blockrun-litellm 0.2.2

LiteLLM adapter for BlockRun — call x402-paid AI models via LiteLLM (custom provider or local OpenAI-compatible proxy)

blockrun-litellm

LiteLLM adapter for BlockRun — call x402-paid AI models through LiteLLM with zero changes to your existing code.

TL;DR — BlockRun's /v1/chat/completions is already OpenAI-compatible at the protocol level. The only thing that differs is authentication: BlockRun uses per-request x402 wallet signatures (non-custodial USDC micropayments on Base / Solana), not a Bearer API key. This package bridges that gap.

中文文档见底部 / Chinese docs at the bottom

---

Two ways to integrate

Mode	Best for	What it looks like
1. Custom provider (in-process)	Apps using the LiteLLM Python library	litellm.completion(model="blockrun/openai/gpt-5.5", ...)
2. Local proxy (sidecar)	Apps using the LiteLLM Proxy Server (or any OpenAI client)	api_base="http://localhost:4001/v1"

Both modes share the same underlying wallet/signing flow (via the blockrun-llm SDK), so they behave identically. Pick whichever fits your deployment.

Verified end-to-end against the live BlockRun gateway

Both modes have been validated against https://blockrun.ai/api using the free nvidia/deepseek-v4-flash model:

$ python -c "
> import litellm
> from blockrun_litellm import register; register()
> r = litellm.completion(
>     model='blockrun/nvidia/deepseek-v4-flash',
>     messages=[{'role':'user','content':'Reply with exactly: pong'}],
>     max_tokens=20, temperature=0.0)
> print(r.choices[0].message.content)"
pong

$ curl -sS http://127.0.0.1:4001/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{"model":"nvidia/deepseek-v4-flash","messages":[{"role":"user","content":"Reply with exactly: proxy-ok"}]}'
{"id":"a710c144c68c42f7a319fb93e9b9b5a0","object":"chat.completion","model":"nvidia/deepseek-v4-flash",
 "choices":[{"index":0,"message":{"role":"assistant","content":"proxy-ok"},...}],"usage":{...}}

---

Install

# Custom provider only (no proxy server)
pip install blockrun-litellm

# Custom provider + local proxy (includes FastAPI/uvicorn)
pip install 'blockrun-litellm[proxy]'

Requires Python ≥ 3.9.

---

Configure your wallet (one-time)

The blockrun-llm SDK signs each request locally with an EVM (Base chain) private key. The key never leaves your machine. Three ways to provide it:

# Option A — environment variable (recommended for servers)
export BLOCKRUN_WALLET_KEY=0xYOUR_BASE_CHAIN_PRIVATE_KEY

# Option B — auto-create + fund a new wallet (interactive, shows QR for funding)
python -c "from blockrun_llm import setup_agent_wallet; setup_agent_wallet()"

# Option C — pass per-call (Python lib mode), see examples below

💡 To validate without spending real USDC, use a free model like nvidia/deepseek-v4-flash — same code path, same wallet flow, $0 settlement.

---

Mode 1 — Custom provider (Python library)

The shortest path if your app already calls litellm.completion() directly.

1a. Register once at startup

import litellm
from blockrun_litellm import register

register()  # idempotent; adds "blockrun" to litellm.custom_provider_map

1b. Call with a blockrun/ model prefix

response = litellm.completion(
    model="blockrun/openai/gpt-5.5",        # blockrun/<provider>/<model>
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    max_tokens=128,
    temperature=0.7,
)

print(response.choices[0].message.content)
print(response.usage)  # prompt_tokens / completion_tokens / total_tokens

The blockrun/ prefix is stripped before being sent to the BlockRun gateway, so openai/gpt-5.5, anthropic/claude-opus-4-5, google/gemini-3-pro, etc. all work — anything in BlockRun's catalog.

1c. Override the wallet per-call (optional)

response = litellm.completion(
    model="blockrun/openai/gpt-5.5",
    messages=[...],
    api_key="0xANOTHER_PRIVATE_KEY",          # passed to blockrun-llm as wallet
)

1d. Async

import asyncio

async def main():
    response = await litellm.acompletion(
        model="blockrun/openai/gpt-5.5",
        messages=[{"role": "user", "content": "Hi"}],
    )
    print(response.choices[0].message.content)

asyncio.run(main())

---

Mode 2 — Local proxy (LiteLLM Proxy Server, langchain, raw curl, …)

If you're running the LiteLLM Proxy Server (litellm --config config.yaml), or any client that just speaks OpenAI HTTP, run our proxy as a sidecar.

