dbx-tools-litellm

Thin LiteLLM integration for Databricks Model Serving. It adds live endpoint discovery and loose model-name resolution, then delegates the unchanged request to LiteLLM's built-in Databricks provider.

Install from PyPI:

uv add dbx-tools-litellm

To install the current main branch directly from the repository instead:

uv add "dbx-tools-litellm @ git+https://github.com/reggie-db/dbx-tools.git@main#subdirectory=packages/py/litellm"

Key features

• resolves a Databricks profile from --profile, then DATABRICKS_CONFIG_PROFILE, then the Databricks CLI's configured default;
• discovers serving endpoints from the selected workspace and caches them per process;
• advertises discovered endpoints and family aliases only under dbx/* by default, keeping them distinct from LiteLLM's native databricks/* provider;
• resolves exact or fuzzy model names with dbx-tools-model, refreshing the live catalogue once after a miss;
• restricts tool-bearing requests to endpoints classified as tool-capable;
• routes Responses-only models through LiteLLM's databricks/responses/... bridge;
• resolves Responses-only proxy calls before provider selection so LiteLLM's native Databricks Responses implementation receives the original body;
• optionally classifies an auto reasoning effort as low, medium, or high for reasoning-capable OpenAI and Claude endpoints;
• marks a stable prefix of Claude requests for Anthropic prompt caching, which GPT endpoints get automatically on the native Responses surface;
• floors rate-limit backoff to the token-per-minute window so a retry lands in a fresh budget instead of amplifying the limit;
• supports LiteLLM chat, embedding, synchronous/asynchronous, and streaming entrypoints, rewriting request content only for the Databricks serving constraints described under Request processing.

Run the proxy

uv run dbx-litellm --port 4000

The launcher listens on 127.0.0.1 by default. Pass an explicit LiteLLM --host value or set HOST to expose it on another interface. Pass --profile my-workspace to override both the environment and CLI default.

The equivalent module invocation is:

uv run python -m dbx_tools.litellm --port 4000

Then point an OpenAI-compatible client at http://127.0.0.1:4000/v1:

curl http://127.0.0.1:4000/v1/chat/completions \
  -H 'content-type: application/json' \
  -d '{"model":"dbx/databricks-claude","messages":[{"role":"user","content":"hi"}]}'

The resolved profile is written to DATABRICKS_CONFIG_PROFILE, so endpoint discovery and LiteLLM's delegated Databricks request use the same workspace credentials.

Relationship to LiteLLM

LiteLLM remains the proxy and provider implementation. It owns the OpenAI-compatible routes, Databricks authentication and transport, parameter mapping, streaming semantics, retries, embeddings, and Chat↔Responses conversion.

This package supplies only the workspace-specific layer LiteLLM does not have: deterministic profile selection, live endpoint discovery, fuzzy names, and capability-aware routing. Request messages and content blocks are rewritten only to satisfy concrete Databricks serving constraints — the ordered pipeline under Request processing (trailing-assistant repair, JSON nudge, Claude prompt-cache marking) and the image payload guard — or when the caller explicitly requests automatic reasoning selection. Tools are not rewritten.

LiteLLM 1.83 loads custom handlers from a Python file beside the config. For an existing LiteLLM config, add config_provider.py next to the YAML:

from dbx_tools.litellm.provider import dbx_provider
from dbx_tools.litellm.reasoning import dbx_auto_reasoning
from dbx_tools.litellm.routing import dbx_responses_router

Then register that adjacent shim under dbx:

model_list:
  - model_name: "dbx/*"
    litellm_params:
      model: "dbx/*"
      allowed_openai_params:
        - reasoning_effort
        - thinking

litellm_settings:
  callbacks:
    - config_provider.dbx_auto_reasoning
    - config_provider.dbx_responses_router
  custom_provider_map:
    - provider: dbx
      custom_handler: config_provider.dbx_provider

The packaged config advertises only dbx/*. A consumer config can opt into LiteLLM's native Databricks provider independently:

model_list:
  - model_name: "databricks/*"
    litellm_params:
      model: "databricks/*"

Set DATABRICKS_CONFIG_PROFILE before starting LiteLLM to override the Databricks CLI default when --profile is not available.

