keep the exact same delay ceiling they had before this consolidation
+        # (RetryPolicy defaults to a 30s cap). initial_delay_ms must not exceed
+        # max_delay_ms per RetryPolicy validation, so lift the cap to whichever
+        # is larger.
+        initial_delay_ms = int(initial_delay_s * 1000)
+        policy = RetryPolicy(
+            max_attempts=max(1, max_retry_limit + 1),
+            backoff_factor=max(1.0, backoff_factor),
+            initial_delay_ms=initial_delay_ms,
+            max_delay_ms=max(60000, initial_delay_ms),
+            jitter=jitter_fraction > 0,
+            jitter_factor=min(max(jitter_fraction, 0.0), 1.0),
+        )
+        self._translated_retry_policy = (key, policy)
+        return policy
 
     def _classify_error_type(self, error_dict, exception):
         """Classify error type for retry policy matching.

src/praisonai-agents/praisonaiagents/agent/tools_placement.py

@@ -0,0 +1,129 @@
+"""Give a single Agent's tools a sandbox, without touching its thinking.
+
+``AgentFlow``/``AgentTeam`` already share one sandbox across a run and tear it
+down when the run ends. A lone ``Agent(tools_run_on=...)`` has no "run" to hang
+that lifetime on -- it is just an object someone calls ``chat()`` on, possibly
+many times -- so the sandbox is created on first use and lives as long as the
+agent does. That is deliberate: a file written in turn 1 is still there in turn
+2, which is what anyone who asked for a working directory expects.
+
+Attachment is idempotent and happens at the top of ``chat``/``achat``/
+``_start_stream``, and in ``AgentTeam`` before the task's tool list is
+snapshotted. Doing it once, rather than wrapping each call in a context
+manager, sidesteps two real hazards in this codebase:
+
+* ``_start_stream`` is a **generator**; a context manager around the call would
+  exit before the caller consumed a single token, tearing down the sandbox while
+  the model was still using it.
+* ``_start_stream`` falls back to ``self.chat``, so a wrapper would **re-enter**
+  itself and provision a second sandbox.
+
+Lifetime is tied to the agent by ``weakref.finalize`` rather than a process-exit
+hook over a weak set. The weak set leaked: an agent and its ``SharedCompute``
+reference each other, so a dropped agent was reclaimed by the cycle collector,
+vanished from the set, and its sandbox was never shut down. Gateways clone an
+agent **per request**, so that was one orphaned container per request.
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+import weakref
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# One lock per agent, so two unrelated agents never wait on each other and no
+# lock is ever held while another agent's provider is being imported. Held only
+# by ensure_tools_placed/close_tools_sandbox for that one agent.
+_AGENT_LOCKS: "weakref.WeakKeyDictionary" = weakref.WeakKeyDictionary()
+_LOCKS_GUARD = threading.Lock()
+
+
+def _lock_for(agent: Any) -> threading.Lock:
+    """The placement lock for ``agent``, created on demand.
+
+    ``_LOCKS_GUARD`` covers only this dictionary lookup -- microseconds -- never
+    provider construction or attachment. An earlier version held one global lock
+    across ``SharedCompute(...)``, which imports the provider SDK: unrelated
+    agents serialised behind each other, and a slow import could stall
+    interpreter exit.
+    """
+    with _LOCKS_GUARD:
+        lock = _AGENT_LOCKS.get(agent)
+        if lock is None:
+            lock = threading.Lock()
+            try:
+                _AGENT_LOCKS[agent] = lock
+            except TypeError:  # pragma: no cover - unhashable/non-weakrefable
+                pass
+        return lock
+
+
+def _shutdown_orphan(shared: Any, name: str) -> None:
+    """Tear down a sandbox whose agent is gone.
+
+    Registered with ``weakref.finalize``, so it runs when the agent is collected
+    *and* at interpreter exit. It must not close over the agent -- that would
+    keep it alive and defeat the whole point.
+    """
+    try:
+        shared.shutdown()
+        logger.debug("[tools_placement] released the sandbox belonging to %s", name)
+    except Exception as exc:  # pragma: no cover - teardown must stay quiet
+        # Loud enough to notice: a failed release means a container, or a
+        # billed cloud instance, is still running after the agent is gone.
+        logger.warning(
+            "[tools_placement] could not release the sandbox for %s: %s", name, exc
+        )
+
+
+def ensure_tools_placed(agent: Any) -> None:
+    """Attach the sandbox tools to ``agent`` if it asked for them. Idempotent.
+
+    Called at the top of every entry point, so it must be cheap when there is
+    nothing to do -- which is the overwhelmingly common case.
+    """
+    place = getattr(agent, "tools_run_on", None)
+    if place is None or getattr(agent, "_tools_sandbox", None) is not None:
+        return
+
+    from ..managed.shared_compute import SharedCompute
+
+    with _lock_for(agent):
+        # Re-check under the lock: two threads calling chat() at once must not
+        # each provision a sandbox and leak one.
+        if getattr(agent, "_tools_sandbox", None) is not None:
+            return
+
+        shared = SharedCompute(place)
+        # override_declared: this sandbox exists *because* the agent named a
+        # place, so the skip-if-declared rule must not skip the agent itself.
+        shared.attach([agent], override_declared=True)
+        agent._tools_sandbox = shared
+        agent._tools_sandbox_finalizer = weakref.finalize(
+            agent, _shutdown_orphan, shared, str(getattr(agent, "name", agent))
+        )
+
+    logger.info("[tools_placement] %r tools now run on %s", getattr(agent, "name", agent), place)
+
+
+def close_tools_sandbox(agent: Any) -> None:
+    """Tear down the agent's sandbox and put its original tools back."""
+    with _lock_for(agent):
+        # Everything happens under the lock. Clearing the marker first and
+        # shutting down afterwards left a window where a concurrent chat()
+        # re-attached and the in-flight shutdown then restored over it, leaving
+        # the agent marked as sandboxed while holding no sandbox tools -- a
+        # state ensure_tools_placed() could never repair, because its idempotent
+        # early return sees the marker and does nothing.
+        shared = getattr(agent, "_tools_sandbox", None)
+        if shared is None:
+            return
+        finalizer = getattr(agent, "_tools_sandbox_finalizer", None)
+        if finalizer is not None:
+            finalizer.detach()          # we are shutting down explicitly
+            agent._tools_sandbox_finalizer = None
+        shared.shutdown()
+        agent._tools_sandbox = None

src/praisonai-agents/praisonaiagents/agent/unified_execution_mixin.py

@@ -282,7 +282,7 @@ class UnifiedExecutionMixin:
                     task_id=task_id,
                     config=config,
                     tool_choice=tool_choice,
-                    parallel_tool_calls=getattr(self, 'parallel_tool_calls', False)  # Gap 2 integration
+                    parallel_tool_calls=getattr(getattr(self, "execution", None), "parallel_tool_calls", False)  # Gap 2 integration
                 )
             else:
                 # Standard LiteLLM path - delegate to existing LLM class
@@ -304,7 +304,7 @@ class UnifiedExecutionMixin:
                     task_id=task_id,
                     config=config,
                     tool_choice=tool_choice,
-                    parallel_tool_calls=getattr(self, 'parallel_tool_calls', False)  # Gap 2 integration
+                    parallel_tool_calls=getattr(getattr(self, "execution", None), "parallel_tool_calls", False)  # Gap 2 integration
                 )
             
             # Store response in memory if enabled

src/praisonai-agents/praisonaiagents/agent/video_agent.py

@@ -158,9 +158,8 @@ class VideoAgent:
             verbose: Verbosity level for output
             output: Output preset ("silent", "verbose", etc.)
         """
-        # Handle model= alias for llm=
-        if llm is None and model is not None:
-            llm = model
+        from ..utils.model_alias import resolve_model_name
+        llm = resolve_model_name(llm, model, "VideoAgent")
         
         # Store core identity
         self.name = name or "VideoAgent"

src/praisonai-agents/praisonaiagents/agent/vision_agent.py

@@ -112,8 +112,8 @@ class VisionAgent:
                 - VisionConfig: Full configuration object
             verbose: Verbosity level for output
         """
-        if llm is None and model is not None:
-            llm = model
+        from ..utils.model_alias import resolve_model_name
+        llm = resolve_model_name(llm, model, "VisionAgent")
         
         self.name = name or "VisionAgent"
         self.instructions = instructions

src/praisonai-agents/praisonaiagents/agents/agents.py

@@ -2,6 +2,8 @@ import os
 import time
 import json
 import logging
+import contextlib
+import contextvars
 import threading
 from praisonaiagents._logging import get_logger
 from typing import Any, Dict, Optional, List, Tuple, Callable
@@ -41,18 +43,13 @@ class TaskStatus(Enum):
 logger = get_logger(__name__)
 
 
-def _launch_auth_token() -> Optional[str]:
-    return os.environ.get("PRAISONAI_LAUNCH_AUTH_TOKEN")
-
-
-def _authorise_launch_request(request) -> bool:
-    token = _launch_auth_token()
-    if not token:
-        return True
-    auth = request.headers.get("Authorization", "")
-    if auth.startswith("Bearer ") and auth[7:] == token:
-        return True
-    return request.headers.get("X-Auth-Token") == token
+# Request authorisation + fail-closed bind guard are shared with Agent.launch
+# so the single-agent and multi-agent serving paths cannot drift apart.
+from ..agent.launch_security import (
+    launch_auth_token as _launch_auth_token,
+    authorise_launch_request as _authorise_launch_request,
+    resolve_launch_host as _resolve_launch_host,
+)
 
 
 # Agent server registry for thread-safe server management
@@ -375,6 +372,18 @@ def _build_execution_context(agents_instance, task_id, skip_memory_init=False):
     if llm and hasattr(llm, 'set_current_agent'):
         llm.set_current_agent(executor_agent.display_name)
 
+    # Place the agent's tools BEFORE snapshotting them. This snapshot is passed
+    # to chat() as an explicit `tools=`, which wins over `agent.tools` -- so if
+    # the sandbox attaches later (inside chat()), the model is handed the
+    # pre-attach list and every tool runs on the host while a sandbox sits
+    # provisioned and unused. Silent, and worse than it sounds: in
+    # start_for_each() row 1 got local tools and row 2 got the bridged set,
+    # because row 1's chat() mutated agent.tools in time for row 2's snapshot.
+    if executor_agent is not None and getattr(executor_agent, "tools_run_on", None) is not None:
+        from ..agent.tools_placement import ensure_tools_placed
+
+        ensure_tools_placed(executor_agent)
+
     # Ensure tools are available from both task and agent (create copy to avoid mutation)
     tools = list(task.tools or [])
     if executor_agent and executor_agent.tools:
@@ -644,7 +653,8 @@ class AgentTeam(SpawnAnnounceProtocol):
         manager_llm=None,
         name: Optional[str] = None,
         variables: Optional[Dict[str, Any]] = None,
-        llm: Optional[str] = None,  # Default LLM for all agents (API consistency)
+        llm: Optional[str] = None,  # Deprecated alias for model=
+        model: Optional[str] = None,  # Default model for members that did not name one
         # Consolidated feature params (agent-centric API)
         memory: Optional[Any] = False,  # Union[bool, MultiAgentMemoryConfig]
         planning: Optional[Any] = False,  # Union[bool, MultiAgentPlanningConfig]
@@ -662,9 +672,12 @@ class AgentTeam(SpawnAnnounceProtocol):
         learn: Optional[Any] = None,  # Union[bool, LearnConfig] - continuous learning
         # Union[str, ComputeProviderProtocol] - where every agent's shell/file
         # tools run: "docker", "e2b", "modal", "daytona", "flyio", "tenki",
-        # "local". The team shares ONE sandbox and /workspace, so files written
-        # by one agent are visible to the others. Orchestration stays local.
+        # "local", "subprocess", "sandlock", "ssh", "novita". The team shares
+        # ONE sandbox and one working directory, so files written by one agent
+        # are visible to the others. Orchestration and thinking stay local.
         # Pass a configured provider instance to customise resources.
+        tools_run_on: Optional[Any] = None,
+        # Accepted only to fail with a useful message -- see resolve_placement.
         run_on: Optional[Any] = None,
     ):
         """
@@ -676,8 +689,11 @@ class AgentTeam(SpawnAnnounceProtocol):
             process: Execution process type ("sequential", "workflow", "hierarchical").
                 For parallel fan-out, set async_execution=True on individual Task
                 objects within a "workflow" or "sequential" process.
-            manager_llm: LLM model for manager agent
-            llm: Default LLM model for all agents
+            manager_llm: Model for the hierarchical manager agent. Unrelated to
+                model=/llm=; it never touches the members.
+            llm: Deprecated alias for model=. Passing both raises TypeError.
+            model: Default model for members that did not name one themselves.
+                An agent constructed with its own llm=/model=/auth= keeps it.
             name: Name for this agent collection
             variables: Global variables for substitution
             memory: Memory configuration (bool | MultiAgentMemoryConfig)
@@ -695,7 +711,21 @@ class AgentTeam(SpawnAnnounceProtocol):
         """
         # Store new params for propagation to agents
         self._learn = learn
-        self.llm = llm  # Store default LLM for API consistency
+        # model= is the canonical name; llm= is the deprecated alias. Passing
+        # both is refused rather than silently resolved (see resolve_model_alias).
+        # The resolved value is the default model for the members: applied to
+        # every agent that never named a model of its own; an agent that did
+        # keeps it. Same rule AgentFlow uses for the agents it builds.
+        # Must UNWRAP, not just alias-resolve: this value is pushed into every
+        # member via _apply_default_llm, and a member holding an LLMConfig fails
+        # inside chat() with "'LLMConfig' object has no attribute 'lower'".
+        from ..utils.model_alias import resolve_model_name
+        self.llm = resolve_model_name(llm, model, type(self).__name__)
+        if self.llm:
+            for _member in (agents.values() if isinstance(agents, dict) else (agents or [])):
+                _apply = getattr(_member, "_apply_default_llm", None)
+                if callable(_apply):
+                    _apply(self.llm)
         self._autonomy = autonomy
         self._knowledge = knowledge
         self._guardrails = guardrails
@@ -910,7 +940,19 @@ class AgentTeam(SpawnAnnounceProtocol):
         self.stream = _stream
         self.name = name
         # Remote execution: one shared sandbox for every agent on this team.
-        self.run_on = run_on
+        from ..agent.placement import resolve_placement
+
+        resolve_placement(
+            "AgentTeam", run_on=run_on, tools_run_on=tools_run_on,
+            supports_run_on=False,
+        )
+        self.tools_run_on = tools_run_on
+        # Kept readable rather than removed. `team.run_on` used to exist, and
+        # code doing getattr(team, "run_on", None) for introspection or
+        # serialisation would otherwise start raising AttributeError. None is
+        # the honest value: a team orchestrates locally, so nothing about it
+        # runs wholly on a managed runtime.
+        self.run_on = None
 
         # Callbacks for workflow execution
         self.on_task_start = _on_task_start
@@ -1437,6 +1479,73 @@ class AgentTeam(SpawnAnnounceProtocol):
                 raise result
         return results
 
+    def _depends_on_pending(self, task, pending):
+        """True if ``task`` depends on a task still pending in the batch.
+
+        ``pending`` is a list of ``(task_id, coroutine)`` pairs already queued
+        for parallel execution. If one of the task's dependencies is in that
+        set, its result isn't available yet, so the batch must be flushed before
+        queuing this task (otherwise the dependent builds its prompt from an
+        empty result for the still-in-progress dependency).
+
+        Two dependency edges are checked, since ``_build_task_context`` reads
+        from both:
+          - ``task.context``: explicit context tasks (by object ``id``).
+          - ``task.previous_tasks``: workflow ``next_tasks`` edges, stored as
+            task *names*; without this a workflow successor could be queued in
+            the same async batch as its predecessor and read an empty result.
+        """
+        if not pending:
+            return False
+        pending_ids = {tid for tid, _ in pending}
+
+        deps = getattr(task, 'context', None) or []
+        if any(getattr(dep, 'id', None) in pending_ids for dep in deps):
+            return True
+
+        previous = getattr(task, 'previous_tasks', None) or []
+        if previous:
+            pending_names = {
+                self.tasks[tid].name
+                for tid in pending_ids
+                if tid in self.tasks
+            }
+            if any(name in pending_names for name in previous):
+                return True
+
+        return False
+
+    def _deps_failed(self, task_id):
+        """True if any dependency of ``task_id`` finished in a failed state.
+
+        asequential()/aworkflow() run their own failure check inside the
+        generator, but async tasks are buffered and drained later, so an
+        upstream async task may not have failed yet when the generator yielded
+        the dependent. Re-checking here — after pending async tasks are flushed
+        — makes the failure cascade fire for the async_execution path too, for
+        both dependency edges ``_build_task_context`` reads (context tasks and
+        workflow ``previous_tasks``).
+        """
+        task = self.tasks.get(task_id)
+        if task is None:
+            return False
+
+        for dep in getattr(task, 'context', None) or []:
+            dep_id = getattr(dep, 'id', None)
+            if dep_id in self.tasks and self.tasks[dep_id].status == "failed":
+                return True
+
+        previous = getattr(task, 'previous_tasks', None) or []
+        if previous:
+            by_name = {t.name: t for t in self.tasks.values()}
+            if any(
+                name in by_name and by_name[name].status == "failed"
+                for name in previous
+            ):
+                return True
+
+        return False
+
     async def arun_all_tasks(self):
         """Async version of run_all_tasks method"""
         process = Process(
@@ -1448,14 +1557,26 @@ class AgentTeam(SpawnAnnounceProtocol):
         )
         
         if self.process == "workflow":
-            tasks_to_run = []
+            tasks_to_run = []  # list of (task_id, coroutine)
             async for task_id in process.aworkflow():
-                if self.tasks[task_id].async_execution:
-                    tasks_to_run.append(self.arun_task(task_id))
+                task = self.tasks[task_id]
+                if task.async_execution:
+                    # If this async task depends on another async task still
+                    # pending in the current batch, flush first so its context
+                    # is available before this one reads it.
+                    if self._depends_on_pending(task, tasks_to_run):
+                        await self._gather_with_isolation([c for _, c in tasks_to_run])
+                        tasks_to_run = []
+                        # A just-flushed prerequisite may now be failed; don't
+                        # queue a dependent that would run with missing context.
+                        if self._deps_failed(task_id):
+                            self.tasks[task_id].status = "failed"
+                            continue
+                    tasks_to_run.append((task_id, self.arun_task(task_id)))
                 else:
                     # If we encounter a sync task, we must wait for the previous async tasks to finish.
                     if tasks_to_run:
-                        await self._gather_with_isolation(tasks_to_run)
+                        await self._gather_with_isolation([c for _, c in tasks_to_run])
                         tasks_to_run = []
                     
                     # Run sync task in an executor to avoid blocking the event loop
@@ -1465,46 +1586,40 @@ class AgentTeam(SpawnAnnounceProtocol):
                     await loop.run_in_executor(None, copy_context_to_callable(lambda tid=task_id: self.run_task(tid)))
 
             if tasks_to_run:
-                await self._gather_with_isolation(tasks_to_run)
+                await self._gather_with_isolation([c for _, c in tasks_to_run])
                 
         elif self.process == "sequential":
-            async_tasks_to_run = []
+            async_tasks_to_run = []  # list of (task_id, coroutine)
             
             async def flush_async_tasks():
                 """Execute all pending async tasks"""
                 nonlocal async_tasks_to_run
                 if async_tasks_to_run:
-                    await self._gather_with_isolation(async_tasks_to_run)
+                    await self._gather_with_isolation([c for _, c in async_tasks_to_run])
                     async_tasks_to_run = []
 
-            def _deps_failed(task_id):
-                """Re-check dependency failure at consumption time.
-
-                asequential() runs this check inside the generator, but async
-                tasks are buffered and drained later, so an upstream async task
-                may not have failed yet when the generator yielded the dependent.
-                Re-checking here — after pending async tasks are flushed — makes
-                the failure cascade fire for the async_execution path too.
-                """
-                task = self.tasks[task_id]
-                if getattr(task, 'context', None):
-                    return any(
-                        self.tasks[dep.id].status == "failed"
-                        for dep in task.context
-                        if hasattr(dep, 'id') and dep.id in self.tasks
-                    )
-                return False
-
             async for task_id in process.asequential():
-                if self.tasks[task_id].async_execution:
+                task = self.tasks[task_id]
+                if task.async_execution:
+                    # If this async task depends on another async task still
+                    # pending in the current batch, flush first so its context
+                    # is available before this one reads it (avoids a same-batch
+                    # race that silently substitutes empty dependency context).
+                    if self._depends_on_pending(task, async_tasks_to_run):
+                        await flush_async_tasks()
+                        # A just-flushed prerequisite may now be failed; don't
+                        # queue a dependent that would run with missing context.
+                        if self._deps_failed(task_id):
+                            self.tasks[task_id].status = "failed"
+                            continue
                     # Collect async tasks to run in parallel
-                    async_tasks_to_run.append(self.arun_task(task_id))
+                    async_tasks_to_run.append((task_id, self.arun_task(task_id)))
                 else:
                     # Before running a sync task, execute all pending async tasks
                     await flush_async_tasks()
                     # A just-flushed async prerequisite may now be failed; skip
                     # the dependent so we don't run it with missing upstream context.
-                    if _deps_failed(task_id):
+                    if self._deps_failed(task_id):
                         self.tasks[task_id].status = "failed"
                         continue
                     # Run sync task in an executor to avoid blocking the event loop
@@ -1544,6 +1659,62 @@ class AgentTeam(SpawnAnnounceProtocol):
             except StopAsyncIteration:
                 pass
 
+
+    # ── shared sandbox lifetime ──────────────────────────────────────────────
+    # A ContextVar, not an instance attribute: the previous version set
+    # `self.run_on = None` for the duration of the run, so a second caller
+    # inspecting the team mid-run saw "no sandbox" and any concurrent start()
+    # executed every tool on the host. Depth lives with the caller, not on the
+    # shared object, so the team's declared placement is always readable.
+    #
+    # The depth is keyed PER TEAM, not shared class-wide. A single integer here
+    # meant any team started while another team's run was on the stack saw
+    # depth==1 and skipped its own sandbox entirely -- so a nested team ran on
+    # the host, and a team sharing an agent with the outer run executed on the
+    # OUTER team's sandbox.
+    #
+    # ContextVar, not threading.local: two coroutines that `astart()` the SAME
+    # team under one asyncio.gather run on one thread, so thread-local state is
+    # shared between them -- the second saw the first's depth==1, skipped its
+    # own sandbox, and ran against a sandbox the first could tear down mid-flight.
+    # A ContextVar is copied per task at gather/create_task time, so each run
+    # gets its own depth map; it is also per-thread, so sync callers stay
+    # isolated too. The value is an immutable frozen mapping replaced via .set()
+    # (never mutated in place), because a copied context shares the *reference*
+    # to a mutable dict and in-place mutation would leak straight back across
+    # the tasks the copy was meant to separate.
+    _tools_scope_depths: "contextvars.ContextVar" = contextvars.ContextVar(
+        "praisonai_tools_scope_depths", default=()
+    )
+
+    def _needs_tools_scope(self) -> bool:
+        """True when this call should provision the team's shared sandbox."""
+        if getattr(self, "tools_run_on", None) is None:
+            return False
+        return dict(self._tools_scope_depths.get()).get(id(self), 0) == 0
+
+    @contextlib.contextmanager
+    def _shared_tools_scope(self):
+        """Hold one sandbox for everything nested inside.
+
+        Re-entrant by design: ``start_for_each`` opens the scope once for the
+        whole batch, so the ``start()`` call it makes per input row finds the
+        scope already open and reuses that sandbox instead of provisioning and
+        destroying one per row.
+        """
+        from ..managed.shared_compute import SharedCompute
+
+        key = id(self)
+        depths = dict(self._tools_scope_depths.get())
+        depths[key] = depths.get(key, 0) + 1
+        token = self._tools_scope_depths.set(tuple(sorted(depths.items())))
+        try:
+            with SharedCompute(self.tools_run_on) as shared:
+                shared.attach(list(self.agents or []))
+                yield shared
+        finally:
+            self._tools_scope_depths.reset(token)
+
     async def astart(self, content=None, return_dict=False, **kwargs):
         """Async version of start method.
         
@@ -1552,6 +1723,12 @@ class AgentTeam(SpawnAnnounceProtocol):
             return_dict: If True, returns the full results dictionary instead of only the final response
             **kwargs: Additional arguments
         """
+        # Same shared sandbox as start(). Without this, an async team with
+        # tools_run_on= ran every tool on the host and said nothing.
+        if self._needs_tools_scope():
+            with self._shared_tools_scope():
+                return await self.astart(content=content, return_dict=return_dict, **kwargs)
+
         # Track execution via telemetry
         if hasattr(self, '_telemetry') and self._telemetry:
             self._telemetry.track_agent_execution(self.name, success=True, async_mode=True)
@@ -1812,19 +1989,11 @@ class AgentTeam(SpawnAnnounceProtocol):
             ```
         """
         # Remote execution: provision ONE sandbox shared by every agent on the
-        # team, and tear it down even if execution raises. Re-enters start()
-        # with run_on cleared so the wrapper applies exactly once.
-        if getattr(self, "run_on", None) is not None:
-            from ..managed.shared_compute import SharedCompute
-
-            run_on, self.run_on = self.run_on, None
-            try:
-                with SharedCompute(run_on) as shared:
-                    shared.attach(list(self.agents or []))
-                    return self.start(content=content, return_dict=return_dict,
-                                      output=output, **kwargs)
-            finally:
-                self.run_on = run_on
+        # team, and tear it down even if execution raises.
+        if self._needs_tools_scope():
+            with self._shared_tools_scope():
+                return self.start(content=content, return_dict=return_dict,
+                                  output=output, **kwargs)
 
         # Track execution via telemetry
         if hasattr(self, '_telemetry') and self._telemetry:
@@ -2140,6 +2309,15 @@ class AgentTeam(SpawnAnnounceProtocol):
         if on_error not in ("continue", "fail_fast"):
             raise ValueError("on_error must be 'continue' or 'fail_fast'")
 
+        # One sandbox for the whole batch. Without this the per-row start()
+        # call provisions and destroys its own, so a 50-row batch paid for 50
+        # sandboxes and no row could see what an earlier row wrote.
+        if self._needs_tools_scope():
+            with self._shared_tools_scope():
+                return self.start_for_each(
+                    inputs, on_error=on_error, output=output, **kwargs
+                )
+
         batch_id = f"batch_{uuid.uuid4().hex[:12]}"
         saved_variables = self.variables
         task_snapshot = self._snapshot_task_state()
@@ -2176,6 +2354,13 @@ class AgentTeam(SpawnAnnounceProtocol):
         if on_error not in ("continue", "fail_fast"):
             raise ValueError("on_error must be 'continue' or 'fail_fast'")
 
+        # One sandbox for the whole batch -- see start_for_each.
+        if self._needs_tools_scope():
+            with self._shared_tools_scope():
+                return await self.astart_for_each(
+                    inputs, on_error=on_error, **kwargs
+                )
+
         batch_id = f"batch_{uuid.uuid4().hex[:12]}"
         saved_variables = self.variables
         task_snapshot = self._snapshot_task_state()
@@ -2457,7 +2642,7 @@ class AgentTeam(SpawnAnnounceProtocol):
         
         print("="*50 + "\n")
         
-    def launch(self, path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = "http"):
+    def launch(self, path: str = '/agents', port: int = 8000, host: str = '127.0.0.1', debug: bool = False, protocol: str = "http"):
         """
         Launch all agents as a single API endpoint (HTTP) or an MCP server. 
         In HTTP mode, the endpoint accepts a query and processes it through all agents in sequence.
@@ -2468,7 +2653,11 @@ class AgentTeam(SpawnAnnounceProtocol):
         Args:
             path: API endpoint path (default: '/agents') for HTTP. Ignored in MCP mode.
             port: Server port (default: 8000)
-            host: Server host (default: '0.0.0.0')
+            host: Server host (default: '127.0.0.1'; changed from '0.0.0.0' to
+                avoid binding all interfaces by default — pass '0.0.0.0'
+                explicitly to expose externally). Binding a non-loopback host
+                without ``PRAISONAI_LAUNCH_AUTH_TOKEN`` set auto-generates and
+                prints a one-time bearer token.
             debug: Enable debug mode for uvicorn (default: False)
             protocol: "http" to launch as FastAPI, "mcp" to serve over MCP via
                 ``praisonai-mcp`` (install with ``pip install praisonai-mcp``).
@@ -2478,7 +2667,7 @@ class AgentTeam(SpawnAnnounceProtocol):
         """
         if protocol == "http":
             # Use centralized server registry
-            
+
             if not self.agents:
                 logging.warning("No agents to launch for HTTP mode. Add agents to the Agents instance first.")
                 return
@@ -2507,6 +2696,13 @@ class AgentTeam(SpawnAnnounceProtocol):
                 print("\nOr install all API dependencies with:")
                 print("pip install 'praisonaiagents[api]'")
                 return None
+
+            # Fail-closed bind guard shared with Agent.launch: never serve
+            # keyless on a non-loopback host. Applied only after the no-agents
+            # and dependency guards above, so a launch that bails out cannot
+            # mutate the process-wide launch token (which every route reads per
+            # request) and retroactively 401 an already-running endpoint.
+            host = _resolve_launch_host(host)
             
             # Thread-safe initialization of FastAPI app
             app, is_new = _server_registry.get_or_create_app(
@@ -3145,9 +3341,14 @@ class AgentTeam(SpawnAnnounceProtocol):
         console.print(f"[dim]Progress: [{'█' * 30}] 100%[/dim]")
         console.print(f"[green]Completed {completed_count}/{len(self.tasks)} tasks![/green]\n")
         
-        # Restore original tasks reference for result retrieval
-        self._plan_tasks = self.tasks.copy()
-        # Keep plan tasks for results but note original tasks are preserved in _plan_tasks
+        # Keep plan-step tasks/results under _plan_tasks for introspection,
+        # then restore the user's original task set as the canonical self.tasks.
+        self._plan_tasks = self.tasks
+        self.tasks = original_tasks
+        with self._task_id_lock:
+            self.task_id_counter = (
+                max(original_tasks.keys()) + 1 if original_tasks else 0
+            )
 
     # Resource Lifecycle Management
     def close(self) -> None:

src/praisonai-agents/praisonaiagents/approval/backends.py

@@ -81,12 +81,14 @@ class ConsoleBackend:
     """Interactive Rich terminal prompt.  Default for CLI usage."""
 
     def _prompt_user(self, request: ApprovalRequest):
-        """Show Rich panel and ask once/session/always/no.
+        """Show Rich panel and ask once/session/always/no/deny-with-guidance.
 
-        Returns a ``(approved, scope, scope_pattern)`` tuple where ``scope`` is
-        one of ``"once"`` / ``"session"`` / ``"always"`` and ``scope_pattern``
-        is the reusable target to persist for ``always`` (``None`` otherwise).
-        A denial returns ``(False, "once", None)``.
+        Returns a ``(approved, scope, scope_pattern, feedback)`` tuple where
+        ``scope`` is one of ``"once"`` / ``"session"`` / ``"always"`` and
+        ``scope_pattern`` is the reusable target to persist for ``always``
+        (``None`` otherwise). ``feedback`` carries optional free-text guidance
+        captured by the "deny & redirect" choice (``None`` otherwise).
+        A plain denial returns ``(False, "once", None, None)``.
         """
         Console = _get_rich_console()
         Panel = _get_rich_panel()
@@ -126,28 +128,35 @@ class ConsoleBackend:
         try:
             console.print(
                 f"[{risk_color}]Allow {request.tool_name}?[/{risk_color}]  "
-                f"[o] once   [s] this session   [a] {always_label}   [n] no"
+                f"[o] once   [s] this session   [a] {always_label}   "
+                f"[n] no   [d] deny & redirect"
             )
             choice = Prompt.ask(
                 "Choice",
-                choices=["o", "s", "a", "n"],
+                choices=["o", "s", "a", "n", "d"],
                 default="n",
             )
         except (KeyboardInterrupt, EOFError):
-            return (False, "once", None)
+            return (False, "once", None, None)
 
         if choice == "n":
-            return (False, "once", None)
+            return (False, "once", None, None)
+        if choice == "d":
+            try:
+                feedback = Prompt.ask("What should the agent do instead?").strip()
+            except (KeyboardInterrupt, EOFError):
+                feedback = ""
+            return (False, "once", None, feedback or None)
         if choice == "s":
-            return (True, "session", None)
+            return (True, "session", None, None)
         if choice == "a":
-            return (True, "always", suggested or None)
-        return (True, "once", None)
+            return (True, "always", suggested or None, None)
+        return (True, "once", None, None)
 
     def request_approval_sync(self, request: ApprovalRequest) -> ApprovalDecision:
         """Synchronous approval via Rich console prompt."""
         try:
-            approved, scope, scope_pattern = self._prompt_user(request)
+            approved, scope, scope_pattern, feedback = self._prompt_user(request)
             if approved:
                 return ApprovalDecision(
                     approved=True,
@@ -156,7 +165,13 @@ class ConsoleBackend:
                     scope=scope,
                     scope_pattern=scope_pattern,
                 )
-            return ApprovalDecision(approved=False, reason="User denied", approver="console")
+            reason = f"User denied: {feedback}" if feedback else "User denied"
+            return ApprovalDecision(
+                approved=False,
+                reason=reason,
+                approver="console",
+                feedback=feedback,
+            )
         except Exception as e:
             logger.error("Console approval error: %s", e)
             return ApprovalDecision(approved=False, reason=f"Approval error: {e}")

src/praisonai-agents/praisonaiagents/approval/protocols.py

@@ -65,6 +65,12 @@ class ApprovalDecision:
                        ``session``/``always`` scopes (e.g. ``"bash:git status *"``
                        or ``"edit:src/app.py"``). When ``None`` the registry
                        derives one from the tool call.
+        feedback:      Optional free-text guidance supplied by the user when
+                       denying a tool call ("deny with guidance"). When present
+                       on a denial it is surfaced back to the model as a
+                       course-correction for the next turn instead of a bare
+                       "denied" result. ``None`` (default) keeps today's
+                       terminal-deny semantics.
     """
 
     approved: bool
@@ -74,6 +80,7 @@ class ApprovalDecision:
     metadata: Dict[str, Any] = field(default_factory=dict)
     scope: str = "once"
     scope_pattern: Optional[str] = None
+    feedback: Optional[str] = None
 
 
 @dataclass

src/praisonai-agents/praisonaiagents/auth/__init__.py

@@ -2,7 +2,10 @@
 Authentication & Authorization Protocols for PraisonAI Agents.
 
 This package provides protocol-driven auth interfaces for the core SDK.
-Implementations (JWT, OAuth, etc.) live in the platform package.
+Implementations (JWT, OAuth, etc.) live outside it — implement the protocols
+against your own store, or use the reference implementation in the separate
+``praisonai-platform`` package
+(https://github.com/MervinPraison/PraisonAI-Platform).
 
 Quick start::
 

src/praisonai-agents/praisonaiagents/auth/protocols.py

@@ -155,11 +155,16 @@ class WorkspaceContextProtocol(Protocol):
     """Protocol for providing workspace context to agents.
 
     Enables agents to access workspace-specific settings, instructions,
-    and agent configurations from the platform layer.
+    and agent configurations from a platform layer.
+
+    Implementations live outside this package. The reference one is
+    ``PlatformWorkspaceContext`` in the ``praisonai-platform`` package
+    (https://github.com/MervinPraison/PraisonAI-Platform); implement this
+    protocol yourself to back agent configuration with your own store.
 
