{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"taskweaver","slug":"taskweaver","name":"TaskWeaver","type":"framework","url":"https://github.com/microsoft/TaskWeaver","page_url":"https://unfragile.ai/taskweaver","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"taskweaver__cap_0","uri":"capability://planning.reasoning.code.first.task.planning.with.llm.driven.decomposition","name":"code-first task planning with llm-driven decomposition","description":"Converts natural language user requests into executable Python code plans through a Planner role that decomposes complex tasks into sub-steps. The Planner uses LLM prompts (defined in planner_prompt.yaml) to generate structured code snippets rather than text-based plans, enabling direct execution of analytics workflows. This approach preserves both chat history and code execution history, including in-memory data structures like DataFrames across stateful sessions.","intents":["I want to break down a complex data analysis task into executable steps automatically","I need my agent to generate Python code that I can inspect and modify before execution","I want to maintain state across multiple interactions without losing intermediate DataFrames or variables"],"best_for":["data analysts building reproducible analytics pipelines","teams automating multi-step ETL workflows with code visibility","developers prototyping agents that need to preserve execution state across conversations"],"limitations":["Planner role is specialized for data analytics tasks; less suitable for non-analytical workflows","Code generation quality depends on LLM capability; complex domain logic may require manual refinement","Stateful execution requires persistent session management; distributed execution across multiple processes requires custom state serialization"],"requires":["Python 3.9+","LLM API access (OpenAI, Anthropic, or local LLM via compatible endpoint)","taskweaver package installed from GitHub","YAML configuration file with Planner role definition"],"input_types":["natural language task descriptions","structured task specifications with parameters"],"output_types":["Python code snippets","execution plans with step-by-step breakdown","structured task metadata"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_1","uri":"capability://code.generation.editing.stateful.code.execution.with.in.memory.data.structure.preservation","name":"stateful code execution with in-memory data structure preservation","description":"Executes generated Python code in an isolated interpreter environment that maintains variables, DataFrames, and other in-memory objects across multiple execution cycles within a session. The CodeInterpreter role manages a persistent Python runtime where code snippets are executed sequentially, with each execution's state (local variables, imported modules, DataFrame mutations) carried forward to subsequent code runs. This is tracked via the memory/attachment.py system that serializes execution context.","intents":["I want to run multiple code snippets in sequence while preserving intermediate results","I need my agent to reference previously computed DataFrames without re-fetching or re-computing them","I want to build up complex data transformations across multiple agent interactions"],"best_for":["data science teams running iterative analytics workflows","developers building agents that perform multi-step data transformations","analysts who need reproducible, step-by-step execution traces"],"limitations":["Execution is single-threaded and sequential; parallel code execution requires explicit task decomposition","In-memory state is lost when session terminates; requires explicit serialization for persistence across restarts","Code execution timeout and resource limits must be configured; runaway code can block the agent","No built-in sandboxing beyond Python's native restrictions; untrusted code execution requires external containerization"],"requires":["Python 3.9+ with standard library","Code Execution Service component running (taskweaver/code_execution_service/)","Session object initialized with persistent state management","Optional: pandas, numpy, and other data processing libraries for analytics tasks"],"input_types":["Python code strings","code with variable references to previous execution context"],"output_types":["execution results (stdout/stderr)","modified in-memory state (variables, DataFrames)","execution metadata (runtime, errors)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_10","uri":"capability://automation.workflow.observability.and.execution.tracing.for.debugging.and.monitoring","name":"observability and execution tracing for debugging and monitoring","description":"Provides observability into agent execution through event-based tracing (EventEmitter pattern) that logs planning decisions, code generation, execution results, and role interactions. Execution traces include timestamps, role attribution, and detailed logs that enable debugging of agent behavior and monitoring of production deployments. Traces can be exported for analysis and are integrated with the memory system to provide full execution history.","intents":["I want to debug why the agent made a particular decision or generated specific code","I need to monitor agent performance and identify bottlenecks","I want to audit all agent actions for compliance and transparency"],"best_for":["teams debugging complex agent behaviors","organizations monitoring production agent deployments","developers optimizing agent performance"],"limitations":["Tracing adds overhead; high-frequency events may impact performance","Trace storage is in-memory; no built-in persistence or log aggregation","Trace format is framework-specific; integration with external monitoring tools requires custom adapters","Sensitive information (API keys, user data) may be logged; requires careful configuration"],"requires":["Python 