Update AugmentedLLM interface to use CreateMessageResult and SamplingMessage types, add MCP-model provider type converters, and add a "Done" agent for Swarm workflow

Author: saqadri

examples/marimo_mcp_basic_agent/notebook.py

@@ -34,7 +34,6 @@ def format_list_tools_result(list_tools_result: ListToolsResult):
             res += f"- **{tool.name}**: {tool.description}\n\n"
         return res
 
-
     tools_str = format_list_tools_result(tools)
     mo.accordion({"View tools": mo.md(tools_str)})
     return format_list_tools_result, tools_str
@@ -47,9 +46,10 @@ async def model(messages, config):
         response = await llm.generate_str(message.content)
         return mo.md(response)
 
-
     chatbot = mo.ui.chat(
-        model, prompts=["What are some files in my filesystem", "Get google.com"], show_configuration_controls=False
+        model,
+        prompts=["What are some files in my filesystem", "Get google.com"],
+        show_configuration_controls=False,
     )
     chatbot
     return chatbot, model
@@ -87,6 +87,7 @@ async def _(Agent, OpenAIAugmentedLLM):
 @app.cell
 def _():
     import marimo as mo
+
     return (mo,)
 
 

examples/mcp_server_aggregator/main.py

@@ -90,7 +90,6 @@ async def example_usage():
             arguments={"url": "https://jsonplaceholder.typicode.com/todos/1"},
         )
         logger.info(f"fetch result: {str(result)}")
-        await asyncio.sleep(5)
     except Exception as e:
         logger.error("Error in example_usage:", data=e)
     finally:

examples/streamlit_mcp_basic_agent/main.py

@@ -3,6 +3,7 @@
 import asyncio
 from mcp_agent.app import MCPApp
 from mcp_agent.agents.agent import Agent
+from mcp_agent.workflows.llm.augmented_llm import RequestParams
 from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
 
 
@@ -52,7 +53,9 @@ async def main():
         with st.chat_message("assistant"):
             response = ""
             with st.spinner("Thinking..."):
-                response = await llm.generate_str(message=prompt, use_history=True)
+                response = await llm.generate_str(
+                    message=prompt, request_params=RequestParams(use_history=True)
+                )
             st.markdown(response)
 
         st.session_state["messages"].append({"role": "assistant", "content": response})

examples/streamlit_mcp_rag_agent/main.py

@@ -2,6 +2,7 @@
 from qdrant_client import QdrantClient
 from mcp_agent.app import MCPApp
 from mcp_agent.agents.agent import Agent
+from mcp_agent.workflows.llm.augmented_llm import RequestParams
 from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
 from agent_state import get_agent_state
 import streamlit as st
@@ -66,7 +67,7 @@ async def main():
             response = ""
             with st.spinner("Thinking..."):
                 response = await state.llm.generate_str(
-                    message=prompt, use_history=True
+                    message=prompt, request_params=RequestParams(use_history=True)
                 )
             st.markdown(response)
 

examples/workflow_evaluator_optimizer/main.py

@@ -2,6 +2,7 @@
 
 from mcp_agent.app import MCPApp
 from mcp_agent.agents.agent import Agent
+from mcp_agent.workflows.llm.augmented_llm import RequestParams
 from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
 
 from mcp_agent.workflows.evaluator_optimizer.evaluator_optimizer import (
@@ -76,7 +77,7 @@ async def example_usage():
 
         result = await evaluator_optimizer.generate_str(
             message=f"Write a cover letter for the following job posting: {job_posting}\n\nCandidate Details: {candidate_details}\n\nCompany information: {company_information}",
-            model="gpt-4o",
+            request_params=RequestParams(model="gpt-4o"),
         )
 
         logger.info(f"{result}")

examples/workflow_orchestrator_worker/graded_report.md

@@ -1,23 +1,78 @@
-**Graded Report on 'The Battle of Glimmerwood'**
+# Graded Report: The Battle of Glimmerwood
 
-**Proofreading Feedback:**
-- Corrected various grammatical errors such as verb tense mismatches and subject-verb agreement.
-- Recommended consistent use of past tense for clarity.
-- Suggested improvements in comma usage and spelling verification to enhance sentence construction.
+## Proofreading Feedback
 
-**Factual and Logical Consistency Evaluation:**
-- Identified incongruities in the villagers' rapid response to the marauders without prior preparation context.
-- Highlighted the need for more background on Elara's leadership capabilities or motivation.
-- Suggested more detailed descriptions of magical elements like Glimmerfoxes and their roles.
-- Recommended clarification on the safeguarding methods for Glimmerstones and their significance.
+### Grammar, Spelling, and Punctuation Corrections:
 
-**APA Style Adherence:**
-- Ensured consistency with APA style for title, margins, font usage, and paragraph indentation.
-- Considered elements like running heads and page numbers depending on the document's purpose.
+1. **Spelling Errors:**
+   - Correct "knowed" to "known."
+   - Correct "shaterred" to "shattered."
+   - Correct "attack" to "attacked."
+   - Correct "aim" to "aimed."
+   - Correct "choas" to "chaos."
+   - Correct "aproached" to "approached."
+   - Correct "captured" to "capture."
+   - Correct "confirm" to "confirmed."
 
