{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-xingyaoww--code-act","slug":"xingyaoww--code-act","name":"code-act","type":"agent","url":"https://github.com/xingyaoww/code-act","page_url":"https://unfragile.ai/xingyaoww--code-act","categories":["chatbots-assistants"],"tags":["llm","llm-agent","llm-finetuning","llm-framework"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-xingyaoww--code-act__cap_0","uri":"capability://planning.reasoning.unified.code.action.space.for.llm.agents","name":"unified-code-action-space-for-llm-agents","description":"Consolidates all LLM agent actions into a single executable Python code representation rather than separate text/JSON/tool-calling modalities. The system uses a Python interpreter integrated with the LLM to generate, execute, and iteratively refine code actions based on execution results in multi-turn conversations. This unified approach eliminates action-space fragmentation and enables the LLM to reason about code semantics directly.","intents":["I want my LLM agent to perform complex multi-step tasks using a single, consistent action representation","I need my agent to dynamically revise its approach based on code execution feedback","I want to reduce the cognitive overhead of mapping between multiple action formats (text, JSON, tool calls)"],"best_for":["researchers building LLM agent systems and benchmarking against traditional approaches","teams building autonomous code-generation or data-processing agents","developers prototyping agents that need to execute and learn from code execution failures"],"limitations":["Requires Python runtime environment — cannot execute non-Python code natively without additional sandboxing layers","Performance depends on LLM's ability to generate syntactically correct Python — malformed code requires error-correction loops","Limited to tasks expressible as Python code — not suitable for agents requiring real-time interaction or non-deterministic external APIs"],"requires":["Python 3.8+","LLM model (Mistral-7b-v0.1 or Llama-2-7b recommended)","Docker or Kubernetes for isolated execution environments"],"input_types":["natural language queries","structured task descriptions"],"output_types":["executable Python code","code execution results (stdout/stderr)","structured data from code execution"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_1","uri":"capability://automation.workflow.isolated.code.execution.engine.with.environment.separation","name":"isolated-code-execution-engine-with-environment-separation","description":"Provides sandboxed Python execution environments using Docker containers or Kubernetes pods, where each conversation session gets its own isolated runtime. The engine manages container lifecycle, handles code injection, captures stdout/stderr, and enforces resource limits to prevent runaway processes. This architecture ensures security, reproducibility, and clean state separation between concurrent agent conversations.","intents":["I need to safely execute untrusted or user-generated Python code without risking the host system","I want each agent conversation to have a clean, isolated Python environment with no cross-contamination","I need to capture and return code execution results (output, errors, return values) back to the LLM for reasoning"],"best_for":["production deployments where code execution security is critical","multi-tenant systems serving multiple concurrent agent conversations","research environments benchmarking agent performance across diverse code execution scenarios"],"limitations":["Docker/Kubernetes overhead adds 200-500ms per code execution due to container startup and teardown","Network I/O from containers to external services may be restricted depending on security policies","Stateful code execution (e.g., persistent file handles) requires explicit container persistence configuration","No built-in support for GPU acceleration — requires additional Kubernetes device plugin configuration"],"requires":["Docker 20.10+ or Kubernetes 1.20+","Python 3.8+ in container image","Sufficient disk/memory for concurrent container instances","Network access for pulling base container images"],"input_types":["Python code strings","execution context (environment variables, pre-loaded modules)"],"output_types":["stdout/stderr capture","execution status (success/failure)","return values or exceptions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_10","uri":"capability://planning.reasoning.execution.result.capture.and.feedback.integration","name":"execution-result-capture-and-feedback-integration","description":"Captures stdout, stderr, return values, and exceptions from code execution and formats them as structured feedback that is fed back to the LLM for reasoning. The system distinguishes between successful execution (with output), runtime errors (with stack traces), and syntax errors (with line numbers). This feedback enables the LLM to understand why code failed and generate corrected versions.","intents":["I want the LLM to see actual error messages when code fails, not just a binary success/failure signal","I need the LLM to understand the output of executed code to reason about next steps","I want to provide rich execution context (stack traces, variable values) to guide error recovery"],"best_for":["agents performing exploratory or iterative tasks where error feedback is critical","systems where code correctness is essential and self-correction is required","research on agent error recovery and learning from execution