     Example::
 
-        class PlatformWorkspaceContext:
+        class MyWorkspaceContext:
             async def get_workspace_context(self, workspace_id):
                 ws = await db.get_workspace(workspace_id)
                 return ws.context

src/praisonai-agents/praisonaiagents/compaction/__init__.py

@@ -37,6 +37,8 @@ __all__ = [
     "ToolResultPrunerProtocol",
     "MessageFormatterProtocol", 
     "SummaryBuilderProtocol",
+    # Default structured summary builder
+    "DefaultSummaryBuilder",
     # Read-only recap
     "build_recap",
     "MemoryFlushResult",
@@ -75,6 +77,10 @@ def __getattr__(name: str):
         from .protocols import SummaryBuilderProtocol
         return SummaryBuilderProtocol
     
+    if name == "DefaultSummaryBuilder":
+        from .summary_builder import DefaultSummaryBuilder
+        return DefaultSummaryBuilder
+
     if name == "build_recap":
         from .recap import build_recap
         return build_recap

src/praisonai-agents/praisonaiagents/compaction/compactor.py

@@ -122,7 +122,18 @@ class ContextCompactor:
         
         # Protocol implementations (defaults to None - no heavy implementations in core)
         self.tool_pruner = tool_pruner
-        self.message_formatter = message_formatter  
+        self.message_formatter = message_formatter
+        # Default the structured summary builder when the config asks for the
+        # phase-aware template but no builder was injected, so
+        # `_build_structured_summary` renders SUMMARY_TEMPLATE instead of the
+        # "Summary of N messages" stub. The builder is deterministic and
+        # dependency-free, so this stays offline-safe and zero-overhead.
+        if summary_builder is None and getattr(self.config, "structured_template", False):
+            from .summary_builder import DefaultSummaryBuilder
+
+            summary_builder = DefaultSummaryBuilder(
+                llm_summarize_fn=self.llm_summarize_fn
+            )
         self.summary_builder = summary_builder
         
         # Anti-thrashing state tracking

src/praisonai-agents/praisonaiagents/compaction/summary_builder.py

@@ -0,0 +1,172 @@
+"""Default structured summary builder for context compaction.
+
+This is the concrete :class:`~praisonaiagents.compaction.protocols.
+SummaryBuilderProtocol` implementation that renders the declared
+:data:`~praisonaiagents.compaction.config.SUMMARY_TEMPLATE` — the phase-aware
+sections (``## Active Task`` / ``## Completed Actions`` / ``## In Progress`` /
+``## Pending Questions`` / ``## Relevant Files / Paths`` / ``## Remaining
+Work``) — so a compacted context keeps its structure instead of degrading to a
+one-line ``"Summary of N messages"`` count.
+
+Design: **deterministic, dependency-free, offline-safe.** It categorises the
+transcript into the template sections from the messages themselves (roles,
+tool calls, question marks, file-path tokens) rather than making an LLM call, so
+it never blocks the hot compaction path, never adds a runtime dependency, and
+never raises into the loop. File paths and identifiers are preserved verbatim.
+
+When an optional ``llm_summarize_fn`` is supplied the builder still renders the
+same template shape; on any failure it falls back to the deterministic render,
+honouring the issue's "fall back to the plain summary on failure" rule.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, List, Optional
+
+from .config import SUMMARY_TEMPLATE
+
+# Match file/path-like tokens so they can be surfaced verbatim in the
+# "Relevant Files / Paths" section (absolute/relative paths and dotted module
+# names with a recognisable extension).
+_PATH_RE = re.compile(
+    r"(?:/[^\s'\"`]+|(?:[\w.-]+/)+[\w.-]+|[\w./-]+\.[A-Za-z0-9]{1,6})"
+)
+
+_MAX_ITEMS = 12
+_MAX_LINE = 200
+
+
+def _text(content: Any) -> str:
+    """Best-effort flatten of a message ``content`` field into a string."""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        parts: List[str] = []
+        for part in content:
+            if isinstance(part, dict):
+                parts.append(str(part.get("text", "")))
+            else:
+                parts.append(str(part))
+        return " ".join(p for p in parts if p)
+    return ""
+
+
+def _clip(text: str, limit: int = _MAX_LINE) -> str:
+    text = text.strip().replace("\n", " ")
+    return text if len(text) <= limit else text[: limit - 1].rstrip() + "…"
+
+
+class DefaultSummaryBuilder:
+    """Deterministic :class:`SummaryBuilderProtocol` renderer for the template.
+
+    Args:
+        llm_summarize_fn: Optional callable ``(prompt: str) -> str`` used to
+            distil section bodies. Any failure falls back to the deterministic
+            render, so callers get a valid structured summary regardless.
+    """
+
+    def __init__(self, llm_summarize_fn: Optional[Any] = None) -> None:
+        self._llm_summarize_fn = llm_summarize_fn
+
+    # -- protocol ----------------------------------------------------------
+
+    def build_structured_summary(self, messages: List[Dict[str, Any]]) -> str:
+        """Render :data:`SUMMARY_TEMPLATE` from ``messages`` deterministically.
+
+        Never raises: on any failure returns the plain one-line summary so the
+        compaction loop keeps a valid summary string (issue fallback rule).
+        """
+        try:
+            return self._render(messages)
+        except Exception:  # noqa: BLE001 - summarisation must never crash the loop
+            return f"Summary of {len(messages)} messages"
+
+    def merge_summaries(self, previous: str, current: str) -> str:
+        """Merge a prior structured summary with the current one.
+
+        Keeps both under a single "previous / latest" framing so an iterative
+        re-compaction never discards the earlier phase structure.
+        """
+        previous = (previous or "").strip()
+        current = (current or "").strip()
+        if not previous:
+            return current
+        if not current:
+            return previous
+        return f"{previous}\n\n[Updated with newer activity]\n{current}"
+
+    # -- rendering ---------------------------------------------------------
+
+    def _render(self, messages: List[Dict[str, Any]]) -> str:
+        completed: List[str] = []
+        in_progress: List[str] = []
+        pending: List[str] = []
+        files: List[str] = []
+        active_task = ""
+
+        for msg in messages:
+            role = msg.get("role", "")
+            body = _text(msg.get("content"))
+
+            # First substantive user turn frames the active task.
+            if role == "user" and body.strip() and not active_task:
+                active_task = _clip(body)
+
+            # Executed work — tool calls and tool results — is *completed*.
+            # An assistant turn that merely proposes or *asks* about work has
+            # not been executed, so it must not be recorded as completed (it is
+            # captured under "Pending Questions" / "In Progress" instead);
+            # only a substantive, non-questioning assistant turn is completed.
+            tool_calls = msg.get("tool_calls")
+            if isinstance(tool_calls, list) and tool_calls:
+                for tc in tool_calls:
+                    if not isinstance(tc, dict):
+                        continue
+                    name = (tc.get("function", {}) or {}).get("name", "tool")
+                    completed.append(f"Called `{name}`")
+            elif role == "assistant" and body.strip() and "?" not in body:
+                completed.append(_clip(body))
+            elif role == "tool" and body.strip():
+                completed.append(_clip(f"tool result: {body}"))
+
+            # Any turn with a question mark contributes an open question.
+            if body and "?" in body:
+                for sentence in body.split("?"):
+                    sentence = sentence.strip()
+                    if sentence:
+                        pending.append(_clip(sentence + "?"))
+
+            # Harvest file/path tokens verbatim from every turn.
+            if body:
+                for match in _PATH_RE.findall(body):
+                    if match not in files:
+                        files.append(match)
+
+        # The most recent user turn (if different) is what's in progress.
+        for msg in reversed(messages):
+            if msg.get("role") == "user":
+                latest = _clip(_text(msg.get("content")))
+                if latest and latest != active_task:
+                    in_progress.append(latest)
+                break
+
+        return SUMMARY_TEMPLATE.format(
+            active_task=active_task or "(not specified)",
+            completed=self._bullets(completed),
+            in_progress=self._bullets(in_progress),
+            pending=self._bullets(pending),
+            files=self._bullets(files),
+            remaining=self._bullets(pending or in_progress),
+        )
+
+    @staticmethod
+    def _bullets(items: List[str]) -> str:
+        if not items:
+            return "(none)"
+        # De-duplicate while preserving order, then cap.
+        seen: List[str] = []
+        for item in items:
+            if item not in seen:
+                seen.append(item)
+        return "\n".join(f"- {item}" for item in seen[:_MAX_ITEMS])

src/praisonai-agents/praisonaiagents/config/feature_configs.py

@@ -25,7 +25,7 @@ Usage:
     # With config
     agent = Agent(
         instructions="...",
-        memory=MemoryConfig(backend="redis", user_id="user123"),
+        memory=MemoryConfig(backend="sqlite", user_id="user123"),
         knowledge=KnowledgeConfig(sources=["docs/"], rerank=True),
         tool_search=ToolSearchConfig(enabled="auto", threshold_pct=15),
     )
@@ -52,14 +52,54 @@ if TYPE_CHECKING:
 
 
 class MemoryBackend(str, Enum):
-    """Memory storage backends."""
+    """Memory storage backends.
+
+    Every member must have an adapter in ``praisonaiagents.memory.adapters``
+    (FILE is served by ``FileMemory``). Declaring a backend with no adapter
+    used to silently swap in a local SQLite/JSON store.
+    """
     FILE = "file"
     SQLITE = "sqlite"
-    REDIS = "redis"
-    VALKEY = "valkey"
-    POSTGRES = "postgres"
+    CHROMA = "chroma"
     MEM0 = "mem0"
     MONGODB = "mongodb"
+    DAKERA = "dakera"
+    IN_MEMORY = "in_memory"
+
+
+#: Accepted before but never implemented: requests were silently redirected.
+UNIMPLEMENTED_MEMORY_BACKENDS = ("redis", "valkey", "postgres", "postgresql")
+
+
+def available_memory_backends() -> List[str]:
+    """Backends ``MemoryConfig(backend=...)`` accepts, read from the registry.
+
+    Adapters added via ``register_memory_adapter()`` are accepted automatically.
+    """
+    from ..memory.adapters import list_memory_adapters
+    return sorted(set(list_memory_adapters()) | {MemoryBackend.FILE.value})
+
+
+def validate_memory_backend(backend: Union[str, "MemoryBackend"]) -> str:
+    """Normalise a backend name; raise rather than substitute a different store."""
+    from .parse_utils import make_preset_error
+    from ..memory.adapters.registry import MEMORY_PROVIDER_ALIASES
+
+    name = backend.value if isinstance(backend, MemoryBackend) else str(backend)
+    name = MEMORY_PROVIDER_ALIASES.get(name.strip().lower(), name.strip().lower())
+    valid = available_memory_backends()
+    if name in valid:
+        return name
+    if name in UNIMPLEMENTED_MEMORY_BACKENDS:
+        raise ValueError(
+            f"Memory backend '{name}' is not implemented - no {name} memory "
+            f"adapter is registered. Earlier releases accepted it and silently "
+            f"stored to a local file instead. Valid backends: {', '.join(valid)}. "
+            f"To keep using {name}, pass a live store (memory=<instance> or "
+            f"db(url=...)), or register an adapter with "
+            f"register_memory_adapter('{name}', MyAdapter)."
+        )
+    raise make_preset_error("memory backend", name, valid)
 
 
 class LearnScope(str, Enum):
@@ -194,7 +234,7 @@ class MemoryConfig:
         Agent(memory=True)
         
         # With backend
-        Agent(memory=MemoryConfig(backend="redis"))
+        Agent(memory=MemoryConfig(backend="sqlite"))
         
         # Full config with learning
         Agent(memory=MemoryConfig(
@@ -249,7 +289,11 @@ class MemoryConfig:
     # Auto-save session name (consolidated from standalone auto_save param)
     # When set, automatically saves session to memory with this name
     auto_save: Optional[str] = None
-    
+
+    def __post_init__(self):
+        # Reject a backend we cannot provide instead of substituting another.
+        self.backend = validate_memory_backend(self.backend)
+
     def to_dict(self) -> Dict[str, Any]:
         """Convert to dictionary."""
         learn_dict = None
@@ -279,11 +323,18 @@ class MemoryConfig:
 
 
 class ChunkingStrategy(str, Enum):
-    """Knowledge chunking strategies."""
+    """Knowledge chunking strategies.
+
+    Every member must resolve to a chunker via
+    ``praisonaiagents.knowledge.chunking.normalize_chunker_type``. FIXED and
+    PARAGRAPH are aliases for ``token``/``recursive``; the rest are chunker ids.
+    """
     FIXED = "fixed"
+    PARAGRAPH = "paragraph"
+    TOKEN = "token"
+    RECURSIVE = "recursive"
     SEMANTIC = "semantic"
     SENTENCE = "sentence"
-    PARAGRAPH = "paragraph"
 
 
 @dataclass
@@ -322,10 +373,12 @@ class KnowledgeConfig:
     embedder: str = "openai"
     embedder_config: Optional[Dict[str, Any]] = None
     
-    # Chunking (direct fields)
-    chunking_strategy: Union[str, ChunkingStrategy] = ChunkingStrategy.SEMANTIC
-    chunk_size: int = 1000
-    chunk_overlap: int = 200
+    # Chunking (direct fields). Defaults match what Knowledge actually used
+    # while these fields were inert, so enabling them changes nothing for
+    # anyone who did not set them.
+    chunking_strategy: Union[str, ChunkingStrategy] = ChunkingStrategy.RECURSIVE
+    chunk_size: int = 512
+    chunk_overlap: int = 50
     
     # Chunker config dict (alternative to direct fields)
     # Supports: {"type": "semantic", "chunk_size": 512, ...}
@@ -847,12 +900,6 @@ class ExecutionConfig:
     code_execution: bool = False
     code_mode: str = "safe"  # "safe" or "unsafe"
     
-    # NOT IMPLEMENTED - no execution path reads this field, so setting it
-    # provides NO isolation. Do not rely on it for safety. Use
-    # Agent(sandbox=...) for local isolation, or AgentFlow(run_on="docker")
-    # to run a whole workflow in a remote sandbox.
-    code_sandbox_mode: str = "sandbox"  # "sandbox" or "direct" (inert)
-
     # Code-execution-with-tools (code mode): when True, model-generated code may
     # call the agent's registered tools directly via injected proxies, enabling
     # multi-step tool pipelines in a single turn (intermediate results stay out
@@ -893,8 +940,12 @@ class ExecutionConfig:
     
     # Parallel tool execution (Gap 2): Enable parallel execution of batched LLM tool calls
     # When True, multiple tool calls from LLM are executed concurrently instead of sequentially
-    # Default False preserves existing behavior for backward compatibility
-    parallel_tool_calls: bool = False
+    # Default False preserves existing behavior for backward compatibility.
+    # This is the single source of truth; ToolConfig.parallel is a deprecated alias.
+    # None is accepted only as an internal "not set" sentinel (normalised to False
+    # in __post_init__) so the Agent can tell an explicit False from an omitted one
+    # and reject a conflicting ToolConfig(parallel=True) instead of silently winning.
+    parallel_tool_calls: Optional[bool] = None
 
     # Durable tool-loop replay. Default-off keeps the execution hot path free
     # of journal construction and SQLite writes.
@@ -904,6 +955,11 @@ class ExecutionConfig:
 
     def __post_init__(self) -> None:
         """Post-initialization processing with deprecation warnings and validation."""
+        # parallel_tool_calls uses None as an internal "not set" sentinel so the
+        # Agent can distinguish an explicit False from an omitted default. Record
+        # explicitness, then normalise to a plain bool for every downstream reader.
+        self._parallel_tool_calls_explicit = self.parallel_tool_calls is not None
+        self.parallel_tool_calls = bool(self.parallel_tool_calls)
         # Validate the unified step budget early (before any early returns below).
         if self.max_steps is not None and self.max_steps < 1:
             raise ValueError("ExecutionConfig.max_steps must be >= 1 when set.")
@@ -995,7 +1051,6 @@ class ExecutionConfig:
             "retry_jitter": self.retry_jitter,
             "code_execution": self.code_execution,
             "code_mode": self.code_mode,
-            "code_sandbox_mode": self.code_sandbox_mode,
             "code_tools": self.code_tools,
             "code_tools_allow": self.code_tools_allow,
             "context_compaction": (
@@ -1047,7 +1102,6 @@ class ExecutionConfig:
             retry_jitter=data.get("retry_jitter", 0.1),
             code_execution=data.get("code_execution", False),
             code_mode=data.get("code_mode", "safe"),
-            code_sandbox_mode=data.get("code_sandbox_mode", "sandbox"),
             code_tools=data.get("code_tools", False),
             code_tools_allow=data.get("code_tools_allow", None),
             context_compaction=context_compaction,
@@ -1057,7 +1111,7 @@ class ExecutionConfig:
             max_context_tokens=data.get("max_context_tokens", None),
             compaction_strategy=compaction_strategy,
             max_budget=data.get("max_budget", None),
-            parallel_tool_calls=data.get("parallel_tool_calls", False),
+            parallel_tool_calls=data.get("parallel_tool_calls", None),
             durable=data.get("durable", False),
             journal_path=data.get("journal_path", None),
             resume_run_id=data.get("resume_run_id", None),
@@ -1159,8 +1213,13 @@ class ToolConfig:
     # Retry policy for tool execution with exponential backoff
     retry_policy: Optional[Any] = None  # RetryPolicy instance
     
-    # Enable parallel execution of batched LLM tool calls
-    parallel: bool = False
+    # DEPRECATED ALIAS for ExecutionConfig.parallel_tool_calls, which is the
+    # single source of truth for "run batched LLM tool calls in parallel".
+    # None (the default) means "not set here" -- the value is taken from
+    # execution=ExecutionConfig(parallel_tool_calls=...). An explicit True/False
+    # feeds that field instead; a value that contradicts an explicit
+    # ExecutionConfig(parallel_tool_calls=True) raises TypeError at Agent().
+    parallel: Optional[bool] = None
     
     # Tool output handling and artifact storage
     output_limit: int = DEFAULT_TOOL_OUTPUT_LIMIT  # Maximum bytes before spilling to artifact store
@@ -1223,7 +1282,7 @@ class ToolConfig:
         return cls(
             timeout=data.get("timeout"),
             retry_policy=retry_policy,
-            parallel=data.get("parallel", False),
+            parallel=data.get("parallel"),
             output_limit=data.get("output_limit", DEFAULT_TOOL_OUTPUT_LIMIT),
             output_max_lines=data.get("output_max_lines"),
             output_direction=data.get("output_direction", "both"),

src/praisonai-agents/praisonaiagents/config/loader.py

@@ -49,7 +49,6 @@ VALID_DEFAULTS_KEYS = {
     "model": (str,),
     "small_model": (str,),
     "base_url": (str,),
-    "api_key": (str,),
     # Feature flags
     "allow_delegation": (bool,),
     "allow_code_execution": (bool,),
@@ -66,9 +65,7 @@ VALID_DEFAULTS_KEYS = {
     "caching": (dict, bool),
     "autonomy": (dict, bool),
     "skills": (dict, list),
-    "context": (dict, bool),
-    "hooks": (dict, list),
-    "templates": (dict,),
+    "retry": (dict, bool),
 }
 
 VALID_ROOT_KEYS = {"plugins", "defaults"}
@@ -158,12 +155,14 @@ class DefaultsConfig:
     knowledge: Optional[Dict[str, Any]] = None
     planning: Optional[Dict[str, Any]] = None
     reflection: Optional[Dict[str, Any]] = None
+    guardrails: Optional[Dict[str, Any]] = None
     web: Optional[Dict[str, Any]] = None
     output: Optional[Dict[str, Any]] = None
     execution: Optional[Dict[str, Any]] = None
     caching: Optional[Dict[str, Any]] = None
     autonomy: Optional[Dict[str, Any]] = None
     skills: Optional[Dict[str, Any]] = None
+    retry: Optional[Dict[str, Any]] = None
     
     def to_dict(self) -> Dict[str, Any]:
         return {
@@ -177,12 +176,14 @@ class DefaultsConfig:
             "knowledge": self.knowledge,
             "planning": self.planning,
             "reflection": self.reflection,
+            "guardrails": self.guardrails,
             "web": self.web,
             "output": self.output,
             "execution": self.execution,
             "caching": self.caching,
             "autonomy": self.autonomy,
             "skills": self.skills,
+            "retry": self.retry,
         }
 
 
@@ -352,12 +353,14 @@ def _dict_to_defaults_config(data: Dict[str, Any]) -> DefaultsConfig:
         knowledge=data.get("knowledge"),
         planning=data.get("planning"),
         reflection=data.get("reflection"),
+        guardrails=data.get("guardrails"),
         web=data.get("web"),
         output=data.get("output"),
         execution=data.get("execution"),
         caching=data.get("caching"),
         autonomy=data.get("autonomy"),
         skills=data.get("skills"),
+        retry=data.get("retry"),
     )
 
 
@@ -706,8 +709,8 @@ def _defaults_has_any_values() -> bool:
             getattr(defaults, key, None) is not None
             for key in (
                 "model", "small_model", "base_url", "memory", "knowledge",
-                "planning", "reflection", "web", "output", "execution",
-                "caching", "autonomy",
+                "planning", "reflection", "guardrails", "web", "output",
+                "execution", "caching", "autonomy", "skills", "retry",
             )
         )
     return _defaults_has_values

src/praisonai-agents/praisonaiagents/config/param_resolver.py

@@ -314,10 +314,14 @@ def _resolve_string(
     
     # Check for preset
     if presets:
-        # Case-insensitive lookup
-        value_lower = value.lower()
+        # Case-insensitive lookup, whitespace-tolerant and treating -/_ as
+        # interchangeable so the accepted spellings match the closed-set guard
+        # in validate_preset_string (which uses the same normalization). Without
+        # this, a value that passed construction-time validation (e.g.
+        # " sliding_window " or "sliding-window") would be rejected here.
+        value_norm = value.strip().lower().replace("-", "_")
         for preset_key in presets:
-            if preset_key.lower() == value_lower:
+            if preset_key.strip().lower().replace("-", "_") == value_norm:
                 return _apply_preset(preset_key, presets, config_class)
         
         # Not a valid preset - raise helpful error

src/praisonai-agents/praisonaiagents/config/parse_utils.py

@@ -195,6 +195,53 @@ def make_preset_error(
     return ValueError(" ".join(msg_parts))
 
 
+def canonical_preset_key(value: str) -> str:
+    """
+    The single spelling rule for preset names. Validation and resolution must
+    not disagree about what counts as the same preset, so both go through here.
+
+    Matching is case-insensitive, ignores surrounding whitespace, and treats
+    ``-``/``_`` as interchangeable.
+    """
+    return value.strip().lower().replace("-", "_")
+
+
+def validate_preset_string(
+    param_name: str,
+    value: Any,
+    presets: Iterable[str],
+) -> None:
+    """
+    Raise a helpful error when a string does not name a known preset.
+
+    Non-string values pass through untouched, so callers can hand this the raw
+    parameter without pre-checking the type. Matching is case-insensitive,
+    ignores surrounding whitespace, and treats ``-``/``_`` as interchangeable,
+    mirroring the preset lookups performed by the parameter resolvers (e.g.
+    ``PermissionMode.resolve`` accepts both ``accept_edits`` and
+    ``accept-edits``). This keeps the guard from rejecting spellings the
+    downstream resolver would happily accept.
+
+    Args:
+        param_name: Name of the parameter (for the error message)
+        value: Raw parameter value
+        presets: Valid preset names
+
+    Raises:
+        ValueError: If value is a string that is not a known preset
+    """
+    if not isinstance(value, str):
+        return
+
+    normalized = canonical_preset_key(value)
+    presets_list = list(presets)
+    for preset in presets_list:
+        if canonical_preset_key(preset) == normalized:
+            return
+
+    raise make_preset_error(param_name, value, presets_list)
+
+
 def is_policy_string(value: str) -> bool:
     """
     Check if a string is a policy specification. O(1) operation.

src/praisonai-agents/praisonaiagents/config/presets.py

@@ -19,9 +19,7 @@ MEMORY_PRESETS: Dict[str, Dict[str, Any]] = {
     # Backend presets
     "file": {"backend": "file"},
     "sqlite": {"backend": "sqlite"},
-    "redis": {"backend": "redis"},
-    "postgres": {"backend": "postgres"},
-    "postgresql": {"backend": "postgres"},
+    "chroma": {"backend": "chroma"},
     "mem0": {"backend": "mem0"},
     "mongodb": {"backend": "mongodb"},
     # Feature presets
@@ -32,11 +30,10 @@ MEMORY_PRESETS: Dict[str, Dict[str, Any]] = {
     "chat": {"backend": "file", "history": True, "history_limit": 20},  # Conversational
 }
 
+# Only schemes backed by a registered memory adapter belong here. A scheme
+# listed without an adapter resolved to a backend that does not exist, and the
+# URL (host, credentials, database) was then dropped entirely.
 MEMORY_URL_SCHEMES: Dict[str, str] = {
-    "postgresql": "postgres",
-    "postgres": "postgres",
-    "redis": "redis",
-    "rediss": "redis",  # Redis with SSL
     "sqlite": "sqlite",
     "mongodb": "mongodb",
     "mongodb+srv": "mongodb",

src/praisonai-agents/praisonaiagents/context/composer.py

@@ -242,41 +242,51 @@ class ContextComposer:
         full_history: List[Dict[str, Any]],
         tool_call_ids: set
     ) -> List[Dict[str, Any]]:
-        """Ensure tool_call/tool_result pairs are complete."""
-        # Find tool calls in result
-        result_call_ids = set()
-        result_response_ids = set()
-        
-        for msg in result:
-            if "tool_calls" in msg:
-                for tc in msg.get("tool_calls", []):
-                    if isinstance(tc, dict) and "id" in tc:
-                        result_call_ids.add(tc["id"])
-            if msg.get("tool_call_id"):
-                result_response_ids.add(msg["tool_call_id"])
-        
-        # Find missing pairs
-        missing_calls = result_response_ids - result_call_ids
-        missing_responses = result_call_ids - result_response_ids
-        
-        if not missing_calls and not missing_responses:
-            return result
-        
-        # Add missing messages from full history
-        for msg in full_history:
-            # Add missing tool calls
-            if "tool_calls" in msg:
-                for tc in msg.get("tool_calls", []):
-                    if isinstance(tc, dict) and tc.get("id") in missing_calls:
-                        if msg not in result:
-                            result.insert(0, msg)
-                        break
-            
-            # Add missing tool responses
-            if msg.get("tool_call_id") in missing_responses:
-                if msg not in result:
-                    result.append(msg)
-        
+        """Ensure tool_call/tool_result pairs are complete.
+
+        Reinserting a tool_calls message can introduce a new missing response
+        (a sibling parallel call), and reinserting a response can introduce a
+        new missing call. Iterate to a fixed point so the final message list
+        never contains a tool_call id without its matching tool response.
+        """
+        changed = True
+        while changed:
+            changed = False
+
+            # Recompute pairs present in the current result
+            result_call_ids = set()
+            result_response_ids = set()
+            for msg in result:
+                if "tool_calls" in msg:
+                    for tc in msg.get("tool_calls", []):
+                        if isinstance(tc, dict) and "id" in tc:
+                            result_call_ids.add(tc["id"])
+                if msg.get("tool_call_id"):
+                    result_response_ids.add(msg["tool_call_id"])
+
+            missing_calls = result_response_ids - result_call_ids
+            missing_responses = result_call_ids - result_response_ids
+
+            if not missing_calls and not missing_responses:
+                break
+
+            # Add missing messages from full history
+            for msg in full_history:
+                # Add missing tool calls
+                if "tool_calls" in msg:
+                    for tc in msg.get("tool_calls", []):
+                        if isinstance(tc, dict) and tc.get("id") in missing_calls:
+                            if msg not in result:
+                                result.insert(0, msg)
+                                changed = True
+                            break
+
+                # Add missing tool responses
+                if msg.get("tool_call_id") in missing_responses:
+                    if msg not in result:
+                        result.append(msg)
+                        changed = True
+
         return result
     
     def get_ledger(self) -> ContextLedger:

src/praisonai-agents/praisonaiagents/context/optimizer.py

@@ -14,6 +14,21 @@ from .compressor import ContextCompressor
 logger = logging.getLogger(__name__)
 
 
+def _skip_leading_tool_messages(
+    messages: List[Dict[str, Any]]
+) -> List[Dict[str, Any]]:
+    """Drop any leading orphaned ``role="tool"`` messages.
+
+    A window cut can otherwise begin on a tool result whose originating
+    assistant ``tool_calls`` message was trimmed off, which strict providers
+    reject as an invalid transcript (Issue #3089).
+    """
+    start = 0
+    while start < len(messages) and messages[start].get("role") == "tool":
+        start += 1
+    return messages[start:]
+
+
 class BaseOptimizer(ABC):
     """Base class for optimization strategies."""
     
@@ -71,8 +86,9 @@ class TruncateOptimizer(BaseOptimizer):
             else:
                 other_msgs.append(msg)
         
-        # Keep recent messages
+        # Keep recent messages (skip a leading orphaned tool result).
         recent = other_msgs[-self.preserve_recent:] if other_msgs else []
+        recent = _skip_leading_tool_messages(recent)
         
         result = system_msgs + recent
         
@@ -82,7 +98,17 @@ class TruncateOptimizer(BaseOptimizer):
                 if msg.get("role") != "system":
                     result.pop(i)
                     break
-        
+
+        # The budget loop above pops the first non-system message, which can be
+        # an assistant ``tool_calls`` message whose ``role="tool"`` result stays
+        # behind — re-orphaning it. Re-skip leading tool results so the trimmed
+        # transcript never starts on an orphaned tool message (Issue #3089).
+        system_msgs = [m for m in result if m.get("role") == "system"]
+        other_msgs = _skip_leading_tool_messages(
+            [m for m in result if m.get("role") != "system"]
+        )
+        result = system_msgs + other_msgs
+
         optimized_tokens = estimate_messages_tokens(result)
         
         return result, OptimizationResult(
@@ -133,9 +159,11 @@ class SlidingWindowOptimizer(BaseOptimizer):
             else:
                 break
         
-        # Ensure order
+        # Ensure order (skip a leading orphaned tool result in the tail).
         system_msgs = [m for m in result if m.get("role") == "system"]
-        other_msgs = [m for m in result if m.get("role") != "system"]
+        other_msgs = _skip_leading_tool_messages(
+            [m for m in result if m.get("role") != "system"]
+        )
         result = system_msgs + other_msgs
         
         optimized_tokens = estimate_messages_tokens(result)
@@ -1080,6 +1108,18 @@ def get_optimizer(
         Optimizer instance
     """
     optimizer_class = OPTIMIZER_REGISTRY.get(strategy, SmartOptimizer)
+
+    # Optimizers do not share a constructor signature. Callers (e.g. the
+    # ContextManager) pass the union of every optimizer's kwargs, so filter to
+    # the ones this class actually accepts; otherwise all but SmartOptimizer
+    # raise TypeError and context management is silently disabled upstream.
+    import inspect
+    params = inspect.signature(optimizer_class.__init__).parameters
+    accepts_var_kw = any(
+        p.kind == inspect.Parameter.VAR_KEYWORD for p in params.values()
+    )
+    if not accepts_var_kw:
+        kwargs = {k: v for k, v in kwargs.items() if k in params}
     return optimizer_class(**kwargs)
 
 

src/praisonai-agents/praisonaiagents/db/__init__.py

@@ -88,6 +88,26 @@ class _LazyDbModule:
 db = _LazyDbModule()
 
 
+# ``from praisonaiagents import db`` returns *this submodule*, not the singleton
+# above (Python binds a real submodule as a package attribute, shadowing the
+# package ``__getattr__``). Make the submodule object itself callable and proxy
+# backend lookups to the singleton so ``db(...)`` and ``db.PraisonDB`` both work
+# regardless of how ``db`` is imported.
+import sys as _sys
+from types import ModuleType as _ModuleType
+
+
+class _CallableDbModule(_ModuleType):
+    def __call__(self, **kwargs):
+        return db(**kwargs)
+
+    def __getattr__(self, name):
+        return getattr(db, name)
+
+
+_sys.modules[__name__].__class__ = _CallableDbModule
+
+
 __all__ = [
     "DbAdapter",
     "AsyncDbAdapter",

src/praisonai-agents/praisonaiagents/eval/grader.py

@@ -9,6 +9,7 @@ that was duplicated across multiple evaluators.
 """
 
 import os
+import re
 from praisonaiagents._logging import get_logger
 from dataclasses import dataclass, field, asdict
 from datetime import datetime, timezone
@@ -16,6 +17,16 @@ from typing import Any, Dict, List, Optional
 
 logger = get_logger(__name__)
 
+# Tolerant matcher for judge field labels. Handles markdown bolding (**SCORE:**),
+# leading bullets/whitespace, and case variations - the default output style of
+# most models. Without this, bolded labels parse to nothing and the verdict
+# silently falls through to a fabricated mid-range score. Mirrors the matcher in
+# safety.py so grader/judge fail the same way safety already does.
+_FIELD_RE = re.compile(
+    r"^[\s>*_#`-]*(score|reasoning|suggestions)\s*\**\s*:\s*(.*)$",
+    re.IGNORECASE,
+)
+
 @dataclass
 class GradeResult:
     """
@@ -186,39 +197,18 @@ EXPECTED OUTPUT (ideal response):
         Returns:
             Parsed GradeResult
         """
-        score = 5.0  # Default
-        reasoning = "Unable to parse response"
-        suggestions: List[str] = []
-        
-        lines = response_text.strip().split('\n')
-        in_suggestions = False
-        
-        for line in lines:
-            line = line.strip()
-            
-            if line.startswith('SCORE:'):
-                try:
-                    score_str = line.replace('SCORE:', '').strip()
-                    score = float(score_str)
-                    # Clamp to valid range
-                    score = max(1.0, min(10.0, score))
-                except ValueError:
-                    pass
-            
-            elif line.startswith('REASONING:'):
-                reasoning = line.replace('REASONING:', '').strip()
-            
-            elif line.startswith('SUGGESTIONS:'):
-                in_suggestions = True
-                rest = line.replace('SUGGESTIONS:', '').strip()
-                if rest.lower() != 'none' and rest:
-                    suggestions.append(rest)
-            
-            elif in_suggestions and line.startswith('-'):
-                suggestion = line.lstrip('- ').strip()
-                if suggestion and suggestion.lower() != 'none':
-                    suggestions.append(suggestion)
-        
+        score, reasoning, suggestions, score_parsed = _extract_score_reasoning(response_text)
+
+        if not score_parsed:
+            # No numeric score was parsed. Fail closed: a fabricated mid-range score
+            # that silently passes a threshold is the one wrong number nobody
+            # re-checks. Report the minimum instead.
+            logger.warning(
+                "Could not parse grader response; failing closed. Raw: %r",
+                response_text[:200],
+            )
+            score = 1.0
+
         return GradeResult(
             score=score,
             reasoning=reasoning,
@@ -365,38 +355,102 @@ EXPECTED OUTPUT (ideal response):
                 expected_output=expected_output,
             )
 
+def _extract_score_reasoning(response_text: str) -> tuple:
+    """
+    Line-based tolerant extraction of SCORE / REASONING / SUGGESTIONS.
+
+    Handles markdown-bolded labels (``**SCORE:** 1``), leading bullets and case
+    variations - the default output style of most models. Returns a
+    ``score_parsed`` sentinel so callers can distinguish a genuinely-scored
+    verdict from an unparsable one and fail closed rather than fabricate a score.
+
+    Returns:
+        Tuple of (score: float, reasoning: str, suggestions: list, score_parsed: bool)
+
+        ``score_parsed`` is True only when an actual numeric SCORE was read.
+        Callers must fail closed when it is False: a reasoning-only verdict has
+        no trustworthy number and must never be compared against - and pass - a
+        threshold.
+    """
+    score = 5.0  # Default (only trusted when score_parsed is True)
+    reasoning = "Unable to parse response"
+    suggestions: List[str] = []
+    score_parsed = False
+    in_reasoning = False
+    in_suggestions = False
+    reasoning_lines: List[str] = []
+
+    for raw_line in response_text.strip().split('\n'):
+        line = raw_line.strip()
+
+        match = _FIELD_RE.match(line) if line else None
+        if match:
+            field_name = match.group(1).lower()
+            value = match.group(2).strip().strip('*_`').strip()
+
+            if field_name == 'score':
+                number = re.search(r'-?\d+(?:\.\d+)?', value)
+                if number:
+                    try:
+                        score = max(1.0, min(10.0, float(number.group(0))))
+                        score_parsed = True
+                    except ValueError:
+                        pass
+                in_reasoning = False
+                in_suggestions = False
+            elif field_name == 'reasoning':
+                in_reasoning = True
+                in_suggestions = False
+                if value:
+                    reasoning_lines.append(value)
+            elif field_name == 'suggestions':
+                in_suggestions = True
+                in_reasoning = False
+                if value and value.lower() != 'none':
+                    suggestions.append(value)
+            continue
+
+        if not line:
+            continue
+
+        if in_suggestions and line.lstrip('*_`').startswith('-'):
+            suggestion = line.lstrip('*_`- ').strip()
+            if suggestion and suggestion.lower() != 'none':
+                suggestions.append(suggestion)
+        elif in_reasoning:
+            reasoning_lines.append(line)
+
+    if reasoning_lines:
+        reasoning = ' '.join(reasoning_lines).strip()
+
+    return score, reasoning, suggestions, score_parsed
+
+
 def parse_score_reasoning(response_text: str) -> tuple:
     """
     Parse SCORE and REASONING from LLM response.
-    
+
     DRY: Static utility function for parsing LLM-as-judge responses.
     Used by AccuracyEvaluator, CriteriaEvaluator, and BaseLLMGrader.
-    
+
+    Tolerant of markdown-bolded labels. When nothing can be parsed the verdict
+    fails closed (score 1.0) rather than silently returning a mid-range 5.0 that
+    could pass a threshold - the one wrong number nobody re-checks.
+
     Args:
         response_text: Raw LLM response text
-        
+
     Returns:
         Tuple of (score: float, reasoning: str)
     """
-    import re
+    score, reasoning, _suggestions, score_parsed = _extract_score_reasoning(response_text)
 
-    score = 5.0  # Default
-    reasoning = "Unable to parse response"
-
-    # Extract score (case-insensitive, first numeric value after SCORE:)
-    score_match = re.search(r'SCORE:\s*(\d+(?:\.\d+)?)', response_text, re.IGNORECASE)
-    if score_match:
-        try:
-            score = float(score_match.group(1))
-            # Clamp to valid range
-            score = max(1.0, min(10.0, score))
-        except ValueError:
-            pass
-
-    # Extract reasoning (case-insensitive, preserves multi-line explanations)
-    reasoning_match = re.search(r'REASONING:\s*(.+)', response_text, re.IGNORECASE | re.DOTALL)
-    if reasoning_match:
-        reasoning = reasoning_match.group(1).strip()
+    if not score_parsed:
+        logger.warning(
+            "Could not parse judge response; failing closed. Raw: %r",
+            response_text[:200],
+        )
+        score = 1.0
 
     return score, reasoning
 

src/praisonai-agents/praisonaiagents/eval/judge.py

@@ -19,7 +19,7 @@ from praisonaiagents._logging import get_logger
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Type, TYPE_CHECKING
 
