What can @flink-app/anthropic-adapter do?

claude api adapter with unified flink interface, streaming response handler for claude api, message history and conversation context management, model selection and configuration management, error handling and retry logic with exponential backoff, request validation and parameter sanitization, token usage tracking and cost estimation, batch request processing with rate limit awareness

@flink-app/anthropic-adapter

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Anthropic Claude adapter for Flink AI framework

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8 capabilities

Best for: claude api adapter with unified flink interface, streaming response handler for claude api, message history and conversation context management
Type: Repository · Free
Score: 28/100
Best alternative: OpenAI Agents SDK

Capabilities8 decomposed

claude api adapter with unified flink interface

Medium confidence

Provides a standardized adapter layer that maps Flink framework's abstract LLM interface to Anthropic's Claude API endpoints. The adapter implements Flink's provider contract, translating framework-agnostic method calls (e.g., `sendMessage()`, `streamResponse()`) into Claude-specific API calls with proper authentication, request formatting, and response parsing. This enables seamless swapping of Claude with other LLM providers within the same Flink application without changing application code.

Solves for

I want to use Claude as my LLM backend in a Flink application without writing provider-specific codeI need to switch between Claude and other LLM providers (OpenAI, Cohere) without refactoring my application logicI want to standardize how my team integrates different LLM APIs across multiple projects

Best for

teams building multi-provider LLM applications using Flink framework

developers who want provider abstraction without vendor lock-in

organizations standardizing on Flink for LLM orchestration

Requires

Flink framework installed (version compatible with adapter)

Anthropic API key (from console.anthropic.com)

Node.js 14+ or compatible JavaScript runtime

Limitations

Adapter is limited to Anthropic Claude models only — does not support other Anthropic products like Bedrock

Requires Flink framework as a dependency; cannot be used standalone without Flink's core architecture

No built-in retry logic or circuit breaker — relies on Flink's error handling layer

What makes it unique

Implements Flink's provider interface contract, enabling drop-in replacement of Claude with other LLM providers through a unified adapter pattern rather than direct API integration. Uses Flink's message serialization and stream handling conventions to ensure consistency across the framework ecosystem.

vs alternatives

Tighter integration with Flink's architecture than direct Claude SDK usage, enabling provider-agnostic application code and easier multi-provider deployments compared to building custom adapter layers.

streaming response handler for claude api

Medium confidence

Implements real-time streaming of Claude's responses using Server-Sent Events (SSE) or similar streaming protocols. The adapter buffers incoming chunks, parses Claude's streaming format (delta events), and emits them through Flink's stream handler, enabling progressive text rendering and real-time token consumption tracking. Handles stream termination, error states, and partial message recovery.

Solves for

I want to display Claude's response word-by-word as it generates, not wait for the full responseI need to track token usage in real-time as Claude streams its responseI want to cancel a long-running Claude request mid-stream if the user stops waiting

Best for

chat interfaces and conversational UIs requiring real-time response display

applications with strict latency requirements where progressive rendering improves perceived performance

token-metered systems that need live cost tracking during generation

Requires

Flink framework with streaming handler support

Anthropic API key with streaming enabled

Client-side streaming support (modern browsers, Node.js streams, etc.)

Limitations

Streaming adds complexity to error handling — partial responses may be lost if connection drops mid-stream

Stream buffering can cause memory overhead for very long responses (>100K tokens)

Requires client-side support for streaming (WebSocket or SSE) — not compatible with simple HTTP request/response patterns

What makes it unique

Integrates Claude's native streaming format with Flink's event-driven stream handler, providing a unified streaming abstraction that works across different transport protocols (HTTP, WebSocket) without application-level protocol awareness.

vs alternatives

Cleaner streaming abstraction than raw Claude SDK streaming, with built-in Flink event semantics and error recovery compared to manual stream parsing in application code.

message history and conversation context management

Medium confidence

Manages multi-turn conversation state by maintaining a message history buffer that tracks user and assistant messages in Claude's expected format (role, content pairs). The adapter handles context window optimization by tracking token counts and implementing sliding-window truncation strategies to keep conversations within Claude's token limits while preserving recent context. Supports system prompts, message filtering, and context injection points.

