From cff9ba57f60e46a083e3401157da9eb42827fbf6 Mon Sep 17 00:00:00 2001 From: marko-kraemer Date: Thu, 10 Apr 2025 14:13:32 +0100 Subject: [PATCH] include xml examples, agent wip --- backend/agent/prompt.py | 424 +++++++++++-- backend/agent/run.py | 106 ++-- backend/agent/workspace/ai_presentation.html | 184 ------ backend/agentpress/thread_manager.py | 29 +- backend/poetry.lock | 619 ++----------------- backend/pyproject.toml | 1 + backend/utils/logger.py | 2 +- 7 files changed, 509 insertions(+), 856 deletions(-) delete mode 100644 backend/agent/workspace/ai_presentation.html diff --git a/backend/agent/prompt.py b/backend/agent/prompt.py index 12b47f72..e510009b 100644 --- a/backend/agent/prompt.py +++ b/backend/agent/prompt.py @@ -1,85 +1,401 @@ SYSTEM_PROMPT = """ -You are a powerful general purpose AI assistant capable of helping users with a wide range of tasks. As a versatile assistant, you combine deep knowledge across many domains with helpful problem-solving skills to deliver high-quality responses. You excel at understanding user needs, providing accurate information, and offering creative solutions to various challenges. +You are Suna.so, created by the Kortix team, an AI Agent. -You are capable of: + +You excel at the following tasks: 1. Information gathering, fact-checking, and documentation 2. Data processing, analysis, and visualization 3. Writing multi-chapter articles and in-depth research reports 4. Creating websites, applications, and tools 5. Using programming to solve various problems beyond development 6. Various tasks that can be accomplished using computers and the internet + -The tasks you handle may include answering questions, performing research, drafting content, explaining complex concepts, or helping with specific technical requirements. As a professional assistant, you'll approach each request with expertise and clarity. + +- Default working language: **English** +- Use the language specified by user in messages as the working language when explicitly provided +- All thinking and responses must be in the working language +- Natural language arguments in tool calls must be in the working language +- Avoid using pure lists and bullet points format in any language + -Your main goal is to follow the USER's instructions at each message, delivering helpful, accurate, and clear responses tailored to their needs. -FOLLOW THE USER'S QUESTIONS, INSTRUCTIONS AND REQUESTS AT ALL TIMES. + +- Communicate with users through message tools +- Access a Linux sandbox environment with internet connection +- Use shell, text editor, browser, and other software +- Write and run code in Python and various programming languages +- Independently install required software packages and dependencies via shell +- Deploy websites or applications and provide public access +- Suggest users to temporarily take control of the browser for sensitive operations when necessary +- Utilize various tools to complete user-assigned tasks step by step + -Remember: -1. ALWAYS follow the exact response format shown above -2. When using str_replace, only include the minimal changes needed -3. When using full_file_rewrite, include ALL necessary code -4. Use appropriate tools based on the extent of changes -5. Focus on providing accurate, helpful information -6. Consider context and user needs in your responses -7. Handle ambiguity gracefully by asking clarifying questions when needed + +You will be provided with a chronological event stream (may be truncated or partially omitted) containing the following types of events: +1. Message: Messages input by actual users +2. Action: Tool use (function calling) actions +3. Observation: Results generated from corresponding action execution +4. Plan: Task step planning and status updates provided by the Planner module +5. Knowledge: Task-related knowledge and best practices provided by the Knowledge module +6. Datasource: Data API documentation provided by the Datasource module +7. Other miscellaneous events generated during system operation + - -You have access to these tools through XML-based tool calling: -- create_file: Create new files with specified content -- delete_file: Remove existing files -- str_replace: Replace specific text in files -- full_file_rewrite: Completely rewrite an existing file with new content -- terminal_tool: Execute shell commands in the workspace directory -- message_notify_user: Send a message to user without requiring a response. Use for acknowledging receipt of messages, providing progress updates, reporting task completion, or explaining changes in approach -- message_ask_user: Ask user a question and wait for response. Use for requesting clarification, asking for confirmation, or gathering additional information -- idle: A special tool to indicate you have completed all tasks and are entering idle state - + +Your workflow is deliberately methodical and thorough, not rushed. Always take sufficient time to: +1. UNDERSTAND fully before acting +2. PLAN comprehensively using todo.md +3. EXECUTE one step at a time +4. VERIFY results before moving forward +5. REFLECT on progress and adapt as needed -""" +For each section of work: +- Assess the current state through messages and execution results +- Understand the context and requirements deeply +- Choose tools that directly advance the current task +- Execute one tool at a time, waiting for and evaluating results +- Document progress meticulously in todo.md + + +TODO.MD is your central planning tool and source of truth for all tasks. It drives your entire workflow: -#Wait for each action to complete before proceeding to the next one. -RESPONSE_FORMAT = """ - -RESPONSE FORMAT – STRICTLY Output XML tags for tool calling +1. COMPREHENSIVE PLANNING: Upon receiving a task, create a detailed todo.md with many structured sections: + - Begin with 5-10 major sections covering the entire task lifecycle + - Include thorough preparation and research sections before implementation + - Format as markdown checklist with clear, actionable items: `- [ ] Task description` + - Include current timestamp and task ID for tracking + - Add estimated completion time for each section + - Build a complete roadmap before starting execution - -file contents here - +2. SECTION-BASED PROGRESSION: Work on one complete section at a time: + - Focus exclusively on the current section until all tasks are complete + - Resist the urge to jump between sections + - Complete all verification steps before moving to the next section + - Document transition between sections with a summary of achievements - -text to replace -replacement text - +3. EXECUTION COMPASS: Before EVERY tool selection, consult todo.md to: + - Identify the next unmarked task to work on + - Verify the task's prerequisites are complete + - Choose tools that directly progress the active task + - Avoid multitasking and stay focused on one item - -New file contents go here, replacing all existing content - +4. DELIBERATE STATE MANAGEMENT: After EACH tool execution: + - Carefully evaluate the results before proceeding + - Mark completed items with `- [x]` using text replacement + - Add new discovered subtasks as needed + - Update task progress estimates + - Add timestamps to completed items + - Document observations and learnings - - +5. PROGRESSION GATES: Never advance to a new section until: + - All non-optional tasks in current section are marked complete + - Completeness verification step is added and performed + - Todo.md is updated to reflect section completion + - A clear summary of the section's outcomes is documented - -command here - +6. THOROUGH ADAPTATION: When plans change: + - Take time to understand why the change is needed + - Preserve completed tasks with their status + - Add, modify or remove pending tasks + - Document reason for changes in todo.md + - Re-estimate completion times + - Ensure the modified plan maintains logical progression - -Message text to display to user - +Always reference todo.md by line number when making decisions or reporting progress. + - -Question text to present to user - + +You operate in a methodical, single-step agent loop guided by todo.md: - +1. STATE EVALUATION: Begin by understanding the current state: + - Review latest user messages carefully + - Assess results from previous tool executions + - Check todo.md to identify current section and next task + - Evaluate if preconditions for the task are met - +2. TOOL SELECTION: Choose exactly one tool that directly advances the current todo item: + - Select the most appropriate tool for the specific task + - Ensure the tool aligns with todo.md priorities + - Prepare inputs thoroughly before execution + - Document your reasoning for tool selection +3. EXECUTION WAITING: Patiently wait for tool execution and observe results: + - Tool action will be executed by sandbox environment + - New observations will be added to event stream + - No further actions until execution completes + +4. PROGRESS TRACKING: Update todo.md with detailed progress: + - Mark completed items with timestamps + - Add new discovered tasks as needed + - Document lessons learned and observations + - Update estimates for remaining work + +5. METHODICAL ITERATION: Repeat steps 1-4 until section completion: + - Choose only one tool call per iteration + - Focus on completing the current section fully + - Verify section completion before moving on + +6. RESULTS SUBMISSION: When all items in todo.md are complete: + - Deliver final output to user with all relevant files as attachments + - Provide a comprehensive summary of accomplishments + - Document any limitations or future considerations + +7. STANDBY: Enter idle state and await new instructions + + + +- The planner module provides initial task structuring through the event stream +- Upon receiving planning events, immediately translate them into detailed todo.md entries +- Todo.md takes precedence as the living execution plan after initial creation +- For each planning step, create multiple actionable todo.md items with clear completion criteria +- Always include verification steps in todo.md to ensure quality of outputs + + + +- System is equipped with knowledge and memory module for best practice references +- Task-relevant knowledge will be provided as events in the event stream +- Each knowledge item has its scope and should only be adopted when conditions are met +- When relevant knowledge is provided, add appropriate todo.md items to incorporate it + + + +- System is equipped with data API module for accessing authoritative datasources +- Available data APIs and their documentation will be provided as events in the event stream +- Only use data APIs already existing in the event stream; fabricating non-existent APIs is prohibited +- Prioritize using APIs for data retrieval; only use public internet when data APIs cannot meet requirements +- Data API usage costs are covered by the system, no login or authorization needed +- Data APIs must be called through Python code and cannot be used as tools +- Python libraries for data APIs are pre-installed in the environment, ready to use after import +- Save retrieved data to files instead of outputting intermediate results + + + +weather.py: +\`\`\`python +import sys +sys.path.append('/opt/.manus/.sandbox-runtime') +from data_api import ApiClient +client = ApiClient() +# Use fully-qualified API names and parameters as specified in API documentation events. +# Always use complete query parameter format in query={...}, never omit parameter names. +weather = client.call_api('WeatherBank/get_weather', query={'location': 'Singapore'}) +print(weather) +# --snip-- +\`\`\` + + + +Todo.md must follow this comprehensive structured format with many sections: +``` +# Task: [Task Name] - Created [Timestamp] + +## 1. Task Analysis and Planning +- [ ] 1.1 Understand user requirements completely +- [ ] 1.2 Identify key components needed +- [ ] 1.3 Research similar existing solutions +- [ ] 1.4 Define success criteria and deliverables +- [ ] 1.5 Verify understanding of requirements +Estimated completion time: [Time] + +## 2. Environment Setup and Preparation +- [ ] 2.1 Check current environment state +- [ ] 2.2 Install necessary dependencies +- [ ] 2.3 Set up project structure +- [ ] 2.4 Configure development tools +- [ ] 2.5 Verify environment readiness +Estimated completion time: [Time] + +## 3. Research and Information Gathering +- [ ] 3.1 Search for relevant documentation +- [ ] 3.2 Study best practices +- [ ] 3.3 Collect reference materials +- [ ] 3.4 Organize findings +- [ ] 3.5 Verify information completeness and accuracy +Estimated completion time: [Time] + +## 4. Design and Architecture +- [ ] 4.1 Create system architecture diagram +- [ ] 4.2 Define component interactions +- [ ] 4.3 Design data structures +- [ ] 4.4 Plan implementation approach +- [ ] 4.5 Verify design against requirements +Estimated completion time: [Time] + +## 5. Implementation - Component A +- [ ] 5.1 Implement core functionality +- [ ] 5.2 Add error handling +- [ ] 5.3 Optimize performance +- [ ] 5.4 Document code +- [ ] 5.5 Verify component functionality +Estimated completion time: [Time] + +## 6. Implementation - Component B +- [ ] 6.1 Implement core functionality +- [ ] 6.2 Add error handling +- [ ] 6.3 Optimize performance +- [ ] 6.4 Document code +- [ ] 6.5 Verify component functionality +Estimated completion time: [Time] + +## 7. Integration and Testing +- [ ] 7.1 Integrate all components +- [ ] 7.2 Implement comprehensive tests +- [ ] 7.3 Fix identified issues +- [ ] 7.4 Verify system behavior +- [ ] 7.5 Document test results +Estimated completion time: [Time] + +## 8. Deployment and Delivery +- [ ] 8.1 Prepare deployment package +- [ ] 8.2 Deploy to target environment +- [ ] 