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README.md

@@ -1,37 +1,75 @@
-# aiXplain Agents SDK
-
-**Build and deploy autonomous AI agents on production-grade infrastructure, instantly.**
-
----
-
-## aiXplain agents
-
-aiXplain Agents SDK gives developers Python and REST APIs to build, run, and deploy autonomous multi-step agents on [AgenticOS](https://docs.aixplain.com/getting-started/agenticos). Agents include built-in memory for short- and long-term context (opt-in), and adapt at runtime by planning steps, selecting tools and models, running code, and refining outputs until tasks are complete.
-
-aiXplain agents include micro-agents for runtime policy enforcement and access control, plus proprietary meta-agents like Evolver for self-improvement.
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="docs/assets/aixplain-logo-light.png">
+    <source media="(prefers-color-scheme: light)" srcset="docs/assets/aixplain-logo-dark.png">
+    <img src="docs/assets/aixplain-logo-dark.png" alt="aiXplain" width="520">
+  </picture>
+</p>
+
+<h1 align="center">aiXplain SDK</h1>
+
+<p align="center">
+  <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-2ea44f?style=flat-square" alt="License"></a>
+  <a href="https://studio.aixplain.com/browse"><img src="https://img.shields.io/badge/Marketplace-900%2B%20models%20%26%20tools-0b74de?style=flat-square" alt="Marketplace size"></a>
+  <a href="https://console.aixplain.com/settings/keys"><img src="https://img.shields.io/badge/%F0%9F%94%91%20PAYG%20API%20key-Console-0b74de?style=flat-square" alt="PAYG API key"></a>
+  <a href="https://discord.gg/aixplain"><img src="https://img.shields.io/badge/Discord-Join-5865F2?style=flat-square&logo=discord&logoColor=white" alt="Discord"></a>
+</p>
+
+**Build, deploy, and govern autonomous AI agents for your business operations.**
+
+aiXplain SDK provides Python and REST APIs for agents that plan, use tools, call models and data, run code, and adapt at runtime. It also works natively with MCP-compatible coding agents and IDEs.
+
+> **Become an agentic-first organization**
+>
+> Designed for business operations: autonomous, governed, MCP-compatible, and built for context management. Your interactive AI assistant is [a click away](https://auth.aixplain.com/).
+>
+> _We operate our business with aiXplain agents, using them across product, business development, and marketing._
+
+## Why aiXplain
+
+- **Autonomous runtime loop** — plan, call tools and models, reflect, and continue without fixed flowcharts.
+- **Multi-agent execution** — delegate work to specialized subagents at runtime.
+- **Governance by default** — runtime access and policy enforcement on every run.
+- **Production observability** — inspect step-level traces, tool calls, and outcomes for debugging.
+- **Model and tool portability** — swap assets without rewriting application glue code.
+- **MCP-native access** — connect MCP clients to [900+ aiXplain-hosted assets](#mcp-servers) with one PAYG API key.
+- **Flexible deployment** — run the same agent definition serverless or private.
 
-With one API key, access 900+ vendor-agnostic models, tools, and integrations in the aiXplain Marketplace with consolidated billing, and swap assets without rewriting pipelines.
+| | aiXplain SDK | Other agent frameworks |
+|---|---|---|
+| Governance | Runtime access and policy enforcement built in | Usually custom code or external guardrails |
+| Models and tools | 900+ models and tools with one API key | Provider-by-provider setup |
+| Deployment | Cloud (instant) or on-prem | Usually self-assembled runtime and infra |
+| Observability | Built-in traces and dashboards | Varies by framework |
+| Coding-agent workflows | Works natively with MCP-compatible coding agents and IDEs | Usually not a first-class workflow target |
 
-### Why aiXplain for developers
+## AgenticOS
 
-- **Autonomy** — agents plan and adapt at runtime instead of following fixed workflows.
-- **Delegation** — route complex work to specialized subagents during execution.
-- **Policy enforcement** — apply runtime guardrails with Inspector and Bodyguard on every run.
-- **Observability** — inspect step-level traces, tool calls, and outcomes for debugging.
-- **Portability** — swap models and tools without rewriting application logic.
-- **Flexible deployment** — run the same agent definition serverless or private.
+AgenticOS is the portable runtime platform behind aiXplain agents. AgentEngine orchestrates planning, execution, and delegation for autonomous agents. AssetServing connects agents to models, tools, and data through a governed runtime layer. Observability captures traces, metrics, and monitoring for every production run across Cloud (instant) and on-prem deployments.
 
