{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-playbooks-ai--playbooks","slug":"playbooks-ai--playbooks","name":"playbooks","type":"agent","url":"https://runplaybooks.ai","page_url":"https://unfragile.ai/playbooks-ai--playbooks","categories":["ai-agents"],"tags":["agent-orchestration","ai-agents","ai-agents-framework","ai-automation","context-engineering","conversational-ai","llm","llm-framework","low-code","mcp","multi-agent-systems","natural-language-programming","software-3-0","workflow-automation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-playbooks-ai--playbooks__cap_0","uri":"capability://code.generation.editing.natural.language.to.executable.playbook.compilation","name":"natural language to executable playbook compilation","description":"Compiles structured natural language playbooks into PBAsm (semantic intermediate representation), a low-level instruction set designed for LLM execution. The compilation pipeline preserves semantic intent across model generations by treating playbooks as executable specifications rather than prompts, enabling forward compatibility and deterministic behavior independent of underlying LLM changes.","intents":["Write AI agent workflows in natural language without learning a programming language","Create reproducible agent behaviors that work across different LLM providers and model versions","Define control flow, state management, and context handling declaratively"],"best_for":["Non-technical domain experts building AI workflows","Teams needing version-stable agent behaviors across model upgrades","Developers prototyping multi-step agent orchestrations quickly"],"limitations":["Compilation targets PBAsm IR which adds abstraction overhead — debugging requires understanding both natural language source and IR bytecode","Complex conditional logic may require hybrid natural language + Python approach for clarity","No IDE-level syntax highlighting or real-time compilation feedback in standard editors"],"requires":["Python 3.9+","Playbooks CLI installed via pip","Valid playbook file with .pb extension"],"input_types":["natural language text (structured format)","Python code blocks (optional hybrid mode)","YAML-like variable declarations"],"output_types":["PBAsm bytecode","Executable playbook object","Compiled agent specification"],"categories":["code-generation-editing","natural-language-programming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_1","uri":"capability://automation.workflow.multi.agent.orchestration.with.channel.based.message.passing","name":"multi-agent orchestration with channel-based message passing","description":"Implements a meeting-based coordination system where agents communicate through typed message channels with built-in batching and routing. The architecture uses an event bus for asynchronous message delivery, supports cross-agent playbook calls, and manages agent lifecycle (creation, initialization, termination) with automatic load balancing for scaling agent pools.","intents":["Coordinate multiple AI agents working on interdependent tasks without manual queue management","Route messages between agents with type safety and delivery guarantees","Scale agent pools dynamically based on workload while maintaining message ordering"],"best_for":["Teams building multi-agent systems (e.g., research teams, code review workflows)","Applications requiring agent-to-agent collaboration with state synchronization","Developers needing observable, debuggable agent communication patterns"],"limitations":["Message batching introduces latency (configurable but adds ~50-200ms per batch cycle)","No built-in persistence for message queues — agent crashes lose in-flight messages unless external durability layer added","Cross-agent playbook calls require agents to be in same runtime; distributed agents need MCP bridge"],"requires":["Python 3.9+","Playbooks framework with agent system initialized","Defined agent types (AIAgent, HumanAgent, or custom subclasses)"],"input_types":["playbook definitions with agent references","message objects with typed payloads","agent configuration (model, tools, context)"],"output_types":["message delivery confirmations","agent execution results","meeting transcripts with message history"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_10","uri":"capability://automation.workflow.testing.framework.with.playbook.aware.assertions","name":"testing framework with playbook-aware assertions","description":"Provides a testing framework for validating playbook behavior through assertions on execution results, agent outputs, and message flows. Tests can verify that playbooks execute correctly, agents produce expected outputs, and multi-agent interactions follow expected patterns, with support for mocking LLM responses and deterministic test execution.","intents":["Write automated tests for playbooks to catch regressions","Verify multi-agent coordination patterns work as expected","Mock LLM responses for deterministic testing without API calls"],"best_for":["Teams building production agent systems requiring reliability","Developers practicing test-driven development with playbooks","CI/CD pipelines validating playbook changes before deployment"],"limitations":["Mocking LLM responses