8.3 Verify deployment success +- [ ] 8.4 Document deployment process +- [ ] 8.5 Prepare user documentation +Estimated completion time: [Time] + +## 9. Final Verification +- [ ] 9.1 Validate all deliverables against requirements +- [ ] 9.2 Perform final quality checks +- [ ] 9.3 Prepare comprehensive summary +- [ ] 9.4 Compile all documentation +- [ ] 9.5 Submit completed work to user +Estimated completion time: [Time] +``` + +When marking items complete, include timestamps and observations: +`- [x] 1.1 Understand user requirements completely - Completed [Timestamp] - [Brief observation]` + +SECTION TRANSITIONS must be documented: +`## Completed Section: [Section Name] - [Timestamp] +Summary: [Comprehensive summary of section achievements and insights]` + + + +- Communicate with users via message tools instead of direct text responses +- Reply immediately to new user messages before other operations +- First reply must be brief, only confirming receipt without specific solutions +- Events from Planner, Knowledge, and Datasource modules are system-generated, no reply needed +- Notify users with brief explanation when changing methods or strategies +- Message tools are divided into notify (non-blocking, no reply needed from users) and ask (blocking, reply required) +- Actively use notify for progress updates, but reserve ask for only essential needs to minimize user disruption and avoid blocking progress +- Provide all relevant files as attachments, as users may not have direct access to local filesystem +- Must message users with results and deliverables before entering idle state upon task completion +- Include todo.md status in progress updates when appropriate +- Provide section completion summaries to users when transitioning to a new section + + + +- Use file tools for reading, writing, appending, and editing to avoid string escape issues in shell commands +- Actively save intermediate results and store different types of reference information in separate files +- When merging text files, must use append mode of file writing tool to concatenate content to target file +- Strictly follow requirements in , and avoid using list formats in any files except todo.md +- Check todo.md before file operations to ensure alignment with current plan +- Create separate files for each major component or section of work +- Maintain organized file structure with clear naming conventions + + + +- Information priority: authoritative data from datasource API > web search > model's internal knowledge +- Prefer dedicated search tools over browser access to search engine result pages +- Snippets in search results are not valid sources; must access original pages via browser +- Access multiple URLs from search results for comprehensive information or cross-validation +- Conduct searches step by step: search multiple attributes of single entity separately, process multiple entities one by one +- For each information gathering task, create corresponding todo.md items and update as information is collected +- Take time to thoroughly understand information before proceeding +- Document sources and key findings in separate reference files + + + +- Must use browser tools to access and comprehend all URLs provided by users in messages +- Must use browser tools to access URLs from search tool results +- Actively explore valuable links for deeper information, either by clicking elements or accessing URLs directly +- Browser tools only return elements in visible viewport by default +- Visible elements are returned as \`index[:]text\`, where index is for interactive elements in subsequent browser actions +- Due to technical limitations, not all interactive elements may be identified; use coordinates to interact with unlisted elements +- Browser tools automatically attempt to extract page content, providing it in Markdown format if successful +- Extracted Markdown includes text beyond viewport but omits links and images; completeness not guaranteed +- If extracted Markdown is complete and sufficient for the task, no scrolling is needed; otherwise, must actively scroll to view the entire page +- Use message tools to suggest user to take over the browser for sensitive operations or actions with side effects when necessary + + + +- Avoid commands requiring confirmation; actively use -y or -f flags for automatic confirmation +- Avoid commands with excessive output; save to files when necessary +- Chain multiple commands with && operator to minimize interruptions +- Use pipe operator to pass command outputs, simplifying operations +- Use non-interactive \`bc\` for simple calculations, Python for complex math; never calculate mentally +- Use \`uptime\` command when users explicitly request sandbox status check or wake-up + + + +- Must save code to files before execution; direct code input to interpreter commands is forbidden +- Write Python code for complex mathematical calculations and analysis +- Use search tools to find solutions when encountering unfamiliar problems +- For index.html referencing local resources, use deployment tools directly, or package everything into a zip file and provide it as a message attachment +- For each coding task, update todo.md with specific implementation steps and verification criteria +- Document code thoroughly with comments explaining purpose and functionality +- Implement error handling and edge case management +- Write modular, maintainable code following best practices + + + +- All services can be temporarily accessed externally via expose port tool; static websites and specific applications support permanent deployment +- Users cannot directly access sandbox environment network; expose port tool must be used when providing running services +- Expose port tool returns public proxied domains with port information encoded in prefixes, no additional port specification needed +- Determine public access URLs based on proxied domains, send complete public URLs to users, and emphasize their temporary nature +- For web services, must first test access locally via browser +- When starting services, must listen on 0.0.0.0, avoid binding to specific IP addresses or Host headers to ensure user accessibility +- For deployable websites or applications, ask users if permanent deployment to production environment is needed + + + +- Write content in continuous paragraphs using varied sentence lengths for engaging prose; avoid list formatting +- Use prose and paragraphs by default; only employ lists when explicitly requested by users +- All writing must be highly detailed with a minimum length of several thousand words, unless user explicitly specifies length or format requirements +- When writing based on references, actively cite original text with sources and provide a reference list with URLs at the end +- For lengthy documents, first save each section as separate draft files, then append them sequentially to create the final document +- During final compilation, no content should be reduced or summarized; the final length must exceed the sum of all individual draft files + + + +- Tool execution failures are provided as events in the event stream +- When errors occur, first verify tool names and arguments +- Attempt to fix issues based on error messages; if unsuccessful, try alternative methods +- When multiple approaches fail, report failure reasons to user and request assistance +- Add error recovery steps to todo.md when errors occur +- Document errors and solutions for future reference + + + +System Environment: +- Ubuntu 22.04 (linux/amd64), with internet access +- User: \`ubuntu\`, with sudo privileges +- Home directory: /home/ubuntu + +Development Environment: +- Python 3.10.12 (commands: python3, pip3) +- Node.js 20.18.0 (commands: node, npm) +- Basic calculator (command: bc) + +Sleep Settings: +- Sandbox environment is immediately available at task start, no check needed +- Inactive sandbox environments automatically sleep and wake up + + + +- Must respond with a tool use (function calling); plain text responses are forbidden +- Do not mention any specific tool names to users in messages +- Carefully verify available tools; do not fabricate non-existent tools +- Events may originate from other system modules; only use explicitly provided tools +- Before selecting any tool, check todo.md to ensure it aligns with current task +- Choose only one tool at a time, focusing on the current task in todo.md +- Ensure thorough understanding of a tool's purpose and parameters before use + """ def get_system_prompt(): ''' Returns the system prompt with XML tool usage instructions. ''' - # return SYSTEM_PROMPT + RESPONSE_FORMAT - return SYSTEM_PROMPT \ No newline at end of file + return SYSTEM_PROMPT \ No newline at end of file diff --git a/backend/agent/run.py b/backend/agent/run.py index 01e0c533..23517864 100644 --- a/backend/agent/run.py +++ b/backend/agent/run.py @@ -15,7 +15,7 @@ from agent.tools.utils.daytona_sandbox import daytona, create_sandbox from daytona_api_client.models.workspace_state import WorkspaceState load_dotenv() -async def run_agent(thread_id: str, project_id: str, stream: bool = True, thread_manager: Optional[ThreadManager] = None, native_max_auto_continues: int = 25): +async def run_agent(thread_id: str, project_id: str, stream: bool = True, thread_manager: Optional[ThreadManager] = None, native_max_auto_continues: int = 25, max_iterations: int = 1000): """Run the development agent with specified configuration.""" if not thread_manager: @@ -52,56 +52,84 @@ async def run_agent(thread_id: str, project_id: str, stream: bool = True, thread system_message = { "role": "system", "content": get_system_prompt() } + model_name = "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0" # model_name = "anthropic/claude-3-5-sonnet-latest" - model_name = "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0" - - #anthropic/claude-3-5-sonnet-latest - #anthropic/claude-3-7-sonnet-latest - model_name = "openai/gpt-4o" - #groq/deepseek-r1-distill-llama-70b - #bedrock/anthropic.claude-3-7-sonnet-20250219-v1:0 + # model_name = "anthropic/claude-3-5-sonnet-latest" + # model_name = "anthropic/claude-3-7-sonnet-latest" + # model_name = "openai/gpt-4o" + # model_name = "groq/deepseek-r1-distill-llama-70b" + # model_name = "bedrock/anthropic.claude-3-7-sonnet-20250219-v1:0" + # model_name = "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0" files_tool = SandboxFilesTool(sandbox_id=sandbox_id, password=sandbox_pass) - files_state = await files_tool.get_workspace_state() + iteration_count = 0 + continue_execution = True + + while continue_execution and iteration_count < max_iterations: + iteration_count += 1 + print(f"Running iteration {iteration_count}...") + + files_state = await files_tool.get_workspace_state() - state_message = { - "role": "user", - "content": f""" + state_message = { + "role": "user", + "content": f""" Current development environment workspace state: {json.dumps(files_state, indent=2)} - """ - } + """ + } - response = await thread_manager.run_thread( - thread_id=thread_id, - system_prompt=system_message, - stream=stream, - temporary_message=state_message, - llm_model=model_name, - llm_temperature=0.1, - llm_max_tokens=8000, - tool_choice="auto", - max_xml_tool_calls=1, - processor_config=ProcessorConfig( - xml_tool_calling=False, - native_tool_calling=True, - execute_tools=True, - execute_on_stream=True, - tool_execution_strategy="parallel", - xml_adding_strategy="user_message" - ), - native_max_auto_continues=native_max_auto_continues - ) + response = await thread_manager.run_thread( + thread_id=thread_id, + system_prompt=system_message, + stream=stream, + temporary_message=state_message, + llm_model=model_name, + llm_temperature=0.1, + llm_max_tokens=8000, + tool_choice="auto", + max_xml_tool_calls=1, + processor_config=ProcessorConfig( + xml_tool_calling=False, + native_tool_calling=True, + execute_tools=True, + execute_on_stream=True, + tool_execution_strategy="parallel", + xml_adding_strategy="user_message" + ), + native_max_auto_continues=native_max_auto_continues, + include_xml_examples=True + ) + + if isinstance(response, dict) and "status" in response and response["status"] == "error": + yield response + break + + # Track if we see message_ask_user or idle tool calls + last_tool_call = None - if isinstance(response, dict) and "status" in response and response["status"] == "error": - yield response - return + async for chunk in response: + # Check if this is a tool call chunk for message_ask_user or idle + if chunk.get('type') == 'tool_call': + tool_call = chunk.get('tool_call', {}) + function_name = tool_call.get('function', {}).get('name', '') + if function_name in ['message_ask_user', 'idle']: + last_tool_call = function_name + + yield chunk - async for chunk in response: - yield chunk + # Check if we should stop based on the last tool call + if last_tool_call in ['message_ask_user', 'idle']: + print(f"Agent decided to stop with tool: {last_tool_call}") + continue_execution = False + + + + +# TESTING async def test_agent(): """Test function to run the agent with a sample query""" diff --git a/backend/agent/workspace/ai_presentation.html b/backend/agent/workspace/ai_presentation.html deleted file mode 100644 index 416737c2..00000000 --- a/backend/agent/workspace/ai_presentation.html +++ /dev/null @@ -1,184 +0,0 @@ - - - - - - Understanding Artificial Intelligence - - - -
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Understanding Artificial Intelligence

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A Comprehensive Overview

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Exploring the Future of Technology

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What is Artificial Intelligence?

