Compare commits
2 Commits
pk/optiona
...
mb/remove-
| Author | SHA1 | Date | |
|---|---|---|---|
|
|
4b4e8b839c | ||
|
|
86c2dd5cfc |
@@ -1 +0,0 @@
|
||||
../../.claude/skills/changelog
|
||||
@@ -1 +0,0 @@
|
||||
../../.claude/skills/cleanup
|
||||
@@ -1 +0,0 @@
|
||||
../../.claude/skills/code-review
|
||||
@@ -1 +0,0 @@
|
||||
../../.claude/skills/docstring
|
||||
@@ -1 +0,0 @@
|
||||
../../.claude/skills/pr-description
|
||||
@@ -1 +0,0 @@
|
||||
../../.claude/skills/pr-submit
|
||||
@@ -1 +0,0 @@
|
||||
../../.claude/skills/update-docs
|
||||
@@ -32,20 +32,6 @@ Create changelog files for the important commits in this PR. The PR number is pr
|
||||
|
||||
6. Use ⚠️ emoji prefix for breaking changes.
|
||||
|
||||
7. **Write changes in user-facing terms first.** Lead with what users of the framework will notice: new APIs, changed behavior, new parameters, fixed bugs they might have hit, etc. Implementation details (internal refactoring, how something is wired up under the hood) can be included as secondary context after the user-facing description, but should never be the *only* content of a changelog entry when there is a user-visible effect.
|
||||
|
||||
**Good** (user-facing first, implementation detail as context):
|
||||
```
|
||||
- Turn completion instructions now persist correctly across full context updates when using `system_instruction`. Previously they were injected as a context system message, which caused warning spam and didn't survive context updates.
|
||||
```
|
||||
|
||||
**Bad** (implementation detail only, no user-facing framing):
|
||||
```
|
||||
- Fixed turn completion instructions being injected as a context system message instead of using `system_instruction`.
|
||||
```
|
||||
|
||||
Ask yourself: "If I'm a developer building on Pipecat, what would I notice changed?" Start there.
|
||||
|
||||
## Example
|
||||
|
||||
For PR #3519 with a new feature and a bug fix:
|
||||
@@ -57,5 +43,5 @@ For PR #3519 with a new feature and a bug fix:
|
||||
|
||||
`changelog/3519.fixed.md`:
|
||||
```
|
||||
- Fixed an issue where something was not working correctly in some user-visible scenario. The root cause was an internal implementation detail.
|
||||
- Fixed an issue where something was not working correctly.
|
||||
```
|
||||
|
||||
@@ -1,8 +1,3 @@
|
||||
---
|
||||
name: cleanup
|
||||
description: Review, refactor, document, and validate code changes in the current branch
|
||||
---
|
||||
|
||||
# Code Cleanup Skill
|
||||
|
||||
The **Code Cleanup Skill** reviews, refactors, and documents code changes in your current branch, ensuring alignment with **Pipecat's architecture, coding standards, and example patterns**.
|
||||
@@ -149,7 +144,7 @@ class InputParams(BaseModel):
|
||||
|
||||
#### Examples
|
||||
|
||||
Validated against `examples/07-interruptible.py`:
|
||||
Validated against `examples/foundational/07-interruptible.py`:
|
||||
|
||||
- Proper `create_transport()` usage
|
||||
- Correct pipeline structure
|
||||
|
||||
@@ -157,11 +157,7 @@ After processing all mapped pairs, check for two kinds of gaps:
|
||||
|
||||
**Missing sections**: Mapped doc pages that are missing standard sections compared to the source. For example, a transport page with no Configuration section, or a service page with no InputParams table when the source defines `InputParams(BaseModel)`. Flag these and offer to add the missing sections.
|
||||
|
||||
If the user wants a new page, do all three of the following:
|
||||
|
||||
#### 8a: Create the doc page
|
||||
|
||||
Create the new `.mdx` file using this template structure:
|
||||
If the user wants a new page, create it using this template structure:
|
||||
```
|
||||
---
|
||||
title: "Service Name"
|
||||
@@ -211,53 +207,6 @@ pip install "pipecat-ai[package-name]"
|
||||
[Event table and example code]
|
||||
```
|
||||
|
||||
#### 8b: Add to docs.json
|
||||
|
||||
Add the new page path to `DOCS_PATH/docs.json` in the correct navigation group. The path format is `server/services/{category}/{provider}` (without the `.mdx` extension).
|
||||
|
||||
Find the matching group in the navigation structure:
|
||||
- **STT** → `"group": "Speech-to-Text"` under Services
|
||||
- **TTS** → `"group": "Text-to-Speech"` under Services
|
||||
- **LLM** → `"group": "LLM"` under Services
|
||||
- **S2S** → `"group": "Speech-to-Speech"` under Services
|
||||
- **Transport** → `"group": "Transport"` under Services
|
||||
- **Serializer** → `"group": "Serializers"` under Services
|
||||
- **Image generation** → `"group": "Image Generation"` under Services
|
||||
- **Video** → `"group": "Video"` under Services
|
||||
- **Memory** → `"group": "Memory"` under Services
|
||||
- **Vision** → `"group": "Vision"` under Services
|
||||
- **Analytics** → `"group": "Analytics & Monitoring"` under Services
|
||||
|
||||
Insert the new entry **alphabetically** within the group's `pages` array. For example, adding a new STT service "foo":
|
||||
```json
|
||||
{
|
||||
"group": "Speech-to-Text",
|
||||
"pages": [
|
||||
"server/services/stt/assemblyai",
|
||||
"server/services/stt/aws",
|
||||
...
|
||||
"server/services/stt/foo",
|
||||
...
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### 8c: Add to supported-services.mdx
|
||||
|
||||
Add a new row to the correct category table in `DOCS_PATH/server/services/supported-services.mdx`.
|
||||
|
||||
Use this format:
|
||||
```
|
||||
| [DisplayName](/server/services/{category}/{provider}) | `pip install "pipecat-ai[package]"` |
|
||||
```
|
||||
|
||||
To determine the correct values:
|
||||
- **DisplayName**: Use the service's human-readable name (e.g., "ElevenLabs", "AWS Polly", "Google Gemini")
|
||||
- **package**: Look at the service's `pyproject.toml` extras or the import pattern in the source code. For example, if the service is in `src/pipecat/services/foo/`, the package is typically `foo`.
|
||||
- If no pip dependencies are required, use `No dependencies required` instead.
|
||||
|
||||
Insert the new row **alphabetically** within the table. Match the column alignment of the existing rows.
|
||||
|
||||
### Step 9: Output summary
|
||||
|
||||
After all edits are complete, print a summary:
|
||||
@@ -272,9 +221,6 @@ After all edits are complete, print a summary:
|
||||
### Updated guides
|
||||
- `guides/learn/speech-to-text.mdx` — Updated code example (renamed `old_param` → `new_param`)
|
||||
|
||||
### New service pages
|
||||
- `server/services/tts/newprovider.mdx` — Created page, added to docs.json (Text-to-Speech), added to supported-services.mdx
|
||||
|
||||
### Unmapped source files
|
||||
- `src/pipecat/services/newprovider/tts.py` — NewProviderTTSService (no doc page exists)
|
||||
|
||||
@@ -301,6 +247,4 @@ Before finishing, verify:
|
||||
- [ ] New parameters have accurate types and defaults from source
|
||||
- [ ] Formatting matches the existing page style
|
||||
- [ ] Guides referencing changed APIs were checked and updated
|
||||
- [ ] New service pages were added to `docs.json` in the correct group, alphabetically
|
||||
- [ ] New service pages were added to `supported-services.mdx` in the correct table, alphabetically
|
||||
- [ ] Unmapped files were reported to the user
|
||||
|
||||
30
.dockerignore
Normal file
30
.dockerignore
Normal file
@@ -0,0 +1,30 @@
|
||||
# flyctl launch added from .gitignore
|
||||
**/.vscode
|
||||
**/env
|
||||
**/__pycache__
|
||||
**/*~
|
||||
**/venv
|
||||
#*#
|
||||
|
||||
# Distribution / packaging
|
||||
**/.Python
|
||||
**/build
|
||||
**/develop-eggs
|
||||
**/dist
|
||||
**/downloads
|
||||
**/eggs
|
||||
**/.eggs
|
||||
**/lib
|
||||
**/lib64
|
||||
**/parts
|
||||
**/sdist
|
||||
**/var
|
||||
**/wheels
|
||||
**/share/python-wheels
|
||||
**/*.egg-info
|
||||
**/.installed.cfg
|
||||
**/*.egg
|
||||
**/MANIFEST
|
||||
**/.DS_Store
|
||||
**/.env
|
||||
fly.toml
|
||||
1
.github/workflows/coverage.yaml
vendored
1
.github/workflows/coverage.yaml
vendored
@@ -42,7 +42,6 @@ jobs:
|
||||
--extra langchain \
|
||||
--extra livekit \
|
||||
--extra piper \
|
||||
--extra runner \
|
||||
--extra sagemaker \
|
||||
--extra tracing \
|
||||
--extra websocket
|
||||
|
||||
8
.github/workflows/format.yaml
vendored
8
.github/workflows/format.yaml
vendored
@@ -32,9 +32,7 @@ jobs:
|
||||
run: uv python install 3.12
|
||||
|
||||
- name: Install development dependencies
|
||||
# `--all-extras` (matching the dev setup in README.md) so pyright can
|
||||
# resolve types from various optional dependencies.
|
||||
run: uv sync --group dev --all-extras --no-extra gstreamer --no-extra local
|
||||
run: uv sync --group dev
|
||||
|
||||
- name: Ruff formatter
|
||||
id: ruff-format
|
||||
@@ -43,7 +41,3 @@ jobs:
|
||||
- name: Ruff linter (all rules)
|
||||
id: ruff-check
|
||||
run: uv run ruff check
|
||||
|
||||
- name: Type check (pyright)
|
||||
id: pyright
|
||||
run: uv run pyright
|
||||
|
||||
4
.github/workflows/python-compatibility.yaml
vendored
4
.github/workflows/python-compatibility.yaml
vendored
@@ -14,7 +14,7 @@ jobs:
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python-version: ['3.11.15', '3.12.13', '3.13.12', '3.14.3']
|
||||
python-version: ['3.10.19', '3.11.14', '3.12.12', '3.13.12']
|
||||
|
||||
name: Python ${{ matrix.python-version }}
|
||||
steps:
|
||||
@@ -42,7 +42,7 @@ jobs:
|
||||
|
||||
- name: Test uv sync with all extras
|
||||
run: |
|
||||
uv sync --group dev --all-extras
|
||||
uv sync --group dev --all-extras --no-extra krisp
|
||||
|
||||
- name: Verify installation
|
||||
run: |
|
||||
|
||||
51
.github/workflows/sync-quickstart.yaml
vendored
Normal file
51
.github/workflows/sync-quickstart.yaml
vendored
Normal file
@@ -0,0 +1,51 @@
|
||||
name: Sync Quickstart to pipecat-quickstart repo
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [main]
|
||||
paths:
|
||||
- 'examples/quickstart/**'
|
||||
workflow_dispatch: # Manual trigger
|
||||
|
||||
jobs:
|
||||
sync-quickstart:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout main repo
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Checkout quickstart repo
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
repository: pipecat-ai/pipecat-quickstart
|
||||
token: ${{ secrets.QUICKSTART_SYNC_TOKEN }}
|
||||
path: quickstart-repo
|
||||
|
||||
- name: Sync files (excluding uv.lock and README.md)
|
||||
run: |
|
||||
# Copy all files except uv.lock and README.md
|
||||
find examples/quickstart -type f \
|
||||
-not -name "README.md" \
|
||||
-not -name "uv.lock" \
|
||||
-exec cp {} quickstart-repo/ \;
|
||||
|
||||
- name: Commit and push changes
|
||||
run: |
|
||||
cd quickstart-repo
|
||||
git config user.name "GitHub Action"
|
||||
git config user.email "action@github.com"
|
||||
git add .
