{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"codeact-agent","slug":"codeact-agent","name":"CodeAct Agent","type":"agent","url":"https://github.com/xingyaoww/code-act","page_url":"https://unfragile.ai/codeact-agent","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"codeact-agent__cap_0","uri":"capability://code.generation.editing.python.code.generation.as.unified.agent.action.space","name":"python code generation as unified agent action space","description":"Generates executable Python code as the primary action mechanism for LLM agents instead of JSON tool calls or text responses. The system consolidates all agent actions (tool invocations, computations, state management) into a single Python code generation target, allowing the LLM to leverage full programming language expressiveness. This unified action space is then executed in isolated environments and results are fed back to the LLM for multi-turn refinement.","intents":["I want my LLM agent to perform complex multi-step operations without being constrained by predefined tool schemas","I need agents to dynamically compose actions based on runtime results rather than following rigid tool-calling patterns","I want to leverage Python's full expressiveness for agent reasoning and action execution"],"best_for":["Research teams building flexible LLM agents for complex reasoning tasks","Developers prototyping agents that need dynamic action composition","Teams migrating from JSON tool-calling to code-based agent paradigms"],"limitations":["Requires Python execution environment — cannot execute arbitrary compiled binaries or non-Python code natively","Code generation quality depends on LLM's Python proficiency — hallucinated imports or syntax errors require error handling loops","Adds execution latency compared to direct tool calls due to code parsing and sandboxing overhead"],"requires":["Python 3.8+","LLM with strong Python code generation capability (Mistral-7b-v0.1 or Llama-2-7b minimum)","Isolated execution environment (Docker, Kubernetes, or local Jupyter kernel)"],"input_types":["natural language queries","structured task descriptions","execution context and previous results"],"output_types":["executable Python code","code execution results (stdout/stderr)","structured data from code output"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_1","uri":"capability://automation.workflow.isolated.code.execution.with.multi.turn.error.recovery","name":"isolated code execution with multi-turn error recovery","description":"Executes LLM-generated Python code in containerized or sandboxed environments (Docker containers, Kubernetes pods, or Jupyter kernels) with automatic capture of execution results, errors, and stdout/stderr. Failed executions are returned to the LLM with full error context, enabling multi-turn refinement loops where the agent can inspect errors and regenerate corrected code. Each conversation maintains its own isolated execution context to prevent state leakage.","intents":["I need to safely run untrusted LLM-generated code without risking my system","I want execution errors to automatically feed back to the agent for self-correction","I need clean environment isolation between different agent conversations"],"best_for":["Production deployments requiring security isolation between agent executions","Research environments where agents need to iteratively debug their own code","Multi-user systems where conversation isolation is critical"],"limitations":["Docker/Kubernetes overhead adds 200-500ms per execution cycle for container startup and teardown","Jupyter kernel approach requires persistent kernel management — kernel crashes require restart logic","No built-in timeout enforcement — runaway code requires external process termination","Cross-execution state sharing requires explicit serialization — no automatic context persistence between turns"],"requires":["Docker daemon running (for Docker execution mode)","Kubernetes cluster (for K8s deployment)","Jupyter kernel (for local/server deployments)","Python 3.8+ in execution environment"],"input_types":["Python code strings","execution context variables","previous execution state"],"output_types":["execution results (stdout)","error messages (stderr)","exception tracebacks","return values from code"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_10","uri":"capability://automation.workflow.error.capture.and.structured.result.formatting","name":"error capture and structured result formatting","description":"Automatically captures execution errors (exceptions, syntax errors, import errors), stdout/stderr output, and return values from executed code. Formats results into structured objects that include error type, traceback, execution duration, and output. This structured format enables the LLM to parse and understand execution outcomes for subsequent reasoning steps.","intents":["I want my agent to understand why code failed and fix it","I need structured execution results that the LLM can parse reliably","I want visibility into both successful and failed code executions"],"best_for":["Agents that need to debug and fix their own code","Systems requiring reliable error handling and recovery","Scenarios where execution transparency is critical"],"limitations":["Traceback formatting varies by Python version — parsing may be fragile","Large error messages may exceed LLM context windows","Binary output (images, pickled objects) cannot be easily serialized to text","Timeout errors may not include full traceback — incomplete error context"],"requires":["Python exception handling (built-in)","sys.stdout/stderr capture mechanism","JSON serialization for result formatting"],"input_types":["executed Python code","execution environment state"],"output_types":["structured error objects (type, message, traceback)","stdout/stderr strings","execution duration","return values"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_11","uri":"capability://memory.knowledge.conversation.history.management.with.mongodb.persistence","name":"conversation history management with mongodb persistence","description":"Stores complete conversation transcripts in MongoDB including user queries, generated code, execution results, and LLM responses. Enables session resumption, conversation browsing, and audit trails. Conversation state includes metadata like timestamps, execution durations, and error counts. Supports querying and filtering conversations by various criteria.","intents":["I want to save and resume agent conversations across sessions","I need an audit trail of what code the agent generated and how it executed","I want to analyze agent behavior across