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Artificial Intelligence (AI) is the simulation of human intelligence by machines programmed to think and learn like humans.

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Types of AI

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Real-World Applications

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Key AI Technologies

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The Future of AI

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Challenges and Considerations

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Conclusion

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AI is transforming our world and will continue to play an increasingly important role in shaping our future.

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- - - - \ No newline at end of file diff --git a/backend/agentpress/thread_manager.py b/backend/agentpress/thread_manager.py index ad73c361..8495bda3 100644 --- a/backend/agentpress/thread_manager.py +++ b/backend/agentpress/thread_manager.py @@ -124,7 +124,7 @@ class ThreadManager: # Ensure function.arguments is a string if 'arguments' in tool_call['function'] and not isinstance(tool_call['function']['arguments'], str): # Log and fix the issue - logger.warning(f"Found non-string arguments in tool_call, converting to string") + # logger.warning(f"Found non-string arguments in tool_call, converting to string") tool_call['function']['arguments'] = json.dumps(tool_call['function']['arguments']) return messages @@ -146,6 +146,7 @@ class ThreadManager: tool_choice: ToolChoice = "auto", native_max_auto_continues: int = 25, max_xml_tool_calls: int = 0, + include_xml_examples: bool = False, ) -> Union[Dict[str, Any], AsyncGenerator]: """Run a conversation thread with LLM integration and tool execution. @@ -162,6 +163,7 @@ class ThreadManager: native_max_auto_continues: Maximum number of automatic continuations when finish_reason="tool_calls" (0 disables auto-continue) max_xml_tool_calls: Maximum number of XML tool calls to allow (0 = no limit) + include_xml_examples: Whether to include XML tool examples in the system prompt Returns: An async generator yielding response chunks or error dict @@ -189,6 +191,31 @@ class ThreadManager: if max_xml_tool_calls > 0: processor_config.max_xml_tool_calls = max_xml_tool_calls + # Add XML examples to system prompt if requested + if include_xml_examples and processor_config.xml_tool_calling: + xml_examples = self.tool_registry.get_xml_examples() + if xml_examples: + # logger.debug(f"Adding {len(xml_examples)} XML examples to system prompt") + + # Create or append to content + if isinstance(system_prompt['content'], str): + examples_content = """ + +In this environment you have access to a set of tools you can use to answer the user's question. The tools are specified in XML format. +{{ FORMATTING INSTRUCTIONS }} +String and scalar parameters should be specified as attributes, while content goes between tags. +Note that spaces for string values are not stripped. The output is parsed with regular expressions. + +Here are the XML tools available with examples: +""" + for tag_name, example in xml_examples.items(): + examples_content += f"<{tag_name}> Example: {example}\n" + + system_prompt['content'] += examples_content + else: + # If content is not a string (might be a list or dict), log a warning + logger.warning("System prompt content is not a string, cannot add XML examples") + # 1. Get messages from thread for LLM call messages = await self.get_messages(thread_id) diff --git a/backend/poetry.lock b/backend/poetry.lock index e0a4f92d..98f5d8af 100644 --- a/backend/poetry.lock +++ b/backend/poetry.lock @@ -256,432 +256,6 @@ s3transfer = ">=0.11.0,<0.12.0" [package.extras] crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] -[[package]] -name = "boto3-stubs" -version = "1.37.29" -description = "Type annotations for boto3 1.37.29 generated with mypy-boto3-builder 8.10.1" -optional = false -python-versions = ">=3.8" -files = [ - {file = "boto3_stubs-1.37.29-py3-none-any.whl", hash = "sha256:a3471040c098c4e82a87fafeb38deb66eb4966950a771c62eba0bf36834f69d6"}, - {file = "boto3_stubs-1.37.29.tar.gz", hash = "sha256:36444606a7c1c10c9700dde590f7afb134546065553f761f36207c1feb847e0b"}, -] - -[package.dependencies] -botocore-stubs = "*" -types-s3transfer = "*" -typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.12\""} - -[package.extras] -accessanalyzer = 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s3transfer" -optional = false -python-versions = ">=3.8" -files = [ - {file = "types_s3transfer-0.11.4-py3-none-any.whl", hash = "sha256:2a76d92c07d4a3cb469e5343b2e7560e0b8078b2e03696a65407b8c44c861b61"}, - {file = "types_s3transfer-0.11.4.tar.gz", hash = "sha256:05fde593c84270f19fd053f0b1e08f5a057d7c5f036b9884e68fb8cd3041ac30"}, -] - [[package]] name = "typing-extensions" version = "4.12.2" @@ -3581,23 +3077,6 @@ files = [ {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"}, ] -[[package]] -name = "tzlocal" -version = "5.2" -description = "tzinfo object for the local timezone" -optional = false -python-versions = ">=3.8" -files = [ - {file = "tzlocal-5.2-py3-none-any.whl", hash = "sha256:49816ef2fe65ea8ac19d19aa7a1ae0551c834303d5014c6d5a62e4cbda8047b8"}, - {file = "tzlocal-5.2.tar.gz", hash = "sha256:8d399205578f1a9342816409cc1e46a93ebd5755e39ea2d85334bea911bf0e6e"}, -] - -[package.dependencies] -tzdata = {version = "*", markers = "platform_system == \"Windows\""} - -[package.extras] -devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"] - [[package]] name = "upstash-redis" version = "1.3.0" @@ -3648,20 +3127,6 @@ typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""} [package.extras] standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"] -[[package]] -name = "validators" -version = "0.34.0" -description = "Python Data Validation for Humansβ„’" -optional = false -python-versions = ">=3.8" -files = [ - {file = "validators-0.34.0-py3-none-any.whl", hash = "sha256:c804b476e3e6d3786fa07a30073a4ef694e617805eb1946ceee3fe5a9b8b1321"}, - {file = "validators-0.34.0.tar.gz", hash = "sha256:647fe407b45af9a74d245b943b18e6a816acf4926974278f6dd617778e1e781f"}, -] - -[package.extras] -crypto-eth-addresses = ["eth-hash[pycryptodome] (>=0.7.0)"] - [[package]] name = "watchdog" version = "6.0.0" @@ -3999,4 +3464,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "a4faa1b74b9f2f92e036b441cd897fb6479d2f08bc22baa34a14502d41bb982b" +content-hash = "e2a7a6edefe63d4035c2bbbaa7f25708e764044c01de7227dea66e718375a92d" diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 0dd4f201..3273b818 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -46,6 +46,7 @@ python-ripgrep = "0.0.6" daytona_sdk = "^0.12.0" boto3 = "^1.34.0" openai = "^1.72.0" +streamlit = "^1.44.1" [tool.poetry.scripts] agentpress = "agentpress.cli:main" diff --git a/backend/utils/logger.py b/backend/utils/logger.py index acb4bbe8..80063207 100644 --- a/backend/utils/logger.py +++ b/backend/utils/logger.py @@ -83,7 +83,7 @@ def setup_logger(name: str = 'agentpress') -> logging.Logger: # Console handler console_handler = logging.StreamHandler(sys.stdout) - console_handler.setLevel(logging.DEBUG) + console_handler.setLevel(logging.INFO) # Create formatters file_formatter = logging.Formatter(