-**Suggestions for Clarity and Engagement:**
-- Advocated for consistent verb tense usage and grammar refinement.
-- Encouraged use of vivid descriptions and varied sentence structures for engagement.
-- Suggested further character development and balanced pacing of events.
+2. **Grammar and Sentence Structure:**
+   - Change "The villagers, who were live peacefully," to "The villagers, who lived peacefully," to correct verb tense.
+   - Change "fur shimmer like moonlight" to "fur shimmered like moonlight" to match subject-verb agreement.
+   - Change "Lead by the cunning Captain Thorn," to "Led by the cunning Captain Thorn," to use the correct past participle.
+   - Adjust "which was believed to grant immortality" to "which were believed to grant immortality" to match noun-verb agreement with the plural "Glimmerstones."
+   - Insert a conjunction for clarity: "a young girl named Elara stood her ground, she rallied the villagers" to "a young girl named Elara stood her ground and rallied the villagers."
+   - Add a comma for clarity: "forests natural defenses" should be "forest's natural defenses".
+   - Adjust "As the bandits aproached the village square, a herd of Glimmerfoxes emerged," to ensure clarity: "As the bandits approached the village square, a herd of Glimmerfoxes emerged, blinding them with their dazzling light. The villagers seized the opportunity to capture the invaders."
+   - Adjust "The Glimmerstones true power" to "The Glimmerstones' true power" to indicate possession properly.
+   - Change "and whispers of a hidden agenda linger among the villagers" to "while whispers of a hidden agenda lingered among the villagers" to ensure proper tense consistency and flow.
 
-By addressing these points, the story's overall readability, coherence, and adherence to APA guidelines will be improved, enhancing the narrative's professional presentation.
+### Overall Suggestions:
+This story effectively uses imagery and fantasy elements to engage readers. However, addressing the above grammatical and structural issues will enhance clarity and reader enjoyment. Consider revisiting verb tenses for consistency throughout the story, and make sure all subject-verb agreements align appropriately. Additionally, watch out for spelling mistakes that could distract from the engaging narrative.
+
+## Logical Consistency and Factual Review
+
+### Logical Inconsistencies and Contradictions:
+
+1. **Ambiguity in Characters' Knowledge and Abilities:**
+   - The story mentions "the Glimmerstones which was believed to grant immortality," but then states "The Glimmerstone's true power was never confirmed." This presents a contradiction in terms of the villagers’ understanding and certainty about the Glimmerstones' powers. If their power was never confirmed, it's illogical for them to form a belief around immortality.
+   
+2. **Timeline and Setting Clarity:**
+   - The event of rallying and trapping the bandits progresses rapidly without a clear timeline or logistical explanation of how Elara instantly organized defense with the villagers and forest creatures. The story transitions swiftly from the attack to victory, which lacks a detailed transition that leads readers through the progression of events.
+
+3. **Plan and Execution Details:**
+   - Elara rallies the villagers and devises a "clever plan" using the forest’s natural defenses. However, the story does not provide details about the plan's execution or the specific actions taken that would logically lead to trapping seasoned bandits. The lack of explanation weakens the plausibility of the villagers and a young girl overcoming notorious bandits.
+
+4. **Defense Strategy and Capabilities:**
+   - The narrative suggests using "the forest's natural defenses" and a blinding light from Glimmerfoxes to defeat the Dark Marauders. Yet, it does not explain the nature of these defenses or how the abilities of the Glimmerfoxes were tactically employed, leading to uncertainty around the feasibility of the villagers' defense strategy.
+
+5. **Character Motivation and Consequences:**
+   - The motivation behind the Dark Marauders seeking the Glimmerstones is clear, but there's ambiguity in the villagers' response and how the aftermath affects their lives and the perception of the imminent threat. The story ends by hinting at a hidden agenda but does not explore the implications or next steps, leaving a narrative gap.
+
+By addressing these inconsistencies and providing additional context and explanations, the story can achieve greater coherence and believability.
+
+## APA Style Evaluation
+
+### Evaluation of APA Style Adherence:
+
+#### 1. **Formatting:**
+- **Running Head & Header:** The story does not contain a running head or headers with page numbers, as suggested by APA formatting standards.
+- **Margins:** There's no information on margins since it's not formatted as a complete document.
+- **Title Page Elements:** The story lacks a proper title page that should include the title, author byline, and institutional affiliation according to APA guidelines.
+- **Heading Levels:** The document uses a single level of heading ("The Battle of Glimmerwood"). APA formatting suggests more structured headings if the document's complexity requires it, starting with Level 1 headings.
+
+#### 2. **References and In-text Citations:**
+- **Reference List:** There is no reference list at the end of the story, which is essential in APA style when external sources are referenced (though this story appears to be fictional, any quotes or specific influences should be cited).
+- **In-text Citations:** There are no in-text citations in the narrative, which are necessary if any part of the content was inspired by or derived from specific works or documents.
+
+### Suggestions for Improvement:
+
+1. **Add a Title Page:** Including elements such as the story title, author's name, and institutional affiliation aligns with APA requirements for academic papers.
+   
+2. **Format Headers:** If turned into a proper document, incorporate a header with a running head and page numbers.
+
+3. **Incorporate References:** If the story draws from any real-world factual content or existing literature, ensure to provide proper in-text citations and a reference list at the conclusion.
+
+4. **Expand Headings (if necessary):** Structure the document with more detailed headings if broken into multiple sections, aligning them with APA style heading levels.
+
+5. **Check Margins & Spacing:** Although not visible here, ensure document margins are set properly if it transitions into a word processor environment.
+
+This evaluation should guide necessary changes to adhere to APA style for both formatting and referencing, enhancing both accessibility and professional presentation of the short story.

