brrragent

brrragent is a Python package for running a synchronous model/tool loop. It provides a common entry point for several model APIs and converts MCP tools into the function-calling formats expected by those APIs.

Overview

Main components:

• run_agent: executes model responses and tool calls until completion.
• McpToolCaller: discovers, filters, routes, and executes tools across one or more MCP servers.
• McpServerConfig: configures Streamable HTTP, legacy HTTP+SSE, stdio, or raw JSON-RPC compatibility connections.
• KeyPool: selects API keys and removes rate-limited keys from rotation.

Model adapters cover OpenAI, OpenAI-compatible APIs, Google Gemini, OpenRouter, and the optional Codex OAuth backend. Structured outputs, retry handling, tool result truncation, and provider routing are handled by the package.

Installation

Install from PyPI:

uv add brrragent

Gemini support uses an optional dependency:

uv add "brrragent[gemini]"

Use uv pip install instead when installing into an existing environment.

Basic use

from brrragent import McpToolCaller, run_agent

with McpToolCaller(endpoint="https://mcp.example.com/mcp") as mcp:
    result = run_agent(
        system_prompt="Follow the request and use available tools when needed.",
        user_prompt="Return a short response.",
        model="openai/gpt-5.5",
        mcp=mcp,
    )

print(result)

Image inputs

run_agent accepts one or more images with Codex OAuth, OpenAI, OpenRouter, OpenAI-compatible providers, and Gemini:

from brrragent import ImageInput, run_agent

result = run_agent(
    system_prompt="Return a concise image description.",
    user_prompt="What is shown?",
    model="codex/gpt-5.6-luna:medium",
    images=[ImageInput.from_file("photo.jpg", detail="low")],
)

Use ImageInput(url) for remote images, ImageInput.from_base64(...) for existing Base64 data, or ImageInput.from_bytes(...) for in-memory content. Multiple images preserve their input order. Detail can be auto, low, high, or original; provider and model support still applies. Codex Spark does not accept image input.

Provider credentials are read from the caller's environment. Common variables are OPENAI_API_KEY, OPENROUTER_API_KEY, OPENLUX_API_KEY, and GEMINI_API_KEY.

Use openlux/<model> for OpenLux-native routing. Legacy yunwu/<model> aliases remain accepted; YUNWU_API_KEY is used only when OPENLUX_API_KEY is unset.

Codex OAuth

Create brrragent-owned Codex credentials with device login:

uv run brrragent-auth login

This writes direct OAuth credentials to ~/.cache/brrragent/codex-auth.json and Codex CLI credentials to ~/.cache/brrragent/codex-home/auth.json, both with 0600 permissions. Default routing does not read shared OpenCode auth or ~/.codex/auth.json.

Override locations with BRRRAGENT_AUTH_PATH, BRRRAGENT_CODEX_HOME, or command flags. Existing callers can still use BRRRAGENT_OPENCODE_AUTH_PATH and explicit CODEX_HOME values.

Use one independently issued profile per application or worker group to avoid refresh-token rotation conflicts:

uv run brrragent-auth login --profile content-worker
BRRRAGENT_PROFILE=content-worker uv run your-application

Prefer per-call selection when one process handles multiple use cases:

from brrragent import CodexAuthConfig, run_agent

result = run_agent(
    system_prompt="Return a concise answer.",
    user_prompt="Summarize this input.",
    model="codex/gpt-5.6-luna:medium",
    codex_auth=CodexAuthConfig(profile="content-worker"),
)

Explicit paths are also supported with CodexAuthConfig(auth_path="...", codex_home="..."). Per-call selection is context-local, so concurrent use cases do not need to mutate process-wide environment variables.

Named profiles live under ~/.cache/brrragent/profiles/<profile>/. Run device login separately for every profile; copying an auth file also copies its rotating refresh token and does not provide isolation. BRRRAGENT_PROFILE_ROOT can move the profile directory. A named profile also takes precedence over a generic CODEX_HOME value.

Prompt caching

Keep reusable tools and system instructions before request-specific content. Use one versioned key per stable prompt without user IDs, timestamps, URLs, or prompt text:

from brrragent import PromptCacheConfig

cache = PromptCacheConfig(key="workflow:v2", ttl="1h")

Pass prompt_cache=cache to run_agent. brrragent maps it to supported cache keys, explicit breakpoints, provider affinity, and Anthropic cache controls. Gemini uses its stable system-first prefix for implicit caching. An optional on_usage callback receives normalized token and cache usage for each request.

MCP servers

One caller can combine multiple endpoints:

from brrragent import McpToolCaller

mcp = McpToolCaller(
    endpoints=[
        "https://mcp.example.com/one",
        "https://mcp.example.com/two",
    ],
    allowed_tools={"*"},
    excluded_tools={"internal_*"},
)

Use explicit server configuration to mix transports or prefix tool names:

from brrragent import McpServerConfig, McpToolCaller

mcp = McpToolCaller(
    servers=[
        McpServerConfig(
            name="remote",
            url="https://mcp.example.com/mcp",
            tool_prefix="remote_",
        ),
        McpServerConfig(
            name="local",
            transport="stdio",
            command="uvx",
            args=("example-mcp-server",),
            tool_prefix="local_",
        ),
    ]
)

transport="auto" tries Streamable HTTP, legacy HTTP+SSE, then raw JSON-RPC POST compatibility. Standard transports use MCP initialization, version negotiation, persistent sessions, and clean shutdown. Duplicate visible tool names require tool_prefix configuration.

HTTP auth objects in McpServerConfig use httpx2.Auth, matching MCP SDK v2. HTTP transports use the operating system trust store; set SSL_CERT_FILE or SSL_CERT_DIR for custom certificate authorities.

BRRRAGENT_MCP_URL configures one default endpoint. BRRRAGENT_MCP_URLS accepts comma-separated endpoints.

Tool filters accept exact names and case-sensitive shell patterns such as *_search. Exclusions apply during discovery and execution.

Public API

from brrragent import (
    AgentUsage,
    KeyPool,
    McpServerConfig,
    McpToolCaller,
    PromptCacheConfig,
    RateLimitExhausted,
    get_default_caller,
    pick_env_key,
    pick_key,
    run_agent,
)

Requirements

• Python 3.12 or newer
• httpx>=0.27.1,<1
• httpx2>=2.5,<3
• mcp>=2.0.0,<3
• openai>=2.45.0,<4
• google-genai>=1.0.0,<3 for the gemini extra

License

MIT