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Connect Mellum 2.1 by running it behind an OpenAI-compatible chat-completions endpoint, then point your coding agent at that endpoint and the model ID it serves. You can serve the original weights with vLLM or SGLang, or use a GGUF quantization with llama.cpp. Tool-using agents need a backend and request format that preserve Mellum’s tool calls; the vLLM command documented by JetBrains enables the required parser settings.
What the connection actually involves
Your existing agent does not normally load Mellum 2.1 directly. Instead, a model server loads Mellum and exposes an HTTP API. The agent sends chat-completion requests containing messages, settings and, when applicable, tool definitions. The server returns the model response, including a structured tool call when Mellum decides that a shell, file or other agent tool is needed.
The practical configuration has three values:
- Base URL: the server address, usually ending in
/v1for an OpenAI-compatible API. - Model ID: the exact identifier accepted by the server.
- API key value: only if your local server or agent requires one. A local development server may accept a placeholder value, but follow that server’s authentication behavior.
JetBrains documents OpenAI-compatible serving for vLLM, SGLang and llama.cpp in the Mellum 2.1 model card and the official GGUF repository. Your agent must support the same chat-completions request and tool-call format as the selected server.
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Choose a serving route before configuring the agent
Use the original model route when you want JetBrains’ documented full-weight setup. Choose GGUF when you need a quantized file and a llama.cpp-compatible runtime. The endpoint pattern is similar in both cases, but command-line flags, model identifiers and tool-call support are backend-specific.
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| Route | Model form | Documented endpoint example | Best fit |
|---|---|---|---|
| vLLM | Original Mellum 2.1 weights | http://localhost:8000/v1/chat/completions |
Agents that need the documented reasoning and tool-call configuration |
| SGLang | Original Mellum 2.1 weights | http://localhost:30000/v1/chat/completions |
Deployments already using SGLang’s OpenAI-compatible server |
| llama.cpp | GGUF quantization | http://localhost:8080/v1 |
Local quantized inference and agents that accept a llama.cpp endpoint |
There was no Inference Provider deployment for this model in the reviewed model card, and the generic Hugging Face Inference Providers documentation did not list it as available at that review. Hosted availability can change, so check the current provider listing before designing a hosted-only deployment.
Prerequisites and compatibility checks
- Choose a server route and install its current release according to that project’s documentation.
- Download access to the original weights or one of the GGUF files in the official repositories.
- An existing coding agent with a custom OpenAI-compatible provider, base-URL or endpoint setting.
- A way to pass the exact served model ID to the agent.
- Tool support in the agent if you expect shell, file or other function calls.
- Enough compute and memory for the selected precision, context length and workload. The published file sizes are not minimum VRAM or RAM requirements.
Mellum 2.1 is described by JetBrains as a 12-billion-parameter mixture-of-experts model with 2.5 billion active parameters and a published context length of 131,072 tokens. Those figures describe the model; they do not guarantee that any particular computer can run a 131,072-token request at useful speed.
Option A: serve the original model with vLLM
Start a basic OpenAI-compatible server
JetBrains’ model-card command launches the original model and advertises the long context length:
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--max-model-len 131072
--reasoning-parser qwen3After startup, the server exposes the OpenAI-compatible API on its documented default port. Your agent normally uses http://localhost:8000/v1 as the base URL, while a lower-level client may call http://localhost:8000/v1/chat/completions directly. Check which form your agent’s field expects; supplying the full path where it expects a base URL can produce a duplicated path such as /v1/v1/chat/completions.
Enable the documented tool-calling setup
For an agent that supplies tools, use the additional vLLM settings shown by JetBrains:
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vllm serve JetBrains/Mellum2.1-12B-A2.5B-Thinking
--max-model-len 131072
--reasoning-parser qwen3
--enable-auto-tool-choice
--tool-call-parser hermes--enable-auto-tool-choice allows the server to select a tool when the request offers tools, while --tool-call-parser hermes tells vLLM how to parse Mellum’s tool-call format. The agent still has to send a compatible tool schema and correctly execute the returned call. These flags are documented for vLLM; do not assume that the same flags exist or have the same meaning in another backend.
Point the coding agent at vLLM
- Open the agent’s custom-provider, local-model or OpenAI-compatible-provider settings.
