{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"sourcegraph-cody","slug":"sourcegraph-cody","name":"Sourcegraph Cody","type":"agent","url":"https://sourcegraph.com/cody","page_url":"https://unfragile.ai/sourcegraph-cody","categories":["code-editors"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"sourcegraph-cody__cap_0","uri":"capability://memory.knowledge.codebase.aware.chat.with.semantic.code.context.retrieval","name":"codebase-aware chat with semantic code context retrieval","description":"Accepts natural language questions about code and retrieves relevant context from the entire codebase using Sourcegraph's Search API, which performs semantic indexing across repositories. The system automatically includes the open file and cursor position as baseline context, then augments with explicit `@` mentions (files, symbols, remote repositories) to construct a rich context window before sending the prompt + context to an LLM backend for response generation. Responses are streamed back to the IDE with inline code snippets and explanations.","intents":["Ask questions about how a specific API or module works without manually searching the codebase","Understand the architecture and design patterns used across a large monorepo","Get explanations of unfamiliar code sections with full repository context","Retrieve code examples from across the codebase that match a pattern or use case"],"best_for":["developers in organizations with large or complex monorepos","teams already using Sourcegraph for code search and intelligence","engineers onboarding to unfamiliar codebases who need rapid context"],"limitations":["Context window size is undocumented; unclear how performance degrades with very large codebases or deep dependency chains","Model selection is opaque — no control over which LLM is used (vendor lock-in to Sourcegraph's backend choice)","Requires Sourcegraph backend access; no offline-only mode available","Context filtering is binary (exclude repositories) rather than prioritization-based; cannot rank certain repos higher","Latency and throughput SLAs are not published; no performance guarantees for large teams"],"requires":["Sourcegraph instance (SaaS at sourcegraph.com or self-hosted Enterprise)","IDE extension installed (VS Code, JetBrains, Visual Studio, or web interface)","Authentication to Sourcegraph (OAuth or token-based)","Repository indexed in Sourcegraph (automatic for SaaS; requires configuration for Enterprise)"],"input_types":["natural language text prompts","code selections or open file context","cursor position in editor","explicit @-mentions of files, symbols, or remote repositories","optional context filters (repository exclusion rules)"],"output_types":["streaming text responses","inline code snippets","markdown-formatted explanations","links to relevant code locations in Sourcegraph"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_1","uri":"capability://code.generation.editing.inline.auto.edit.with.typing.pattern.analysis","name":"inline auto-edit with typing pattern analysis","description":"Monitors cursor position and recent character edits in the editor to detect incomplete code patterns (e.g., partial function calls, unfinished conditionals). When at least one character has been typed, the system analyzes the typing pattern and surrounding context to generate inline edit suggestions that complete or refactor the code. Suggestions are presented as inline diffs that can be accepted or rejected without disrupting the editing flow.","intents":["Complete boilerplate code (imports, function signatures, class definitions) without breaking focus","Refactor or expand code as I type based on codebase patterns","Receive context-aware suggestions that respect the repository's coding style and conventions","Quickly generate repetitive code structures (loops, conditionals, error handling)"],"best_for":["developers who prefer in-editor suggestions over explicit chat interactions","teams with consistent coding patterns and conventions across the codebase","fast typists who want to minimize context-switching to chat interfaces"],"limitations":["Requires at least one character to be typed before activating (cold-start problem); cannot suggest completions for empty lines","Mechanism for detecting typing patterns is undocumented (unclear if diff-based, AST-based, or heuristic-based)","No control over suggestion frequency or aggressiveness; may generate unwanted suggestions in some contexts","Latency between typing and suggestion display is not specified; could disrupt fast typing if response time exceeds ~200ms","Limited to single-file context; unclear if cross-file patterns are considered"],"requires":["IDE extension installed (VS Code, JetBrains, or Visual Studio)","Sourcegraph backend access for context retrieval","Repository indexed in Sourcegraph","Active editing session with cursor in a code file"],"input_types":["character input events from editor","cursor position","recent edit history (last N characters)","open file context","surrounding code structure"],"output_types":["inline code suggestions","diff-based edit previews","acceptance/rejection UI controls"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_10","uri":"capability://automation.workflow.enterprise.self.hosted.deployment.with.on.premises.data.handling","name":"enterprise self-hosted deployment with on-premises data handling","description":"Provides Sourcegraph Enterprise deployment options for organizations that require on-premises or air-gapped infrastructure. Cody can be deployed as part of a self-hosted Sourcegraph instance, with data