|
||||
|
||||
# Only commit if there are changes
|
||||
if ! git diff --staged --quiet; then
|
||||
git commit -m "Sync from pipecat main repo
|
||||
|
||||
Updated files from examples/quickstart/
|
||||
Commit: ${{ github.sha }}
|
||||
"
|
||||
git push
|
||||
else
|
||||
echo "No changes to sync"
|
||||
fi
|
||||
1
.github/workflows/tests.yaml
vendored
1
.github/workflows/tests.yaml
vendored
@@ -46,7 +46,6 @@ jobs:
|
||||
--extra langchain \
|
||||
--extra livekit \
|
||||
--extra piper \
|
||||
--extra runner \
|
||||
--extra sagemaker \
|
||||
--extra tracing \
|
||||
--extra websocket
|
||||
|
||||
148
.github/workflows/update-docs.yml
vendored
148
.github/workflows/update-docs.yml
vendored
@@ -1,148 +0,0 @@
|
||||
name: Update Documentation on PR Merge
|
||||
|
||||
on:
|
||||
pull_request_target:
|
||||
types: [closed]
|
||||
branches: [main]
|
||||
paths:
|
||||
- "src/pipecat/services/**"
|
||||
- "src/pipecat/transports/**"
|
||||
- "src/pipecat/serializers/**"
|
||||
- "src/pipecat/processors/**"
|
||||
- "src/pipecat/audio/**"
|
||||
- "src/pipecat/turns/**"
|
||||
- "src/pipecat/observers/**"
|
||||
- "src/pipecat/pipeline/**"
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
pr_number:
|
||||
description: "PR number to generate docs for"
|
||||
required: true
|
||||
type: string
|
||||
|
||||
jobs:
|
||||
update-docs:
|
||||
if: >-
|
||||
github.event_name == 'workflow_dispatch' ||
|
||||
github.event.pull_request.merged == true
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: read
|
||||
id-token: write
|
||||
steps:
|
||||
- name: Checkout pipecat
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Checkout docs
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
repository: pipecat-ai/docs
|
||||
token: ${{ secrets.DOCS_SYNC_TOKEN }}
|
||||
path: _docs
|
||||
|
||||
- name: Resolve PR number
|
||||
id: pr
|
||||
run: |
|
||||
if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
|
||||
echo "number=${{ inputs.pr_number }}" >> "$GITHUB_OUTPUT"
|
||||
else
|
||||
echo "number=${{ github.event.pull_request.number }}" >> "$GITHUB_OUTPUT"
|
||||
fi
|
||||
|
||||
- name: Update documentation
|
||||
uses: anthropics/claude-code-action@v1
|
||||
env:
|
||||
DOCS_SYNC_TOKEN: ${{ secrets.DOCS_SYNC_TOKEN }}
|
||||
with:
|
||||
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
github_token: ${{ secrets.GITHUB_TOKEN }}
|
||||
prompt: |
|
||||
You are updating documentation for the pipecat-ai/docs repository based on
|
||||
changes merged in PR #${{ steps.pr.outputs.number }} of pipecat-ai/pipecat.
|
||||
|
||||
## Setup
|
||||
|
||||
1. Read the skill instructions at `.claude/skills/update-docs/SKILL.md`
|
||||
2. Read the source-to-doc mapping at `.claude/skills/update-docs/SOURCE_DOC_MAPPING.md`
|
||||
3. The docs repository is checked out at `./_docs/`
|
||||
|
||||
## Get the diff
|
||||
|
||||
Run `gh pr diff ${{ steps.pr.outputs.number }}` to see what changed in the PR.
|
||||
Also run `gh pr diff ${{ steps.pr.outputs.number }} --name-only` to get the list of changed files.
|
||||
Filter to source files matching the directories listed in SKILL.md Step 3.
|
||||
|
||||
If no relevant source files were changed, exit with "No documentation changes needed."
|
||||
|
||||
## Follow the skill instructions
|
||||
|
||||
Apply the SKILL.md workflow (Steps 3-9) with these adaptations for automation:
|
||||
|
||||
### Docs path
|
||||
Use `./_docs/` — it's already checked out. Do not ask for a path.
|
||||
|
||||
### Branch management
|
||||
- Branch name: `docs/pr-${{ steps.pr.outputs.number }}`
|
||||
- Work inside `./_docs/` for all doc edits and git operations
|
||||
- Check if the branch already exists on the remote:
|
||||
```bash
|
||||
cd _docs && git fetch origin docs/pr-${{ steps.pr.outputs.number }} 2>/dev/null
|
||||
```
|
||||
- If it exists: check it out (supports workflow re-runs)
|
||||
- If not: create it from main
|
||||
|
||||
### Git config
|
||||
Before committing in `_docs`, set:
|
||||
```bash
|
||||
git config user.name "github-actions[bot]"
|
||||
git config user.email "github-actions[bot]@users.noreply.github.com"
|
||||
```
|
||||
|
||||
### No interactive questions
|
||||
Do not ask questions. If you encounter gaps (unmapped files, missing sections,
|
||||
ambiguous changes), note them in the PR body under "## Gaps identified".
|
||||
|
||||
### Creating the docs PR
|
||||
After committing all changes in `_docs`, push and create a PR:
|
||||
```bash
|
||||
cd _docs
|
||||
git push -u origin docs/pr-${{ steps.pr.outputs.number }}
|
||||
GH_TOKEN=$DOCS_SYNC_TOKEN gh pr create \
|
||||
--repo pipecat-ai/docs \
|
||||
--label auto-docs \
|
||||
--label pipecat \
|
||||
--title "docs: update for pipecat PR #${{ steps.pr.outputs.number }}" \
|
||||
--body "$(cat <<'BODY'
|
||||
Automated documentation update for [pipecat PR #${{ steps.pr.outputs.number }}](https://github.com/pipecat-ai/pipecat/pull/${{ steps.pr.outputs.number }}).
|
||||
|
||||
## Changes
|
||||
<summarize each doc page updated and what changed>
|
||||
|
||||
## Gaps identified
|
||||
<any unmapped files, missing doc pages, or missing sections — or "None">
|
||||
BODY
|
||||
)"
|
||||
```
|
||||
|
||||
### Re-run handling
|
||||
If `gh pr create` fails because a PR from that branch already exists,
|
||||
push the updated commits and use `gh pr edit` to update the body instead.
|
||||
|
||||
### No-op
|
||||
If after analyzing the diff you determine no documentation changes are needed
|
||||
(e.g., only skip-listed files changed, or changes don't affect public API docs),
|
||||
exit cleanly without creating a branch or PR. Output "No documentation changes needed."
|
||||
|
||||
## Important rules
|
||||
- Only modify files inside `./_docs/` — never modify pipecat source code
|
||||
- Follow the conservative editing rules from SKILL.md Step 6
|
||||
- Read each doc page fully before editing (SKILL.md Guidelines)
|
||||
- Use `GH_TOKEN=$DOCS_SYNC_TOKEN` for all `gh` commands targeting pipecat-ai/docs
|
||||
claude_args: |
|
||||
--model claude-sonnet-4-5-20250929
|
||||
--max-turns 30
|
||||
--allowedTools "Read,Write,Edit,Glob,Grep,Bash"
|
||||
@@ -1,13 +1,8 @@
|
||||
repos:
|
||||
- repo: local
|
||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||
rev: v0.12.1
|
||||
hooks:
|
||||
- id: ruff
|
||||
name: ruff
|
||||
entry: uv run ruff check --fix
|
||||
language: system
|
||||
types: [python]
|
||||
language_version: python3
|
||||
args: [--fix]
|
||||
- id: ruff-format
|
||||
name: ruff-format
|
||||
entry: uv run ruff format
|
||||
language: system
|
||||
types: [python]
|
||||
|
||||
@@ -11,7 +11,7 @@ build:
|
||||
jobs:
|
||||
post_install:
|
||||
- pip install uv
|
||||
- UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --group docs --all-extras --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra mlx-whisper
|
||||
- UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
|
||||
|
||||
sphinx:
|
||||
configuration: docs/api/conf.py
|
||||
|
||||
174
AGENTS.md
174
AGENTS.md
@@ -1,174 +0,0 @@
|
||||
# AGENTS.md
|
||||
|
||||
This file provides guidance to AI coding agents when working with code in this repository.
|
||||
|
||||
## Project Overview
|
||||
|
||||
Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
|
||||
|
||||
## Common Commands
|
||||
|
||||
```bash
|
||||
# Setup development environment
|
||||
uv sync --group dev --all-extras --no-extra gstreamer --no-extra local
|
||||
|
||||
# Install pre-commit hooks
|
||||
uv run pre-commit install
|
||||
|
||||
# Run all tests
|
||||
uv run pytest
|
||||
|
||||
# Run a single test file
|
||||
uv run pytest tests/test_name.py
|
||||
|
||||
# Run a specific test
|
||||
uv run pytest tests/test_name.py::test_function_name
|
||||
|
||||
# Preview changelog
|
||||
uv run towncrier build --draft --version Unreleased
|
||||
|
||||
# Lint and format check
|
||||
uv run ruff check
|
||||
uv run ruff format --check
|
||||
|
||||
# Update dependencies (after editing pyproject.toml)
|
||||
uv lock && uv sync
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
### Frame-Based Pipeline Processing
|
||||
|
||||
All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
|
||||
|
||||
```
|
||||
[Processor1] → [Processor2] → ... → [ProcessorN]
|
||||
```
|
||||
|
||||
**Key components:**
|
||||
|
||||
- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
|
||||
|
||||
- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
|
||||
|
||||
- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
|
||||
|
||||
- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
|
||||
|
||||
- **Transports** (`src/pipecat/transports/`): Transports are frame processors used for external I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`, `BaseInputTransport` and `BaseOutputTransport`.
|
||||
|
||||
- **Pipeline Task (`src/pipecat/pipeline/task.py`)**: Runs and manages a pipeline. Pipeline tasks send the first frame, `StartFrame`, to the pipeline in order for processors to know they can start processing and pushing frames. Pipeline tasks internally create a pipeline with two additional processors, a source processor before the user-defined pipeline and a sink processor at the end. Those are used for multiple things: error handling, pipeline task level events, heartbeat monitoring, etc.
|
||||
|
||||
- **Pipeline Runner (`src/pipecat/pipeline/runner.py`)**: High-level entry point for executing pipeline tasks. Handles signal management (SIGINT/SIGTERM) for graceful shutdown and optional garbage collection. Run a single pipeline task with `await runner.run(task)` or multiple concurrently with `await asyncio.gather(runner.run(task1), runner.run(task2))`.
|
||||
|
||||
- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
|
||||
|
||||
- **Serializers** (`src/pipecat/serializers/`): Convert frames to/from wire formats for WebSocket transports. `FrameSerializer` base class defines `serialize()` and `deserialize()`. Telephony serializers (Twilio, Plivo, Vonage, Telnyx, Exotel, Genesys) handle provider-specific protocols and audio encoding (e.g., μ-law).