Solves for

I want to maintain a conversation with Claude across multiple turns without manually managing message historyI need to automatically trim old messages when approaching Claude's token limit to keep conversations runningI want to inject system instructions or context into the middle of a conversation dynamically

Best for

multi-turn chatbot applications requiring stateful conversations

long-running agents that need to maintain context across many interactions

applications with variable context windows (e.g., different Claude models with different limits)

Requires

Flink framework with message serialization support

Token counter utility (built-in or external)

Claude API key

Limitations

No built-in persistence — conversation history is in-memory only; requires external storage integration for durability

Token counting is approximate (uses heuristic estimation) — actual token usage may differ from estimates

Sliding-window truncation loses older context; no semantic importance weighting for which messages to drop

What makes it unique

Implements context window management as a first-class adapter concern rather than application responsibility, with automatic token-aware truncation and Flink-native message serialization that preserves conversation semantics across provider boundaries.

vs alternatives

Reduces boilerplate for conversation state management compared to manual message array handling, with built-in token awareness that prevents silent context loss unlike naive history appending.

model selection and configuration management

Medium confidence

Provides a configuration layer that maps Flink's abstract model selection to specific Claude model versions (e.g., claude-3-opus, claude-3-sonnet) with version pinning and fallback strategies. The adapter validates model availability, manages per-model configuration overrides (temperature, max_tokens, top_p), and handles model deprecation warnings. Supports dynamic model switching based on cost/performance trade-offs or availability.

Solves for

I want to specify which Claude model version my application uses without hardcoding API callsI need to override model parameters (temperature, max tokens) for specific use casesI want to automatically fall back to a cheaper model if my primary model is unavailable

Best for

applications supporting multiple Claude model tiers (Opus, Sonnet, Haiku) with different cost/quality profiles

cost-optimized systems that need to switch models based on request complexity

teams managing model versions across development, staging, and production environments

Requires

Flink framework with configuration provider

Anthropic API key with access to desired Claude models

Model configuration file or environment variables

Limitations

No automatic model selection based on task complexity — requires explicit configuration or application logic

Fallback strategies are static; no learning from past failures to improve fallback decisions

Model deprecation warnings are advisory only — adapter does not prevent use of deprecated models

What makes it unique

Abstracts Claude model selection through Flink's configuration system, enabling environment-specific model choices and fallback chains without application code changes. Integrates with Flink's configuration provider pattern for centralized model management.

vs alternatives

Cleaner model management than hardcoded model IDs in application code, with Flink-native configuration inheritance and fallback support compared to manual model selection logic.

error handling and retry logic with exponential backoff

Medium confidence

Implements standardized error handling for Claude API failures (rate limits, timeouts, authentication errors) with exponential backoff retry strategies. The adapter distinguishes between retryable errors (rate limits, transient network failures) and non-retryable errors (invalid API key, malformed request), applying appropriate retry policies. Integrates with Flink's error handler interface to propagate errors with context and metadata.

Solves for

I want my application to automatically retry failed Claude requests without crashingI need to handle rate limiting gracefully without losing user requestsI want detailed error information to debug why a Claude request failed

Best for

production applications requiring resilience to transient API failures

high-volume systems that need rate limit handling

applications with strict uptime requirements

Requires

Flink framework with error handler support

Anthropic API key

Network timeout configuration (typically 30-60 seconds)

Limitations

Exponential backoff is fixed (no jitter) — may cause thundering herd on API recovery

Retry budget is global; no per-request or per-user rate limiting

No circuit breaker pattern — will continue retrying even if API is down for extended periods

What makes it unique

Integrates error handling with Flink's error handler interface, providing standardized retry semantics across all Flink adapters rather than Claude-specific error handling. Distinguishes retryable vs non-retryable errors at the adapter level, reducing application-level error handling complexity.

vs alternatives

More robust than raw Claude SDK error handling, with Flink-aware error propagation and standardized retry policies compared to manual try-catch blocks in application code.

request validation and parameter sanitization

Medium confidence

Validates incoming requests against Claude's API schema before sending, checking parameter types, ranges, and required fields. The adapter sanitizes user inputs to prevent injection attacks (prompt injection, token smuggling) and enforces Anthropic's content policy constraints. Provides detailed validation error messages that help developers identify configuration issues early rather than at API call time.