remaining within the organization's infrastructure. The deployment model supports various configurations (on-premises, VPC, air-gapped) depending on organizational requirements. Authentication and context retrieval use the same Sourcegraph Search API as SaaS, but all data processing occurs within the organization's infrastructure.","intents":["Deploy Cody in regulated industries (healthcare, finance) with strict data residency requirements","Ensure that code and prompts never leave the organization's infrastructure","Integrate Cody with existing enterprise security and compliance frameworks","Maintain full control over LLM model selection and updates"],"best_for":["enterprises in regulated industries with data residency requirements","organizations with strict security or compliance policies","teams that want to maintain full control over infrastructure and data"],"limitations":["Deployment options and configurations are not documented; unclear if on-premises, VPC, and air-gapped deployments are all supported","LLM model selection for Enterprise is not documented; unclear if organizations can choose their own models or if Sourcegraph provides specific models","Data handling and privacy guarantees for Enterprise are not documented; unclear if data is used for model training or analytics","Pricing for Enterprise deployment is not transparent; requires contacting sales","Maintenance and update procedures for self-hosted instances are not documented","Support SLAs and response times for Enterprise are not specified"],"requires":["Sourcegraph Enterprise license (contact sales for pricing)","Infrastructure for self-hosted deployment (Kubernetes, Docker, or other container orchestration)","Network isolation or VPC configuration (depending on deployment model)","Authentication system integration (LDAP, SAML, or other enterprise auth)"],"input_types":["deployment configuration (on-premises, VPC, air-gapped)","infrastructure specifications","authentication and security policies"],"output_types":["self-hosted Sourcegraph instance with Cody","data remaining within organization's infrastructure","audit logs and compliance reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_11","uri":"capability://text.generation.language.llm.backend.abstraction.with.undocumented.model.selection","name":"llm backend abstraction with undocumented model selection","description":"Routes all LLM inference requests (chat, completions, debugging, templates) to a backend LLM service, but the specific model(s) used, selection logic, and fallback mechanisms are undocumented. The system abstracts away model details from the user, presenting a unified 'Cody' interface regardless of the underlying LLM. This allows Sourcegraph to change models or use multiple models without requiring user configuration, but creates vendor lock-in and opacity about model capabilities and limitations.","intents":["Use a unified AI coding assistant without worrying about which LLM is powering it","Benefit from model improvements as Sourcegraph updates its backend without requiring user action","Access the 'latest LLMs' and 'most powerful models' without explicit selection"],"best_for":["developers who want a simple, unified AI assistant without model selection complexity","organizations that trust Sourcegraph to choose appropriate models for their use cases"],"limitations":["Model identity is undocumented; unclear if Cody uses Claude, GPT-4, Llama, or proprietary models","Model version is undocumented; unclear if models are updated automatically or on a schedule","No user control over model selection; cannot choose between different models for different tasks","Model capabilities and limitations are not documented; unclear what the models can and cannot do","Fallback mechanisms are undocumented; unclear what happens if the primary model is unavailable","Cost implications are unclear; pricing does not specify per-token or per-model costs","Vendor lock-in is high; switching to alternative tools requires re-authentication and context reconfiguration"],"requires":["Sourcegraph backend access","Authentication to Sourcegraph","Network connectivity to Sourcegraph servers (for SaaS)"],"input_types":["natural language prompts","code context","codebase context"],"output_types":["LLM-generated responses","code suggestions","explanations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_12","uri":"capability://text.generation.language.freemium.saas.access.with.opaque.free.tier.limits","name":"freemium saas access with opaque free tier limits","description":"Offers Cody as a freemium service on Sourcegraph.com with an undocumented free tier and paid tiers. The free tier limits are not specified (unclear if there are usage limits, feature restrictions, or context size limits), and pricing for paid tiers is not transparent (only Enterprise pricing of $49/user/month is documented, with unclear Cody inclusion). This creates uncertainty about cost and value for individual developers and small teams.","intents":["Try Cody for free without committing to a paid plan","Access Cody as part of an Enterprise Sourcegraph subscription","Understand the cost implications of using Cody at scale"],"best_for":["individual developers and small teams evaluating Cody","enterprises already using Sourcegraph Enterprise"],"limitations":["Free tier limits are undocumented; unclear if there are usage limits, feature restrictions, or context size limits","Free tier feature set is undocumented; unclear if all features are available in free tier or if some are paid-only","Paid tier pricing is undocumented; only Enterprise pricing ($49/user/month) is documented, with unclear Cody inclusion","Cost model is unclear; unclear if pricing is per-user, per-query, per-token, or usage-based","Free trial duration is undocumented; unclear if there is a trial period or if free tier is permanent","Upgrade path is unclear; unclear how to transition from free to paid or what triggers a paywall","Data usage for free tier is undocumented; unclear if free tier data is used for model training or analytics"],"requires":["Sourcegraph.com account (free signup)","Repository indexed in Sourcegraph (automatic for public repos)"],"input_types":["natural language prompts","code context"],"output_types":["chat responses","code suggestions"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_2","uri":"capability://code.generation.editing.llm.powered.code.completion.with.repository.context","name":"llm-powered code completion with repository context","description":"Generates code completion suggestions by sending the current file context, cursor position, and retrieved codebase context to an LLM backend. The system analyzes the code structure at the cursor position and generates contextually relevant completions that align with the repository's patterns, naming conventions, and API usage. Completions are ranked and presented as a list of options that can be inserted with a single keystroke.","intents":["Complete function calls, method chains, and API usage patterns based on how they are used elsewhere in the codebase","Generate variable names and identifiers that match the repository's naming conventions","Suggest imports and module references that are consistent with the codebase structure","Receive completions that understand the semantic intent of the code being written, not just syntactic patterns"],"best_for":["developers working in large codebases with consistent patterns and conventions","teams using multiple programming languages where context-aware completion reduces errors","engineers who want completions that respect the codebase's architectural decisions"],"limitations":["LLM model selection and version are undocumented; unclear if completions use the same model as chat or a specialized completion model","Completion latency is not specified; could introduce noticeable delays if LLM inference takes >500ms","No control over completion ranking or filtering; may suggest incorrect or deprecated patterns","Context window for completion is likely smaller than chat to maintain low latency; unclear what is included","No explicit feedback mechanism to improve completion quality over time"],"requires":["IDE extension installed (VS Code, JetBrains, or Visual Studio)","Sourcegraph backend access","Repository indexed in Sourcegraph","Active editing session with cursor in a code file"],"input_types":["partial code at cursor position","open file context","cursor position","retrieved codebase context (files, symbols, patterns)","language/file type information"],"output_types":["ranked list of code completion suggestions","completion text with optional formatting","metadata (confidence, source location in codebase)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_3","uri":"capability://code.generation.editing.templated.prompt.execution.with.codebase.context","name":"templated prompt execution with codebase context","description":"Provides a library of pre-built prompt templates (e.g., 'Explain this code', 'Generate tests', 'Refactor for performance') that can be executed with a single click or custom prompts can be created. Each template is parameterized with the current file, selection, or codebase context, and when executed, sends the template + context to the LLM backend. Results are displayed in the chat interface or inline in the editor, with the ability to iterate or refine the prompt.","intents":["Generate unit tests for a selected function without manually writing test boilerplate","Refactor code for performance, readability, or security with codebase-aware suggestions","Generate documentation or comments for code sections based on codebase style","Create custom prompts for team-specific tasks (e.g., 'Check for SQL injection vulnerabilities')"],"best_for":["teams with standardized code review or refactoring workflows","developers who want to automate repetitive code generation tasks","organizations that want to enforce coding standards or security practices via templated prompts"],"limitations":["Pre-built templates are not documented; unclear which templates are available or how comprehensive they are","Custom prompt creation mechanism is undocumented; unclear if templates support variables, conditionals, or complex logic","No versioning or sharing mechanism for custom prompts across teams (unclear if this is a limitation)","Template execution latency depends on LLM response time; no SLA or performance guarantees","Results quality depends on template design and LLM capability; no feedback loop to improve templates"],"requires":["IDE extension or web interface","Sourcegraph backend access","Repository indexed in Sourcegraph","Code selection or open file context (depending on template)"],"input_types":["template selection (pre-built or custom)","code selection or file context","optional template parameters (e.g., refactoring goal, test framework)","codebase context (automatically retrieved)"],"output_types":["generated code (tests, refactored code, documentation)","chat responses with explanations","inline suggestions or diffs"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_4","uri":"capability://code.generation.editing.debugging.assistance.with.error.context.and.code.analysis","name":"debugging