|
||||
|
||||
- **RTVI** (`src/pipecat/processors/frameworks/rtvi.py`): Real-Time Voice Interface protocol bridging clients and the pipeline. `RTVIProcessor` handles incoming client messages (text input, audio, function call results). `RTVIObserver` converts pipeline frames to outgoing messages: user/bot speaking events, transcriptions, LLM/TTS lifecycle, function calls, metrics, and audio levels.
|
||||
|
||||
- **Observers** (`src/pipecat/observers/`): Monitor frame flow without modifying the pipeline. Passed to `PipelineTask` via the `observers` parameter. Implement `on_process_frame()` and `on_push_frame()` callbacks.
|
||||
|
||||
### Important Patterns
|
||||
|
||||
- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
|
||||
|
||||
- **Turn Management**: Turn management is done through `LLMUserAggregator` and
|
||||
`LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
|
||||
|
||||
- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
|
||||
|
||||
- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to. An `InterruptionFrame` carries an optional `asyncio.Event` that is set when the frame reaches the pipeline sink. If a processor stops an `InterruptionFrame` from propagating downstream (i.e., doesn't push it), it **must** call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()` callers.
|
||||
|
||||
- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
|
||||
|
||||
- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to execute fast.
|
||||
|
||||
- **Async Task Management**: Always use `self.create_task(coroutine, name)` instead of raw `asyncio.create_task()`. The `TaskManager` automatically tracks tasks and cleans them up on processor shutdown. Use `await self.cancel_task(task, timeout)` for cancellation.
|
||||
|
||||
- **Error Handling**: Use `await self.push_error(msg, exception, fatal)` to push errors upstream. Services should use `fatal=False` (the default) so application code can handle errors and take action (e.g. switch to another service).
|
||||
|
||||
### Key Directories
|
||||
|
||||
| Directory | Purpose |
|
||||
| -------------------------- | -------------------------------------------------- |
|
||||
| `src/pipecat/frames/` | Frame definitions (100+ types) |
|
||||
| `src/pipecat/processors/` | FrameProcessor base + aggregators, filters, audio |
|
||||
| `src/pipecat/pipeline/` | Pipeline orchestration |
|
||||
| `src/pipecat/services/` | AI service integrations (60+ providers) |
|
||||
| `src/pipecat/transports/` | Transport layer (Daily, LiveKit, WebSocket, Local) |
|
||||
| `src/pipecat/serializers/` | Frame serialization for WebSocket protocols |
|
||||
| `src/pipecat/observers/` | Pipeline observers for monitoring frame flow |
|
||||
| `src/pipecat/audio/` | VAD, filters, mixers, turn detection, DTMF |
|
||||
| `src/pipecat/turns/` | User turn management |
|
||||
|
||||
## Code Style
|
||||
|
||||
- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
|
||||
- **Deprecations**: Use the `.. deprecated:: <version>` Sphinx directive in docstrings (never inline tags like `[DEPRECATED]`), and pair it with a runtime `warnings.warn(..., DeprecationWarning)` at the call site. See `CONTRIBUTING.md` for full conventions.
|
||||
- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
|
||||
- **Type hints**: Required for complex async code.
|
||||
- **Dataclass vs Pydantic**: Use `@dataclass` for frames and internal pipeline data (high-frequency, no validation needed). Use Pydantic `BaseModel` for configuration, parameters, metrics, and external API data (benefits from validation and serialization). Specifically:
|
||||
- `@dataclass`: Frame types, context aggregator pairs, internal data containers
|
||||
- `BaseModel`: Service `InputParams`, transport/VAD/turn params, metrics data, API request/response models, serializer params
|
||||
|
||||
### Docstring Example
|
||||
|
||||
```python
|
||||
class MyService(LLMService):
|
||||
"""Description of what the service does.
|
||||
|
||||
More detailed description.
|
||||
|
||||
Event handlers available:
|
||||
|
||||
- on_connected: Called when we are connected
|
||||
|
||||
Example::
|
||||
|
||||
@service.event_handler("on_connected")
|
||||
async def on_connected(service, frame):
|
||||
...
|
||||
"""
|
||||
|
||||
def __init__(self, param1: str, **kwargs):
|
||||
"""Initialize the service.
|
||||
|
||||
Args:
|
||||
param1: Description of param1.
|
||||
**kwargs: Additional arguments passed to parent.
|
||||
"""
|
||||
super().__init__(**kwargs)
|
||||
|
||||
|
||||
# Pydantic params class with a deprecated field
|
||||
class MyParams(BaseModel):
|
||||
"""Configuration parameters for MyService.
|
||||
|
||||
Parameters:
|
||||
new_setting: Replacement for ``old_setting``.
|
||||
old_setting: Legacy setting, no longer used.
|
||||
|
||||
.. deprecated:: 1.2.0
|
||||
Use ``new_setting`` instead. Will be removed in 2.0.0.
|
||||
"""
|
||||
|
||||
new_setting: str = "default"
|
||||
old_setting: str | None = None
|
||||
```
|
||||
|
||||
## Service Implementation
|
||||
|
||||
When adding a new service:
|
||||
|
||||
1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
|
||||
2. Implement required abstract methods
|
||||
3. Handle necessary frames
|
||||
4. By default, all frames should be pushed in the direction they came
|
||||
5. Push `ErrorFrame` on failures
|
||||
6. Add metrics tracking via `MetricsData` if relevant
|
||||
7. Follow the pattern of existing services in `src/pipecat/services/`
|
||||
|
||||
## Testing
|
||||
|
||||
Test utilities live in `src/pipecat/tests/utils.py`. Use `run_test()` to send frames through a pipeline and assert expected output frames in each direction. Use `SleepFrame(sleep=N)` to add delays between frames.
|
||||
2276
CHANGELOG.md
2276
CHANGELOG.md
File diff suppressed because it is too large
Load Diff
62
CHANGELOG.md.template
Normal file
62
CHANGELOG.md.template
Normal file
@@ -0,0 +1,62 @@
|
||||
# Changelog
|
||||
|
||||
All notable changes to the **<project name>** SDK will be documented in this file.
|
||||
|
||||
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
|
||||
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
|
||||
|
||||
Please make sure to add your changes to the appropriate categories:
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Added
|
||||
|
||||
<!-- for new functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Changed
|
||||
|
||||
<!-- for changed functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Deprecated
|
||||
|
||||
<!-- for soon-to-be removed functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Removed
|
||||
|
||||
<!-- for removed functionality -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Fixed
|
||||
|
||||
<!-- for fixed bugs -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Performance
|
||||
|
||||
<!-- for performance-relevant changes -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Security
|
||||
|
||||
<!-- for security-relevant changes -->
|
||||
|
||||
- n/a
|
||||
|
||||
### Other
|
||||
|
||||
<!-- for everything else -->
|
||||
|
||||
- n/a
|
||||
|
||||
## [0.1.0] - YYYY-MM-DD
|
||||
|
||||
Initial release.
|
||||
158
CLAUDE.md
158
CLAUDE.md
@@ -1 +1,157 @@
|
||||
@AGENTS.md
|
||||
# CLAUDE.md
|
||||
|
||||
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
|
||||
|
||||
## Project Overview
|
||||
|
||||
Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
|
||||
|
||||
## Common Commands
|
||||
|
||||
```bash
|
||||
# Setup development environment
|
||||
uv sync --group dev --all-extras --no-extra gstreamer --no-extra krisp
|
||||
|
||||
# Install pre-commit hooks
|
||||
uv run pre-commit install
|
||||
|
||||
# Run all tests
|
||||
uv run pytest
|
||||
|
||||
# Run a single test file
|
||||
uv run pytest tests/test_name.py
|
||||
|
||||
# Run a specific test
|
||||
uv run pytest tests/test_name.py::test_function_name
|
||||
|
||||
# Preview changelog
|
||||
uv run towncrier build --draft --version Unreleased
|
||||
|
||||
# Lint and format check
|
||||
uv run ruff check
|
||||
uv run ruff format --check
|
||||
|
||||
# Update dependencies (after editing pyproject.toml)
|
||||
uv lock && uv sync
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
### Frame-Based Pipeline Processing
|
||||
|
||||
All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
|
||||
|
||||
```
|
||||
[Processor1] → [Processor2] → ... → [ProcessorN]
|
||||
```
|
||||
|
||||
**Key components:**
|
||||
|
||||
- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
|
||||
|
||||
- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
|
||||
|
||||
- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
|
||||
|
||||
- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
|
||||
|
||||
- **Transports** (`src/pipecat/transports/`): Transports are frame processors used for external I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`, `BaseInputTransport` and `BaseOutputTransport`.
|
||||
|
||||
- **Pipeline Task (`src/pipecat/pipeline/task.py`)**: Runs and manages a pipeline. Pipeline tasks send the first frame, `StartFrame`, to the pipeline in order for processors to know they can start processing and pushing frames. Pipeline tasks internally create a pipeline with two additional processors, a source processor before the user-defined pipeline and a sink processor at the end. Those are used for multiple things: error handling, pipeline task level events, heartbeat monitoring, etc.
|
||||
|
||||
- **Pipeline Runner (`src/pipecat/pipeline/runner.py`)**: High-level entry point for executing pipeline tasks. Handles signal management (SIGINT/SIGTERM) for graceful shutdown and optional garbage collection. Run a single pipeline task with `await runner.run(task)` or multiple concurrently with `await asyncio.gather(runner.run(task1), runner.run(task2))`.
|
||||
|
||||
- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
|
||||
|
||||
- **Serializers** (`src/pipecat/serializers/`): Convert frames to/from wire formats for WebSocket transports. `FrameSerializer` base class defines `serialize()` and `deserialize()`. Telephony serializers (Twilio, Plivo, Vonage, Telnyx, Exotel, Genesys) handle provider-specific protocols and audio encoding (e.g., μ-law).
|
||||
|
||||
- **RTVI** (`src/pipecat/processors/frameworks/rtvi.py`): Real-Time Voice Interface protocol bridging clients and the pipeline. `RTVIProcessor` handles incoming client messages (text input, audio, function call results). `RTVIObserver` converts pipeline frames to outgoing messages: user/bot speaking events, transcriptions, LLM/TTS lifecycle, function calls, metrics, and audio levels.
|
||||
|
||||
- **Observers** (`src/pipecat/observers/`): Monitor frame flow without modifying the pipeline. Passed to `PipelineTask` via the `observers` parameter. Implement `on_process_frame()` and `on_push_frame()` callbacks.
|
||||
|
||||
### Important Patterns
|
||||
|
||||
- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
|
||||
|
||||
- **Turn Management**: Turn management is done through `LLMUserAggregator` and
|
||||
`LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
|
||||
|
||||
- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
|
||||
|
||||
- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to. An `InterruptionFrame` carries an optional `asyncio.Event` that is set when the frame reaches the pipeline sink. If a processor stops an `InterruptionFrame` from propagating downstream (i.e., doesn't push it), it **must** call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()` callers.
|
||||
|
||||
- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
|
||||
|
||||
- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to execute fast.
|
||||
|
||||
- **Async Task Management**: Always use `self.create_task(coroutine, name)` instead of raw `asyncio.create_task()`. The `TaskManager` automatically tracks tasks and cleans them up on processor shutdown. Use `await self.cancel_task(task, timeout)` for cancellation.