Solves for

I want to catch configuration errors before they reach the Claude API and waste quotaI need to prevent malicious prompt injection attacks in user-submitted contentI want clear error messages when I misconfigure the adapter

Best for

applications accepting user-generated prompts that need injection protection

development teams wanting early validation feedback

security-conscious applications with strict input validation requirements

Requires

Flink framework with validation provider

JSON schema for Claude API parameters

Input sanitization library (built-in or external)

Limitations

Validation is schema-based only; cannot detect semantic issues (e.g., contradictory instructions)

Prompt injection detection is heuristic-based; sophisticated attacks may bypass validation

Sanitization may alter user intent in edge cases (e.g., removing special characters that are semantically important)

What makes it unique

Implements validation as an adapter-level concern with Flink-native validation provider integration, catching errors before API calls rather than relying on Claude's API validation. Includes prompt injection detection specific to Claude's instruction-following behavior.

vs alternatives

Earlier error detection than relying on Claude API validation, with built-in injection prevention compared to applications that validate only after API responses.

token usage tracking and cost estimation

Medium confidence

Tracks input and output token consumption for each Claude request and maintains running totals for billing and quota management. The adapter estimates costs based on current Claude pricing (input/output token rates) and provides per-request and aggregate cost metrics. Integrates with Flink's metrics system to expose token usage as observable metrics for monitoring and alerting.

Solves for

I want to know how much each Claude request costs before it runsI need to track total token consumption across my application for billing purposesI want to set up alerts if token usage exceeds a budget threshold

Best for

cost-conscious applications needing budget tracking

multi-tenant systems that need per-user or per-project cost attribution

applications with strict token budgets or quota limits

Requires

Flink framework with metrics provider

Claude API key (to fetch actual token counts from responses)

Current Claude pricing data (from Anthropic pricing page)

Limitations

Token counting is approximate for input (uses heuristic estimation) — actual tokens may differ by 5-10%

Cost estimation uses static pricing; does not account for volume discounts or custom pricing agreements

No built-in cost allocation across multiple users or projects — requires application-level tracking

What makes it unique

Integrates token tracking with Flink's metrics system, exposing token usage as first-class observable metrics rather than application-level logging. Provides both per-request and aggregate cost tracking with Flink-native metric aggregation.

vs alternatives

More integrated cost tracking than manual token counting, with Flink metrics integration for monitoring compared to applications that log token usage without structured metrics.

batch request processing with rate limit awareness

Medium confidence

Enables processing multiple Claude requests in batches while respecting Anthropic's rate limits (requests per minute, tokens per minute). The adapter queues requests, monitors rate limit headers from API responses, and throttles submission rate to stay within limits. Supports parallel batch processing with configurable concurrency limits and provides progress tracking for batch operations.

Solves for

I want to process 1000 documents with Claude without hitting rate limitsI need to parallelize Claude requests while respecting API quotasI want visibility into batch processing progress and estimated completion time

Best for

bulk processing applications (document analysis, content generation, classification)

data pipeline stages that need to process large datasets with Claude

background job systems that can tolerate queuing delays

Requires

Flink framework with queue/batch support

Anthropic API key with sufficient rate limits

Memory for queueing (typically <100MB for 10K requests)

Limitations

Batch processing is in-memory only; no persistence for long-running batches across restarts

Rate limit awareness is reactive (based on API response headers) — cannot predict limits in advance

No automatic retry for failed batch items — failed requests must be resubmitted manually

What makes it unique

Implements rate limit-aware batch processing as an adapter-level abstraction, handling queue management and throttling internally rather than requiring application-level rate limit handling. Integrates with Flink's batch processing patterns for consistent semantics.

vs alternatives

Simpler batch processing than manual queue management with rate limit tracking, with Flink-native batch semantics compared to applications that implement their own rate limiting.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @flink-app/anthropic-adapter, ranked by overlap. Discovered automatically through the match graph.