assistance with error context and code analysis","description":"Analyzes error messages, stack traces, and surrounding code context to identify root causes and suggest fixes. When a developer encounters an error (either by pasting it into chat or selecting error-related code), the system retrieves relevant code context from the codebase and sends the error + context to the LLM backend to generate debugging recommendations. Suggestions may include identifying the problematic code section, explaining the error, and proposing fixes with code examples.","intents":["Understand the root cause of an error without manually tracing through the codebase","Get fix suggestions that are consistent with the repository's patterns and conventions","Quickly resolve runtime errors, type errors, or logic bugs with codebase-aware analysis","Learn from errors by understanding how similar issues are handled elsewhere in the codebase"],"best_for":["developers debugging complex issues in large codebases","teams with non-obvious error patterns or custom error handling conventions","engineers who want to learn from codebase patterns while fixing bugs"],"limitations":["Error analysis mechanism is undocumented; unclear if it parses stack traces, analyzes ASTs, or relies on LLM interpretation","Context retrieval for errors may be inaccurate if the error message doesn't clearly indicate the problematic code","LLM-generated fixes may not account for all edge cases or may suggest suboptimal solutions","No integration with debuggers or runtime tools; analysis is static and based on code inspection","Latency for error analysis is not specified; could be slow for large stack traces or complex codebases"],"requires":["IDE extension or chat interface","Sourcegraph backend access","Repository indexed in Sourcegraph","Error message, stack trace, or problematic code selection"],"input_types":["error messages or stack traces (text)","problematic code selection","surrounding code context","codebase context (automatically retrieved)"],"output_types":["root cause analysis (text explanation)","suggested fixes (code snippets)","links to similar code patterns in the codebase","explanations of error handling conventions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_5","uri":"capability://memory.knowledge.context.aware.code.search.and.retrieval.via.mentions","name":"context-aware code search and retrieval via @-mentions","description":"Allows developers to explicitly specify which files, symbols, or remote repositories should be included in the context for a chat query by using `@` mention syntax (e.g., '@file.ts', '@MyClass', '@github.com/org/repo'). The system resolves these mentions to actual code artifacts using Sourcegraph's Search API and includes them in the context window sent to the LLM. This provides fine-grained control over context without requiring manual copy-paste or relying on automatic context detection.","intents":["Ask questions about specific files or symbols without manually copying code into chat","Include context from remote repositories or external codebases in the same query","Ensure that specific code sections are included in the context, avoiding irrelevant results from automatic retrieval","Reference code across multiple repositories in a single query (useful for microservices or multi-repo projects)"],"best_for":["developers working with multiple repositories or microservices","teams that need precise control over context inclusion","engineers working on cross-repository refactorings or integrations"],"limitations":["@-mention resolution mechanism is undocumented; unclear how ambiguous mentions are resolved (e.g., multiple files with the same name)","Remote repository mentions require the repository to be indexed in Sourcegraph; no support for arbitrary GitHub URLs","No support for complex context queries (e.g., 'all files that import MyClass'); only direct file/symbol mentions","Mention syntax is not documented in detail; unclear if it supports wildcards, regex, or other advanced patterns","No autocomplete for @-mentions; developers must know the exact file or symbol name"],"requires":["IDE extension or chat interface","Sourcegraph backend access","Repository indexed in Sourcegraph (for local mentions)","Remote repositories indexed in Sourcegraph (for @-mentions to external repos)"],"input_types":["natural language prompt with @-mentions","@-mention syntax (file paths, symbol names, repository URLs)","optional context filters"],"output_types":["resolved code context (files, symbols, code snippets)","chat response with context-aware answers","links to mentioned code in Sourcegraph"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_6","uri":"capability://memory.knowledge.repository.context.filtering.with.exclusion.rules","name":"repository context filtering with exclusion rules","description":"Allows administrators or developers to define context filters that exclude specific repositories from being included in Cody's context retrieval. When a filter is active, Sourcegraph's Search API excludes the filtered repositories from results, ensuring that sensitive, deprecated, or irrelevant code is not included in LLM context. Filters are applied at the query level and can be configured per user, team, or organization.","intents":["Exclude deprecated or legacy repositories from context to avoid outdated patterns","Prevent sensitive code (e.g., security-critical, proprietary) from being included in LLM context","Focus context on relevant repositories when working with large monorepos or multi-repo setups","Ensure compliance