|
||||
|
||||
- **Error Handling**: Use `await self.push_error(msg, exception, fatal)` to push errors upstream. Services should use `fatal=False` (the default) so application code can handle errors and take action (e.g. switch to another service).
|
||||
|
||||
### Key Directories
|
||||
|
||||
| Directory | Purpose |
|
||||
| -------------------------- | -------------------------------------------------- |
|
||||
| `src/pipecat/frames/` | Frame definitions (100+ types) |
|
||||
| `src/pipecat/processors/` | FrameProcessor base + aggregators, filters, audio |
|
||||
| `src/pipecat/pipeline/` | Pipeline orchestration |
|
||||
| `src/pipecat/services/` | AI service integrations (60+ providers) |
|
||||
| `src/pipecat/transports/` | Transport layer (Daily, LiveKit, WebSocket, Local) |
|
||||
| `src/pipecat/serializers/` | Frame serialization for WebSocket protocols |
|
||||
| `src/pipecat/observers/` | Pipeline observers for monitoring frame flow |
|
||||
| `src/pipecat/audio/` | VAD, filters, mixers, turn detection, DTMF |
|
||||
| `src/pipecat/turns/` | User turn management |
|
||||
|
||||
## Code Style
|
||||
|
||||
- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
|
||||
- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
|
||||
- **Type hints**: Required for complex async code.
|
||||
- **Dataclass vs Pydantic**: Use `@dataclass` for frames and internal pipeline data (high-frequency, no validation needed). Use Pydantic `BaseModel` for configuration, parameters, metrics, and external API data (benefits from validation and serialization). Specifically:
|
||||
- `@dataclass`: Frame types, context aggregator pairs, internal data containers
|
||||
- `BaseModel`: Service `InputParams`, transport/VAD/turn params, metrics data, API request/response models, serializer params
|
||||
|
||||
### Docstring Example
|
||||
|
||||
```python
|
||||
class MyService(LLMService):
|
||||
"""Description of what the service does.
|
||||
|
||||
More detailed description.
|
||||
|
||||
Event handlers available:
|
||||
|
||||
- on_connected: Called when we are connected
|
||||
|
||||
Example::
|
||||
|
||||
@service.event_handler("on_connected")
|
||||
async def on_connected(service, frame):
|
||||
...
|
||||
"""
|
||||
|
||||
def __init__(self, param1: str, **kwargs):
|
||||
"""Initialize the service.
|
||||
|
||||
Args:
|
||||
param1: Description of param1.
|
||||
**kwargs: Additional arguments passed to parent.
|
||||
"""
|
||||
super().__init__(**kwargs)
|
||||
```
|
||||
|
||||
## Service Implementation
|
||||
|
||||
When adding a new service:
|
||||
|
||||
1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
|
||||
2. Implement required abstract methods
|
||||
3. Handle necessary frames
|
||||
4. By default, all frames should be pushed in the direction they came
|
||||
5. Push `ErrorFrame` on failures
|
||||
6. Add metrics tracking via `MetricsData` if relevant
|
||||
7. Follow the pattern of existing services in `src/pipecat/services/`
|
||||
|
||||
## Testing
|
||||
|
||||
Test utilities live in `src/pipecat/tests/utils.py`. Use `run_test()` to send frames through a pipeline and assert expected output frames in each direction. Use `SleepFrame(sleep=N)` to add delays between frames.
|
||||
|
||||
@@ -23,7 +23,7 @@ Create your integration following the patterns and examples shown in the "Integr
|
||||
Your repository must contain these components:
|
||||
|
||||
- **Source code** - Complete implementation following Pipecat patterns
|
||||
- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples))
|
||||
- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
|
||||
- **README.md** - Must include:
|
||||
- Introduction and explanation of your integration
|
||||
- Installation instructions
|
||||
@@ -65,25 +65,12 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
|
||||
|
||||
#### Websocket-based Services
|
||||
|
||||
**Base class:** `WebsocketSTTService`
|
||||
|
||||
**Use for:** Services where you manage the websocket connection directly. Combines `STTService` with `WebsocketService` for automatic reconnection and keepalive support.
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [CartesiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/stt.py)
|
||||
- [ElevenLabsRealtimeSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/stt.py)
|
||||
|
||||
#### SDK-based Streaming Services
|
||||
|
||||
**Base class:** `STTService`
|
||||
|
||||
**Use for:** Streaming services where the provider's Python SDK manages the connection internally.
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
|
||||
- [GoogleSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/stt.py)
|
||||
- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
|
||||
|
||||
#### File-based Services
|
||||
|
||||
@@ -121,59 +108,55 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- **`_process_context(self, context: LLMContext)`** — The main method that processes an LLM context and generates a response. Each LLM service overrides `process_frame` to extract context from `LLMContextFrame` and calls `_process_context`.
|
||||
|
||||
- **`adapter_class`** — Class attribute pointing to a `BaseLLMAdapter` subclass. Defaults to `OpenAILLMAdapter`. Non-OpenAI services must implement their own adapter (see `src/pipecat/adapters/base_llm_adapter.py`) with methods:
|
||||
- `get_llm_invocation_params(context)` — Extract provider-specific params from universal context
|
||||
- `to_provider_tools_format(tools_schema)` — Convert standard tools to provider format
|
||||
- `get_messages_for_logging(context)` — Format messages for logging
|
||||
- Reference adapters: `src/pipecat/adapters/services/` (anthropic, gemini, bedrock, etc.)
|
||||
|
||||
- **Frame sequence:** Output must follow this frame sequence pattern:
|
||||
- `LLMFullResponseStartFrame` — Signals the start of an LLM response
|
||||
- `LLMTextFrame` — Contains LLM content, typically streamed as tokens
|
||||
- `LLMFullResponseEndFrame` — Signals the end of an LLM response
|
||||
- `LLMFullResponseStartFrame` - Signals the start of an LLM response
|
||||
- `LLMTextFrame` - Contains LLM content, typically streamed as tokens
|
||||
- `LLMFullResponseEndFrame` - Signals the end of an LLM response
|
||||
|
||||
- **Thought frames (reasoning models):** If the model supports extended thinking / chain-of-thought, emit thought frames alongside the response:
|
||||
- `LLMThoughtStartFrame` — Signals the start of a thought
|
||||
- `LLMThoughtTextFrame` — Contains thought content, streamed as tokens
|
||||
- `LLMThoughtEndFrame` — Signals the end of a thought
|
||||
|
||||
- **Context aggregation** is handled by the framework via `LLMContext` + `LLMContextAggregatorPair`. The LLM service just processes context it receives — no need to implement aggregators.
|
||||
- **Context aggregation:** Implement context aggregation to collect user and assistant content:
|
||||
- Aggregators come in pairs with a `user()` instance and `assistant()` instance
|
||||
- Context must adhere to the `LLMContext` universal format
|
||||
- Aggregators should handle adding messages, function calls, and images to the context
|
||||
|
||||
### TTS (Text-to-Speech) Services
|
||||
|
||||
#### WebsocketTTSService
|
||||
#### AudioContextWordTTSService
|
||||
|
||||
**Use for:** Websocket-based streaming services (with or without word timestamps)
|
||||
**Use for:** Websocket-based services supporting word/timestamp alignment
|
||||
|
||||
**Examples:**
|
||||
**Example:**
|
||||
|
||||
- [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
|
||||
- [ElevenLabsTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
|
||||
|
||||
#### InterruptibleTTSService
|
||||
|
||||
**Use for:** Websocket-based services without word timestamps that reconnect on interruption (e.g. don't support a context ID or interruption message)
|
||||
**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
|
||||
|
||||
**Example:**
|
||||
|
||||
- [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
|
||||
|
||||
#### WordTTSService
|
||||
|
||||
**Use for:** HTTP-based services supporting word/timestamp alignment
|
||||
|
||||
**Example:**
|
||||
|
||||
- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
|
||||
|
||||
#### TTSService
|
||||
|
||||
**Use for:** HTTP-based services (word timestamps are supported in the base class)
|
||||
**Use for:** HTTP-based services without word/timestamp alignment
|
||||
|
||||
**Examples:**
|
||||
**Example:**
|
||||
|
||||
- [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
|
||||
- [OpenAITTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/openai/tts.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- For websocket services, use asyncio WebSocket implementation
|
||||
- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
|
||||
- Handle idle service timeouts with keepalives
|
||||
- TTS services push both audio (`TTSAudioRawFrame`) and text (`TTSTextFrame`) frames
|
||||
- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
|
||||
|
||||
### Telephony Serializers
|
||||
|
||||
@@ -217,25 +200,14 @@ Vision services process images and provide analysis such as descriptions, object
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- Must implement `run_vision` method that takes a `UserImageRawFrame` and returns an `AsyncGenerator[Frame, None]`
|
||||
- The method processes the image frame and yields frames with analysis results
|
||||
- Must yield the frame sequence: `VisionFullResponseStartFrame`, `VisionTextFrame`, `VisionFullResponseEndFrame`
|
||||
- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
|
||||
- The method processes the latest image in the context and yields frames with analysis results
|
||||
- Typically yields `TextFrame` objects containing descriptions or answers
|
||||
|
||||
## Implementation Guidelines
|
||||
|
||||
### Naming Conventions
|
||||
|
||||
#### Package and Repository Naming
|
||||
|
||||
Use the `pipecat-{vendor}` naming convention for your PyPI package and repository:
|
||||
|
||||
- `pipecat-{vendor}` — for single-service integrations (e.g., `pipecat-deepdub`)
|
||||
- `pipecat-{vendor}-{type}` — when a vendor offers multiple service types (e.g., `pipecat-upliftai-stt`, `pipecat-upliftai-tts`)
|
||||
|
||||
This convention makes community packages easily discoverable via PyPI search and clearly identifies them as part of the Pipecat ecosystem.
|
||||
|
||||
#### Class Naming
|
||||
|
||||
- **STT:** `VendorSTTService`
|
||||
- **LLM:** `VendorLLMService`
|
||||
- **TTS:**
|
||||
@@ -259,105 +231,49 @@ def can_generate_metrics(self) -> bool:
|
||||
return True
|
||||
```
|
||||
|
||||
### Service Settings
|
||||
### Dynamic Settings Updates
|
||||
|
||||
Every AI service (STT, LLM, TTS, image generation, etc.) exposes a **Settings dataclass** that serves two roles:
|
||||
STT, LLM, and TTS services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
|
||||
|
||||
1. **Store mode** — the service's `self._settings` holds the current value of every runtime-updatable field.
|
||||
2. **Delta mode** — an update frame (e.g. `TTSUpdateSettingsFrame`) specifies only the fields that should change; unspecified fields remain `NOT_GIVEN`.
|
||||
|
||||
#### Defining your Settings class
|
||||
|
||||
Extend `STTSettings`, `TTSSettings`, `LLMSettings`, or `ImageGenSettings` (or, if your service directly subclasses `AIService`, `ServiceSettings`). The base classes already provide common fields (e.g. `model`, `voice`, `language`). You only need to add **service-specific knobs that should be runtime-updatable**:
|
||||
Each service declares a settings dataclass that extends the appropriate base (`STTSettings`, `TTSSettings`, `LLMSettings`). Fields default to `NOT_GIVEN` so that update objects can represent sparse deltas:
|
||||
|
||||
```python
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from pipecat.services.settings import TTSSettings, NOT_GIVEN
|
||||
from pipecat.services.settings import STTSettings, NOT_GIVEN
|
||||
|
||||
@dataclass
|
||||
class MyTTSSettings(TTSSettings):
|
||||
"""Settings for MyTTS service.