feedback"],"limitations":["Large output (e.g., printing 1MB of data) can exceed LLM context windows — requires output truncation","Sensitive information in output (API keys, passwords) may leak to the LLM — requires output sanitization","Stack traces can be verbose and confusing to LLMs — may require summarization or filtering","No support for interactive debugging — LLM cannot inspect kernel state or set breakpoints"],"requires":["Code execution engine with stdout/stderr capture","Exception handling and stack trace formatting","Output truncation/sanitization logic"],"input_types":["raw stdout/stderr from code execution","exception objects with stack traces","return values from code"],"output_types":["formatted execution feedback","truncated/sanitized output","structured error information"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_11","uri":"capability://data.processing.analysis.benchmark.evaluation.against.agent.task.datasets","name":"benchmark-evaluation-against-agent-task-datasets","description":"Provides integration with agent evaluation benchmarks (e.g., M³ToolEval) to measure CodeAct performance on standardized task datasets. The system includes evaluation harnesses that run agents on benchmark tasks, collect results, and compute success metrics. This enables quantitative comparison of CodeAct against alternative agent architectures (text-based, JSON-based, tool-calling).","intents":["I want to measure how well CodeAct agents perform on standard benchmarks","I need to compare CodeAct against other agent architectures with fair evaluation","I want to identify which types of tasks CodeAct excels at vs. struggles with"],"best_for":["researchers publishing agent architecture papers or comparisons","teams evaluating CodeAct for their use case before committing to adoption","organizations building internal benchmarks for agent performance"],"limitations":["Benchmark datasets may not reflect real-world task distributions — results may not generalize","Evaluation is computationally expensive — running full benchmarks can take hours/days","Benchmarks are static — cannot capture performance on novel or evolving task types","Success metrics are task-specific — no universal metric for agent quality"],"requires":["Benchmark dataset (M³ToolEval or similar)","Evaluation harness code","Sufficient compute for running experiments","Metrics collection and analysis tools"],"input_types":["benchmark task descriptions","expected outputs or success criteria"],"output_types":["success rates per task","performance metrics (latency, token usage)","error analysis and failure modes"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_12","uri":"capability://memory.knowledge.conversation.history.management.and.context.windowing","name":"conversation-history-management-and-context-windowing","description":"Manages conversation state across multi-turn interactions, including message history, code blocks, execution results, and LLM responses. The system implements context windowing strategies to fit conversation history within the LLM's context window, using techniques like summarization, truncation, or selective history retention. This enables long conversations while respecting model constraints.","intents":["I want to maintain full conversation history for auditing and debugging","I need to fit long conversations into the LLM's limited context window without losing critical information","I want to resume conversations from previous sessions without losing context"],"best_for":["long-running agent sessions where context management is critical","systems requiring full conversation audit trails","applications where conversation resumption is important"],"limitations":["Context windowing strategies (summarization, truncation) may lose important details","Conversation history storage (MongoDB) adds operational complexity and latency","Large conversation histories can cause performance degradation in UI rendering","No built-in support for multi-conversation context — each conversation is isolated"],"requires":["Conversation state management system","Persistent storage (MongoDB or similar)","Context windowing logic","Summarization or truncation algorithms"],"input_types":["user messages","code blocks","execution results","LLM responses"],"output_types":["conversation transcripts","windowed context for LLM","conversation metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_2","uri":"capability://planning.reasoning.multi.turn.code.generation.and.refinement.loop","name":"multi-turn-code-generation-and-refinement-loop","description":"Implements a feedback loop where the LLM generates code, the system executes it, captures results (success/failure/output), and feeds execution feedback back to the LLM for iterative refinement. The system maintains conversation history and execution context across turns, allowing the LLM to reason about why code failed and generate corrected versions. This pattern enables self-correction without human intervention.","intents":["I want my agent to automatically fix code that fails execution rather than giving up","I need the agent to learn from execution errors and adapt its approach in subsequent attempts","I want to maintain full conversation history including code, execution results, and reasoning for debugging and auditing"],"best_for":["autonomous agents performing exploratory data analysis or code generation tasks","systems where human-in-the-loop correction is expensive or unavailable","research on agent self-correction and error recovery