requires predefined response sets — complex agent behaviors may need many mock scenarios","Testing multi-agent systems is complex — test setup can be verbose for coordinating multiple agents","No built-in performance benchmarking — latency and throughput testing requires custom instrumentation"],"requires":["Python 3.9+","pytest or unittest framework","Playbooks testing utilities imported"],"input_types":["playbook definitions","test cases with expected outputs","mock LLM responses"],"output_types":["test pass/fail results","assertion error messages","execution traces for failed tests"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_11","uri":"capability://data.processing.analysis.capture.functions.for.dynamic.context.extraction","name":"capture functions for dynamic context extraction","description":"Enables playbooks to define capture functions that extract and structure data from LLM responses, user input, or external sources into typed variables. Capture functions support pattern matching, data transformation, and validation, allowing playbooks to parse unstructured LLM output into structured data for downstream processing.","intents":["Extract structured data from LLM responses without manual parsing","Validate and transform user input before using it in playbooks","Define reusable data extraction patterns across multiple playbooks"],"best_for":["Applications requiring structured data extraction from LLM outputs","Workflows with complex data validation requirements","Teams building data pipelines that consume LLM-generated content"],"limitations":["Capture functions are defined per playbook — no global library of reusable extractors","Pattern matching is limited to simple regex or structured formats — complex parsing requires Python code","Validation errors in capture functions can halt playbook execution; error recovery requires explicit handling"],"requires":["Python 3.9+","Playbook with capture function definitions","Input data matching expected format"],"input_types":["LLM responses","user input","external data sources"],"output_types":["typed variables","structured data objects","validation results"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_12","uri":"capability://automation.workflow.trigger.based.control.flow.and.conditional.execution","name":"trigger-based control flow and conditional execution","description":"Supports trigger-based control flow where playbook steps execute conditionally based on events, user input, or external signals. Triggers can be time-based (wait for duration), event-based (wait for message), or condition-based (wait for variable state), enabling reactive agent workflows that respond to external stimuli without polling.","intents":["Pause playbook execution until a specific event occurs (user input, external signal)","Implement time-based delays and scheduling within playbooks","Build reactive workflows that respond to external triggers"],"best_for":["Interactive agent applications with user-in-the-loop workflows","Event-driven systems requiring agent responses to external triggers","Long-running agents that need to wait for external conditions"],"limitations":["Trigger evaluation is polling-based by default — high-frequency triggers add CPU overhead","Complex trigger conditions require Python code — natural language trigger definitions are limited","Timeout handling for triggers can be tricky — long waits may exceed LLM context window limits"],"requires":["Python 3.9+","Playbook with trigger definitions","Event source or external signal provider"],"input_types":["trigger specifications (time, event, condition)","external events or signals"],"output_types":["trigger evaluation results","conditional execution paths"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_13","uri":"capability://automation.workflow.built.in.playbook.library.for.common.agent.patterns","name":"built-in playbook library for common agent patterns","description":"Provides a library of pre-built playbooks implementing common agent patterns (research, code review, data analysis, etc.) that can be imported and customized. Built-in playbooks serve as templates and examples, reducing boilerplate and enabling rapid prototyping of standard agent workflows.","intents":["Quickly prototype common agent workflows without building from scratch","Learn playbook patterns by studying well-designed examples","Extend built-in playbooks for domain-specific variations"],"best_for":["Teams new to Playbooks learning best practices","Rapid prototyping and MVP development","Applications implementing standard agent patterns (research, analysis, review)"],"limitations":["Built-in playbooks may not match exact domain requirements — customization is often necessary","Library is limited to common patterns — specialized workflows require custom playbook development","Updates to built-in playbooks may break customizations if not carefully versioned"],"requires":["Python 3.9+","Playbooks framework installed","Import built-in playbook in custom playbook"],"input_types":["built-in playbook definitions","customization parameters"],"output_types":["instantiated playbook with customizations","agent execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_2","uri":"capability://tool.use.integration.mcp.model.context.protocol.agent.integration.and.remote.execution","name":"mcp (model context protocol) agent integration and remote execution","description":"Integrates the Model Context Protocol to enable agents to invoke remote tools and services through standardized MCP server connections. Remote agents (RemoteAIAgent) execute playbooks in isolated processes or containers, with automatic serialization of execution state, context, and results back to the calling agent, supporting distributed multi-agent systems.","intents":["Connect agents to external tools and APIs via MCP without writing custom integration code","Execute agent playbooks in remote environments (containers, serverless, separate machines)","Build distributed agent systems where agents can delegate work to specialized remote agents"],"best_for":["Teams integrating with MCP-compatible tools (Claude, Anthropic ecosystem)","Distributed systems requiring agent isolation and sandboxing","Applications needing to scale agent execution across multiple machines"],"limitations":["Remote execution adds network latency (typically 100-500ms per round-trip) and requires reliable connectivity","State serialization/deserialization overhead for complex execution contexts — large context windows may exceed network payload limits","Debugging remote agent execution requires log aggregation; VSCode debugger only works for local agents"],"requires":["Python 3.9+","MCP server running and accessible (local or network)","RemoteAIAgent class instantiated with server connection details","Serializable execution state (no unpicklable objects in context)"],"input_types":["playbook definitions","MCP tool schemas","execution context objects"],"output_types":["tool invocation results","serialized execution state","remote agent responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_3","uri":"capability://planning.reasoning.execution.state.management.with.call.stack.and.resumable.execution","name":"execution state management with call stack and resumable execution","description":"Maintains execution state across playbook steps using a call stack that tracks variable bindings, control flow position, and LLM context. Playbooks can pause at breakpoints, wait for external events, or be resumed from checkpoints, enabling long-lived agent workflows that survive interruptions and support interactive debugging with VSCode integration.","intents":["Pause agent execution to wait for user input or external events without losing context","Resume interrupted playbooks from the exact point they paused","Debug agent behavior step-by-step with breakpoints and variable inspection"],"best_for":["Interactive agent applications requiring user-in-the-loop workflows","Long-running agents that need to survive process restarts","Development teams debugging complex agent behaviors"],"limitations":["Execution state must be serializable — complex Python objects in context require custom pickling","Resuming from checkpoints requires the same playbook version; code changes may invalidate saved state","VSCode debugger integration only works for local execution; remote agents require log-based debugging"],"requires":["Python 3.9+","Playbooks execution runtime initialized","Optional: VSCode 1.80+ with Playbooks extension for visual debugging"],"input_types":["playbook definitions","execution checkpoint (serialized state)","breakpoint specifications"],"output_types":["execution state snapshots","variable bindings at breakpoint","resumable execution context"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_4","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support.and.caching","name":"llm provider abstraction with multi-provider support and caching","description":"Abstracts LLM API differences through LLMHelper, supporting OpenAI, Anthropic, and Ollama with unified function-calling schemas, retry logic, and built-in response caching. The system preprocesses messages through a context compaction pipeline that manages token budgets, implements semantic context management, and constructs InterpreterPrompts that guide LLM execution of PBAsm instructions.","intents":["Switch between LLM providers (OpenAI, Anthropic, Ollama) without changing playbook code","Reduce LLM API costs through intelligent response caching and context compaction","Ensure consistent function-calling behavior across different LLM APIs"],"best_for":["Teams evaluating multiple LLM providers without vendor lock-in","Cost-sensitive applications needing caching and context optimization","Developers building provider-agnostic agent systems"],"limitations":["Caching is response-level only — no semantic deduplication of similar queries","Context compaction may lose nuance in edge cases; token budget constraints can truncate important context","Provider-specific features (vision, structured output) require manual playbook adjustments per provider"],"requires":["Python 3.9+","API key for at least one provider (OPENAI_API_KEY, ANTHROPIC_API_KEY, or local Ollama instance)","Model configuration in playbooks config file"],"input_types":["playbook execution context","message history","function schemas"],"output_types":["LLM responses","function call specifications","cached responses (if