pipecat-ai-flows
Conversation Flow management for Pipecat AI applications
Decision gist · record as of 2026-08-14
No. This package is deprecated and archived. Install pipecat-ai 1.5.0+ instead, which includes Pipecat Flows under pipecat.flows with the same API. If you are maintaining legacy code using pipecat-ai-flows 1.4.0, plan a migration to the integrated version; do not start new projects with this package.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- This package is deprecated; new code should use pipecat.flows from pipecat-ai 1.5.0+ instead.
- Low install friction, but maintenance is abandoned: the repository is archived and the package is frozen.
License · maintenance · safety
BSD 2-Clause License (permissive) — BSD 2-Clause License (permissive) poses no restriction on use or redistribution, but the abandoned status means no future license updates or clarifications will arrive.
last release 2026-07-05 (40 days) · last repo commit 2026-07-05 · 622 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 142,853 downloads/mo, #11,199 on PyPI
Alternatives
Verify before relying
# Deprecated: use pipecat-ai 1.5.0+ instead
pip install pipecat-ai
from pipecat.flows import FlowManager, NodeConfig
flow = FlowManager()- Whether existing code using pipecat-ai-flows 1.4.0 will continue to work without updates or if pipecat-ai 1.5.0+ introduces breaking changes.
- Whether the pipecat.flows API in pipecat-ai 1.5.0+ is truly identical or if subtle behavioral differences exist.
What it is and what it does
Pipecat Flows provides conversation flow and state machine management for building AI applications with Pipecat. It lets you define multi-step dialogue logic, route between conversation states, and orchestrate LLM interactions in a structured way. The package depends on pipecat-ai, loguru, and docstring_parser.
However, this package is now deprecated and frozen. As of pipecat-ai 1.5.0, Pipecat Flows has been merged into the core pipecat-ai package under the pipecat.flows namespace. The standalone pipecat-ai-flows package will receive no further updates. Existing users should migrate by installing pipecat-ai and updating imports from pipecat_flows to pipecat.flows; the API remains the same.
Use it for
- Building multi-turn conversation systems where dialogue must branch based on user input or LLM decisions.
- Implementing state machines for complex AI agent workflows that need to track context and manage transitions.
- Orchestrating conversation flows in existing Pipecat applications that haven't yet migrated to pipecat-ai 1.5.0+.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
This package is deprecated and archived. Install pipecat-ai 1.5.0+ instead, which includes Pipecat Flows under pipecat.flows with the same API. If you are maintaining legacy code using pipecat-ai-flows 1.4.0, plan a migration to the integrated version; do not start new projects with this package.
Install
pipecat-ai-flows on PyPI
Before you install
Low install friction, but maintenance is abandoned: the repository is archived and the package is frozen. The last release was 40 days ago. All active development has moved into pipecat-ai itself, making this package a legacy artifact.
Requires Python 3.11 or later. This package is deprecated; new code should use pipecat.flows from pipecat-ai 1.5.0+ instead.
License in practice
BSD 2-Clause License (permissive) poses no restriction on use or redistribution, but the abandoned status means no future license updates or clarifications will arrive.
Quickstart
# Deprecated: use pipecat-ai 1.5.0+ instead
pip install pipecat-ai
from pipecat.flows import FlowManager, NodeConfig
flow = FlowManager()
Verify before relying
- Whether existing code using pipecat-ai-flows 1.4.0 will continue to work without updates or if pipecat-ai 1.5.0+ introduces breaking changes.
- Whether the pipecat.flows API in pipecat-ai 1.5.0+ is truly identical or if subtle behavioral differences exist.
Package facts
| License | BSD 2-Clause License permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagespipecat-ailogurudocstring_parser |
| Maintenance | Abandoned 40 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 142,853 / month, #11,199 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseTopic :: Communications :: ConferencingTopic :: Multimedia :: Sound/AudioTopic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: pipecat_ai_flows-1.4.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “conversation flow management”
- pipecat-ai-flowsManages conversation flows and state machines for Pipecat AI…
- honcho-aiPython SDK for the Honcho conversational memory platform, enabling…
- summarization-pydantic-aiManages conversation history for Pydantic AI agents by automatically…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also pipecat-ai · pipecatcloud · pipecat-ai-whisker · pipecat-ai-prebuilt · langflow · langflow-base · promptflow-core · azureml-train · lfx · azure-ai-language-conversations