documentation missing in detail aspect of multimindsdk

[DarshanKumar89]

MultiMind SDK Python vs JavaScript (Develop Branch) – Deep Technical Analysis

1. Feature-by-Feature Comparison

Fine-Tuning (Training) – Python: Full support for advanced fine-tuning methods (LoRA, Q-LoRA, Adapters, UniPELT+, MAML, etc.) is implemented. The Python SDK provides a UniPELTPlusTuner class and related tuners to fine-tune models with parameter-efficient techniques. It accepts training/eval datasets and integrates with PyTorch, allowing training loops and hyperparameter control. JavaScript: The JS SDK exposes methods like fineTuneModel(...) and advancedFineTune(...) to mirror these capabilities, but under the hood they rely on the Python backend. There is no native training loop in Node.js – instead, the JS SDK acts as a bridge to invoke Python’s fine-tuning engine (e.g. spawning a Python process or calling a backend API). Thus, fine-tuning is available via JS but not implemented in pure JS (it delegates to Python). For example, a LoRA fine-tuning call in JS uses the Python logic to train the model.

Retrieval-Augmented Generation (RAG) – Python: Implemented as a robust system (RAGClient) supporting hybrid retrieval (vector database + knowledge graph). The Python SDK handles document chunking, metadata storage, and integrates vector stores (FAISS, ChromaDB) and embedding models (OpenAI, HuggingFace, etc.). In Python, you can add documents and query via the RAGClient, which manages vector indexes and optional knowledge-graph retrieval. JavaScript: The JS SDK provides queryRAG(...) and an “advanced RAG” interface (addDocumentsToRAG, queryAdvancedRAG) corresponding to Python’s RAG features. These methods too leverage the Python backend. For instance, adding documents in JS will hand off to the Python process to index them in FAISS/Chroma, and querying uses Python’s retrieval pipeline. The develop branch of the JS SDK includes stubs for document processing and hybrid search, but the heavy lifting (vector search, etc.) is done by Python. In summary, core RAG functionality is present in both (JS simply proxies to Python’s implementation), whereas advanced features like knowledge-graph retrieval exist only in Python (the JS mentions “Knowledge Graphs” capability but relies on Python).

Agent Framework (Agents, Tools, Memory) – Python: Fully featured. The Python SDK defines a generic Agent class and specialized agents with support for tool integration, memory, and multi-step reasoning. Developers can add tools (agent.add_tool(...)) and memory modules (agent.add_memory(...)) to an agent. The Python agent can orchestrate complex task flows (it supports task orchestration and prompt chaining) and even compose multiple models/tools in one workflow. For example, an agent can incorporate a web search tool and short-term memory, as shown in the Python usage example. Advanced patterns (like reflexive agents that evaluate and rewrite outputs in a loop) are being developed on Python side (e.g. JudgeAgent and RewriterAgent for self-refinement) – these constitute a DAG of agent actions in Python’s design. JavaScript: The JS SDK offers basic and advanced agents APIs but with partial implementation. It provides methods like generateWithAgent(prompt) for simple single-step agents and runAdvancedAgent(prompt, context) for multi-step agents with tools. The JS README even notes support for “Advanced Agents: Multi-step reasoning, tool usage, and memory”. However, these capabilities are largely wrappers: when an advanced agent is run via JS, it delegates to the Python engine where the actual tool execution and memory management occur. The JS develop branch does not implement a full agent reasoning loop on its own – it relies on a Python subprocess (via the bridge) to carry out the agent’s plan (e.g. performing a calculation tool or web search as in the example). Features like long-term memory or complex toolchains are only truly implemented in Python; the JS can invoke them but not modify their internals. In short, both SDKs expose agent orchestration, but only Python contains the full logic for tools, memory, and task planning. The JS SDK can create and run agents, but these agents operate by commanding the Python backend.

Pipelines and DAG Execution – Python: Agents and tasks can be arranged in flexible pipelines. The develop branch of the Python SDK emphasizes a declarative DAG approach for agent workflows. For instance, the planned MetaControllerAgent can mutate an agent pipeline (DAG) at runtime, and an AgentArena can run multiple agents in parallel and select the best result. This indicates Python’s architecture either already supports or is soon to support directed acyclic graphs of agent steps (conditional branches, parallel tool calls, feedback loops). The core Python SDK has “Task Orchestration” as a feature and is evolving towards self-improving agents with reflexive loops. JavaScript: There is no direct DAG or parallel agent execution support in the JS SDK as of the develop branch. The JS side does not implement modules like MetaControllerAgent or parallel agent voting – these advanced orchestration features are absent or only hinted at. The JS advanced agent runs steps sequentially through Python; it cannot yet define a branching workflow or run concurrent agents on its own. Essentially, complex DAG-based agent orchestration is Python-only in the current state (with features under active development in Python’s develop branch, and not yet ported to JS).

