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telnyx-ai-inference-python

Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.

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价格未确认★ 212 GitHub Stars目录更新于 · 2026年10月9日agent-skill

概览

Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.

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Telnyx Ai Inference - Python

Installation

pip install telnyx

Setup

import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("TELNYX_API_KEY"),  # This is the default and can be omitted
)

All examples below assume client is already initialized as shown above.

Error Handling

All API calls can fail with network errors, rate limits (429), validation errors (422), or authentication errors (401). Always handle errors in production code:

import telnyx

try:
    result = client.messages.send(to="+13125550001", from_="+13125550002", text="Hello")
except telnyx.APIConnectionError:
    print("Network error — check connectivity and retry")
except telnyx.RateLimitError:
    # 429: rate limited — wait and retry with exponential backoff
    import time
    time.sleep(1)  # Check Retry-After header for actual delay
except telnyx.APIStatusError as e:
    print(f"API error {e.status_code}: {e.message}")
    if e.status_code == 422:
        print("Validation error — check required fields and formats")

Common error codes: 401 invalid API key, 403 insufficient permissions, 404 resource not found, 422 validation error (check field formats), 429 rate limited (retry with exponential backoff).

Important Notes

Pagination: List methods return an auto-paginating iterator. Use for item in page_result: to iterate through all pages automatically.

Transcribe speech to text

Transcribe speech to text. This endpoint is consistent with the OpenAI Transcription API and may be used with the OpenAI JS or Python SDK.

POST /ai/audio/transcriptions

response = client.ai.audio.transcribe(
    model="distil-whisper/distil-large-v2",
)
print(response.text)

Returns: duration (number), segments (array[object]), text (string), words (array[object])

Create a chat completion

Deprecated: Use POST /v2/ai/openai/chat/completions instead. Chat with a language model. This endpoint is consistent with the OpenAI Chat Completions API and may be used with the OpenAI JS or Python SDK.

POST /ai/chat/completions — Required: messages

Optional: api_key_ref (string), best_of (integer), early_stopping (boolean), enable_thinking (boolean), frequency_penalty (number), guided_choice (array[string]), guided_json (object), guided_regex (string), length_penalty (number), logprobs (boolean), max_tokens (integer), min_p (number), model (string), n (number), presence_penalty (number), response_format (object), seed (integer), stop (object), stream (boolean), temperature (number), tool_choice (enum: none, auto, required), tools (array[object]), top_logprobs (integer), top_p (number), use_beam_search (boolean)

response = client.ai.chat.create_completion(
    messages=[{
        "role": "system",
        "content": "You are a friendly chatbot.",
    }, {
        "role": "user",
        "content": "Hello, world!",
    }],
)
print(response)

List conversations

Retrieve a list of all AI conversations configured by the user. Supports PostgREST-style query parameters for filtering. Examples are included for the standard metadata fields, but you can filter on any field in the metadata JSON object.

GET /ai/conversations

conversations = client.ai.conversations.list()
print(conversations.data)

Returns: created_at (date-time), id (uuid), last_message_at (date-time), metadata (object), name (string)

Create a conversation

Create a new AI Conversation.

POST /ai/conversations

Optional: metadata (object), name (string)

conversation = client.ai.conversations.create()
print(conversation.id)

Returns: created_at (date-time), id (uuid), last_message_at (date-time), metadata (object), name (string)

Aggregate Conversation Insights

Aggregate conversation insights by specified fields

GET /ai/conversations/conversation-insights/aggregates

response = client.ai.conversations.conversation_insights.aggregate()
print(response.data)

Returns: record_count (integer)

Get Insight Template Groups

Get all insight groups

GET /ai/conversations/insight-groups

page = client.ai.conversations.insight_groups.retrieve_insight_groups()
page = page.data[0]
print(page.id)

Returns: created_at (date-time), description (string), id (uuid), insights (array[object]), name (string), webhook (string)

Create Insight Template Group

Create a new insight group

POST /ai/conversations/insight-groups — Required: name

Optional: description (string), webhook (string)

insight_template_group_detail = client.ai.conversations.insight_groups.insight_groups(
    name="my-resource",
)
print(insight_template_group_detail.data)

Returns: created_at (date-time), description (string), id (uuid), insights (array[object]), name (string), webhook (string)

