ToolLibGen: LLM-Driven Tool Synthesis

• ToolLibGen is a framework that integrates collaborative lexicon management with LLM-driven code synthesis to generate, cluster, and aggregate tools for both linguistic resources and coding applications.
• The system employs a multi-phase pipeline—generating question-specific Python functions, clustering by semantic similarity, and aggregating validated code artifacts—to streamline tool creation for LLM reasoning.
• Empirical evaluations indicate improved retrieval accuracy and task success rates, with metrics such as +36% pass@1 in private API scenarios and sub-100ms lookup times in lexicon applications.

ToolLibGen refers to multiple advanced tool generation and management frameworks in computational linguistics and AI-enabled code generation. The term has been used for: (1) a generative lexicon management toolkit for collaborative construction and programmatic manipulation of lexical resources; (2) a scalable system for automatic tool creation, structured aggregation, and retrieval for LLM reasoning; and (3) a pipeline architecture for private-library-oriented LLM code synthesis. The following article provides a comprehensive overview of ToolLibGen across these domains, referencing implementations by Henry & Bassac (2005) and recent LLM-centric research.

1. System Architectures and Core Concepts

ToolLibGen designates systems unifying tool creation, aggregation, and collaborative use in two major settings: linguistic resource engineering and automatic code tool generation for LLM-enhanced reasoning.

In the lexicon engineering context, ToolLibGen is a client–server architecture with an LDAP (Lightweight Directory Access Protocol) server repository (the Directory Information Base, DIB) for storing lexeme entries. Clients can be graphical (e.g., JXplorer) for collaborative editing or programmatic (e.g., Python modules such as entree.py) for automated querying, updating, and exporting lexicon data, with data expressed as LDAP attributes reflecting Generative Lexicon theory (telic, agentive, formal qualia roles) (0805.2537).

In LLM reasoning and autonomous tool composition, ToolLibGen is an end-to-end pipeline that extracts task-specific Python functions as tools from Chain-of-Thought (CoT) reasoning traces, clusters them by semantic proximity, and refactors each cluster into aggregated and interface-regularized tool libraries. This automation utilizes a multi-phase pipeline with iterative LLM prompting and agent-based review, ultimately producing a structured library $\mathcal{L} = \{C_1^*, C_2^*, \ldots, C_K^*\}$, where each $C_k^*$ is a consolidated, validated code artifact (Yue et al., 9 Oct 2025).

Private-library-oriented variants connect LLM-powered code generation models to unknown/private APIs with two-stage pipelines: retrieval (APIFinder module via dense vector search) and code synthesis (APICoder module using prompt-engineered code LLMs pre-trained on public or private code corpora) (Zan et al., 2023).

2. Phased Pipeline for Tool Creation, Clustering, and Aggregation

End-to-End Pipeline (LLM Tool Synthesis)

Given a dataset of questions and corresponding CoT traces $\mathcal{D} = \{(Q_i,\,\text{CoT}_i)\}_{i=1}^N$, the ToolLibGen pipeline executes three principal phases (Yue et al., 9 Oct 2025):

• Phase 1: Question-Specific Tool Generation
  • For each problem $(Q_i,\,\text{CoT}_i)$, a generic LLM abstracts reusable Python functions $\mathcal{T}_i$.
  • Every candidate function $t \in \mathcal{T}_i$ is validated: a solver LLM attempts to solve $Q_i$ using $t$; failed attempts trigger LLM-based refinement until correct output or maximum iterations.
• Phase 2: Tool Clustering
  • Tools $\mathcal{T} = \bigcup_i \mathcal{T}_i$ are sampled, and a general LLM proposes a hierarchical cluster tree to assign each tool to a leaf node $C_k$ using semantic similarity, formalized via cosine distance between tool embeddings $v_t = E(d_t)$, where $d_t$ concatenates the tool name and description.
• Phase 3: Automated Tool Aggregation
  • For each cluster $C_k$, a Code Agent designs a high-level class blueprint to subsume all tool functionalities, generates Python code accordingly, and a Reviewing Agent ensures behavioral equivalence with the original tools through targeted solution attempts.
  • Refinement is looped up to a fixed turn to guarantee that all original task requirements are satisfied by the aggregated code artifact $C_k^*$.

3. Tool Representation, Storage, and Retrieval

Lexicon Engineering (Linguistics)

Each lexeme is an LDAP entry with objectClass=lexeme, accompanied by a flat list of attributes that instantiate qualia structures, argument roles, type hierarchies, and morphosyntactic properties. The toolkit supports graphical creation (through JXplorer), direct batch editing (using LDIF/XML), and programmatic manipulation (via entree.py), with access control and collaborative real-time updates (0805.2537).

LLM Tool Management

In the LLM-generated tool domain, tools are maintained as Python functions/classes with meta-descriptions. Clustering is hierarchically organized (typically to depth four), using LLM prompts to produce a semantic tree spanning specific subdomains. The library supports efficient tool querying through semantic search over tool embeddings.

In the private-library-oriented context, APIFinder embeds API descriptions into vector space (using dual BERT encoders), retrieves relevant API candidates from a FAISS index, and presents the top-$K$ choices to users or downstream models. Subsequent code is synthesized by LLMs equipped with API-informed prompts (Zan et al., 2023).

Context	Storage	Retrieval Method
LDAP Lexicon	LDAP DIB (entries)	Graphical/API (filtering)
LLM-Created	Python codebase	Embedding-based search
Private Libs	API doc index (FAISS)	Dense retriever + LLM

4. Collaborative and Automated Functionality

ToolLibGen implementations support both collaborative human editing and full automation:

• Collaborative Creation (Lexicons): Multiple users edit or curate entries in real time, supported by LDAP replication and modular access controls. CLI and Python-based tools facilitate integration with NLP pipelines and validation workflows (0805.2537).
• Multi-Agent Automation (LLM Tools):
  • Code Agent: Designs class blueprints, synthesizes code, and self-corrects via syntax checking.
  • Reviewing Agent: Validates functional equivalence using LLM-based solution trajectories and feedback, ensuring all source questions are solvable after aggregation (Yue et al., 9 Oct 2025).
  • Human-in-the-loop retrieval (Private APIs): The system can present ranked API candidates and allow developers to select the most appropriate for further code generation (Zan et al., 2023).

5. Evaluation and Empirical Results

In LLM code tool generation, experimental results emphasize improvements in retrieval accuracy and downstream reasoning:

• Tool retrieval accuracy (top-$k$) and end-to-end problem-solving success are evaluated against manually curated and clustered tool libraries.
• Compared to unstructured tool collections, a structured and aggregated library allows for higher retrieval precision and scalable performance as the number of question-specific tools increases.
• On private-library benchmarks (e.g., TorchDataEval, MonkeyEval), pre-trained CodeGenAPI models, when aided by precise API retrieval, substantially outperform off-the-shelf LLM code generators, with perfect API basics yielding up to +36% pass@1 on some tasks (Zan et al., 2023).
• In LDAP lexicon management, empirical results on ~70 French compound nouns show sub-100ms per-compound lookup and validation times on contemporary hardware, with planned scaling to millions of entries (0805.2537).

Metric	Lexicon Toolkit	LLM ToolLibGen	Private-APICodeGen
Retrieval top-$k$	—	Improved (structured)	>50% recall@5
Pass@1 (task completion)	Not reported	Increases w/ structure	up to +36%
Scalability	$10^6$ lexemes	$10^3$-$10^5$ tools	Large APIs w/ FAISS
Latency	<100ms/query	LLM-dependent	Model + retrieval time

6. Applications, Limitations, and Future Directions

Applications

• Linguistic Resource Construction: Dynamic maintenance and collaborative authoring of generative lexicon resources with semantic constraints and rule-based validation, particularly for compositional phenomena like anaphoric reference in compounds (0805.2537).
• Automated Tool Synthesis for LLMs: Scaling tool-augmented reasoning across varying domains, transforming spontaneous LLM-generated code fragments into robust, reusable libraries, improving interpretability, maintainability, and performance (Yue et al., 9 Oct 2025).
• Private Library Code Generation: Enabling LLMs to generate code for enterprise/private APIs using latent retrieval, developer interaction, and continuous LLM adaptation, with direct support for legacy and proprietary environments (Zan et al., 2023).

Limitations and Future Work

• The clustering protocol in LLM-based ToolLibGen is semantically guided but lacks explicit, backpropagated loss; granularity control and inter-cluster coherence are subjects for further refinement (Yue et al., 9 Oct 2025).
• Handling very large or dynamic API spaces efficiently, and addressing privacy/security concerns when exposing private-library documentation to LLMs, are identified as active R&D areas (Zan et al., 2023).
• In lexicon engineering, ongoing work includes automated coherence checking, semi-automatic qualia acquisition, and enhanced GUI interfaces (0805.2537).

A plausible implication is that the ToolLibGen paradigm—combining scalable automatic tool aggregation with robust, multi-agent validation—will be foundational for next-generation interpretable, modular LLM-augmented systems and for ongoing advances in collaborative linguistics resource engineering.