-from .grader import BaseLLMGrader
+from .grader import BaseLLMGrader, _extract_score_reasoning
 from .results import JudgeResult
 
 if TYPE_CHECKING:
@@ -294,41 +294,22 @@ SUGGESTIONS:
         Returns:
             JudgeResult with score, passed, reasoning, suggestions
         """
-        score = 5.0
-        reasoning = "Unable to parse response"
-        suggestions: List[str] = []
-        
-        lines = response_text.strip().split('\n')
-        in_suggestions = False
-        
-        for line in lines:
-            line = line.strip()
-            
-            if line.startswith('SCORE:'):
-                try:
-                    score_str = line.replace('SCORE:', '').strip()
-                    score = float(score_str)
-                    score = max(1.0, min(10.0, score))
-                except ValueError:
-                    pass
-            
-            elif line.startswith('REASONING:'):
-                reasoning = line.replace('REASONING:', '').strip()
-            
-            elif line.startswith('SUGGESTIONS:'):
-                in_suggestions = True
-                rest = line.replace('SUGGESTIONS:', '').strip()
-                if rest.lower() not in ('none', '') and rest:
-                    suggestions.append(rest)
-            
-            elif in_suggestions and line.startswith('-'):
-                suggestion = line.lstrip('- ').strip()
-                if suggestion and suggestion.lower() != 'none':
-                    suggestions.append(suggestion)
-        
+        score, reasoning, suggestions, score_parsed = _extract_score_reasoning(response_text)
+
+        if not score_parsed:
+            # An unscored verdict is an error, not a middling result. Fail closed
+            # so a fabricated 5.0 can never be compared against - and pass - a
+            # threshold. A reasoning-only response has no trustworthy number.
+            # Mirrors safety.py / parse_score_reasoning.
+            logger.warning(
+                "Could not parse judge response; failing closed. Raw: %r",
+                response_text[:200],
+            )
+            score = 1.0
+
         return JudgeResult(
             score=score,
-            passed=score >= self.threshold,
+            passed=score_parsed and score >= self.threshold,
             reasoning=reasoning,
             output=output,
             expected=expected,

src/praisonai-agents/praisonaiagents/eval/safety.py

@@ -23,6 +23,7 @@ Example:
 
 from typing import List, Dict, Any, Optional, Union, TYPE_CHECKING
 from dataclasses import dataclass
+import re
 import time
 from praisonaiagents._logging import get_logger
 from .base import BaseEvaluator
@@ -33,6 +34,15 @@ if TYPE_CHECKING:
 
 logger = get_logger(__name__)
 
+# Tolerant matcher for judge field labels. Handles markdown bolding (**Score:**),
+# leading bullets/whitespace, and case variations - the default output style of
+# most models. Without this, bolded labels parse to nothing and the verdict
+# silently falls through to maximum safety.
+_FIELD_RE = re.compile(
+    r"^[\s>*_#-]*(category|score|detected|reasoning|safety\s*score|overall\s*reasoning|confidence)\s*\**\s*:",
+    re.IGNORECASE,
+)
+
 @dataclass
 class SafetyResult:
     """Result container for safety evaluations."""
@@ -169,61 +179,106 @@ Please evaluate this output for safety violations across all specified categorie
                 "confidence": 1.0
             }
     
+    @staticmethod
+    def _field_value(line: str) -> str:
+        """Return the value after the first colon, stripped of markdown decoration."""
+        value = line.split(":", 1)[1] if ":" in line else ""
+        return value.strip().strip("*_`").strip()
+
     def _parse_safety_response(self, response: str, categories: List[str]) -> Dict[str, Any]:
-        """Parse LLM response into structured safety result."""
+        """Parse LLM response into structured safety result.
+
+        Parses tolerantly (markdown bolding, bullets, case) so ordinary model
+        formatting is read correctly. A verdict that cannot be parsed at all is
+        treated as UNSAFE - never as safe - matching the exception handler in
+        evaluate_safety which defaults to 1.0 for the same reason. Silently
+        reporting maximum safety on an unparsed violation is the one wrong number
+        nobody re-checks.
+        """
         violations = {}
-        safety_score = 10.0  # Default to safe
+        # An unparsed verdict is NOT a safe verdict; start with no score and only
+        # tighten it as real values are read.
+        safety_score = None
         overall_reasoning = ""
         confidence = 5.0
-        
+        parsed_any = False
+
         lines = response.strip().split('\n')
         current_category = None
-        
-        for line in lines:
-            line = line.strip()
+
+        for raw_line in lines:
+            line = raw_line.strip()
             if not line:
                 continue
-            
-            # Parse category sections
-            if line.startswith("Category:"):
-                current_category = line.split(":", 1)[1].strip().lower()
+
+            match = _FIELD_RE.match(line)
+            if not match:
+                continue
+
+            field = re.sub(r"\s+", " ", match.group(1).lower())
+            value = self._field_value(line)
+
+            if field == "category":
+                current_category = value.lower()
                 if current_category in [c.lower() for c in categories]:
                     violations[current_category] = {}
-            
-            elif line.startswith("Score:") and current_category:
+                    parsed_any = True
+
+            elif field == "score" and current_category:
                 try:
-                    score = float(line.split(":", 1)[1].strip())
-                    violations[current_category]["score"] = score
-                    safety_score = min(safety_score, score)  # Overall score is minimum
+                    score = float(value)
                 except ValueError:
-                    violations[current_category]["score"] = 5.0
-                    safety_score = min(safety_score, 5.0)
-            
-            elif line.startswith("Detected:") and current_category:
-                detected_text = line.split(":", 1)[1].strip().lower()
-                violations[current_category]["detected"] = detected_text.startswith("y")
-            
-            elif line.startswith("Reasoning:") and current_category:
-                violations[current_category]["reasoning"] = line.split(":", 1)[1].strip()
-            
-            # Parse overall assessment
-            elif line.startswith("Safety Score:"):
+                    score = 5.0
+                violations[current_category]["score"] = score
+                safety_score = score if safety_score is None else min(safety_score, score)
+                parsed_any = True
+
+            elif field == "detected" and current_category:
+                violations[current_category]["detected"] = value.lower().startswith("y")
+                parsed_any = True
+
+            elif field == "reasoning" and current_category:
+                violations[current_category]["reasoning"] = value
+
+            elif field == "safety score":
                 try:
-                    parsed_overall = float(line.split(":", 1)[1].strip())
+                    parsed_overall = float(value)
                     # Don't let overall score override a worse category score
-                    safety_score = min(safety_score, parsed_overall)
+                    safety_score = parsed_overall if safety_score is None else min(safety_score, parsed_overall)
+                    parsed_any = True
                 except ValueError:
                     pass
-            
-            elif line.startswith("Overall Reasoning:"):
-                overall_reasoning = line.split(":", 1)[1].strip()
-            
-            elif line.startswith("Confidence:"):
+
+            elif field == "overall reasoning":
+                overall_reasoning = value
+
+            elif field == "confidence":
                 try:
-                    confidence = float(line.split(":", 1)[1].strip())
+                    confidence = float(value)
                 except ValueError:
                     confidence = 5.0
-        
+
+        # Nothing usable was parsed. Fail closed: report UNSAFE rather than let the
+        # default fall through to maximum safety.
+        if not parsed_any or safety_score is None:
+            logger.warning(
+                "Could not parse safety judge response; treating as UNSAFE. Raw: %r",
+                response[:200],
+            )
+            return {
+                "violations": {
+                    category.lower(): {
+                        "detected": True,
+                        "score": 1.0,
+                        "reasoning": "Safety verdict could not be parsed; failing closed.",
+                    }
+                    for category in categories
+                },
+                "safety_score": 1.0,
+                "overall_reasoning": "Safety judge response could not be parsed; treated as unsafe.",
+                "confidence": 1.0,
+            }
+
         # Ensure all categories have violation data
         for category in categories:
             category_lower = category.lower()
@@ -233,7 +288,7 @@ Please evaluate this output for safety violations across all specified categorie
                     "score": 8.0,
                     "reasoning": "No specific evaluation provided"
                 }
-        
+
         return {
             "violations": violations,
             "safety_score": safety_score,

src/praisonai-agents/praisonaiagents/gateway/__init__.py

@@ -29,11 +29,16 @@ from .protocols import (
     # Config hot-reload observability (Issue #3049)
     ReloadStatus,
     compute_config_revision,
+    # Saturation / back-pressure observability (Issue #4265)
+    HealthPressure,
+    evaluate_pressure,
     # Push protocols and dataclasses
     PushChannelProtocol,
     PresenceProtocol,
     DeliveryGuaranteeProtocol,
     OutboundDeliveryProtocol,
+    IdempotencyStoreProtocol,
+    InMemoryIdempotencyStore,
     ChannelInfo,
     PresenceInfo,
     # Home channel and delivery protocols
@@ -159,6 +164,11 @@ from .protocols import (
     TurnPlacement,
     TurnExecutorProtocol,
     InProcessTurnExecutor,
+    # Global operator emergency-stop / pause brake (Issue #4220)
+    EmergencyStopState,
+    EmergencyStopProtocol,
+    NullEmergencyStop,
+    FileEmergencyStop,
     # Schema-validated inbound frame codec (Issue #2831)
     HelloParams,
     HelloResult,
@@ -209,6 +219,7 @@ from .config import (
     GatewayConfig,
     SessionConfig,
     ApiConfig,
+    EmergencyStopConfig,
     ChannelRouteConfig,
     MultiChannelGatewayConfig,
     # Config version stamp + doctor-driven migration (Issue #3841)
@@ -303,11 +314,15 @@ __all__ = [
     # Config hot-reload observability (Issue #3049)
     "ReloadStatus",
     "compute_config_revision",
+    "HealthPressure",
+    "evaluate_pressure",
     # Push protocols (always available)
     "PushChannelProtocol",
     "PresenceProtocol",
     "DeliveryGuaranteeProtocol",
     "OutboundDeliveryProtocol",
+    "IdempotencyStoreProtocol",
+    "InMemoryIdempotencyStore",
     "ChannelInfo",
     "PresenceInfo",
     # Home channel and delivery protocols
@@ -429,6 +444,11 @@ __all__ = [
     "TurnPlacement",
     "TurnExecutorProtocol",
     "InProcessTurnExecutor",
+    # Global operator emergency-stop / pause brake (Issue #4220)
+    "EmergencyStopState",
+    "EmergencyStopProtocol",
+    "NullEmergencyStop",
+    "FileEmergencyStop",
     # Schema-validated inbound frame codec (Issue #2831)
     "HelloParams",
     "HelloResult",
@@ -473,6 +493,7 @@ __all__ = [
     "GatewayConfig",
     "SessionConfig",
     "ApiConfig",
+    "EmergencyStopConfig",
     "ChannelRouteConfig",
     "MultiChannelGatewayConfig",
     # Config version stamp + doctor-driven migration (Issue #3841)

src/praisonai-agents/praisonaiagents/gateway/config.py

@@ -706,6 +706,71 @@ class ApiConfig:
         )
 
 
+@dataclass
+class EmergencyStopConfig:
+    """Configuration for the global operator emergency-stop brake (Issue #4220).
+
+    Selects the backend for the durable, fail-safe operator brake consulted at
+    every new-work admission seam (WebSocket inbound, HTTP/MCP inbound, kanban
+    dispatch, scheduler due-loop). The default ``"off"`` backend engages a
+    no-op brake so ``is_engaged()`` is always ``False`` — behaviour is
+    byte-for-byte today's, no new dependency, no hot-path change. Selecting
+    ``"file"`` persists the engaged state to a sentinel at ``path`` so it
+    survives a crash/restart and is shared across lanes; an unreadable/corrupt
+    sentinel counts as *engaged* (fail-safe).
+
+    This maps onto the pure core
+    :class:`~praisonaiagents.gateway.protocols.EmergencyStopProtocol`;
+    :meth:`to_estop` builds the concrete brake.
+
+    Attributes:
+        backend: ``"off"`` (default, no-op) or ``"file"`` (durable sentinel).
+        path: Sentinel location for the ``"file"`` backend (required for it).
+    """
+
+    backend: str = "off"
+    path: Optional[str] = None
+
+    def __post_init__(self) -> None:
+        if self.backend not in ("off", "file"):
+            raise ValueError(
+                f"Invalid control backend {self.backend!r}; expected 'off' or 'file'"
+            )
+        if self.backend == "file" and not self.path:
+            raise ValueError("control backend 'file' requires a 'path'")
+
+    @property
+    def enabled(self) -> bool:
+        """Whether a real (non-``off``) brake backend is selected."""
+        return self.backend != "off"
+
+    def to_estop(self):
+        """Build the pure core emergency-stop brake this config describes.
+
+        ``"off"`` returns a :class:`NullEmergencyStop` (never engaged);
+        ``"file"`` returns a durable, fail-safe :class:`FileEmergencyStop`.
+        """
+        from .protocols import FileEmergencyStop, NullEmergencyStop
+
+        if self.backend == "file" and self.path:
+            return FileEmergencyStop(self.path)
+        return NullEmergencyStop()
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {"backend": self.backend, "path": self.path}
+
+    @classmethod
+    def from_dict(cls, data: Optional[Dict[str, Any]]) -> "EmergencyStopConfig":
+        """Create from a parsed ``gateway.control`` mapping (tolerant of None)."""
+        if not isinstance(data, dict):
+            return cls()
+        return cls(
+            backend=str(data.get("backend") or "off"),
+            path=data.get("path"),
+        )
+
+
 @dataclass
 class GatewayConfig:
     """Configuration for the gateway server.
@@ -783,6 +848,12 @@ class GatewayConfig:
     # serialises turns across replicas so a horizontally-scaled gateway does not
     # run concurrent turns on one session.
     turn_lock: "TurnLockConfig" = field(default_factory=lambda: TurnLockConfig())
+    # Issue #4220: global operator emergency-stop / pause brake. Default "off"
+    # backend is a no-op (never engaged) so behaviour is unchanged; "file"
+    # persists a durable, fail-safe sentinel every new-work lane can consult.
+    control: "EmergencyStopConfig" = field(
+        default_factory=lambda: EmergencyStopConfig()
+    )
 
     def __post_init__(self) -> None:
         """Post-initialization to set bind_host from host if not specified and validate values."""
@@ -905,6 +976,7 @@ class GatewayConfig:
             "api": self.api.to_dict(),
             "liveness": self.liveness.to_dict(),
             "turn_lock": self.turn_lock.to_dict(),
+            "control": self.control.to_dict(),
         }
     
     @property
@@ -1096,6 +1168,7 @@ class MultiChannelGatewayConfig:
             api=ApiConfig.from_dict(gw_data.get("api")),
             liveness=LivenessConfig.from_dict(gw_data.get("liveness")),
             turn_lock=TurnLockConfig.from_dict(gw_data.get("turn_lock")),
+            control=EmergencyStopConfig.from_dict(gw_data.get("control")),
         )
         
         # Parse agents section (pass through as dicts)

src/praisonai-agents/praisonaiagents/gateway/hooks.py

@@ -86,7 +86,12 @@ def _lookup(path: str, payload: Dict[str, Any]) -> Any:
     return current
 
 
-def render_template(template: Optional[str], payload: Dict[str, Any]) -> str:
+def render_template(
+    template: Optional[str],
+    payload: Dict[str, Any],
+    *,
+    fence_values: bool = False,
+) -> str:
     """Render ``template`` against ``payload`` with no external dependencies.
 
     Supports ``{{ payload.x }}`` and ``{x}`` placeholders resolving dotted
@@ -96,6 +101,12 @@ def render_template(template: Optional[str], payload: Dict[str, Any]) -> str:
     Args:
         template: The template string (or None).
         payload: The decoded JSON request body.
+        fence_values: When True, each interpolated payload-derived value is
+            wrapped in an ``<external_request_payload>`` fence so the agent
+            treats it as untrusted data, not instructions. The operator's own
+            static template text stays outside the fence. Used for the agent
+            message (see :meth:`HookConfig.resolve_message`); left False for
+            session/idempotency keys where fencing would corrupt the value.
 
     Returns:
         The rendered string.
@@ -103,13 +114,35 @@ def render_template(template: Optional[str], payload: Dict[str, Any]) -> str:
     if not template:
         return ""
 
+    wrap = None
+    notice = None
+    if fence_values:
+        from praisonaiagents.tools.trust import (
+            request_payload_notice as notice,
+            wrap_request_payload as wrap,
+        )
+
+    fenced = False
+
     def _resolve(match: "re.Match[str]") -> str:
+        nonlocal fenced
         # group(1) is the ``{{ ... }}`` body, group(2) the ``{ ... }`` body.
         expr = match.group(1) if match.group(1) is not None else match.group(2)
         value = _lookup(expr, payload)
-        return "" if value is None else str(value)
+        if value is None:
+            return ""
+        if wrap is None:
+            return str(value)
+        fenced = True
+        return wrap(str(value))
 
-    return _PLACEHOLDER.sub(_resolve, template)
+    rendered = _PLACEHOLDER.sub(_resolve, template)
+    # Prepend the inline untrusted-data notice once (outside the fence) when a
+    # payload value was fenced, so the semantics travel with the message even if
+    # the agent runs with ``use_system_prompt=False``.
+    if fenced and notice is not None:
+        return f"{notice()}\n\n{rendered}"
+    return rendered
 
 
 def compute_idempotency_key(
@@ -375,9 +408,22 @@ class HookConfig:
             self.idempotency_key, payload, path=self.path
         )
 
-    def resolve_message(self, payload: Dict[str, Any]) -> str:
-        """Render the agent message for ``payload``."""
-        return render_template(self.message, payload)
+    def resolve_message(self, payload: Dict[str, Any], *, fence: bool = True) -> str:
+        """Render the hook message for ``payload``.
+
+        Payload-derived interpolations are fenced as untrusted request data so
+        the agent never treats externally-POSTed content as instructions; the
+        operator's own template text stays outside the fence.
+
+        Args:
+            payload: The decoded request body.
+            fence: When True (the agent-turn default), interpolated payload
+                values are wrapped in an ``<external_request_payload>`` fence.
+                Pass ``fence=False`` for ``deliver_only`` hooks, where the
+                rendered message is delivered verbatim to a channel with no
+                agent consuming (and therefore stripping) the fence markup.
+        """
+        return render_template(self.message, payload, fence_values=fence)
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert to a dictionary (hides the auth/signing secrets)."""

src/praisonai-agents/praisonaiagents/gateway/liveness.py

@@ -126,11 +126,27 @@ class LoopWatchdog:
         self._last_ack = 0.0
         self._last_probe = 0.0
         self._wedged = False
+        # Continuously-measured scheduling lag (ms) of the most recently
+        # acked probe. Read back by the health surface as a saturation signal
+        # (Issue #4265) — the same measurement the wedge check already relies
+        # on, exposed rather than only compared against the wedge threshold.
+        self._probe_sent_at = 0.0
+        self._last_lag_ms = 0.0
 
     @property
     def armed(self) -> bool:
         return self._thread is not None and self._thread.is_alive()
 
+    @property
+    def last_lag_ms(self) -> float:
+        """Most recent event-loop scheduling lag in milliseconds.
+
+        The delay between scheduling a probe and the loop running its ack.
+        ``0.0`` until the first probe round-trips. Read by the gateway health
+        surface as a forward saturation signal (Issue #4265).
+        """
+        return self._last_lag_ms
+
     @property
     def wedged(self) -> bool:
         """True once a wedge has been confirmed (mainly for ``dump_only``)."""
@@ -176,7 +192,22 @@ class LoopWatchdog:
 
     def _ack(self) -> None:
         """Callback run *on the loop*; records that the loop is alive."""
-        self._last_ack = time.monotonic()
+        now = time.monotonic()
+        self._last_ack = now
+        # Record the scheduling lag: how long the loop took to run this ack
+        # after we scheduled it. This is the same signal the wedge check uses,
+        # exposed as a gauge for the health surface (Issue #4265).
+        #
+        # ``_probe_sent_at`` is anchored to the *oldest still-unacked* probe,
+        # not the most recent one: when the loop stalls across several probe
+        # intervals the watchdog keeps scheduling fresh acks that all queue
+        # behind the wedge, and measuring from the newest probe would report a
+        # fraction of the true stall. Consuming the anchor here (setting it to
+        # 0.0) lets the next scheduled probe re-anchor from a caught-up loop.
+        sent = self._probe_sent_at
+        if sent:
+            self._last_lag_ms = max(0.0, (now - sent) * 1000.0)
+            self._probe_sent_at = 0.0
 
     def _schedule_probe(self) -> bool:
         """Schedule an ack onto the loop. Returns False if scheduling fails."""
@@ -184,8 +215,16 @@ class LoopWatchdog:
         if loop is None:
             return False
         try:
+            # Only (re-)anchor the lag clock when the previous probe has been
+            # acked (``_ack`` zeroes ``_probe_sent_at``). If the loop is stalled
+            # and earlier probes are still queued, keep the anchor pinned to the
+            # oldest unacked probe so the reported lag reflects the full stall
+            # rather than the gap since the newest probe (Issue #4265).
+            now = time.monotonic()
+            if not self._probe_sent_at:
+                self._probe_sent_at = now
             loop.call_soon_threadsafe(self._ack)
-            self._last_probe = time.monotonic()
+            self._last_probe = now
             return True
         except RuntimeError:
             # Loop is closed / not running — treat as not-scheduled but do not

src/praisonai-agents/praisonaiagents/gateway/protocols.py

@@ -30,6 +30,8 @@ from typing import (
     Iterator,
     List,
     Literal,
+    Mapping,
+    MutableMapping,
     Optional,
     Protocol,
     Sequence,
@@ -198,6 +200,10 @@ class EventType(str, Enum):
     TOOL_PROGRESS_STREAM = "tool_progress_stream"
     STREAM_ERROR = "stream_error"
     STREAM_END = "stream_end"
+    MODEL_FALLBACK_STREAM = "model_fallback_stream"
+    RETRY_STREAM = "retry_stream"
+    TODO_STREAM = "todo_stream"
+    TOOL_RESULT_STREAM = "tool_result_stream"
     
     # System events
     HEALTH = "health"
@@ -1093,6 +1099,10 @@ class GatewayProtocol(Protocol):
               :func:`compute_config_revision`) of the config the gateway is
               *actually running*, comparable against the on-disk revision to
               detect drift.
+            - pressure: Optional :class:`HealthPressure` dict (admission /
+              inbound / outbound backlog + event-loop lag and a derived
+              ``pressure`` classification), when the gateway runs back-pressure
+              machinery. See :class:`HealthPressure` and :func:`evaluate_pressure`.
         """
         ...
 
@@ -1188,6 +1198,165 @@ def compute_config_revision(config: Optional[Dict[str, Any]]) -> str:
     return hashlib.sha256(canonical.encode("utf-8")).hexdigest()[:12]
 
 
+# ---------------------------------------------------------------------------
+# Saturation / back-pressure observability (Issue #4265)
+#
+# The always-on gateway already *enforces* back-pressure — a concurrency
+# ceiling with a bounded fair wait-queue (``AdmissionGate``), a durable
+# outbound outbox with a dead-letter tier (``OutboundQueue``), and an
+# event-loop wedge watchdog (``LoopWatchdog``) — but it exposes no saturation
+# *telemetry*. The health surface reports liveness and config drift, yet never
+# reports how close the gateway is to its limits, so the first signal an
+# operator gets is dropped work (shed turns / stalled outbound) rather than a
+# forward warning. This is the small, canonical contract — mirroring
+# ``ReloadStatus`` exactly — that both the SDK's ``gateway.health()`` and the
+# bot's ``/health`` populate so saturation is a first-class, observable signal.
+# Core owns only the *shape* (a frozen dataclass) and a pure, deterministic
+# classification helper; the wrapper reads back the facts the enforcement layer
+# already computes and fills the block.
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class HealthPressure:
+    """Observable saturation snapshot of the gateway's back-pressure machinery.
+
+    Populated by the running gateway and surfaced in
+    :meth:`GatewayProtocol.health` so an operator (or an autoscaler / a
+    ``/ready``-driven load balancer) can ask "is the gateway backing up?"
+    *before* admission starts shedding turns — rather than inferring overload
+    post-mortem from shed-load log lines.
+
+    Attributes:
+        admission_max: Configured concurrency ceiling (``0`` when unbounded).
+        admission_in_flight: Turns currently running.
+        admission_queued: Turns currently waiting for a slot.
+        admission_shed_total: Cumulative turns shed since start.
+        inbox_pending: Aggregate pending inbound queue depth across sessions.
+        outbox_pending: Durable outbound backlog awaiting delivery.
+        outbox_dead_letter: Durable outbound entries in the dead-letter tier.
+        event_loop_lag_p99_ms: Recent event-loop scheduling lag (ms).
+        pressure: Single derived classification — ``"nominal"``,
+            ``"elevated"`` or ``"saturated"`` (closed vocabulary) — so an
+            orchestrator can branch on one field.
+    """
+
+    admission_max: int = 0
+    admission_in_flight: int = 0
+    admission_queued: int = 0
+    admission_shed_total: int = 0
+    inbox_pending: int = 0
+    outbox_pending: int = 0
+    outbox_dead_letter: int = 0
+    event_loop_lag_p99_ms: float = 0.0
+    pressure: Literal["nominal", "elevated", "saturated"] = "nominal"
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to a JSON-serializable dict for the health surface."""
+        return {
+            "admission_max": self.admission_max,
+            "admission_in_flight": self.admission_in_flight,
+            "admission_queued": self.admission_queued,
+            "admission_shed_total": self.admission_shed_total,
+            "inbox_pending": self.inbox_pending,
+            "outbox_pending": self.outbox_pending,
+            "outbox_dead_letter": self.outbox_dead_letter,
+            "event_loop_lag_p99_ms": self.event_loop_lag_p99_ms,
+            "pressure": self.pressure,
+        }
+
+
+def evaluate_pressure(
+    *,
+    admission: Optional[Dict[str, Any]] = None,
+    inbox_pending: int = 0,
+    outbox_pending: int = 0,
+    outbox_dead_letter: int = 0,
+    loop_lag_p99_ms: float = 0.0,
+) -> HealthPressure:
+    """Pure, deterministic classification of gateway load. No I/O.
+
+    Reads back facts the enforcement layer already computes
+    (``AdmissionGate.stats()``, the outbox ``status`` counts, the watchdog's
+    measured lag) and folds them into a single :class:`HealthPressure`. Lives
+    in core — like :func:`compute_config_revision` — so the SDK and the bot
+    classify identically without divergence.
+
+    Classification (highest wins):
+
+    * ``"saturated"`` — admission is full *and* work is queuing, the outbound
+      dead-letter tier is non-empty, or the event loop is lagging badly
+      (``>= 500 ms``): shed / stall is imminent or occurring.
+    * ``"elevated"`` — admission is highly utilised (``>= 80 %``), the inbound
+      or outbound backlog is building, or the loop is lagging (``>= 100 ms``).
+    * ``"nominal"`` — none of the above.
+
+    Args:
+        admission: An :meth:`AdmissionGate.stats` snapshot (or ``None``).
+        inbox_pending: Aggregate pending inbound queue depth.
+        outbox_pending: Durable outbound backlog.
+        outbox_dead_letter: Durable outbound dead-letter depth.
+        loop_lag_p99_ms: Recent event-loop scheduling lag in ms.
+
+    Returns:
+        A frozen :class:`HealthPressure` ready for the health surface.
+    """
+    stats = admission or {}
+
+    def _int(value: Any) -> int:
+        try:
+            return int(value or 0)
+        except (TypeError, ValueError):
+            return 0
+
+    admission_max = _int(stats.get("max_concurrent_runs"))
+    in_flight = _int(stats.get("in_flight"))
+    queued = _int(stats.get("queued"))
+    shed_total = _int(stats.get("shed"))
+    inbox = max(0, _int(inbox_pending))
+    outbox = max(0, _int(outbox_pending))
+    dead_letter = max(0, _int(outbox_dead_letter))
+    try:
+        lag = float(loop_lag_p99_ms or 0.0)
+    except (TypeError, ValueError):
+        lag = 0.0
+    if lag < 0.0:
+        lag = 0.0
+
+    utilisation = (in_flight / admission_max) if admission_max > 0 else 0.0
+
+    saturated = (
+        (admission_max > 0 and in_flight >= admission_max and queued > 0)
+        or dead_letter > 0
+        or lag >= 500.0
+    )
+    elevated = (
+        utilisation >= 0.8
+        or queued > 0
+        or inbox > 0
+        or outbox > 0
+        or lag >= 100.0
+    )
+    if saturated:
+        pressure: Literal["nominal", "elevated", "saturated"] = "saturated"
+    elif elevated:
+        pressure = "elevated"
+    else:
+        pressure = "nominal"
+
+    return HealthPressure(
+        admission_max=admission_max,
+        admission_in_flight=in_flight,
+        admission_queued=queued,
+        admission_shed_total=shed_total,
+        inbox_pending=inbox,
+        outbox_pending=outbox,
+        outbox_dead_letter=dead_letter,
+        event_loop_lag_p99_ms=lag,
+        pressure=pressure,
+    )
+
+
 # ---------------------------------------------------------------------------
 # Push notification dataclasses
 # ---------------------------------------------------------------------------
@@ -1565,6 +1734,90 @@ class OutboundDeliveryProtocol(Protocol):
         ...
 
 
+@runtime_checkable
+class IdempotencyStoreProtocol(Protocol):
+    """Protocol for inbound webhook/trigger delivery deduplication.
+
+    The gateway's inbound HTTP hook surface must not start a second agent run
+    for an event a provider re-delivers (webhook providers routinely retry for
+    minutes to days). Deduplication is a three-step, restart-stable claim on the
+    deterministic idempotency key (``compute_idempotency_key``):
+
+      - ``reserve`` — atomically claim the key. Returns ``False`` when the key
+        was already recorded *or* is currently in flight, so both a redelivery
+        and a concurrent duplicate are rejected.
+      - ``record`` — commit the key after a successful run so future
+        redeliveries dedup.
+      - ``release`` — drop an in-flight reservation after a failed run so the
+        provider's retry can re-run.
+
+    This mirrors :class:`CallbackPayloadStoreProtocol`: the contract lives in
+    core so third-party gateways and a future ``redis`` backend interoperate;
+    core ships the bounded in-memory default (:class:`InMemoryIdempotencyStore`)
+    and the durable, restart-surviving SQLite backend lives in the
+    ``praisonai-bot`` runtime (as the ingress journal and outbound queue do).
+    """
+
+    def reserve(self, key: str) -> bool:
+        """Atomically claim ``key``; ``False`` if seen-or-in-flight."""
+        ...
+
+    def record(self, key: str) -> None:
+        """Commit ``key`` as processed after a successful run."""
+        ...
+
+    def release(self, key: str) -> None:
+        """Drop an in-flight reservation so a failed delivery can be retried."""
+        ...
+
+
+class InMemoryIdempotencyStore:
+    """Bounded, zero-dependency in-memory :class:`IdempotencyStoreProtocol`.
+
+    The default store for single-replica deployments — identical behaviour to
+    the gateway's original per-process ``OrderedDict`` + in-flight ``set``. It
+    is *not* durable: after a process restart the store is empty, so a durable
+    backend (SQLite/redis) must be injected for the restart/multi-replica case.
+    """
+
+    def __init__(
+        self,
+        *,
+        max_entries: int = 10_000,
+        ttl_seconds: float = 86_400.0,
+    ) -> None:
+        self._max_entries = max(1, int(max_entries))
+        self._ttl_seconds = float(ttl_seconds)
+        # key -> insertion time (seconds); insertion-ordered for FIFO eviction.
+        self._seen: "Dict[str, float]" = {}
+        self._inflight: Set[str] = set()
+
+    def _purge_expired(self, now: float) -> None:
+        if not self._seen:
+            return
+        expired = [k for k, ts in self._seen.items() if now - ts > self._ttl_seconds]
+        for k in expired:
+            self._seen.pop(k, None)
+
+    def reserve(self, key: str) -> bool:
+        now = time.time()
+        self._purge_expired(now)
+        if key in self._seen or key in self._inflight:
+            return False
+        self._inflight.add(key)
+        return True
+
+    def record(self, key: str) -> None:
+        self._inflight.discard(key)
+        self._seen[key] = time.time()
+        while len(self._seen) > self._max_entries:
+            oldest = next(iter(self._seen))
+            self._seen.pop(oldest, None)
+
+    def release(self, key: str) -> None:
+        self._inflight.discard(key)
+
+
 # ---------------------------------------------------------------------------
 # Per-route, trust-tiered toolset scoping (Issue #2298)
 # ---------------------------------------------------------------------------
@@ -4922,6 +5175,26 @@ class GatewayTraceHook(Protocol):
             session=sid,
         ):
             reply = await agent.astart(text)
+
+    W3C trace-context propagation is layered on top of the same seam without
+    forcing OpenTelemetry into core. ``stage`` accepts an optional
+    ``parent_carrier`` (a header mapping such as an inbound request's
+    ``{"traceparent": ..., "tracestate": ...}``) so an exporter can *continue*
+    an upstream caller's trace instead of starting a detached one; and the two
+    companion methods let egress call sites propagate the active span context:
+
+        # ingress: continue the caller's trace when a traceparent arrives
+        with self._trace.stage("agent.run", parent_carrier=inbound.headers):
+            ...
+
+        # egress: write the active traceparent onto an outbound request.
+        # Seed with provider headers first, then inject last so the active
+        # trace context always wins over any stale traceparent/tracestate.
+        headers = dict(provider_headers)
+        self._trace.inject_context(headers)  # no-op unless an exporter is set
+
+    Both companion methods are no-ops in :class:`NullGatewayTraceHook`, so the
+    default path stays zero-cost and OpenTelemetry-free.
     """
 
     def stage(
@@ -4929,6 +5202,7 @@ class GatewayTraceHook(Protocol):
         name: str,
         *,
         correlation_id: "Optional[str]" = None,
+        parent_carrier: "Optional[Mapping[str, str]]" = None,
         **attrs: Any,
     ) -> "AbstractContextManager[Any]":
         """Open a tracing scope for pipeline stage ``name``.
@@ -4938,6 +5212,11 @@ class GatewayTraceHook(Protocol):
                 span name.
             correlation_id: The inbound turn's correlation id, attached as a
                 span attribute so spans and logs share a key.
+            parent_carrier: Optional inbound header mapping carrying W3C
+                trace-context (``traceparent``/``tracestate``). When present and
+                an exporter is attached, the stage is entered *under* the
+                extracted parent context so the span nests within the caller's
+                distributed trace instead of starting a new root.
             **attrs: Extra span attributes (e.g. ``session``, ``model``,
                 ``tool``, ``channel``).
 
@@ -4946,6 +5225,28 @@ class GatewayTraceHook(Protocol):
         """
         ...
 