Get Insight Template Group

Get insight group by ID

GET /ai/conversations/insight-groups/{group_id}

insight_template_group_detail = client.ai.conversations.insight_groups.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_group_detail.data)

Returns: created_at (date-time), description (string), id (uuid), insights (array[object]), name (string), webhook (string)

Update Insight Template Group

Update an insight template group

PUT /ai/conversations/insight-groups/{group_id}

Optional: description (string), name (string), webhook (string)

insight_template_group_detail = client.ai.conversations.insight_groups.update(
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_group_detail.data)

Returns: created_at (date-time), description (string), id (uuid), insights (array[object]), name (string), webhook (string)

Delete Insight Template Group

Delete insight group by ID

DELETE /ai/conversations/insight-groups/{group_id}

client.ai.conversations.insight_groups.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Assign Insight Template To Group

Assign an insight to a group

POST /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/assign

client.ai.conversations.insight_groups.insights.assign(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Unassign Insight Template From Group

Remove an insight from a group

DELETE /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/unassign

client.ai.conversations.insight_groups.insights.delete_unassign(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Get Insight Templates

Get all insights

GET /ai/conversations/insights

page = client.ai.conversations.insights.list()
page = page.data[0]
print(page.id)

Returns: created_at (date-time), id (uuid), insight_type (enum: custom, default), instructions (string), json_schema (object), name (string), webhook (string)

Create Insight Template

Create a new insight

POST /ai/conversations/insights — Required: instructions, name

Optional: json_schema (object), webhook (string)

insight_template_detail = client.ai.conversations.insights.create(
    instructions="You are a helpful assistant.",
    name="my-resource",
)
print(insight_template_detail.data)

Returns: created_at (date-time), id (uuid), insight_type (enum: custom, default), instructions (string), json_schema (object), name (string), webhook (string)

Get Insight Template

Get insight by ID

GET /ai/conversations/insights/{insight_id}

insight_template_detail = client.ai.conversations.insights.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.data)

Returns: created_at (date-time), id (uuid), insight_type (enum: custom, default), instructions (string), json_schema (object), name (string), webhook (string)

Update Insight Template

Update an insight template

PUT /ai/conversations/insights/{insight_id}

Optional: instructions (string), json_schema (object), name (string), webhook (string)

insight_template_detail = client.ai.conversations.insights.update(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.data)

Returns: created_at (date-time), id (uuid), insight_type (enum: custom, default), instructions (string), json_schema (object), name (string), webhook (string)

Delete Insight Template

Delete insight by ID

DELETE /ai/conversations/insights/{insight_id}

client.ai.conversations.insights.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)

Get a conversation

Retrieve a specific AI conversation by its ID.

GET /ai/conversations/{conversation_id}

conversation = client.ai.conversations.retrieve(
    "conversation_id",
)
print(conversation.data)

Returns: created_at (date-time), id (uuid), last_message_at (date-time), metadata (object), name (string)

Update conversation metadata

Update metadata for a specific conversation.

PUT /ai/conversations/{conversation_id}

Optional: metadata (object)

conversation = client.ai.conversations.update(
    conversation_id="550e8400-e29b-41d4-a716-446655440000",
)
print(conversation.data)

Returns: created_at (date-time), id (uuid), last_message_at (date-time), metadata (object), name (string)

Delete a conversation

Delete a specific conversation by its ID.

DELETE /ai/conversations/{conversation_id}

client.ai.conversations.delete(
    "conversation_id",
)

Get insights for a conversation

Retrieve insights for a specific conversation

GET /ai/conversations/{conversation_id}/conversations-insights

response = client.ai.conversations.retrieve_conversations_insights(
    "conversation_id",
)
print(response.data)

Returns: conversation_insights (array[object]), created_at (date-time), id (string), status (enum: pending, in_progress, completed, failed)

Create Message

Add a new message to the conversation. Used to insert a new messages to a conversation manually ( without using chat endpoint )

POST /ai/conversations/{conversation_id}/message — Required: role

Optional: content (string), metadata (object), name (string), sent_at (date-time), tool_call_id (string), tool_calls (array[object]), tool_choice (object)

client.ai.conversations.add_message(
    conversation_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    role="user",
)

Get conversation messages

Retrieve messages for a specific conversation, including tool calls made by the assistant.