3.9+","EventEmitter implementation in framework","Logging configuration","Optional: external monitoring tools (DataDog, New Relic) with custom adapters"],"input_types":["execution events","role interactions","code generation and execution results"],"output_types":["structured execution logs","traces with timestamps and role attribution","performance metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_11","uri":"capability://automation.workflow.evaluation.and.testing.framework.for.agent.performance.assessment","name":"evaluation and testing framework for agent performance assessment","description":"Provides evaluation infrastructure for assessing agent performance on benchmarks and custom test cases. The framework includes evaluation datasets, metrics, and testing utilities that enable quantitative assessment of agent capabilities. Evaluation results are tracked and can be compared across different configurations or model versions, supporting iterative improvement of agent prompts and settings.","intents":["I want to measure how well my agent performs on standard benchmarks","I need to test agent behavior on custom datasets before production deployment","I want to compare agent performance across different LLM models or configurations"],"best_for":["teams iterating on agent prompts and configurations","organizations validating agent performance before deployment","researchers benchmarking agent capabilities"],"limitations":["Evaluation datasets are limited; custom benchmarks require manual creation","Metrics are task-specific; no universal evaluation metric for all agent tasks","Evaluation is computationally expensive; running full benchmarks requires significant LLM API costs","Results are sensitive to LLM non-determinism; repeated evaluations may show variance"],"requires":["Python 3.9+","Evaluation datasets (provided or custom)","LLM API access for evaluation runs","Metrics definition and implementation"],"input_types":["test cases with expected outputs","agent configurations","evaluation metrics"],"output_types":["evaluation results (pass/fail, scores)","performance metrics","comparison reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_12","uri":"capability://memory.knowledge.session.management.with.stateful.conversation.and.execution.history","name":"session management with stateful conversation and execution history","description":"Manages agent sessions that maintain conversation history, execution context, and state across multiple user interactions. Each session has a unique identifier and persists the full interaction history including user messages, agent responses, generated code, and execution results. Sessions can be resumed, allowing users to continue conversations from previous states. Session state includes the current execution context (variables, DataFrames) and conversation history, enabling the agent to maintain continuity across interactions.","intents":["I want to pause an agent interaction and resume it later without losing context","I need to maintain separate sessions for different tasks or users","I want to access the full history of a conversation including generated code and results"],"best_for":["teams running long-running analytics workflows across multiple sessions","applications supporting multiple concurrent users","developers building agent systems with session persistence"],"limitations":["Session state is in-memory by default; no built-in persistence to disk or database","Session resumption requires manual session ID management; no automatic session recovery","Large sessions (many interactions) consume significant memory; no automatic session cleanup","Session isolation is process-level; distributed deployments require custom session sharing"],"requires":["Python 3.9+","Session management module (taskweaver/session/session.py)","Optional: database for persistent session storage","Optional: distributed cache (Redis) for multi-process session sharing"],"input_types":["user messages","session identifiers","session configuration"],"output_types":["session state (conversation history, execution context)","session metadata (creation time, last interaction)","session identifiers for resumption"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_2","uri":"capability://planning.reasoning.role.based.multi.agent.orchestration.with.controlled.communication","name":"role-based multi-agent orchestration with controlled communication","description":"Implements a role-based architecture where specialized agents (Planner, CodeInterpreter, External Roles like WebExplorer) communicate exclusively through a central Planner mediator. Each role is defined with specific capabilities and responsibilities, and all inter-role communication flows through the Planner to ensure coordinated task execution. Roles are configured via YAML definitions that specify their prompts, capabilities, and communication protocols, enabling extensibility without modifying core framework code.","intents":["I want to add specialized agents (e.g., web scraper, image analyzer) without modifying the core framework","I need to ensure agents coordinate through a central orchestrator to avoid conflicting actions","I want to define custom roles with specific domain expertise for my analytics tasks"],"best_for":["teams building multi-agent systems with clear role separation","organizations extending TaskWeaver with domain-specific agents","developers who need controlled, auditable agent communication flows"],"limitations":["All communication routes through Planner, creating a potential bottleneck for high-frequency inter-agent communication","Role definitions are static per session; dynamic role creation/removal requires session restart","External roles require explicit integration; no automatic discovery or plugin-based role loading","Communication overhead increases with number of roles; scaling to 10+ roles