matrx-ai
Unified multi-provider AI client, orchestration, and tool system for Python. One UnifiedAIClient that speaks to 12+ provider SDKs (OpenAI, Anthropic, Google, Groq, Cerebras, xAI, Together, ElevenLabs, Hugging Face, Cohere, Fireworks, Replicate) through a common UnifiedConfig / UnifiedMessage / UnifiedResponse contract, with streaming, tool calls, agents, and an orchestrator that runs a model until its task is complete.
Install
pip install matrx-ai
Python 3.13+ required. Depends on matrx-utils, matrx-connect, matrx-graph, plus the provider SDKs. Host applications inject their own ORM models at startup via matrx_ai.configure(...).
What's in the box
- Unified client (
UnifiedAIClient): one async interface over every supported provider; request translation + response normalization handled by per-provider adapters. - Config + message types:
UnifiedConfig,UnifiedMessage,MessageList,UnifiedResponse, enums (Provider,Role,ContentType,FinishReason), usage trackers (TokenUsage,TimingUsage,ToolCallUsage). - Orchestrator:
AIMatrixRequest,CompletedRequest,execute_until_complete(...)— autonomous multi-turn execution with tool loops. - Agents (
matrx_ai.agents): template + session-based agent system; caching; services. - Tools (
matrx_ai.tools): registry + built-in implementations (ctx_patch,ctx_create, shell, code, …). All host-specific integration points (ContextManifest, writeback persistence, test helpers) come in viaconfigure(). - Context (
matrx_ai.context): re-exportsAppContext+Emitterfrommatrx-connect. - Persistence (
matrx_ai.db): Supabase-backed conversation/request storage. ORM models are host-injected. - Providers (
matrx_ai.providers): per-provider translators, request shaping, streaming adapters, parameter modifiers (e.g. GPT-5 stripstemperature/top_p).
Usage
One-shot call
from matrx_ai import UnifiedAIClient, UnifiedConfig, UnifiedMessage, Provider, Role
client = UnifiedAIClient()
config = UnifiedConfig(
provider=Provider.ANTHROPIC,
ai_model="claude-sonnet-4-6",
messages=[UnifiedMessage(role=Role.USER, content="Summarize the Magna Carta.")],
stream=False,
)
response = await client.run(config)
print(response.text)
Streaming
config = UnifiedConfig(
provider=Provider.OPENAI,
ai_model="gpt-4o",
messages=[UnifiedMessage(role=Role.USER, content="Explain RAFT.")],
stream=True,
)
async for chunk in client.stream(config):
print(chunk.delta, end="", flush=True)
Autonomous execution (tools + multi-turn)
from matrx_ai import execute_until_complete, AIMatrixRequest
request = AIMatrixRequest(
config=config,
tools=["web_search", "code_executor"],
max_iterations=10,
)
completed = await execute_until_complete(request)
print(completed.final_response.text)
print(completed.timing_usage, completed.token_usage)
Host integration — matrx_ai.configure(...)
matrx-ai is designed to be embedded in a larger application (like aidream) that supplies ORM models, settings, and context-object classes. It is also designed to run without any of that configuration — most features work on their own; the DB-dependent ones raise ExtNotConfiguredError at call time if the host didn't wire them up.
import matrx_ai
matrx_ai.configure(
db_models={"AiModel": AiModel, "CxConversation": CxConversation, ...},
db_bases={"CxConversationBase": CxConversationBase, ...},
db_instances={"guest_executions_manager": guest_manager},
db_extras={"ContentBlocksDTO": ContentBlocksDTO},
settings=settings,
get_supabase_client=get_async_supabase_client,
# Context-object model classes (used by ctx_patch / ctx_create tools)
context_object_cls=ContextObject,
context_object_type_cls=ContextObjectType,
context_source_cls=ContextSource,
persist_mode_cls=PersistMode,
context_manifest_cls=ContextManifest,
load_manifest_from_ctx=load_manifest_from_ctx,
schedule_context_writeback=schedule_writeback,
# …anything else the host wants to make available
)
This is the reference implementation of the "capability-within, injection-without" pattern used throughout the Matrx family — the package stores injected values in a module-level _ext registry; feature code retrieves them at call time via get_ext("…"). import matrx_ai always works in a minimal environment; only features that need a specific injection will raise if it's missing.
For a client host, get_jwt may be a synchronous or asynchronous zero-argument provider. matrx-ai resolves it at each authenticated server request and does not cache its result. Once a host configures that provider, a failure or missing token refuses the authenticated request instead of falling back to anonymous access. Without a provider, the public tool-catalog fetch remains unauthenticated.
Dependency posture
Hard deps: matrx-utils, matrx-connect, matrx-graph, plus the provider SDKs (Anthropic, OpenAI, Google GenAI, Groq, Cerebras, xAI, Together, Cohere, Fireworks, Replicate, Tiktoken) and core libs (pydantic, httpx, aiohttp, supabase, asyncpg, json-repair, numpy, rich).
matrx-ai does not depend on matrx-orm. The host app that embeds matrx-ai may use matrx-orm (aidream does), but matrx-ai itself accepts ORM model classes through configure(db_models=..., db_bases=..., ...) rather than importing them.
Replaces
The old root-level ai/ folder in the aidream monorepo. matrx-ai is now the canonical AI layer.
Contributing
See CLAUDE.md for package-specific rules (including the _ext injection pattern and the forbidden-imports list). Deep per-subsystem docs live in matrx_ai/MODULE_README.md. This package lives in the aidream monorepo at github.com/AI-Matrix-Engine/aidream-current.
License
MIT.
1 maintainer
Release files for matrx-ai 0.5.328
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