Merge branch 'main' of https://github.com/evalstate/fast-agent

Author: evalstate

README.md

@@ -12,13 +12,14 @@
 > [!TIP]
 > Documentation site is in production here : https://fast-agent.ai. Feel free to feed back what's helpful and what's not. llms.txt link is here: https://fast-agent.ai/llms.txt
 
-**`fast-agent`** enables you to create and interact with sophisticated Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling. Both Anthropic (Haiku, Sonnet, Opus) and OpenAI models (gpt-4o family, o1/o3 family) are supported.
+**`fast-agent`** enables you to create and interact with sophisticated Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling. Both Anthropic (Haiku, Sonnet, Opus) and OpenAI models (gpt-4o/gpt-4.1 family, o1/o3 family) are supported.
 
 The simple declarative syntax lets you concentrate on composing your Prompts and MCP Servers to [build effective agents](https://www.anthropic.com/research/building-effective-agents).
 
 `fast-agent` is multi-modal, supporting Images and PDFs for both Anthropic and OpenAI endpoints via Prompts, Resources and MCP Tool Call results. The inclusion of passthrough and playback LLMs enable rapid development and test of Python glue-code for your applications.
 
-> [!TIP] > `fast-agent` is now MCP Native! Coming Soon - Full Documentation Site and Further MCP Examples.
+> [!TIP]
+> `fast-agent` is now MCP Native! Coming Soon - Full Documentation Site and Further MCP Examples.
 
 ### Agent Application Development
 
@@ -70,7 +71,7 @@ Or start an interactive chat with the Agent:
 
 ```python
 async with fast.run() as agent:
-  await agent()
+  await agent.interactive()
 ```
 
 Here is the complete `sizer.py` Agent application, with boilerplate code:
@@ -87,7 +88,7 @@ fast = FastAgent("Agent Example")
 )
 async def main():
   async with fast.run() as agent:
-    await agent()
+    await agent.interactive()
 
 if __name__ == "__main__":
     asyncio.run(main())

examples/data-analysis/analysis-campaign.py

@@ -33,7 +33,7 @@
      - Extracted compelling insights suitable for social media promotion
     """,
     request_params=RequestParams(maxTokens=8192),
-    model="gpt-4o",
+    model="gpt-4.1",
 )
 @fast.evaluator_optimizer(
     "analysis_tool",
@@ -55,7 +55,7 @@
     """,
     servers=["fetch", "brave"],  # Using the fetch MCP server for Brave search
     request_params=RequestParams(temperature=0.3),
-    model="gpt-4o",
+    model="gpt-4.1",
 )
 # Social media content generator
 @fast.agent(

examples/mcp/state-transfer/fastagent.config.yaml

@@ -5,7 +5,7 @@
 # Check here for current details: https://fast-agent.ai/models/
 
 # set the default model for fast-agent below:
-default_model: gpt-4o
+default_model: gpt-4.1
 
 # Logging and Console Configuration:
 logger:

examples/researcher/fastagent.config.yaml

@@ -4,7 +4,6 @@
 #
 #
 
-execution_engine: asyncio
 logger:
   type: console
   level: error
@@ -53,14 +52,10 @@ mcp:
       args: ["mcp-server-fetch"]
     sequential:
       command: "npx"
-      args: ["-y","@modelcontextprotocol/server-sequential-thinking"]
-
+      args: ["-y", "@modelcontextprotocol/server-sequential-thinking"]
 #    webmcp:
 #      command: "node"
 #      args: ["/home/ssmith/.webmcp/server.cjs"]
 #      env:
 #         WEBMCP_SERVER_TOKEN: 96e22896d8143fc1d61fec09208fc5ed
 