hit)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_5","uri":"capability://code.generation.editing.hybrid.python.natural.language.playbook.execution","name":"hybrid python + natural language playbook execution","description":"Supports mixing natural language playbook steps with embedded Python code blocks, executed through PythonExecutor and StreamingPythonExecutor. Python code runs in sandboxed environments with access to playbook variables and agent context, enabling complex logic, data transformations, and tool integrations without leaving the playbook language.","intents":["Implement complex logic (loops, conditionals, data processing) in Python within playbooks","Access external Python libraries and APIs directly from playbooks","Stream long-running Python operations (file processing, API calls) without blocking agent execution"],"best_for":["Developers comfortable with Python building hybrid workflows","Applications requiring data transformations or complex business logic","Teams integrating existing Python libraries into agent workflows"],"limitations":["Python execution is not sandboxed by default — arbitrary code can access filesystem and network","StreamingPythonExecutor adds complexity for managing async execution; errors in streaming code are harder to debug","Large Python blocks reduce playbook readability and make version control diffs harder to review"],"requires":["Python 3.9+","Playbooks framework with PythonExecutor initialized","Python code must be valid and importable (dependencies installed)"],"input_types":["natural language playbook steps","Python code blocks (triple-quoted or indented)","playbook variables and context"],"output_types":["Python execution results","modified playbook variables","streamed output (if using StreamingPythonExecutor)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_6","uri":"capability://automation.workflow.observability.and.monitoring.with.langfuse.integration","name":"observability and monitoring with langfuse integration","description":"Integrates with Langfuse for comprehensive observability, tracking LLM calls, agent execution traces, message flows, and performance metrics. The event bus system emits structured events for all playbook execution steps, enabling real-time monitoring, cost analysis, and debugging of multi-agent systems through centralized trace collection.","intents":["Monitor agent execution in production with detailed traces and performance metrics","Analyze LLM API costs and token usage across playbooks and agents","Debug multi-agent systems by replaying execution traces and message flows"],"best_for":["Production teams running AI agents at scale","Cost-conscious teams tracking LLM spending per playbook","Developers debugging complex multi-agent interactions"],"limitations":["Langfuse integration adds network latency for trace submission (typically 50-200ms per batch)","Trace data can be verbose for long-running playbooks — storage costs scale with execution complexity","Real-time monitoring requires Langfuse account and API key; no local-only observability option"],"requires":["Python 3.9+","Langfuse account and API key (LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY)","Playbooks configured with Langfuse integration enabled"],"input_types":["playbook execution events","LLM API calls and responses","agent message flows"],"output_types":["structured traces in Langfuse","performance metrics and dashboards","cost analysis reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_7","uri":"capability://automation.workflow.configuration.system.with.model.caching.and.batching.tuning","name":"configuration system with model, caching, and batching tuning","description":"Provides a hierarchical configuration system that loads settings from environment variables, config files, and runtime overrides, enabling tuning of model selection, message batching behavior, LLM caching strategies, and observability settings. Configuration precedence is clearly defined, allowing per-environment customization without code changes.","intents":["Configure different LLM models for development vs. production without code changes","Tune message batching and caching behavior for performance optimization","Enable/disable observability and monitoring per environment"],"best_for":["Teams managing multiple deployment environments (dev, staging, prod)","Applications needing to optimize costs and latency per environment","Developers tuning agent performance without modifying playbook code"],"limitations":["Configuration precedence can be confusing — environment variables override config files, which override defaults","No schema validation for config files — invalid settings fail at runtime, not load time","Hot-reloading configuration is not supported; changes require agent restart"],"requires":["Python 3.9+","Configuration file (YAML or environment variables)","Valid model names and API keys for selected providers"],"input_types":["environment variables","YAML/JSON config files","runtime configuration objects"],"output_types":["loaded configuration object","model and provider settings","batching and caching parameters"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_8","uri":"capability://automation.workflow.interactive.terminal.agent.chat.interface","name":"interactive