GET /ai/conversations/{conversation_id}/messages

page = client.ai.conversations.messages.list(
    conversation_id="550e8400-e29b-41d4-a716-446655440000",
)
page = page.data[0]
print(page.role)

Returns: created_at (date-time), role (enum: user, assistant, tool), sent_at (date-time), text (string), tool_calls (array[object])

Get Tasks by Status

Retrieve tasks for the user that are either queued, processing, failed, success or partial_success based on the

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文件元数据

name: telnyx-ai-inference-python
description: >-
  Access Telnyx LLM inference APIs, embeddings, and AI analytics for call
  insights and summaries. This skill provides Python SDK examples.
metadata:
  author: telnyx
  product: ai-inference
  language: python
  generated_by: telnyx-openapi-pipeline

查看原始文本

---
name: telnyx-ai-inference-python
description: >-
  Access Telnyx LLM inference APIs, embeddings, and AI analytics for call
  insights and summaries. This skill provides Python SDK examples.
metadata:
  author: telnyx
  product: ai-inference
  language: python
  generated_by: telnyx-openapi-pipeline
---

<!-- Auto-generated from Telnyx OpenAPI specs. Do not edit. -->

# Telnyx Ai Inference - Python

## Installation

```bash
pip install telnyx
```

## Setup

```python
import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("TELNYX_API_KEY"),  # This is the default and can be omitted
)
```

All examples below assume `client` is already initialized as shown above.

## Error Handling

All API calls can fail with network errors, rate limits (429), validation errors (422),
or authentication errors (401). Always handle errors in production code:

```python
import telnyx

try:
    result = client.messages.send(to="+13125550001", from_="+13125550002", text="Hello")
except telnyx.APIConnectionError:
    print("Network error — check connectivity and retry")
except telnyx.RateLimitError:
    # 429: rate limited — wait and retry with exponential backoff
    import time
    time.sleep(1)  # Check Retry-After header for actual delay
except telnyx.APIStatusError as e:
    print(f"API error {e.status_code}: {e.message}")
    if e.status_code == 422:
        print("Validation error — check required fields and formats")
```

Common error codes: `401` invalid API key, `403` insufficient permissions,
`404` resource not found, `422` validation error (check field formats),
`429` rate limited (retry with exponential backoff).

## Important Notes

- **Pagination:** List methods return an auto-paginating iterator. Use `for item in page_result:` to iterate through all pages automatically.

## Transcribe speech to text

Transcribe speech to text. This endpoint is consistent with the [OpenAI Transcription API](https://platform.openai.com/docs/api-reference/audio/createTranscription) and may be used with the OpenAI JS or Python SDK.

`POST /ai/audio/transcriptions`

```python
response = client.ai.audio.transcribe(
    model="distil-whisper/distil-large-v2",
)
print(response.text)
```

Returns: `duration` (number), `segments` (array[object]), `text` (string), `words` (array[object])

## Create a chat completion

**Deprecated**: Use `POST /v2/ai/openai/chat/completions` instead. Chat with a language model. This endpoint is consistent with the [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat) and may be used with the OpenAI JS or Python SDK.

`POST /ai/chat/completions` — Required: `messages`

Optional: `api_key_ref` (string), `best_of` (integer), `early_stopping` (boolean), `enable_thinking` (boolean), `frequency_penalty` (number), `guided_choice` (array[string]), `guided_json` (object), `guided_regex` (string), `length_penalty` (number), `logprobs` (boolean), `max_tokens` (integer), `min_p` (number), `model` (string), `n` (number), `presence_penalty` (number), `response_format` (object), `seed` (integer), `stop` (object), `stream` (boolean), `temperature` (number), `tool_choice` (enum: none, auto, required), `tools` (array[object]), `top_logprobs` (integer), `top_p` (number), `use_beam_search` (boolean)

```python
response = client.ai.chat.create_completion(
    messages=[{
        "role": "system",
        "content": "You are a friendly chatbot.",
    }, {
        "role": "user",
        "content": "Hello, world!",
    }],
)
print(response)
```

## List conversations

Retrieve a list of all AI conversations configured by the user. Supports [PostgREST-style query parameters](https://postgrest.org/en/stable/api.html#horizontal-filtering-rows) for filtering. Examples are included for the standard metadata fields, but you can filter on any field in the metadata JSON object.

