knowns vs OpenAI Agents SDK

Stores project tasks as markdown files in .knowns/tasks/ directory with Git-friendly format, enabling AI agents to maintain persistent memory across sessions. Tasks include acceptance criteria, implementation plans, and @doc/path/@task-N references that create a context graph. When an AI agent is assigned a task, it parses all embedded references, recursively follows links to documentation, and builds a complete context graph before implementation — solving the stateless AI problem where context must be re-explained each session.

Unique: Uses Git-tracked markdown files with @reference syntax for context linking instead of a centralized database, making the entire knowledge base human-readable, version-controlled, and portable. The reference resolution happens at read-time (when AI agent accesses a task) rather than at write-time, enabling dynamic context graphs that adapt as documentation changes.

vs alternatives: Unlike Jira or Linear which store context in proprietary databases, knowns makes task context Git-trackable and AI-readable; unlike simple markdown folders, it provides structured reference linking and recursive context resolution for AI agents.

Implements a Model Context Protocol (MCP) server that exposes the task and documentation system to AI agents via standardized protocol bindings. When an AI agent connects via MCP, it can query tasks, resolve references, and retrieve full context graphs without parsing markdown directly. The MCP server translates internal FileStore operations into MCP resource and tool endpoints, enabling seamless integration with Claude, GPT, and other MCP-compatible agents.

Unique: Implements MCP as a first-class integration point rather than an afterthought, making the entire task/doc system queryable via standard protocol. The MCP server translates FileStore operations into protocol-native endpoints, enabling AI agents to resolve context graphs without understanding knowns' internal markdown structure.

vs alternatives: Provides standardized MCP integration vs. custom API endpoints; enables any MCP-compatible agent to access context without custom adapters; follows protocol standards for interoperability.

Implements knowns as a TypeScript codebase that compiles to JavaScript and runs on Node.js, Deno, and browser runtimes. The build system uses Vite for bundling and supports multiple entry points (CLI, server, web UI). Core logic is runtime-agnostic, with platform-specific adapters for file I/O, HTTP, and other system operations. This enables the same codebase to run as a CLI tool, HTTP server, web application, and embedded library.

Unique: Implements a single TypeScript codebase with runtime-agnostic core logic and platform-specific adapters, enabling deployment as CLI, server, and web application without code duplication. Vite-based build system supports multiple entry points and targets.

vs alternatives: More flexible than single-runtime tools (CLI-only or server-only); enables code reuse across platforms; simpler than maintaining separate implementations for each runtime.

Provides a React-based web interface that renders the same task and documentation data as the CLI. The web UI includes a Kanban board for visual task management, a documentation browser for exploring linked docs, and a task detail view with full context. The UI communicates with the knowns server via HTTP API and WebSocket for real-time updates. All UI state is derived from the FileStore, ensuring consistency with CLI and other interfaces.

Unique: Implements web UI as a separate React application that communicates with knowns server via standard HTTP API and WebSocket, rather than embedding UI logic in the server. This enables independent UI updates and scaling.

vs alternatives: Lighter than Jira/Linear UI (no complex state management) but more polished than plain CLI; provides visual overview for non-technical stakeholders while maintaining CLI-first developer experience.

Parses @doc/path and @task-N reference syntax embedded in task descriptions and documentation, then recursively resolves all linked documents to build a complete context graph. When an AI agent requests a task, the system traverses the reference tree, fetches all linked documentation, and returns a flattened context structure. This enables AI agents to understand not just the immediate task but all architectural decisions, patterns, and related work that inform implementation.

Unique: Uses a simple @reference syntax embedded directly in markdown rather than a separate link database, making references human-readable and editable. Resolution happens at read-time with recursive traversal, enabling dynamic context graphs that adapt as documentation changes without requiring index updates.

vs alternatives: Simpler than graph database approaches (no schema, no query language) but more powerful than flat document lists; enables AI agents to discover context through reference chains rather than requiring explicit context specification.