Automatic reasoning effort

Automatic effort is opt-in. On Chat Completions, send "reasoning_effort": "auto":

{
  "model": "claude sonnet",
  "messages": [{ "role": "user", "content": "Debug this distributed deadlock" }],
  "reasoning_effort": "auto"
}

On Responses, use the native reasoning shape:

{
  "model": "gpt 5 codex",
  "input": "Debug this distributed deadlock",
  "reasoning": { "effort": "auto" }
}

The callback resolves databricks-meta-llama-3-1-8b-instruct against the live catalogue as its fallback classifier preference, asks the discovered endpoint for a score from 0.01 through 1.00, then maps that score through the target Databricks endpoint's inferred reasoning levels. Scores below 0.34 use low, scores below 0.67 use medium, and higher scores use high. An exact 1.00 uses the GPT-5.6 ultra tier, whose LiteLLM wire value is xhigh; models without that level remain at high. Integer classifier output is treated as a percentage (73 becomes 0.73), except 1, which remains the maximum score.

Explicit low, medium, high, xhigh, or thinking values are never overridden. Unsupported targets have auto removed and use their normal provider default.

The classifier sees at most eight recent non-system turns and 6,000 characters. Full Chat transcripts are sampled directly. Short follow-ups can recover prior turns from metadata.thread_id, metadata.conversation_id, or metadata.session_id; Responses chains are linked through previous_response_id. Context and classification scores use diskcache with a TTL, so retries and follow-ups avoid repeated classifier calls without retaining an unbounded transcript.

Configuration:

• DBX_TOOLS_LITELLM_REASONING_MODEL overrides the classifier endpoint;
• DBX_TOOLS_LITELLM_REASONING_CACHE_DIR changes the disk-cache directory;
• DBX_TOOLS_LITELLM_REASONING_CACHE_TTL_SECONDS sets the context and result TTL (default: 86,400 seconds);
• DBX_TOOLS_LITELLM_REASONING_TIMEOUT_SECONDS sets the classifier timeout (default: 5 seconds).

For Claude targets, LiteLLM's Databricks transformer maps the selected reasoning_effort to the backend's native extended-thinking token budget.

The one-line dbx-access record includes thinking_requested=<level> for every request. Automatic requests also include thinking_selected=<level> after the classifier maps the score through the resolved model's capabilities. The existing reasoning=<tokens> field remains the number of reasoning tokens reported by the provider, not the selected effort level.

Request processing

Every delegated Chat Completions request runs through a small, ordered pipeline (provider._prepare_messages) before it reaches Databricks. Each step exists to satisfy a concrete Databricks serving constraint that an OpenAI-style client does not know about. Order matters, because each step can change what the next one sees:

1. Trailing-assistant repair (_repair_trailing_assistant). Databricks rejects a transcript whose last message is an assistant turn with "This model does not support assistant message prefill. The conversation must end with a user message." Codex hits this on retry, when a stream that disconnected mid-turn is resumed with its partial answer replayed as the final message. The repair drops trailing assistant turns (including an unanswered tool call) so the transcript ends where the model can continue. It never empties the list.
2. JSON nudge (_ensure_json_mentioned). OpenAI-family endpoints refuse response_format: {"type": "json_object"} unless the prompt itself contains the word "json". This is a prompt-content rule, so no parameter filtering satisfies it. When json mode is requested but unmentioned, the nudge appends a short instruction to the last non-system turn — the one role guaranteed to survive into input on the Responses bridge. This is exactly how Mem0's memory extraction trips the rule; the nudge fixes every client at once. Runs after the repair so it never appends to a turn that is then dropped.
3. Prompt-cache marking (_apply_prompt_cache, Claude only). See below. Runs last so its breakpoints land on boundaries the earlier steps have already settled.

The image payload guard (payload_guard.DbxPayloadGuard) is a separate pre-call hook, not part of the message pipeline. Databricks rejects any request body over 32 MiB; chat clients inline uploaded images as base64 and resend them every turn, so a couple of photos push a long chat past the cap and every turn then fails with an opaque 400. The guard measures the serialized request and, if it is over target, downscales base64 images (largest first) with Pillow until it fits, raising a clear size-named error only if it still cannot.

Prompt caching

Caching behaviour differs by model family because the two Databricks serving surfaces expose it differently. The proxy leaves the automatic case alone and fills the explicit case that OpenAI-style clients never trigger.