terminal agent chat interface","description":"Provides a CLI-based chat interface for interacting with agents through the terminal, supporting real-time message streaming, multi-turn conversations, and integration with HumanAgent for user-in-the-loop workflows. The terminal interface handles message formatting, streaming output, and user input collection without requiring a web UI.","intents":["Test and interact with agents directly from the command line during development","Build conversational AI applications without implementing a custom chat UI","Enable user-in-the-loop agent workflows where agents request human input"],"best_for":["Developers prototyping and testing agents quickly","CLI-first applications and tools","Teams without frontend engineering resources"],"limitations":["Terminal interface is text-only — no support for rich media, images, or formatted output beyond ANSI colors","Streaming output can be garbled if multiple agents write simultaneously; no built-in output buffering","No session persistence — conversation history is lost when terminal closes unless explicitly saved"],"requires":["Python 3.9+","Terminal with ANSI color support (most modern terminals)","Playbooks agent initialized and configured"],"input_types":["user text input from terminal","agent playbook definitions"],"output_types":["formatted agent responses","streamed text output","conversation history (optional)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-playbooks-ai--playbooks__cap_9","uri":"capability://automation.workflow.web.based.playground.and.visual.agent.debugging","name":"web-based playground and visual agent debugging","description":"Provides a web UI for testing playbooks, visualizing agent execution flows, and debugging multi-agent interactions. The playground frontend displays execution traces, message flows between agents, variable states, and LLM responses in real-time, with support for pausing execution and inspecting state at breakpoints.","intents":["Visualize multi-agent message flows and execution order","Debug agent behavior by inspecting variable states and LLM responses","Test playbooks interactively without writing test code"],"best_for":["Teams building complex multi-agent systems","Non-technical stakeholders reviewing agent behavior","Developers debugging intricate agent coordination patterns"],"limitations":["Web playground requires running a local server — not suitable for headless/serverless deployments","Visualization can become cluttered with many agents or long message histories","Real-time updates require WebSocket connection — network latency can cause stale state display"],"requires":["Python 3.9+","Playbooks web server running (playbooks serve command)","Modern web browser with WebSocket support"],"input_types":["playbook definitions","agent configurations","execution traces"],"output_types":["visual execution flow diagrams","message sequence displays","variable state snapshots"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Playbooks CLI installed via pip","Valid playbook file with .pb extension","Playbooks framework with agent system initialized","Defined agent types (AIAgent, HumanAgent, or custom subclasses)","pytest or unittest framework","Playbooks testing utilities imported","Playbook with capture function definitions","Input data matching expected format","Playbook with trigger definitions"],"failure_modes":["Compilation targets PBAsm IR which adds abstraction overhead — debugging requires understanding both natural language source and IR bytecode","Complex conditional logic may require hybrid natural language + Python approach for clarity","No IDE-level syntax highlighting or real-time compilation feedback in standard editors","Message batching introduces latency (configurable but adds ~50-200ms per batch cycle)","No built-in persistence for message queues — agent crashes lose in-flight messages unless external durability layer added","Cross-agent playbook calls require agents to be in same runtime; distributed agents need MCP bridge","Mocking LLM responses requires predefined response sets — complex agent behaviors may need many mock scenarios","Testing multi-agent systems is complex — test setup can be verbose for coordinating multiple agents","No built-in performance benchmarking — latency and throughput testing requires custom instrumentation","Capture functions are defined per playbook — no global library of reusable extractors","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.18070453512845985,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2026-04-06T02:12:58Z"},"community":{"stars":83,"forks":13,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=playbooks-ai--playbooks","compare_url":"https://unfragile.ai/compare?artifact=playbooks-ai--playbooks"}},"signature":"k91bxH0Jj+ANbuF6HZ4FAg2rtQxsLz6Gl4e0tkSS5P2Fj39UCZUsrkw5oS0nw8A+utjJfg3aupp+RrdghlD9AA==","signedAt":"2026-06-21T21:38:11.478Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/playbooks-ai--playbooks","artifact":"https://unfragile.ai/playbooks-ai--playbooks","verify":"https://unfragile.ai/api/v1/verify?slug=playbooks-ai--playbooks","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"}}