2a. Start the proxy

export BLOCKRUN_WALLET_KEY=0xYOUR_KEY
blockrun-litellm-proxy --port 4001
# → uvicorn running at http://127.0.0.1:4001

Flags:

Flag	Default	Purpose
--host	127.0.0.1	Bind interface. Keep loopback unless you set BLOCKRUN_PROXY_TOKEN.
--port	4001	Bind port
--api-url	https://blockrun.ai/api	Override BlockRun gateway endpoint
--log-level	info	critical/error/warning/info/debug/trace

Optional shared-secret guard:

export BLOCKRUN_PROXY_TOKEN=$(openssl rand -hex 32)
# clients must now send:  Authorization: Bearer $BLOCKRUN_PROXY_TOKEN

2b. Point LiteLLM Proxy at it

Drop this into your config.yaml:

model_list:
  - model_name: gpt-5.5
    litellm_params:
      model: openai/openai/gpt-5.5   # first 'openai/' = LiteLLM provider; rest = BlockRun model id
      api_base: http://localhost:4001/v1
      api_key: "dummy"                # ignored if BLOCKRUN_PROXY_TOKEN is unset

  - model_name: claude-opus-4-5
    litellm_params:
      model: openai/anthropic/claude-opus-4-5
      api_base: http://localhost:4001/v1
      api_key: "dummy"

litellm_settings:
  drop_params: True   # silently drop OpenAI params BlockRun doesn't support

Run LiteLLM Proxy as usual:

litellm --config config.yaml --port 4000

Then call it like any OpenAI endpoint:

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.5",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

2c. Or skip LiteLLM entirely

The proxy speaks OpenAI HTTP, so anything that takes an api_base works:

# OpenAI Python SDK pointed straight at the BlockRun proxy
from openai import OpenAI

client = OpenAI(api_key="dummy", base_url="http://localhost:4001/v1")
resp = client.chat.completions.create(
    model="openai/gpt-5.5",
    messages=[{"role": "user", "content": "Hi"}],
)
print(resp.choices[0].message.content)

# Plain curl
curl http://localhost:4001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "openai/gpt-5.5", "messages": [{"role":"user","content":"Hi"}]}'

2d. Endpoints exposed

Method	Path	Notes
POST	/v1/chat/completions	OpenAI Chat Completions. stream=True returns text/event-stream; otherwise JSON.
GET	/v1/models	BlockRun model catalog
GET	/healthz	Liveness probe (no upstream call)
GET	/docs	Auto-generated Swagger UI

---

Supported parameters

All of these are forwarded to BlockRun unchanged:

OpenAI param	Supported	Notes
model	✅	Any BlockRun model id, e.g. openai/gpt-5.5
messages	✅	Full role/content/tool_calls schema
max_tokens	✅	Defaults to 1024 if omitted
temperature	✅	0–2
top_p	✅
tools / tool_choice	✅	Function calling
stream	✅	OpenAI-style SSE (text/event-stream). Provider mode yields LiteLLM GenericStreamingChunk objects; proxy mode emits data: <json>\n\n events terminated by data: [DONE]. Free models stream directly; paid models stream after the in-band 402-sign-retry dance.
frequency_penalty / presence_penalty / logprobs / n	⚠️	Silently dropped — enable litellm_settings.drop_params: True to suppress LiteLLM warnings

BlockRun-specific extras (also accepted):

Param	Purpose
search: True	Enable xAI Live Search (for search-enabled models)
search_parameters: {...}	Full Live Search config
fallback_models: ["..."]	Auto-retry on transient upstream errors

---

Examples

The examples/ directory has copy-paste-ready snippets:

• examples/python_lib.py — full LiteLLM Python library usage
• examples/litellm_config.yaml — LiteLLM Proxy Server config
• examples/raw_openai_sdk.py — pointing the OpenAI SDK at the proxy