`GET /ai/conversations`

```python
conversations = client.ai.conversations.list()
print(conversations.data)
```

Returns: `created_at` (date-time), `id` (uuid), `last_message_at` (date-time), `metadata` (object), `name` (string)

## Create a conversation

Create a new AI Conversation.

`POST /ai/conversations`

Optional: `metadata` (object), `name` (string)

```python
conversation = client.ai.conversations.create()
print(conversation.id)
```

Returns: `created_at` (date-time), `id` (uuid), `last_message_at` (date-time), `metadata` (object), `name` (string)

## Aggregate Conversation Insights

Aggregate conversation insights by specified fields

`GET /ai/conversations/conversation-insights/aggregates`

```python
response = client.ai.conversations.conversation_insights.aggregate()
print(response.data)
```

Returns: `record_count` (integer)

## Get Insight Template Groups

Get all insight groups

`GET /ai/conversations/insight-groups`

```python
page = client.ai.conversations.insight_groups.retrieve_insight_groups()
page = page.data[0]
print(page.id)
```

Returns: `created_at` (date-time), `description` (string), `id` (uuid), `insights` (array[object]), `name` (string), `webhook` (string)

## Create Insight Template Group

Create a new insight group

`POST /ai/conversations/insight-groups` — Required: `name`

Optional: `description` (string), `webhook` (string)

```python
insight_template_group_detail = client.ai.conversations.insight_groups.insight_groups(
    name="my-resource",
)
print(insight_template_group_detail.data)
```

Returns: `created_at` (date-time), `description` (string), `id` (uuid), `insights` (array[object]), `name` (string), `webhook` (string)

## Get Insight Template Group

Get insight group by ID

`GET /ai/conversations/insight-groups/{group_id}`

```python
insight_template_group_detail = client.ai.conversations.insight_groups.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_group_detail.data)
```

Returns: `created_at` (date-time), `description` (string), `id` (uuid), `insights` (array[object]), `name` (string), `webhook` (string)

## Update Insight Template Group

Update an insight template group

`PUT /ai/conversations/insight-groups/{group_id}`

Optional: `description` (string), `name` (string), `webhook` (string)

```python
insight_template_group_detail = client.ai.conversations.insight_groups.update(
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_group_detail.data)
```

Returns: `created_at` (date-time), `description` (string), `id` (uuid), `insights` (array[object]), `name` (string), `webhook` (string)

## Delete Insight Template Group

Delete insight group by ID

`DELETE /ai/conversations/insight-groups/{group_id}`

```python
client.ai.conversations.insight_groups.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Assign Insight Template To Group

Assign an insight to a group

`POST /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/assign`

```python
client.ai.conversations.insight_groups.insights.assign(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Unassign Insight Template From Group

Remove an insight from a group

`DELETE /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/unassign`

```python
client.ai.conversations.insight_groups.insights.delete_unassign(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Get Insight Templates

Get all insights

`GET /ai/conversations/insights`

```python
page = client.ai.conversations.insights.list()
page = page.data[0]
print(page.id)
```

Returns: `created_at` (date-time), `id` (uuid), `insight_type` (enum: custom, default), `instructions` (string), `json_schema` (object), `name` (string), `webhook` (string)

## Create Insight Template

Create a new insight

`POST /ai/conversations/insights` — Required: `instructions`, `name`

Optional: `json_schema` (object), `webhook` (string)

```python
insight_template_detail = client.ai.conversations.insights.create(
    instructions="You are a helpful assistant.",
    name="my-resource",
)
print(insight_template_detail.data)
```

Returns: `created_at` (date-time), `id` (uuid), `insight_type` (enum: custom, default), `instructions` (string), `json_schema` (object), `name` (string), `webhook` (string)

## Get Insight Template

Get insight by ID

`GET /ai/conversations/insights/{insight_id}`

