agentflow

name

agentflow

description

Expert guidance for building, debugging, and extending applications with AgentFlow (10xscale-agentflow). TRIGGER when: code imports from agentflow (e.g. `from agentflow import`, `StateGraph`, `Agent`, `ToolNode`, `AgentState`); user references `agentflow.json` or CLI commands (`agentflow init`, `agentflow api`, `agentflow play`, `agentflow build`, `agentflow skills`); user is building graph-based multi-agent workflows, tools, memory, checkpointing, or streaming with this framework. SKIP: generic Python or multi-agent questions not referencing agentflow; other frameworks (LangGraph, CrewAI, AutoGen) unless comparing.

Agentflow Project Skill

Use this skill when working in an Agentflow project. Agentflow is a multi-agent framework that wraps official OpenAI and Google SDK capabilities behind a unified graph, agent, tool, state, storage, API, CLI, and TypeScript client interface.

Treat https://agentflow.10xscale.ai/ as the first source of truth for public package names, install commands, and user-facing behavior. Use implementation source after the docs establish the intended API.

Workflow

1. Identify the published package or docs surface involved:

   • PyPI core Python SDK: 10xscale-agentflow (pip install 10xscale-agentflow), source at https://github.com/10xHub/Agentflow/tree/main/agentflow/agentflow
   • PyPI API/CLI SDK: 10xscale-agentflow-cli (pip install 10xscale-agentflow-cli), source at https://github.com/10xHub/Agentflow/tree/main/agentflow-api/agentflow_cli
   • npm TypeScript SDK: @10xscale/agentflow-client (npm install @10xscale/agentflow-client), source at https://github.com/10xHub/Agentflow/tree/main/agentflow-client/src
   • Main docs: https://agentflow.10xscale.ai/
   • Playground/UI: agentflow play command after installed cli

2. Read the matching reference file before changing behavior:

   Core Python SDK

   • Architecture and package flow: .github/skills/agentflow/references/architecture.md
   • Agent constructor, provider, reasoning, retry, fallback, output_schema: .github/skills/agentflow/references/agents-and-tools.md
   • Graph construction, nodes, edges, compile, interrupts, config keys: .github/skills/agentflow/references/state-graph.md
   • State, messages, and content blocks: .github/skills/agentflow/references/state-and-messages.md
   • Thread and checkpointing: .github/skills/agentflow/references/checkpointing-and-threads.md
   • Dependency injection (InjectQ): .github/skills/agentflow/references/dependency-injection.md
   • Multimodal files and media stores: .github/skills/agentflow/references/media-and-files.md
   • Long-term memory stores (MemoryConfig, QdrantStore, Mem0Store): .github/skills/agentflow/references/memory-and-store.md
   • Streaming, StreamChunk, SSE, ResponseGranularity: .github/skills/agentflow/references/streaming.md
   • Stream emitter for tool progress updates: .github/skills/agentflow/references/stream-emitter.md
   • Observability hooks, validators, and runtime jumps: .github/skills/agentflow/references/callbacks-and-command.md
   • Prebuilt agents (ReactAgent, PlanActReflectAgent, StructuredOutputAgent, SupervisorTeamAgent, SwarmAgent, RAGAgent) and tools: .github/skills/agentflow/references/prebuilt-agents-and-tools.md
   • Event publishers and A2A/ACP runtime protocols: .github/skills/agentflow/references/publishers-and-runtime-protocols.md
   • Context management, ID generation, and background tasks: .github/skills/agentflow/references/context-id-background.md
   • Provider internals and adapters: .github/skills/agentflow/references/providers-and-adapters.md
   • Prompt-injection and validation safety: .github/skills/agentflow/references/security-and-validators.md
   • Realtime audio-to-audio voice agents (AudioAgent, Gemini Live, arealtime, WebSocket bridge): .github/skills/agentflow/references/realtime.md

   API/CLI SDK

   • CLI commands and generated project files: .github/skills/agentflow/references/cli-commands.md
   • agentflow.json and dependency loading: .github/skills/agentflow/references/api-configuration.md
   • API auth and authorization: .github/skills/agentflow/references/auth-and-authorization.md
   • API environment, settings, and middleware: .github/skills/agentflow/references/api-settings-and-middleware.md
   • Rate limiting (config, backends, headers, custom backend): .github/skills/agentflow/references/rate-limiting.md
   • REST routes and error behavior: .github/skills/agentflow/references/rest-api-and-errors.md
   • API Snowflake IDs and thread naming: .github/skills/agentflow/references/id-and-thread-name-generators.md
   • API server and deployment runtime: .github/skills/agentflow/references/production-runtime.md

   TypeScript client SDK

   • REST and TypeScript client surface: .github/skills/agentflow/references/api-client.md
   • Browser/client-side tool execution: .github/skills/agentflow/references/remote-tools.md
   • TypeScript auth helpers and structured errors: .github/skills/agentflow/references/client-auth-and-errors.md
   • TypeScript messages, invoke, and stream details: .github/skills/agentflow/references/client-messages-invoke-stream.md
   • TypeScript thread, memory, and file APIs: .github/skills/agentflow/references/client-threads-memory-files.md

   Testing and QA

   • Unit testing without LLM calls (TestAgent, QuickTest, MockToolRegistry, agentflow test): .github/skills/agentflow/references/unit-testing.md
   • Evaluation framework (EvalSet, criteria, AgentEvaluator, QuickEval, UserSimulator, agentflow eval): .github/skills/agentflow/references/evaluation.md
   • Testing helpers overview: .github/skills/agentflow/references/testing-and-evaluation.md

3. Prefer existing Agentflow abstractions over new custom wiring:

   • Build workflows with StateGraph, Agent, ToolNode, AgentState, and Message.
   • Use prebuilt agents (ReactAgent, PlanActReflectAgent, StructuredOutputAgent, SupervisorTeamAgent, SwarmAgent, RAGAgent) for common patterns before hand-writing graph loops.
   • Persist conversation state with checkpointers; use stores only for cross-thread memory.
   • Put business services in InjectQ instead of global variables.
   • Keep API/CLI graph modules storage-agnostic and wire dependencies through agentflow.json.

4. Verify against source when implementation details matter. Public names and expected behavior should match https://agentflow.10xscale.ai/; source under https://github.com/10xHub/Agentflow (core), https://github.com/10xHub/agentflow-cli (API/CLI), and https://github.com/10xHub/agentflow-client (TypeScript) explains how that behavior is implemented.

Local Conventions

• A compiled graph is normally loaded once by the API server and reused per request.
• Public package naming matters: use 10xscale-agentflow, 10xscale-agentflow-cli, and @10xscale/agentflow-client in user-facing docs and examples, not repository folder names.
• Every persisted interaction should include config.thread_id.
• Tools need docstrings and type annotations so model-facing schemas are useful.
• Injectable tool and node parameters (state, config, tool_call_id) are hidden from the model schema.
• For production, avoid process-local storage for shared state; use durable checkpointer/store backends.
• Add observability or audit side effects by registering a GraphLifecycleHook on CallbackManager — do not wrap ainvoke() / astream() calls in application code to achieve the same result.
• reasoning_config is on by default at medium effort; disable explicitly with reasoning_config=None when not needed.
• Provider is auto-detected from the model name; use base_url for third-party OpenAI-compatible APIs (Ollama, DeepSeek, OpenRouter).