may introduce latency"],"requires":["YAML configuration files defining each role (see taskweaver/planner/planner_prompt.yaml)","LLM endpoint for each role that requires language generation","Role implementation classes inheriting from base Role interface","Session object managing role lifecycle"],"input_types":["YAML role definitions","role-specific prompts and instructions","inter-role messages (structured JSON)"],"output_types":["role responses (text, code, structured data)","execution logs with role attribution","task completion status per role"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_3","uri":"capability://tool.use.integration.plugin.system.for.wrapping.custom.algorithms.and.external.tools","name":"plugin system for wrapping custom algorithms and external tools","description":"Extends TaskWeaver's capabilities through a plugin architecture where custom algorithms, APIs, and domain-specific tools are wrapped as callable functions with YAML-defined schemas. Plugins are registered with the framework and made available to the CodeInterpreter role, which can invoke them as part of generated code. Each plugin has a YAML configuration specifying function signature, parameters, return types, and documentation, enabling the LLM to understand and call plugins correctly without hardcoding integration logic.","intents":["I want to integrate my custom ML model or algorithm into the agent without modifying core code","I need to expose external APIs (databases, web services) as callable functions in generated code","I want to define reusable domain-specific tools that the agent can discover and use automatically"],"best_for":["teams with custom analytics libraries or proprietary algorithms","organizations integrating TaskWeaver with existing tool ecosystems","developers building domain-specific agent extensions"],"limitations":["Plugin discovery is static; plugins must be registered at framework initialization","YAML schema definition is manual; no automatic schema inference from Python function signatures","Plugin execution runs in the same process as the agent; resource-intensive plugins can block execution","Error handling in plugins must be explicit; framework provides limited automatic error recovery"],"requires":["Python 3.9+","Plugin base class from taskweaver.plugin module","YAML configuration file for each plugin with schema definition","Plugin implementation class with execute() method"],"input_types":["YAML plugin configuration files","Python plugin implementation classes","function parameters matching schema definition"],"output_types":["plugin execution results (any JSON-serializable type)","plugin metadata (schema, documentation)","execution logs with plugin attribution"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_4","uri":"capability://memory.knowledge.memory.and.attachment.system.for.preserving.execution.context","name":"memory and attachment system for preserving execution context","description":"Manages conversation history and code execution history through an attachment-based memory system (taskweaver/memory/attachment.py) that serializes execution context including variables, DataFrames, and intermediate results. Attachments are JSON-serializable objects that capture the state of the Python interpreter after each code execution, enabling the framework to reconstruct context for subsequent planning and execution cycles. This system bridges the gap between natural language conversation history and code execution state.","intents":["I want the agent to remember intermediate computation results without re-running code","I need to inspect what data structures and variables exist at any point in the conversation","I want to export execution history including both chat and code state for auditing or replay"],"best_for":["teams requiring full execution traceability and auditability","data analysts who need to inspect intermediate results","developers building agent systems with replay or debugging capabilities"],"limitations":["Attachment serialization adds overhead; large DataFrames (>100MB) may cause performance degradation","JSON serialization limits attachment types; custom Python objects require custom serializers","Memory grows unbounded with conversation length; no automatic pruning or summarization","Attachment reconstruction is synchronous; no lazy loading for large datasets"],"requires":["Python 3.9+","Session object with memory management enabled","JSON serialization support for all objects in execution context","Optional: pandas for DataFrame serialization"],"input_types":["execution context (variables, state)","conversation history","code execution results"],"output_types":["serialized attachments (JSON)","execution history with state snapshots","context for LLM planning"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_5","uri":"capability://text.generation.language.llm.agnostic.provider.integration.with.multi.model.support","name":"llm-agnostic provider integration with multi-model support","description":"Abstracts LLM provider differences through a unified interface that supports OpenAI, Anthropic, and local LLM endpoints with compatible APIs. The framework decouples LLM selection from agent logic through configuration, enabling role-specific model assignment (e.g., Planner uses GPT-4, CodeInterpreter uses GPT-3.5). LLM calls are made through a provider abstraction layer that handles API differences, token counting, and response parsing, allowing seamless model switching without code changes.","intents":["I want to use different LLM providers (OpenAI, Anthropic, local models) interchangeably","I need to assign different models to different roles based on cost/capability tradeoffs","I want to run TaskWeaver with local LLMs without cloud dependencies"],"best_for":["teams evaluating multiple LLM providers","organizations with local LLM infrastructure","developers building