```python
insight_template_detail = client.ai.conversations.insights.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.data)
```

Returns: `created_at` (date-time), `id` (uuid), `insight_type` (enum: custom, default), `instructions` (string), `json_schema` (object), `name` (string), `webhook` (string)

## Update Insight Template

Update an insight template

`PUT /ai/conversations/insights/{insight_id}`

Optional: `instructions` (string), `json_schema` (object), `name` (string), `webhook` (string)

```python
insight_template_detail = client.ai.conversations.insights.update(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.data)
```

Returns: `created_at` (date-time), `id` (uuid), `insight_type` (enum: custom, default), `instructions` (string), `json_schema` (object), `name` (string), `webhook` (string)

## Delete Insight Template

Delete insight by ID

`DELETE /ai/conversations/insights/{insight_id}`

```python
client.ai.conversations.insights.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Get a conversation

Retrieve a specific AI conversation by its ID.

`GET /ai/conversations/{conversation_id}`

```python
conversation = client.ai.conversations.retrieve(
    "conversation_id",
)
print(conversation.data)
```

Returns: `created_at` (date-time), `id` (uuid), `last_message_at` (date-time), `metadata` (object), `name` (string)

## Update conversation metadata

Update metadata for a specific conversation.

`PUT /ai/conversations/{conversation_id}`

Optional: `metadata` (object)

```python
conversation = client.ai.conversations.update(
    conversation_id="550e8400-e29b-41d4-a716-446655440000",
)
print(conversation.data)
```

Returns: `created_at` (date-time), `id` (uuid), `last_message_at` (date-time), `metadata` (object), `name` (string)

## Delete a conversation

Delete a specific conversation by its ID.

`DELETE /ai/conversations/{conversation_id}`

```python
client.ai.conversations.delete(
    "conversation_id",
)
```

## Get insights for a conversation

Retrieve insights for a specific conversation

`GET /ai/conversations/{conversation_id}/conversations-insights`

```python
response = client.ai.conversations.retrieve_conversations_insights(
    "conversation_id",
)
print(response.data)
```

Returns: `conversation_insights` (array[object]), `created_at` (date-time), `id` (string), `status` (enum: pending, in_progress, completed, failed)

## Create Message

Add a new message to the conversation. Used to insert a new messages to a conversation manually ( without using chat endpoint )

`POST /ai/conversations/{conversation_id}/message` — Required: `role`

Optional: `content` (string), `metadata` (object), `name` (string), `sent_at` (date-time), `tool_call_id` (string), `tool_calls` (array[object]), `tool_choice` (object)

```python
client.ai.conversations.add_message(
    conversation_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    role="user",
)
```

## Get conversation messages

Retrieve messages for a specific conversation, including tool calls made by the assistant.

`GET /ai/conversations/{conversation_id}/messages`

```python
page = client.ai.conversations.messages.list(
    conversation_id="550e8400-e29b-41d4-a716-446655440000",
)
page = page.data[0]
print(page.role)
```

Returns: `created_at` (date-time), `role` (enum: user, assistant, tool), `sent_at` (date-time), `text` (string), `tool_calls` (array[object])

## Get Tasks by Status

Retrieve tasks for the user that are either `queued`, `processing`, `failed`, `success` or `partial_success` based on the

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安装前审查: 避免自动安装

许可证: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The error handling example uses `client.messages.send` which is not part of the AI inference API, potentially confusing users.
The SKILL.md does not explicitly state limitations or safe operating boundaries (e.g., rate limits, data privacy considerations).
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 212 stars, 22 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: team-telnyx/ai
许可证: MIT
版本: 1.0.0
最近 GitHub 推送: 2026年9月5日
目录更新于: 2026年10月9日
技能指令路径: providers/claude/plugins/telnyx-ai/skills/telnyx-ai-inference-python/SKILL.md @ 0443b296fd5b

版本来自目录元数据，使用前请核实来源发布记录。

质量

67/100

有潜力

信任

56/100

Do not auto-install

审计

72/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
The error handling example uses `client.messages.send` which is not part of the AI inference API, potentially confusing users.
The SKILL.md does not explicitly state limitations or safe operating boundaries (e.g., rate limits, data privacy considerations).
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 212 stars, 22 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

Agent 接入

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

打开安装 API LLM 文本格式打开 Manifest 自动解析计划

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创作者: team-telnyx
来源: team-telnyx/ai
收录方: OpenAgentSkill 社区索引

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