-
-
-

examples/workflows/evaluator.py

@@ -40,7 +40,7 @@
     Summarize your evaluation as a structured response with:
     - Overall quality rating.
     - Specific feedback and areas for improvement.""",
-    model="gpt-4o",
+    model="gpt-4.1",
 )
 # Define the evaluator-optimizer workflow
 @fast.evaluator_optimizer(

examples/workflows/fastagent.config.yaml

@@ -3,7 +3,6 @@
 #
 #
 
-execution_engine: asyncio
 logger:
   type: file
   level: error

examples/workflows/orchestrator.py

@@ -13,7 +13,7 @@
 @fast.agent(
     "author",
     instruction="""You are to role play a poorly skilled writer, 
-    who makes frequent grammar, punctuations and spelling errors. You enjoy
+    who makes frequent grammar, punctuation and spelling errors. You enjoy
     writing short stories, but the narrative doesn't always make sense""",
     servers=["filesystem"],
 )
@@ -25,7 +25,7 @@
             the closest match to a user's request, make the appropriate tool calls, 
             and return the URI and CONTENTS of the closest match.""",
     servers=["fetch", "filesystem"],
-    model="gpt-4o",
+    model="gpt-4.1",
 )
 @fast.agent(
     name="writer",
@@ -40,19 +40,17 @@
             Identify any awkward phrasing or structural issues that could improve clarity. 
             Provide detailed feedback on corrections.""",
     servers=["fetch"],
-    model="gpt-4o",
+    model="gpt-4.1",
 )
 # Define the orchestrator to coordinate the other agents
 @fast.orchestrator(
-    name="orchestrate",
-    agents=["finder", "writer", "proofreader"],
-    plan_type="full",
+    name="orchestrate", agents=["finder", "writer", "proofreader"], plan_type="full", model="sonnet"
 )
 async def main() -> None:
     async with fast.run() as agent:
-        await agent.author(
-            "write a 250 word short story about kittens discovering a castle, and save it to short_story.md"
-        )
+        # await agent.author(
+        #     "write a 250 word short story about kittens discovering a castle, and save it to short_story.md"
+        # )
 
         # The orchestrator can be used just like any other agent
         task = """Load the student's short story from short_story.md, 

examples/workflows/parallel.py

@@ -25,7 +25,7 @@
     instruction="""Verify the factual consistency within the story. Identify any contradictions,
     logical inconsistencies, or inaccuracies in the plot, character actions, or setting. 
     Highlight potential issues with reasoning or coherence.""",
-    model="gpt-4o",
+    model="gpt-4.1",
 )
 @fast.agent(
     name="style_enforcer",

fastagent.config.yaml

@@ -1,9 +1,5 @@
 # Please edit this configuration file to match your environment (on Windows).
 # Examples in comments below - check/change the paths.
-#
-#
-
-execution_engine: asyncio
 logger:
   type: file
   level: error

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "fast-agent-mcp"
-version = "0.2.16"
+version = "0.2.17"
 description = "Define, Prompt and Test MCP enabled Agents and Workflows"
 readme = "README.md"
 license = { file = "LICENSE" }
@@ -34,9 +34,6 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-temporal = [
-    "temporalio>=1.8.0",
-]
 openai = [
     "openai>=1.58.1",
 ]

schema/mcp-agent.config.schema.json

@@ -589,42 +589,6 @@
       },
       "title": "OpenTelemetrySettings",
       "type": "object"
-    },
-    "TemporalSettings": {
-      "description": "Temporal settings for the fast-agent application.",
-      "properties": {
-        "host": {
-          "title": "Host",
-          "type": "string"
-        },
-        "namespace": {
-          "default": "default",
-          "title": "Namespace",
-          "type": "string"
-        },
-        "task_queue": {
-          "title": "Task Queue",
-          "type": "string"
-        },
-        "api_key": {
-          "anyOf": [
-            {
-              "type": "string"
-            },
-            {
-              "type": "null"
-            }
-          ],
-          "default": null,
-          "title": "Api Key"
-        }
-      },
-      "required": [
-        "host",
-        "task_queue"
-      ],
-      "title": "TemporalSettings",
-      "type": "object"
     }
   },
   "additionalProperties": true,
@@ -647,8 +611,7 @@
     "execution_engine": {
       "default": "asyncio",
       "enum": [
-        "asyncio",
-        "temporal"
+        "asyncio"
       ],
       "title": "Execution Engine",
       "type": "string",
@@ -666,18 +629,6 @@
       "default": "haiku",
       "title": "Default Model"
     },
-    "temporal": {
-      "anyOf": [
-        {
-          "$ref": "#/$defs/TemporalSettings"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "default": null,
-      "description": "Settings for Temporal workflow orchestration"
-    },
     "anthropic": {
       "anyOf": [
         {
@@ -776,4 +727,4 @@
   "title": "MCP Agent Configuration Schema",
   "type": "object",
   "$schema": "http://json-schema.org/draft-07/schema#"
-}
+}