 <div align="center">
-  <img src="docs/assets/aixplain-workflow-teamagent.png" alt="aiXplain team-agent runtime flow" title="aiXplain"/>
+  <img src="docs/assets/aixplain-agentic-os-architecture.svg" alt="aiXplain AgenticOS architecture" title="aiXplain"/>
 </div>
 
-## AgenticOS
+---
 
-AgenticOS is the runtime behind aiXplain Agents. It orchestrates multi-step execution, routes model and tool calls with fallback policies, enforces governance at runtime, records step-level traces, and supports both serverless and private deployment.
+## MCP Server Marketplace
 
-<div align="center">
-  <img src="docs/assets/aixplain-agentic-os-architecture.png" alt="aiXplain AgenticOS architecture" title="aiXplain"/>
-</div>
+[aiXplain Marketplace](https://studio.aixplain.com/browse) now also exposes MCP servers for **900+ models and tools**, allowing external clients to access selected **tool, integration, and model assets**, for example **Opus 4.6, Kimi, Qwen, Airtable, and Slack**, through **aiXplain-hosted MCP endpoints** with a single API key 🔑.
+
+Read the full MCP setup guide in the [MCP servers docs](https://docs.aixplain.com/api-reference/mcp-servers).
+
+```json
+{
+  "ms1": {
+    "url": "https://models-mcp.aixplain.com/mcp/<AIXPLAIN_ASSET_ID>",
+    "headers": {
+      "Authorization": "Bearer <AIXPLAIN_APIKEY>",
+      "Accept": "application/json, text/event-stream"
+    }
+  }
+}
+```
 
 ---
 
@@ -49,54 +87,98 @@ Get your API key from your [aiXplain account](https://console.aixplain.com/setti
 ### Create and run your first agent (v2)
 
 ```python
+from uuid import uuid4
 from aixplain import Aixplain
 
 aix = Aixplain(api_key="<AIXPLAIN_API_KEY>")
 
 search_tool = aix.Tool.get("tavily/tavily-web-search/tavily")
+search_tool.allowed_actions = ["search"]
 
 agent = aix.Agent(
-    name="Research agent",
+    name=f"Research agent {uuid4().hex[:8]}",
     description="Answers questions with concise web-grounded findings.",
     instructions="Use the search tool when needed and cite key findings.",
     tools=[search_tool],
 )
 agent.save()
 
-result = agent.run(query="Summarize the latest AgenticOS updates.")
+result = agent.run(
+    query="Who is the CEO of OpenAI? Answer in one sentence.",
+)
 print(result.data.output)
 ```
 
 ### Build a multi-agent team (v2)
 
 ```python
+from uuid import uuid4
 from aixplain import Aixplain
+from aixplain.v2 import EditorConfig, EvaluatorConfig, EvaluatorType, Inspector, InspectorAction, InspectorActionConfig, InspectorSeverity, InspectorTarget
 
 aix = Aixplain(api_key="<AIXPLAIN_API_KEY>")
 search_tool = aix.Tool.get("tavily/tavily-web-search/tavily")
-
-planner = aix.Agent(
-    name="Planner",
-    instructions="Break requests into clear subtasks."
+search_tool.allowed_actions = ["search"]
+
+def never_edit(text: str) -> bool:
+    return False
+
+def passthrough(text: str) -> str:
+    return text
+
+noop_inspector = Inspector(
+    name=f"noop-output-inspector-{uuid4().hex[:8]}",
+    severity=InspectorSeverity.LOW,
+    targets=[InspectorTarget.OUTPUT],
+    action=InspectorActionConfig(type=InspectorAction.EDIT),
+    evaluator=EvaluatorConfig(
+        type=EvaluatorType.FUNCTION,
+        function=never_edit,
+    ),
+    editor=EditorConfig(
+        type=EvaluatorType.FUNCTION,
+        function=passthrough,
+    ),
 )
 
 researcher = aix.Agent(
-    name="Researcher",
+    name=f"Researcher {uuid4().hex[:8]}",
     instructions="Find and summarize reliable sources.",
     tools=[search_tool],
 )
 
 team_agent = aix.Agent(
-    name="Research team",
-    instructions="Delegate work to subagents, then return one final answer.",
-    subagents=[planner, researcher],
+    name=f"Research team {uuid4().hex[:8]}",
+    instructions="Research the topic and return exactly 5 concise bullet points.",
+    subagents=[researcher],
+    inspectors=[noop_inspector],
 )
-team_agent.save()
+team_agent.save(save_subcomponents=True)
 
-response = team_agent.run(query="Compare top open-source agent frameworks in 5 bullets.")
+response = team_agent.run(
+    query="Compare OpenAI and Anthropic in exactly 5 concise bullet points.",
+)
 print(response.data.output)
 ```
 
