copilotkit vs haystack
Two of the top projects in Agent frameworks and SDKs, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
copilotkit
React and Angular SDK for adding agent chat and generative UI to apps
haystack
Python framework for building RAG pipelines and agents from modular components
Verdict
copilotkit ranks higher, 90 to 89. Of the 4 score parts we measure for both, copilotkit leads on 1, haystack on 1 and 2 are even.
- Adoptioncopilotkit leads by 7 points, 89 to 82.
- FreshnessEven at 100.
- Maintenancehaystack leads by 5 points, 93 to 88.
- Agent-readyEven at 70.
Pick copilotkit if you are
Frontend and full-stack teams shipping agent UIs inside their own products
- Works with many agent backends: LangGraph, CrewAI, Mastra, Google ADK, PydanticAI, Strands, LlamaIndex
- Covers React, Angular, Vue, React Native, plus Slack and Teams channels
Pick haystack if you are
Python developers building production RAG systems and agents
- Pipelines support loops, branches and conditional routing, so context flow is explicit and traceable
- Agent hooks (before_llm, before_tool, on_exit) plus step_count and token_usage tracking
| What we compare | copilotkit | haystack |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 89, widely used | 82, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 88, healthy | 93, healthy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 38.7k | 27k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2026 |
| Language | TypeScript | Python |
| Docker | No | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
copilotkit
CopilotKit is a TypeScript SDK and runtime that connects an existing agent (LangGraph, CrewAI, Mastra, Google ADK, PydanticAI and others) to a frontend over the AG-UI protocol. It provides chat components, generative UI, shared agent/UI state, human-in-the-loop approvals and frontend tools for React, Angular, Vue, React Native, Slack and Teams. A separate Intelligence layer adds memory, thread persistence and Continual Learning.
Who it is for: Frontend and full-stack teams shipping agent UIs inside their own products
Strengths
- Works with many agent backends: LangGraph, CrewAI, Mastra, Google ADK, PydanticAI, Strands, LlamaIndex
- Covers React, Angular, Vue, React Native, plus Slack and Teams channels
- Three generative UI modes: registered components, A2UI specs, sandboxed MCP Apps
- Hooks such as useAgent give programmatic control beneath the prebuilt chat components
Weaknesses
- Memory, persistent threads and Continual Learning sit in Intelligence, not the MIT SDK
- Slack and Teams are GA only on cloud-hosted Intelligence
- Needs a separately built agent or the built-in agent plus an LLM API key
- Angular, Vue and React Native are listed as supported, while React/Next.js is the GA surface
- no GPU
- Needs Node.js/npm, LLM provider API key (e.g. OpenAI)
- Models: OpenAI (built-in agent), LangGraph, CrewAI, Mastra, Google ADK
haystack
Haystack is a Python framework for composing LLM applications as pipelines of components covering retrieval, routing, memory, tool calling and generation. One Pipeline runs sync or async and streams tokens, and the Agent class supports lifecycle hooks and concurrent tool calls. It integrates with OpenAI, Anthropic, Mistral, Cohere, Hugging Face, Google, Azure OpenAI, AWS Bedrock and local models.
Who it is for: Python developers building production RAG systems and agents
Strengths
- Pipelines support loops, branches and conditional routing, so context flow is explicit and traceable
- Agent hooks (before_llm, before_tool, on_exit) plus step_count and token_usage tracking
- Model-agnostic: many hosted providers and local models, swappable without rewriting pipelines
- Installs with pip install haystack-ai; Apache-2.0 licensed
Weaknesses
- Collects anonymous component-usage telemetry by default; opt-out is documented separately
- Serving as REST or MCP needs the separate Hayhooks project
- Governance, access controls and managed observability are in the paid Enterprise Platform
- Many integrations live in a separate haystack-core-integrations repo
- GPU optional
- Docker
- Needs Python, LLM provider or local model
- Models: OpenAI, Anthropic, Mistral, Cohere, Hugging Face
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