API69

Claude API

Anthropic's API for Claude models — tool use, vision, extended thinking, 200K context. Opus/Sonnet/Haiku.

streaming response supporthigh-throughput message batchingstructured tool use via function calling

3 shared capabilities

Framework31

mlflow-anthropic

Anthropic integration package for MLflow Tracing

mlflow trace artifact storage and retrieval for claude interactionserror and failure tracking for claude api interactionsreal-time trace streaming and live monitoring dashboard

3 shared capabilities

Repository44

Claudraband – Claude Code for the Power User

Hello everyone.Claudraband wraps a Claude Code TUI in a controlled terminal to enable extended workflows. It uses tmux for visible controlled sessions or xterm.js for headless sessions (a little slower), but everything is mediated by an actual Claude Code TUI.One example of a workflow I use now is h

claude api integration with power-user workflow optimizationerror handling and api response parsing

2 shared capabilities

Framework38

ai-sdk-provider-claude-code

AI SDK v6 provider for Claude via Claude Agent SDK (use Pro/Max subscription)

claude-based conversational agent integrationdynamic response generation

2 shared capabilities

Repository43

Rudel – Claude Code Session Analytics

We built rudel.ai after realizing we had no visibility into our own Claude Code sessions. We were using it daily but had no idea which sessions were efficient, why some got abandoned, or whether we were actually improving over time.So we built an analytics layer for it. After connecting our own sess

claude api session conversation capture and persistence

1 shared capability

Repository41

BrowserOS – "Claude Cowork" in the browser

Hey HN! We're Nithin and Nikhil, twin brothers building BrowserOS (YC S24). We're an open-source, privacy-first alternative to the AI browsers from big labs.The big differentiator: on BrowserOS you can use local LLMs or BYOK and run the agent entirely on the client side, so your company&#x

browser-native claude api integration with streaming

1 shared capability

Best For

✓teams building multi-provider LLM applications using Flink framework
✓developers who want provider abstraction without vendor lock-in
✓organizations standardizing on Flink for LLM orchestration
✓chat interfaces and conversational UIs requiring real-time response display
✓applications with strict latency requirements where progressive rendering improves perceived performance
✓token-metered systems that need live cost tracking during generation
✓multi-turn chatbot applications requiring stateful conversations
✓long-running agents that need to maintain context across many interactions

Known Limitations

⚠Adapter is limited to Anthropic Claude models only — does not support other Anthropic products like Bedrock
⚠Requires Flink framework as a dependency; cannot be used standalone without Flink's core architecture
⚠No built-in retry logic or circuit breaker — relies on Flink's error handling layer
⚠Streaming responses depend on Flink's stream handler implementation; performance varies by Flink version
⚠Streaming adds complexity to error handling — partial responses may be lost if connection drops mid-stream
⚠Stream buffering can cause memory overhead for very long responses (>100K tokens)

Requirements

Flink framework installed (version compatible with adapter)Anthropic API key (from console.anthropic.com)Node.js 14+ or compatible JavaScript runtimenpm or yarn package managerFlink framework with streaming handler supportAnthropic API key with streaming enabledClient-side streaming support (modern browsers, Node.js streams, etc.)Network connection with stable latency (<500ms recommended)

Input / Output

Accepts: text prompts, conversation history (array of message objects), system instructions (string), structured JSON parameters (temperature, max_tokens, etc.), streaming configuration (chunk size, timeout), abort signal for cancellation, user messages (text), system prompts (text), token limit configuration (number), model identifier (string, e.g., 'claude-3-opus-20240229'), configuration object (temperature, max_tokens, top_p, etc.), fallback model list (array of model identifiers), API error response (HTTP status, body), retry configuration (max attempts, initial backoff, multiplier), request context (for logging and debugging), request parameters (object), user prompts (text), system instructions (text), configuration overrides (object), request parameters (to estimate input tokens), API response (to get actual output tokens), pricing configuration (input/output token rates), array of requests (each with prompt, parameters), concurrency limit (number), rate limit configuration (requests/min, tokens/min)

Produces: text responses, streamed text chunks, structured JSON (if Claude returns JSON), token usage metadata, streamed text chunks (delta events), token usage metadata (partial and final), stream status events (start, progress, complete, error), formatted message array (Claude API format), token usage statistics, truncation metadata (which messages were dropped), resolved model identifier, validated configuration object, deprecation warnings (if applicable), retry decision (boolean: retry or fail), backoff delay (milliseconds), error metadata (type, code, message, retryable flag), validation result (pass/fail), validation errors (array of error objects with field, message, suggestion), sanitized parameters (object), token usage object (input_tokens, output_tokens), cost estimate (USD), metrics (for Flink metrics system), batch results (array of responses), batch progress metrics (completed, failed, pending), rate limit status (current usage, remaining quota)