- Set the base URL to the vLLM server, commonly
http://localhost:8000/v1. - Set the model field to the identifier exposed by the server, normally
JetBrains/Mellum2.1-12B-A2.5B-Thinkingfor this launch command. - Enter an API key only if the agent requires a value. Do not confuse a locally accepted placeholder with authenticated access.
- Send a short coding request, then a request that requires one harmless tool call. Confirm both the text response and the structured tool event.
Option B: serve Mellum with SGLang
The model card also shows SGLang serving the original weights on port 30000 and receiving requests at /v1/chat/completions. The resulting base URL is typically http://localhost:30000/v1.
Use the current SGLang launch syntax from the model card and SGLang’s documentation, then configure your agent with that base URL and the model ID reported by the running server. The documented SGLang example establishes the compatible endpoint, but it does not specify a Mellum-specific tool-call parser equivalent to vLLM’s Hermes setting. If your agent depends on tools, verify the current SGLang tool-calling implementation and response format before switching a production workflow.
Validate SGLang before adding tools
- Start SGLang with the Mellum model and the documented port.
- Call the server’s model-list or chat-completions endpoint using the exact model ID it reports.
- Connect the agent with the SGLang base URL ending in
/v1. - Test ordinary text generation first.
- Test a non-destructive tool call only after confirming that the agent receives structured responses rather than tool syntax embedded in plain text.
Option C: use a GGUF quantization with llama.cpp
Select a quantization deliberately
The official GGUF repository publishes several file sizes:
| Quantization | Published file size |
|---|---|
| MXFP4_MOE | 7.0 GB |
| Q4_K_M | 8.1 GB |
| Q6_K | 10.9 GB |
| Q8_0 | 12.9 GB |
| BF16 | 24.3 GB |
These are download/file sizes, not guaranteed minimum GPU memory, system RAM or performance thresholds. Runtime overhead, context length, concurrent requests and offloading can require substantially more memory. The repository characterizes Q4_K_M as a balanced, recommended choice; treat that as the repository’s guidance rather than a universal quality or speed guarantee.
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Launch llama.cpp
The repository’s example uses the Hugging Face model reference with a quantization suffix:
llama-server -hf JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M
--ctx-size 131072 --temp 0.6 --top-p 0.95 --top-k 20This starts a local OpenAI-compatible server at http://localhost:8080/v1. The --ctx-size 131072 setting requests the published context length, while temperature, top-p and top-k control sampling. A larger context setting does not mean every request will fit comfortably in memory.
Configure agents shown in the GGUF repository
The repository includes examples for Pi, Hermes Agent and OpenClaw. Their configuration syntax is version-sensitive, so use the live snippets in the GGUF repository as the authoritative form when you apply them.
Pi
The documented Pi setup registers a llama-cpp provider in ~/.pi/agent/models.json, sets baseUrl to http://localhost:8080/v1, uses api: "openai-completions", and supplies the GGUF model identifier. Start Pi from the project directory after saving the configuration. Keep the model identifier consistent with the server’s advertised name; a quantization suffix used in the launch command may not be copied literally into every client field.
Hermes Agent
The repository’s Hermes Agent example selects a custom provider and the llama.cpp base URL. Replicate the current provider and model fields from that example, then run a plain completion followed by a safe tool test. If the agent prints tool syntax as text, inspect its expected tool-call format and the server’s current support before changing prompts.
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The OpenClaw example sets a custom base URL, model ID, provider ID and OpenAI compatibility. Enter all four values rather than only changing the URL; OpenClaw uses the provider identity and compatibility setting when constructing requests.
Option D: connect another OpenAI-compatible agent or SDK
For an agent not covered by the Mellum examples, look for settings named custom provider, OpenAI-compatible endpoint, base URL, model or API key. The integration is generic: Mellum does not require a special client library when the server exposes standard chat completions.
The OpenAI Agents SDK documents the same pattern with a custom base_url, an AsyncOpenAI client and an OpenAIChatCompletionsModel. Its documentation distinguishes a global client, a provider selected for one run and a model assigned to one agent. See the Agents SDK model documentation for the current code and scope rules.
- Create the SDK client with the Mellum server’s base URL, such as
http://localhost:8080/v1orhttp://localhost:8000/v1. - Assign the exact server model ID to the chat-completions model wrapper.
- Pass an API key value only when required by the server or SDK validation.
- Register the agent’s tools using the SDK’s normal function schema.
- Run a text-only request, then inspect the raw response for a structured tool call before allowing the agent to execute it.
Make tool calling reliable
Tool use is a compatibility chain, not just a model setting. The model must emit a call, the server must parse and return it in the API response, and the agent must recognize the response and execute the matching function.
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- Server: use the documented vLLM reasoning parser, automatic tool choice and Hermes parser when running the vLLM route.
- Request: confirm that the agent sends tools in the chat-completions request rather than merely describing them in a prompt.
- Response: inspect whether the server returns a structured tool-call object with a function name and arguments.
- Agent: verify that it sends the tool result back in the next turn using the role and fields expected by the server.
- Safety: begin with read-only or harmless tools. Do not grant shell or file-write permissions until the round trip is correct.
Tool behavior can differ between vLLM, SGLang and llama.cpp. The vLLM flags in the model card should not be treated as a portable configuration for every backend.
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Check model IDs and endpoint paths
“Model not found” is usually an identifier mismatch rather than a model failure. Compare the client’s model value with the name reported by the server’s model-list endpoint and with the launch command. GGUF deployments add another source of confusion: the server command may use a quantization suffix such as :Q4_K_M, while an agent example may use a repository-style identifier. Copy the convention required by the selected runtime instead of normalizing the string yourself.
Also distinguish these URL forms:
http://localhost:8000/v1— base URL for an agent that appends the API route.http://localhost:8000/v1/chat/completions— complete route for a client that calls the endpoint directly.http://localhost:8080/v1— the llama.cpp base URL shown in the GGUF instructions.
If requests fail with a 404, print the final URL generated by the agent. A duplicated /v1 or a missing /v1 is a common configuration error.
Context length, memory and throughput
The published 131,072-token context is useful when an agent must inspect a large repository, but it is a maximum model setting rather than a promise of speed or capacity on your machine. Memory use varies with precision or quantization, active context, generated tokens, server overhead, batching and hardware offload. The reviewed sources do not establish a universal minimum GPU, VRAM or RAM configuration.
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Choose the quantization and context together. A smaller GGUF file can make local startup possible, but a request that fills a 131,072-token context still creates substantial key-value-cache and runtime demands. If the server runs out of memory, reduce the context setting, lower concurrency, select a smaller quantization or move inference to hardware with more available memory. Do not infer a safe hardware purchase from the download size alone.
Verify the integration with a staged test
- Health check: confirm the server starts without model-loading errors and reports the expected model.
- Plain completion: ask for a short code explanation and verify that the response arrives through the agent.
- Context test: provide a small project file or repository excerpt and confirm that the agent preserves the expected context.
- Tool schema test: expose one harmless read-only tool and inspect the outgoing request for a proper tool definition.
- Tool-call test: verify that the server returns a structured call, the agent executes it, and the result is sent back as a tool message.
- Failure test: stop the server and confirm that the agent reports a clear connection error rather than silently falling back to another model.
Performance figures: useful context, not a guarantee
JetBrains labels its evaluation results self-reported. The agentic runs used Pi v0.73.1 with shell and file tools, a 114,000-token context, up to 16,000 generated tokens per turn and Mellum 2.1’s default temperature of 1.0. Those conditions are not a controlled comparison for every agent, backend or quantization.
| Evaluation | JetBrains-reported result |
|---|---|
| SWE-bench Verified | 47.0% |
| Terminal-Bench 2.1 | 17.4% |
| SWE-bench Pro | 28.0% |
The GGUF repository also reports quantization comparisons against BF16 logits on Wikitext-2 with a 512-token context, including KLD and top-token-match measurements. Those measurements describe output similarity under that test setup; they do not predict the speed, memory use or coding-agent success rate you will get locally.
Quick Recap
Keep the deployment maintainable
- Record the server version, model revision, quantization, context setting and sampling parameters alongside the agent configuration.
- Pin or otherwise track model and runtime updates so a changed parser or model ID does not silently break tool use.
- Keep local endpoints bound to the intended interface and protect any endpoint exposed beyond the development machine.
- Recheck hosted-provider availability before replacing local serving with a hosted plan; availability was not established for this model at the reviewed date.
- When reporting results, include the backend, quantization, context and agent version. A benchmark number without those conditions is easy to misinterpret.
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