|
||||
class MySTTSettings(STTSettings):
|
||||
"""Settings for my STT service.
|
||||
|
||||
Parameters:
|
||||
speaking_rate: Speed multiplier (0.5–2.0).
|
||||
region: Cloud region for the service.
|
||||
"""
|
||||
|
||||
speaking_rate: float | None = field(default_factory=lambda: NOT_GIVEN)
|
||||
region: str = field(default_factory=lambda: NOT_GIVEN)
|
||||
```
|
||||
|
||||
**What goes in Settings vs. `__init__` params:**
|
||||
|
||||
| Belongs in Settings | Stays as `__init__` params |
|
||||
| -------------------------------------------------------- | ----------------------------------------- |
|
||||
| Model name, voice, language | API keys, auth tokens |
|
||||
| Service-specific tuning knobs (rate, pitch, temperature) | Base URLs, endpoint overrides |
|
||||
| Anything users may want to change mid-session | Audio encoding, sample format |
|
||||
| | Connection parameters (timeouts, retries) |
|
||||
|
||||
The rule of thumb: if a caller might send an update frame to change it at runtime, it belongs in Settings. Everything else is init-only config stored as `self._xxx`.
|
||||
|
||||
#### Wiring settings into `__init__`
|
||||
|
||||
Accept an **optional** `settings` parameter. Build a `default_settings` object with all fields set to real values, then merge any caller overrides with `apply_update`.
|
||||
|
||||
Add a `Settings` **class attribute** that points to your settings dataclass. This lets callers access the settings class through the service itself (e.g. `MyTTSService.Settings(...)`) without a separate import:
|
||||
The service stores its current settings in `self._settings` and declares the type with a class-level annotation for editor support:
|
||||
|
||||
```python
|
||||
from typing import Optional
|
||||
class MySTTService(STTService):
|
||||
_settings: MySTTSettings
|
||||
|
||||
class MyTTSService(TTSService):
|
||||
Settings = MyTTSSettings
|
||||
_settings: Settings
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str,
|
||||
settings: Optional[Settings] = None,
|
||||
**kwargs,
|
||||
):
|
||||
# 1. Defaults — every field has a real value (store mode).
|
||||
default_settings = self.Settings(
|
||||
model="my-model-v1",
|
||||
voice="default-voice",
|
||||
language="en",
|
||||
speaking_rate=1.0,
|
||||
def __init__(self, *, model: str, language: str, region: str, **kwargs):
|
||||
# An initial value should be provided for every settings field.
|
||||
# This will be validated at service start.
|
||||
# (If you track sample_rate, it can be a placeholder value like 0; see
|
||||
# "Sample Rate Handling").
|
||||
super().__init__(
|
||||
settings=MySTTSettings(model=model, language=language, region=region), **kwargs
|
||||
)
|
||||
|
||||
# 2. Merge caller overrides (only given fields win).
|
||||
if settings is not None:
|
||||
default_settings.apply_update(settings)
|
||||
|
||||
# 3. Pass the fully-populated settings to the base class.
|
||||
super().__init__(settings=default_settings, **kwargs)
|
||||
|
||||
# 4. Init-only config stored separately.
|
||||
self._api_key = api_key
|
||||
```
|
||||
|
||||
This pattern lets callers override only what they care about:
|
||||
|
||||
```python
|
||||
# Uses all defaults
|
||||
svc = MyTTSService(api_key="sk-xxx")
|
||||
|
||||
# Overrides just the voice — access Settings through the service class
|
||||
svc = MyTTSService(
|
||||
api_key="sk-xxx",
|
||||
settings=MyTTSService.Settings(voice="custom-voice"),
|
||||
)
|
||||
```
|
||||
|
||||
#### Reacting to runtime changes
|
||||
|
||||
AI services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
|
||||
|
||||
To react to runtime setting changes, override `_update_settings`. The base implementation applies the delta to `self._settings` and returns a `dict` mapping each changed field name to its **pre-update** value. Your override should call `super()` first, then act on the changed fields. A common implementation might look like:
|
||||
|
||||
```python
|
||||
async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
|
||||
"""Apply a settings update, reconfiguring the connection if needed."""
|
||||
async def _update_settings(self, update: STTSettings) -> dict[str, Any]:
|
||||
"""Apply a settings update, reconfiguring the recognizer if needed."""
|
||||
changed = await super()._update_settings(update)
|
||||
|
||||
if not changed:
|
||||
@@ -376,7 +292,7 @@ Note that, in this example, the service requires a reconnect to apply the new la
|
||||
If your service can't yet apply certain settings at runtime, call `self._warn_unhandled_updated_settings(changed)` with any unhandled field names so users get a clear log message:
|
||||
|
||||
```python
|
||||
async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
|
||||
async def _update_settings(self, update: STTSettings) -> dict[str, Any]:
|
||||
changed = await super()._update_settings(update)
|
||||
|
||||
if not changed:
|
||||
@@ -409,7 +325,7 @@ Note that `self.sample_rate` is a `@property` set in the TTSService base class,
|
||||
|
||||
Use Pipecat's tracing decorators:
|
||||
|
||||
- **STT:** `@traced_stt` - decorate `_handle_transcription(self, transcript, is_final, language)` (the standard method name convention)
|
||||
- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
|
||||
- **LLM:** `@traced_llm` - decorate the `_process_context()` method
|
||||
- **TTS:** `@traced_tts` - decorate the `run_tts()` method
|
||||
|
||||
@@ -417,9 +333,8 @@ Use Pipecat's tracing decorators:
|
||||
|
||||
### Packaging and Distribution
|
||||
|
||||
- Name your package `pipecat-{vendor}` (see [Naming Conventions](#naming-conventions))
|
||||
- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
|
||||
- Publish to PyPI for easier installation
|
||||
- Consider releasing to PyPI for easier installation
|
||||
- Follow semantic versioning principles
|
||||
- Maintain a changelog
|
||||
|
||||
@@ -432,15 +347,17 @@ For REST-based communication, use aiohttp. Pipecat includes this as a required d
|
||||
- Wrap API calls in appropriate try/catch blocks
|
||||
- Handle rate limits and network failures gracefully
|
||||
- Provide meaningful error messages
|
||||
- When errors occur, raise exceptions AND push errors to notify the pipeline:
|
||||
- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
|
||||
|
||||
```python
|
||||
from pipecat.frames.frames import ErrorFrame
|
||||
|
||||
try:
|
||||
# Your API call
|
||||
result = await self._make_api_call()
|
||||
except Exception as e:
|
||||
# Push error upstream to notify the pipeline
|
||||
await self.push_error(f"{self} error: {e}", exception=e)
|
||||
# Push error frame to pipeline
|
||||
await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
|
||||
# Raise or handle as appropriate
|
||||
raise
|
||||
```
|
||||
|
||||
50
README.md
50
README.md
@@ -8,7 +8,7 @@
|
||||
|
||||
**Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
|
||||
|
||||
> Want to dive right in? Run `pipecat init quickstart` or follow the [quickstart guide](https://docs.pipecat.ai/getting-started/quickstart).
|
||||
> Want to dive right in? Try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
|
||||
|
||||
## 🚀 What You Can Build
|
||||
|
||||
@@ -28,10 +28,6 @@
|
||||
|
||||
## 🌐 Pipecat Ecosystem
|
||||
|
||||
### 🧩 Multi-agent systems
|
||||
|
||||
Need multiple AI agents working together? [Pipecat Subagents](https://github.com/pipecat-ai/pipecat-subagents) lets you build distributed multi-agent systems where each agent runs its own pipeline and communicates through a shared message bus. Hand off conversations between specialists, dispatch background tasks, and scale agents across processes or machines.
|
||||
|
||||
### 📱 Client SDKs
|
||||
|
||||
Building client applications? You can connect to Pipecat from any platform using our official SDKs:
|
||||
@@ -69,10 +65,6 @@ claude plugin marketplace add pipecat-ai/skills
|
||||
|
||||
and install any of the available plugins.
|
||||
|
||||
### 🧩 Community Integrations
|
||||
|
||||
Build and share your own Pipecat service integrations! Browse existing [community integrations](https://docs.pipecat.ai/api-reference/server/services/community-integrations) or check out our [guide](COMMUNITY_INTEGRATIONS.md) to create your own.