UnfragileRank

Adoption9%(30% weight)

Quality26%(20% weight)

Ecosystem58%(15% weight)

Match Graph25%(30% weight)

Freshness90%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Repository

8 capabilities

Visit @flink-app/anthropic-adapter→

Repository Details

Package Details

npm

Registry

2.0.0-alpha.95

Version

356

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About

Anthropic Claude adapter for Flink AI framework

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Capabilities8 decomposed

claude api adapter with unified flink interface

Medium confidence

Solves for

Best for

teams building multi-provider LLM applications using Flink framework

developers who want provider abstraction without vendor lock-in

organizations standardizing on Flink for LLM orchestration

Requires

Flink framework installed (version compatible with adapter)

Anthropic API key (from console.anthropic.com)

Node.js 14+ or compatible JavaScript runtime

Limitations

Adapter is limited to Anthropic Claude models only — does not support other Anthropic products like Bedrock

Requires Flink framework as a dependency; cannot be used standalone without Flink's core architecture

No built-in retry logic or circuit breaker — relies on Flink's error handling layer

What makes it unique

vs alternatives

streaming response handler for claude api

Medium confidence

Solves for

Best for

chat interfaces and conversational UIs requiring real-time response display

applications with strict latency requirements where progressive rendering improves perceived performance

token-metered systems that need live cost tracking during generation

Requires

Flink framework with streaming handler support

Anthropic API key with streaming enabled

Client-side streaming support (modern browsers, Node.js streams, etc.)

Limitations

Streaming adds complexity to error handling — partial responses may be lost if connection drops mid-stream

Stream buffering can cause memory overhead for very long responses (>100K tokens)

Requires client-side support for streaming (WebSocket or SSE) — not compatible with simple HTTP request/response patterns

What makes it unique

vs alternatives

Cleaner streaming abstraction than raw Claude SDK streaming, with built-in Flink event semantics and error recovery compared to manual stream parsing in application code.

message history and conversation context management

Medium confidence

Solves for

Best for

multi-turn chatbot applications requiring stateful conversations

long-running agents that need to maintain context across many interactions

applications with variable context windows (e.g., different Claude models with different limits)

Requires

Flink framework with message serialization support

Token counter utility (built-in or external)

Claude API key

Limitations

No built-in persistence — conversation history is in-memory only; requires external storage integration for durability

Token counting is approximate (uses heuristic estimation) — actual token usage may differ from estimates

Sliding-window truncation loses older context; no semantic importance weighting for which messages to drop

What makes it unique

vs alternatives

Reduces boilerplate for conversation state management compared to manual message array handling, with built-in token awareness that prevents silent context loss unlike naive history appending.

model selection and configuration management

Medium confidence

Solves for

Best for

applications supporting multiple Claude model tiers (Opus, Sonnet, Haiku) with different cost/quality profiles

cost-optimized systems that need to switch models based on request complexity

teams managing model versions across development, staging, and production environments

Requires

Flink framework with configuration provider

Anthropic API key with access to desired Claude models

Model configuration file or environment variables

Limitations

No automatic model selection based on task complexity — requires explicit configuration or application logic

Fallback strategies are static; no learning from past failures to improve fallback decisions

Model deprecation warnings are advisory only — adapter does not prevent use of deprecated models

What makes it unique

vs alternatives

Cleaner model management than hardcoded model IDs in application code, with Flink-native configuration inheritance and fallback support compared to manual model selection logic.

error handling and retry logic with exponential backoff

Medium confidence

Solves for

Best for

production applications requiring resilience to transient API failures

high-volume systems that need rate limit handling

applications with strict uptime requirements

Requires

Flink framework with error handler support

Anthropic API key

Network timeout configuration (typically 30-60 seconds)

Limitations

Exponential backoff is fixed (no jitter) — may cause thundering herd on API recovery

Retry budget is global; no per-request or per-user rate limiting

No circuit breaker pattern — will continue retrying even if API is down for extended periods

What makes it unique

vs alternatives

More robust than raw Claude SDK error handling, with Flink-aware error propagation and standardized retry policies compared to manual try-catch blocks in application code.