Provides a command-line interface (knowns/kn commands) for creating, updating, and organizing tasks and documentation with built-in Kanban board state management. Tasks move through predefined states (backlog, in-progress, review, done) tracked in markdown frontmatter. The CLI supports batch operations, filtering, and status transitions. A companion web UI (React-based) renders the same data as a visual Kanban board, with both interfaces operating on the shared .knowns/ file store.

Unique: Implements a dual-interface design where CLI and web UI operate on the same file-based storage, avoiding database synchronization issues. Kanban state is stored in markdown frontmatter, making workflow status Git-trackable and mergeable.

vs alternatives: Lighter than Jira/Linear (no server, no database) but more structured than plain markdown folders; CLI-first design appeals to developers while web UI provides visual overview for non-technical stakeholders.

Maintains a version history of all task and documentation changes using a VersionStore layer that tracks file mutations over time. Each change is recorded with timestamp and metadata, enabling rollback to previous states. The versioning system operates transparently on top of the FileStore, capturing all mutations whether they come from CLI, web UI, or API calls. This enables audit trails and recovery from accidental deletions or edits.

Unique: Implements versioning at the FileStore layer (below CLI/web UI) rather than as a separate feature, capturing all mutations regardless of interface. Version history is stored alongside data files, making it portable and Git-compatible.

vs alternatives: Provides version history without relying on Git commits; enables rollback without understanding Git; simpler than full Git integration but less powerful than Git's branching model.

Stores project documentation as markdown files in .knowns/docs/ with YAML frontmatter for metadata (title, tags, created, updated). Documentation supports standard markdown syntax plus knowns-specific reference syntax (@doc/path, @task-N) for linking to other docs and tasks. The system treats documentation as first-class entities that can be queried, linked, and versioned alongside tasks. A documentation browser in the web UI enables visual navigation of the doc structure.

Unique: Treats documentation as first-class entities with structured metadata and reference linking, rather than as unstructured markdown files. Documentation is queryable, linkable, and versionable alongside tasks, creating a unified knowledge system.

vs alternatives: Simpler than wiki systems (no database, no special syntax) but more structured than plain markdown folders; enables AI agents to discover and link documentation through reference chains.

openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tracking Modes Server-Managed Conversations Realtime and Voice Agents Realtime System Overview RealtimeSession Orchestration OpenAI Realtime WebSocket Model Audio Pipeline and Voice Activity Detection Realtime Configuration Realtime Tool Execution and Guardrails Interruption Handling

Getting Started | openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tracking Modes Server-Managed Conversations Realtime and Voice Agents Realtime System Overview RealtimeSession Orchestration OpenAI Realtime WebSocket Model Audio Pipeline and Voice Activity Detection Realtime Configuration Realtime Tool Execution and Guardrails Int

Core Concepts | openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tracking Modes Server-Managed Conversations Realtime and Voice Agents Realtime System Overview RealtimeSession Orchestration OpenAI Realtime WebSocket Model Audio Pipeline and Voice Activity Detection Realtime Configuration Realtime Tool Execution and Guardrails Inter

openai/openai-agents-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki openai/openai-agents-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 7 May 2026 ( 3a11cf ) Overview Getting Started Core Concepts Agent Architecture Runner and Execution Flow RunResult and Output Management RunState and Resumption Context and Dependency Injection Run Configuration Tools and Capabilities Tool System Overview Function Tools Hosted Tools Local Runtime Tools Agent as Tool Tool Use Behavior Tool Approval and Human-in-the-Loop Multi-Agent Coordination Handoff System Manager Pattern vs Handoffs Handoff Configuration Handoff History Management Safety and Validation Guardrail Architecture Input and Output Guardrails Tool Guardrails Guardrail Execution Strategies Tripwire Mechanism Model Integration Model Abstraction Layer OpenAI Responses API OpenAI Chat Completions API LiteLLM Multi-Provider Support Model Settings and Configuration Retry Policies Streaming Responses Session and Memory Management Session Protocol Session Implementations Conversation Tr