---

How it works (under the hood)

┌─────────────────┐    OpenAI dict     ┌──────────────────────┐    POST /v1/chat/completions  ┌────────────────┐
│ Your app /      │ ─────────────────▶ │  blockrun-litellm    │ ────────────────────────────▶ │  blockrun.ai   │
│ LiteLLM /       │                    │  (provider OR proxy) │ ◀──── 402 + payment-required ─│  gateway       │
│ OpenAI SDK      │                    │  ↓                   │                               │                │
└─────────────────┘                    │  blockrun-llm SDK    │ ───── EIP-712 signed retry ──▶│                │
                                       │  (local signing)     │ ◀──── 200 + chat response ────│                │
                                       └──────────────────────┘                               └────────────────┘
                                                ▲
                                                │ private key (stays local, signs only)
                                       ┌──────────────────────┐
                                       │ BLOCKRUN_WALLET_KEY  │
                                       │   or ~/.blockrun/    │
                                       └──────────────────────┘

1. Caller sends an OpenAI Chat Completions dict.
2. blockrun-litellm whitelists the params and dispatches through blockrun-llm.
3. blockrun-llm posts to BlockRun, receives a 402 with payment requirements, signs an EIP-712 payment locally with your wallet, and retries.
4. BlockRun verifies the signature on-chain, settles the USDC micropayment, runs the inference, and returns the response.
5. blockrun-litellm returns the dumped pydantic model as a plain OpenAI dict (or litellm.ModelResponse in provider mode).

---

FAQ

Q: Does this support streaming? Yes, as of v0.2.0. Pass stream=True and the adapter routes through blockrun-llm's chat_completion_stream() (SDK ≥ 0.20.0). The 402 → sign-locally → retry-with-PAYMENT-SIGNATURE dance happens before the first chunk; once the upstream switches to text/event-stream, chunks are forwarded straight through (provider mode → litellm.GenericStreamingChunk, proxy mode → OpenAI-style data: <json>\n\n SSE). Caveats inherited from the gateway: search_parameters and the Responses-API models (codex, gpt-5.4-pro) reject streaming server-side with 400.

Q: Where does my private key live? On your machine only — BLOCKRUN_WALLET_KEY env var, or ~/.blockrun/.session if you used setup_agent_wallet(). The proxy and provider both read from those sources via blockrun-llm. Only EIP-712 signatures are transmitted.

Q: How do I switch between Base and Solana? Today this adapter wires to BlockRun's Base gateway (USDC on Base). Solana support tracks the blockrun-llm SolanaLLMClient and will be added in a follow-up release.

Q: Can I run the proxy in Docker / k8s? Yes — it's a vanilla FastAPI app. Pass the wallet key via secret (env var), bind to 0.0.0.0 only inside a private network, and set BLOCKRUN_PROXY_TOKEN for an additional auth layer.

Q: Is this affiliated with LiteLLM (BerriAI)? No — this is an independent adapter built by the BlockRun team. LiteLLM is a great project; we're just plugging into its custom-provider hooks.

---

Development

git clone https://github.com/BlockRunAI/blockrun-litellm
cd blockrun-litellm
pip install -e '.[proxy,dev]'
pytest

---

License

MIT. See LICENSE.

---

中文文档

BlockRun 的 LiteLLM 适配层 —— 用 LiteLLM 调用 BlockRun 上的 AI 模型，完全零改动。

一句话： BlockRun 的 /v1/chat/completions 协议层就是 OpenAI 兼容的，唯一区别是认证方式 —— BlockRun 用 x402 钱包签名（按次 USDC 微支付，非托管），不是 Bearer API Key。这个包就是把这层差异填平。

两种对接方式

模式	适用	写法
1. 自定义 Provider（进程内）	用 LiteLLM Python 库的应用	litellm.completion(model="blockrun/openai/gpt-5.5", ...)
2. 本地代理（sidecar）	用 LiteLLM Proxy Server 的、或任何 OpenAI 客户端	api_base="http://localhost:4001/v1"