• GPT (native Responses): GPT-5.4+ endpoints route through LiteLLM's databricks/responses/... bridge to the native Responses surface, which applies automatic, OpenAI-style prefix caching. No marking is needed; a repeated prefix reads from cache and reports cached_tokens. Changing reasoning.effort between turns does not evict the cache, because effort is a top-level parameter and not part of the cached input prefix.
• Claude (emulated Responses / chat): Databricks refuses the native Responses passthrough for Claude ("Responses API passthrough is not supported for model databricks-claude-..."), so these turns go through LiteLLM's Responses-to-Chat emulation onto chat/completions. Anthropic caching on Databricks is explicit: a request is cached only where a content block carries cache_control. OpenAI-style clients (Codex, Open WebUI) never send it and the emulation does not add it, so without intervention the whole transcript is re-billed as fresh input every turn — which repeatedly trips the workspace input-tokens-per-minute limit on long chats.

_apply_prompt_cache closes that gap for Claude targets by stamping cache_control: {"type": "ephemeral"} on two rolling breakpoints: the first system message (stable for the life of the chat) and the last stable turn (the message before the volatile final turn, already present and cache-written on the previous turn). Anthropic matches the longest cached prefix at each breakpoint, so two breakpoints cache effectively the whole history except the newest turn. The final turn is left unmarked because it is new every request and would only ever write, never read. This is a no-op for non-Claude models and for single-turn requests, which have no stable prefix. LiteLLM's Databricks transformer preserves cache_control for Claude, so marking the blocks here is sufficient; the endpoint honours it and returns cache_creation_input_tokens and cache_read_input_tokens.

Databricks disables the stateful Responses store (store / previous_response_id) workspace-wide by default, so the full transcript is re-sent every turn on both families. Caching is what keeps the re-sent prefix from being billed and rate-limited each time.

Rate-limit retries

The packaged router retries rate limits five times with exponential backoff and honors provider Retry-After headers. Timeouts and internal server errors get three retries. Authentication, bad requests, and content-policy failures are not retried.

Databricks' REQUEST_LIMIT_EXCEEDED is a per-minute token budget, and a retry re-sends the whole request body. Retrying inside the same minute only adds more tokens to an already-exceeded window and cannot succeed — the amplification that turns one rate limit into a spiral of failed reconnects. So when the server does not send a Retry-After, rate-limit backoff is floored to the rate-limit window (RATE_LIMIT_WINDOW_SECONDS) so every retry lands in a fresh window rather than piling into the current one. A server Retry-After, when present, is authoritative and overrides the floor.

Streaming dbx requests apply the same bounded retry protection when a rate limit arrives before the first response chunk. A failure after content has already streamed is returned immediately because restarting would duplicate partial output in the client.

Runtime behavior

Endpoint discovery is lazy. The first request lists serving endpoints, later requests reuse that catalogue, and an unresolved name triggers one refresh before the unresolved endpoint id is delegated to Databricks. /v1/models refreshes the profile's live serving endpoints and uses that discovery as the exact model list, including endpoints such as databricks-gpt-5-6-sol. LiteLLM's bundled registry supplies metadata for matching live models and is used as a fallback only when live discovery fails. The response then appends one basic alias for each recognized deployed family. Exact models and aliases are advertised as dbx/databricks-gpt-5-6-sol, dbx/databricks-gpt, and similar ids. The aliases flow through the same fuzzy resolver and do not replace exact models. A custom config that declares databricks/* opts into native model ids alongside the dbx ids.

The proxy owns one process-wide SDK client and bearer cache. A fresh cache read returns without locking; a stale read acquires an RLock, checks again, and performs one synchronous SDK authentication load. SDK background refresh is disabled so parallel requests cannot start a second refresh path.

LiteLLM's CustomLLM interface has no native Responses hook. For a Responses-only endpoint, the packaged proxy's pre-call hook changes only the model identifier to databricks/<resolved-endpoint>; LiteLLM's native Databricks Responses implementation receives the original body. Other model families use LiteLLM's own Responses-to-Chat fallback.

Modules

• backend - profile-resolved workspace client, endpoint cache, and model resolution;
• models — Responses-only endpoint routing policy;
• provider — LiteLLM CustomLLM adapter and exported dbx_provider singleton; owns the Chat Completions message pipeline (trailing-assistant repair, JSON nudge, Claude prompt-cache marking) and the rate-limit-aware streaming retry;
• payload_guard — pre-call hook that downscales oversize base64 images to keep requests under the 32 MiB serving limit;
• reasoning — opt-in effort classification and TTL-backed follow-up context;
• routing — model-only proxy hook for native Responses-only calls;
• access_log — one-line per-request dbx-access telemetry;
• cli - profile-resolving launcher for the packaged LiteLLM proxy config.

For standalone Python endpoint resolution and invocation helpers, use dbx-tools-model. For the TypeScript local proxy, use @dbx-tools/cli-model-proxy.