cost-optimized agent systems"],"limitations":["Token counting varies by provider; no unified token budgeting across models","Response format differences require provider-specific parsing logic","Local LLM support requires compatible API (OpenAI-compatible endpoint); proprietary APIs not supported","Model-specific features (e.g., vision, function calling) require conditional code paths"],"requires":["LLM API key or endpoint URL for chosen provider","Configuration file specifying provider and model name","Python 3.9+ with requests library for API calls","Optional: local LLM server running on compatible endpoint"],"input_types":["prompts (text)","conversation history","system instructions"],"output_types":["LLM responses (text, code)","token usage metadata","structured outputs (JSON if requested)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_6","uri":"capability://code.generation.editing.code.generation.with.context.aware.variable.and.library.management","name":"code generation with context-aware variable and library management","description":"Generates Python code snippets that reference variables and libraries from previous execution context, enabling the CodeInterpreter to write code that builds on prior state without re-importing or re-computing. The code generation process (driven by the CodeInterpreter role) has access to the current execution context (available variables, imported modules, DataFrames) and generates code that leverages this context. This is achieved through prompt engineering that includes context information and validation that generated code references only available symbols.","intents":["I want the agent to generate code that reuses previously computed DataFrames without re-fetching data","I need the agent to know which libraries are already imported to avoid redundant imports","I want generated code to reference variables from previous steps in the same session"],"best_for":["data analysts running iterative analytics workflows","teams building agents that perform multi-step transformations","developers optimizing agent efficiency by avoiding redundant computation"],"limitations":["Context information must be explicitly passed to LLM; large contexts (>10K tokens) may exceed model limits","No static analysis of generated code; undefined variable references are caught only at runtime","Variable naming conflicts are not automatically resolved; agent must manage namespace carefully","Context information is text-based; complex object types may not be accurately represented in prompts"],"requires":["Python 3.9+","CodeInterpreter role with access to execution context","LLM with sufficient context window to include execution state","Code validation logic to check for undefined references"],"input_types":["task descriptions","current execution context (variables, imports)","previous code snippets"],"output_types":["Python code snippets","code with variable references","code with library calls"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_7","uri":"capability://automation.workflow.interactive.console.and.web.ui.for.agent.interaction","name":"interactive console and web ui for agent interaction","description":"Provides two user interfaces for interacting with TaskWeaver agents: a console-based chat interface (taskweaver/chat/console/chat.py) for terminal-based interaction and a web UI for browser-based access. Both interfaces manage session state, display execution results and code, and enable users to provide feedback or corrections. The console interface uses event-driven architecture (EventEmitter) to handle asynchronous agent responses, while the web UI provides a more polished experience with code syntax highlighting and result visualization.","intents":["I want to interact with the agent through a simple command-line interface","I need a web-based UI to share agent interactions with non-technical stakeholders","I want to see generated code and execution results in a readable format"],"best_for":["developers prototyping agents locally via CLI","teams deploying agents for end-user interaction","organizations needing both technical and non-technical interfaces"],"limitations":["Console interface lacks code syntax highlighting; web UI requires separate deployment","Both interfaces are single-session; no multi-user session management","Web UI requires Node.js/React build; no pre-built Docker image provided","Session state is in-memory; no persistence across server restarts"],"requires":["Python 3.9+ for console interface","Node.js 14+ and React for web UI development","Running TaskWeaver session with configured LLM","Optional: Docker for containerized deployment"],"input_types":["natural language user queries","user feedback on generated code"],"output_types":["agent responses (text)","generated code with syntax highlighting","execution results and logs","session history"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_8","uri":"capability://tool.use.integration.external.role.integration.for.specialized.tasks.web.exploration.image.analysis","name":"external role integration for specialized tasks (web exploration, image analysis)","description":"Extends TaskWeaver with specialized external roles (e.g., WebExplorer for web scraping, ImageReader for image analysis) that are coordinated through the Planner. External roles are implemented as separate agents with their own LLM prompts and capabilities, communicating with the Planner through the standard message-passing protocol. This enables TaskWeaver to handle tasks beyond pure data analytics by delegating to specialized agents while maintaining the code-first execution model.","intents":["I want the agent to fetch data from web pages as part of an analytics workflow","I need the agent to analyze images and extract information for downstream processing","I want to add domain-specific capabilities without modifying the core framework"],"best_for":["teams