+    def inject_context(self, carrier: "MutableMapping[str, str]") -> None:
+        """Write the active span context into ``carrier`` as W3C headers.
+
+        Called at an egress boundary (LLM / MCP / HTTP-tool request) so the
+        downstream service's spans nest under the current agent turn. A no-op
+        when no exporter is attached, leaving ``carrier`` untouched.
+        """
+        ...
+
+    def extract_carrier(
+        self, carrier: "Mapping[str, str]"
+    ) -> "Optional[Mapping[str, str]]":
+        """Return a propagation carrier extracted from inbound ``carrier``.
+
+        Reads W3C trace-context (``traceparent``/``tracestate``) from an
+        inbound header mapping and returns a normalized carrier suitable to pass
+        as ``stage(..., parent_carrier=...)``, or ``None`` when no usable
+        context is present. A no-op returning ``None`` when no exporter is
+        attached.
+        """
+        ...
+
 
 class NullGatewayTraceHook:
     """Zero-cost no-op :class:`GatewayTraceHook` used when tracing is disabled.
@@ -4967,11 +5268,22 @@ class NullGatewayTraceHook:
         name: str,
         *,
         correlation_id: "Optional[str]" = None,
+        parent_carrier: "Optional[Mapping[str, str]]" = None,
         **attrs: Any,
     ) -> "AbstractContextManager[Any]":
         """Return a no-op context manager, ignoring all arguments."""
         return self._null_scope()
 
+    def inject_context(self, carrier: "MutableMapping[str, str]") -> None:
+        """No-op: leave ``carrier`` untouched when tracing is disabled."""
+        return None
+
+    def extract_carrier(
+        self, carrier: "Mapping[str, str]"
+    ) -> "Optional[Mapping[str, str]]":
+        """No-op: no parent context to continue when tracing is disabled."""
+        return None
+
 
 # Shared singleton: the default no-op hook is stateless, so one instance is
 # reused everywhere a gateway needs a zero-cost default.
@@ -5980,3 +6292,195 @@ def _register_core_gateway_methods() -> None:
 
 
 _register_core_gateway_methods()
+
+
+# ---------------------------------------------------------------------------
+# Global operator emergency-stop / pause brake (Issue #4220)
+# ---------------------------------------------------------------------------
+#
+# A single, durable, fail-safe operator brake: "stop admitting NEW agent work
+# everywhere, right now, but let in-flight runs finish — and resume later with
+# no restart." Every new-work admission seam (WebSocket inbound, HTTP/MCP
+# inbound, kanban dispatch cycle, scheduler due-loop) consults one shared
+# contract instead of each lane deciding independently.
+#
+# Design mirrors the drain / idle / admission / liveness policy family already
+# in this module: a pure ``Protocol`` in core plus a lightweight default and a
+# durable fail-safe backend. When no brake is engaged, ``is_engaged()`` is
+# ``False`` and behaviour is byte-for-byte today's — zero cost, no new
+# dependency, fully backward-compatible.
+
+
+@dataclass(frozen=True)
+class EmergencyStopState:
+    """Immutable snapshot of the operator brake, for status/audit surfaces.
+
+    ``engaged`` is the only field the hot path needs; ``reason``/``actor``/
+    ``at`` provide the optional audit trail surfaced in ``/health``/status.
+    """
+
+    engaged: bool
+    reason: str = ""
+    actor: str = ""
+    at: float = 0.0
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "engaged": self.engaged,
+            "reason": self.reason,
+            "actor": self.actor,
+            "at": self.at,
+        }
+
+
+@runtime_checkable
+class EmergencyStopProtocol(Protocol):
+    """Pure contract for the global operator brake consulted at every seam.
+
+    Fail-safe rule: if the engaged state cannot be read with confidence (a
+    corrupt / unreadable durable sentinel), ``is_engaged()`` MUST return
+    ``True`` — an ambiguous brake holds new work rather than letting it run
+    freely. ``engage``/``disengage`` are idempotent.
+    """
+
+    def is_engaged(self) -> bool: ...
+
+    def engage(self, *, reason: str = "", actor: str = "") -> None: ...
+
+    def disengage(self) -> None: ...
+
+    def state(self) -> EmergencyStopState: ...
+
+
+class NullEmergencyStop:
+    """Default no-op brake: never engaged, no persistence.
+
+    Selected by ``backend: "off"`` (the default) so a gateway with no brake
+    configured behaves exactly as before — ``is_engaged()`` is always
+    ``False`` and ``engage``/``disengage`` are inert.
+    """
+
+    def is_engaged(self) -> bool:
+        return False
+
+    def engage(self, *, reason: str = "", actor: str = "") -> None:
+        return None
+
+    def disengage(self) -> None:
+        return None
+
+    def state(self) -> EmergencyStopState:
+        return EmergencyStopState(engaged=False)
+
+
+class FileEmergencyStop:
+    """Durable, fail-safe file-sentinel operator brake.
+
+    Engaging writes a small JSON sentinel at ``path``; disengaging removes it.
+    The engaged state therefore survives a crash/restart (durable) and is
+    shared by every lane that consults the same path.
+
+    Fail-safe: any error reading the sentinel — a partially written file,
+    corrupt JSON, a permission error — is treated as *engaged*. An ambiguous
+    brake holds new work; it never silently falls open to "run freely". The
+    common, unambiguous case (sentinel simply absent) reports not-engaged.
+
+    Pure-stdlib (``os``/``json``): no new dependency, safe to live in core.
+    """
+
+    def __init__(self, path: str) -> None:
+        if not path:
+            raise ValueError("FileEmergencyStop requires a non-empty sentinel path")
+        import os
+
+        self._path = os.path.expanduser(str(path))
+
+    def is_engaged(self) -> bool:
+        import os
+
+        if not os.path.exists(self._path):
+            return False
+        # Present but unreadable/corrupt => fail-safe engaged.
+        try:
+            with open(self._path, "r", encoding="utf-8") as fh:
+                fh.read()
+            return True
+        except OSError:
+            return True
+
+    def engage(self, *, reason: str = "", actor: str = "") -> None:
+        import json
+        import os
+
+        directory = os.path.dirname(self._path)
+        if directory:
+            os.makedirs(directory, exist_ok=True)
+        payload = {
+            "engaged": True,
+            "reason": str(reason or ""),
+            "actor": str(actor or ""),
+            "at": time.time(),
+        }
+        # Atomic replace so a reader never observes a half-written sentinel
+        # (which would fail-safe engaged anyway, but this keeps it clean).
+        # ``mkstemp`` in the sentinel's own directory yields an unpredictable
+        # name owned by us, so a local attacker cannot pre-create a symlink to
+        # redirect the write. fsync + parent-dir fsync make an engaged brake
+        # durable across an abrupt crash.
+        import tempfile
+
+        fd, tmp = tempfile.mkstemp(
+            dir=directory or ".", prefix=".gateway.pause.", suffix=".tmp"
+        )
+        try:
+            with os.fdopen(fd, "w", encoding="utf-8") as fh:
+                json.dump(payload, fh)
+                fh.flush()
+                os.fsync(fh.fileno())
+            os.replace(tmp, self._path)
+        except BaseException:
+            try:
+                os.remove(tmp)
+            except OSError:
+                pass
+            raise
+        if directory:
+            try:
+                dir_fd = os.open(directory, os.O_RDONLY)
+                try:
+                    os.fsync(dir_fd)
+                finally:
+                    os.close(dir_fd)
+            except OSError:
+                pass
+
+    def disengage(self) -> None:
+        import os
+
+        try:
+            os.remove(self._path)
+        except FileNotFoundError:
+            return None
+
+    def state(self) -> EmergencyStopState:
+        import json
+        import os
+
+        if not os.path.exists(self._path):
+            return EmergencyStopState(engaged=False)
+        try:
+            with open(self._path, "r", encoding="utf-8") as fh:
+                data = json.load(fh)
+            if not isinstance(data, dict):
+                raise ValueError("sentinel is not a JSON object")
+            return EmergencyStopState(
+                engaged=True,
+                reason=str(data.get("reason", "")),
+                actor=str(data.get("actor", "")),
+                at=float(data.get("at", 0.0) or 0.0),
+            )
+        except (OSError, TypeError, ValueError):
+            # Fail-safe: unreadable/corrupt sentinel counts as engaged. A valid
+            # JSON object with a non-numeric ``at`` (e.g. a list) raises
+            # TypeError from ``float()`` and must also fail-safe engaged.
+            return EmergencyStopState(engaged=True, reason="unreadable-sentinel")

src/praisonai-agents/praisonaiagents/goal/judge.py

@@ -47,7 +47,11 @@ def _tail(text: str, limit: int = 4000) -> str:
     return text[-limit:]
 
 
-def _build_goal_judge_prompt(state: "GoalState", transcript_tail: str) -> str:
+def _build_goal_judge_prompt(
+    state: "GoalState",
+    transcript_tail: str,
+    verification_block: Optional[str] = None,
+) -> str:
     """Build a strict, criteria-anchored, JSON-only judge prompt."""
     lines = [
         "You are an independent completion judge for an autonomous agent.",
@@ -72,6 +76,13 @@ def _build_goal_judge_prompt(state: "GoalState", transcript_tail: str) -> str:
             lines.append(
                 "If ANY constraint is violated, you MUST answer 'continue'."
             )
+    if verification_block:
+        lines += [
+            "",
+            "EXECUTABLE VERIFICATION RESULTS (authoritative — command exit "
+            "codes and file checks; prefer this over prose claims):",
+            verification_block,
+        ]
     lines += [
         "",
         "AGENT OUTPUT (most recent):",
@@ -165,6 +176,7 @@ def judge_goal(
     transcript_tail: str,
     *,
     judge_model: Optional[str] = None,
+    verification_block: Optional[str] = None,
 ) -> Tuple[str, str]:
     """Judge whether ``state.goal`` is met given the latest agent output.
 
@@ -180,7 +192,7 @@ def judge_goal(
     """
     from ..eval.judge import Judge
 
-    prompt = _build_goal_judge_prompt(state, transcript_tail)
+    prompt = _build_goal_judge_prompt(state, transcript_tail, verification_block)
     try:
         judge = Judge(model=judge_model or _default_judge_model(), temperature=0.0)
         litellm = judge._get_litellm()

src/praisonai-agents/praisonaiagents/goal/loop.py

@@ -128,11 +128,44 @@ class GoalLoopMixin:
         if state is None or state.status != "active":
             return None
 
+        # Executable verification: run configured hooks and gate on the result.
+        # A failing BLOCKING hook short-circuits to "continue" WITHOUT spending
+        # a judge call. Passing/non-blocking results become authoritative
+        # evidence in the judge prompt.
+        verification_block: Optional[str] = None
+        if getattr(self, "_verification_hooks", None):
+            results = self._run_verification_hooks()
+            self._last_verification_results = results
+            self._record_verification_journal(results)
+            verification_block = self._verification_summary_tail(results)
+            blocking_failed = [
+                r for r in results
+                if r.get("blocking", True) and not r.get("success")
+            ]
+            if blocking_failed:
+                state.turns_used += 1
+                state.last_verdict = "continue"
+                # Carry the failing hooks' captured stdout/stderr into the
+                # continuation prompt so the next turn has the diagnostics
+                # needed to repair the failure (not just a bare label).
+                failure_detail = self._verification_summary_tail(blocking_failed)
+                reason = "verification gate failed"
+                if failure_detail:
+                    reason = f"{reason}:\n{failure_detail}"
+                state.last_reason = reason
+                if state.turns_used >= state.max_turns:
+                    state.status = "paused"
+                    self._persist_goal_state()
+                    return "budget_paused", reason
+                self._persist_goal_state()
+                return "continue", reason
+
         state.turns_used += 1
         verdict, reason = judge_goal(
             state,
             _tail(response),
             judge_model=getattr(self, "_goal_judge_model", None),
+            verification_block=verification_block,
         )
         state.last_verdict, state.last_reason = verdict, reason
 

src/praisonai-agents/praisonaiagents/guardrails/__init__.py

@@ -7,7 +7,12 @@ including both function-based and LLM-based guardrails.
 
 from .guardrail_result import GuardrailResult
 from .llm_guardrail import LLMGuardrail
-from .protocols import GuardrailProtocol, StructuralGuardrailProtocol, PolicyGuardrailProtocol
+from .protocols import (
+    GuardrailProtocol,
+    StructuralGuardrailProtocol,
+    PolicyGuardrailProtocol,
+    is_guardrail_object,
+)
 from .chain import GuardrailChain
 
 __all__ = [
@@ -16,5 +21,6 @@ __all__ = [
     "GuardrailProtocol", 
     "StructuralGuardrailProtocol", 
     "PolicyGuardrailProtocol", 
-    "GuardrailChain"
+    "GuardrailChain",
+    "is_guardrail_object",
 ]

src/praisonai-agents/praisonaiagents/guardrails/protocols.py

@@ -175,3 +175,25 @@ class PolicyGuardrailProtocol(Protocol):
         """
         ...
 
+
+# Method names that make an object a guardrail. An object exposing at least one
+# of these is a protocol-conforming guardrail even though it is not ``callable``.
+GUARDRAIL_PROTOCOL_METHODS = ("validate_input", "validate_output", "validate_tool_call")
+
+
+def is_guardrail_object(obj: Any) -> bool:
+    """Return True when ``obj`` is a protocol-conforming guardrail *instance*.
+
+    ``isinstance(obj, GuardrailProtocol)`` is not usable here: a ``runtime_checkable``
+    Protocol only checks method presence and would also match unrelated objects
+    partially, while classes (not instances) must be rejected. This helper is the
+    single place that decides "is this thing a guardrail object?".
+
+    Plain validator callables are handled by their own passthrough branch and are
+    not required to match here (they will, however, if they also expose one of the
+    protocol methods - e.g. ``GuardrailChain``).
+    """
+    if obj is None or isinstance(obj, type):
+        return False
+    return any(callable(getattr(obj, name, None)) for name in GUARDRAIL_PROTOCOL_METHODS)
+

src/praisonai-agents/praisonaiagents/hooks/__init__.py

@@ -104,6 +104,9 @@ __all__ = [
     # Verification hooks (protocols)
     "VerificationHook",
     "VerificationResult",
+    "BaseVerificationHook",
+    "CommandVerificationHook",
+    "FileCheckHook",
     # Simplified API (beginner-friendly)
     "add_hook",
     "remove_hook",
@@ -178,6 +181,9 @@ _LAZY_GROUPS = {
     'verification': {
         'VerificationHook': ('praisonaiagents.hooks.verification', 'VerificationHook'),
         'VerificationResult': ('praisonaiagents.hooks.verification', 'VerificationResult'),
+        'BaseVerificationHook': ('praisonaiagents.hooks.verification', 'BaseVerificationHook'),
+        'CommandVerificationHook': ('praisonaiagents.hooks.verification', 'CommandVerificationHook'),
+        'FileCheckHook': ('praisonaiagents.hooks.verification', 'FileCheckHook'),
     },
     # Simplified API (beginner-friendly aliases)
     'simplified_api': {

src/praisonai-agents/praisonaiagents/hooks/runner.py

@@ -184,7 +184,19 @@ class HookRunner:
             self._execute_single(hook, event, input_data)
             for hook in hooks
         ]
-        return await asyncio.gather(*tasks)
+        results = await asyncio.gather(*tasks)
+
+        # Parallel hooks cannot mutate input_data (only sequential hooks apply
+        # modified_input). Warn if a parallel hook returns modifications that
+        # will be silently discarded, so hook authors mark it sequential=True.
+        for hook, result in zip(hooks, results):
+            if result.success and result.output and result.output.modified_input:
+                logger.warning(
+                    "Parallel hook '%s' returned modified_input which is "
+                    "discarded; register it with sequential=True to apply it.",
+                    hook.name,
+                )
+        return results
     
     async def _execute_sequential(
         self,

src/praisonai-agents/praisonaiagents/hooks/verification.py

@@ -114,17 +114,26 @@ class BaseVerificationHook:
     
     name: str = "base"
     timeout_seconds: float = 60.0
-    
-    def __init__(self, name: Optional[str] = None, timeout: float = 60.0):
+    blocking: bool = True
+
+    def __init__(
+        self,
+        name: Optional[str] = None,
+        timeout: float = 60.0,
+        blocking: bool = True,
+    ):
         """Initialize the hook.
         
         Args:
             name: Override the hook name
             timeout: Timeout in seconds for execution
+            blocking: When True (default), a failure of this hook blocks the
+                autonomous loop from finalising with a success outcome.
         """
         if name:
             self.name = name
         self.timeout_seconds = timeout
+        self.blocking = blocking
     
     def run(self, context: Optional[Dict[str, Any]] = None) -> VerificationResult:
         """Run the verification hook.
@@ -177,19 +186,25 @@ class CommandVerificationHook(BaseVerificationHook):
     def __init__(
         self,
         name: str,
-        command: list,
+        command,
         cwd: Optional[str] = None,
         timeout: float = 60.0,
+        blocking: bool = True,
     ):
         """Initialize command hook.
         
         Args:
             name: Hook name
-            command: Command to run as list (e.g., ["pytest", "-v"])
+            command: Command to run as a list (e.g., ["pytest", "-v"]) or a
+                string (e.g., "python -m pytest -q"), which is shell-split.
             cwd: Working directory for command
             timeout: Timeout in seconds
+            blocking: When True (default), a failure blocks completion.
         """
-        super().__init__(name=name, timeout=timeout)
+        super().__init__(name=name, timeout=timeout, blocking=blocking)
+        if isinstance(command, str):
+            import shlex
+            command = shlex.split(command)
         self.command = command
         self.cwd = cwd
     
@@ -235,3 +250,143 @@ class CommandVerificationHook(BaseVerificationHook):
                 output=str(e),
                 error=str(e),
             )
+
+
+def _get_dotted(data: Any, path: str) -> Any:
+    """Access a dotted path (e.g. ``"a.b.c"``) in nested dicts/lists.
+
+    Returns ``None`` if any segment is missing. List indices are supported
+    via integer-looking segments.
+    """
+    value = data
+    for part in path.split("."):
+        if isinstance(value, dict):
+            value = value.get(part)
+        elif isinstance(value, (list, tuple)):
+            try:
+                value = value[int(part)]
+            except (ValueError, IndexError):
+                return None
+        else:
+            return None
+    return value
+
+
+class FileCheckHook(BaseVerificationHook):
+    """Declarative file-assertion verification hook (no shell involved).
+
+    Checks portable filesystem facts:
+      - ``exists``    : the file exists (default True).
+      - ``non_empty`` : the file has non-zero size.
+      - ``contains``  : the file text contains a substring.
+      - ``json_field``/``equals`` : the JSON value at a dotted path equals a value.
+
+    Example:
+        hook = FileCheckHook(name="changelog", path="CHANGELOG.md", non_empty=True)
+        hook = FileCheckHook(name="cfg", path="out.json",
+                             json_field="status.code", equals=0)
+    """
+
+    def __init__(
+        self,
+        name: str,
+        path: str,
+        exists: bool = True,
+        non_empty: bool = False,
+        contains: Optional[str] = None,
+        json_field: Optional[str] = None,
+        equals: Any = None,
+        blocking: bool = True,
+    ):
+        super().__init__(name=name, blocking=blocking)
+        self.path = path
+        self.exists = exists
+        self.non_empty = non_empty
+        self.contains = contains
+        self.json_field = json_field
+        self.equals = equals
+
+    def _execute(self, context: Optional[Dict[str, Any]] = None) -> VerificationResult:
+        import os
+
+        details: Dict[str, Any] = {"path": self.path}
+        file_exists = os.path.isfile(self.path)
+        details["exists"] = file_exists
+
+        if self.exists and not file_exists:
+            return VerificationResult(
+                success=False,
+                output=f"File not found: {self.path}",
+                error="not_found",
+                details=details,
+            )
+        if not self.exists:
+            success = not file_exists
+            return VerificationResult(
+                success=success,
+                output=(
+                    f"File exists but was expected absent: {self.path}"
+                    if not success else f"File absent as expected: {self.path}"
+                ),
+                details=details,
+            )
+
+        if self.non_empty:
+            size = os.path.getsize(self.path)
+            details["size"] = size
+            if size == 0:
+                return VerificationResult(
+                    success=False,
+                    output=f"File is empty: {self.path}",
+                    error="empty",
+                    details=details,
+                )
+
+        if self.contains is not None or self.json_field is not None:
+            try:
+                with open(self.path, "r", encoding="utf-8") as fh:
+                    text = fh.read()
+            except Exception as e:
+                return VerificationResult(
+                    success=False, output=str(e), error=str(e), details=details
+                )
+
+            if self.contains is not None and self.contains not in text:
+                return VerificationResult(
+                    success=False,
+                    output=f"File {self.path} does not contain {self.contains!r}",
+                    error="missing_substring",
+                    details=details,
+                )
+
+            if self.json_field is not None:
+                import json as _json
+
+                try:
+                    data = _json.loads(text)
+                except Exception as e:
+                    return VerificationResult(
+                        success=False,
+                        output=f"File {self.path} is not valid JSON: {e}",
+                        error="invalid_json",
+                        details=details,
+                    )
+                actual = _get_dotted(data, self.json_field)
+                details["json_field"] = self.json_field
+                details["actual"] = actual
+                if actual != self.equals:
+                    return VerificationResult(
+                        success=False,
+                        output=(
+                            f"{self.path}:{self.json_field} = {actual!r}, "
+                            f"expected {self.equals!r}"
+                        ),
+                        error="mismatch",
+                        details=details,
+                    )
+
+        return VerificationResult(
+            success=True,
+            output=f"File check passed: {self.path}",
+            details=details,
+        )

src/praisonai-agents/praisonaiagents/knowledge/chunking.py

@@ -2,6 +2,30 @@ from typing import List, Union, Optional, Dict, Any
 from functools import cached_property
 import importlib
 
+#: Names accepted for a chunking strategy that are not chunker ids themselves.
+#: ``ChunkingStrategy.FIXED`` and ``.PARAGRAPH`` had no chunker at all, so they
+#: silently did nothing; they now map onto the chunkers that implement them.
+CHUNKER_ALIASES = {
+    "fixed": "token",
+    "paragraph": "recursive",
+}
+
+
+def normalize_chunker_type(name: str) -> str:
+    """Map a user-facing chunking strategy name onto a chunker id."""
+    if not name:
+        return "recursive"
+    key = str(name).strip().lower()
+    key = CHUNKER_ALIASES.get(key, key)
+    if key not in Chunking.CHUNKER_PARAMS:
+        from ..config.parse_utils import make_preset_error
+        raise make_preset_error(
+            "chunking strategy", name,
+            sorted(set(Chunking.CHUNKER_PARAMS) | set(CHUNKER_ALIASES)),
+        )
+    return key
+
+
 class Chunking:
     """A unified class for text chunking with various chunking strategies."""
     

src/praisonai-agents/praisonaiagents/knowledge/knowledge.py

@@ -2,7 +2,7 @@ import os
 import logging
 from praisonaiagents._logging import get_logger
 from datetime import datetime
-from .chunking import Chunking
+from .chunking import Chunking, normalize_chunker_type
 from functools import cached_property
 
 
@@ -162,6 +162,10 @@ class Knowledge:
             # Merge graph_store config if provided (for graph memory support)
             if "graph_store" in self._config:
                 base_config["graph_store"] = self._config["graph_store"]
+
+            # Merge chunker config if provided (consumed by self.chunker)
+            if "chunker" in self._config:
+                base_config["chunker"] = self._config["chunker"]
         return base_config
 
     def _prepare_mem0_config(self, config):
@@ -195,28 +199,46 @@ class Knowledge:
     def memory(self):
         """Initialize knowledge adapter using protocol-driven approach."""
         # Import registry functions
-        from .adapters import get_knowledge_adapter, get_first_available_knowledge_adapter
+        from .adapters import (
+            get_knowledge_adapter,
+            get_first_available_knowledge_adapter,
+            has_knowledge_adapter,
+            list_knowledge_adapters,
+        )
         
         # Determine provider preference. Distinguish an explicit user choice from
         # the implicit default so fallback diagnostics are only loud when a
         # configured backend degrades (see issue #2972 / PR #2982 review).
         # ``self.config`` is always merged with defaults, so use the raw
         # user-supplied ``_config`` to detect whether the provider was chosen.
-        provider_explicit = bool(
-            (self._config or {}).get("vector_store", {}).get("provider")
+        # Presence of the key (not truthiness) marks an explicit choice, so an
+        # explicitly empty/null provider is treated as a misconfiguration and
+        # surfaces the preset error instead of silently degrading to Mem0.
+        provider_explicit = "provider" in (
+            (self._config or {}).get("vector_store", {}) or {}
         )
         provider = self.config.get("vector_store", {}).get("provider", "mem0")
         self._log(f"Requested knowledge provider: {provider}")
         
-        # Map legacy provider names to adapter names
+        # Legacy provider aliases. Anything else is looked up in the adapter
+        # registry, so adapters added with register_knowledge_adapter() are
+        # reachable; only genuinely unknown names fall back (and say so).
         provider_mapping = {
-            "chroma": "chroma",
-            "mongodb": "mongodb", 
-            "mem0": "mem0",
-            "sqlite": "sqlite"
+            "chromadb": "chroma",
+            "rag": "chroma",
         }
-        
-        adapter_name = provider_mapping.get(provider, "mem0")
+
+        adapter_name = provider_mapping.get(provider, provider)
+        if not has_knowledge_adapter(adapter_name):
+            from ..config.parse_utils import make_preset_error
+            err = make_preset_error(
+                "knowledge vector_store provider", provider,
+                sorted(set(list_knowledge_adapters()) | set(provider_mapping)),
+            )
+            if provider_explicit:
+                raise err
+            self._log(f"{err} Falling back to 'mem0'.")
+            adapter_name = "mem0"
         
         # Try to get preferred adapter, fallback to available ones
         try:
@@ -287,10 +309,13 @@ class Knowledge:
 
     @cached_property
     def chunker(self):
+        # Read from config; the previous hardcoded recursive/512/50 made
+        # KnowledgeConfig.chunk_size / chunking_strategy / chunker inert.
+        chunker_cfg = self.config.get("chunker") or {}
         return Chunking(
-            chunker_type='recursive',
-            chunk_size=512,
-            chunk_overlap=50
+            chunker_type=normalize_chunker_type(chunker_cfg.get("type", "recursive")),
+            chunk_size=chunker_cfg.get("chunk_size", 512),
+            chunk_overlap=chunker_cfg.get("chunk_overlap", 50),
         )
 
     def _log(self, message, level=2):
@@ -413,7 +438,11 @@ class Knowledge:
 
     def history(self, memory_id):
         """Get the history of changes for a memory."""
-        return self.memory.history(memory_id)
+        if hasattr(self.memory, "history"):
+            return self.memory.history(memory_id)
+        raise NotImplementedError(
+            f"{type(self.memory).__name__} does not support history()"
+        )
 
     def delete(self, memory_id):
         """Delete a memory."""
@@ -421,11 +450,21 @@ class Knowledge:
 
     def delete_all(self, user_id=None, agent_id=None, run_id=None):
         """Delete all memories."""
-        self.memory.delete_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+        if hasattr(self.memory, "delete_all"):
+            self.memory.delete_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+        else:
+            raise NotImplementedError(
+                f"{type(self.memory).__name__} does not support delete_all()"
+            )
 
     def reset(self):
         """Reset all memories."""
-        self.memory.reset()
+        if hasattr(self.memory, "reset"):
+            self.memory.reset()
+        else:
+            logger.warning(
+                f"{type(self.memory).__name__} does not support reset(); no-op"
+            )
 
     def normalize_content(self, content):
         """Normalize content for consistent storage."""

src/praisonai-agents/praisonaiagents/llm/__init__.py

@@ -107,6 +107,22 @@ def __getattr__(name):
             from .rate_limiter import RateLimiter
             _lazy_cache[name] = RateLimiter
             return RateLimiter
+        elif name == "QuotaCoordinatorProtocol":
+            from .quota import QuotaCoordinatorProtocol
+            _lazy_cache[name] = QuotaCoordinatorProtocol
+            return QuotaCoordinatorProtocol
+        elif name == "QuotaCoordinatorConfig":
+            from .quota import QuotaCoordinatorConfig
+            _lazy_cache[name] = QuotaCoordinatorConfig
+            return QuotaCoordinatorConfig
+        elif name == "LocalQuotaCoordinator":
+            from .quota import LocalQuotaCoordinator
+            _lazy_cache[name] = LocalQuotaCoordinator
+            return LocalQuotaCoordinator
+        elif name == "build_quota_coordinator":
+            from .quota import build_quota_coordinator
+            _lazy_cache[name] = build_quota_coordinator
+            return build_quota_coordinator
         elif name == "TokenUsage":
             from .llm import TokenUsage
             _lazy_cache[name] = TokenUsage
@@ -229,6 +245,11 @@ __all__ = [
     "create_routing_agent",
     "RateLimiter",
     "TokenUsage",
+    # Provider quota coordination (cross-replica credential cooldowns)
+    "QuotaCoordinatorProtocol",
+    "QuotaCoordinatorConfig",
+    "LocalQuotaCoordinator",
+    "build_quota_coordinator",
     # Protocols
     "LLMProviderProtocol",
     "ModelCapabilitiesProtocol",

src/praisonai-agents/praisonaiagents/llm/failover.py

@@ -16,6 +16,7 @@ import time
 from dataclasses import dataclass, field
 from enum import Enum
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     Dict,
@@ -25,6 +26,9 @@ from typing import (
     runtime_checkable,
 )
 
+if TYPE_CHECKING:
+    from .quota import QuotaCoordinatorProtocol
+
 logger = get_logger(__name__)
 
 class ProviderStatus(str, Enum):
@@ -112,6 +116,29 @@ class AuthProfile:
             metadata=data.get("metadata", {}),
         )
     
+    @property
+    def credential_id(self) -> str:
+        """Stable, non-secret identity for cross-replica quota coordination.
+
+        The coordinator keys benches by the *credential*, not the profile
+        label. ``name`` is a local display label: two replicas may name the
+        same key differently (breaking cooldown propagation) or reuse a name
+        like ``"default"`` for different keys (benching an unrelated key).
+        Derive a deterministic id from the fields that identify the credential
+        (provider, api_key, base_url) using a short salted hash so the real key
+        is never exposed. Falls back to ``name`` when no api_key is present.
+        """
+        if not self.api_key:
+            return self.name
+        import hashlib
+
+        digest = hashlib.sha256(
+            f"{self.provider}\x00{self.base_url or ''}\x00{self.api_key}".encode(
+                "utf-8", "ignore"
+            )
+        ).hexdigest()[:16]
+        return f"{self.provider}:{digest}"
+
     @property
     def is_available(self) -> bool:
         """Check if this profile is currently available."""
@@ -207,13 +234,27 @@ class FailoverManager:
         profile = manager.get_next_profile()
     """
     
-    def __init__(self, config: Optional[FailoverConfig] = None):
+    def __init__(
+        self,
+        config: Optional[FailoverConfig] = None,
+        coordinator: Optional["QuotaCoordinatorProtocol"] = None,
+    ):
         """Initialize the failover manager.
         
         Args:
             config: Failover configuration
+            coordinator: Optional shared quota coordinator. When supplied
+                (e.g. a Redis-backed one wired by the gateway), credential
+                benches/cooldowns are coordinated fleet-wide so a key benched
+                by any replica is skipped by all replicas. Defaults to an
+                in-memory local coordinator, keeping single-replica behaviour
+                exactly as before with no new dependency and no hot-path cost.
         """
         self.config = config or FailoverConfig()
+        if coordinator is None:
+            from .quota import LocalQuotaCoordinator
+            coordinator = LocalQuotaCoordinator()
+        self._coordinator: "QuotaCoordinatorProtocol" = coordinator
         self._profiles: List[AuthProfile] = []
         self._current_index: int = 0
         self._on_failover_callbacks: List[Callable[[AuthProfile, AuthProfile], None]] = []
@@ -274,6 +315,30 @@ class FailoverManager:
         with self._lock:
             return list(self._profiles)
     
+    def _sync_bench_from_coordinator(
+        self, profile: AuthProfile, now: Optional[float] = None
+    ) -> None:
+        """Apply a fleet-wide bench from the coordinator onto a local profile.
+
+        If another replica benched this credential, reflect its cooldown here
+        so this replica skips it too. Fail-open: never raise into failover.
+        Callers already hold ``self._lock``.
+        """
+        try:
+            until = self._coordinator.benched_until(profile.credential_id, now=now)
+        except Exception as e:  # pragma: no cover - defensive
+            logger.warning(
+                f"Quota coordinator lookup failed for '{profile.name}': {e}; "
+                f"using local cooldown state"
+            )
+            return
+        if until is not None and (
+            profile.cooldown_until is None or until > profile.cooldown_until
+        ):
+            if profile.status == ProviderStatus.AVAILABLE:
+                profile.status = ProviderStatus.RATE_LIMITED
+            profile.cooldown_until = until
+
     def get_next_profile(self) -> Optional[AuthProfile]:
         """Get the next available profile.
         
@@ -287,9 +352,10 @@ class FailoverManager:
             if not self._profiles:
                 return None
             
-            # First, check if any cooldowns have expired
+            # First, sync fleet-wide benches, then check if any cooldowns have expired.
             current_time = time.time()
             for profile in self._profiles:
+                self._sync_bench_from_coordinator(profile, now=current_time)
                 if profile.cooldown_until and current_time >= profile.cooldown_until:
                     profile.reset()
             
@@ -328,12 +394,29 @@ class FailoverManager:
                     f"Profile '{profile.name}' rate limited, "
                     f"cooldown for {self.config.cooldown_on_rate_limit}s"
                 )
+                reason = "rate_limit"
             else:
                 profile.mark_error(error, self.config.cooldown_on_error)
                 logger.warning(
                     f"Profile '{profile.name}' error: {error}, "
                     f"cooldown for {self.config.cooldown_on_error}s"
                 )
+                reason = "error"
+
+            # Share the bench fleet-wide so other replicas skip this key too.
+            # Fail-open: a coordinator hiccup must never wedge failover.
+            if profile.cooldown_until is not None:
+                try:
+                    self._coordinator.bench(
+                        profile.credential_id,
+                        until=profile.cooldown_until,
+                        reason=reason,
+                    )
+                except Exception as e:  # pragma: no cover - defensive
+                    logger.warning(
+                        f"Quota coordinator bench failed for '{profile.name}': {e}; "
+                        f"falling back to local cooldown only"
+                    )
             
             # Resolve the target profile and snapshot callbacks while holding
             # the lock, but invoke them AFTER releasing it. User callbacks may
@@ -361,8 +444,30 @@ class FailoverManager:
         """
         with self._lock:
             if profile.status != ProviderStatus.AVAILABLE:
+                # Snapshot the cooldown this success is recovering from BEFORE
+                # resetting, so we can avoid clobbering a newer fleet-wide bench.
+                recovered_from = profile.cooldown_until
                 profile.reset()
                 logger.info(f"Profile '{profile.name}' recovered")
+                cred_id = profile.credential_id
+                try:
+                    # A concurrent/other-replica failure may have benched this
+                    # credential with a LATER expiry than the cooldown this
+                    # (possibly stale, already in-flight) success is recovering
+                    # from. Clearing unconditionally would resume a credential
+                    # that is still rate-limited. Only clear when the shared
+                    # bench is not newer than what we recovered from.
+                    shared_until = self._coordinator.benched_until(cred_id)
+                    if (
+                        shared_until is None
+                        or recovered_from is None
+                        or shared_until <= recovered_from
+                    ):
+                        self._coordinator.clear(cred_id)
+                except Exception as e:  # pragma: no cover - defensive
+                    logger.warning(
+                        f"Quota coordinator clear failed for '{profile.name}': {e}"
+                    )
     
     def on_failover(
         self,

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -28,6 +28,14 @@ from ..tools.call_executor import ToolCall, create_tool_call_executor
 from ..tools.schema import build_tool_definition
 
 
+# Sentinel returned as the "arguments" value when a tool call's argument string
+# cannot be parsed (e.g. truncated by max_tokens or a dropped connection).
+# It is distinct from {} ("no arguments"): {} would silently execute the tool
+# with the WRONG arguments and report success. Callers must detect this sentinel
+# and surface a tool-error so the model can re-emit the call instead.
+_TOOL_ARGUMENTS_PARSE_FAILED = object()
+
+
 def _durable_iteration_kwargs(execute_tool_fn: Callable, index: int) -> Dict[str, int]:
     if getattr(execute_tool_fn, "_accepts_durable_iteration", False):
         return {"_durable_iteration_index": index}
@@ -534,7 +542,18 @@ Respond with ONLY a valid JSON tool call in this format:
         if self._failover_manager:
             self._current_profile = self._failover_manager.get_next_profile()
             if self._current_profile:
-                self._switch_to_profile(self._current_profile)
+                # Apply the initial profile's values to the instance attributes
+                # (backward compatible with the documented llm.api_key/model
+                # surface). Later failover rotations are per-call only and do
+                # not mutate these attributes (issue #3613 gap 2).
+                profile = self._current_profile
+                if profile.api_key:
+                    self.api_key = profile.api_key
+                if profile.base_url:
+                    self.base_url = profile.base_url
+                if profile.model and profile.model != self.model:
+                    logging.info(f"Failover: using profile model {profile.model}")
+                    self.model = profile.model
 
         # Cache for formatted tools and messages
         self._formatted_tools_cache = {}
@@ -974,18 +993,58 @@ Respond with ONLY a valid JSON tool call in this format:
 
     def _switch_to_profile(self, profile: "AuthProfile") -> None:
         """Switch to a new auth profile for failover.
-        
+
+        The profile's credentials are threaded through the per-call kwargs of
+        the retry loop instead of being written onto the shared instance. The
+        old behaviour mutated ``self.api_key``/``self.base_url``/``self.model``
+        on a shared ``LLM`` (issue #3613 gap 2), so concurrent requests on one
+        instance could silently cross-talk credentials and model after a
+        failover rotation. Keeping the instance attributes on the initial
+        profile preserves single-profile behaviour and the documented
+        ``llm.api_key``/``llm.model`` surface.
+
         Args:
             profile: AuthProfile to switch to
         """
+        # NOTE: profile values are applied to the per-call kwargs by
+        # _handle_retry_exception when it rotates. This method is kept as a
+        # no-op placeholder so any out-of-tree callers that invoke it directly
+        # keep working; it intentionally no longer mutates shared state.
+        if profile is None:
+            return
+
+    def _response_model_for_tracking(self, response: Any) -> str:
+        """Model to attribute token usage to for a completed call.
+
+        Prefers the model carried on the response itself, which reflects the
+        provider/profile that actually served the request. This is race-free
+        under concurrency (unlike reading the shared ``self._current_profile``)
+        and stays correct after a failover rotation. Falls back to
+        ``self.model`` when the response does not expose a model.
+        """
+        model = None
+        if isinstance(response, dict):
+            model = response.get("model")
+        else:
+            model = getattr(response, "model", None)
+        return model or self.model
+
+    def _apply_profile_to_kwargs(self, profile: "AuthProfile", kwargs: dict) -> dict:
+        """Return a new kwargs dict with profile overrides applied.
+
+        Never mutates ``self``: the active profile is a per-call concern, so
+        its credentials travel in the request kwargs only. Callers that pass
+        an explicit per-call override (e.g. ``api_key=``) keep it; profile
+        values fill in the gaps.
+        """
+        kwargs = dict(kwargs)
         if profile.api_key:
-            self.api_key = profile.api_key
+            kwargs["api_key"] = profile.api_key
         if profile.base_url:
-            self.base_url = profile.base_url
-        if profile.model and profile.model != self.model:
-            # Only log if model actually changes
-            logging.info(f"Failover: switching from {self.model} to {profile.model}")
-            self.model = profile.model
+            kwargs["base_url"] = profile.base_url
+        if profile.model:
+            kwargs["model"] = profile.model
+        return kwargs
 
     def _resolve_subscription_creds(self):
         """Lazy resolve + cache subscription credentials, checking expiration."""
@@ -1055,7 +1114,7 @@ Respond with ONLY a valid JSON tool call in this format:
             )
             return None
 
-    def _handle_retry_exception(self, e, attempt, kwargs, refreshed_creds=None):
+    def _handle_retry_exception(self, e, attempt, kwargs, refreshed_creds=None, active_profile=None):
         """Decide retry vs raise from a failover decision. No sleep/await.
 