+<div align="center">
+  <img src="docs/assets/aixplain-workflow-teamagent.png" alt="aiXplain team-agent runtime flow" title="aiXplain"/>
+</div>
+
+Execution order:
+
+```text
+Human prompt: "Compare OpenAI and Anthropic in exactly 5 concise bullet points."
+
+Team agent
+├── Planner: breaks the goal into research and synthesis steps
+├── Orchestrator: routes work to the right subagent
+├── Researcher subagent
+│   └── Tavily search tool: finds and summarizes reliable sources
+├── Inspector: checks the final output through a simple runtime policy
+├── Orchestrator: decides whether another pass is needed
+└── Responder: returns one final answer
+```
 </details>
 
 <details>
@@ -130,13 +212,14 @@ You can still access legacy docs at [docs.aixplain.com/1.0](https://docs.aixplai
 
 aiXplain applies runtime governance and enterprise controls by default:
 
+- **We do not train on your data** — your data is not used to train foundation models.
 - **No data retained by default** — agent memory is opt-in (short-term and long-term).
 - **SOC 2 Type II certified** — enterprise security and compliance posture.
 - **Runtime policy enforcement** — Inspector and Bodyguard govern every agent execution.
-- **Sovereign deployment options** — serverless or private (on-prem, VPC, and air-gapped).
+- **Portable deployment options** — Cloud (instant) or on-prem (including VPC and air-gapped environments).
 - **Encryption** — TLS 1.2+ in transit and encrypted storage at rest.
 
-Learn more at [aiXplain Security](https://aixplain.com/security/) and [Sovereignty](https://aixplain.com/sovereignty/).
+Learn more at aiXplain [Security](https://aixplain.com/security/) and aiXplain [pricing](https://aixplain.com/pricing/).
 
 ---
 
@@ -148,7 +231,7 @@ Start free, then scale with usage-based pricing.
 - **Subscription plans** — reduce effective consumption-based rates.
 - **Custom enterprise pricing** — available for advanced scale and deployment needs.
 
-Learn more at [aiXplain Pricing](https://aixplain.com/pricing/).
+Learn more at aiXplain [pricing](https://aixplain.com/pricing/).
 
 ---
 

aixplain/v1/modules/agent/__init__.py

@@ -576,7 +576,7 @@ def run(
         max_iterations: int = 5,
         trace_request: bool = False,
         progress_verbosity: Optional[str] = "compact",
-        run_response_generation: bool = True,
+        run_response_generation: bool = False,
         **kwargs,
     ) -> AgentResponse:
         """Runs an agent call.
@@ -595,7 +595,7 @@ def run(
             max_iterations (int, optional): maximum number of iterations between the agent and the tools. Defaults to 10.
             trace_request (bool, optional): return the request id for tracing the request. Defaults to False.
             progress_verbosity (Optional[str], optional): Progress display mode - "full" (detailed), "compact" (brief), or None (no progress). Defaults to "compact".
-            run_response_generation (bool, optional): Whether to run response generation. Defaults to True.
+            run_response_generation (bool, optional): Whether to run response generation. Defaults to False.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -668,7 +668,11 @@ def run(
             poll_url = response["url"]
             end = time.time()
             result = self.sync_poll(
-                poll_url, name=name, timeout=timeout, wait_time=wait_time, progress_verbosity=progress_verbosity
+                poll_url,
+                name=name,
+                timeout=timeout,
+                wait_time=wait_time,
+                progress_verbosity=progress_verbosity,
             )
             result_data = result.get("data") or {}
             if result.status == ResponseStatus.FAILED:
@@ -733,7 +737,7 @@ def run_async(
         expected_output: Optional[Union[BaseModel, Text, dict]] = None,
         evolve: Union[Dict[str, Any], EvolveParam, None] = None,
         trace_request: bool = False,
-        run_response_generation: bool = True,
+        run_response_generation: bool = False,
     ) -> AgentResponse:
         """Runs asynchronously an agent call.
 
@@ -751,7 +755,7 @@ def run_async(
             expected_output (Union[BaseModel, Text, dict], optional): expected output. Defaults to None.
             evolve (Union[Dict[str, Any], EvolveParam, None], optional): evolve the agent configuration. Can be a dictionary, EvolveParam instance, or None.
             trace_request (bool, optional): return the request id for tracing the request. Defaults to False.
-            run_response_generation (bool, optional): Whether to run response generation. Defaults to True.
+            run_response_generation (bool, optional): Whether to run response generation. Defaults to False.
 
         Returns:
             dict: polling URL in response