底层都走 blockrun-llm SDK 做签名和 x402 支付，两种模式行为一致。按你的部署方式选一种就行。

快速上手

安装

# 只装自定义 provider
pip install blockrun-litellm

# 同时装本地代理（带 FastAPI/uvicorn）
pip install 'blockrun-litellm[proxy]'

配钱包（一次性）

# 方式 A — 环境变量（服务端推荐）
export BLOCKRUN_WALLET_KEY=0xYOUR_BASE_CHAIN_PRIVATE_KEY

# 方式 B — 自动创建并扫码充值（交互式）
python -c "from blockrun_llm import setup_agent_wallet; setup_agent_wallet()"

私钥只在本地用于 EIP-712 签名，永远不会离开你的机器。

💡 想零成本试一遍？用免费模型 nvidia/deepseek-v4-flash —— 代码完全一样，钱包流程一样，结算 $0。

模式 1：自定义 Provider

import litellm
from blockrun_litellm import register

register()  # 启动时调一次即可

response = litellm.completion(
    model="blockrun/openai/gpt-5.5",   # blockrun/<provider>/<model>
    messages=[{"role": "user", "content": "你好"}],
    max_tokens=128,
)
print(response.choices[0].message.content)

异步版本：await litellm.acompletion(...) 同理。

模式 2：本地代理

# 1) 启动 sidecar
export BLOCKRUN_WALLET_KEY=0xYOUR_KEY
blockrun-litellm-proxy --port 4001

# 2) LiteLLM Proxy 配置 (config.yaml)

model_list:
  - model_name: gpt-5.5
    litellm_params:
      model: openai/openai/gpt-5.5
      api_base: http://localhost:4001/v1
      api_key: "dummy"

litellm_settings:
  drop_params: True

或者直接拿任何 OpenAI 客户端用：

from openai import OpenAI
client = OpenAI(api_key="dummy", base_url="http://localhost:4001/v1")
resp = client.chat.completions.create(
    model="openai/gpt-5.5",
    messages=[{"role": "user", "content": "你好"}],
)

支持的参数

OpenAI 参数	支持	备注
model / messages / max_tokens / temperature / top_p	✅
tools / tool_choice	✅	函数调用
stream	✅	OpenAI 标准 SSE（text/event-stream）。Provider 模式 yield LiteLLM GenericStreamingChunk；Proxy 模式发 data: <json>\n\n 事件并以 data: [DONE] 结尾。免费模型直接开流；付费模型走带内 402→签名→重试再开流。
frequency_penalty / presence_penalty / logprobs / n	⚠️	静默丢弃 —— 建议 LiteLLM 配 drop_params: True 抑制告警

BlockRun 额外参数：

参数	作用
search: True	启用 xAI Live Search（搜索类模型）
search_parameters: {...}	完整 Live Search 配置
fallback_models: ["..."]	上游抖动自动重试列表

常见问题

Q：支持流式吗？ v0.2.0 起完全支持。stream=True 时适配层走 blockrun-llm 的 chat_completion_stream()（SDK ≥ 0.20.0），402 → 本地签名 → 带 PAYMENT-SIGNATURE 重试这条链在第一个 chunk 之前完成；上游切到 text/event-stream 后 chunks 直接透传（Provider 模式 → litellm.GenericStreamingChunk，Proxy 模式 → OpenAI 标准 data: <json>\n\n）。后端继承的限制：search_parameters 和 Responses-API 模型（codex、gpt-5.4-pro）在服务端就拒绝流式（400）。

Q：私钥放哪？ 只在本地 —— BLOCKRUN_WALLET_KEY 环境变量，或 setup_agent_wallet() 创建的 ~/.blockrun/.session。Provider 和 Proxy 都通过 blockrun-llm 读取。链上只看到签名，看不到私钥。

Q：Docker / k8s 部署？ 代理是普通的 FastAPI 应用。密钥用 secret 注入，对外只暴露内网，可选 BLOCKRUN_PROXY_TOKEN 加一层 Bearer 鉴权。

Q：和 BerriAI 是什么关系？ 没关系。这是 BlockRun 团队独立维护的适配层，挂在 LiteLLM 的 custom provider 接口上。

开发

git clone https://github.com/BlockRunAI/blockrun-litellm
cd blockrun-litellm
pip install -e '.[proxy,dev]'
pytest

License

MIT