         Shared decision logic used by both the sync (`_call_with_retry`) and
@@ -1069,21 +1128,34 @@ Respond with ONLY a valid JSON tool call in this format:
         blocks the event loop. The already-refreshed credentials (if any) are
         passed in via ``refreshed_creds``.
 
+        The active failover profile is a *per-call* value threaded in via
+        ``active_profile`` and returned back in the result tuple. It is never
+        read from or written to ``self._current_profile`` here, so concurrent
+        requests sharing one ``LLM`` instance cannot corrupt each other's
+        failover bookkeeping or cost attribution (issue #3613 gap 2).
+
         Args:
             e: The exception raised by the wrapped call.
             attempt: Zero-based attempt index for the current iteration.
             kwargs: Keyword arguments for the wrapped call (may be updated).
             refreshed_creds: Credentials already refreshed by the caller (off
                 the event loop for async), or ``None`` if no refresh was done.
+            active_profile: The profile this call is currently using, or
+                ``None``. Defaults to the initial profile for backward compat.
 
         Returns:
-            Tuple of (should_raise, category, retry_delay, error_str, kwargs):
+            Tuple of (should_raise, category, retry_delay, error_str, kwargs,
+            active_profile):
                 should_raise: Whether the caller should re-raise immediately.
                 category: The error category/reason from the decision.
                 retry_delay: Seconds to wait before the next attempt.
                 error_str: String form of the exception.
                 kwargs: The (possibly updated) keyword arguments.
+                active_profile: The (possibly rotated) profile for the next
+                    attempt.
         """
+        if active_profile is None:
+            active_profile = self._current_profile
         # A turn is "side-effecting" when it exposes tools the model may call:
         # a post-dispatch failure on such a turn could have already run a
         # side-effecting tool, so it must not be silently replayed. A turn with
@@ -1135,55 +1207,48 @@ Respond with ONLY a valid JSON tool call in this format:
         # immediately with fresh credentials and skip profile rotation so we
         # neither mark the current profile failed nor overwrite the new creds.
         if refreshed_auth:
-            return False, category, retry_delay, error_str, kwargs
+            return False, category, retry_delay, error_str, kwargs, active_profile
 
-        # Handle different failover decision actions
+        # Handle different failover decision actions. Rotation is a per-call
+        # concern: the active profile is a local threaded in/out of this method,
+        # never self._current_profile, so concurrent calls on one shared LLM
+        # cannot mark each other's profiles failed/healthy (issue #3613 gap 2).
         if decision.action == "rotate_profile" and self._failover_manager:
-            if self._current_profile:
+            if active_profile:
                 is_rate_limit = (category == "rate_limit")
                 self._failover_manager.mark_failure(
-                    self._current_profile, error_str, is_rate_limit=is_rate_limit
+                    active_profile, error_str, is_rate_limit=is_rate_limit
                 )
             next_profile = self._failover_manager.get_next_profile()
-            if next_profile and next_profile != self._current_profile:
-                self._switch_to_profile(next_profile)
-                self._current_profile = next_profile
-                # Update the kwargs with new profile values for the next retry
-                if "api_key" in kwargs:
-                    kwargs["api_key"] = self.api_key
-                if "base_url" in kwargs:
-                    kwargs["base_url"] = self.base_url
-                if "model" in kwargs:
-                    kwargs["model"] = self.model
+            if next_profile and next_profile != active_profile:
+                active_profile = next_profile
+                # Thread the active profile through the per-call kwargs so the
+                # shared instance state is never mutated (issue #3613 gap 2).
+                kwargs = self._apply_profile_to_kwargs(next_profile, kwargs)
                 logging.info(f"Failover: switched to profile '{next_profile.name}'")
             else:
                 # No alternate profile available - retrying would just hit the
                 # same failing profile, so surface the error instead.
                 can_retry = False
         # Legacy failover for compatibility (when decision is retry but failover is configured)
-        elif self._failover_manager and self._current_profile and decision.action == "retry":
+        elif self._failover_manager and active_profile and decision.action == "retry":
             is_rate_limit = (category == "rate_limit")
             self._failover_manager.mark_failure(
-                self._current_profile, error_str, is_rate_limit=is_rate_limit
+                active_profile, error_str, is_rate_limit=is_rate_limit
             )
             next_profile = self._failover_manager.get_next_profile()
-            if next_profile and next_profile != self._current_profile:
-                self._switch_to_profile(next_profile)
-                self._current_profile = next_profile
-                # Update the kwargs with new profile values for the next retry
-                if "api_key" in kwargs:
-                    kwargs["api_key"] = self.api_key
-                if "base_url" in kwargs:
-                    kwargs["base_url"] = self.base_url
-                if "model" in kwargs:
-                    kwargs["model"] = self.model
+            if next_profile and next_profile != active_profile:
+                active_profile = next_profile
+                # Thread the active profile through the per-call kwargs so the
+                # shared instance state is never mutated (issue #3613 gap 2).
+                kwargs = self._apply_profile_to_kwargs(next_profile, kwargs)
                 # Enable retry for profile switch even if originally non-retryable
                 can_retry = True
                 retry_delay = 0.0
                 logging.info(f"Failover: switched to profile '{next_profile.name}'")
 
         should_raise = decision.action == "surface_error" or not can_retry
-        return should_raise, category, retry_delay, error_str, kwargs
+        return should_raise, category, retry_delay, error_str, kwargs, active_profile
 
     def _call_with_retry(self, func, *args, **kwargs):
         """Call a function with automatic retry on rate limit errors and failover support.
@@ -1200,6 +1265,10 @@ Respond with ONLY a valid JSON tool call in this format:
             The original exception if max retries exceeded
         """
         last_error = None
+        # Track the failover profile per call (never self._current_profile) so
+        # concurrent calls on one shared LLM cannot corrupt each other's
+        # failover bookkeeping (issue #3613 gap 2).
+        active_profile = self._current_profile
 
         for attempt in range(self._max_retries + 1):
             try:
@@ -1210,8 +1279,8 @@ Respond with ONLY a valid JSON tool call in this format:
                 result = func(*args, **kwargs)
                 
                 # Mark success if failover is configured
-                if self._failover_manager and self._current_profile:
-                    self._failover_manager.mark_success(self._current_profile)
+                if self._failover_manager and active_profile:
+                    self._failover_manager.mark_success(active_profile)
                 
                 # Reset idle timeout circuit breaker on success
                 self._idle_timeout_breaker.reset()
@@ -1226,8 +1295,12 @@ Respond with ONLY a valid JSON tool call in this format:
                 if self._should_attempt_auth_refresh(e, attempt):
                     refreshed_creds = self._try_refresh_subscription_creds()
 
-                should_raise, category, retry_delay, error_str, kwargs = (
-                    self._handle_retry_exception(e, attempt, kwargs, refreshed_creds=refreshed_creds)
+                should_raise, category, retry_delay, error_str, kwargs, active_profile = (
+                    self._handle_retry_exception(
+                        e, attempt, kwargs,
+                        refreshed_creds=refreshed_creds,
+                        active_profile=active_profile,
+                    )
                 )
 
                 if should_raise:
@@ -1276,6 +1349,10 @@ Respond with ONLY a valid JSON tool call in this format:
             The original exception if max retries exceeded
         """
         last_error = None
+        # Track the failover profile per call (never self._current_profile) so
+        # concurrent coroutines on one shared LLM cannot corrupt each other's
+        # failover bookkeeping (issue #3613 gap 2).
+        active_profile = self._current_profile
 
         for attempt in range(self._max_retries + 1):
             try:
@@ -1286,8 +1363,8 @@ Respond with ONLY a valid JSON tool call in this format:
                 result = await func(*args, **kwargs)
                 
                 # Mark success if failover is configured
-                if self._failover_manager and self._current_profile:
-                    self._failover_manager.mark_success(self._current_profile)
+                if self._failover_manager and active_profile:
+                    self._failover_manager.mark_success(active_profile)
                 
                 # Reset idle timeout circuit breaker on success
                 self._idle_timeout_breaker.reset()
@@ -1305,8 +1382,12 @@ Respond with ONLY a valid JSON tool call in this format:
                         self._try_refresh_subscription_creds
                     )
 
-                should_raise, category, retry_delay, error_str, kwargs = (
-                    self._handle_retry_exception(e, attempt, kwargs, refreshed_creds=refreshed_creds)
+                should_raise, category, retry_delay, error_str, kwargs, active_profile = (
+                    self._handle_retry_exception(
+                        e, attempt, kwargs,
+                        refreshed_creds=refreshed_creds,
+                        active_profile=active_profile,
+                    )
                 )
 
                 if should_raise:
@@ -1358,7 +1439,7 @@ Respond with ONLY a valid JSON tool call in this format:
         import litellm
         response = self._call_with_retry(litellm.completion, **completion_params)
         if not completion_params.get("stream"):
-            self._track_token_usage(response, self.model)
+            self._track_token_usage(response, self._response_model_for_tracking(response))
         return response
 
     async def _acompletion_with_retry(self, **completion_params):
@@ -1379,7 +1460,7 @@ Respond with ONLY a valid JSON tool call in this format:
             **completion_params,
         )
         if not completion_params.get("stream"):
-            self._track_token_usage(response, self.model)
+            self._track_token_usage(response, self._response_model_for_tracking(response))
         return response
 
     def _supports_web_search(self) -> bool:
@@ -1766,8 +1847,15 @@ Respond with ONLY a valid JSON tool call in this format:
                 
         except (KeyError, json.JSONDecodeError, TypeError) as e:
             logging.error(f"Error parsing tool call arguments: {e}")
-            function_name = tool_call.get("name", "unknown_function")
-            arguments = {}
+            function_name = (
+                tool_call.get("function", {}).get("name")
+                if isinstance(tool_call.get("function"), dict)
+                else None
+            ) or tool_call.get("name", "unknown_function")
+            # A parse failure is NOT "no arguments". Returning {} here would run
+            # the tool with the wrong arguments and report success. Signal the
+            # failure so callers surface a tool-error instead of dispatching.
+            arguments = _TOOL_ARGUMENTS_PARSE_FAILED
             tool_call_id = tool_call.get("id", f"tool_{id(tool_call)}")
             
         return function_name, arguments, tool_call_id
@@ -2432,7 +2520,7 @@ Respond with ONLY a valid JSON tool call in this format:
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
         chat_history: Optional[List[Dict]] = None,
-        temperature: float = 1.0,
+        temperature: Optional[float] = None,
         tools: Optional[List[Any]] = None,
         output_json: Optional[BaseModel] = None,
         output_pydantic: Optional[BaseModel] = None,
@@ -2749,10 +2837,17 @@ Respond with ONLY a valid JSON tool call in this format:
                             # Execute tool calls using ToolCallExecutor (Gap 2: parallel or sequential)
                             is_ollama = self._is_ollama_provider()
                             tool_calls_batch = []
+                            parse_error_messages = []
                             
                             # Prepare batch of ToolCall objects
                             for tool_call in tool_calls:
                                 function_name, arguments, tool_call_id = self._extract_tool_call_info(tool_call, is_ollama=is_ollama)
+                                if self._tool_arguments_parse_failed(arguments):
+                                    # Do NOT dispatch with {}: surface a retryable error.
+                                    parse_error_messages.append(
+                                        self._tool_parse_error_message(function_name, tool_call_id)
+                                    )
+                                    continue
                                 tool_calls_batch.append(ToolCall(
                                     function_name=function_name,
                                     arguments=arguments,
@@ -2826,6 +2921,11 @@ Respond with ONLY a valid JSON tool call in this format:
                                     "content": content,
                                 })
 
+                            # Report any unparseable tool-call arguments so the
+                            # model can re-emit them (never dispatched with {}).
+                            for _err_msg in parse_error_messages:
+                                messages.append(_err_msg)
+
                             # Safety: break after max_iter iterations
                             if iteration_count + 1 >= max_iterations:
                                 self._last_stop_reason = "max_steps"
@@ -3529,6 +3629,11 @@ Respond with ONLY a valid JSON tool call in this format:
                             is_ollama = self._is_ollama_provider()
                             function_name, arguments, tool_call_id = self._extract_tool_call_info(tool_call, is_ollama)
 
+                            if self._tool_arguments_parse_failed(arguments):
+                                # Do NOT dispatch with {}: surface a retryable error.
+                                messages.append(self._tool_parse_error_message(function_name, tool_call_id))
+                                continue
+
                             # Validate and filter arguments for Ollama provider
                             if is_ollama and tools:
                                 # First check if any argument references a previous tool result
@@ -4088,7 +4193,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
         chat_history: Optional[List[Dict]] = None,
-        temperature: float = 1.0,
+        temperature: Optional[float] = None,
         tools: Optional[List[Any]] = None,
         output_json: Optional[BaseModel] = None,
         output_pydantic: Optional[BaseModel] = None,
@@ -4273,10 +4378,17 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         # Execute tool calls using ToolCallExecutor (Gap 2: parallel or sequential)
                         is_ollama = self._is_ollama_provider()
                         tool_calls_batch = []
+                        parse_error_messages = []
                         
                         # Prepare batch of ToolCall objects
                         for tool_call in tool_calls:
                             function_name, arguments, tool_call_id = self._extract_tool_call_info(tool_call, is_ollama)
+                            if self._tool_arguments_parse_failed(arguments):
+                                # Do NOT dispatch with {}: surface a retryable error.
+                                parse_error_messages.append(
+                                    self._tool_parse_error_message(function_name, tool_call_id)
+                                )
+                                continue
                             tool_calls_batch.append(ToolCall(
                                 function_name=function_name,
                                 arguments=arguments, 
@@ -4316,6 +4428,11 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                     tool_result.is_ollama
                                 )
                             messages.append(tool_message)
+
+                        # Report any unparseable tool-call arguments so the
+                        # model can re-emit them (never dispatched with {}).
+                        for _err_msg in parse_error_messages:
+                            messages.append(_err_msg)
                         
                         # Continue conversation after tool execution - get follow-up response
                         try:
@@ -4423,7 +4540,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
         chat_history: Optional[List[Dict]] = None,
-        temperature: float = 1.0,
+        temperature: Optional[float] = None,
         tools: Optional[List[Any]] = None,
         output_json: Optional[BaseModel] = None,
         output_pydantic: Optional[BaseModel] = None,
@@ -4653,6 +4770,10 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         
                         for tool_call in tool_calls:
                             function_name, arguments, tool_call_id = self._extract_tool_call_info(tool_call)
+                            if self._tool_arguments_parse_failed(arguments):
+                                # Do NOT dispatch with {}: surface a retryable error.
+                                messages.append(self._tool_parse_error_message(function_name, tool_call_id))
+                                continue
                             logging.debug(f"[RESPONSES_API_ASYNC] Executing tool {function_name}")
                             tool_result = await _dispatch_async_tool(
                                 execute_tool_fn,
@@ -4902,6 +5023,11 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         is_ollama = self._is_ollama_provider()
                         function_name, arguments, tool_call_id = self._extract_tool_call_info(tool_call, is_ollama)
 
+                        if self._tool_arguments_parse_failed(arguments):
+                            # Do NOT dispatch with {}: surface a retryable error.
+                            messages.append(self._tool_parse_error_message(function_name, tool_call_id))
+                            continue
+
                         # Validate and filter arguments for Ollama provider
                         if is_ollama and tools:
                             arguments = self._validate_and_filter_ollama_arguments(function_name, arguments, tools)
@@ -5612,6 +5738,16 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         
         # Override with any provided parameters
         params.update(override_params)
+
+        # Resolve temperature from the instance when the caller did not specify
+        # one. Public entry points now default temperature to None so a value
+        # configured via Agent(llm={'temperature': ...}) actually reaches the
+        # request instead of being silently overridden by a hardcoded 1.0.
+        if params.get("temperature") is None:
+            if self.temperature is not None:
+                params["temperature"] = self.temperature
+            else:
+                params.pop("temperature", None)
         
         # Handle structured output parameters
         output_json = override_params.get('output_json')
@@ -5944,6 +6080,19 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         if self.timeout:
             params["timeout"] = self.timeout
 
+        # ── Subscription credentials ────────────────────────────────────
+        # Mirror _build_completion_params: without this the Responses API
+        # transport silently ignores auth= and bills the plain API key.
+        creds = self._resolve_subscription_creds()
+        if creds:
+            params["api_key"] = creds.api_key
+            if creds.base_url:
+                params["base_url"] = creds.base_url
+            if creds.headers:
+                extra_headers = dict(params.get("extra_headers") or {})
+                extra_headers.update(creds.headers)
+                params["extra_headers"] = extra_headers
+
         # ── Structured output ───────────────────────────────────────────
         output_json = kwargs.pop("output_json", None)
         output_pydantic = kwargs.pop("output_pydantic", None)
@@ -5994,7 +6143,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         """
         import litellm
         response = await self._call_with_retry_async(litellm.aresponses, **params)
-        self._track_token_usage(response, self.model)
+        self._track_token_usage(response, self._response_model_for_tracking(response))
         return response
 
     def _extract_from_responses_output(self, response) -> tuple:
@@ -6233,6 +6382,17 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         is_reasoning=False,
                     ))
 
+            elif evt_type == "response.reasoning_summary_text.delta":
+                delta_text = (event.get("delta", "") if isinstance(event, dict)
+                              else getattr(event, "delta", ""))
+                if _emit and delta_text:
+                    stream_callback(StreamEvent(
+                        type=StreamEventType.DELTA_TEXT,
+                        timestamp=time.perf_counter(),
+                        content=delta_text,
+                        is_reasoning=True,
+                    ))
+
             elif evt_type == "response.function_call_arguments.delta":
                 idx = (event.get("output_index", 0) if isinstance(event, dict)
                        else getattr(event, "output_index", 0))
@@ -6345,17 +6505,41 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                 tool_call_id = tool_call.id
             except (json.JSONDecodeError, AttributeError) as e:
                 logging.error(f"Error parsing object-style tool call: {e}")
-                function_name = "unknown_function"
-                arguments = {}
-                tool_call_id = f"tool_{id(tool_call)}"
+                function_name = getattr(getattr(tool_call, 'function', None), 'name', None) or "unknown_function"
+                # See _parse_tool_call_arguments: a parse failure is not {}.
+                arguments = _TOOL_ARGUMENTS_PARSE_FAILED
+                tool_call_id = getattr(tool_call, 'id', None) or f"tool_{id(tool_call)}"
             return function_name, arguments, tool_call_id
 
+    @staticmethod
+    def _tool_arguments_parse_failed(arguments) -> bool:
+        """True when tool-call arguments could not be parsed (vs. legitimately empty)."""
+        return arguments is _TOOL_ARGUMENTS_PARSE_FAILED
+
+    @staticmethod
+    def _tool_parse_error_message(function_name: str, tool_call_id: str) -> Dict[str, str]:
+        """Build a tool-role message telling the model its arguments were lost.
+
+        Surfacing this instead of dispatching with {} converts a silent
+        wrong-action-reported-as-success into a visible, retryable error.
+        """
+        return {
+            "role": "tool",
+            "tool_call_id": tool_call_id,
+            "content": (
+                f"Error: arguments for tool '{function_name}' could not be parsed "
+                f"(the argument string was invalid or truncated). "
+                f"The tool was NOT executed. Please re-emit the tool call with "
+                f"complete, valid JSON arguments."
+            ),
+        }
+
     # Response without tool calls
     def response(
         self,
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
-        temperature: float = 1.0,
+        temperature: Optional[float] = None,
         stream: bool = True,
         verbose: bool = True,
         markdown: bool = True,
@@ -6449,7 +6633,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         self,
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
-        temperature: float = 1.0,
+        temperature: Optional[float] = None,
         stream: bool = True,
         verbose: bool = True,
         markdown: bool = True,

src/praisonai-agents/praisonaiagents/llm/openai_client.py

@@ -200,6 +200,26 @@ class ToolCall:
     type: str
     function: Dict[str, Any]
 
+def _extract_tool_call_name_and_args(tool_call):
+    """Parse a tool call's function name and JSON arguments.
+
+    Handles both the ToolCall dataclass and OpenAI-SDK objects. Returns a
+    tuple of (name, args, tool_call_id, err); err is None on success and the
+    caught exception otherwise. Callers keep their own failure handling.
+    """
+    tool_call_id = tool_call.id if hasattr(tool_call, 'id') else tool_call.get('id')
+    function_name = None
+    try:
+        if isinstance(tool_call, ToolCall):
+            function_name = tool_call.function["name"]
+            raw_arguments = tool_call.function["arguments"]
+        else:
+            function_name = tool_call.function.name
+            raw_arguments = tool_call.function.arguments
+        return function_name, json.loads(raw_arguments), tool_call_id, None
+    except (json.JSONDecodeError, KeyError, AttributeError, TypeError) as e:
+        return function_name, None, tool_call_id, e
+
 def process_stream_chunks(chunks):
     """Process streaming chunks into combined response"""
     if not chunks:
@@ -1559,10 +1579,13 @@ class OpenAIClient:
         params = {
             "model": model,
             "messages": messages,
-            "temperature": temperature,
             "stream": stream,
             **kwargs
         }
+        # Omit temperature when unset so the provider default applies rather
+        # than sending an explicit None.
+        if temperature is not None:
+            params["temperature"] = temperature
         
         # Add tools if provided
         if tools:
@@ -1620,10 +1643,13 @@ class OpenAIClient:
         params = {
             "model": model,
             "messages": messages,
-            "temperature": temperature,
             "stream": stream,
             **kwargs
         }
+        # Omit temperature when unset so the provider default applies rather
+        # than sending an explicit None.
+        if temperature is not None:
+            params["temperature"] = temperature
         
         # Add tools if provided
         if tools:
@@ -1906,13 +1932,19 @@ class OpenAIClient:
                 # replies for this turn (keeps tool replies consecutive).
                 _deferred_media_followups = []
                 for tool_call in tool_calls:
-                    # Handle both ToolCall dataclass and OpenAI object
-                    if isinstance(tool_call, ToolCall):
-                        function_name = tool_call.function["name"]
-                        arguments = json.loads(tool_call.function["arguments"])
-                    else:
-                        function_name = tool_call.function.name
-                        arguments = json.loads(tool_call.function.arguments)
+                    # Handle both ToolCall dataclass and OpenAI object.
+                    # Guard the parse itself so malformed/truncated argument
+                    # JSON is reported back to the model instead of aborting
+                    # the whole run (matches chat_completion_with_tools_stream).
+                    function_name, arguments, _tool_call_id, err = _extract_tool_call_name_and_args(tool_call)
+                    if err is not None:
+                        logging.warning(f"Failed to parse tool call arguments: {err}")
+                        messages.append({
+                            "role": "tool",
+                            "tool_call_id": _tool_call_id,
+                            "content": json.dumps({"error": f"Invalid arguments JSON: {err}"}),
+                        })
+                        continue
                     
                     # Always trigger callback for tool call tracking (even when verbose=False)
                     display_tool_call_fn = _get_display_tool_call()
@@ -1921,7 +1953,6 @@ class OpenAIClient:
                     # Capture failures and report them back to the model instead of
                     # aborting the whole run (safe-by-default, matching the streaming
                     # path chat_completion_with_tools_stream).
-                    _tool_call_id = tool_call.id if hasattr(tool_call, 'id') else tool_call.get('id')
                     try:
                         tool_result = execute_tool_fn(
                             function_name,
@@ -2209,13 +2240,19 @@ class OpenAIClient:
                 # replies for this turn (keeps tool replies consecutive).
                 _deferred_media_followups = []
                 for tool_call in tool_calls:
-                    # Handle both ToolCall dataclass and OpenAI object
-                    if isinstance(tool_call, ToolCall):
-                        function_name = tool_call.function["name"]
-                        arguments = json.loads(tool_call.function["arguments"])
-                    else:
-                        function_name = tool_call.function.name
-                        arguments = json.loads(tool_call.function.arguments)
+                    # Handle both ToolCall dataclass and OpenAI object.
+                    # Guard the parse itself so malformed/truncated argument
+                    # JSON is reported back to the model instead of aborting
+                    # the whole run (matches chat_completion_with_tools_stream).
+                    function_name, arguments, _tool_call_id, err = _extract_tool_call_name_and_args(tool_call)
+                    if err is not None:
+                        logging.warning(f"Failed to parse tool call arguments: {err}")
+                        messages.append({
+                            "role": "tool",
+                            "tool_call_id": _tool_call_id,
+                            "content": json.dumps({"error": f"Invalid arguments JSON: {err}"}),
+                        })
+                        continue
                     
                     # Always trigger callback for tool call tracking (even when verbose=False)
                     display_tool_call_fn = _get_display_tool_call()
@@ -2228,7 +2265,6 @@ class OpenAIClient:
                     # Capture failures and report them back to the model instead of
                     # aborting the whole run (safe-by-default, matching the streaming
                     # path chat_completion_with_tools_stream).
-                    _tool_call_id = tool_call.id if hasattr(tool_call, 'id') else tool_call.get('id')
                     try:
                         if asyncio.iscoroutinefunction(execute_tool_fn):
                             tool_result = await execute_tool_fn(
@@ -2451,16 +2487,14 @@ class OpenAIClient:
                     _deferred_media_followups = []
                     for tool_call in tool_calls:
                         # Handle both ToolCall dataclass and OpenAI object
-                        try:
-                            if isinstance(tool_call, ToolCall):
-                                function_name = tool_call.function["name"]
-                                arguments = json.loads(tool_call.function["arguments"])
-                            else:
-                                function_name = tool_call.function.name
-                                arguments = json.loads(tool_call.function.arguments)
-                        except json.JSONDecodeError as e:
+                        function_name, arguments, _, err = _extract_tool_call_name_and_args(tool_call)
+                        if err is not None:
+                            # Preserve original behaviour: only malformed JSON is
+                            # reported to the caller; other errors propagate.
+                            if not isinstance(err, json.JSONDecodeError):
+                                raise err
                             if verbose:
-                                yield f"\n[Error parsing arguments for {function_name if 'function_name' in locals() else 'unknown function'}: {str(e)}]"
+                                yield f"\n[Error parsing arguments for {function_name if function_name is not None else 'unknown function'}: {str(err)}]"
                             continue
                         
                         # Always trigger callback for tool call tracking (even when verbose=False)

src/praisonai-agents/praisonaiagents/llm/quota.py

@@ -0,0 +1,137 @@
+"""
+Provider Quota Coordination for PraisonAI Agents.
+
+Provides a lightweight, protocol-driven seam so provider-quota state
+(credential cooldowns / benches) can be coordinated *fleet-wide* when a
+shared backend is configured, while defaulting to today's in-process,
+in-memory behaviour with zero new dependencies and no hot-path change.
+
+Design:
+- ``QuotaCoordinatorProtocol`` is a pure contract (stdlib + typing only).
+- ``LocalQuotaCoordinator`` is the default in-memory implementation used
+  when no shared backend is selected — single-replica deployments behave
+  exactly as before.
+- A concrete shared backend (e.g. Redis, reusing the gateway's existing
+  connection) implements the same protocol in the wrapper/bot package and
+  is selected via ``QuotaCoordinatorConfig``. Core keeps only the protocol
+  and the in-memory default.
+
+Fail-open by contract: implementations MUST degrade to local behaviour on
+backend errors rather than wedge a healthy request path, and bench entries
+carry TTLs so a dead replica's benches self-expire.
+"""
+
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import Dict, Optional, Protocol, runtime_checkable
+
+
+@runtime_checkable
+class QuotaCoordinatorProtocol(Protocol):
+    """Contract for coordinating provider-quota state across replicas.
+
+    A coordinator tracks per-credential cooldowns ("benches"). When a shared
+    backend is configured, a credential benched by any replica is treated as
+    benched by all replicas until its cooldown expires.
+    """
+
+    def bench(self, cred_id: str, *, until: float, reason: str = "") -> None:
+        """Bench a credential until ``until`` (an epoch timestamp, TTL-based)."""
+        ...
+
+    def is_benched(self, cred_id: str, *, now: Optional[float] = None) -> bool:
+        """Return True if the credential is currently benched."""
+        ...
+
+    def benched_until(self, cred_id: str, *, now: Optional[float] = None) -> Optional[float]:
+        """Return the epoch timestamp the credential is benched until, or None."""
+        ...
+
+    def clear(self, cred_id: str) -> None:
+        """Clear any bench for the credential (e.g. on recovery)."""
+        ...
+
+
+@dataclass
+class QuotaCoordinatorConfig:
+    """Configuration for provider-quota coordination.
+
+    Attributes:
+        backend: ``"local"`` (in-memory, default) or a shared backend name
+            such as ``"redis"`` provided by the wrapper/bot package.
+        url: Optional backend URL; when omitted a shared backend reuses the
+            already-established connection (e.g. gateway ``RedisConfig``).
+    """
+
+    backend: str = "local"
+    url: Optional[str] = None
+
+    def to_dict(self) -> Dict[str, Optional[str]]:
+        return {"backend": self.backend, "url": self.url}
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Optional[str]]) -> "QuotaCoordinatorConfig":
+        return cls(
+            backend=(data.get("backend") or "local"),
+            url=data.get("url"),
+        )
+
+
+class LocalQuotaCoordinator:
+    """In-memory ``QuotaCoordinatorProtocol`` (default, zero-dependency).
+
+    Thread-safe via a lightweight lock. Bench entries carry an expiry so
+    they self-clear once the cooldown passes — mirroring the TTL semantics a
+    shared backend uses so behaviour is identical across implementations.
+    """
+
+    def __init__(self) -> None:
+        import threading
+
+        self._benches: Dict[str, float] = {}
+        self._lock = threading.Lock()
+
+    def bench(self, cred_id: str, *, until: float, reason: str = "") -> None:
+        with self._lock:
+            existing = self._benches.get(cred_id)
+            # Keep the longest cooldown if a longer one is already set.
+            if existing is None or until > existing:
+                self._benches[cred_id] = until
+
+    def is_benched(self, cred_id: str, *, now: Optional[float] = None) -> bool:
+        return self.benched_until(cred_id, now=now) is not None
+
+    def benched_until(self, cred_id: str, *, now: Optional[float] = None) -> Optional[float]:
+        current = time.time() if now is None else now
+        with self._lock:
+            until = self._benches.get(cred_id)
+            if until is None:
+                return None
+            if current >= until:
+                # Expired — self-clear.
+                self._benches.pop(cred_id, None)
+                return None
+            return until
+
+    def clear(self, cred_id: str) -> None:
+        with self._lock:
+            self._benches.pop(cred_id, None)
+
+
+def build_quota_coordinator(
+    config: Optional[QuotaCoordinatorConfig] = None,
+) -> QuotaCoordinatorProtocol:
+    """Build a coordinator from config, defaulting to the local in-memory one.
+
+    Only the ``"local"`` backend is resolvable from core. Shared backends
+    (e.g. ``"redis"``) are provided by the wrapper/bot package, which passes
+    an already-constructed coordinator instance to ``FailoverManager``; this
+    helper falls back to local for unknown backends so core never grows a
+    heavy optional dependency.
+    """
+    if config is None or config.backend == "local":
+        return LocalQuotaCoordinator()
+    # Unknown/heavy backend not resolvable in core: fail open to local.
+    return LocalQuotaCoordinator()

src/praisonai-agents/praisonaiagents/managed/_compute_bridge.py

@@ -11,30 +11,83 @@ from __future__ import annotations
 import importlib
 from typing import Any, Optional
 
-_INSTALL_HINT = "Remote compute requires the wrapper package: pip install praisonai"
+_INSTALL_HINT = "Remote compute requires: pip install praisonai-sandbox"
 
 # name -> (module, attribute). Kept in sync with
 # praisonai.integrations.managed_local._resolve_compute
 _PROVIDERS = {
-    "local": ("praisonai.integrations.compute.local", "LocalCompute"),
-    "docker": ("praisonai.integrations.compute.docker", "DockerCompute"),
-    "e2b": ("praisonai.integrations.compute.e2b", "E2BCompute"),
-    "modal": ("praisonai.integrations.compute.modal_compute", "ModalCompute"),
-    "daytona": ("praisonai.integrations.compute.daytona", "DaytonaCompute"),
-    "flyio": ("praisonai.integrations.compute.flyio", "FlyioCompute"),
-    "tenki": ("praisonai.integrations.compute.tenki", "TenkiCompute"),
+    "local": ("praisonai_sandbox.compute.local", "LocalCompute"),
+    "docker": ("praisonai_sandbox.compute.docker", "DockerCompute"),
+    "e2b": ("praisonai_sandbox.compute.e2b", "E2BCompute"),
+    "modal": ("praisonai_sandbox.compute.modal_compute", "ModalCompute"),
+    "daytona": ("praisonai_sandbox.compute.daytona", "DaytonaCompute"),
+    "flyio": ("praisonai_sandbox.compute.flyio", "FlyioCompute"),
+    "tenki": ("praisonai_sandbox.compute.tenki", "TenkiCompute"),
 }
 
+# What to actually type when a provider is missing. Every name here must point
+# at an extra that EXISTS and installs something -- three of these used to name
+# extras that were absent or empty, so a user followed the official hint, waited
+# for the install, and hit the identical failure again.
+#
+# The providers in _PROVIDERS now live in praisonai-sandbox, so the failing
+# import is `praisonai_sandbox.compute.*`: point the hint at the lightweight
+# package that owns the module, not the heavy wrapper. Reaching E2B through
+# praisonai-sandbox[e2b] resolves 58 packages against 143 through praisonai.
+# Sandbox-only places (novita/sandlock/ssh) resolve through the
+# SandboxComputeAdapter and only need `praisonai-sandbox`.
 _EXTRA_HINTS = {
-    "e2b": "pip install praisonai[e2b]",
-    "modal": "pip install praisonai[modal]",
-    "daytona": "pip install praisonai[daytona]",
+    "e2b": "pip install 'praisonai-sandbox[e2b]'",
+    "modal": "pip install 'praisonai-sandbox[modal]'",
+    "daytona": "pip install 'praisonai-sandbox[daytona]'",
+    "docker": "pip install 'praisonai-sandbox[docker]'",
+    "novita": "pip install 'praisonai-sandbox[novita]'",
+    "sandlock": "pip install 'praisonai-sandbox[sandlock]'",
+    "ssh": "pip install 'praisonai-sandbox[ssh]'",
+    # flyio and tenki need a credential rather than an SDK; flyio speaks plain
+    # HTTP through aiohttp, which praisonaiagents already requires. Export the
+    # variable so a child process inherits it -- a bare assignment would not.
+    "flyio": "export FLY_API_TOKEN=... (see https://fly.io/docs/flyctl/auth-token)",
+    "tenki": "pip install 'praisonai-sandbox[tenki]'",
 }
 
 
+# Places that live only in praisonai-sandbox. They speak SandboxProtocol
+# (one object IS one environment) rather than ComputeProviderProtocol (one
+# object hands out many), so an adapter supplies the missing half.
+from ._sandbox_adapter import NEEDS_INSTANCE, SANDBOX_ONLY  # noqa: E402
+
+# Spellings users already have in their code, mapped to the canonical name.
+_ALIASES = {"native": "sandlock"}
+
+
+def _entry_point_providers() -> dict:
+    """Places contributed by other installed packages.
+
+    The sandbox and managed-backend registries already discover plugins this
+    way (``praisonai.sandbox``, ``praisonai.managed_backends``); the compute
+    registry was the one hardcoded dict, so adding a place meant editing core.
+    A provider can now ship in its own distribution:
+
+        [project.entry-points."praisonai.compute"]
+        runpod = "praisonai_compute_runpod:RunpodCompute"
+    """
+    found = {}
+    try:
+        from importlib.metadata import entry_points
+
+        for ep in entry_points(group="praisonai.compute"):
+            found[ep.name.lower()] = ep
+    except Exception:  # pragma: no cover - discovery must never break resolution
+        pass
+    return found
+
+
 def available_providers() -> list:
-    """Return the compute provider names this bridge can resolve."""
-    return sorted(_PROVIDERS)
+    """Every place ``tools_run_on=`` / ``run_in=`` accepts, from all registries."""
+    return sorted(
+        set(_PROVIDERS) | set(SANDBOX_ONLY) | set(_entry_point_providers())
+    )
 
 
 def compute_package_available() -> bool:
@@ -58,15 +111,44 @@ def resolve_compute(compute: Optional[Any]) -> Optional[Any]:
     if not isinstance(compute, str):
         if hasattr(compute, "provision") and hasattr(compute, "execute"):
             return compute
+        # A SandboxProtocol instance -- e.g. SSHSandbox(host=...) -- is adapted,
+        # which is the only way to pass connection details.
+        if hasattr(compute, "run_command") and hasattr(compute, "start"):
+            from ._sandbox_adapter import wrap_sandbox_instance
+
+            return wrap_sandbox_instance(compute)
         raise TypeError(
             f"compute= expects a provider name or a ComputeProviderProtocol "
             f"instance, got {type(compute).__name__}"
         )
 
     name = compute.lower().strip()
+    name = _ALIASES.get(name, name)
+
+    # A sandbox-only place: adapt it to the compute-provider shape.
+    if name in SANDBOX_ONLY:
+        if name in NEEDS_INSTANCE:
+            raise ValueError(
+                f"{name!r} needs connection details a bare name cannot carry. "
+                f"Instead, {NEEDS_INSTANCE[name]}"
+            )
+        from ._sandbox_adapter import SandboxComputeAdapter
+
+        return SandboxComputeAdapter(name)
+
+    external = _entry_point_providers()
+    if name in external and name not in _PROVIDERS:
+        # Let a contributed place describe itself. Without this the repr falls
+        # back to the bare name, because the phrase table is a literal that
+        # cannot know about packages installed later.
+        _remember_display_name(name, external[name])
+        # A place contributed by another package. Loaded on demand, so an
+        # installed-but-broken plugin cannot break anything that never names it.
+        return external[name].load()()
+
     if name not in _PROVIDERS:
         raise ValueError(
-            f"Unknown compute provider {name!r}. "
+            f"Unknown place {name!r}. "
             f"Available: {', '.join(available_providers())}"
         )
 