|
||||
|
||||
### 📺️ Pipecat TV Channel
|
||||
|
||||
Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
|
||||
@@ -83,28 +75,27 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/simple-chatbot/image.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
|
||||
<br/>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/daily-multi-translation"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/daily-multi-translation/image.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/vision/vision-moondream.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/assets/moondream.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
|
||||
</p>
|
||||
|
||||
## 🧩 Available services
|
||||
|
||||
| Category | Services |
|
||||
| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/api-reference/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/api-reference/server/services/stt/aws), [Azure](https://docs.pipecat.ai/api-reference/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/api-reference/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/api-reference/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/api-reference/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/api-reference/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/api-reference/server/services/stt/gladia), [Google](https://docs.pipecat.ai/api-reference/server/services/stt/google), [Gradium](https://docs.pipecat.ai/api-reference/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/api-reference/server/services/stt/groq), [Mistral](https://docs.pipecat.ai/api-reference/server/services/stt/mistral), [NVIDIA](https://docs.pipecat.ai/api-reference/server/services/stt/nvidia), [OpenAI (Whisper)](https://docs.pipecat.ai/api-reference/server/services/stt/openai), [Sarvam](https://docs.pipecat.ai/api-reference/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/api-reference/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/api-reference/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/api-reference/server/services/stt/whisper), [xAI](https://docs.pipecat.ai/api-reference/server/services/stt/xai) |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/api-reference/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/api-reference/server/services/llm/aws), [Azure](https://docs.pipecat.ai/api-reference/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/api-reference/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/api-reference/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/api-reference/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/api-reference/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/api-reference/server/services/llm/grok), [Groq](https://docs.pipecat.ai/api-reference/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/api-reference/server/services/llm/mistral), [Nebius](https://docs.pipecat.ai/api-reference/server/services/llm/nebius), [Novita](https://docs.pipecat.ai/api-reference/server/services/llm/novita), [NVIDIA NIM](https://docs.pipecat.ai/api-reference/server/services/llm/nvidia), [Ollama](https://docs.pipecat.ai/api-reference/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/api-reference/server/services/llm/openai), [OpenAI Responses](https://docs.pipecat.ai/api-reference/server/services/llm/openai-responses), [OpenRouter](https://docs.pipecat.ai/api-reference/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/api-reference/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/api-reference/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/api-reference/server/services/llm/sambanova), [Sarvam](https://docs.pipecat.ai/api-reference/server/services/llm/sarvam), [Together AI](https://docs.pipecat.ai/api-reference/server/services/llm/together) |
|
||||
| Text-to-Speech | [Async](https://docs.pipecat.ai/api-reference/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/api-reference/server/services/tts/aws), [Azure](https://docs.pipecat.ai/api-reference/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/api-reference/server/services/tts/camb), [Cartesia](https://docs.pipecat.ai/api-reference/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/api-reference/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/api-reference/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/api-reference/server/services/tts/fish), [Google](https://docs.pipecat.ai/api-reference/server/services/tts/google), [Gradium](https://docs.pipecat.ai/api-reference/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/api-reference/server/services/tts/groq), [Hume](https://docs.pipecat.ai/api-reference/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/api-reference/server/services/tts/inworld), [Kokoro](https://docs.pipecat.ai/api-reference/server/services/tts/kokoro), [LMNT](https://docs.pipecat.ai/api-reference/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/api-reference/server/services/tts/minimax), [Mistral](https://docs.pipecat.ai/api-reference/server/services/tts/mistral), [Neuphonic](https://docs.pipecat.ai/api-reference/server/services/tts/neuphonic), [NVIDIA](https://docs.pipecat.ai/api-reference/server/services/tts/nvidia), [OpenAI](https://docs.pipecat.ai/api-reference/server/services/tts/openai), [Piper](https://docs.pipecat.ai/api-reference/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/api-reference/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/api-reference/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/api-reference/server/services/tts/sarvam), [Smallest](https://docs.pipecat.ai/api-reference/server/services/tts/smallest), [Soniox](https://docs.pipecat.ai/api-reference/server/services/tts/soniox), [Speechmatics](https://docs.pipecat.ai/api-reference/server/services/tts/speechmatics), [xAI](https://docs.pipecat.ai/api-reference/server/services/tts/xai), [XTTS](https://docs.pipecat.ai/api-reference/server/services/tts/xtts) |
|
||||
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/api-reference/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/api-reference/server/services/s2s/gemini), [Grok Voice Agent](https://docs.pipecat.ai/api-reference/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/api-reference/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/api-reference/server/services/s2s/ultravox), |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/api-reference/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/api-reference/server/services/transport/fastapi-websocket), [LiveKit (WebRTC)](https://docs.pipecat.ai/api-reference/server/services/transport/livekit), [SmallWebRTCTransport](https://docs.pipecat.ai/api-reference/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/api-reference/server/services/transport/websocket-server), [WhatsApp](https://docs.pipecat.ai/api-reference/server/services/transport/whatsapp), Local |
|
||||
| Serializers | [Exotel](https://docs.pipecat.ai/api-reference/server/services/serializers/exotel), [Genesys](https://docs.pipecat.ai/api-reference/server/services/serializers/genesys), [Plivo](https://docs.pipecat.ai/api-reference/server/services/serializers/plivo), [Twilio](https://docs.pipecat.ai/api-reference/server/services/serializers/twilio), [Telnyx](https://docs.pipecat.ai/api-reference/server/services/serializers/telnyx), [Vonage](https://docs.pipecat.ai/api-reference/server/services/serializers/vonage) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/api-reference/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/api-reference/server/services/transport/lemonslice), [Tavus](https://docs.pipecat.ai/api-reference/server/services/video/tavus), [Simli](https://docs.pipecat.ai/api-reference/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/api-reference/server/services/memory/mem0) |
|
||||
| Vision & Image | [fal](https://docs.pipecat.ai/api-reference/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/api-reference/server/services/image-generation/google-imagen), [Moondream](https://docs.pipecat.ai/api-reference/server/services/vision/moondream) |
|
||||
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/api-reference/server/utilities/audio/silero-vad-analyzer), [Krisp Viva](https://docs.pipecat.ai/guides/features/krisp-viva), [Koala](https://docs.pipecat.ai/api-reference/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/api-reference/server/utilities/audio/aic-filter), [RNNoise](https://docs.pipecat.ai/api-reference/server/utilities/audio/rnnoise-filter) |
|
||||
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/api-reference/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/api-reference/server/services/analytics/sentry) |
|
||||
| Community | [Browse community integrations →](https://docs.pipecat.ai/api-reference/server/services/community-integrations) |
|
||||
| Category | Services |
|
||||
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [Hathora](https://docs.pipecat.ai/server/services/stt/hathora), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hathora](https://docs.pipecat.ai/server/services/tts/hathora), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
|
||||
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox), |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Serializers | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
|
||||
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/google-imagen), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) |
|
||||
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter) |
|
||||
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/api-reference/server/services/supported-services)
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
## ⚡ Getting started
|
||||
|
||||
@@ -146,15 +137,15 @@ You can get started with Pipecat running on your local machine, then move your a
|
||||
|
||||
## 🧪 Code examples
|
||||
|
||||
- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples) — small snippets that build on each other, introducing one or two concepts at a time
|
||||
- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
|
||||
- [Example apps](https://github.com/pipecat-ai/pipecat-examples) — complete applications that you can use as starting points for development
|
||||
|
||||
## 🛠️ Contributing to the framework
|
||||
|
||||
### Prerequisites
|
||||
|
||||
**Minimum Python Version:** 3.11
|
||||
**Recommended Python Version:** >= 3.12
|
||||
**Minimum Python Version:** 3.10
|
||||
**Recommended Python Version:** 3.12
|
||||
|
||||
### Setup Steps
|
||||
|
||||
@@ -170,6 +161,7 @@ You can get started with Pipecat running on your local machine, then move your a
|
||||
```bash
|
||||
uv sync --group dev --all-extras \
|
||||
--no-extra gstreamer \
|
||||
--no-extra krisp \
|
||||
--no-extra local \
|
||||
```
|
||||
|
||||
|
||||
1
changelog/3696.added.md
Normal file
1
changelog/3696.added.md
Normal file
@@ -0,0 +1 @@
|
||||
- Added `TextAggregationMetricsData` metric measuring the time from the first LLM token to the first complete sentence, representing the latency cost of sentence aggregation in the TTS pipeline.
|
||||
1
changelog/3696.changed.md
Normal file
1
changelog/3696.changed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Added `text_aggregation_mode` parameter to `TTSService` and all TTS subclasses with a new `TextAggregationMode` enum (`SENTENCE`, `TOKEN`). All text now flows through text aggregators regardless of mode, enabling pattern detection and tag handling in TOKEN mode.
|
||||
1
changelog/3696.deprecated.md
Normal file
1
changelog/3696.deprecated.md
Normal file
@@ -0,0 +1 @@
|
||||
- ⚠️ Deprecated `aggregate_sentences` parameter on `TTSService` and all TTS subclasses. Use `text_aggregation_mode=TextAggregationMode.SENTENCE` or `text_aggregation_mode=TextAggregationMode.TOKEN` instead.
|
||||
19
changelog/3714.added.md
Normal file
19
changelog/3714.added.md
Normal file
@@ -0,0 +1,19 @@
|
||||
- Added support for using strongly-typed objects instead of dicts for updating service settings at runtime.
|
||||
|
||||
Instead of, say:
|
||||
|
||||
```python
|
||||
await task.queue_frame(
|
||||
STTUpdateSettingsFrame(settings={"language": Language.ES})
|
||||
)
|
||||
```
|
||||
|
||||
you'd do:
|
||||
|
||||
```python
|
||||
await task.queue_frame(
|
||||
STTUpdateSettingsFrame(delta=DeepgramSTTSettings(language=Language.ES))
|
||||
)
|
||||
```
|
||||
|
||||
Each service now vends strongly-typed classes like `DeepgramSTTSettings` representing the service's runtime-updatable settings.
|
||||
1
changelog/3714.changed.md
Normal file
1
changelog/3714.changed.md
Normal file
@@ -0,0 +1 @@
|
||||
- ⚠️ Refactored runtime-updatable service settings to use strongly-typed classes (`TTSSettings`, `STTSettings`, `LLMSettings`, and service-specific subclasses) instead of plain dicts. Each service's `_settings` now holds these strongly-typed objects. For service maintainers, see changes in COMMUNITY_INTEGRATIONS.md.
|
||||
1
changelog/3714.deprecated.2.md
Normal file
1
changelog/3714.deprecated.2.md
Normal file
@@ -0,0 +1 @@
|
||||
- Dict-based `*UpdateSettingsFrame(settings={...})` is deprecated in favor of passing typed settings delta objects with `*UpdateSettingsFrame(delta={...})`.
|
||||
3
changelog/3714.deprecated.md
Normal file
3
changelog/3714.deprecated.md
Normal file
@@ -0,0 +1,3 @@
|
||||
- Deprecated `set_model()`, `set_voice()`, and `set_language()` on AI services in favor of runtime updates via `TTSUpdateSettingsFrame`, `STTUpdateSettingsFrame`, and `LLMUpdateSettingsFrame`.
|
||||
|
||||
⚠️ Note, too, a subtle behavior change in these deprecated methods. Whereas previously only `set_language()` caused the service to actually react to the update (e.g. by reconnecting to a remote service so it an pick up the change), now all these methods do. This change was made as part of a refactor making them all work the same way under the hood.
|
||||
1
changelog/3759.performance.md
Normal file
1
changelog/3759.performance.md
Normal file
@@ -0,0 +1 @@
|
||||
- Switched `GradiumTTSService` from `InterruptibleWordTTSService` to `AudioContextWordTTSService`, eliminating websocket disconnect/reconnect on every interruption by using `client_req_id`-based multiplexing.
|
||||
1
changelog/3786.changed.md
Normal file
1
changelog/3786.changed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Word timestamp support has been moved from `WordTTSService` into `TTSService` via a new `supports_word_timestamps` parameter. Services that previously extended `WordTTSService`, `AudioContextWordTTSService`, or `WebsocketWordTTSService` now pass `supports_word_timestamps=True` to their parent `__init__` instead.
|
||||
5
changelog/3786.deprecated.md
Normal file
5
changelog/3786.deprecated.md
Normal file
@@ -0,0 +1,5 @@
|
||||
- Deprecated `WordTTSService`, `WebsocketWordTTSService`, `AudioContextWordTTSService`, and `InterruptibleWordTTSService`. Use their non-word counterparts with `supports_word_timestamps=True` instead:
|
||||
- `WordTTSService` → `TTSService(supports_word_timestamps=True)`
|
||||
- `WebsocketWordTTSService` → `WebsocketTTSService(supports_word_timestamps=True)`
|
||||
- `AudioContextWordTTSService` → `AudioContextTTSService(supports_word_timestamps=True)`
|
||||
- `InterruptibleWordTTSService` → `InterruptibleTTSService(supports_word_timestamps=True)`
|
||||
1
changelog/3803.fixed.md
Normal file
1
changelog/3803.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed Poetry compatibility by inlining `local-smart-turn-v3` dependencies (`transformers`, `onnxruntime`) into core dependencies instead of using a self-referential extra.
|
||||
1
changelog/3803.removed.md
Normal file
1
changelog/3803.removed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Removed `local-smart-turn-v3` optional extra from `pyproject.toml`. The `transformers` and `onnxruntime` packages are now always installed as core dependencies since they are required by the default turn stop strategy, `TurnAnalyzerUserTurnStopStrategy` which uses `LocalSmartTurnAnalyzerV3`.
|
||||
1
changelog/3806.added.md
Normal file
1
changelog/3806.added.md
Normal file
@@ -0,0 +1 @@
|
||||
- Added `output_medium` parameter to `AgentInputParams` and `OneShotInputParams` in Ultravox service to control initial output medium (text or voice) at call creation time.
|
||||
1
changelog/3806.changed.2.md
Normal file
1
changelog/3806.changed.2.md
Normal file
@@ -0,0 +1 @@
|
||||
- Improved Ultravox TTFB measurement accuracy by using VAD speech end time instead of `UserStoppedSpeakingFrame` timing.