with data governance policies by excluding certain repositories from AI analysis"],"best_for":["organizations with large monorepos or multi-repo setups","teams with sensitive code that should not be analyzed by LLMs","enterprises with data governance or compliance requirements"],"limitations":["Filtering is binary (exclude only); no support for prioritization or ranking of repositories","Filter configuration mechanism is undocumented; unclear if filters are defined via UI, API, or configuration files","No support for fine-grained filtering (e.g., exclude specific files or directories within a repository)","Filter scope is unclear; unclear if filters apply globally, per-user, per-team, or per-organization","No audit trail for filter changes; unclear how to track which repositories are excluded and why"],"requires":["Sourcegraph Enterprise or SaaS with admin access","Repository indexed in Sourcegraph","Filter configuration (mechanism unclear)"],"input_types":["repository names or identifiers to exclude","filter scope (global, user, team, organization)","optional filter description or rationale"],"output_types":["filtered context results (excluded repositories removed)","filter status or confirmation"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_7","uri":"capability://tool.use.integration.multi.ide.extension.with.unified.authentication.and.context.sync","name":"multi-ide extension with unified authentication and context sync","description":"Provides Cody as a native extension for VS Code, JetBrains IDEs, Visual Studio, and a web-based interface, with unified authentication (OAuth or token-based) and synchronized context across all platforms. When a developer switches between IDEs or devices, their authentication session, chat history, and context preferences are maintained. The extension integrates with each IDE's native APIs to access file context, cursor position, and editor state, ensuring consistent behavior across platforms.","intents":["Use Cody consistently across multiple IDEs without re-authenticating or reconfiguring context","Access chat history and context preferences across different devices and development environments","Integrate Cody into existing IDE workflows without switching tools or context","Maintain a unified development experience across VS Code, JetBrains, Visual Studio, and web"],"best_for":["developers who use multiple IDEs or switch between devices","teams with heterogeneous IDE preferences (some using VS Code, others using JetBrains)","organizations that want a consistent AI coding assistant experience across the development stack"],"limitations":["Feature parity across IDEs is not documented; unclear if all features are available in all IDEs (Visual Studio marked 'Experimental')","Authentication sync mechanism is undocumented; unclear if sessions are stored locally or synchronized via Sourcegraph servers","Chat history sync is not explicitly mentioned; unclear if chat history is preserved across IDEs or devices","Context sync latency is not specified; could introduce delays if context preferences are fetched from Sourcegraph servers","Web interface feature set may be limited compared to native IDE extensions"],"requires":["IDE extension installed (VS Code, JetBrains, Visual Studio, or web browser)","Sourcegraph account (SaaS or Enterprise)","Authentication credentials (OAuth or API token)"],"input_types":["IDE-specific context (file, cursor position, selection)","authentication credentials","context preferences and filters"],"output_types":["unified chat interface","IDE-native suggestions and completions","synchronized context and preferences"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_8","uri":"capability://automation.workflow.cli.based.codebase.querying.and.batch.operations","name":"cli-based codebase querying and batch operations","description":"Provides a command-line interface (CLI) for querying Cody without opening an IDE, enabling automation, CI/CD integration, and batch operations. The CLI accepts natural language prompts or templated commands, retrieves codebase context via Sourcegraph's Search API, and returns results as structured output (JSON, markdown, or plain text). This enables use cases like automated code review, batch refactoring, or integration with development workflows.","intents":["Query the codebase from CI/CD pipelines or automation scripts without opening an IDE","Perform batch operations (e.g., refactor all instances of a pattern, generate tests for multiple files)","Integrate Cody into development workflows (e.g., pre-commit hooks, code review automation)","Generate reports or analysis of code patterns across the entire codebase"],"best_for":["DevOps engineers and automation specialists integrating Cody into CI/CD pipelines","teams performing large-scale refactorings or code generation tasks","organizations that want to automate code review or quality checks"],"limitations":["CLI interface and command syntax are not documented; unclear what commands are available or how to use them","Output format options are not specified; unclear if JSON, markdown, or other formats are supported","Batch operation capabilities are not documented; unclear if the CLI supports parallel execution or rate limiting","Authentication mechanism for CLI is not documented; unclear if it uses API tokens, OAuth, or other methods","Integration with CI/CD systems is not documented; unclear if there are pre-built integrations for GitHub Actions, GitLab CI, etc."],"requires":["Cody CLI installed (installation method unclear)","Sourcegraph backend access","Repository indexed in