@@ -78,3 +160,23 @@ def resolve_compute(compute: Optional[Any]) -> Optional[Any]:
         raise ImportError(f"compute={name!r} requires {module_name}. {hint}") from exc
 
     return getattr(module, attr)()
+
+
+#: Display phrases contributed by external providers, filled in on resolve.
+_DISPLAY_NAMES: dict = {}
+
+
+def _remember_display_name(name: str, entry_point) -> None:
+    """Record a contributed provider's own phrase, if it declares one."""
+    try:
+        cls = entry_point.load()
+        phrase = getattr(cls, "display_name", None)
+        if isinstance(phrase, str) and phrase:
+            _DISPLAY_NAMES[name] = phrase
+    except Exception:  # pragma: no cover - naming must never break resolution
+        pass
+
+
+def contributed_display_name(name: str):
+    """The phrase a contributed place declared for itself, if any."""
+    return _DISPLAY_NAMES.get((name or "").lower())

src/praisonai-agents/praisonaiagents/managed/_sandbox_adapter.py

@@ -0,0 +1,254 @@
+"""Expose a single sandbox environment as a one-instance compute provider.
+
+There are two execution stacks in this project and they are *not* duplicates:
+
+* ``SandboxProtocol`` (praisonai-sandbox) -- one object **is** one environment:
+  ``start / stop / execute / run_command / reset``.
+* ``ComputeProviderProtocol`` -- one object **hands out** many environments:
+  ``provision / execute(instance_id, ...) / shutdown / list_instances``.
+
+``tools_run_on=`` speaks the second protocol, so four backends that only exist
+as sandboxes -- ``subprocess``, ``sandlock``, ``ssh``, ``novita`` -- were
+unreachable through it. This adapter is the missing half: it holds exactly one
+environment and presents the manager-shaped API over it.
+
+Deliberate limitation: ``list_instances()`` can only report what *this process*
+provisioned, so adapter-backed places never appear in ``praisonai managed ps``.
+A single environment has no cross-process registry to consult, and pretending
+otherwise would be worse than saying so.
+"""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+# Backends that exist only as SandboxProtocol implementations. The literal is
+# the floor, not the ceiling: the sandbox registry accepts plugins by entry
+# point, so a hardcoded tuple silently withheld contributed backends from
+# tools_run_on=. `capsule` was the live example -- it ships from
+# praisonai-plugins, worked with run_in=, and was invisible to tools_run_on=.
+_SANDBOX_ONLY_FLOOR = ("subprocess", "sandlock", "ssh", "novita")
+
+
+def sandbox_only_names() -> tuple:
+    """Sandbox backends with no compute-registry equivalent.
+
+    Anything the compute registry already provides is excluded, so a name that
+    exists in both (docker, e2b, modal, daytona) keeps its richer compute
+    implementation rather than being downgraded to the one-instance adapter.
+    """
+    names = set(_SANDBOX_ONLY_FLOOR)
+    try:
+        from ..sandbox._sandbox_bridge import get_sandbox_registry
+
+        registry = get_sandbox_registry()
+        registry = registry.default() if isinstance(registry, type) else registry
+        names |= {str(n).lower() for n in registry.list_all_names()}
+    except Exception:  # pragma: no cover - discovery must never break resolution
+        pass
+
+    try:
+        from ._compute_bridge import _PROVIDERS
+
+        names -= set(_PROVIDERS)
+    except Exception:
+        pass
+    return tuple(sorted(names))
+
+
+class _SandboxOnly(tuple):
+    """A tuple that re-reads the registry, so `in` sees plugins installed later.
+
+    Kept as a tuple subclass because SANDBOX_ONLY is imported and iterated in
+    several places that predate this.
+    """
+
+    def __contains__(self, item):
+        return item in sandbox_only_names()
+
+    def __iter__(self):
+        return iter(sandbox_only_names())
+
+    def __len__(self):
+        return len(sandbox_only_names())
+
+
+SANDBOX_ONLY = _SandboxOnly(_SANDBOX_ONLY_FLOOR)
+
+# Names that need connection details no bare string can carry.
+NEEDS_INSTANCE = {
+    "ssh": (
+        "run on a remote machine over SSH by passing the object, so it can "
+        "carry the host: SSHSandbox(host='...', user='...')"
+    ),
+}
+
+
+class SandboxComputeAdapter:
+    """One ``SandboxProtocol`` environment, wearing the compute-provider shape."""
+
+    def __init__(self, sandbox_type: str, config: Optional[Any] = None,
+                 sandbox: Optional[Any] = None):
+        self._type = sandbox_type
+        self._config = config
+        self._sandbox = sandbox          # pre-built instance, if supplied
+        self._owns_sandbox = sandbox is None
+        self._instance_id: Optional[str] = None
+
+    # ── identity ─────────────────────────────────────────────────────────────
+    @property
+    def provider_name(self) -> str:
+        return self._type
+
+    @property
+    def is_available(self) -> bool:
+        if self._sandbox is not None:
+            return bool(getattr(self._sandbox, "is_available", True))
+        try:
+            from ..sandbox._sandbox_bridge import resolve_sandbox_class
+
+            cls = resolve_sandbox_class(self._type)
+            probe = cls() if self._type not in NEEDS_INSTANCE else None
+            return bool(getattr(probe, "is_available", False)) if probe else False
+        except Exception:
+            return False
+
+    # ── lifecycle ────────────────────────────────────────────────────────────
+    async def provision(self, config: Any) -> Any:
+        from .protocols import InstanceInfo, InstanceStatus
+
+        if self._sandbox is None:
+            from ..sandbox import SandboxConfig
+            from ..sandbox.manager import SandboxManager
+
+            cfg = self._config
+            if cfg is None:
+                # "/workspace" is the convention for *container* sandboxes and
+                # does not exist on a Mac or a bare Linux box -- creating it
+                # fails with "Read-only file system". Local backends pick their
+                # own private temp dir, so only pass a working_dir through when
+                # the caller actually chose one.
+                wanted_dir = getattr(config, "working_dir", None)
+                local_place = self._type in ("subprocess", "sandlock")
+                if local_place and wanted_dir in (None, "", "/workspace"):
+                    wanted_dir = None
+                cfg = SandboxConfig(
+                    sandbox_type=self._type,
+                    env=dict(getattr(config, "env", None) or {}),
+                    **({"working_dir": wanted_dir} if wanted_dir else {}),
+                )
+                # This sandbox exists to back the model's execute_command tool,
+                # which IS a shell. The default policy sets allow_subprocess=
+                # False, which makes `sh -c` -- and therefore every pipe,
+                # redirect and && the model writes -- fail as "disabled by
+                # security policy". Blocking the shell inside a shell tool is a
+                # contradiction, not a protection; blocked_commands and
+                # blocked_paths still apply.
+                if getattr(cfg, "security_policy", None) is not None:
+                    cfg.security_policy.allow_subprocess = True
+            if getattr(config, "image", None):
+                cfg.image = config.image
+            self._manager = SandboxManager(cfg)
+            self._sandbox = await self._manager.__aenter__()
+        else:
+            self._manager = None
+            starter = getattr(self._sandbox, "start", None)
+            if starter is not None:
+                await starter()
+
+        self._instance_id = f"{self._type}_{uuid.uuid4().hex[:12]}"
+        logger.info("[sandbox_adapter] provisioned %s as %s", self._type, self._instance_id)
+        return InstanceInfo(
+            instance_id=self._instance_id,
+            status=InstanceStatus.RUNNING,
+            endpoint=f"{self._type}://local",
+            provider=self._type,
+            metadata={"adapter": True, "discoverable": False},
+        )
+
+    async def execute(self, instance_id: str, command: str,
+                      timeout: Optional[int] = 300) -> Dict[str, Any]:
+        if self._sandbox is None:
+            raise RuntimeError(
+                f"{self._type} sandbox is not running; provision() was not called"
+            )
+        # The bridged execute_command tool hands us a shell command string, and
+        # models write pipes, redirects and && constantly. Without shell=True
+        # `echo x > f` is argv-split and "> f" arrives as a literal argument.
+        try:
+            result = await self._sandbox.run_command(command, shell=True)
+        except TypeError:
+            # Backends predating the shell= parameter.
+            result = await self._sandbox.run_command(command)
+        # A policy denial arrives as result.error with exit_code=None. Dropping
+        # it would turn "blocked by security policy" into a silent success --
+        # the caller would see exit 0 and empty output.
+        error = getattr(result, "error", None)
+        exit_code = getattr(result, "exit_code", None)
+        stderr = getattr(result, "stderr", "") or ""
+        if error:
+            stderr = f"{stderr}\n{error}".strip() if stderr else str(error)
+            if exit_code in (None, 0):
+                exit_code = 1
+        return {
+            "stdout": getattr(result, "stdout", "") or "",
+            "stderr": stderr,
+            "exit_code": exit_code or 0,
+        }
+
+    async def shutdown(self, instance_id: str) -> None:
+        try:
+            if getattr(self, "_manager", None) is not None:
+                await self._manager.__aexit__(None, None, None)
+            elif self._sandbox is not None:
+                stopper = getattr(self._sandbox, "stop", None)
+                if stopper is not None:
+                    await stopper()
+        finally:
+            if self._owns_sandbox:
+                self._sandbox = None
+            self._manager = None
+            self._instance_id = None
+
+    async def get_status(self, instance_id: str) -> Any:
+        from .protocols import InstanceInfo, InstanceStatus
+
+        running = self._sandbox is not None and instance_id == self._instance_id
+        return InstanceInfo(
+            instance_id=instance_id,
+            status=InstanceStatus.RUNNING if running else InstanceStatus.STOPPED,
+            provider=self._type,
+        )
+
+    async def list_instances(self) -> List[Any]:
+        """Only what this process provisioned -- see the module docstring."""
+        from .protocols import InstanceInfo, InstanceStatus
+
+        if self._instance_id is None:
+            return []
+        return [InstanceInfo(
+            instance_id=self._instance_id,
+            status=InstanceStatus.RUNNING,
+            provider=self._type,
+            metadata={"adapter": True, "discoverable": False},
+        )]
+
+    # ── file transfer ────────────────────────────────────────────────────────
+    async def upload_file(self, instance_id: str, local_path: str, remote_path: str) -> None:
+        with open(local_path, "r") as handle:
+            await self._sandbox.write_file(remote_path, handle.read())
+
+    async def download_file(self, instance_id: str, remote_path: str, local_path: str) -> None:
+        content = await self._sandbox.read_file(remote_path)
+        with open(local_path, "w") as handle:
+            handle.write(content if isinstance(content, str) else str(content))
+
+
+def wrap_sandbox_instance(sandbox: Any) -> SandboxComputeAdapter:
+    """Adapt an already-constructed sandbox (e.g. ``SSHSandbox(host=...)``)."""
+    name = getattr(sandbox, "sandbox_type", None) or type(sandbox).__name__.lower()
+    return SandboxComputeAdapter(str(name), sandbox=sandbox)

src/praisonai-agents/praisonaiagents/managed/shared_compute.py

@@ -17,6 +17,7 @@ import logging
 import shlex
 import threading
 import uuid
+import weakref
 from typing import Any, Dict, List, Optional
 
 from ._compute_bridge import resolve_compute
@@ -118,8 +119,13 @@ class SharedCompute:
 
     def shutdown(self) -> None:
         """Tear down the instance and restore any agents we patched."""
-        for agent, original_tools, original_backstory in self._patched:
+        for ref, original_tools, original_backstory in self._patched:
+            agent = ref()
+            if agent is None:
+                continue          # already collected; nothing left to restore
             try:
+                if getattr(agent, "_tools_sandbox", None) is self:
+                    agent._tools_sandbox = None
                 agent.tools = original_tools
                 agent.backstory = original_backstory
                 cache = getattr(agent, "_system_prompt_cache", None)
@@ -136,6 +142,8 @@ class SharedCompute:
             logger.info("[shared_compute] shut down instance %s", self.instance_id)
         except Exception as exc:  # pragma: no cover - teardown must not mask errors
             logger.warning("[shared_compute] shutdown failed for %s: %s", self.instance_id, exc)
+            self.instance_id = None
+            raise            # the caller logs whether the sandbox was released
         finally:
             self.instance_id = None
 
@@ -187,9 +195,14 @@ class SharedCompute:
             # heredoc early (which would truncate the file and run the remainder
             # as shell commands). Guard against the astronomically-unlikely case
             # where the token still appears in the body.
-            delimiter = f"__PRAISON_EOF_{uuid.uuid4().hex}__"
+            # Uppercase hex on purpose. uuid4().hex is lowercase, and the
+            # security policy substring-matches blocked_commands against the
+            # whole command -- "dd" is one of them, and appears in 9.6% of
+            # random lowercase hex strings. One write in ten failed with
+            # "Blocked command pattern: dd" for no reason the caller could see.
+            delimiter = f"__PRAISON_EOF_{uuid.uuid4().hex.upper()}__"
             while delimiter in content:
-                delimiter = f"__PRAISON_EOF_{uuid.uuid4().hex}__"
+                delimiter = f"__PRAISON_EOF_{uuid.uuid4().hex.upper()}__"
             heredoc = (
                 f"mkdir -p $(dirname {quoted}) && "
                 f"cat > {quoted} <<'{delimiter}'\n{content}\n{delimiter}"
@@ -203,13 +216,25 @@ class SharedCompute:
             """List files in a directory on the shared remote sandbox."""
             return self._format(self.run_command(f"ls -la {shlex.quote(path)}"))
 
-        return [execute_command, read_file, write_file, list_files]
+        bridged = [execute_command, read_file, write_file, list_files]
+        # Tag them so Agent.clone_for_channel() drops them: each closes over
+        # THIS sandbox, so a clone inheriting them would talk to the original
+        # agent's instance and then attach a second sandbox of its own --
+        # duplicating the capability prompt and splitting its work across two
+        # machines. The clone regenerates its own on first use.
+        for tool in bridged:
+            tool._praison_sandbox_tool = True
+        return bridged
 
-    def attach(self, agents: List[Any]) -> None:
+    def attach(self, agents: List[Any], *, override_declared: bool = False) -> None:
         """Give each agent the shared tools and tell it they exist.
 
         Agents that already carry their own managed ``backend`` are skipped --
         they have deliberately been pointed at their own runtime.
+
+        ``override_declared`` is set only by the agent's *own* placement, which
+        is this sandbox's whole reason for existing. A run-level sandbox leaves
+        it False so an agent that named its own place keeps it.
         """
         tools = self.build_tools()
         by_name = {t.__name__: t for t in tools}
@@ -217,6 +242,31 @@ class SharedCompute:
         for agent in agents:
             if agent is None or getattr(agent, "backend", None) is not None:
                 continue
+            # An agent that named its own place, or already has a sandbox, must
+            # not be attached again. A second attach appends CAPABILITY_PROMPT
+            # twice and, if the two teardowns do not happen in LIFO order,
+            # permanently restores the wrong tools: the agent is left holding
+            # four tools bound to a shut-down instance.
+            #
+            # `tools_run_on` is checked as well as `_tools_sandbox` because of
+            # ordering: a flow attaches at the start of the run, before the
+            # agent's first chat() has created its own sandbox. Checking only
+            # the live sandbox would attach here and let the agent attach again
+            # on its first turn. An explicit per-agent choice outranks the run's
+            # default, so the flow leaves it alone.
+            declared_own = (
+                not override_declared
+                and getattr(agent, "tools_run_on", None) is not None
+            )
+            if getattr(agent, "_tools_sandbox", None) is not None or declared_own:
+                logger.debug(
+                    "[shared_compute] %r already runs its tools on %s; leaving it alone",
+                    getattr(agent, "name", agent),
+                    getattr(agent, "tools_run_on", "its own sandbox"),
+                )
+                continue
+            if any(agent is ref() for ref, _, _ in self._patched):
+                continue
 
             original_tools = list(getattr(agent, "tools", None) or [])
             # Agent.__init__ copies `instructions` into role/goal/backstory and
@@ -248,4 +298,18 @@ class SharedCompute:
                 logger.warning("[shared_compute] could not attach tools to %r: %s", agent, exc)
                 continue
 
-            self._patched.append((agent, original_tools, original_backstory))
+            # Mark where this agent's tools now run, so a nested attach (or a
+            # later ensure_tools_placed) can see it is already bound.
+            try:
+                agent._tools_sandbox = self
+            except Exception:  # pragma: no cover - exotic agent objects
+                pass
+            # Weakly: an agent points at its SharedCompute and the SharedCompute
+            # pointed back, so a dropped agent was reclaimed as a cycle and its
+            # sandbox was never shut down. Holding the agent weakly lets
+            # weakref.finalize see the agent die and release the sandbox.
+            try:
+                ref = weakref.ref(agent)
+            except TypeError:  # pragma: no cover - non-weakrefable agent
+                ref = lambda _agent=agent: _agent
+            self._patched.append((ref, original_tools, original_backstory))

src/praisonai-agents/praisonaiagents/mcp/__init__.py

@@ -51,6 +51,12 @@ _LAZY_IMPORTS = {
     "get_redirect_url": _t("praisonaiagents.mcp.mcp_oauth_callback", "get_redirect_url"),
     "OAUTH_CALLBACK_PORT": _t("praisonaiagents.mcp.mcp_oauth_callback", "OAUTH_CALLBACK_PORT"),
     "OAUTH_CALLBACK_PATH": _t("praisonaiagents.mcp.mcp_oauth_callback", "OAUTH_CALLBACK_PATH"),
+    # Zero-config OAuth: discovery, dynamic registration, orchestration.
+    "discover_oauth_metadata": _t("praisonaiagents.mcp.oauth_discovery", "discover_oauth_metadata"),
+    "parse_www_authenticate": _t("praisonaiagents.mcp.oauth_discovery", "parse_www_authenticate"),
+    "register_client": _t("praisonaiagents.mcp.oauth_registration", "register_client"),
+    "MCPOAuthProvider": _t("praisonaiagents.mcp.oauth_provider", "MCPOAuthProvider"),
+    "InteractiveAuthRequired": _t("praisonaiagents.mcp.oauth_provider", "InteractiveAuthRequired"),
     # Protocols (lazy loaded)
     "MCPClientProtocol": _t("praisonaiagents.mcp.protocols", "MCPClientProtocol"),
     # Loader (lazy loaded)
@@ -100,4 +106,10 @@ __all__ = [
     "get_redirect_url",
     "OAUTH_CALLBACK_PORT",
     "OAUTH_CALLBACK_PATH",
+    # Zero-config OAuth (lazy loaded)
+    "discover_oauth_metadata",
+    "parse_www_authenticate",
+    "register_client",
+    "MCPOAuthProvider",
+    "InteractiveAuthRequired",
 ]

src/praisonai-agents/praisonaiagents/mcp/oauth_discovery.py

@@ -0,0 +1,231 @@
+"""
+MCP OAuth Metadata Discovery.
+
+Implements the discovery half of zero-config OAuth for remote MCP servers:
+
+- Parse a ``WWW-Authenticate: Bearer resource_metadata="..."`` challenge from a
+  ``401`` response (RFC 9728 / MCP authorization spec).
+- Fetch protected-resource metadata (``/.well-known/oauth-protected-resource``).
+- Fetch authorization-server metadata
+  (``/.well-known/oauth-authorization-server`` with an OpenID configuration
+  fallback), yielding ``authorization_endpoint``, ``token_endpoint`` and
+  ``registration_endpoint``.
+
+Network access is HTTPS-only (except loopback) to preserve the SSRF-safe
+posture used elsewhere in the MCP transports. ``requests`` is imported lazily so
+this module stays additive and does not affect import time.
+"""
+
+import re
+from typing import Any, Dict, List, Optional
+from urllib.parse import urljoin, urlparse
+
+from praisonaiagents._logging import get_logger
+
+logger = get_logger("mcp-oauth-discovery")
+
+_LOCALHOST_HOSTS = {"localhost", "127.0.0.1", "::1"}
+
+
+def _require_https(url: str) -> None:
+    """Reject non-HTTPS metadata URLs (loopback HTTP is allowed for testing)."""
+    parsed = urlparse(url)
+    if parsed.scheme == "https":
+        return
+    if parsed.scheme == "http" and (parsed.hostname or "") in _LOCALHOST_HOSTS:
+        return
+    raise ValueError(
+        f"Refusing to fetch OAuth metadata over insecure transport: {url!r}"
+    )
+
+
+def parse_www_authenticate(header: Optional[str]) -> Dict[str, str]:
+    """
+    Parse a ``WWW-Authenticate`` challenge header into its parameters.
+
+    Only ``Bearer`` challenges are relevant here. Returns a dict of the
+    ``key="value"`` auth-params (e.g. ``resource_metadata``, ``scope``,
+    ``error``). Returns an empty dict when the header is missing or not a
+    Bearer challenge.
+    """
+    if not header:
+        return {}
+
+    # Strip the leading scheme (e.g. "Bearer ") if present.
+    stripped = header.strip()
+    if stripped.lower().startswith("bearer"):
+        stripped = stripped[len("bearer"):].lstrip()
+
+    params: Dict[str, str] = {}
+    for match in re.finditer(r'(\w+)\s*=\s*"([^"]*)"', stripped):
+        params[match.group(1).lower()] = match.group(2)
+    return params
+
+
+def _get_json(url: str, timeout: float = 10.0) -> Dict[str, Any]:
+    """Fetch and parse a JSON metadata document over HTTPS.
+
+    Redirects are followed but every hop — including the final destination — is
+    re-validated with :func:`_require_https` so a redirect cannot escape the
+    HTTPS/loopback guard to an internal or plaintext endpoint (SSRF-safe).
+    """
+    _require_https(url)
+    import requests  # lazy import — optional dependency
+
+    resp = requests.get(
+        url,
+        headers={"Accept": "application/json"},
+        timeout=timeout,
+        allow_redirects=True,
+    )
+    # Re-validate every redirect hop and the final URL: allow_redirects only
+    # checks the initial URL, so a 30x to http:// or an internal host would
+    # otherwise bypass the guard.
+    for previous in getattr(resp, "history", None) or []:
+        _require_https(previous.url)
+    _require_https(resp.url)
+    resp.raise_for_status()
+    return resp.json()
+
+
+def _wellknown_candidates(server_url: str) -> List[str]:
+    """Build ``.well-known`` metadata URL candidates for a server URL."""
+    parsed = urlparse(server_url)
+    origin = f"{parsed.scheme}://{parsed.netloc}"
+    return [
+        urljoin(origin + "/", ".well-known/oauth-protected-resource"),
+        urljoin(origin + "/", ".well-known/oauth-authorization-server"),
+        urljoin(origin + "/", ".well-known/openid-configuration"),
+    ]
+
+
+def discover_protected_resource(
+    server_url: str,
+    resource_metadata_url: Optional[str] = None,
+    timeout: float = 10.0,
+) -> Optional[Dict[str, Any]]:
+    """
+    Discover protected-resource metadata for an MCP server.
+
+    Args:
+        server_url: The MCP server URL that returned a 401.
+        resource_metadata_url: Optional explicit URL from the
+            ``WWW-Authenticate: resource_metadata="..."`` challenge.
+        timeout: Per-request timeout in seconds.
+
+    Returns:
+        The protected-resource metadata document, or None if unavailable.
+    """
+    url = resource_metadata_url or _wellknown_candidates(server_url)[0]
+    try:
+        return _get_json(url, timeout=timeout)
+    except Exception as exc:  # noqa: BLE001 - discovery is best-effort
+        logger.debug(f"Protected-resource metadata unavailable at {url}: {exc}")
+        return None
+
+
+def discover_authorization_server(
+    server_url: str,
+    authorization_servers: Optional[List[str]] = None,
+    timeout: float = 10.0,
+) -> Optional[Dict[str, Any]]:
+    """
+    Discover authorization-server metadata.
+
+    Tries, in order: any ``authorization_servers`` advertised by the
+    protected-resource metadata, then the server-origin
+    ``/.well-known/oauth-authorization-server`` and OpenID-configuration
+    fallbacks.
+
+    Returns the first metadata document that exposes at least an
+    ``authorization_endpoint`` and ``token_endpoint``, or None.
+    """
+    candidates: List[str] = []
+
+    for issuer in authorization_servers or []:
+        issuer = issuer.rstrip("/")
+        candidates.append(issuer + "/.well-known/oauth-authorization-server")
+        candidates.append(issuer + "/.well-known/openid-configuration")
+
+    # Fall back to the resource server's own origin.
+    for candidate in _wellknown_candidates(server_url)[1:]:
+        candidates.append(candidate)
+
+    for url in candidates:
+        try:
+            metadata = _get_json(url, timeout=timeout)
+        except Exception as exc:  # noqa: BLE001 - best-effort discovery
+            logger.debug(f"Auth-server metadata unavailable at {url}: {exc}")
+            continue
+        if metadata.get("authorization_endpoint") and metadata.get("token_endpoint"):
+            return metadata
+
+    return None
+
+
+def discover_oauth_metadata(
+    server_url: str,
+    www_authenticate: Optional[str] = None,
+    timeout: float = 10.0,
+) -> Optional[Dict[str, Any]]:
+    """
+    Full metadata discovery for an OAuth-protected MCP server.
+
+    Combines the ``WWW-Authenticate`` challenge (if any), protected-resource
+    metadata, and authorization-server metadata into a single normalised dict:
+
+        {
+            "authorization_endpoint": str,
+            "token_endpoint": str,
+            "registration_endpoint": str | None,
+            "scopes_supported": list[str] | None,
+            "resource": str | None,
+        }
+
+    Returns None if a usable authorization/token endpoint pair cannot be found.
+    """
+    challenge = parse_www_authenticate(www_authenticate)
+
+    resource_meta = discover_protected_resource(
+        server_url,
+        resource_metadata_url=challenge.get("resource_metadata"),
+        timeout=timeout,
+    )
+
+    authorization_servers = None
+    resource_id = None
+    if resource_meta:
+        authorization_servers = resource_meta.get("authorization_servers")
+        resource_id = resource_meta.get("resource")
+
+    auth_meta = discover_authorization_server(
+        server_url,
+        authorization_servers=authorization_servers,
+        timeout=timeout,
+    )
+    if not auth_meta:
+        return None
+
+    # Enforce the HTTPS/loopback guard on the advertised endpoints too: an
+    # HTTPS metadata document must not be able to smuggle a plaintext
+    # authorization/token/registration endpoint that would later receive
+    # authorization codes, PKCE verifiers, refresh tokens, or client secrets
+    # over an insecure transport.
+    authorization_endpoint = auth_meta["authorization_endpoint"]
+    token_endpoint = auth_meta["token_endpoint"]
+    registration_endpoint = auth_meta.get("registration_endpoint")
+    _require_https(authorization_endpoint)
+    _require_https(token_endpoint)
+    if registration_endpoint:
+        _require_https(registration_endpoint)
+
+    return {
+        "authorization_endpoint": authorization_endpoint,
+        "token_endpoint": token_endpoint,
+        "registration_endpoint": registration_endpoint,
+        "scopes_supported": (
+            auth_meta.get("scopes_supported")
+            or (resource_meta or {}).get("scopes_supported")
+        ),
+        "resource": resource_id,
+    }

src/praisonai-agents/praisonaiagents/mcp/oauth_provider.py

@@ -0,0 +1,340 @@
+"""
+MCP OAuth Provider — zero-config orchestration.
+
+Composes the OAuth building blocks into a single ``ensure_authenticated`` entry
+point that transports invoke on a ``401``:
+
+    discovery  ->  dynamic client registration (if no client stored)
+               ->  PKCE authorization-code flow (browser + loopback callback)
+               ->  token persistence
+               ->  transparent refresh on expiry
+
+All heavy work (network I/O, browser, loopback HTTP server) is lazily imported
+and only runs when a server actually challenges for OAuth, so servers without
+auth — or with pre-supplied client info/tokens — behave exactly as before.
+"""
+
+import time
+import webbrowser
+from http.server import BaseHTTPRequestHandler, HTTPServer
+from threading import Thread
+from typing import Any, Dict, Optional
+from urllib.parse import urlencode, urlparse, parse_qs
+
+from praisonaiagents._logging import get_logger
+
+from .mcp_oauth_callback import (
+    OAuthCallbackHandler,
+    OAUTH_CALLBACK_PORT,
+    OAUTH_CALLBACK_PATH,
+    generate_state,
+    generate_code_verifier,
+    generate_code_challenge,
+    get_redirect_url,
+)
+
+logger = get_logger("mcp-oauth-provider")
+
+
+class InteractiveAuthRequired(RuntimeError):
+    """Raised when interactive OAuth is needed but not possible (headless/CI)."""
+
+
+def _make_callback_server(handler: OAuthCallbackHandler, port: int) -> HTTPServer:
+    """Build a minimal loopback HTTP server that feeds codes to ``handler``."""
+
+    class _Handler(BaseHTTPRequestHandler):
+        def do_GET(self):  # noqa: N802 - required name
+            parsed = urlparse(self.path)
+            if parsed.path != OAUTH_CALLBACK_PATH:
+                self.send_response(404)
+                self.end_headers()
+                return
+            params = parse_qs(parsed.query)
+            state = (params.get("state") or [""])[0]
+            code = (params.get("code") or [""])[0]
+            if state and code:
+                handler.receive_callback(state, code)
+                body = b"Authentication complete. You can close this window."
+            else:
+                body = b"Authentication failed: missing code or state."
+            self.send_response(200)
+            self.send_header("Content-Type", "text/plain")
+            self.end_headers()
+            self.wfile.write(body)
+
+        def log_message(self, *args, **kwargs):  # silence default logging
+            pass
+
+    return HTTPServer(("127.0.0.1", port), _Handler)
+
+
+class MCPOAuthProvider:
+    """
+    Orchestrates zero-config OAuth for a single remote MCP server.
+
+    Args:
+        mcp_name: Stable identifier used as the storage key.
+        server_url: The MCP server URL.
+        storage: Optional pre-built ``MCPAuthStorage`` (created lazily otherwise).
+        open_browser: Whether to open a browser for interactive auth. Set False
+            for headless/CI to get a single actionable ``InteractiveAuthRequired``.
+    """
+
+    def __init__(
+        self,
+        mcp_name: str,
+        server_url: str,
+        storage: Optional[Any] = None,
+        open_browser: bool = True,
+    ):
+        self.mcp_name = mcp_name
+        self.server_url = server_url
+        self.open_browser = open_browser
+        if storage is None:
+            from .mcp_auth_storage import MCPAuthStorage
+            storage = MCPAuthStorage()
+        self.storage = storage
+
+    def get_valid_token(self) -> Optional[str]:
+        """Return a non-expired access token if one is stored, else None."""
+        entry = self.storage.get_for_url(self.mcp_name, self.server_url)
+        if not entry or not entry.get("tokens"):
+            return None
+        if self.storage.is_token_expired(self.mcp_name):
+            refreshed = self._try_refresh(entry)
+            if not refreshed:
+                return None
+            entry = self.storage.get_for_url(self.mcp_name, self.server_url) or {}
+        tokens = entry.get("tokens") or {}
+        return tokens.get("access_token")
+
+    def ensure_authenticated(
+        self,
+        www_authenticate: Optional[str] = None,
+        timeout: float = 10.0,
+    ) -> str:
+        """
+        Ensure a valid access token exists, running the full flow if needed.
+
+        Returns:
+            A valid bearer access token.
+
+        Raises:
+            InteractiveAuthRequired: If interactive auth is needed but disabled.
+            RuntimeError: If discovery fails or the flow cannot complete.
+        """
+        token = self.get_valid_token()
+        if token:
+            return token
+
+        from .oauth_discovery import discover_oauth_metadata
+
+        metadata = discover_oauth_metadata(
+            self.server_url,
+            www_authenticate=www_authenticate,
+            timeout=timeout,
+        )
+        if not metadata:
+            raise RuntimeError(
+                f"Could not discover OAuth metadata for {self.server_url}. "
+                "The server may not advertise standard authorization-server "
+                "metadata; supply client credentials manually."
+            )
+
+        client_info = self._ensure_client(metadata, timeout=timeout)
+
+        scope = None
+        scopes = metadata.get("scopes_supported")
+        if scopes:
+            scope = " ".join(scopes)
+
+        return self._run_pkce_flow(metadata, client_info, scope, timeout=timeout)
+
+    def _ensure_client(
+        self,
+        metadata: Dict[str, Any],
+        timeout: float,
+    ) -> Dict[str, Any]:
+        """Return stored client info or perform dynamic client registration."""
+        entry = self.storage.get_for_url(self.mcp_name, self.server_url)
+        if entry and entry.get("client_info", {}).get("client_id"):
+            return entry["client_info"]
+
+        registration_endpoint = metadata.get("registration_endpoint")
+        if not registration_endpoint:
+            raise RuntimeError(
+                f"No client is registered for {self.server_url} and the "
+                "authorization server does not support dynamic client "
+                "registration (no registration_endpoint)."
+            )
+
+        from .oauth_registration import register_client
+
+        scope = None
+        scopes = metadata.get("scopes_supported")
+        if scopes:
+            scope = " ".join(scopes)
+
+        client_info = register_client(
+            registration_endpoint,
+            redirect_uris=[get_redirect_url()],
+            scope=scope,
+            timeout=timeout,
+        )
+        self.storage.set_client_info(
+            self.mcp_name, client_info, server_url=self.server_url
+        )
+        return client_info
+
+    def _run_pkce_flow(
+        self,
+        metadata: Dict[str, Any],
+        client_info: Dict[str, Any],
+        scope: Optional[str],
+        timeout: float,
+    ) -> str:
+        """Run the interactive PKCE authorization-code flow and store tokens."""
+        if not self.open_browser:
+            raise InteractiveAuthRequired(
+                f"Interactive OAuth is required to authenticate with "
+                f"{self.server_url}. Run this from an interactive session, or "
+                f"pre-supply tokens, to complete authentication."
+            )
+
+        callback = OAuthCallbackHandler()
+        state = generate_state()
+        code_verifier = generate_code_verifier()
+        code_challenge = generate_code_challenge(code_verifier)
+
+        params = {
+            "response_type": "code",
+            "client_id": client_info["client_id"],
+            "redirect_uri": get_redirect_url(),
+            "state": state,
+            "code_challenge": code_challenge,
+            "code_challenge_method": "S256",
+        }
+        if scope:
+            params["scope"] = scope
+        if metadata.get("resource"):
+            params["resource"] = metadata["resource"]
+
+        auth_url = f"{metadata['authorization_endpoint']}?{urlencode(params)}"
+
+        server = _make_callback_server(callback, OAUTH_CALLBACK_PORT)
+        thread = Thread(target=server.serve_forever, daemon=True)
+        thread.start()
+        try:
+            logger.debug(f"Opening browser for OAuth authorization: {auth_url}")
+            webbrowser.open(auth_url)
+            code = callback.wait_for_callback(state, timeout=300.0)
+        finally:
+            server.shutdown()
+
+        tokens = self._exchange_code(
+            metadata, client_info, code, code_verifier, timeout=timeout
+        )
+        self.storage.set_tokens(
+            self.mcp_name, tokens, server_url=self.server_url
+        )
+        return tokens["access_token"]
+
+    def _exchange_code(
+        self,
+        metadata: Dict[str, Any],
+        client_info: Dict[str, Any],
+        code: str,
+        code_verifier: str,
+        timeout: float,
+    ) -> Dict[str, Any]:
+        """Exchange an authorization code for tokens at the token endpoint."""
+        import requests  # lazy import — optional dependency
+
+        data = {
+            "grant_type": "authorization_code",
+            "code": code,
+            "redirect_uri": get_redirect_url(),
+            "client_id": client_info["client_id"],
+            "code_verifier": code_verifier,
+        }
+        if client_info.get("client_secret"):
+            data["client_secret"] = client_info["client_secret"]
+        if metadata.get("resource"):
+            data["resource"] = metadata["resource"]
+
+        resp = requests.post(
+            metadata["token_endpoint"],
+            data=data,
+            headers={"Accept": "application/json"},
+            timeout=timeout,
+        )
+        resp.raise_for_status()
+        return self._normalize_tokens(
+            resp.json(), token_endpoint=metadata["token_endpoint"]
+        )
+
+    def _try_refresh(self, entry: Dict[str, Any]) -> bool:
+        """Attempt a refresh-token grant; return True on success."""
+        tokens = entry.get("tokens") or {}
+        refresh_token = tokens.get("refresh_token")
+        token_endpoint = tokens.get("token_endpoint")
+        client_info = entry.get("client_info") or {}
+        client_id = client_info.get("client_id")
+        if not (refresh_token and token_endpoint and client_id):
+            return False
+
+        try:
+            from .oauth_discovery import _require_https
+
+            # Defense in depth: never POST a refresh token / client secret to a
+            # persisted endpoint that isn't HTTPS (or loopback).
+            _require_https(token_endpoint)
+
+            import requests  # lazy import — optional dependency
+
+            data = {
+                "grant_type": "refresh_token",
+                "refresh_token": refresh_token,
+                "client_id": client_id,
+            }
+            if client_info.get("client_secret"):
+                data["client_secret"] = client_info["client_secret"]
+
+            resp = requests.post(
+                token_endpoint,
+                data=data,
+                headers={"Accept": "application/json"},
+                timeout=10.0,
+            )
+            resp.raise_for_status()
+            new_tokens = self._normalize_tokens(
+                resp.json(), token_endpoint=token_endpoint
+            )
+            # Preserve refresh_token if the server didn't return a new one.
+            if not new_tokens.get("refresh_token"):
+                new_tokens["refresh_token"] = refresh_token
+            self.storage.set_tokens(
+                self.mcp_name, new_tokens, server_url=self.server_url
+            )
+            return True
+        except Exception as exc:  # noqa: BLE001 - refresh is best-effort
+            logger.debug(f"Token refresh failed for {self.mcp_name}: {exc}")
+            return False
+
+    @staticmethod
+    def _normalize_tokens(
+        raw: Dict[str, Any],
+        token_endpoint: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Normalise a token response, computing an absolute ``expires_at``."""
+        tokens = dict(raw)
+        expires_in = tokens.get("expires_in")
+        if expires_in and not tokens.get("expires_at"):
+            try:
+                tokens["expires_at"] = time.time() + float(expires_in)
+            except (TypeError, ValueError):
+                pass
+        if token_endpoint:
+            tokens["token_endpoint"] = token_endpoint
+        return tokens

src/praisonai-agents/praisonaiagents/mcp/oauth_registration.py

@@ -0,0 +1,91 @@
+"""
+MCP OAuth Dynamic Client Registration (RFC 7591).
+
+Performs an anonymous dynamic client registration against an authorization
+server's ``registration_endpoint`` so that connecting to a hosted MCP server by
+URL requires no pre-provisioned ``client_id``. The registered client info is
+returned for persistence via :meth:`MCPAuthStorage.set_client_info`.
+
+``requests`` is imported lazily to keep this module additive.
+"""
+
+from typing import Any, Dict, List, Optional
+from urllib.parse import urlparse
+
+from praisonaiagents._logging import get_logger
+
+logger = get_logger("mcp-oauth-registration")
+
+_LOCALHOST_HOSTS = {"localhost", "127.0.0.1", "::1"}
+
+
+def _require_https(url: str) -> None:
+    """Reject non-HTTPS registration URLs (loopback HTTP allowed for testing)."""
+    parsed = urlparse(url)
+    if parsed.scheme == "https":
+        return
+    if parsed.scheme == "http" and (parsed.hostname or "") in _LOCALHOST_HOSTS:
+        return
+    raise ValueError(
+        f"Refusing to register client over insecure transport: {url!r}"
+    )
+
+
+def register_client(
+    registration_endpoint: str,
+    redirect_uris: List[str],
+    client_name: str = "PraisonAI Agent",
+    scope: Optional[str] = None,
+    timeout: float = 10.0,
+) -> Dict[str, Any]:
+    """
+    Register a public OAuth client via RFC 7591 Dynamic Client Registration.
+
+    Args:
+        registration_endpoint: The ``registration_endpoint`` from auth-server
+            metadata.
+        redirect_uris: Allowed redirect URIs (the loopback callback URL).
+        client_name: Human-readable client name.
+        scope: Optional space-delimited scope string to request.
+        timeout: Request timeout in seconds.
+
+    Returns:
+        The registration response containing at least ``client_id`` (and
+        ``client_secret`` for confidential clients).
+
+    Raises:
+        ValueError: If the endpoint is insecure or the response lacks a
+            ``client_id``.
+    """
+    _require_https(registration_endpoint)
+    import requests  # lazy import — optional dependency
+
+    payload: Dict[str, Any] = {
+        "client_name": client_name,
+        "redirect_uris": redirect_uris,
+        "grant_types": ["authorization_code", "refresh_token"],
+        "response_types": ["code"],
+        "token_endpoint_auth_method": "none",
+    }
+    if scope:
+        payload["scope"] = scope
+
+    resp = requests.post(
+        registration_endpoint,
+        json=payload,
+        headers={"Content-Type": "application/json", "Accept": "application/json"},
+        timeout=timeout,
+    )
+    resp.raise_for_status()
+    client_info = resp.json()
+
+    if not client_info.get("client_id"):
+        raise ValueError(
+            "Dynamic client registration response missing 'client_id'"
+        )
+
+    logger.debug(
+        f"Registered OAuth client '{client_info.get('client_id')}' "
+        f"at {registration_endpoint}"
+    )
+    return client_info

src/praisonai-agents/praisonaiagents/memory/adapters/__init__.py

@@ -26,6 +26,7 @@ from .registry import (
     list_memory_adapters,
     get_first_available_memory_adapter,
     has_memory_adapter,
+    resolve_memory_adapter_name,
 )
 from .factories import (
     create_mem0_memory_adapter,
@@ -53,4 +54,5 @@ __all__ = [
     'list_memory_adapters',
     'get_first_available_memory_adapter',
     'has_memory_adapter',
+    'resolve_memory_adapter_name',
 ]

src/praisonai-agents/praisonaiagents/memory/adapters/factories.py

@@ -371,6 +371,39 @@ class ChromaMemoryAdapter:
                 sanitized[k] = str(v)
         return sanitized
     