examples/workflow_orchestrator_worker/main.py

@@ -3,6 +3,7 @@
 
 from mcp_agent.app import MCPApp
 from mcp_agent.agents.agent import Agent
+from mcp_agent.workflows.llm.augmented_llm import RequestParams
 from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
 from mcp_agent.workflows.orchestrator.orchestrator import Orchestrator
 
@@ -158,7 +159,9 @@ async def example_usage():
             plan_type="full",
         )
 
-        result = await orchestrator.generate_str(message=task, model="gpt-4o")
+        result = await orchestrator.generate_str(
+            message=task, request_params=RequestParams(model="gpt-4o")
+        )
         logger.info(f"{result}")
 
 

examples/workflow_parallel/main.py

@@ -3,6 +3,7 @@
 
 from mcp_agent.app import MCPApp
 from mcp_agent.agents.agent import Agent
+from mcp_agent.workflows.llm.augmented_llm import RequestParams
 from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
 
 # from mcp_agent.workflows.parallel.fan_in import FanIn
@@ -84,7 +85,8 @@ async def example_usage():
         )
 
         result = await parallel.generate_str(
-            message=f"Student short story submission: {SHORT_STORY}", model="gpt-4o"
+            message=f"Student short story submission: {SHORT_STORY}",
+            request_params=RequestParams(model="gpt-4o"),
         )
 
         logger.info(f"{result}")

examples/workflow_swarm/main.py

@@ -2,7 +2,7 @@
 import os
 
 from mcp_agent.app import MCPApp
-from mcp_agent.workflows.swarm.swarm import SwarmAgent
+from mcp_agent.workflows.swarm.swarm import DoneAgent, SwarmAgent
 from mcp_agent.workflows.swarm.swarm_anthropic import AnthropicSwarm
 from mcp_agent.human_input.handler import console_input_callback
 
@@ -41,7 +41,7 @@ def initiate_flight_credits():
 
 def case_resolved():
     """Resolve the case"""
-    return "Case resolved. No further questions."
+    return DoneAgent()
 
 
 # Agents

pyproject.toml

@@ -49,6 +49,7 @@ build-backend = "hatchling.build"
 
 [dependency-groups]
 dev = [
+    "anthropic>=0.42.0",
     "pre-commit>=4.0.1",
     "pydantic>=2.10.4",
     "pyyaml>=6.0.2",

src/mcp_agent/context.py

@@ -33,11 +33,11 @@
 from mcp_agent.logging.logger import LoggingConfig
 from mcp_agent.logging.transport import create_transport
 from mcp_agent.mcp_server_registry import ServerRegistry
+from mcp_agent.workflows.llm.llm_selector import ModelSelector
 
 if TYPE_CHECKING:
     from mcp_agent.human_input.types import HumanInputCallback
     from mcp_agent.executor.workflow_signal import SignalWaitCallback
-    from mcp_agent.workflows.llm.llm_selector import ModelSelector
 else:
     # Runtime placeholders for the types
     HumanInputCallback = Any
@@ -55,7 +55,7 @@ class Context(BaseModel):
     human_input_handler: Optional[HumanInputCallback] = None
     signal_notification: Optional[SignalWaitCallback] = None
     upstream_session: Optional[ServerSession] = None  # TODO: saqadri - figure this out
-    model_selector: Optional["ModelSelector"] = None
+    model_selector: Optional[ModelSelector] = None
 