Genetic/Evolutionary Agents – Python: The concept of “Genetic AI” agents (self-evolving agent pipelines using evolutionary algorithms) is in the works. The Python develop branch outlines components like MetaControllerAgent, AgentMutator, AgentArena, etc., to enable mutation and selection of agent strategies. These would allow multiple agent variants to compete or a pipeline to evolve over time (mutating the DAG, tracking performance in an EvolutionMemory, etc.). As of now, these are design specs and issue trackers – some building blocks (like basic feedback loops) exist, but full genetic algorithms for agents are not yet fully implemented even in Python (they’re listed as “Open” features on the roadmap). JavaScript: None of these genetic-agent features are implemented in the JS SDK. The JS develop branch does not have any mention of evolutionary agents, population-based search, or pipeline mutation. These are planned Python-side enhancements that will likely be abstracted to JS only after Python implementation stabilizes. So, genetic agent orchestration is exclusively a Python roadmap feature, with no JS support at present.

Memory Systems – Python: Comprehensive memory support. Agents can have short-term memory (e.g. conversation context window) and long-term memory via vector stores or knowledge graphs. The Python SDK provides memory modules that can be attached to agents, and the RAG subsystem itself can serve as a long-term memory (via document vectors and graph triples). There is also discussion of advanced memory like an EvolutionMemory to remember best pipelines across runs. In practice, Python’s Agent uses in-memory histories and can integrate with external memory (like a vector DB). JavaScript: The JS SDK acknowledges memory (“Advanced Agents: ... memory” in docs) but lacks a standalone memory module. If a JS agent uses memory, it’s because the Python backend is maintaining conversation context or retrieving from a vector store. For example, runAdvancedAgent in JS may allow an agent to recall prior tool outputs, but this context handling is done by Python’s agent logic. There is no JS class for a Memory store; the JS SDK simply passes along context or uses the RAG functions for persistent memory. Thus, memory is effectively read/write through to Python. No dedicated JS memory plugins exist yet.

Tool Use and Plugins – Python: Built-in support for common tools and easy extension for custom tools. The Python agent framework allows integration of tools such as web search, calculators, database queries, etc., by registering tool functions (agent.add_tool(name, func)). The Python SDK likely includes a library of standard tools (or examples) and a plugin-like mechanism to add new ones. Additionally, Python’s architecture is modular: new model integrations or components can be added by subclassing base classes (e.g. adding a new ModelClient or a custom converter). JavaScript: The JS SDK exposes agent tool usage in a very high-level way (e.g., runAdvancedAgent can invoke tools based on the prompt). However, defining or registering a new tool on the JS side is not currently documented – JS doesn’t have an add_tool method for agents. Instead, the advanced agent likely uses Python’s tool plugins. Essentially, if a tool (say a web search) exists, the Python backend provides it. The JS developer can request an agent do something that requires a tool, and the Python side will execute it if available. As for plugins beyond tools (like adding new model backends), the JS relies on Python’s plugin system. For example, if Python supports a new model or retrieval plugin, the JS can call it via the bridge but cannot independently manage plugins. In short, tool integration is present in both, but all implementations reside in Python. The JS SDK is “plugin-ready” to the extent that it will call into whatever plugins the Python SDK has (for instance, a custom Python tool or model is accessible through JS by naming it in the config).

Model Routing and Multi-Model – Python: Rich support via the Model Client System. Python’s develop branch includes a unified ModelClient API for different model types and sizes. Notably, it has a FederatedRouter class that can dynamically route queries to different models (local vs. cloud, or based on input content). Python also provides LSTMModelClient (for classic neural nets), MoEModelClient (mixture-of-experts ensemble), MultiModalClient (for unified multimodal inputs across text/image/etc.), and others. These allow complex multi-model orchestration: e.g., route short queries to a small model and long queries to GPT-4, as demonstrated with FederatedRouter. JavaScript: The JS SDK mirrors these capabilities by offering factory methods like createLSTMModelClient, createMoEModelClient, createMultiModalClient, and createFederatedRouter. In the JS develop branch, these methods exist and accept similar parameters (e.g. a routing function for MoE/Federated). Under the hood, however, these JS model clients are essentially proxies. When you create an LSTMModelClient in JS, it likely initializes a corresponding model in the Python subprocess (e.g. loading the PyTorch model file via Python). The JS object then represents a handle to that model. The FederatedRouter logic (deciding “which model ID to route to”) can run in JS (since the router lambda is provided in JS), but executing the chosen model’s generate happens in Python. Both SDKs therefore expose multi-model orchestration, but the core routing and model execution is in Python’s domain. The JS develop branch is kept in sync with Python’s feature set here – for example, when Python added FederatedRouter, the JS sync-features script updated the JS to include startGateway and routing methods. Summarily, multi-model support is implemented in Python and surfaced in JS. The JS can route among remote and local models by calling Python’s FederatedRouter (or by simple logic then calling specific model clients).