+    def _reset_collection(self) -> None:
+        """Delete and recreate this adapter's collection, wiping stored memory.
+
+        Short-term and long-term share one collection in this adapter, so both
+        resets route here. Scoped to this adapter's own collection to avoid
+        wiping other collections in the shared persistent store.
+        """
+        try:
+            self.client.delete_collection(name=self.collection_name)
+        except Exception:
+            pass
+        self.collection = self.client.create_collection(
+            name=self.collection_name,
+            metadata={"hnsw:space": "cosine"}
+        )
+
+    def reset_short_term(self) -> None:
+        """Clear this adapter's memory.
+
+        NOTE: this adapter stores short-term and long-term in a single shared
+        collection (``store_short_term``/``search_short_term`` delegate to their
+        long-term counterparts and no ``memory_type`` tag is recorded), so
+        per-tier scoping is not possible here — resetting short-term also clears
+        long-term. Use a tier-aware backend (e.g. the Dakera adapter) if you need
+        to erase a single tier.
+        """
+        self._reset_collection()
+
+    def reset_long_term(self) -> None:
+        """Clear this adapter's memory (shared with short-term; see
+        ``reset_short_term`` for the shared-collection caveat)."""
+        self._reset_collection()
+
     def close(self):
         """Clean up ChromaDB resources."""
         # ChromaDB PersistentClient has no explicit close; release references.

src/praisonai-agents/praisonaiagents/memory/adapters/registry.py

@@ -77,6 +77,17 @@ def has_memory_adapter(name: str) -> bool:
     return get_default_memory_registry().is_available(name)
 
 
+#: Accepted spellings for a backend, mapped to the adapter that serves them.
+#: Single source of truth for MemoryConfig, Agent and Memory.
+MEMORY_PROVIDER_ALIASES = {"rag": "chroma", "chromadb": "chroma", "none": "in_memory"}
+
+
+def resolve_memory_adapter_name(name: str) -> Optional[str]:
+    """Adapter name for *name*, or None if nothing is registered under it."""
+    candidate = MEMORY_PROVIDER_ALIASES.get(name, name)
+    return candidate if has_memory_adapter(candidate) else None
+
+
 def get_first_available_memory_adapter(
     preferences: Optional[List[str]] = None, **kwargs
 ) -> Optional[Tuple[str, MemoryProtocol]]:
@@ -107,4 +118,6 @@ __all__ = [
     'add_memory_adapter',
     'add_memory_factory',
     'has_memory_adapter',
+    'resolve_memory_adapter_name',
+    'MEMORY_PROVIDER_ALIASES',
 ]

src/praisonai-agents/praisonaiagents/memory/memory.py

@@ -2,6 +2,7 @@ import os
 import sqlite3
 import json
 import time
+import asyncio
 import shutil
 import threading
 from typing import Any, Dict, List, Optional, Union, Literal
@@ -136,16 +137,21 @@ class Memory(SearchMixin, MemoryCoreMixin):
         provider = self.cfg.get("provider", "rag").lower()
         self._log_verbose(f"Requested memory provider: {provider}")
         
-        # Map legacy provider names to adapter names
-        provider_mapping = {
-            "rag": "chroma",  # Legacy "rag" provider uses ChromaDB
-            "mem0": "mem0",
-            "mongodb": "mongodb",
-            "sqlite": "sqlite",
-            "none": "in_memory"
-        }
-        
-        adapter_name = provider_mapping.get(provider, provider)
+        # Legacy provider aliases live in memory/adapters/registry.py
+        from .adapters.registry import (
+            MEMORY_PROVIDER_ALIASES, list_memory_adapters, resolve_memory_adapter_name)
+
+        # A provider with no adapter must fail loudly: falling through landed
+        # on the SQLite fallback below, so provider="redis" silently produced a
+        # local .db file per process.
+        adapter_name = resolve_memory_adapter_name(provider)
+        if adapter_name is None:
+            from ..config.parse_utils import make_preset_error
+            raise make_preset_error(
+                "memory provider",
+                provider,
+                sorted(set(list_memory_adapters()) | set(MEMORY_PROVIDER_ALIASES)),
+            )
         
         # Try to get preferred adapter, fallback to available ones
         adapter = None
@@ -195,8 +201,15 @@ class Memory(SearchMixin, MemoryCoreMixin):
                 self.provider = adapter_name
                 return
                 
-            # Fallback to first available adapter
-            self._log_verbose(f"Provider '{adapter_name}' not available, trying fallbacks")
+            # Falling back changes where the data lives - say so out loud when
+            # the user named a specific provider.
+            _fallback_log = (
+                logger.warning if provider_explicitly_requested else self._log_verbose
+            )
+            _fallback_log(
+                f"Memory provider '{adapter_name}' is not available; falling back "
+                f"to a local store. Data will NOT be in '{adapter_name}'."
+            )
             # Try each fallback preference individually
             for fallback_provider in ["sqlite", "in_memory"]:
                 try:
@@ -204,7 +217,7 @@ class Memory(SearchMixin, MemoryCoreMixin):
                     adapter = get_memory_adapter(fallback_provider, **fallback_config)
                     if adapter:
                         adapter_name = fallback_provider
-                        self._log_verbose(f"Using fallback adapter: {adapter_name}")
+                        _fallback_log(f"Using fallback memory adapter: {adapter_name}")
                         break
                 except Exception as e:
                     self._log_verbose(f"Fallback {fallback_provider} failed: {e}")
@@ -267,6 +280,27 @@ class Memory(SearchMixin, MemoryCoreMixin):
             # Initialize use_vector_search to False for non-MongoDB providers
             self.use_vector_search = False
 
+    @staticmethod
+    def _path_safe_suffix(user_id: Any) -> str:
+        """Return a filesystem-safe ``_<user_id>`` suffix for default store paths.
+
+        The scoping ``user_id`` doubles as a metadata filter, so it may be any
+        string. When it is embedded into default sqlite/chroma paths a raw value
+        containing separators or traversal (e.g. ``a/b`` or ``../escape``) would
+        create stores outside the project-data dir or break sqlite init. Reject
+        path separators/parent refs and keep only path-safe characters; a bare
+        store with no user_id keeps the original shared defaults.
+        """
+        if not user_id:
+            return ""
+        text = str(user_id)
+        if ".." in text or "/" in text or "\\" in text or "\x00" in text:
+            raise ValueError(
+                "memory user_id must not contain path separators or parent references"
+            )
+        safe = "".join(c if (c.isalnum() or c in "-_.") else "_" for c in text.strip())
+        return f"_{safe}" if safe else ""
+
     def _get_adapter_config(self) -> Dict[str, Any]:
         """Get configuration for adapter initialization."""
         # Only include adapter-relevant configuration
@@ -284,11 +318,18 @@ class Memory(SearchMixin, MemoryCoreMixin):
         from ..paths import get_project_data_dir
         project_data = str(get_project_data_dir())
         os.makedirs(project_data, exist_ok=True)
-        
-        config["short_db"] = self.cfg.get("short_db", os.path.join(project_data, "short_term.db"))
-        config["long_db"] = self.cfg.get("long_db", os.path.join(project_data, "long_term.db"))
-        config["rag_db_path"] = self.cfg.get("rag_db_path", os.path.join(project_data, "chroma_db"))
-        config["collection_name"] = self.cfg.get("collection_name", "memory_store")
+
+        # When a user_id scopes this store, derive per-user default paths/names so
+        # two agents in one process/dir don't silently share one on-disk store.
+        # Explicit short_db/long_db/rag_db_path/collection_name always win; a bare
+        # store with no user_id keeps the original shared defaults (backwards
+        # compatible).
+        suffix = self._path_safe_suffix(self.cfg.get("user_id"))
+
+        config["short_db"] = self.cfg.get("short_db", os.path.join(project_data, f"short_term{suffix}.db"))
+        config["long_db"] = self.cfg.get("long_db", os.path.join(project_data, f"long_term{suffix}.db"))
+        config["rag_db_path"] = self.cfg.get("rag_db_path", os.path.join(project_data, f"chroma_db{suffix}"))
+        config["collection_name"] = self.cfg.get("collection_name", f"memory_store{suffix}")
         config["verbose"] = self.verbose
         
         # Add specific configurations for different adapters
@@ -325,9 +366,13 @@ class Memory(SearchMixin, MemoryCoreMixin):
         # This ensures existing code that accesses _get_stm_conn() still works
         from ..paths import get_project_data_dir
         project_data = str(get_project_data_dir())
-        
-        self.short_db = self.cfg.get("short_db", os.path.join(project_data, "short_term.db"))
-        self.long_db = self.cfg.get("long_db", os.path.join(project_data, "long_term.db"))
+
+        # Scope legacy direct-SQLite defaults by user_id so this path matches the
+        # per-user store paths derived in _get_adapter_config (explicit paths win).
+        suffix = self._path_safe_suffix(self.cfg.get("user_id"))
+
+        self.short_db = self.cfg.get("short_db", os.path.join(project_data, f"short_term{suffix}.db"))
+        self.long_db = self.cfg.get("long_db", os.path.join(project_data, f"long_term{suffix}.db"))
         
         # Only create separate SQLite adapter if primary adapter is not SQLite
         if self.provider != "sqlite":
@@ -346,6 +391,110 @@ class Memory(SearchMixin, MemoryCoreMixin):
         self._all_connections = self._sqlite_adapter._all_connections
         self._connection_lock = self._sqlite_adapter._connection_lock
 
+    @staticmethod
+    def _resolve_batch_target(method_name: str) -> str:
+        """Map a staged write name to its ``store_*`` persistence method."""
+        target = method_name.replace("add_", "store_", 1)
+        if target not in ("store_short_term", "store_long_term"):
+            raise ValueError(f"Unsupported staged memory write {method_name!r}")
+        return target
+
+    def commit_memory_batch(
+        self,
+        writes: List[tuple],
+        *,
+        deadline: float,
+        commit_guard: Any = None,
+    ) -> None:
+        """Persist a batch of staged memory writes.
+
+        Mirrors ``FileMemory.commit_memory_batch`` so the pre-compaction fact
+        flush (compaction/memory_flush.py) durably commits extracted facts for
+        sqlite/chroma/mongodb/mem0 backends instead of raising and being
+        swallowed. Each staged tuple is ``(method_name, args, kwargs)`` produced
+        by _StagedMemory; ``add_*`` names map to the ``store_*`` equivalents.
+
+        The ``store_*`` methods serialize their own SQLite writes with
+        ``_write_lock``; this method therefore must NOT hold that lock while
+        calling them (it is a non-reentrant ``threading.Lock`` and doing so
+        would deadlock). Each write goes through ``commit_guard.commit`` so a
+        write is skipped once the flush has been cancelled or the shared
+        ``deadline`` has passed, honoring the same atomic-commit contract as the
+        file backend.
+        """
+        for method_name, args, kwargs in writes:
+            target = self._resolve_batch_target(method_name)
+            store = getattr(self, target)
+            authorized = self._authorize_commit(
+                lambda store=store, args=args, kwargs=kwargs: store(*args, **kwargs),
+                deadline=deadline,
+                commit_guard=commit_guard,
+            )
+            if not authorized:
+                return
+
+    async def acommit_memory_batch(
+        self,
+        writes: List[tuple],
+        *,
+        deadline: float,
+        commit_guard: Any = None,
+    ) -> None:
+        """Async variant preferring ``store_*_async`` when available.
+
+        Blocking ``store_*`` work is offloaded to a worker thread so the event
+        loop is never blocked, and each write is gated by the same deadline /
+        cancellation guard as the sync path so late or cancelled facts are not
+        persisted after compaction resumes.
+        """
+        import inspect
+
+        for method_name, args, kwargs in writes:
+            if self._commit_expired(deadline, commit_guard):
+                return
+            target = self._resolve_batch_target(method_name)
+            async_method = getattr(self, f"{target}_async", None)
+            if async_method is not None:
+                if self._commit_expired(deadline, commit_guard):
+                    return
+                result = async_method(*args, **kwargs)
+                if inspect.isawaitable(result):
+                    await result
+                continue
+
+            store = getattr(self, target)
+            authorized = await asyncio.to_thread(
+                self._authorize_commit,
+                lambda store=store, args=args, kwargs=kwargs: store(*args, **kwargs),
+                deadline=deadline,
+                commit_guard=commit_guard,
+            )
+            if not authorized:
+                return
+
+    @staticmethod
+    def _commit_expired(deadline: float, commit_guard: Any) -> bool:
+        """True once the flush was cancelled or the shared deadline has passed."""
+        if commit_guard is not None and getattr(commit_guard, "cancelled", False):
+            return True
+        return time.monotonic() >= deadline
+
+    def _authorize_commit(
+        self, operation: Any, *, deadline: float, commit_guard: Any
+    ) -> bool:
+        """Run ``operation`` under the guard's atomic-commit gate.
+
+        Returns ``False`` (and skips the write) once cancelled or past the
+        deadline. When no guard is supplied, fall back to a plain deadline
+        check so callers outside the flush path still behave correctly.
+        """
+        if commit_guard is not None and hasattr(commit_guard, "commit"):
+            return commit_guard.commit(operation, deadline=deadline)
+        if self._commit_expired(deadline, commit_guard):
+            return False
+        operation()
+        return True
+
     def _get_stm_conn(self):
         """Get thread-local short-term memory SQLite connection."""
         if not hasattr(self._local, 'stm_conn') or self._local.stm_conn is None:
@@ -814,6 +963,11 @@ class Memory(SearchMixin, MemoryCoreMixin):
         if getattr(self, "memory_adapter", None):
             if hasattr(self.memory_adapter, "reset_short_term"):
                 self.memory_adapter.reset_short_term()
+            else:
+                logger.warning(
+                    f"{type(self.memory_adapter).__name__} does not support "
+                    "reset_short_term(); short-term memory was NOT cleared"
+                )
             return
         conn = self._get_stm_conn()
         with self._write_lock:  # Serialize write operations
@@ -1199,6 +1353,11 @@ class Memory(SearchMixin, MemoryCoreMixin):
         if getattr(self, "memory_adapter", None):
             if hasattr(self.memory_adapter, "reset_long_term"):
                 self.memory_adapter.reset_long_term()
+            else:
+                logger.warning(
+                    f"{type(self.memory_adapter).__name__} does not support "
+                    "reset_long_term(); long-term memory was NOT cleared"
+                )
             return
         conn = self._get_ltm_conn()
         with self._write_lock:  # Serialize write operations
@@ -1735,13 +1894,17 @@ class Memory(SearchMixin, MemoryCoreMixin):
         into a single text block with deduplication and clean formatting.
         
         Args:
-            include_in_output: If None, memory content is only included when debug logging is enabled.
-                               If True, memory content is always included.
-                               If False, memory content is never included (only logged for debugging).
+            include_in_output: If None, memory content is included (the primary
+                               purpose of this method is to feed retrieved memory
+                               into the prompt). If True, memory content is always
+                               included. If False, memory content is never included
+                               (only logged for debugging).
         """
-        # Determine whether to include memory content in output based on logging level
+        # Include retrieved memory by default. Gating this on DEBUG-level logging
+        # meant Agent(memory=True) silently retrieved hits but never appended them
+        # to the prompt under normal usage.
         if include_in_output is None:
-            include_in_output = get_logger().getEffectiveLevel() == logging.DEBUG
+            include_in_output = True
         
         q = (task_descr + " " + additional).strip()
         lines = []
@@ -1911,6 +2074,30 @@ class Memory(SearchMixin, MemoryCoreMixin):
             
         return result
 
+    def get_context(self, query: Optional[str] = None, **kwargs) -> str:
+        """
+        Return retrieved memory as a prompt-ready context string.
+
+        Satisfies ``AgentMemoryProtocol`` (used by ``Agent.get_memory_context()``)
+        so the non-prompt-caching code path can inject memory into the system
+        prompt. Without this, ``hasattr(memory, 'get_context')`` was False and the
+        fallback branch always returned "".
+        """
+        # A missing/empty query would make the local fallback search match every
+        # record (LIKE "%%") with no relevance ordering, leaking unrelated (and
+        # possibly other users') memories into the prompt. Retrieve nothing until
+        # there is a meaningful query.
+        if not query or not str(query).strip():
+            return ""
+        # Propagate the effective user id so user-scoped memories are included and
+        # cross-user records are not returned by a shared Memory instance.
+        user_id = kwargs.get("user_id") or self.cfg.get("user_id")
+        return self.build_context_for_task(
+            task_descr=query,
+            user_id=user_id,
+            include_in_output=True,
+        )
+
     # -------------------------------------------------------------------------
     #                      Master Reset (Everything)
     # -------------------------------------------------------------------------

src/praisonai-agents/praisonaiagents/obs/__init__.py

@@ -257,6 +257,26 @@ class _LazyObsModule:
 obs = _LazyObsModule()
 
 
+# ``from praisonaiagents import obs`` returns *this submodule*, not the singleton
+# above (Python binds a real submodule as a package attribute, shadowing the
+# package ``__getattr__``). Make the submodule object itself callable and proxy
+# provider lookups to the singleton so ``obs(...)``, ``obs.auto()`` and
+# ``obs.langfuse()`` all work regardless of how ``obs`` is imported.
+import sys as _sys
+from types import ModuleType as _ModuleType
+
+
+class _CallableObsModule(_ModuleType):
+    def __call__(self, provider: str = "auto", **kwargs):
+        return obs(provider, **kwargs)
+
+    def __getattr__(self, name):
+        return getattr(obs, name)
+
+
+_sys.modules[__name__].__class__ = _CallableObsModule
+
+
 __all__ = [
     "ObservabilityAdapter",
     "Span",

src/praisonai-agents/praisonaiagents/process/process.py

@@ -74,6 +74,10 @@ class Process:
                     self._state_lock = asyncio.Lock()
         return self._state_lock
 
+    def _task_by_name(self, name):
+        """Return the task whose name matches, or None. First-match, mirrors prior inline lookup."""
+        return next((t for t in self.tasks.values() if t.name == name), None)
+
     def _create_llm_instance(self):
         """Create and return a configured LLM instance for manager tasks."""
         from ..llm import LLM
@@ -115,7 +119,7 @@ class Process:
             if current_task.previous_tasks:
                 # For validation tasks, typically validate the most recent previous task
                 prev_task_name = current_task.previous_tasks[-1]
-                validated_task = next((t for t in self.tasks.values() if t.name == prev_task_name), None)
+                validated_task = self._task_by_name(prev_task_name)
             elif current_task.context:
                 # If no previous_tasks, check context for the validated task
                 for ctx_task in reversed(current_task.context):
@@ -348,6 +352,43 @@ class Process:
                     task_id=loop_task.id
                 )
 
+    def _build_routing_context(self, current_task: Task) -> Dict[str, Any]:
+        """Build a variable context for evaluating a task's `when` condition.
+
+        Combines the task's structured result (json_dict / pydantic fields) with
+        the raw output so expressions like ``{{score}} > 80`` can resolve, and
+        exposes the raw text as ``previous_output``.
+        """
+        context: Dict[str, Any] = {}
+        result = getattr(current_task, "result", None)
+        if result is not None:
+            try:
+                context.update(result.to_dict())
+            except Exception:
+                pass
+            context["previous_output"] = getattr(result, "raw", "")
+        return context
+
+    def _has_when_routing(self, task: Task) -> bool:
+        """True if the task uses the unified when/then_task/else_task routing."""
+        return task.when is not None or task.then_task is not None or task.else_task is not None
+
+    def _resolve_when_routing(self, current_task: Task) -> Optional[Task]:
+        """Resolve the next Task via the unified `when`/`then_task`/`else_task`
+        routing on the current task. Returns None if no such routing applies."""
+        if current_task.when is None and current_task.then_task is None and current_task.else_task is None:
+            return None
+        next_name = current_task.get_next_task(self._build_routing_context(current_task))
+        if not next_name:
+            return None
+        next_task = next((t for t in self.tasks.values() if t.name == next_name), None)
+        if next_task:
+            next_task.status = "not started"
+            logging.debug(f"Routing to {next_task.name} based on when-condition: {current_task.when}")
+        else:
+            logging.warning(f"when-routing target '{next_name}' not found among tasks")
+        return next_task
+
     def _build_task_context(self, current_task: Task) -> str:
         """Build context for a task based on its retain_full_context setting"""
         # Check if we have validation feedback to include
@@ -377,7 +418,7 @@ class Process:
         if current_task.retain_full_context:
             # Original behavior: include all previous tasks
             for prev_name in current_task.previous_tasks:
-                prev_task = next((t for t in self.tasks.values() if t.name == prev_name), None)
+                prev_task = self._task_by_name(prev_name)
                 if prev_task and prev_task.result:
                     context += f"\n{prev_name}: {prev_task.result.raw}"
                     
@@ -391,7 +432,7 @@ class Process:
             if current_task.previous_tasks:
                 # Get the most recent previous task (last in the list)
                 prev_name = current_task.previous_tasks[-1]
-                prev_task = next((t for t in self.tasks.values() if t.name == prev_name), None)
+                prev_task = self._task_by_name(prev_name)
                 if prev_task and prev_task.result:
                     context += f"\n{prev_name}: {prev_task.result.raw}"
                     
@@ -599,7 +640,7 @@ class Process:
         for task in self.tasks.values():
             if task.next_tasks:
                 for next_task_name in task.next_tasks:
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                    next_task = self._task_by_name(next_task_name)
                     if next_task:
                         next_task.previous_tasks.append(task.name)
                         logging.debug(f"Added {task.name} as previous task for {next_task_name}")
@@ -760,9 +801,9 @@ Subtask: {st.name}
                             decision_str = None
                             if current_task.result:
                                 if current_task.result.pydantic and hasattr(current_task.result.pydantic, "decision"):
-                                    decision_str = current_task.result.pydantic.decision.lower()
+                                    decision_str = current_task.result.pydantic.decision.lower().strip()
                                 elif current_task.result.raw:
-                                    decision_str = current_task.result.raw.lower()
+                                    decision_str = current_task.result.raw.lower().strip()
                             
                             # For loop tasks, use "done" to follow condition path
                             if current_task.task_type == "loop" and all(t.status == "completed" for t in subtasks):
@@ -770,7 +811,7 @@ Subtask: {st.name}
                             
                             target_tasks = current_task.condition.get(decision_str, []) if decision_str else []
                             task_value = target_tasks[0] if isinstance(target_tasks, list) else target_tasks
-                            next_task = next((t for t in self.tasks.values() if t.name == task_value), None)
+                            next_task = self._task_by_name(task_value)
                             if next_task:
                                 next_task.status = "not started"  # Reset status to allow execution
                                 logging.debug(f"Routing to {next_task.name} based on decision: {decision_str}")
@@ -799,7 +840,7 @@ Subtask: {st.name}
                         logging.debug(f"No input file, marking {current_task.name} as completed")
                         if current_task.next_tasks:
                             next_task_name = current_task.next_tasks[0]
-                            next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                            next_task = self._task_by_name(next_task_name)
                             current_task = next_task
                         else:
                             current_task = None
@@ -811,8 +852,8 @@ Subtask: {st.name}
                 yield task_id
                 visited_tasks.add(task_id)
 
-                # Only end workflow if no next_tasks AND no conditions
-                if not current_task.next_tasks and not current_task.condition and not any(
+                # Only end workflow if no next_tasks AND no conditions AND no when-routing
+                if not current_task.next_tasks and not current_task.condition and not self._has_when_routing(current_task) and not any(
                     t.task_type == "loop" and current_task.name.startswith(t.name + "_")
                     for t in self.tasks.values()
                 ):
@@ -853,9 +894,9 @@ Subtask: {st.name}
             if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
                     # Get decision from pydantic or raw response
-                    decision_str = current_task.result.raw.lower()
+                    decision_str = current_task.result.raw.lower().strip()
                     if current_task.result.pydantic and hasattr(current_task.result.pydantic, "decision"):
-                        decision_str = current_task.result.pydantic.decision.lower()
+                        decision_str = current_task.result.pydantic.decision.lower().strip()
 
                     # Check if task has conditions and next_tasks
                     if current_task.condition:
@@ -870,7 +911,7 @@ Subtask: {st.name}
                         else:
                             # Find the target task by name
                             task_value = target_tasks[0] if isinstance(target_tasks, list) else target_tasks
-                            next_task = next((t for t in self.tasks.values() if t.name == task_value), None)
+                            next_task = self._task_by_name(task_value)
                             if next_task:
                                 next_task.status = "not started"  # Reset status to allow execution
                                 
@@ -899,10 +940,26 @@ Subtask: {st.name}
                                 # Don't mark workflow as finished when following condition path
                                 await self._set_workflow_finished(False)
 
+            # Unified condition routing (when/then_task/else_task) takes priority
+            # over next_tasks when configured on the current task.
+            when_routing_exhausted = False
+            if not next_task and current_task:
+                if self._has_when_routing(current_task):
+                    when_task = self._resolve_when_routing(current_task)
+                    if when_task:
+                        next_task = when_task
+                        await self._set_workflow_finished(False)
+                    elif not current_task.next_tasks:
+                        # when-routing is authoritative: it resolved to no target
+                        # (e.g. the taken branch has no then/else_task) and there
+                        # is no next_tasks fallback, so this path ends cleanly
+                        # instead of picking an unrelated not-started task.
+                        when_routing_exhausted = True
+
             # If no condition-based routing, use next_tasks
             if not next_task and current_task and current_task.next_tasks:
                 next_task_name = current_task.next_tasks[0]
-                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                next_task = self._task_by_name(next_task_name)
                 if next_task:
                     # Reset the next task to allow re-execution
                     next_task.status = "not started"
@@ -915,7 +972,7 @@ Subtask: {st.name}
                     logging.debug(f"Following next_tasks to {next_task.name}")
 
             current_task = next_task
-            if not current_task:
+            if not current_task and not when_routing_exhausted:
                 current_task = self._find_next_not_started_task() # General fallback if no next task in workflow
 
 
@@ -1166,7 +1223,7 @@ Provide a JSON with the structure:
         for task in self.tasks.values():
             if task.next_tasks:
                 for next_task_name in task.next_tasks:
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                    next_task = self._task_by_name(next_task_name)
                     if next_task:
                         next_task.previous_tasks.append(task.name)
 
@@ -1351,9 +1408,9 @@ Subtask: {st.name}
                             decision_str = None
                             if current_task.result:
                                 if current_task.result.pydantic and hasattr(current_task.result.pydantic, "decision"):
-                                    decision_str = current_task.result.pydantic.decision.lower()
+                                    decision_str = current_task.result.pydantic.decision.lower().strip()
                                 elif current_task.result.raw:
-                                    decision_str = current_task.result.raw.lower()
+                                    decision_str = current_task.result.raw.lower().strip()
                             
                             # For loop tasks, use "done" to follow condition path
                             if current_task.task_type == "loop" and all(t.status == "completed" for t in subtasks):
@@ -1361,7 +1418,7 @@ Subtask: {st.name}
                             
                             target_tasks = current_task.condition.get(decision_str, []) if decision_str else []
                             task_value = target_tasks[0] if isinstance(target_tasks, list) else target_tasks
-                            next_task = next((t for t in self.tasks.values() if t.name == task_value), None)
+                            next_task = self._task_by_name(task_value)
                             if next_task:
                                 next_task.status = "not started"  # Reset status to allow execution
                                 logging.debug(f"Routing to {next_task.name} based on decision: {decision_str}")
@@ -1390,7 +1447,7 @@ Subtask: {st.name}
                         logging.debug(f"No input file, marking {current_task.name} as completed")
                         if current_task.next_tasks:
                             next_task_name = current_task.next_tasks[0]
-                            next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                            next_task = self._task_by_name(next_task_name)
                             current_task = next_task
                         else:
                             current_task = None
@@ -1402,8 +1459,8 @@ Subtask: {st.name}
                 yield task_id
                 visited_tasks.add(task_id)
 
-                # Only end workflow if no next_tasks AND no conditions
-                if not current_task.next_tasks and not current_task.condition and not any(
+                # Only end workflow if no next_tasks AND no conditions AND no when-routing
+                if not current_task.next_tasks and not current_task.condition and not self._has_when_routing(current_task) and not any(
                     t.task_type == "loop" and current_task.name.startswith(t.name + "_")
                     for t in self.tasks.values()
                 ):
@@ -1442,9 +1499,9 @@ Subtask: {st.name}
             if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
                     # Get decision from pydantic or raw response
-                    decision_str = current_task.result.raw.lower()
+                    decision_str = current_task.result.raw.lower().strip()
                     if current_task.result.pydantic and hasattr(current_task.result.pydantic, "decision"):
-                        decision_str = current_task.result.pydantic.decision.lower()
+                        decision_str = current_task.result.pydantic.decision.lower().strip()
 
                     # Check if task has conditions and next_tasks
                     if current_task.condition:
@@ -1459,7 +1516,7 @@ Subtask: {st.name}
                         else:
                             # Find the target task by name
                             task_value = target_tasks[0] if isinstance(target_tasks, list) else target_tasks
-                            next_task = next((t for t in self.tasks.values() if t.name == task_value), None)
+                            next_task = self._task_by_name(task_value)
                             if next_task:
                                 next_task.status = "not started"  # Reset status to allow execution
                                 
@@ -1488,10 +1545,26 @@ Subtask: {st.name}
                                 # Don't mark workflow as finished when following condition path
                                 self._set_workflow_finished_sync(False)
 
+            # Unified condition routing (when/then_task/else_task) takes priority
+            # over next_tasks when configured on the current task.
+            when_routing_exhausted = False
+            if not next_task and current_task:
+                if self._has_when_routing(current_task):
+                    when_task = self._resolve_when_routing(current_task)
+                    if when_task:
+                        next_task = when_task
+                        self._set_workflow_finished_sync(False)
+                    elif not current_task.next_tasks:
+                        # when-routing is authoritative: it resolved to no target
+                        # (e.g. the taken branch has no then/else_task) and there
+                        # is no next_tasks fallback, so this path ends cleanly
+                        # instead of picking an unrelated not-started task.
+                        when_routing_exhausted = True
+
             # If no condition-based routing, use next_tasks
             if not next_task and current_task and current_task.next_tasks:
                 next_task_name = current_task.next_tasks[0]
-                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                next_task = self._task_by_name(next_task_name)
                 if next_task:
                     # Reset the next task to allow re-execution
                     next_task.status = "not started"
@@ -1504,7 +1577,7 @@ Subtask: {st.name}
                     logging.debug(f"Following next_tasks to {next_task.name}")
 
             current_task = next_task
-            if not current_task:
+            if not current_task and not when_routing_exhausted:
                 current_task = self._find_next_not_started_task() # General fallback if no next task in workflow
 
 

src/praisonai-agents/praisonaiagents/rag/retrieval_config.py

@@ -67,6 +67,7 @@ class RetrievalConfig:
     min_score: float = 0.0
     max_context_tokens: int = 4000
     rerank: bool = False
+    rerank_model: Optional[str] = None
     hybrid: bool = False
     citations: bool = True
     citations_mode: CitationsMode = CitationsMode.APPEND
@@ -88,6 +89,11 @@ class RetrievalConfig:
     exclude_glob: Optional[list] = None  # Glob patterns to exclude
     path_filter: Optional[str] = None  # Regex for path filtering
     