mechanisms"],"limitations":["Infinite refinement loops possible if LLM cannot learn from repeated failures — requires explicit max-turn limits (typically 5-10 turns)","Execution feedback must be informative (good error messages) — cryptic errors may not guide LLM toward correct solution","Context window constraints limit conversation history — long multi-turn sessions may require summarization or context pruning","No guarantee of convergence — some tasks may be unsolvable by the LLM regardless of feedback"],"requires":["LLM with sufficient context window (32k tokens for Mistral-7b-v0.1 recommended)","Code execution engine with result capture","Conversation state management (in-memory or persistent store)"],"input_types":["initial user query","execution results from previous turns","error messages and stack traces"],"output_types":["refined Python code","final execution results","conversation transcript with all iterations"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_3","uri":"capability://code.generation.editing.fine.tuned.llm.model.variants.for.code.action.generation","name":"fine-tuned-llm-model-variants-for-code-action-generation","description":"Provides pre-trained and fine-tuned LLM variants (CodeActAgent-Mistral-7b-v0.1 with 32k context, CodeActAgent-Llama-7b with 4k context) optimized for generating executable Python code as agent actions. These models are instruction-tuned to produce syntactically correct, executable code that integrates with the CodeAct execution engine. The fine-tuning process aligns the model's output distribution toward valid Python code and away from natural language explanations.","intents":["I want to use a smaller, open-source LLM that's optimized for code generation without relying on proprietary APIs","I need a model that understands the CodeAct action format and generates code that executes correctly","I want to deploy locally or on-premises without sending data to external LLM providers"],"best_for":["organizations with data privacy requirements or restricted API access","researchers comparing CodeAct against other agent architectures","teams building agents where inference latency is critical (local deployment)"],"limitations":["Mistral-7b variant requires 16GB+ VRAM for inference; Llama-7b requires 8GB+ VRAM","Smaller context window (4k for Llama) limits ability to handle complex multi-file code generation tasks","Fine-tuning is specific to CodeAct paradigm — models may not generalize well to other code generation tasks","No built-in support for multi-language code generation — optimized for Python only"],"requires":["GPU with sufficient VRAM (RTX 3090 / A100 recommended)","vLLM or llama.cpp for efficient inference","Python 3.8+","Model weights (downloadable from HuggingFace)"],"input_types":["natural language task descriptions","conversation history with previous code and execution results"],"output_types":["Python code strings","code with inline comments explaining reasoning"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_4","uri":"capability://automation.workflow.web.based.chat.ui.with.conversation.persistence","name":"web-based-chat-ui-with-conversation-persistence","description":"Provides a full-featured web interface for interacting with CodeAct agents, with conversation history stored in MongoDB and rendered in a chat-like format. The UI handles message rendering, code syntax highlighting, execution result display, and conversation management. It communicates with the LLM service and code execution engine via backend APIs, abstracting the complexity of agent orchestration from end users.","intents":["I want a user-friendly interface for non-technical users to interact with code-generating agents","I need to persist and retrieve conversation history for auditing, debugging, or resuming sessions","I want to visualize code execution results (stdout, errors, structured output) alongside the conversation"],"best_for":["production deployments serving multiple end users","teams building internal tools or customer-facing agent applications","research environments where conversation logging is required for analysis"],"limitations":["MongoDB dependency adds operational complexity — requires separate database setup and maintenance","Real-time collaboration features not supported — concurrent edits from multiple users may cause conflicts","Large conversation histories (1000+ turns) may cause UI performance degradation due to DOM rendering","Code execution results are displayed as text — no interactive visualization of data structures or plots"],"requires":["Node.js 14+ for frontend","MongoDB 4.4+ for persistence","Backend API server (Python/FastAPI or similar)","Modern web browser (Chrome, Firefox, Safari)"],"input_types":["natural language text input","file uploads (for context or data)"],"output_types":["rendered conversation with code blocks","formatted execution results","downloadable conversation transcripts"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_5","uri":"capability://tool.use.integration.python.script.interface.for.programmatic.agent.access","name":"python-script-interface-for-programmatic-agent-access","description":"Provides a lightweight Python API for programmatically invoking CodeAct agents without a web UI, enabling integration into automated workflows, batch processing, or custom applications. The interface abstracts the LLM service and code execution engine behind a simple function-call API, handling orchestration, state management, and result collection internally.","intents":["I