request validation and parameter sanitization

Medium confidence

Solves for

Best for

applications accepting user-generated prompts that need injection protection

development teams wanting early validation feedback

security-conscious applications with strict input validation requirements

Requires

Flink framework with validation provider

JSON schema for Claude API parameters

Input sanitization library (built-in or external)

Limitations

Validation is schema-based only; cannot detect semantic issues (e.g., contradictory instructions)

Prompt injection detection is heuristic-based; sophisticated attacks may bypass validation

Sanitization may alter user intent in edge cases (e.g., removing special characters that are semantically important)

What makes it unique

vs alternatives

Earlier error detection than relying on Claude API validation, with built-in injection prevention compared to applications that validate only after API responses.

token usage tracking and cost estimation

Medium confidence

Solves for

Best for

cost-conscious applications needing budget tracking

multi-tenant systems that need per-user or per-project cost attribution

applications with strict token budgets or quota limits

Requires

Flink framework with metrics provider

Claude API key (to fetch actual token counts from responses)

Current Claude pricing data (from Anthropic pricing page)

Limitations

Token counting is approximate for input (uses heuristic estimation) — actual tokens may differ by 5-10%

Cost estimation uses static pricing; does not account for volume discounts or custom pricing agreements

No built-in cost allocation across multiple users or projects — requires application-level tracking

What makes it unique

vs alternatives

More integrated cost tracking than manual token counting, with Flink metrics integration for monitoring compared to applications that log token usage without structured metrics.

batch request processing with rate limit awareness

Medium confidence

Solves for

Best for

bulk processing applications (document analysis, content generation, classification)

data pipeline stages that need to process large datasets with Claude

background job systems that can tolerate queuing delays

Requires

Flink framework with queue/batch support

Anthropic API key with sufficient rate limits

Memory for queueing (typically <100MB for 10K requests)

Limitations

Batch processing is in-memory only; no persistence for long-running batches across restarts

Rate limit awareness is reactive (based on API response headers) — cannot predict limits in advance

No automatic retry for failed batch items — failed requests must be resubmitted manually

What makes it unique

vs alternatives

Simpler batch processing than manual queue management with rate limit tracking, with Flink-native batch semantics compared to applications that implement their own rate limiting.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Alternatives to @flink-app/anthropic-adapter

OpenAI Agents SDK60Framework

OpenAI's official agent framework — agents, handoffs, guardrails, sessions, built-in tracing.

Compare →

Claude Agent SDK59Framework

Anthropic's official agent SDK — the Claude Code harness (tools, MCP, subagents, permissions) as a library.

Compare →

Pipecat59Framework

Open-source realtime voice-agent framework — composable STT/LLM/TTS pipelines, every provider, WebRTC.

Compare →

LiveKit Agents59Framework

LiveKit's realtime agent framework — voice/video agents as WebRTC participants, telephony included.

Compare →

See all alternatives to @flink-app/anthropic-adapter→

@flink-app/anthropic-adapter

Capabilities8 decomposed

claude api adapter with unified flink interface

streaming response handler for claude api

message history and conversation context management

model selection and configuration management

error handling and retry logic with exponential backoff

request validation and parameter sanitization

token usage tracking and cost estimation

batch request processing with rate limit awareness

Related Artifactssharing capabilities

Claude API

mlflow-anthropic

Claudraband – Claude Code for the Power User

ai-sdk-provider-claude-code

Rudel – Claude Code Session Analytics

BrowserOS – "Claude Cowork" in the browser

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

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Alternatives to @flink-app/anthropic-adapter

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@flink-app/anthropic-adapter

Capabilities8 decomposed

claude api adapter with unified flink interface

streaming response handler for claude api

message history and conversation context management

model selection and configuration management

error handling and retry logic with exponential backoff

request validation and parameter sanitization

token usage tracking and cost estimation

batch request processing with rate limit awareness

Related Artifactssharing capabilities

Claude API

mlflow-anthropic

Claudraband – Claude Code for the Power User

ai-sdk-provider-claude-code

Rudel – Claude Code Session Analytics

BrowserOS – "Claude Cowork" in the browser

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to @flink-app/anthropic-adapter

Are you the builder of @flink-app/anthropic-adapter?

Get the weekly brief

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