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BerriAIv1.106.4Tested (smoke test)

LiteLLM: what’s new in v1.106.4 and our verdict

Python SDK and proxy that gives one OpenAI-style interface to many model providers.

Official site

Release Ruler for LiteLLM: marked at its Tested release date on the timeline of every AI model we track.

At a glance

Evidence
Tested (smoke test)
Released
Oct 25, 2026
Last verified
Oct 25, 2026
Pricing
Partly verified
Best for
local LLM tools and model platforms

Evidence and ranking

Evidence behind this verdict

  1. Sources read2 cited sources, latest fetched Oct 25, 2026. (done)
  2. Facts re-checkedLast verified Oct 25, 2026. (done)
  3. Run by usSmoke test on Oct 25, 2026 (scope below). (done)

Passed Test log result: Passed. Read the log

Where we rank it (editorial)

Listed here but not yet ranked. Ranks are editorial picks, not benchmark scores.

Pricing: Open source; no pricing page read. (checked 2026-10-25)

Our verdict

Useful as a single interface in front of local and hosted models; check the license files and pin the version because it releases daily.

Good for

  • Teams that call several model providers
  • Anyone pointing tools at a local OpenAI-compatible server

Skip if

  • You only ever use one provider and its own SDK

Tested. Tested 2026-10-25 (America/Chicago). LiteLLM 1.106.4 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 2.3 s. Test environment: Linux x86_64 virtual machine (Debian 13), 8 vCPU, 15 GiB RAM, no GPU; no vendor account, login or API key. Models were local (Qwen2.5-3B-Instruct (Q4_K_M) through Ollama 0.40.11); we tested the tool, not any vendor's hosted models. Not tested: Any vendor-hosted model: everything ran on a local 3B model, so tool-call reliability with frontier models is not measured here; Multi-agent flows, memory, long conversations, streaming, tracing and cloud deployment; Anything beyond one tool and one question; three trials is a smoke test, not a benchmark; The LiteLLM proxy server, budgets, keys and routing. These are smoke tests, not a benchmark. Full log: /tests/litellm.

Evidence
Tested (smoke test, 2026-10-25, local 3B model (Qwen2.5-3B)). A smoke test is not a benchmark.
What we ran
LiteLLM 1.106.4 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 2.3 s.
Model used
Qwen2.5-3B-Instruct (Q4_K_M) through Ollama 0.40.11
Environment
Linux x86_64 virtual machine (Debian 13), 8 vCPU, 15 GiB RAM, no GPU; no vendor account, login or API key
Not tested
Any vendor-hosted model: everything ran on a local 3B model, so tool-call reliability with frontier models is not measured here; Multi-agent flows, memory, long conversations, streaming, tracing and cloud deployment; Anything beyond one tool and one question; three trials is a smoke test, not a benchmark; The LiteLLM proxy server, budgets, keys and routing.
Date
(America/Chicago)

Best for

Key facts

LiteLLM is a Python SDK and gateway that calls many model providers through an OpenAI-compatible interface. The GitHub license field reads NOASSERTION, so check the repository's license files; v1.106.4 was tagged on 2026-10-25.

  • License: NOASSERTION (mixed; see the repository)
  • Platforms: Python
  • Pricing model: Open source
  • Free tier: SDK and open-source proxy
  • Paid plans: Enterprise pricing not read
  • GitHub: BerriAI/litellm: 62,037 stars on 2026-10-25, last push 2026-10-25
  • Latest release we saw: v1.106.4, 2026-10-25
  • How to start: uv pip install litellm and read docs.litellm.ai.
  • v1.106.4 (2026-10-25): the newest release on the repository when we read it.

How to set it up: Get started with LiteLLM

Install steps are exactly what we ran on 2026-10-25; the test log lists what was and was not covered.

  1. Install

    We installed the SDK; its MCP integration needed the mcp package, which was not installed automatically.

    uv pip install litellm mcp

  2. Point at a local server

    Use an openai/ model name and the local base URL.

    litellm.completion(model="openai/qwen2.5-3b", api_base="http://127.0.0.1:11434/v1", messages=[...])

Pros and cons

Pros

  • One interface across providers
  • Passed a one-tool task 3 of 3 against a local model

Cons

  • The mcp package was not installed automatically for the MCP integration
  • Very frequent releases
  • GitHub reports NOASSERTION for the license, so read the repo's license files

Alternatives and comparisons

  • OpenAI Agents SDK: Lightweight Python framework for multi-agent workflows, with tools, handoffs, sessions and tracing.
  • LangChain: Open-source framework for building LLM applications and agents in Python and JavaScript, from the team behind LangGraph and LangSmith.
  • Ollama: The common way to run open models locally on macOS, Windows and Linux, with a cloud option and an MIT-licensed open-source core.
  • Cohere: Enterprise AI company: Command models, Rerank and the North platform.

FAQ

What is LiteLLM?

A Python SDK and proxy that lets you call many model providers through one OpenAI-style interface.

Did LiteLLM work with a local model in our test?

Yes: 3 of 3 trials passed a one-tool task against a local 3B model. The MCP integration needed us to install the mcp package ourselves. See /tests/litellm.

What license is LiteLLM under?

GitHub shows NOASSERTION for the repository, which usually means mixed or custom licenses; read the repository's license files before use.

What we have not verified

  • Proxy server, budgets, hosted LiteLLM pricing
  • Behaviour with hosted models

Sources

Last verified · Agentic Weekly

  • Cohere: Enterprise AI company: Command models, Rerank and the North platform.
  • DeepSeek: Model API that works with the OpenAI and Anthropic SDK formats and can back agent tools such as Claude Code, GitHub Copilot and OpenCode.
  • Hugging Face: The hub where the ML community shares models, datasets and apps, with PRO, Team and Enterprise tiers.

How to cite this page

Agentic Weekly, "LiteLLM v1.106.4: what's new, verdict & setup", last updated 2026-10-25, https://theagenticweekly.com/tools/litellm

Keep the Reported or Tested label when you quote a verdict. Plain-text version: https://theagenticweekly.com/tools/litellm.md. More for AI assistants: /ai, /tools.json, /llms.txt.