want to integrate CodeAct agents into my existing Python application or data pipeline","I need to run batch agent tasks programmatically without manual UI interaction","I want to build custom orchestration logic on top of the CodeAct agent"],"best_for":["developers building agent-powered applications or services","data engineering teams automating code generation or data transformation tasks","researchers running large-scale agent experiments with custom evaluation loops"],"limitations":["No built-in error handling for network failures — requires custom retry logic","Synchronous API blocks on code execution — long-running tasks may timeout","Limited observability — no built-in logging or tracing of agent decisions","State management is in-memory only — no persistence across process restarts"],"requires":["Python 3.8+","CodeAct package installed","LLM service running (local or remote)","Code execution engine accessible (Docker/Kubernetes)"],"input_types":["Python strings (task descriptions)","Python dictionaries (configuration)","file paths (for context or data)"],"output_types":["Python dictionaries with execution results","code strings","structured data from code execution"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_6","uri":"capability://automation.workflow.docker.containerized.deployment.with.llm.serving","name":"docker-containerized-deployment-with-llm-serving","description":"Packages CodeAct components (LLM service, code execution engine, optional web UI) into Docker containers with vLLM or llama.cpp for efficient LLM inference. The deployment includes container orchestration, volume mounting for model weights, and networking configuration to enable communication between components. This approach simplifies deployment on single machines or small clusters.","intents":["I want to deploy CodeAct on a single server or laptop without managing complex infrastructure","I need reproducible deployments that work consistently across different machines","I want to package CodeAct with all dependencies for easy distribution or sharing"],"best_for":["small teams or solo developers deploying agents on single machines","prototyping and development environments","edge deployments or resource-constrained environments"],"limitations":["Single-machine deployments lack high availability — container failure causes service downtime","No built-in load balancing — concurrent requests may queue or timeout","Storage is ephemeral — code execution results are lost if container restarts","GPU sharing between containers requires careful configuration — may cause resource contention"],"requires":["Docker 20.10+","8GB+ RAM for LLM inference","GPU with 8GB+ VRAM (optional but recommended)","Docker Compose (for multi-container orchestration)"],"input_types":["Dockerfile configuration","docker-compose.yml","environment variables"],"output_types":["running Docker containers","exposed ports for API access","mounted volumes for persistence"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_7","uri":"capability://automation.workflow.kubernetes.orchestrated.deployment.with.auto.scaling","name":"kubernetes-orchestrated-deployment-with-auto-scaling","description":"Deploys CodeAct components as Kubernetes pods with horizontal pod autoscaling, persistent volume claims for model weights and conversation data, and service discovery for inter-component communication. The deployment includes ConfigMaps for configuration management, Secrets for API keys, and resource limits to prevent resource exhaustion. This architecture enables production-grade multi-tenant deployments with automatic scaling based on load.","intents":["I need to deploy CodeAct at scale serving hundreds of concurrent users","I want automatic scaling based on request load without manual intervention","I need high availability with automatic failover and load balancing"],"best_for":["production deployments serving multiple teams or external users","organizations with existing Kubernetes infrastructure","systems requiring high availability and automatic scaling"],"limitations":["Kubernetes complexity adds operational overhead — requires expertise in cluster management, networking, and storage","Cold start latency for new pods may be 30-60 seconds due to model weight loading","Persistent volume performance depends on underlying storage backend — may become bottleneck for high-throughput deployments","Debugging distributed issues is more complex than single-machine deployments"],"requires":["Kubernetes 1.20+","Persistent volume provisioner (NFS, EBS, or similar)","GPU node pool (for LLM inference)","Helm (optional, for templating)"],"input_types":["Kubernetes manifests (YAML)","Helm values files","ConfigMaps and Secrets"],"output_types":["running Kubernetes pods","exposed services with load balancing","persistent volumes for data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_8","uri":"capability://automation.workflow.slurm.hpc.cluster.deployment.for.research.workloads","name":"slurm-hpc-cluster-deployment-for-research-workloads","description":"Enables deployment of CodeAct on high-performance computing clusters using SLURM job scheduling, allowing researchers to run large-scale agent experiments across multiple compute nodes. The deployment integrates with SLURM's job submission, resource allocation, and monitoring systems, enabling batch processing of agent tasks with fine-grained control over compute resources (CPU, GPU, memory, time limits).","intents":["I want to run large-scale agent benchmarking