|
||||
1
changelog/3806.changed.md
Normal file
1
changelog/3806.changed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Aligned `UltravoxRealtimeLLMService` frame handling with OpenAI/Gemini realtime services: added `InterruptionFrame` handling with metrics cleanup, processing metrics at response boundaries, and improved agent transcript handling for both voice and text output modalities.
|
||||
1
changelog/3807.changed.md
Normal file
1
changelog/3807.changed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Updated `OpenAIRealtimeLLMService` default model to `gpt-realtime-1.5`.
|
||||
1
changelog/3808.fixed.md
Normal file
1
changelog/3808.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed `SentryMetrics` method signatures to match updated `FrameProcessorMetrics` base class, resolving `TypeError` when using `start_time`/`end_time` keyword arguments.
|
||||
1
changelog/3809.added.md
Normal file
1
changelog/3809.added.md
Normal file
@@ -0,0 +1 @@
|
||||
- Added `TurnMetricsData` as a generic metrics class for turn detection, with e2e processing time measurement. `KrispVivaTurn` now emits `TurnMetricsData` with `e2e_processing_time_ms` tracking the interval from VAD speech-to-silence transition to turn completion.
|
||||
1
changelog/3809.changed.md
Normal file
1
changelog/3809.changed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Added `api_key` parameter to `KrispVivaSDKManager`, `KrispVivaTurn`, and `KrispVivaFilter` for Krisp SDK v1.6.1+ licensing. Falls back to `KRISP_VIVA_API_KEY` environment variable.
|
||||
1
changelog/3809.deprecated.md
Normal file
1
changelog/3809.deprecated.md
Normal file
@@ -0,0 +1 @@
|
||||
- Deprecated `SmartTurnMetricsData` in favor of `TurnMetricsData`. `BaseSmartTurn` now emits `TurnMetricsData` directly.
|
||||
1
changelog/3811.changed.md
Normal file
1
changelog/3811.changed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Bumped `nltk` minimum version from 3.9.1 to 3.9.3 to resolve a security vulnerability.
|
||||
1
changelog/3813.fixed.md
Normal file
1
changelog/3813.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed STT TTFB metrics not being reported for `SonioxSTTService` and `AWSTranscribeSTTService` due to missing `can_generate_metrics()` override.
|
||||
1
changelog/3814.added.md
Normal file
1
changelog/3814.added.md
Normal file
@@ -0,0 +1 @@
|
||||
- Added `on_audio_context_interrupted()` and `on_audio_context_completed()` callbacks to `AudioContextTTSService`. Subclasses can override these to perform provider-specific cleanup instead of overriding `_handle_interruption()`.
|
||||
1
changelog/3814.fixed.md
Normal file
1
changelog/3814.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed an issue where `AudioContextTTSService`-based providers (AsyncAI, ElevenLabs, Inworld, Rime) did not close or clean up their server-side audio contexts after normal speech completion, only on interruption.
|
||||
4
changelog/3819.changed.md
Normal file
4
changelog/3819.changed.md
Normal file
@@ -0,0 +1,4 @@
|
||||
- `ServiceSettingsUpdateFrame`s are now `UninterruptibleFrame`s. Generally speaking, you don't want a user interruption to prevent a service setting change from going into effect. Note that you usually don't use `ServiceSettingsUpdateFrame` directly, you use one of its subclasses:
|
||||
- `LLMUpdateSettingsFrame`
|
||||
- `TTSUpdateSettingsFrame`
|
||||
- `STTUpdateSettingsFrame`
|
||||
1
changelog/3822.fixed.md
Normal file
1
changelog/3822.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed STT TTFB metrics measuring timeout expiry time instead of actual transcript arrival time.
|
||||
1
changelog/3825.fixed.md
Normal file
1
changelog/3825.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed `InterimTranscriptionFrame` and `TranslationFrame` being unintentionally pushed downstream in `LLMUserAggregator`. They are now consumed like `TranscriptionFrame`.
|
||||
1
changelog/3828.fixed.md
Normal file
1
changelog/3828.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed misleading "Empty audio frame received for STT service" warnings when using audio filters (e.g. `RNNoiseFilter`, `KrispVivaFilter`, `AICFilter`) that buffer audio internally.
|
||||
1
changelog/3837.fixed.md
Normal file
1
changelog/3837.fixed.md
Normal file
@@ -0,0 +1 @@
|
||||
- Fixed issues with `RimeNonJsonTTSService` where trailing punctuation is sometimes vocalized
|
||||
1
changelog/3838.removed.md
Normal file
1
changelog/3838.removed.md
Normal file
@@ -0,0 +1 @@
|
||||
- ⚠️ Removed `PlayHTTTSService` and `PlayHTHttpTTSService`. PlayHT has been shut down and is no longer available.
|
||||
1
changelog/3851.removed.md
Normal file
1
changelog/3851.removed.md
Normal file
@@ -0,0 +1 @@
|
||||
- ⚠️ Removed `ProcessingMetricsData` and all `start_processing_metrics()`/`stop_processing_metrics()` methods from `FrameProcessor` and `FrameProcessorMetrics`. These metrics were inconsistently implemented across services and overlapped with the better-defined TTFB metric. TTFB, LLM token usage, TTS character usage, and text aggregation metrics are unaffected.
|
||||
@@ -1 +0,0 @@
|
||||
- Added a `session_id` field to `RunnerArguments` so bots can log or trace a per-session identifier in local development the same way they can in Pipecat Cloud. The development runner now mints a UUID at every construction site, and paths that already returned a `sessionId` to the caller (Daily `/start`, dial-in webhook) share that same UUID with the runner args instead of generating two. The SmallWebRTC `/api/offer` endpoint also accepts an optional `session_id` query parameter so the `/sessions/{session_id}/...` proxy can thread it through.
|
||||
@@ -1 +0,0 @@
|
||||
- Updated the default `SonioxTTSService` model from `tts-rt-v1-preview` to the generally available `tts-rt-v1`.
|
||||
@@ -1 +0,0 @@
|
||||
- Added a `max_buffer_delay_ms` constructor argument to `CartesiaTTSService` for controlling Cartesia's server-side text buffering. When unset, Pipecat picks a sensible default based on `text_aggregation_mode`: `0` in `SENTENCE` mode (custom buffering — avoids stacking client-side aggregation on top of Cartesia's default 3000ms server buffer) and unset in `TOKEN` mode (Cartesia's managed buffering applies). Pass an explicit value (0–5000ms) to override.
|
||||
@@ -1 +0,0 @@
|
||||
- Default `cartesia_version` for `CartesiaTTSService` bumped from `2025-04-16` to `2026-03-01`, matching `CartesiaHttpTTSService` and unlocking the `use_normalized_timestamps` and `max_buffer_delay_ms` fields.
|
||||
@@ -1 +0,0 @@
|
||||
- ⚠️ `CartesiaTTSService` now sends `use_normalized_timestamps: true` instead of the deprecated `use_original_timestamps` field. Word timestamps now reflect what was actually spoken (post text-normalization and pronunciation-dictionary substitution), matching the convention Pipecat uses for ElevenLabs. This is a behavior change for `sonic-3` users, who were previously receiving timestamps tied to the input transcript.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `CartesiaHttpTTSService` pushing two `ErrorFrame`s on a non-200 response — one with the API's error text and a second, less informative "Unknown error" frame from the outer exception handler. It now pushes a single frame that includes the HTTP status code and returns cleanly.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed Cartesia tag helpers (`SPELL`, `EMOTION_TAG`, `PAUSE_TAG`, `VOLUME_TAG`, `SPEED_TAG`) raising `TypeError` when called on an instance (e.g. `tts.SPELL("hi")`). They're now `@staticmethod` and callable from both the class and an instance.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `CartesiaTTSService` surfacing `flush_done` messages from Cartesia as `ErrorFrame`s. The latest API emits a `flush_done` per transcript when server-side buffering is disabled; Pipecat now consumes them silently since each turn already has its own `context_id`.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed an issue where `LocalSmartTurnAnalyzerV3` was imported unconditionally for user turn stop strategies. It is now only imported when `default_user_turn_stop_strategies()` is called. This improves startup time and removes the `transformers` "PyTorch/TensorFlow/Flax not found" warning when the default stop strategies are not used.
|
||||
@@ -1 +0,0 @@
|
||||
- Broadened `tool_resources` to `app_resources` for easy access not just in tool handlers but in other places like custom `FrameProcessor`s. Three changes: a rename (`tool_resources` → `app_resources`), a new `app_resources` property on `PipelineTask`, and a new `pipeline_task` property on `FrameProcessor`. Tool handlers now read `params.app_resources`; custom processors read `self.pipeline_task.app_resources`. The previous `tool_resources` aliases (on `PipelineTask`, `FunctionCallParams`, and `FrameProcessorSetup`) keep working but are deprecated as of 1.2.0 and emit `DeprecationWarning`s.
|
||||
@@ -1 +0,0 @@
|
||||
- Lowered the per-message log in `SmallWebRTCInputTransport._handle_app_message` from `debug` to `trace`. App messages can be high-frequency and were noisy at debug level; set the loguru level to `TRACE` to see them again.
|
||||
@@ -1 +0,0 @@
|
||||
- Added a `mip_opt_out` constructor argument to `DeepgramTTSService` and `DeepgramHttpTTSService` so callers can opt out of the Deepgram Model Improvement Program. When set, the value is forwarded to Deepgram as a query parameter on the speak request. Defaults to `None`, which preserves the existing behavior. See https://dpgr.am/deepgram-mip for pricing implications before enabling.
|
||||
@@ -1 +0,0 @@
|
||||
- Changed the default model for `GrokRealtimeLLMService` to `grok-voice-think-fast-1.0`, xAI's recommended Voice Agent model. The previous default of `grok-voice-fast-1.0` has been deprecated by xAI and is being removed.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `GrokRealtimeLLMService` ignoring the configured model. The model was stored in `Settings` but never sent to xAI, so every session silently fell back to xAI's server-side default. The model is now passed via the `?model=` query parameter on the WebSocket URL as xAI's Voice Agent API requires.
|
||||
@@ -1 +0,0 @@
|
||||
- Added an opt-in `add_tool_change_messages` flag to the LLM aggregators (set via `LLMContextAggregatorPair(..., add_tool_change_messages=True)`) that appends a developer-role message to the context whenever `LLMSetToolsFrame` changes the set of advertised standard tools. Helps the LLM stay coherent across mid-conversation tool changes, mitigating several flavors of tool-call-related hallucination: calling tools that have been removed, avoiding tools that have been re-added, and hallucinating output (made-up answers or tool-call-shaped non-tool-calls) when tools are unavailable.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `LLMTurnCompletionUserTurnStopStrategy` in `pipecat.turns.user_stop`. When installed, the strategy gates `on_user_turn_stopped` on a `UserTurnInferenceCompletedFrame` (a new fieldless system frame emitted by any component that can judge turn completeness — e.g. the `UserTurnCompletionLLMServiceMixin` on `✓`). A `finalization_timeout` provides a safety net if no completion frame ever arrives.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `deferred(strategy)` and `DeferredUserTurnStopStrategy` in `pipecat.turns.user_stop`. Wraps a stop strategy so it fires only the inference-triggered event and suppresses `on_user_turn_stopped`, leaving finalization to another strategy in the chain such as `LLMTurnCompletionUserTurnStopStrategy`.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `FilterIncompleteUserTurnStrategies` in `pipecat.turns.user_turn_strategies` — a `UserTurnStrategies` specialization that wraps the detector chain with `deferred(...)` and appends `LLMTurnCompletionUserTurnStopStrategy` as the finalizer. Common case: `user_turn_strategies=FilterIncompleteUserTurnStrategies()`. Pass `config=UserTurnCompletionConfig(...)` to customize timeouts and prompts.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `ExternalUserTurnCompletionStopStrategy` in `pipecat.turns.user_stop` — a generic stop strategy that finalizes the user turn whenever a `UserTurnInferenceCompletedFrame` arrives, regardless of which component produced it. `LLMTurnCompletionUserTurnStopStrategy` now extends this base; future producers (Flux, custom end-of-turn classifiers, etc.) can use the base directly or subclass it to add producer-specific setup.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `on_user_turn_inference_triggered`, a new event on the user turn controller, processor, aggregator and stop strategies that fires when a strategy has enough signal to start LLM inference. By default it fires together with `on_user_turn_stopped`; a gating strategy can fire only the inference-triggered event and defer finalization to a peer.