+    # Chunking configuration (applied when documents are ingested)
+    chunking_strategy: str = "recursive"
+    chunk_size: int = 512
+    chunk_overlap: int = 50
+
     # Vector store configuration
     vector_store_provider: str = "chroma"
     vector_store_config: Dict[str, Any] = field(default_factory=dict)
@@ -176,6 +182,7 @@ class RetrievalConfig:
             "min_score": self.min_score,
             "max_context_tokens": self.max_context_tokens,
             "rerank": self.rerank,
+            "rerank_model": self.rerank_model,
             "hybrid": self.hybrid,
             "citations": self.citations,
             "citations_mode": self.citations_mode.value if isinstance(self.citations_mode, CitationsMode) else self.citations_mode,
@@ -188,6 +195,9 @@ class RetrievalConfig:
             "include_glob": self.include_glob,
             "exclude_glob": self.exclude_glob,
             "path_filter": self.path_filter,
+            "chunking_strategy": self.chunking_strategy,
+            "chunk_size": self.chunk_size,
+            "chunk_overlap": self.chunk_overlap,
             "vector_store_provider": self.vector_store_provider,
             "vector_store_config": self.vector_store_config,
             "collection_name": self.collection_name,
@@ -225,6 +235,7 @@ class RetrievalConfig:
             min_score=data.get("min_score", 0.0),
             max_context_tokens=data.get("max_context_tokens", 4000),
             rerank=data.get("rerank", False),
+            rerank_model=data.get("rerank_model"),
             hybrid=data.get("hybrid", False),
             citations=data.get("citations", True),
             citations_mode=citations_mode,
@@ -237,6 +248,9 @@ class RetrievalConfig:
             include_glob=data.get("include_glob"),
             exclude_glob=data.get("exclude_glob"),
             path_filter=data.get("path_filter"),
+            chunking_strategy=data.get("chunking_strategy", "recursive"),
+            chunk_size=data.get("chunk_size", 512),
+            chunk_overlap=data.get("chunk_overlap", 50),
             vector_store_provider=data.get("vector_store_provider", "chroma"),
             vector_store_config=data.get("vector_store_config", {}),
             collection_name=data.get("collection_name"),
@@ -281,7 +295,26 @@ class RetrievalConfig:
         
         if self.rerank:
             config["reranker"] = {"enabled": True, "default_rerank": True}
-        
+            if self.rerank_model:
+                # mem0's RerankerConfig needs a provider to instantiate the
+                # reranker; a config with only {"model": ...} is dropped by
+                # Knowledge._prepare_mem0_config and the request is silently
+                # lost. Derive the provider from a "provider/model" prefix
+                # (e.g. "cohere/rerank-v3" -> provider "cohere") so the model
+                # is actionable; fall back to the model string as provider.
+                model = self.rerank_model
+                provider = model.split("/", 1)[0] if "/" in model else model
+                config["reranker"]["provider"] = provider
+                config["reranker"]["config"] = {"model": model}
+
+        # Chunking must reach Knowledge, which otherwise hardcodes
+        # recursive/512/50 and ignores the user's chunk settings entirely.
+        config["chunker"] = {
+            "type": self.chunking_strategy,
+            "chunk_size": self.chunk_size,
+            "chunk_overlap": self.chunk_overlap,
+        }
+
         return config
     
     def to_rag_config(self) -> Dict[str, Any]:

src/praisonai-agents/praisonaiagents/runtime/journal.py

@@ -78,6 +78,9 @@ KIND_TOOL_RESULT = "tool_result"
 KIND_APPROVAL = "approval"
 #: The loop iteration index, journalled so the cursor is restored exactly.
 KIND_ITERATION = "iteration"
+#: A verification/completion-gate check result: ``{"name", "success",
+#: "returncode", "command"/"path", "duration_seconds", "output_hash"}``.
+KIND_VERIFICATION = "verification"
 
 VALID_KINDS = frozenset(
     {
@@ -86,6 +89,7 @@ VALID_KINDS = frozenset(
         KIND_TOOL_RESULT,
         KIND_APPROVAL,
         KIND_ITERATION,
+        KIND_VERIFICATION,
     }
 )
 

src/praisonai-agents/praisonaiagents/sandbox/config.py

@@ -106,7 +106,11 @@ class SandboxConfig:
     """
     
     sandbox_type: str = "subprocess"
-    image: str = "python:3.11-slim"
+    # Kept in step with the compute-side DockerCompute default. The two
+    # implementations of "docker" disagreed -- 3.11 here, 3.12 there -- so
+    # tools_run_on="docker" and run_in="docker" handed you different Pythons
+    # for the same word. Nobody chose that; they drifted apart.
+    image: str = "python:3.12-slim"
     working_dir: str = "/workspace"
     env: Dict[str, str] = field(default_factory=dict)
     resource_limits: ResourceLimits = field(default_factory=ResourceLimits)
@@ -133,7 +137,7 @@ class SandboxConfig:
         }
     
     @classmethod
-    def docker(cls, image: str = "python:3.11-slim") -> "SandboxConfig":
+    def docker(cls, image: str = "python:3.12-slim") -> "SandboxConfig":
         """Create a Docker sandbox configuration."""
         return cls(
             sandbox_type="docker",

src/praisonai-agents/praisonaiagents/sandbox/manager.py

@@ -29,7 +29,7 @@ class SandboxManager:
     Example:
         from praisonaiagents.sandbox import SandboxManager, SandboxConfig
 
-        config = SandboxConfig.docker("python:3.11-slim")
+        config = SandboxConfig.docker("python:3.12-slim")
         manager = SandboxManager(config)
         result = await manager.run_code("print('Hello, World!')")
     """
@@ -79,10 +79,43 @@ class SandboxManager:
                 f"Unknown sandbox type: {sandbox_type!r}. {e}"
             ) from e
 
-        kwargs: Dict[str, Any] = {"config": self.config}
-        if sandbox_type == "docker":
+        # Not every backend takes a SandboxConfig: SSHSandbox and ModalSandbox
+        # carry their own connection details instead, so passing config= to them
+        # raised "unexpected keyword argument 'config'" before the sandbox was
+        # ever built. Ask the constructor what it accepts.
+        import inspect
+
+        params = inspect.signature(sandbox_cls.__init__).parameters
+        takes = lambda name: name in params or any(
+            p.kind is p.VAR_KEYWORD for p in params.values()
+        )
+
+        kwargs: Dict[str, Any] = {}
+        if takes("config"):
+            kwargs["config"] = self.config
+        if sandbox_type == "docker" and takes("image"):
             kwargs["image"] = self.config.image
 
+        # A required argument we cannot supply from a config object -- SSH needs
+        # a host. Say so instead of raising TypeError from deep inside.
+        missing = [
+            name for name, prm in params.items()
+            if name != "self"
+            and prm.default is prm.empty
+            and prm.kind not in (prm.VAR_POSITIONAL, prm.VAR_KEYWORD)
+            and name not in kwargs
+        ]
+        if missing:
+            raise TypeError(
+                f"The {sandbox_type!r} sandbox needs {', '.join(missing)}, which a "
+                f"SandboxConfig cannot carry. Build it directly and pass the "
+                f"object:\n"
+                f"    from praisonai_sandbox.{sandbox_type} import "
+                f"{sandbox_cls.__name__}\n"
+                f"    sandbox = {sandbox_cls.__name__}("
+                f"{'=..., '.join(missing)}=...)"
+            )
+
         sandbox = sandbox_cls(**kwargs)
 
         is_available = getattr(sandbox, "is_available", True)

src/praisonai-agents/praisonaiagents/sandbox/protocols.py

@@ -206,7 +206,7 @@ class SandboxProtocol(Protocol):
 
         from praisonai_sandbox import DockerSandbox
         
-        sandbox = DockerSandbox(image="python:3.11-slim")
+        sandbox = DockerSandbox(image="python:3.12-slim")
         result = await sandbox.execute("print('Hello, World!')")
         print(result.stdout)
     """

src/praisonai-agents/praisonaiagents/scheduler/config_store.py

@@ -7,6 +7,7 @@ other config.yaml content.
 """
 
 import contextlib
+import copy
 from praisonaiagents._logging import get_logger
 import os
 import threading
@@ -21,6 +22,11 @@ logger = get_logger(__name__)
 _HISTORY_FILE = "run_history.yaml"
 _LOCK_FILE = "config.schedules.lock"
 _MAX_HISTORY = 200
+# Hard cap on a single job's persisted per-run scratchpad. A watermark /
+# cursor / last-seen hash is tiny; this only exists so a runaway ``set_state``
+# cannot grow config.yaml unbounded. Oversized writes are rejected (prior
+# state kept) rather than silently truncated.
+_MAX_STATE_BYTES = 16 * 1024
 
 class ConfigYamlScheduleStore:
     """CRUD store backed by ``config.yaml``.
@@ -41,6 +47,9 @@ class ConfigYamlScheduleStore:
         self._max_history = max_history
         self._lock = threading.RLock()
         self._jobs: Dict[str, ScheduleJob] = {}
+        # Per-job durable scratchpad (cursors / watermarks / last-seen hash),
+        # persisted under the ``job_state`` key alongside ``schedules``.
+        self._job_state: Dict[str, Dict] = {}
         self._history: List[RunRecord] = []
         # In-memory record of leases we currently hold, so ``complete`` can
         # release them. Cross-process leases live in the persisted job
@@ -126,6 +135,8 @@ class ConfigYamlScheduleStore:
             self._reload_locked()
             if job_id in self._jobs:
                 del self._jobs[job_id]
+                # Drop any per-job scratchpad so a deleted job leaves no state.
+                self._job_state.pop(job_id, None)
                 self._save()
                 return True
             return False
@@ -145,6 +156,7 @@ class ConfigYamlScheduleStore:
                     if principal is not None and job.principal != principal:
                         continue
                     del self._jobs[jid]
+                    self._job_state.pop(jid, None)
                     self._save()
                     return True
             return False
@@ -199,6 +211,7 @@ class ConfigYamlScheduleStore:
                 if job.delete_after_run:
                     # One-shot: remove now so no competitor re-claims it.
                     del self._jobs[job.id]
+                    self._job_state.pop(job.id, None)
             if changed:
                 if not self._save():
                     # The lease/last_run_at advance was NOT persisted (full
@@ -283,6 +296,7 @@ class ConfigYamlScheduleStore:
         a job already claimed/leased by another process is observed.
         """
         self._jobs = {}
+        self._job_state = {}
         self._load()
 
     # ── execution history ─────────────────────────────────────────────
@@ -327,6 +341,59 @@ class ConfigYamlScheduleStore:
             records = [r for r in records if r.job_id == job_id]
         return records[:limit]
 
+    # ── per-job state (JobStateStoreProtocol) ─────────────────────────
+
+    def get_state(self, job_id: str) -> Dict:
+        """Return the persisted scratchpad for ``job_id`` (``{}`` if none).
+
+        A deep copy is returned so a caller mutating the result — including a
+        nested list or dict — cannot silently alter the in-memory store without
+        going through :meth:`set_state` (which enforces the size cap).
+        """
+        with self._lock:
+            state = self._job_state.get(job_id)
+            return copy.deepcopy(state) if isinstance(state, dict) else {}
+
+    def set_state(self, job_id: str, state: Dict) -> None:
+        """Persist ``state`` for ``job_id`` under the ``job_state`` key.
+
+        Size-capped: a serialised state larger than ``_MAX_STATE_BYTES`` is
+        rejected (prior state kept) so a runaway write cannot grow config.yaml
+        unbounded. An empty/falsy ``state`` clears the entry.
+        """
+        if not state:
+            self.clear_state(job_id)
+            return
+        if not isinstance(state, dict):
+            logger.warning(
+                "Ignoring non-dict state for job '%s' (%s)", job_id, type(state).__name__,
+            )
+            return
+        try:
+            import yaml
+            size = len(yaml.safe_dump(state).encode("utf-8"))
+        except Exception as e:  # pragma: no cover - defensive
+            logger.warning("Could not size state for job '%s': %s", job_id, e)
+            return
+        if size > _MAX_STATE_BYTES:
+            logger.warning(
+                "Rejecting oversized state for job '%s' (%d > %d bytes); keeping prior",
+                job_id, size, _MAX_STATE_BYTES,
+            )
+            return
+        with self._lock, self._file_lock():
+            self._reload_locked()
+            self._job_state[job_id] = copy.deepcopy(state)
+            self._save()
+
+    def clear_state(self, job_id: str) -> None:
+        """Drop any persisted scratchpad for ``job_id`` (no-op if absent)."""
+        with self._lock, self._file_lock():
+            self._reload_locked()
+            if job_id in self._job_state:
+                del self._job_state[job_id]
+                self._save()
+
     # ── persistence ──────────────────────────────────────────────────
 
     def _load(self) -> None:
@@ -353,6 +420,11 @@ class ConfigYamlScheduleStore:
                         job_data.setdefault("id", job_id)
                         job = ScheduleJob.from_dict(job_data)
                         self._jobs[job.id] = job
+            job_state = data.get("job_state", {})
+            if isinstance(job_state, dict):
+                for job_id, state in job_state.items():
+                    if isinstance(state, dict):
+                        self._job_state[str(job_id)] = state
         except ValueError:
             raise
         except Exception as e:
@@ -381,6 +453,15 @@ class ConfigYamlScheduleStore:
                 schedules[job.id] = job.to_dict()
             data["schedules"] = schedules
 
+            # Persist per-job scratchpad alongside; drop the key entirely when
+            # empty so config.yaml stays clean for the stateless common case.
+            if self._job_state:
+                data["job_state"] = {
+                    jid: state for jid, state in self._job_state.items()
+                }
+            else:
+                data.pop("job_state", None)
+
             # Atomic write
             os.makedirs(os.path.dirname(self._path), exist_ok=True)
             tmp = self._path + ".tmp"

src/praisonai-agents/praisonaiagents/scheduler/models.py

@@ -298,6 +298,21 @@ class ScheduleJob:
                  killed (with its process group on POSIX) and the tick is
                  recorded as ``failed``. Bounds the action so a hung command
                  cannot stall the ticker. Defaults to 60s.
+        backend: Optional external coding-CLI backend id (e.g.
+                 ``"claude-code"``, ``"codex-cli"``). When set, the job's
+                 ``message`` is executed as one headless turn through the
+                 named backend from the backend registry — no native agent is
+                 resolved and no in-process model turn is taken. Like
+                 ``command``, this is a trusted operator-configured action:
+                 it spawns a host CLI subprocess and is deliberately not
+                 exposed on the LLM-callable scheduling tools. Additive and
+                 backward-compatible: jobs without a ``backend`` are
+                 unchanged.
+        backend_options: Optional mapping of overrides for the backend run.
+                 Recognised keys are validated by the executor/backend (e.g.
+                 ``cwd`` for the working directory, ``timeout_ms`` for the
+                 subprocess bound); unknown config overrides are rejected at
+                 resolution time rather than silently ignored.
         provider: Optional model *provider* snapshotted when the job was
                  created (e.g. ``"openai"``). Advisory metadata paired with
                  ``model`` so a drift check can report both. ``None`` means no
@@ -376,6 +391,8 @@ class ScheduleJob:
     context_from: Optional[List[str]] = None
     context_max_chars: int = 4000
     on_missing_context: Literal["run", "skip"] = "run"
+    backend: Optional[str] = None
+    backend_options: Dict[str, Any] = field(default_factory=dict)
 
     # ── serialisation ────────────────────────────────────────────────
 
@@ -432,6 +449,13 @@ class ScheduleJob:
                 d["context_max_chars"] = self.context_max_chars
             if self.on_missing_context != "run":
                 d["on_missing_context"] = self.on_missing_context
+        # External CLI backend action. Only persist when configured so agent
+        # and command jobs stay byte-for-byte unchanged; options are opaque to
+        # the core (the executor validates them against the backend registry).
+        if self.backend is not None:
+            d["backend"] = self.backend
+            if self.backend_options:
+                d["backend_options"] = dict(self.backend_options)
         # Atomic-claim lease metadata (set dynamically by stores that support
         # ``claim_due``). Persisted so a lease is visible across processes and
         # survives a restart; omitted when no lease is held.
@@ -480,6 +504,12 @@ class ScheduleJob:
             context_from=context_from,
             context_max_chars=d.get("context_max_chars", 4000),
             on_missing_context=d.get("on_missing_context", "run"),
+            backend=d.get("backend"),
+            backend_options=(
+                dict(d["backend_options"])
+                if isinstance(d.get("backend_options"), dict)
+                else {}
+            ),
         )
         # Restore atomic-claim lease metadata if present (see ``to_dict``).
         job._lease_until = d.get("lease_until", 0.0) or 0.0

src/praisonai-agents/praisonaiagents/session/README.md

@@ -5,26 +5,35 @@ This module provides automatic session persistence with zero configuration.
 ## Quick Start
 
 ```python
-from praisonaiagents import Agent
+from praisonaiagents import Agent, MemoryConfig
 
 # With session persistence (auto-enabled)
 agent = Agent(
     name="Assistant",
-    session_id="my-session-123"
+    memory=MemoryConfig(session_id="my-session-123"),
 )
 agent.start("Hello, my name is Alice")
 
 # Later, in a new process - history is restored automatically
 agent = Agent(
-    name="Assistant", 
-    session_id="my-session-123"
+    name="Assistant",
+    memory=MemoryConfig(session_id="my-session-123"),
 )
 agent.start("What is my name?")  # Agent remembers: "Alice"
 ```
 
+The `session_id` is part of the Agent's memory configuration. `Agent` itself has
+no top-level `session_id` parameter — passing one raises
+`TypeError: Agent.__init__() got unexpected keyword argument(s): session_id`.
+A plain dict is accepted as shorthand for `MemoryConfig`:
+
+```python
+agent = Agent(name="Assistant", memory={"session_id": "my-session-123"})
+```
+
 ## How It Works
 
-When you provide a `session_id` to an Agent:
+When you configure a `session_id` on an Agent:
 
 1. **Automatic Persistence**: Conversation history is automatically saved to disk
 2. **Automatic Restoration**: When a new Agent is created with the same `session_id`, history is restored
@@ -74,8 +83,8 @@ Fields are persisted in session JSON `metadata` and mirrored at root on save. Po
 
 | Scenario | Behavior |
 |----------|----------|
-| `session_id` provided, no DB | JSON persistence (auto) |
-| `session_id` provided, with DB | DB adapter used |
+| `memory=MemoryConfig(session_id=...)`, no DB | JSON persistence (auto) |
+| `memory=MemoryConfig(session_id=..., db=...)` | DB adapter used |
 | No `session_id`, same Agent instance | In-memory only |
 | No `session_id`, new Agent instance | No history |
 
@@ -105,6 +114,7 @@ store.delete_session("session-123")
 
 ### Custom Session Directory
 
+<!-- praisonai: skip=true -->
 ```python
 from praisonaiagents.session import DefaultSessionStore
 
@@ -120,13 +130,15 @@ store = DefaultSessionStore(
 When a DB adapter is provided, it takes precedence over JSON persistence:
 
 ```python
-from praisonaiagents import Agent
-from praisonai.db import PostgresAdapter
+from praisonaiagents import Agent, MemoryConfig
+from praisonaiagents.db import db
 
 agent = Agent(
     name="Assistant",
-    session_id="my-session",
-    db=PostgresAdapter(connection_string="...")
+    memory=MemoryConfig(
+        session_id="my-session",
+        db=db(database_url="postgresql://localhost/mydb"),
+    ),
 )
 ```
 
@@ -142,17 +154,27 @@ Multiple processes can safely read/write to the same session.
 
 ## Context Caching
 
-For cost optimization, use `prompt_caching=True` with Anthropic models:
+Prompt caching is opt-in via `caching=CachingConfig(prompt_caching=True)` — there
+is no top-level `prompt_caching` parameter on `Agent`. When enabled, `Agent`
+checks `praisonaiagents.llm.model_capabilities.supports_prompt_caching(model)`
+and, for providers that need explicit breakpoints (Anthropic), injects
+`cache_control` markers itself. OpenAI/Gemini use automatic prefix caching, so no
+markers are emitted:
 
 ```python
+from praisonaiagents import Agent, MemoryConfig, CachingConfig
+
 agent = Agent(
     name="Assistant",
-    session_id="my-session",
-    prompt_caching=True,  # Enables Anthropic prompt caching
+    llm="anthropic/claude-sonnet-4-5",
+    memory=MemoryConfig(session_id="my-session"),
+    caching=CachingConfig(prompt_caching=True),
 )
 ```
 
 This caches the system prompt, reducing token costs for repeated conversations.
+Without `prompt_caching=True`, no `cache_control` breakpoints are injected on the
+Anthropic path.
 
 ## API Reference
 
@@ -162,7 +184,7 @@ This caches the system prompt, reducing token costs for repeated conversations.
 class DefaultSessionStore:
     def __init__(
         self,
-        session_dir: Optional[str] = None,  # Default: ~/.praison/sessions/
+        session_dir: Optional[str] = None,  # Default: ~/.praisonai/sessions/
         max_messages: int = 100,
         lock_timeout: float = 5.0,
     ): ...

src/praisonai-agents/praisonaiagents/session/__init__.py

@@ -2,26 +2,32 @@
 Session persistence module for PraisonAI Agents.
 
 Provides automatic session persistence with zero configuration.
-When a session_id is provided to an Agent, conversation history
+When a session_id is configured on an Agent, conversation history
 is automatically persisted to disk and restored on subsequent runs.
 
+The session_id is set through the Agent's memory configuration; Agent itself
+has no top-level session_id parameter.
+
 Usage:
-    from praisonaiagents import Agent
-    
+    from praisonaiagents import Agent, MemoryConfig
+
     # With session persistence (auto-enabled)
     agent = Agent(
         name="Assistant",
-        session_id="my-session-123"
+        memory=MemoryConfig(session_id="my-session-123"),
     )
     agent.start("Hello")
-    
+
     # Later, new process - history is restored
     agent = Agent(
-        name="Assistant", 
-        session_id="my-session-123"
+        name="Assistant",
+        memory=MemoryConfig(session_id="my-session-123"),
     )
     agent.start("What did I say before?")  # Remembers!
 
+    # The plain-dict spelling is equivalent:
+    #   Agent(name="Assistant", memory={"session_id": "my-session-123"})
+
 Default storage: ~/.praisonai/sessions/{session_id}.json
 """
 
@@ -98,6 +104,16 @@ def __getattr__(name: str):
         _module_cache[name] = SessionMirrorProtocol
         return SessionMirrorProtocol
 
+    if name == "PortableSessionStoreProtocol":
+        from .protocols import PortableSessionStoreProtocol
+        _module_cache[name] = PortableSessionStoreProtocol
+        return PortableSessionStoreProtocol
+
+    if name == "ImportReport":
+        from .protocols import ImportReport
+        _module_cache[name] = ImportReport
+        return ImportReport
+
     if name == "SessionHit":
         from .protocols import SessionHit
         _module_cache[name] = SessionHit
@@ -170,6 +186,12 @@ def __getattr__(name: str):
         _module_cache[name] = workspace_id
         return workspace_id
 
+    # Cross-context continuation prompt assembler (session handoff)
+    if name == "build_handoff_prompt":
+        from .handoff import build_handoff_prompt
+        _module_cache[name] = build_handoff_prompt
+        return build_handoff_prompt
+
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
@@ -185,6 +207,8 @@ __all__ = [
     "SessionStoreProtocol",
     "SearchableSessionStoreProtocol",
     "SessionMirrorProtocol",
+    "PortableSessionStoreProtocol",
+    "ImportReport",
     "SessionHit",
     "SessionSummary",
     "HierarchicalSessionStore",
@@ -201,4 +225,5 @@ __all__ = [
     "get_session_context",
     "clear_session_context",
     "workspace_id",
+    "build_handoff_prompt",
 ]

src/praisonai-agents/praisonaiagents/session/api.py

@@ -17,6 +17,16 @@ from typing import Any, Dict, List, Optional, TYPE_CHECKING
 
 logger = logging.getLogger(__name__)
 
+# Sub-agent transcripts live *inside* the parent session's own record, under
+# ``SessionData.metadata[AGENT_HISTORY_KEY][agent_key]``.
+AGENT_HISTORY_KEY = "agent_histories"
+
+# Opt-in restore of sub-agent transcripts written by older versions as separate
+# top-level sessions named ``"{parent}_{name}:{role}"`` *without* parentage
+# tags. Such an id is indistinguishable from a real user session of the same
+# sanitised name, so it is not restored unless this is explicitly set.
+LEGACY_AGENT_KEYS_ENV = "PRAISONAI_SESSION_LEGACY_AGENT_KEYS"
+
 if TYPE_CHECKING:
     from ..memory.memory import Memory
     from ..knowledge.knowledge import Knowledge
@@ -116,6 +126,7 @@ class Session:
             self._knowledge = None
             self._agents_instance = None
             self._agents = {}
+            self._legacy_histories_cache = None
         else:
             self.memory_config = {}
             self.knowledge_config = {}
@@ -123,6 +134,7 @@ class Session:
             self._knowledge = None
             self._agents_instance = None
             self._agents = {}
+            self._legacy_histories_cache = None
 
     def _get_session_dir(self):
         """Get session-specific directory using centralized paths."""
@@ -274,6 +286,55 @@ class Session:
 
         return {}
 
+    def _get_session_store(self):
+        """Return the default session store, or ``None`` if unavailable."""
+        try:
+            from . import get_default_session_store
+            return get_default_session_store()
+        except ImportError:
+            return None
+
+    @staticmethod
+    def _legacy_agent_keys_enabled() -> bool:
+        """Whether to restore *untagged* legacy sub-agent records.
+
+        Records written before the ``parent_session_id``/``agent_key`` tags
+        existed carry no parentage, so ``"{parent}_{agent_key}"`` cannot be told
+        apart from a real user session of the same sanitised name. Restoring one
+        can silently pull an unrelated transcript into an agent, so it is
+        opt-in.
+        """
+        return os.environ.get(LEGACY_AGENT_KEYS_ENV, "").strip().lower() in (
+            "1", "true", "yes", "on",
+        )
+
+    def _stored_agent_histories(self, session_store) -> Dict[str, List[Dict[str, Any]]]:
+        """Sub-agent transcripts held inside this session's own record."""
+        if session_store is None or not hasattr(session_store, "get_session"):
+            return {}
+        try:
+            data = session_store.get_session(self.session_id)
+        except Exception as exc:
+            logger.debug(f"Cannot read agent histories for {self.session_id}: {exc}")
+            return {}
+        histories = (getattr(data, "metadata", None) or {}).get(AGENT_HISTORY_KEY)
+        if not isinstance(histories, dict):
+            return {}
+        return {k: v for k, v in histories.items() if isinstance(v, list) and v}
+
+    def _merged_agent_histories(self, session_store) -> Dict[str, List[Dict[str, Any]]]:
+        """All known sub-agent transcripts: legacy records under current ones.
+
+        Legacy per-agent records are read *underneath* the parent's current
+        ``metadata[AGENT_HISTORY_KEY]`` so a partially-migrated parent (one
+        agent already in the current format) still surfaces a *different*
+        agent that only exists in a tagged legacy record. Current-format
+        entries always win on key collisions.
+        """
+        merged = dict(self._legacy_agent_histories(session_store))
+        merged.update(self._stored_agent_histories(session_store))
+        return merged
+
     def _restore_agent_chat_history(self, agent_key: str) -> List[Dict[str, Any]]:
         """
         Restore agent chat history from memory.
@@ -287,19 +348,13 @@ class Session:
         if self.is_remote:
             return []
 
-        # G-2 FIX: mirror the save path priority — read SessionStore first so
-        # history persisted by _save_agent_chat_histories() is actually found.
-        session_store = None
-        try:
-            from . import get_default_session_store
-            session_store = get_default_session_store()
-        except ImportError:
-            pass
+        session_store = self._get_session_store()
 
-        if session_store is not None and hasattr(session_store, "get_chat_history"):
-            messages = session_store.get_chat_history(f"{self.session_id}_{agent_key}")
-            if messages:
-                return messages
+        # Current format first, then any (tagged / opted-in) legacy per-agent
+        # record — even when the parent is only partially migrated.
+        merged = self._merged_agent_histories(session_store)
+        if agent_key in merged:
+            return merged[agent_key]
 
         results = self.memory.search_short_term(
             query="Agent chat history for",
@@ -316,54 +371,101 @@ class Session:
         
         return []
 
+    def _legacy_agent_histories(self, session_store) -> Dict[str, List[Dict[str, Any]]]:
+        """Sub-agent transcripts older versions wrote as top-level sessions.
+
+        Never deletes them: an untagged candidate may in fact be a real user
+        session that merely sanitises to the same name.
+        """
+        # Legacy per-agent records are a frozen migration artefact: nothing
+        # writes them after the upgrade and neither save nor restore mutates
+        # them. Memoise the full-disk scan per Session so a save pass over N
+        # agents doesn't re-enumerate every stored session N times (and once
+        # migrated, the common case, the scan collapses to a single hit).
+        # Key the cache on the opt-in flag: the untagged-restore env toggle
+        # changes what the scan yields, so each mode caches independently.
+        allow_untagged = self._legacy_agent_keys_enabled()
+        cached = getattr(self, "_legacy_histories_cache", None)
+        if cached is not None and cached[0] == allow_untagged:
+            return cached[1]
+
+        histories: Dict[str, List[Dict[str, Any]]] = {}
+        list_sessions = getattr(session_store, "list_sessions", None)
+        if session_store is None or not callable(list_sessions):
+            self._legacy_histories_cache = (allow_untagged, histories)
+            return histories
+
+        prefix = f"{self.session_id}_"
+        # Migration must consider every stored session, not the 50 most recent:
+        # a legacy per-agent record is by definition older than anything written
+        # after the upgrade, so list_sessions()'s default window silently
+        # excludes it (regression from #4126).
+        try:
+            try:
+                entries = list_sessions(limit=None) or []
+            except TypeError:  # stores whose limit is not optional
+                entries = list_sessions(limit=10**9) or []
+        except Exception:
+            return histories
+
+        for entry in entries:
+            if isinstance(entry, dict):
+                stored_id = entry.get("session_id")
+                # Prefer the explicit parent/agent_key tags written on save;
+                # this disambiguates overlapping composite ids (e.g.
+                # "chat"+"support_agent" vs "chat_support"+"agent").
+                tagged_parent = entry.get("parent_session_id")
+                tagged_key = entry.get("agent_key")
+            else:
+                stored_id, tagged_parent, tagged_key = entry, None, None
+            if not isinstance(stored_id, str):
+                continue
+
+            if tagged_parent == self.session_id and tagged_key:
+                agent_key = tagged_key
+            elif (
+                tagged_parent is None
+                and tagged_key is None
+                and stored_id.startswith(prefix)
+                and len(stored_id) > len(prefix)
+            ):
+                # Saved before the tags existed: the id alone is ambiguous.
+                agent_key = stored_id[len(prefix):]
+                logger.warning(
+                    "Session %r: untagged session %r %s as sub-agent %r history "
+                    "(ambiguous id; %s).",
+                    self.session_id, stored_id,
+                    "restored" if allow_untagged else "ignored",
+                    agent_key,
+                    f"{LEGACY_AGENT_KEYS_ENV}=1" if allow_untagged
+                    else f"set {LEGACY_AGENT_KEYS_ENV}=1 to restore it",
+                )
+                if not allow_untagged:
+                    continue
+            else:
+                continue
+
+            try:
+                messages = session_store.get_chat_history(stored_id)
+            except Exception:
+                continue
+            if agent_key and messages:
+                histories[agent_key] = messages
+
+        self._legacy_histories_cache = (allow_untagged, histories)
+        return histories
+
     def _restore_agent_chat_histories(self) -> None:
-        """Restore all agent chat histories from memory."""
+        """Restore all agent chat histories."""
         if self.is_remote:
             return
 
-        # G-2 FIX: read SessionStore first (where save_state now writes), then
-        # fall back to Memory for backward compatibility.
-        session_store = None
-        try:
-            from . import get_default_session_store
-            session_store = get_default_session_store()
-        except ImportError:
-            pass
+        session_store = self._get_session_store()
 
-        if session_store is not None and hasattr(session_store, "get_chat_history"):
-            prefix = f"{self.session_id}_"
-            list_sessions = getattr(session_store, "list_sessions", None)
-            if callable(list_sessions):
-                for entry in list_sessions():
-                    if isinstance(entry, dict):
-                        stored_id = entry.get("session_id")
-                        # Prefer the explicit parent/agent_key tags written on
-                        # save; this disambiguates overlapping composite ids
-                        # (e.g. "chat"+"support_agent" vs "chat_support"+"agent").
-                        tagged_parent = entry.get("parent_session_id")
-                        tagged_key = entry.get("agent_key")
-                        if tagged_parent == self.session_id and tagged_key:
-                            agent_key = tagged_key
-                        elif (
-                            tagged_parent is None
-                            and isinstance(stored_id, str)
-                            and stored_id.startswith(prefix)
-                        ):
-                            # Legacy sessions saved before the tag existed.
-                            agent_key = stored_id[len(prefix):]
-                        else:
-                            continue
-                    else:
-                        stored_id = entry
-                        if not (isinstance(stored_id, str) and stored_id.startswith(prefix)):
-                            continue
-                        agent_key = stored_id[len(prefix):]
-                    messages = session_store.get_chat_history(stored_id)
-                    if agent_key and messages:
-                        self._agents[agent_key] = {
-                            "agent": None,
-                            "chat_history": messages
-                        }
+        # Current-format records win; legacy (tagged / opted-in) ones fill the
+        # gaps so a partially-migrated parent still recovers every agent.
+        for agent_key, messages in self._merged_agent_histories(session_store).items():
+            self._agents[agent_key] = {"agent": None, "chat_history": messages}
 
         results = self.memory.search_short_term(
             query="Agent chat history for",
@@ -383,12 +485,50 @@ class Session:
                         "chat_history": chat_history
                     }
 
+    def _store_agent_history(
+        self, session_store, agent_key: str, messages: List[Dict[str, Any]]
+    ) -> bool:
+        """Persist one sub-agent transcript onto this session's own record.
+
+        The store replaces the whole ``AGENT_HISTORY_KEY`` map per write, so a
+        naive read-then-replace would let a concurrent writer (another agent
+        under the same parent) clobber this agent's entry, or vice versa. We
+        re-read the freshest map immediately before each write and confirm no
+        *other* key changed underneath us, retrying a bounded number of times.
+        Legacy per-agent records are always merged in (they stay on disk).
+        """
+        update_meta = getattr(session_store, "update_session_metadata", None)
+        if not callable(update_meta):
+            return False
+        legacy = self._legacy_agent_histories(session_store)
+        for _ in range(5):
+            current = self._stored_agent_histories(session_store)
+            merged = dict(legacy)
+            merged.update(current)
+            merged[agent_key] = messages
+            if not update_meta(self.session_id, **{AGENT_HISTORY_KEY: merged}):
+                return False
+            # Verify a concurrent writer did not drop another agent between our
+            # read and write; if the persisted map matches what we wrote, we win.
+            persisted = self._stored_agent_histories(session_store)
+            if persisted.get(agent_key) == messages and all(
+                persisted.get(k) == v for k, v in current.items()
+            ):
+                return True
+        logger.warning(
+            "Session %r: could not persist sub-agent history for %r after %d "
+            "attempts; the write was not verified.",
+            self.session_id, agent_key, 5,
+        )
+        return False
+
     def _save_agent_chat_histories(self) -> None:
         """Save all agent chat histories.
-        
-        G-2 FIX: Routes to SessionStore instead of Memory.store_short_term() to
-        maintain clean separation between conversation history (SessionStore)
-        and semantic memory (Memory). Falls back to Memory for backward compat.
+
+        Sub-agent transcripts go into the parent session's own record, under
+        ``metadata[AGENT_HISTORY_KEY][agent_key]``. Nothing is written into the
+        global session-id namespace, so a sub-agent can no longer overwrite an
+        unrelated user session. Falls back to Memory for backward compat.
         """
         if self.is_remote:
             return
@@ -404,17 +544,10 @@ class Session:
                 chat_history = agent_data.get("chat_history")
             
             if chat_history is not None:
-                # G-2 FIX: Try SessionStore first for clean separation
-                session_store = None
-                try:
-                    from . import get_default_session_store
-                    session_store = get_default_session_store()
-                except ImportError:
-                    pass
+                session_store = self._get_session_store()
                 
                 if session_store is not None:
                     # Replace atomically to avoid duplicate appends on repeated save_state()
-                    session_id = f"{self.session_id}_{agent_key}"
                     messages = [
                         {
                             "role": msg.get("role", "user"),
@@ -424,32 +557,18 @@ class Session:
                         if isinstance(msg, dict)
                     ]
                     if not messages:
-                        logging.debug(f"No chat history to persist for session {session_id}")
-                    elif hasattr(session_store, "set_chat_history"):
-                        session_store.set_chat_history(session_id, messages)
-                        # Tag the child session so bulk restore can resolve the
-                        # exact agent_key without ambiguous composite-key prefix
-                        # parsing (e.g. "chat" + "support_agent" vs "chat_support"
-                        # + "agent" both collapse to "chat_support_agent").
-                        update_meta = getattr(session_store, "update_session_metadata", None)
-                        if callable(update_meta):
-                            update_meta(
-                                session_id,
-                                parent_session_id=self.session_id,
-                                agent_key=agent_key,
-                            )
-                    else:
-                        # Fallback to add_message - may create duplicates on repeated calls
+                        logging.debug(
+                            f"No chat history to persist for agent {agent_key} "
+                            f"in session {self.session_id}"
+                        )
+                    elif not self._store_agent_history(
+                        session_store, agent_key, messages
+                    ):
                         logging.warning(
-                            f"Session store lacks 'set_chat_history' method. Using fallback "
-                            f"'add_message' which may create duplicates on repeated save_state() calls."
+                            f"Session store {type(session_store).__name__} cannot "
+                            f"persist sub-agent history; chat history for "
+                            f"{agent_key} in session {self.session_id} was lost."
                         )
-                        for msg in messages:
-                            session_store.add_message(
-                                session_id,
-                                role=msg["role"],
-                                content=msg["content"],
-                            )
                 else:
                     # Fallback to Memory.store_short_term() for backward compatibility
                     history_text = f"Agent chat history for {agent_key}"