experiments across a cluster","I need to submit batch agent jobs to a shared HPC cluster with resource quotas","I want to leverage GPU clusters for parallel agent execution"],"best_for":["academic research teams with access to HPC clusters","large-scale agent benchmarking and evaluation studies","experiments requiring distributed execution across many compute nodes"],"limitations":["SLURM integration is research-focused — not suitable for production services requiring real-time responsiveness","Job submission overhead (queue wait time) makes interactive agent development difficult","Requires SLURM cluster access and expertise — not portable to cloud environments without additional setup","No built-in support for dynamic job scaling — requires manual job submission for each experiment"],"requires":["SLURM cluster with GPU nodes","Python 3.8+ on compute nodes","Shared filesystem (NFS or similar) for model weights and data","SLURM job submission scripts"],"input_types":["SLURM job scripts","experiment configuration files","task lists for batch processing"],"output_types":["SLURM job logs","experiment results in structured format","performance metrics and benchmarks"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xingyaoww--code-act__cap_9","uri":"capability://code.generation.editing.jupyter.kernel.based.stateful.code.execution","name":"jupyter-kernel-based-stateful-code-execution","description":"Uses Jupyter kernels to maintain execution state across multiple code blocks within a single conversation, enabling variables, imports, and function definitions to persist between turns. The system manages kernel lifecycle (creation, execution, cleanup) and handles kernel communication via the Jupyter protocol. This enables stateful multi-turn code execution where later code can reference earlier definitions.","intents":["I want code executed in later turns to access variables and functions defined in earlier turns","I need to maintain a persistent Python environment across multiple agent interactions","I want to avoid re-importing libraries or re-defining functions on each code execution"],"best_for":["data analysis agents that build up state across multiple queries","interactive development scenarios where state persistence is essential","agents performing exploratory tasks requiring iterative refinement"],"limitations":["Kernel state can become inconsistent if code modifies global state unpredictably — requires careful code generation","Memory leaks possible if large objects are created and not garbage collected — requires explicit cleanup","Kernel crashes cause loss of all state — no automatic recovery or checkpointing","Debugging kernel state issues is difficult — requires introspection of kernel variables"],"requires":["Jupyter kernel (ipykernel or similar)","Python 3.8+","Jupyter protocol support in execution engine"],"input_types":["Python code strings","execution context (previous code blocks)"],"output_types":["execution results","kernel state (variables, imports)","error messages"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LLM model (Mistral-7b-v0.1 or Llama-2-7b recommended)","Docker or Kubernetes for isolated execution environments","Docker 20.10+ or Kubernetes 1.20+","Python 3.8+ in container image","Sufficient disk/memory for concurrent container instances","Network access for pulling base container images","Code execution engine with stdout/stderr capture","Exception handling and stack trace formatting","Output truncation/sanitization logic"],"failure_modes":["Requires Python runtime environment — cannot execute non-Python code natively without additional sandboxing layers","Performance depends on LLM's ability to generate syntactically correct Python — malformed code requires error-correction loops","Limited to tasks expressible as Python code — not suitable for agents requiring real-time interaction or non-deterministic external APIs","Docker/Kubernetes overhead adds 200-500ms per code execution due to container startup and teardown","Network I/O from containers to external services may be restricted depending on security policies","Stateful code execution (e.g., persistent file handles) requires explicit container persistence configuration","No built-in support for GPU acceleration — requires additional Kubernetes device plugin configuration","Large output (e.g., printing 1MB of data) can exceed LLM context windows — requires output truncation","Sensitive information in output (API keys, passwords) may leak to the LLM — requires output sanitization","Stack traces can be verbose and confusing to LLMs — may require summarization or filtering","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4682944177652939,"quality":0.4,"ecosystem":0.52,"match_graph":0.25,"freshness":0.27,"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":"inactive","updated_at":"2026-05-06T17:25:02.177Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2024-05-23T23:14:59Z"},"community":{"stars":1651,"forks":135,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=xingyaoww--code-act","compare_url":"https://unfragile.ai/compare?artifact=xingyaoww--code-act"}},"signature":"NtagsRtQorWi9WbEWAMLz8yAypYoM2TW3UUMG7+YdhORPkRMuZdmSc079X16Kh5MtYaB/G7pzOH/UTifipAvAA==","signedAt":"2026-06-21T07:10:21.918Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xingyaoww--code-act","artifact":"https://unfragile.ai/xingyaoww--code-act","verify":"https://unfragile.ai/api/v1/verify?slug=xingyaoww--code-act","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"}}