Compliance & Monitoring – Python: The Python SDK has an enterprise compliance module. A ComplianceMonitor class provides GDPR/HIPAA compliance checks, PII detection, logging, audit trails, etc.. It can perform runtime checks on data and model outputs, produce alerts, and even provide a compliance dashboard or reports. These features are integrated with model invocation – e.g., the monitor can wrap around model calls to catch disallowed content. Python’s compliance layer is quite extensive, including advanced mechanisms like differential privacy and watermarking (as per the roadmap). JavaScript: The JS SDK includes a checkCompliance(...) method to invoke these checks. As expected, this is a thin wrapper that sends the request to Python’s ComplianceMonitor. There is no separate JS implementation of PII redaction or logging – the JS relies on the Python process to handle it. In practice, if you call sdk.checkCompliance({ ... }) in Node, the SDK will forward the parameters (model ID, data categories, etc.) to Python, and Python’s compliance engine returns the result. The JS develop docs also list compliance under “Advanced Workflows” along with LoRA and document processing – indicating it is acknowledged as a feature. But the heavy logic (regulations, checking text against rules or using AI classifiers) is only in Python. So compliance monitoring is available in JS via delegation; only Python has the actual implementation (e.g. ComplianceMonitor.check_compliance() is in Python).

Model Conversion & Deployment – Python: Full support for converting models between formats and optimizing for deployment. The Python SDK supports conversion to ONNX, GGUF (quantized GPT format), TFLite, TorchScript, etc., and includes optimizations like quantization and operator fusing. There is likely a ModelConverter utility or functions like pytorch_to_onnx() implemented in Python. These utilize frameworks like ONNX runtime, etc., and can be run on GPU or CPU with various flags. JavaScript: The JS develop branch has corresponding methods, for example sdk.pytorchToONNX(inputPath, outputPath, config) and similar for TFLite or GGUF. As expected, these simply call Python’s conversion pipeline. The Node process itself cannot convert a PyTorch model to ONNX (no native PyTorch/ONNX in Node); instead, the JS SDK likely invokes a Python subprocess that runs the conversion (possibly by calling torch.onnx.export() internally). So, model conversion features are implemented in Python only, with JS exposing the function signatures. Both SDKs list support for formats like ONNX and TFLite, but only Python executes those conversions. (The JS feature-sync-report.json confirms that conversion methods are present in the API to keep parity with Python).

Gateway API (Serving) – Python: The Python SDK can launch a local RESTful API “Gateway” (likely based on FastAPI or Flask) if installed with the [gateway] extra. This allows serving models or agents via HTTP endpoints for integration with external applications. The develop branch’s README mentions pip install multimind-sdk[gateway] and a gateway usage. JavaScript: Interestingly, the JS SDK also provides startGateway({host, port, ...}) to spin up a gateway. In the JS context, this might either (a) start the Python’s FastAPI server on the given host/port via the bridge, or (b) launch an Express.js server in Node. Given the unified design, it’s likely just invoking the Python gateway. The JS gateway.ts (as listed in the project structure) serves to call the backend. Therefore, the Gateway feature (HTTP serving of the SDK) is accessible from both, but the actual server implementation is Python’s. The JS call simply instructs Python to start listening (with options like CORS, rate limiting which Python’s gateway honors). There is no independent Node-based serving logic. In short, gateway/serving is a Python feature controllable via JS.

2. High-Level Architecture of Each SDK

Python SDK Architecture: The MultiMind Python SDK is a monolithic, modular framework written in Python. Its core is organized into multiple components (as seen in the multimind/ package structure):

• Agents and Tools: The multimind.agents module defines the Agent base class and specialized agents. It handles agent logic (planning, tool usage, memory). Tools and memory are integrated here (the agent class methods manage these). Agents can be extended or composed (the design supports roles like Judge/Rewriter agents, though those are in development).