Sourcegraph","Authentication credentials (API token or OAuth)"],"input_types":["natural language prompts","templated commands","file paths or patterns","optional context filters"],"output_types":["structured output (JSON, markdown, plain text)","exit codes for automation","batch operation results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__cap_9","uri":"capability://text.generation.language.web.based.chat.interface.with.sourcegraph.integration","name":"web-based chat interface with sourcegraph integration","description":"Provides a browser-based chat interface accessible from Sourcegraph.com or self-hosted Sourcegraph instances, allowing developers to query the codebase without installing an IDE extension. The web interface integrates with Sourcegraph's code search and navigation, allowing developers to launch Cody chat from search results or code pages. Context is retrieved from the same Sourcegraph Search API as IDE extensions, and results include links back to code locations in Sourcegraph.","intents":["Query the codebase from a browser without installing IDE extensions","Launch Cody chat directly from Sourcegraph search results or code pages","Share chat sessions or results with team members via URLs","Access Cody from any device without IDE setup"],"best_for":["developers who prefer browser-based tools or work on multiple machines","teams that want to share Cody queries or results with non-developers","organizations that want to provide Cody access without requiring IDE extension installation"],"limitations":["Feature set compared to IDE extensions is not documented; unclear if all features (autocomplete, inline edits) are available in web interface","Web interface performance and latency are not specified; could be slower than native IDE extensions","Session management and chat history persistence are not documented","Sharing mechanism for chat sessions is not documented; unclear if URLs are shareable or if sessions are private","Integration with Sourcegraph code navigation may be limited compared to IDE extensions"],"requires":["Sourcegraph.com account or self-hosted Sourcegraph instance","Web browser with JavaScript support","Repository indexed in Sourcegraph"],"input_types":["natural language prompts in chat interface","code selections from Sourcegraph code pages","context from Sourcegraph search results"],"output_types":["chat responses with code snippets","links to code locations in Sourcegraph","shareable URLs (if supported)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sourcegraph-cody__headline","uri":"capability://code.generation.editing.ai.powered.code.assistant.with.full.codebase.context","name":"ai-powered code assistant with full codebase context","description":"Sourcegraph Cody is an AI coding assistant that provides contextual code suggestions and completions by leveraging the entire codebase, making it ideal for developers working with large repositories.","intents":["best AI coding assistant","AI code editor for large monorepos","AI coding assistant with codebase context","top tools for code completion","AI tools for software development"],"best_for":["developers working with complex codebases","teams using monorepos"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Sourcegraph instance (SaaS at sourcegraph.com or self-hosted Enterprise)","IDE extension installed (VS Code, JetBrains, Visual Studio, or web interface)","Authentication to Sourcegraph (OAuth or token-based)","Repository indexed in Sourcegraph (automatic for SaaS; requires configuration for Enterprise)","IDE extension installed (VS Code, JetBrains, or Visual Studio)","Sourcegraph backend access for context retrieval","Repository indexed in Sourcegraph","Active editing session with cursor in a code file","Sourcegraph Enterprise license (contact sales for pricing)","Infrastructure for self-hosted deployment (Kubernetes, Docker, or other container orchestration)"],"failure_modes":["Context window size is undocumented; unclear how performance degrades with very large codebases or deep dependency chains","Model selection is opaque — no control over which LLM is used (vendor lock-in to Sourcegraph's backend choice)","Requires Sourcegraph backend access; no offline-only mode available","Context filtering is binary (exclude repositories) rather than prioritization-based; cannot rank certain repos higher","Latency and throughput SLAs are not published; no performance guarantees for large teams","Requires at least one character to be typed before activating (cold-start problem); cannot suggest completions for empty lines","Mechanism for detecting typing patterns is undocumented (unclear if diff-based, AST-based, or heuristic-based)","No control over suggestion frequency or aggressiveness; may generate unwanted suggestions in some contexts","Latency between typing and suggestion display is not specified; could disrupt fast typing if response time exceeds ~200ms","Limited to single-file context; unclear if cross-file patterns are considered","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.695Z","last_scraped_at":null,"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=sourcegraph-cody","compare_url":"https://unfragile.ai/compare?artifact=sourcegraph-cody"}},"signature":"PuHgG09wudAKrbgNdJRyBduTP9zi2Sfwu9SF/GZzNCdp5/jTHUlqu8QcJGtLm2XoeunSx616e2sOWbtgxlklDg==","signedAt":"2026-06-19T17:49:03.463Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sourcegraph-cody","artifact":"https://unfragile.ai/sourcegraph-cody","verify":"https://unfragile.ai/api/v1/verify?slug=sourcegraph-cody","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"}}