|
||||
@@ -1 +0,0 @@
|
||||
- Deprecated `LLMUserAggregatorParams.filter_incomplete_user_turns`. Use `user_turn_strategies=FilterIncompleteUserTurnStrategies()` (or add `LLMTurnCompletionUserTurnStopStrategy` to a custom `user_turn_strategies.stop`) instead. Setting the legacy flag still works for one release: the aggregator emits a `DeprecationWarning` and rewires the strategies as if you had passed `FilterIncompleteUserTurnStrategies` directly.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `on_user_turn_stopped` firing prematurely when `filter_incomplete_user_turns` was enabled. The event now fires only after the LLM confirms the user turn is complete (`✓`); previously the smart-turn detector's tentative stop was bubbling up before the LLM had a chance to veto it, causing observers, transcript appenders and UI indicators to receive an early — and sometimes duplicated — signal.
|
||||
@@ -1,6 +0,0 @@
|
||||
- Added first-class RTVI support for the UI Agent Protocol:
|
||||
- Adds `ui-event`, `ui-snapshot`, and `ui-cancel-task` client-to-server messages, plus `ui-command` and `ui-task` server-to-client messages, with paired `*Data` / `*Message` pydantic models.
|
||||
- Adds built-in command payload models for `Toast`, `Navigate`, `ScrollTo`, `Highlight`, `Focus`, `Click`, `SetInputValue`, and `SelectText`; matching default handlers live in `@pipecat-ai/client-react`.
|
||||
- Adds `RTVIProcessor.on_ui_message` for inbound `ui-event`, `ui-snapshot`, and `ui-cancel-task` messages.
|
||||
- Adds five UI pipeline frames, mirroring the `client-message` frame-and-event pattern: downstream code pushes `RTVIUICommandFrame` / `RTVIUITaskFrame` for the observer to wrap into outbound `UICommandMessage` / `UITaskMessage` envelopes, while the processor pushes inbound `RTVIUIEventFrame`, `RTVIUISnapshotFrame`, and `RTVIUICancelTaskFrame` alongside `on_ui_message`.
|
||||
- Bumps the RTVI `PROTOCOL_VERSION` from `1.2.0` to `1.3.0`.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `TTSSpeakFrame(append_to_context=True)` greetings sometimes splitting across two assistant messages in the LLM context and not surfacing in `on_assistant_turn_stopped`. The `LLMAssistantPushAggregationFrame` emitted at the end of a TTS context now carries a PTS just past the last word so it can't overtake clock-queued `TTSTextFrame`s in the transport's output, and `LLMAssistantAggregator` now triggers `on_assistant_turn_started`/`on_assistant_turn_stopped` when it receives the frame outside an LLM response cycle (restoring v0.0.104 behavior for greeting transcripts).
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `ElevenLabsTTSService` and `ElevenLabsHttpTTSService` producing merged words (e.g. `bookLook`) when using Flash models. Flash often splits sentences mid-stream into alignment chunks that begin with a real inter-word space, but the previous fix unconditionally stripped that space from every chunk. Leading spaces are now stripped only on the first alignment chunk of an utterance, so subsequent chunks correctly flush partial words across boundaries.
|
||||
@@ -1 +0,0 @@
|
||||
- AWS Transcribe STT, Polly TTS, Bedrock LLM, and the Bedrock AgentCore processor now resolve credentials via the standard boto3 provider chain (EC2 instance profiles, EKS pod roles / IRSA, ECS task roles, SSO, `~/.aws/credentials`) when explicit credentials and `AWS_*` environment variables are absent. Services running with IAM roles no longer need to export static credentials.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed AWS Polly TTS, Bedrock LLM, and the Bedrock AgentCore processor erroring out when only one of `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY` was set in the environment. The half-populated kwargs are no longer forwarded to aioboto3; partial env-var configurations now fall through to the boto3 credential chain like fully-unset configurations do.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed a path traversal issue in the development runner's `/files/{filename:path}` download endpoint. Previously, when the runner was started with `--folder`, a request like `/files/..%2F..%2Fetc%2Fpasswd` could escape the configured folder because `%2F`-encoded separators bypassed Starlette's path normalisation. The endpoint now resolves the joined path and rejects any filename that escapes the allowed base with a 403, and also returns 404 (instead of an implicit `null` 200) when `--folder` is unset.
|
||||
@@ -1 +0,0 @@
|
||||
- Changed the default Inworld TTS model from `inworld-tts-1.5-max` to `inworld-tts-2` (Realtime TTS-2) across `InworldHttpTTSService`, `InworldTTSService`, and the `InworldRealtimeLLMService` cascade. Existing users can pin the prior model explicitly via the `model`/`tts_model` argument; both `inworld-tts-1.5-max` and `inworld-tts-1.5-mini` remain valid model IDs.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `ElevenLabsTTSService` and `ElevenLabsHttpTTSService` writing romanized/normalized text to the LLM context. With non-Latin input (e.g., Chinese), the assistant transcript was getting populated with pinyin (`Ni Hao !` instead of `你好!`), which then degraded subsequent LLM turns. The services now consume `alignment` by default and only switch to `normalizedAlignment` / `normalized_alignment` when `pronunciation_dictionary_locators` is configured (where `alignment` has overlapping restarts that produce duplicated/garbled words, per #4316). Both fields are read with preferred-with-fallback semantics since each is nullable per the API schema.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `keyterms` support to ElevenLabs STT services so Scribe V2 callers can bias transcription for both file-based and realtime transcription.
|
||||
@@ -1 +0,0 @@
|
||||
- Deprecated `ResampyResampler` in favor of `SOXRAudioResampler` (or the `create_file_resampler()` / `create_stream_resampler()` factories). Instantiating `ResampyResampler` now emits a `DeprecationWarning`. The class will be removed in Pipecat 2.0 along with the default `resampy` and `numba` dependencies.
|
||||
@@ -1 +0,0 @@
|
||||
- Changed the default model for `GrokLLMService` from `grok-3` to `grok-4.20-non-reasoning`. xAI is retiring `grok-3` on May 15, 2026.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `watchdog_min_timeout` parameter to `DeepgramFluxSTT` and `DeepgramFluxSageMakerSTT` (default `0.5` seconds) to control the minimum silence duration before the watchdog sends a silence packet to prevent dangling turns. The actual threshold is `max(chunk_duration * 2, watchdog_min_timeout)`, so it also adapts automatically to the audio chunk size in use.
|
||||
@@ -1 +0,0 @@
|
||||
- `DeepgramFluxSTT` watchdog silence threshold is now dynamic: `max(chunk_duration * 2, watchdog_min_timeout)` instead of a fixed 500 ms. This prevents false silence injections when large audio chunks are sent at lower frequency.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed a deadlock in `TTSService` that could permanently stall pipeline processing when all three conditions occurred together: `pause_frame_processing=True`, an interruption arrived before any TTS audio was played, and an `UninterruptibleFrame` (e.g. `TTSUpdateSettingsFrame`, `FunctionCallResultFrame`) was in the processing queue at that moment. The process task would block on `__process_event.wait()` indefinitely because `BotStoppedSpeakingFrame` never arrives (no audio was played) and the interruption handler did not resume processing. Affects services using `pause_frame_processing=True` such as ElevenLabs, Rime, AsyncAI, Gradium, and ResembleAI.
|
||||
@@ -1 +0,0 @@
|
||||
- `ElevenLabsTTSService` now sends `close_context` to the server as soon as the turn is complete (on `on_turn_context_completed`) rather than waiting until all audio has finished playing back. The `isFinal` message from ElevenLabs is now used to signal `TTSStoppedFrame` and clean up the audio context, improving turn transition timing.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed interruptions being delayed when a slow non-uninterruptible frame was processing and an uninterruptible frame was waiting in the queue. The bot would stall until the slow frame finished instead of cancelling it immediately on interruption.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `TTSService` dropping uninterruptible frames (e.g. `FunctionCallResultFrame`) from its internal serialization queue when an interruption occurs. Previously, the queue was recreated on every interruption, silently discarding any queued frames. The queue is now reset instead of recreated, preserving uninterruptible frames so they are always delivered downstream.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed a race condition in the Daily transport that caused `AttributeError: 'NoneType' object has no attribute 'send_app_message'` when tearing down a pipeline. Both `DailyInputTransport` and `DailyOutputTransport` share the same `DailyTransportClient` and both call `cleanup()`, which was releasing the underlying `CallClient` on the first call — leaving the second caller with a `None` client.
|
||||
@@ -1 +0,0 @@
|
||||
- Restored `cancel_on_interruption=False` support for `AWSNovaSonicLLMService` and `OpenAIRealtimeLLMService`. These services previously honored the flag by simply not cancelling in-flight function calls on interruption; the introduction of the new async-tool mechanism (which threads started/intermediate/final messages through the LLM context) broke that path because the realtime services didn't know how to interpret those messages. Note that new-style streamed intermediate results (`FunctionCallResultProperties(is_final=False)`) are not supported on these realtime services. Similar fixes for other impacted realtime services are forthcoming.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed two misspelled Gemini TTS voice names in `GeminiTTSService.AVAILABLE_VOICES`.
|
||||
@@ -1 +0,0 @@
|
||||
- Updated `InworldHttpTTSService` and `InworldTTSService` to use PCM audio encoding by default, which returns audio bytes without headers.
|
||||
@@ -1 +0,0 @@
|
||||
- Extended the `cancel_on_interruption=False` regression fix to `GrokRealtimeLLMService`, `AzureRealtimeLLMService`, and `UltravoxRealtimeLLMService`. Grok and Azure use the same approach as in #4441 (each service detects async-tool messages in the LLM context and routes the final result to its formal tool-result channel; Azure inherits transitively from `OpenAIRealtimeLLMService`). Ultravox needed a different approach because its API freezes the conversation between `client_tool_invocation` and the matching `client_tool_result` — for async-registered functions it now ships a placeholder `client_tool_result` immediately when the function is invoked (to unfreeze the conversation), then injects the real result as user-side text once the tool finishes. Streamed intermediate results (`FunctionCallResultProperties(is_final=False)`) are still not supported on any of these realtime services. `GeminiLiveLLMService` and `InworldRealtimeLLMService` are excluded for now: Gemini Live's async-tool path needs deeper investigation, and Inworld appears to have a pre-existing problem with even simple tool calling on its Realtime API.
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user