     # Registries
     server_registry: Optional[ServerRegistry] = None

src/mcp_agent/workflows/evaluator_optimizer/evaluator_optimizer.py

@@ -8,6 +8,7 @@
     MessageParamT,
     MessageT,
     ModelT,
+    RequestParams,
 )
 from mcp_agent.agents.agent import Agent
 from mcp_agent.logging.logger import get_logger
@@ -149,12 +150,7 @@ def __init__(
     async def generate(
         self,
         message: str | MessageParamT | List[MessageParamT],
-        use_history: bool = True,
-        max_iterations: int = 10,
-        model: str = None,
-        stop_sequences: List[str] = None,
-        max_tokens: int = 2048,
-        parallel_tool_calls: bool = True,
+        request_params: RequestParams | None = None,
     ) -> List[MessageT]:
         """Generate an optimized response through evaluation-guided refinement"""
         refinement_count = 0
@@ -169,12 +165,7 @@ async def generate(
                 await stack.enter_async_context(self.optimizer)
             response = await self.optimizer_llm.generate(
                 message=message,
-                use_history=use_history,
-                max_iterations=max_iterations,
-                model=model,
-                stop_sequences=stop_sequences,
-                max_tokens=max_tokens,
-                parallel_tool_calls=parallel_tool_calls,
+                request_params=request_params,
             )
 
         best_response = response
@@ -199,8 +190,7 @@ async def generate(
                 evaluation_result = await self.evaluator_llm.generate_structured(
                     message=eval_prompt,
                     response_model=EvaluationResult,
-                    model=model,
-                    max_tokens=max_tokens,
+                    request_params=request_params,
                 )
 
             # Track iteration
@@ -254,12 +244,7 @@ async def generate(
 
                 response = await self.optimizer_llm.generate(
                     message=refinement_prompt,
-                    use_history=use_history,
-                    max_iterations=max_iterations,
-                    model=model,
-                    stop_sequences=stop_sequences,
-                    max_tokens=max_tokens,
-                    parallel_tool_calls=parallel_tool_calls,
+                    request_params=request_params,
                 )
 
             refinement_count += 1
@@ -269,22 +254,12 @@ async def generate(
     async def generate_str(
         self,
         message: str | MessageParamT | List[MessageParamT],
-        use_history: bool = True,
-        max_iterations: int = 10,
-        model: str = None,
-        stop_sequences: List[str] = None,
-        max_tokens: int = 2048,
-        parallel_tool_calls: bool = True,
+        request_params: RequestParams | None = None,
     ) -> str:
         """Generate an optimized response and return it as a string"""
         response = await self.generate(
             message=message,
-            use_history=use_history,
-            max_iterations=max_iterations,
-            model=model,
-            stop_sequences=stop_sequences,
-            max_tokens=max_tokens,
-            parallel_tool_calls=parallel_tool_calls,
+            request_params=request_params,
         )
 
         return "\n".join(self.optimizer_llm.message_str(r) for r in response)
@@ -293,29 +268,17 @@ async def generate_structured(
         self,
         message: str | MessageParamT | List[MessageParamT],
         response_model: Type[ModelT],
-        use_history: bool = True,
-        max_iterations: int = 10,
-        model: str = None,
-        stop_sequences: List[str] = None,
-        max_tokens: int = 2048,
-        parallel_tool_calls: bool = True,
+        request_params: RequestParams | None = None,
     ) -> ModelT:
         """Generate an optimized structured response"""
         response_str = await self.generate_str(
-            message=message,
-            use_history=use_history,
-            max_iterations=max_iterations,
-            model=model,
-            stop_sequences=stop_sequences,
-            max_tokens=max_tokens,
-            parallel_tool_calls=parallel_tool_calls,
+            message=message, request_params=request_params
         )
 
         return await self.optimizer.generate_structured(
             message=response_str,
             response_model=response_model,
-            model=model,
-            max_tokens=max_tokens,
+            request_params=request_params,
         )
 
     def _build_eval_prompt(