building multi-modal analytics workflows","organizations integrating web data into analytics pipelines","developers extending TaskWeaver with specialized capabilities"],"limitations":["External roles are not built-in; require custom implementation and registration","Web exploration requires handling dynamic content, JavaScript rendering, and anti-scraping measures","Image analysis depends on external APIs or local models; no built-in vision capability","External role communication adds latency; each role invocation requires LLM call"],"requires":["External role implementation class","YAML configuration for external role","LLM endpoint for external role","Optional: web scraping libraries (BeautifulSoup, Selenium), vision APIs (OpenAI Vision, Claude Vision)"],"input_types":["task descriptions requiring external role","URLs or image data","structured parameters for external role"],"output_types":["web page content or structured data","image analysis results","data ready for downstream processing"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__cap_9","uri":"capability://automation.workflow.configuration.driven.framework.setup.with.yaml.based.customization","name":"configuration-driven framework setup with yaml-based customization","description":"Enables framework configuration through YAML files that define roles, LLM providers, plugins, and execution parameters without requiring code changes. Configuration files specify role prompts (e.g., planner_prompt.yaml), LLM endpoints, plugin registrations, and execution settings. This declarative approach allows non-developers to customize agent behavior and enables version control of agent configurations alongside code. Configuration is validated at startup to catch errors early.","intents":["I want to customize agent behavior without modifying Python code","I need to version control agent configurations alongside my codebase","I want to enable non-developers to adjust agent prompts and settings"],"best_for":["teams with non-technical stakeholders who need to customize agents","organizations requiring configuration version control","developers building reusable agent templates"],"limitations":["YAML configuration is static; dynamic configuration changes require framework restart","Complex customizations still require Python code; YAML is limited to declarative configuration","Configuration validation is basic; invalid YAML may not be caught until runtime","No configuration inheritance or templating; large configurations become repetitive"],"requires":["YAML configuration files in framework directory","Python 3.9+ with PyYAML library","Understanding of TaskWeaver configuration schema","Optional: JSON schema for configuration validation"],"input_types":["YAML configuration files","role definitions","LLM provider settings","plugin registrations"],"output_types":["parsed configuration objects","validation errors","configured framework instance"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"taskweaver__headline","uri":"capability://planning.reasoning.code.first.ai.agent.framework.for.data.analytics","name":"code-first ai agent framework for data analytics","description":"TaskWeaver is a code-first AI agent framework that transforms user requests into executable code plans, enabling complex data analytics tasks with rich data structures and stateful conversations.","intents":["best code-first AI agent framework","code-first framework for data analytics","AI framework for executing complex data tasks","top frameworks for intelligent task automation"],"best_for":["data scientists","developers working on AI projects"],"limitations":["requires programming knowledge"],"requires":["Python"],"input_types":["user requests","code snippets"],"output_types":["executable code plans","data analysis results"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM API access (OpenAI, Anthropic, or local LLM via compatible endpoint)","taskweaver package installed from GitHub","YAML configuration file with Planner role definition","Python 3.9+ with standard library","Code Execution Service component running (taskweaver/code_execution_service/)","Session object initialized with persistent state management","Optional: pandas, numpy, and other data processing libraries for analytics tasks","EventEmitter implementation in framework","Logging configuration"],"failure_modes":["Planner role is specialized for data analytics tasks; less suitable for non-analytical workflows","Code generation quality depends on LLM capability; complex domain logic may require manual refinement","Stateful execution requires persistent session management; distributed execution across multiple processes requires custom state serialization","Execution is single-threaded and sequential; parallel code execution requires explicit task decomposition","In-memory state is lost when session terminates; requires explicit serialization for persistence across restarts","Code execution timeout and resource limits must be configured; runaway code can block the agent","No built-in sandboxing beyond Python's native restrictions; untrusted code execution requires external containerization","Tracing adds overhead; high-frequency events may impact performance","Trace storage is in-memory; no built-in persistence or log aggregation","Trace format is framework-specific; integration with external monitoring tools requires custom adapters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=taskweaver","compare_url":"https://unfragile.ai/compare?artifact=taskweaver"}},"signature":"J3ElifQSua8Sex6gznbvquBtkp3ZPDSDH0Z93o9jD6HCvlIPr4r5BUBkvys8AtE7+f7xiw6AmFFS2S06XMFABA==","signedAt":"2026-06-20T05:03:10.189Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/taskweaver","artifact":"https://unfragile.ai/taskweaver","verify":"https://unfragile.ai/api/v1/verify?slug=taskweaver","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}