Claude/Gemini/Codex 10-100x faster with pandō vs OpenAI Agents SDK

Pandō compresses prompts and context before sending to LLMs (Claude, Gemini, Codex) using a proprietary compression algorithm that reduces token count while preserving semantic meaning. This works by identifying and removing redundant information, collapsing repetitive patterns, and applying lossless compression techniques to the input prompt. The compressed prompt is then sent to the target LLM API, reducing both latency and cost proportional to the compression ratio achieved.

Unique: Applies CAD (Computer-Aided Design) principles to code prompts — treating prompt structure as a designable artifact that can be optimized for compression without semantic loss, rather than treating prompts as opaque text strings

vs alternatives: Claims 10-100x speedup over direct LLM calls by compressing prompts before transmission, whereas standard LLM APIs process full context unoptimized

Pandō provides a unified interface that accepts prompts and routes them to Claude, Gemini, or Codex while automatically applying compression before transmission. The abstraction layer handles provider-specific API differences (authentication, request/response formats, rate limiting) and transparently applies compression optimization. This allows developers to switch between LLM providers or use multiple providers without changing application code, while benefiting from compression on all providers.

Unique: Combines provider abstraction with automatic compression — most multi-provider frameworks (LangChain, LiteLLM) handle routing but don't optimize prompts, whereas Pandō compresses before routing to reduce costs across all providers simultaneously

vs alternatives: More efficient than LangChain or LiteLLM for cost optimization because it compresses prompts before sending to any provider, whereas those frameworks send full context unoptimized

Pandō applies CAD (Computer-Aided Design) principles to code prompts by parsing code structure (AST-level or semantic understanding) and intelligently selecting which parts of a codebase are relevant to include in the prompt. Rather than including entire files or arbitrary context windows, it identifies dependencies, related functions, and relevant patterns, then structures the prompt to emphasize important code while compressing boilerplate and repetitive patterns. This enables more effective code generation with smaller context windows.

Unique: Treats code prompts as designable artifacts (CAD metaphor) that can be optimized for both compression and relevance — uses semantic code understanding to select context rather than naive token-counting or file-based selection like most code generation tools

vs alternatives: More intelligent than Copilot's context selection because it understands code structure and dependencies rather than using simple recency/frequency heuristics, enabling better generations with smaller context

Pandō provides batch processing capabilities that compress multiple prompts in parallel and estimate the cost savings and latency improvements before sending to LLMs. The system analyzes a batch of prompts, applies compression to each, calculates compression ratios, and projects API costs and response times. This enables developers to understand the impact of compression on their workload and make informed decisions about which prompts to optimize.

Unique: Provides pre-execution cost/latency estimation for compressed prompts — most LLM tools only show costs after API calls, whereas Pandō estimates impact before committing resources

vs alternatives: More transparent than direct LLM API usage because it shows compression impact and cost savings upfront, enabling informed optimization decisions

Pandō handles streaming LLM responses from compressed prompts by decompressing and reconstructing the output in real-time as tokens arrive. The system maintains state about the compression context used for the original prompt and applies inverse transformations to the streamed response, ensuring that code generation and other outputs are properly reconstructed even when using streaming APIs. This enables low-latency streaming interactions while maintaining compression benefits.

Unique: Applies compression to streaming responses by maintaining decompression state across token boundaries — most streaming implementations don't compress because stateless token-by-token processing makes compression difficult

vs alternatives: Enables streaming with compression benefits, whereas standard streaming APIs send uncompressed tokens, resulting in higher latency and cost for the same quality

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