src/mcp_agent/agents/base_agent.py

@@ -20,6 +20,7 @@
     Union,
 )
 
+from a2a_types.types import AgentCapabilities, AgentCard, AgentSkill
 from mcp.types import (
     CallToolResult,
     EmbeddedResource,
@@ -58,6 +59,11 @@
     from mcp_agent.context import Context
 
 
+DEFAULT_CAPABILITIES = AgentCapabilities(
+    streaming=False, pushNotifications=False, stateTransitionHistory=False
+)
+
+
 class BaseAgent(MCPAggregator, AgentProtocol):
     """
     A base Agent class that implements the AgentProtocol interface.
@@ -586,7 +592,7 @@ async def generate(
 
     async def structured(
         self,
-        prompt: List[PromptMessageMultipart],
+        multipart_messages: List[PromptMessageMultipart],
         model: Type[ModelT],
         request_params: RequestParams | None = None,
     ) -> Tuple[ModelT | None, PromptMessageMultipart]:
@@ -603,7 +609,7 @@ async def structured(
             An instance of the specified model, or None if coercion fails
         """
         assert self._llm
-        return await self._llm.structured(prompt, model, request_params)
+        return await self._llm.structured(multipart_messages, model, request_params)
 
     async def apply_prompt_messages(
         self, prompts: List[PromptMessageMultipart], request_params: RequestParams | None = None
@@ -626,13 +632,51 @@ async def apply_prompt_messages(
     def agent_type(self) -> AgentType:
         """
         Return the type of this agent.
+        """
+        return AgentType.BASIC
+
+    async def agent_card(self) -> AgentCard:
+        """
+        Return an A2A card describing this Agent
+        """
 
-        This is used for display purposes in the interactive prompt and other UI elements.
+        skills: List[AgentSkill] = []
+        tools: ListToolsResult = await self.list_tools()
+        for tool in tools.tools:
+            skills.append(await self.convert(tool))
 
-        Returns:
-            String representing the agent type
+        return AgentCard(
+            name=self.name,
+            description=self.instruction,
+            url=f"fast-agent://agents/{self.name}/",
+            version="0.1",
+            capabilities=DEFAULT_CAPABILITIES,
+            defaultInputModes=["text/plain"],
+            defaultOutputModes=["text/plain"],
+            provider=None,
+            documentationUrl=None,
+            authentication=None,
+            skills=skills,
+        )
+
+    async def convert(self, tool: Tool) -> AgentSkill:
         """
-        return AgentType.BASIC
+        Convert a Tool to an AgentSkill.
+        """
+
+        _, tool_without_namespace = await self._parse_resource_name(tool.name, "tool")
+        return AgentSkill(
+            id=tool.name,
+            name=tool_without_namespace,
+            description=tool.description,
+            tags=["tool"],
+            examples=None,
+            inputModes=None,  # ["text/plain"],
+            # cover TextContent | ImageContent ->
+            # https://github.com/modelcontextprotocol/modelcontextprotocol/pull/223
+            # https://github.com/modelcontextprotocol/modelcontextprotocol/pull/93
+            outputModes=None,  # ,["text/plain", "image/*"],
+        )
 
     @property
     def message_history(self) -> List[PromptMessageMultipart]: