{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-relace-relace-search","slug":"relace-relace-search","name":"Relace: Relace Search","type":"model","url":"https://openrouter.ai/models/relace~relace-search","page_url":"https://unfragile.ai/relace-relace-search","categories":["rag-knowledge"],"tags":["relace","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-relace-relace-search__cap_0","uri":"capability://search.retrieval.parallel.agentic.codebase.exploration.with.view.file.and.grep.tools","name":"parallel agentic codebase exploration with view_file and grep tools","description":"Relace-search executes 4-12 parallel tool invocations (view_file for file content retrieval and grep for pattern matching) to systematically explore a codebase and identify relevant files matching a user query. Unlike RAG systems that rely on pre-computed embeddings and vector similarity, this approach uses an agentic loop that dynamically decides which files to inspect based on intermediate results, enabling context-aware navigation through code structure.","intents":["Find all files in a codebase that implement a specific interface or inherit from a base class","Locate where a particular function or variable is defined, imported, or used across multiple files","Discover files matching a complex search pattern that requires understanding code semantics, not just text matching","Retrieve relevant code context for a refactoring task without manually specifying file paths"],"best_for":["Developers working with large, unfamiliar codebases who need semantic code search beyond keyword matching","Teams building code analysis tools or IDE integrations that require intelligent file discovery","LLM-powered agents that need to gather relevant code context before generating fixes or explanations"],"limitations":["Parallel tool execution adds latency compared to single-pass vector search; 4-12 sequential or concurrent tool calls required per query","Effectiveness depends on codebase structure and naming conventions; poorly organized or obfuscated code may require more exploration steps","No built-in caching of exploration results; repeated similar queries re-execute the full agentic loop","Requires codebase to be accessible via view_file and grep tools; does not work with binary files or non-text formats"],"requires":["Access to Relace API via OpenRouter or direct endpoint","Codebase files accessible through view_file tool (local filesystem, Git repository, or cloud storage with appropriate permissions)","grep-compatible pattern matching support (POSIX regex or similar)","API credentials or authentication token for OpenRouter"],"input_types":["text (natural language query describing what to search for)","code (optional: code snippet to use as search context)"],"output_types":["structured data (list of relevant file paths with line numbers)","text (file contents of matched files)","metadata (match context, relevance scores if available)"],"categories":["search-retrieval","tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-relace-relace-search__cap_1","uri":"capability://planning.reasoning.dynamic.tool.call.sequencing.for.multi.step.code.discovery","name":"dynamic tool-call sequencing for multi-step code discovery","description":"Relace-search implements an agentic reasoning loop that decides which files to inspect next based on results from previous view_file and grep tool calls. The model maintains state across tool invocations, using earlier findings to inform subsequent queries—for example, discovering an import statement in one file and then automatically exploring the imported module. This enables multi-hop reasoning across the codebase without explicit user guidance.","intents":["Follow import chains to find the actual implementation of a function that is re-exported across multiple modules","Trace a type definition through inheritance hierarchies to understand the full interface contract","Discover all callers of a function by first finding the definition, then searching for references","Map dependencies between files to understand coupling and identify refactoring opportunities"],"best_for":["Developers debugging complex call chains or dependency issues in large codebases","Code analysis agents that need to traverse semantic relationships (imports, inheritance, references) automatically","Teams building intelligent code navigation tools that go beyond simple text search"],"limitations":["Multi-step reasoning increases total latency; each additional hop adds one or more tool-call round trips","Agent may get stuck in cycles or explore irrelevant paths if codebase structure is ambiguous or circular","No explicit loop-detection or depth limits documented; potential for unbounded exploration in pathological cases","Requires the model to correctly interpret tool outputs and decide next steps; hallucination or misinterpretation can lead to incorrect file selection"],"requires":["Relace API with agentic reasoning enabled","Codebase with clear import/reference patterns that the agent can parse","Sufficient API quota to handle multi-step tool invocations"],"input_types":["text (high-level query like 'find all callers of function X')"],"output_types":["structured data (ordered list of files discovered through multi-step reasoning)","text (file contents and context from each discovery step)"],"categories":["planning-reasoning","tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-relace-relace-search__cap_2","uri":"capability://search.retrieval.parallel.grep.pattern.matching.across.codebase","name":"parallel grep pattern matching across codebase","description":"Relace-search executes multiple grep tool calls in parallel (up to 12 concurrent invocations) to search for patterns across the entire codebase simultaneously. Each grep call can target different patterns, file types, or directory scopes, allowing the agent to explore multiple hypotheses about where relevant code might be located without sequential bottlenecks. Results from parallel grep calls are aggregated and ranked to identify the most relevant matches.","intents":["Search for all occurrences of a function name, variable, or class across the entire codebase in a single operation","Find files matching multiple related patterns simultaneously (e.g., 'find all files that import module X AND contain function Y')","Discover code that matches a complex regex pattern across thousands of files without sequential scanning","Identify all usages of a deprecated API or pattern to plan a migration"],"best_for":["Large-scale codebases (10k+ files) where sequential grep would be prohibitively slow","Teams performing codebase-wide refactorings or migrations that require finding all instances of a pattern","Developers who need to understand the scope of a change before implementing it"],"limitations":["Parallel execution is bounded by the model's tool-call budget (4-12 concurrent calls); very large pattern sets may require multiple rounds","Grep results are text-based and may include false positives (e.g., matching strings in comments or documentation)","No semantic filtering; cannot distinguish between a function definition and a string literal containing the function name","Regex complexity and performance vary by codebase; pathological patterns may timeout or return incomplete results"],"requires":["Relace API with parallel tool execution support","Codebase accessible via grep tool (POSIX-compatible regex engine)","Well-formed regex patterns; invalid patterns will fail silently or return no results"],"input_types":["text (natural language query or explicit regex patterns)"],"output_types":["structured data (file paths and line numbers of matches)","text (matched lines with context)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-relace-relace-search__cap_3","uri":"capability://search.retrieval.file.content.retrieval.with.view.file.tool","name":"file content retrieval with view_file tool","description":"Relace-search uses the view_file tool to retrieve the full or partial contents of files identified during exploration. The tool supports efficient retrieval of specific line ranges, enabling the agent to fetch only relevant portions of large files rather than loading entire codebases into context. Multiple view_file calls can be parallelized to retrieve contents from different files simultaneously.","intents":["Retrieve the implementation of a function or class after discovering its file path","Fetch specific line ranges from a file to provide context for code analysis or generation","Load multiple files in parallel to understand how they interact or depend on each other","Extract code snippets for documentation or code review without manual file navigation"],"best_for":["Agents that need to retrieve code context after identifying relevant files through search","Developers building code analysis tools that require efficient partial file loading","LLM-powered code generation systems that need to understand existing code patterns"],"limitations":["Line-range queries require knowing approximate line numbers; off-by-one errors or incorrect ranges may miss relevant code","Large files (>100k lines) may be slow to retrieve or exceed context windows if fully loaded","No built-in syntax highlighting or language-aware parsing; returns raw text","Binary files or non-UTF8 encodings may fail or return corrupted content"],"requires":["Relace API with view_file tool enabled","File paths must be known or discovered through prior search/exploration","Sufficient API quota for file retrieval operations"],"input_types":["text (file path and optional line range specification)"],"output_types":["text (file contents, optionally filtered to specific line ranges)","structured data (metadata like file size, encoding, last modified time if available)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-relace-relace-search__cap_4","uri":"capability://planning.reasoning.agentic.context.ranking.and.relevance.filtering","name":"agentic context ranking and relevance filtering","description":"Relace-search implements an agentic ranking mechanism that evaluates the relevance of discovered files based on the original user query and intermediate exploration results. The model uses reasoning to filter out false positives and prioritize files that are most likely to contain the answer, rather than returning all matches indiscriminately. This ranking is dynamic and can be refined across multiple exploration rounds.","intents":["Reduce noise in search results by filtering out files that match the pattern but are not semantically relevant","Prioritize the most important or central files when multiple matches exist","Understand which files are most likely to contain the information needed to answer the user's question","Avoid overwhelming the user with hundreds of matches when a smaller set of key files would suffice"],"best_for":["Developers searching large codebases who need high-quality results, not exhaustive lists","Code analysis agents that need to focus on the most relevant context to avoid context window overflow","Teams building code navigation tools that prioritize user experience over completeness"],"limitations":["Ranking is heuristic-based and may miss relevant files if the model misinterprets the query intent","No explicit relevance scores or confidence metrics provided; ranking is implicit in result ordering","Ranking quality depends on the model's understanding of code semantics; may fail on domain-specific or unfamiliar code patterns","Cannot guarantee that all relevant files are returned; some may be filtered out as false positives"],"requires":["Relace API with reasoning capabilities enabled","Clear, well-formed user queries that the model can interpret"],"input_types":["text (user query describing what to search for)"],"output_types":["structured data (ranked list of relevant files, typically 5-20 results)","text (optional explanations of why files were ranked as relevant)"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-relace-relace-search__cap_5","uri":"capability://memory.knowledge.codebase.aware.context.window.optimization","name":"codebase-aware context window optimization","description":"Relace-search intelligently manages context by retrieving only the most relevant file portions and avoiding unnecessary full-file loads. The system estimates which code snippets are most likely to be useful for answering the user's query and prioritizes those for retrieval, effectively compressing the codebase into a focused context window. This enables analysis of very large codebases that would otherwise exceed LLM context limits.","intents":["Analyze large codebases (100k+ lines) without exceeding LLM context windows","Provide focused code context to an LLM for analysis or generation tasks","Reduce API costs by minimizing the amount of code retrieved and processed","Enable interactive exploration of codebases where each query returns only relevant snippets"],"best_for":["Developers working with large enterprise codebases that exceed typical LLM context limits","Cost-conscious teams that want to minimize token usage in code analysis workflows","Interactive code exploration tools that need to provide fast, focused results"],"limitations":["Context compression may miss relevant code that is not directly referenced in the query","Line-range estimation errors can result in incomplete or fragmented code snippets","No explicit control over context window size; optimization is automatic and may not match user preferences","Effectiveness depends on codebase structure; poorly organized code may require more context than the system allocates"],"requires":["Relace API with context optimization enabled","Codebase with reasonable structure and naming conventions"],"input_types":["text (user query)"],"output_types":["structured data (selected file paths and line ranges)","text (code snippets optimized for context window)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Access to Relace API via OpenRouter or direct endpoint","Codebase files accessible through view_file tool (local filesystem, Git repository, or cloud storage with appropriate permissions)","grep-compatible pattern matching support (POSIX regex or similar)","API credentials or authentication token for OpenRouter","Relace API with agentic reasoning enabled","Codebase with clear import/reference patterns that the agent can parse","Sufficient API quota to handle multi-step tool invocations","Relace API with parallel tool execution support","Codebase accessible via grep tool (POSIX-compatible regex engine)","Well-formed regex patterns; invalid patterns will fail silently or return no results"],"failure_modes":["Parallel tool execution adds latency compared to single-pass vector search; 4-12 sequential or concurrent tool calls required per query","Effectiveness depends on codebase structure and naming conventions; poorly organized or obfuscated code may require more exploration steps","No built-in caching of exploration results; repeated similar queries re-execute the full agentic loop","Requires codebase to be accessible via view_file and grep tools; does not work with binary files or non-text formats","Multi-step reasoning increases total latency; each additional hop adds one or more tool-call round trips","Agent may get stuck in cycles or explore irrelevant paths if codebase structure is ambiguous or circular","No explicit loop-detection or depth limits documented; potential for unbounded exploration in pathological cases","Requires the model to correctly interpret tool outputs and decide next steps; hallucination or misinterpretation can lead to incorrect file selection","Parallel execution is bounded by the model's tool-call budget (4-12 concurrent calls); very large pattern sets may require multiple rounds","Grep results are text-based and may include false positives (e.g., matching strings in comments or documentation)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.24,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=relace-relace-search","compare_url":"https://unfragile.ai/compare?artifact=relace-relace-search"}},"signature":"0/eZEgxIFaSETML+LUXmua1cVwhOi40g4Vmcyx+eHrGAspSYZUNGUiQZBoPERC4a47Jz7Y2RCDrvBQvGdDCeDg==","signedAt":"2026-06-16T00:57:55.544Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/relace-relace-search","artifact":"https://unfragile.ai/relace-relace-search","verify":"https://unfragile.ai/api/v1/verify?slug=relace-relace-search","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}