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Documentation Index

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create-context-graph supports 8 agent frameworks. Each generates a different agent.py implementation while sharing the same FastAPI backend, Neo4j client, and Next.js frontend. This page helps you pick the right one.

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Comparison table

FrameworkCLI flag valueLLM providerStreamingAsync modelAPI key required
PydanticAIpydanticaiAnthropic (configurable)FullNative asyncANTHROPIC_API_KEY
Claude Agent SDKclaude-agent-sdkAnthropicFullNative asyncANTHROPIC_API_KEY
OpenAI Agents SDKopenai-agentsOpenAIFullNative asyncOPENAI_API_KEY
LangGraphlanggraphAnthropic (configurable)FullNative asyncANTHROPIC_API_KEY
Anthropic Toolsanthropic-toolsAnthropicFullNative asyncANTHROPIC_API_KEY
StrandsstrandsAnthropicTools onlyThread-bridgedANTHROPIC_API_KEY
CrewAIcrewaiAnthropicTools onlyThread-bridgedANTHROPIC_API_KEY
Google ADKgoogle-adkGoogle GeminiFullNative asyncGOOGLE_API_KEY
Full streaming means text tokens stream to the browser as they’re generated, and tool calls appear in real-time in the timeline. Tools-only streaming means tool call events stream in real-time, but the final text response arrives all at once after the agent completes.

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Choosing a framework

Both offer full token-by-token text streaming, excellent tool execution, and native async/await support. They are the default choices for new projects.PydanticAI adds type-safe tool definitions using Pydantic models and RunContext, making tool parameters explicit and validated at the Python type system level.Claude Agent SDK provides the most direct Anthropic integration with a straightforward agentic loop.
uvx create-context-graph --domain healthcare --framework pydanticai --demo
uvx create-context-graph --domain healthcare --framework claude-agent-sdk --demo
If you’re already using OpenAI’s API and models, this framework integrates naturally using @function_tool decorators and Runner.run() / Runner.run_streamed().
Broad semantic queries (e.g., “who are the top players?”) may return empty results because the text-matching tools require specific search terms. This is a characteristic of the OpenAI tool-call pattern, not a bug.
An OPENAI_API_KEY is required. The CLI will warn you if it is not set.
uvx create-context-graph --domain gaming --framework openai-agents --demo
If you’re building within the LangChain ecosystem and want access to LangChain’s tool ecosystem, agent memory integrations, and LangSmith observability, LangGraph is the right choice.The generated agent uses create_react_agent() and graph.astream_events() for full streaming.
uvx create-context-graph --domain financial-services --framework langgraph --demo
CrewAI’s Agent + Task + Crew paradigm is designed for multi-agent collaboration. The generated template uses a single agent, but you can extend it into multi-agent crews for complex, role-based workflows.CrewAI runs synchronously in a worker thread via asyncio.to_thread(). Tool calls bridge back to the async event loop using asyncio.run_coroutine_threadsafe(), which is handled automatically in the generated code.
uvx create-context-graph --domain manufacturing --framework crewai --demo
A lightweight, no-framework approach that uses the Anthropic API directly. It implements a custom @register_tool registry and a bounded agentic loop (maximum 15 iterations) so you can see exactly how tool-use cycles work under the hood.Good for teams who want to own the agentic loop code without a framework abstraction on top.
uvx create-context-graph --domain data-journalism --framework anthropic-tools --demo
Uses Google’s Gemini models via the Agent Development Kit (ADK). Requires a separate GOOGLE_API_KEY — the CLI will warn you if it is not set.Best if you’re building within the Google Cloud ecosystem. Uses FunctionTool calling and runner.run_async() for full streaming.
uvx create-context-graph --domain trip-planning --framework google-adk --demo
Uses Anthropic’s Claude model via the Strands SDK with @tool decorators. Like CrewAI, Strands is synchronous and runs in a worker thread, so streaming is tools-only — real-time tool call events with text arriving at the end.
uvx create-context-graph --domain wildlife-management --framework strands --demo

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Streaming behavior in detail

PydanticAI, Claude Agent SDK, OpenAI Agents SDK, LangGraph, Anthropic Tools, and Google ADK all support full token-by-token text streaming.The backend emits Server-Sent Events (SSE) over POST /chat/stream. As the agent runs:
  1. tool_start events fire when a tool is invoked — the timeline shows a live spinner.
  2. text_delta events stream each text token to the frontend as it arrives.
  3. tool_end events fire when a tool completes — graph data is included and the visualization updates incrementally.
  4. A final done event closes the stream.
Text deltas are batched approximately every 50 ms before updating the ReactMarkdown renderer to avoid excessive re-renders.

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Framework-specific dependencies

The CLI injects the correct dependencies into the generated project’s pyproject.toml automatically. For reference:
FrameworkKey dependencies
PydanticAIpydantic-ai>=0.1
Claude Agent SDKclaude-agent-sdk>=0.1, anthropic>=0.30
OpenAI Agents SDKopenai-agents>=0.1
LangGraphlanggraph>=0.1, langchain-anthropic>=0.3
CrewAIcrewai[anthropic]>=0.1
Strandsstrands-agents[anthropic]>=0.1
Google ADKgoogle-adk>=0.1, nest-asyncio>=1.5
Anthropic Toolsanthropic>=0.30
All 8 frameworks share the same conversation memory backend (neo4j-agent-memory with local sentence-transformers embeddings by default — no OPENAI_API_KEY required). If OPENAI_API_KEY is present in .env, the generated project automatically upgrades to OpenAI text-embedding-3-small embeddings.

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API key requirements

Most frameworks require ANTHROPIC_API_KEY. Two frameworks require different keys and will not work without them.
FrameworkRequired keyWhat happens without it
PydanticAIANTHROPIC_API_KEYAgent startup fails
Claude Agent SDKANTHROPIC_API_KEYAgent startup fails
OpenAI Agents SDKOPENAI_API_KEYAgent startup fails
LangGraphANTHROPIC_API_KEYAgent startup fails
Anthropic ToolsANTHROPIC_API_KEYAgent startup fails
StrandsANTHROPIC_API_KEYAgent startup fails
CrewAIANTHROPIC_API_KEYAgent startup fails
Google ADKGOOGLE_API_KEYAgent startup fails
The CLI prints a warning during scaffolding if you select openai-agents or google-adk without the corresponding key set in your environment. You can provide keys via CLI flags: 
uvx create-context-graph --domain healthcare --framework openai-agents \
  --openai-api-key $OPENAI_API_KEY --demo

uvx create-context-graph --domain trip-planning --framework google-adk \
  --google-api-key $GOOGLE_API_KEY --demo

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Switching frameworks after scaffolding

Each project is scaffolded for a single framework. To switch, scaffold a new project with the different --framework flag and point it at the same Neo4j instance (using --neo4j-uri, --neo4j-username, --neo4j-password) to share existing data. 
# Original project
uvx create-context-graph my-app --domain healthcare --framework pydanticai --demo

# New project, same domain, different framework, same Neo4j
uvx create-context-graph my-app-langgraph --domain healthcare --framework langgraph \
  --neo4j-uri neo4j://localhost:7687

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