• Pipelines and Orchestration: Rather than a single monolithic class, Python uses composition of agents and clients to build workflows. There is support for declarative pipeline configuration via YAML/CLI as well (noting the README’s mention of “Declarative YAML + CLI + SDK interfaces”). Internally, complex workflows can be represented as DAGs of agents or steps. The architecture includes experimental controllers (MetaControllerAgent) to modify these pipelines at runtime.

• Clients (Models and RAG): Under multimind.client, the SDK defines various Client classes for interacting with models and data:

  • BaseLLM / ModelClient classes unify different model APIs (local PyTorch models, OpenAI API calls, etc.) behind a common interface.
  • LSTMModelClient, MoEModelClient, MultiModalClient are subclasses that handle specific model types (e.g. an ONNX-loaded LSTM or a mixture-of-experts ensemble).
  • FederatedRouter is an orchestration component that holds multiple ModelClients and a routing function to dispatch calls to one of them.
  • RAGClient manages retrieval-augmented generation. It interfaces with vector databases and (optionally) knowledge graph storage, providing methods to add documents and query them. Internally it may use embedding generators and store vectors via FAISS/Chroma.

• Fine-Tuning and Training: The multimind.fine_tuning module contains classes like UniPELTPlusTuner and possibly LoRATuner, etc. These handle setting up a training loop using deep learning frameworks (PyTorch). The architecture abstracts away model specifics by using the ModelClient interface for loading/saving models, so the fine-tuner can work with any supported model type. There is also support for adapter training (e.g. adding LoRA layers to models) built-in.

• Compliance and Logging: In multimind.compliance, the SDK implements compliance checks. The ComplianceMonitor class encapsulates rules and logging for data passing through the system. It likely uses external libraries or custom logic to detect PII and ensure usage policies (e.g., it might parse model outputs for sensitive info). The compliance subsystem hooks into model generation or data ingestion points to perform real-time monitoring. A logging/audit component records events, enabling an audit trail and possibly feeding into a dashboard or alerts.

• Gateway and Plugins: The gateway (if enabled) is probably implemented as an ASGI app (FastAPI) inside the Python SDK. It uses the above components to expose HTTP endpoints (for example, an endpoint to generate via an agent, or add documents to RAG). The gateway is optional, activated with extra dependencies. Regarding plugins, the Python SDK’s modular design allows integration of new models or tools by subclassing or registering via entry points. For instance, new Tool plugins can be created and added to the agent’s registry. The develop branch emphasizes extensibility, as seen in “Custom Converters: Extensible converter architecture” and the ability to incorporate non-transformer models via BaseLLM wrappers.

Inter-Component Communication (Python): The Python SDK components interoperate in-process – e.g., an Agent may internally use a RAGClient to retrieve context, or call a ModelClient to generate text. There is likely a central coordinator or just the developer’s orchestration code that ties them. The design is modular but unified: all components can be imported and used together (the SDK is meant to be a “one-stop” AI toolkit).

JavaScript SDK Architecture: The JavaScript SDK (TypeScript codebase) is structured as a wrapper and bridge around the Python functionality. Key architectural points:

• The JS repository’s src/ directory mirrors many of the Python features in separate modules (files). For example, agent.ts, fineTune.ts, rag.ts, adapters.ts, evaluation.ts, models.ts, advancedFineTuning.ts, advancedRAG.ts, modelConversion.ts, compliance.ts, advancedAgent.ts, modelClientSystem.ts, gateway.ts, contextTransfer.ts are all present. Each of these corresponds to a feature area of the Python SDK.

• Core JS Class: The MultiMindSDK class (defined in index.ts) serves as the main entry point. It aggregates the methods from all those modules into a unified API (so that, for example, sdk.fineTuneModel() is implemented by calling a function in fineTune.ts). This mirrors the Python approach of one SDK that does everything, but in JS it’s mostly facade methods.

• Bridge to Python: The src/bridge/multimind-bridge.ts module is critical. This sets up communication with the Python backend. While the exact implementation isn’t visible in docs, the intended design (as indicated by the developers) is that the JS SDK will spawn a Python subprocess running the MultiMind Python SDK in a special mode or server. The JS SDK then sends commands to this subprocess – likely via STDIN/STDOUT, RPC calls, or an IPC mechanism. In effect, the Python process acts as a backend server for the JS front-end. This is hinted by a comment in the codebase: “bridge setup – backend bridge”. The JS initialize() method presumably launches or connects to the Python service (perhaps by running python -m multimind.sdk.bridge_server or similar). After initialization, each JS SDK call is translated into a request over the bridge, and results are returned. This architecture allows the JS library to offer the same breadth of features without reimplementing them; it simply marshals data to/from Python.

  • The communication could be JSON-RPC or a custom protocol. Given the complexity of some data (e.g., binary model weights for conversion), the bridge might handle file paths or use a socket. The details aside, the key is that for any heavy operation, the JS calls Python. For example, calling sdk.generateWithAgent(prompt, config) in Node will send a message to the Python process to create an agent (if not already), run agent.run(prompt) in Python, then return the Python agent’s output.

  • The JS sync-features.js script in the repo suggests that the JS SDK is kept in sync with Python by reading some spec or metadata from the Python side. Indeed, a feature-sync-report.json is present, likely listing which features are implemented. This implies an automated layer where the JS knows which Python version or feature set is available and can throw “Not implemented” errors for features not ready.

• Browser/Node differences: The JS SDK is primarily for Node.js (server-side) usage, not for in-browser use (since it needs Python). It is distributed via npm (multimind-sdk on npm). Under the hood it may spawn a child process using Node’s child_process module to run Python – thus the user must have the Python SDK installed or available. The tagline “without needing to manage backend code” means JS developers can call AI features without themselves writing Python, but the library itself does manage a backend Python process.

• Structure of Modules: Each TS file (e.g. fineTune.ts) likely defines functions that package up the call for that feature. For instance, fineTune.ts might export fineTuneModel(config) which internally does something like bridge.call('fine_tune', config). The bridge module handles sending that to Python and awaiting result. The project structure shows this one-to-one mapping:

  • agent.ts – functions for basic agent usage (e.g. generateWithAgent).
  • advancedAgent.ts – functions for advanced agents (createAdvancedAgent, runAdvancedAgent).
  • fineTune.ts & advancedFineTuning.ts – basic and advanced fine-tuning (the latter for LoRA, etc.).
  • rag.ts & advancedRAG.ts – basic vs. advanced RAG (with document management).
  • models.ts – loading/listing models.
  • modelClientSystem.ts – LSTM, MoE, MultiModal, Federated clients creation.
  • adapters.ts – manage model adapters (if a model has modular adapters).
  • evaluation.ts – model evaluation and comparison methods.
  • compliance.ts – compliance monitor and check calls.
  • modelConversion.ts – conversion utilities (PyTorch to ONNX, etc.).
  • contextTransfer.ts – context transfer functions for chat histories (the CLI context transfer tool is exposed here).
  • gateway.ts – functions to start/stop the backend gateway server via Node calls.
  • Plus a contextTransfer.ts and possibly others.

• Communication and State: The JS SDK likely maintains some state – for example, when sdk.initialize() is called, it might start the Python process and perhaps create some persistent contexts (like an agent or model pool). The sdk.close() method will terminate the Python subprocess. In between, calls like createAgent() in JS might correspond to constructing a Python Agent object and storing a handle (ID) in the bridge, so subsequent generateWithAgent knows which agent to use. The design likely involves IDs or references for Python-side objects that the JS side can reuse without reinitializing. For instance, createAdvancedAgent(config) could create an agent in Python and return an identifier, which runAdvancedAgent(input) then uses to execute on that same agent.

• Error Handling: The JS SDK wraps errors from the backend and throws JS exceptions. The docs mention “comprehensive error handling” where all methods throw descriptive errors if operations fail. Likely, if the Python side returns an error (stack trace or message), the bridge translates it to a JS Error with that message.

In summary, the JS SDK architecture is essentially a client-server model: Node.js acts as the client, and Python as the server performing AI computations. The JavaScript code is carefully organized to mirror Python’s feature set and provide a familiar API to JS developers, while internally every significant method call goes through the multimind-bridge to Python. This design ensures consistency: both SDKs offer the “same” features, and the JS stays up-to-date with Python by delegating logic rather than duplicating it.

3. Functional Comparison of Exposed Capabilities

Below is an outline of major functionality areas and how each SDK supports them:

• Agent Creation & Execution: Both SDKs allow creating agents and generating text with them. In Python, one would instantiate Agent (or a subclass) and call agent.run(prompt). In JS, one uses sdk.createAgent(config) or directly sdk.generateWithAgent(prompt, config). Both support basic single-turn Q&A agents. For multi-step agents (with tools/memory), Python offers rich control (adding tools, customizing the agent’s step logic), whereas JS provides the runAdvancedAgent method that under the hood runs a Python advanced agent with tool use. So functionally, a developer can achieve an agent solving a multi-step task in both languages, but only Python allows fine-grained customization of the agent’s internals. The JS SDK covers common use cases by exposing a fixed set of agent behaviors (basic vs. advanced vs. specialized as listed in docs).

• Multi-Agent Orchestration: In Python, you can orchestrate multiple agents together – e.g., have an agent call another agent, or run agents in parallel and choose a result. This isn’t a single function but a pattern enabled by Python’s API (and forthcoming features like AgentArena for parallelism). In JS, there is no direct multi-agent orchestration API (no equivalent of running agents in parallel or a built-in arena/voter). One could still manually intermix multiple agent calls sequentially from JS, but anything beyond sequential execution (like concurrently running agents or evolutionary selection) would require logic outside the JS SDK. Thus, functional parity diverges here: Python supports complex multi-agent workflows (especially in develop branch, via planned MetaControllerAgent, etc.), while JS currently supports one agent at a time (advanced agent can simulate multi-step internally, but not multiple independent agents competing).

• Tools and External Actions: Python’s agent can use tools (e.g. web search, calculators, custom functions) by registering those tools. Many common tools can be integrated (the developer can implement a tool function in Python and add it). The ToolAgent concept (mentioned in blog posts) suggests Python has a pattern for tools usage. In JS, tool use is available only through the advanced agent mechanism (you instruct the agent via prompt to use tools, and the Python backend executes them if configured). There isn’t a direct JS function like sdk.addToolToAgent as of develop. Functionally, both SDKs let an agent perform external actions – but Python requires explicitly coding the tool’s function, whereas in JS, you rely on predefined tools on the backend. The ability to integrate a new tool at runtime is a Python-only function at the moment.

• Memory and Context: For maintaining conversation context or long-term info, Python provides constructs like short-term memory (built into Agent’s state or via a ConversationMemory object) and long-term memory via the RAG system (documents can serve as knowledge memory). JS doesn’t have a separate memory API, but functionally you can achieve context carry-over by keeping an agent instance alive in Python through the JS bridge. For example, calling createAgent() once and then multiple generateWithAgent() calls will use the same Python agent, which can preserve its internal state (conversation history) between calls. So, multi-turn conversational memory works in JS by reusing an agent. But long-term memory (vector stores) requires using the RAG functions; JS provides addDocumentsToRAG and queryAdvancedRAG to simulate memory queries. In short, both support memory usage in practice, but Python offers more explicit control (choose memory type, size, etc.) whereas JS uses the default behaviors of the underlying Python agent.

• Model Fine-Tuning: As detailed, Python has classes for fine-tuning that handle training internally (with support for multiple methods). The developer can fine-tune a model on new data and get a fine-tuned model artifact. JS exposes this via sdk.fineTuneModel(config) and sdk.advancedFineTune(config). Functionally, a JS developer can kick off a fine-tuning job and receive a result (likely some identifier or confirmation; possibly the fine-tuned model is saved on disk where Python runs). Monitoring training (progress, loss) is something Python can do via logs; the JS SDK might not stream those logs by default. Both SDKs can fine-tune models, but the JS SDK depends on Python’s environment (if Python isn’t properly configured with Torch and GPUs, the fine-tune call will fail).

• Evaluation and Comparison: Python includes model evaluation utilities – e.g., evaluateModel(config) to evaluate a model on a dataset and compareModels([modelA, modelB], task) to get performance metrics side by side. These likely rely on datasets and metrics implemented in Python. The JS SDK has the corresponding methods evaluateModel and compareModels as part of MultiMindSDK. Thus, functionally, one can trigger an evaluation from JS and get the results. The heavy lifting (running the model on test data, computing metrics) occurs in Python. Both SDKs therefore offer evaluation capabilities, though only Python can be extended if you need custom metrics or evaluation logic.

• Retrieval & Document Management: Both SDKs allow adding documents and querying them (the core of RAG). In Python, RAGClient.add_documents(list_of_Document) and RAGClient.query(query, ...) are used. In JS, sdk.addDocumentsToRAG(docs) and sdk.queryAdvancedRAG({query, …}) provide the same functionality. The Document format in JS is a simple JS object with text and metadata, which the SDK sends to Python’s Document class. So, functionally identical – a developer can build a knowledge base and query it in both languages. Advanced retrieval options (e.g., hybrid search combining vectors and keyword, or use of a knowledge graph) are currently more accessible in Python (through parameters on RAGClient like retrieval_mode="hybrid", knowledge_graph=True). The JS queryAdvancedRAG likely has flags for hybrid search too, meaning it can request a hybrid retrieval if the Python backend supports it. Overall, RAG operations are fully available in both, with Python providing more customization of retrieval pipelines and JS providing convenience.

• Multi-Modal and Custom Models: The Python SDK’s model client system supports not just text LLMs but also integration of image, audio models via MultiModalClient. It also supports non-transformer models (e.g., RWKV RNNs, as noted on the website) by wrapping them in ModelClient. If a developer wants to use a custom model, in Python they can subclass BaseModelClient or use provided wrappers to load it. The JS SDK can create a createMultiModalClient linking multiple modality-specific clients, and can call models by name (the supported model list in JS includes many model types). However, adding a completely new model type in JS would require adding it in Python first. Functionally, JS can use any model that Python SDK knows about (including open-source models like Mistral, LLaMA, etc., or remote APIs). Both SDKs thus allow multi-modal and multi-provider usage, but Python is the one that actually implements model loading.

• Context Transfer (AI-to-AI handoff): A notable feature in documentation is Context Transfer – converting conversation history from one model format to another. The JS CLI showcases multimind-cli --source chatgpt --target claude --input convo.json --output prompt.txt. The JS SDK likely provides functions for this (perhaps in contextTransfer.ts). Python, on its side, has utilities to parse conversation logs and produce prompts for different models (ensuring formatting differences are handled). Both SDKs in develop have this feature area: JS offers it via CLI and possibly sdk.transferContext(...) and sdk.quickTransfer(...) methods, Python via CLI and an internal API. They function similarly – letting users bridge conversations between platforms. Again, the JS implementation calls into Python’s conversion logic.

• CLI and Utility Functions: The Python SDK comes with a powerful CLI (for those who install the package) covering many functions (fine-tune, context transfer, list models, run compliance scans, etc.). The JS SDK also provides a CLI (the multimind-cli when installed globally via npm) which proxies to the same operations in a Node environment. Functionally, the CLI usage is similar (though implemented separately for Node vs Python shells). This means developers on either side can use command-line commands to perform batch operations. The JS CLI is essentially a thin wrapper that calls the SDK’s functions, which in turn call Python – so even the CLI in JS ultimately hits the Python logic. Both CLIs cover context transfer, model listing, batch processing, etc., ensuring feature parity from a user perspective.

In summary, the functional surface of the two SDKs is very similar – deliberately so. The JS SDK was designed to expose all the major features of the Python SDK. Where differences lie is under the hood and in extensibility: Python executes the tasks and allows customization through code, whereas JS triggers the tasks and keeps the complexity hidden in the backend. A developer can orchestrate fine-tuning, RAG, agents, model conversion, etc., using either SDK; with Python they get full control to modify or extend the behavior (owing to it being open Python code), while with JS they get ease-of-use and integration into Node.js apps, at the cost of needing the Python backend and not being able to change its internals from JS.

4. Missing/Incomplete Features in the JavaScript SDK (vs. Python)

The table below highlights features that are present or fully developed in Python’s develop branch but missing or not fully implemented in the JS SDK. For each, we reference the Python implementation (file paths or class names on the develop branch) as evidence:

Feature Area	JS SDK Status (Develop)	Python Implementation (Reference)
Advanced Agent DAG & Reflexive Loops	Not implemented in JS. JS cannot define custom agent DAGs or self-refining loops. Advanced patterns like an agent judging and rewriting its output (feedback loop) are absent.	Python: Supported via agent pipeline architecture. E.g. the design for JudgeAgent -> RewriterAgent loops exists in Python. The develop roadmap includes MetaControllerAgent for mutable DAGs – Python code under multimind/agents/ will host these classes. (JS has no corresponding module or API for this.)
Parallel/Multi-Agent Execution (Genetic Arena)	Not available in JS. No support for running agents in parallel or genetic selection of outputs.	Python: Planned via AgentArena and related “Genetic AI” features (Issue #33). Python’s upcoming AgentArena (to be in multimind/agents/arena.py) will execute agents in parallel and choose best output. JS has no such feature and cannot utilize multiple concurrent agents.
Agent Mutation & Evolution	Not available in JS. JS cannot mutate agent workflows or apply evolutionary algorithms.	Python: Under development. Classes like AgentMutator (for pipeline mutation) and EvolutionMemory are outlined in Python. These would live in Python’s agent module (e.g., multimind/agents/mutator.py). JS has no exposure to these experimental features.
Custom Tool Integration at Runtime	Limited in JS. Can use only pre-defined tools via prompts; no API to add new tool functions in JS.	Python: Fully supported. Developers can register custom tools by calling Agent.add_tool(name, tool_function) in Python (implemented in multimind/agents/agent.py). For example, adding a “search” tool with a Python function is possible. JS lacks an equivalent method – it depends on tools that are hard-coded on the Python side.
Extensible Memory Modules	Not exposed in JS. No interface to plug in a new memory store or tweak memory parameters.	Python: The multimind/agents/memory subsystem (if present) or Agent’s memory integration is available. Python supports short-term vs long-term memory distinction and could integrate vector DB as memory. In JS, aside from using RAG, there is no way to choose memory type – it’s handled internally by Python’s agent.
Datasets and Data Loading	Not in JS. JS doesn’t provide helpers for loading or preprocessing datasets for training/eval.	Python: Likely uses HuggingFace datasets or custom Dataset classes when fine-tuning (e.g., passing a torch Dataset to tuner.train()). Python’s requirements.txt suggests integration with data libraries. JS has no concept of a Dataset object – training data must be provided in a format the Python expects (often file paths or already prepared in Python).
Fine-Tune Progress Callbacks	Missing in JS. No facility to get intermediate training metrics or live progress.	Python: You can directly access training loop in UniPELTPlusTuner.train() (and modify it or attach callbacks in code). While not a distinct class, the Python user could instrument the training. JS’s advancedFineTune() will only return when training is complete – it doesn’t stream logs or allow callback hooks. (This is an implicit gap; the JS SDK does not yet implement event callbacks for progress that the Python could emit.)
Compliance Dashboard & UI	Not in JS. The JS SDK cannot display or query a compliance dashboard or detailed logs.	Python: A compliance dashboard is mentioned as a feature. The Python ComplianceMonitor.get_dashboard_metrics() exists to retrieve compliance stats. This suggests Python might integrate with a dashboard (perhaps via the Gateway or an external UI). JS has no method for “dashboard” – it can only invoke checkCompliance() and get a pass/fail or report. Advanced monitoring (trends, alerts) aren’t accessible through JS.
Plugin Model Integrations	JS depends on Python only. No mechanism to register a new model type on the JS side.	Python: New model integrations can be added by subclassing BaseLLM or writing a new ModelClient. For example, integrating a new API or library is done in Python’s code (e.g., adding a multimind/clients/myModelClient.py). The JS SDK will only see it after it’s in Python. There is no JS plugin system; it’s tied to Python’s.
CLI Utilities (some)	Partial in JS. The JS CLI supports context transfer, listing models, etc., but not everything Python CLI does.	Python: The CLI (via multimind_cli) supports fine-tune jobs, compliance scans, etc. For instance, Python CLI can run multimind-cli fine-tune ... or other advanced commands. The JS CLI, as of 0.1.x, is mainly focused on context transfer and examples. Fine-tuning and other operations can be done via code in JS but may not have dedicated CLI commands. (This is a minor discrepancy and may be addressed in future JS releases.)
Documentation & Examples (code parity)	Fewer code examples in JS. The JS repo has basic examples (example/ folder) but not as many scenarios as Python’s examples.	Python: Rich examples under multimind-sdk/examples/ (and extensive docs in .md files) covering pipelines, multi-agent workflows, etc. The JS example/ covers agent usage, advanced usage, and CLI usage, but some complex Python examples (e.g., multi-agent reflexive loops or federated orchestration) are not replicated in JS yet, indicating those features aren’t fully tested on JS.

Notes: The JS SDK’s feature-sync-report.json in the develop branch corroborates many of the above – it likely lists certain features as “stub” or “calls backend”. The bottom line is that any feature requiring heavy computation or complex coordination (training, retrieval, etc.) is implemented only in Python. The JS SDK is intentionally kept lightweight, delegating those tasks. If a feature is missing in Python, it will certainly be missing in JS. Conversely, if something is present in Python but very new (e.g., the genetic agents), the JS SDK may not expose it until it stabilizes.

For every entry in the table, the Python file or class where it lives is identified. For example, tool integration lives in the Agent class (likely multimind/agents/agent.py – see Agent.add_tool usage) and compliance in multimind/compliance/monitor.py (ComplianceMonitor – see usage). These Python implementations serve as the source-of-truth that the JS SDK ultimately relies on, highlighting the asymmetry in implementation depth between the two.