multiple conversations"],"best_for":["Production deployments requiring audit trails","Multi-user systems where conversation history is valuable","Research teams analyzing agent behavior"],"limitations":["MongoDB adds external dependency — requires database administration","Large conversations with extensive execution results may exceed MongoDB document size limits (16MB)","No built-in encryption — requires external security measures for sensitive data","Query performance degrades with large conversation volumes — requires indexing strategy","Data retention policies must be managed manually"],"requires":["MongoDB instance (4.0+)","pymongo library for Python integration","Network access to MongoDB server","Sufficient storage for conversation data"],"input_types":["conversation metadata (user, timestamp)","user queries","generated code","execution results"],"output_types":["conversation documents (JSON)","conversation history retrieval","conversation statistics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_12","uri":"capability://planning.reasoning.dynamic.code.refinement.through.error.driven.iteration","name":"dynamic code refinement through error-driven iteration","description":"Implements a feedback loop where execution errors are returned to the LLM with full context (error type, traceback, failed code), and the LLM generates corrected code in the next turn. The system tracks error history and can provide hints about common failure patterns. Supports multiple refinement iterations until code succeeds or user-defined iteration limits are reached.","intents":["I want my agent to automatically fix code that fails to execute","I need agents to learn from execution errors and improve their code generation","I want transparent visibility into the debugging process"],"best_for":["Complex tasks where first-attempt code often fails","Scenarios where agent self-correction is more valuable than human intervention","Research on agent error recovery and learning"],"limitations":["Infinite loops possible if agent generates same error repeatedly — requires iteration limits","Each refinement iteration adds latency (2-5 seconds per turn) — long debugging sessions become slow","LLM may not understand error context — some errors require human insight","Context window fills quickly with error history — long debugging sessions may exceed token limits","No learning across conversations — agent doesn't retain lessons from previous errors"],"requires":["Code execution engine with error capture","LLM with instruction-following for code generation","Iteration limit configuration (e.g., max 5 refinement attempts)","Error context formatting for LLM consumption"],"input_types":["failed code","error messages and tracebacks","execution context"],"output_types":["refined code","execution results","iteration count and history"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_2","uri":"capability://planning.reasoning.multi.turn.agent.interaction.with.execution.informed.reasoning","name":"multi-turn agent interaction with execution-informed reasoning","description":"Implements a conversation loop where the LLM generates code, the system executes it, captures results, and feeds execution output back to the LLM for subsequent reasoning steps. The LLM can inspect execution results, errors, and state changes to dynamically adjust its next action. This creates a feedback loop where agent behavior is informed by real execution outcomes rather than simulated tool responses.","intents":["I want my agent to adapt its strategy based on actual execution results, not mocked responses","I need agents to handle errors gracefully by inspecting failure reasons and regenerating solutions","I want transparent visibility into what code the agent generated and how it executed"],"best_for":["Complex reasoning tasks requiring iterative refinement (data analysis, debugging, exploration)","Scenarios where execution results significantly impact next steps","Teams building transparent, auditable agent systems"],"limitations":["Each turn adds latency for LLM inference + code execution + result processing — typical cycle is 2-5 seconds","Context window fills quickly with execution results — long-running tasks may exceed token limits","LLM must understand execution output format — poorly formatted results confuse subsequent reasoning","No built-in memory persistence — conversation history requires external storage (MongoDB, etc.)"],"requires":["LLM with instruction-following capability for code generation","Code execution engine (Docker, Kubernetes, or Jupyter)","Conversation state management system","Optional: MongoDB or similar for persistent conversation history"],"input_types":["user queries","previous execution results","error messages","conversation history"],"output_types":["generated Python code","execution results","final agent response","conversation transcript"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_3","uri":"capability://automation.workflow.web.based.chat.interface.with.conversation.persistence","name":"web-based chat interface with conversation persistence","description":"Provides a full-featured web UI for interacting with CodeAct agents through a chat-like interface. Conversation history is persisted in MongoDB, enabling users to resume sessions, review agent reasoning, and inspect generated code and execution results. The interface handles multi-turn interactions, displays code generation and execution output, and manages conversation state across browser sessions.","intents":["I want a user-friendly interface to interact with CodeAct agents without writing Python code","I need to review and audit the code my agent generated and how it executed","I want to save and resume agent conversations across sessions"],"best_for":["Non-technical users interacting with agents through a GUI","Teams requiring audit trails and conversation history","Production deployments serving multiple concurrent users"],"limitations":["Requires MongoDB for conversation persistence — adds external dependency","Web UI latency depends on network round-trip time — not suitable for real-time applications","Browser-based execution display may struggle with large code outputs or binary results","No built-in authentication — requires external auth layer for multi-user deployments"],"requires":["Web server (Flask, FastAPI, or similar)","MongoDB instance","Modern web browser (Chrome, Firefox, Safari, Edge)","Network connectivity to server"],"input_types":["text queries via chat input","file uploads (optional)","conversation context from MongoDB"],"output_types":["rendered chat messages","syntax-highlighted code display","execution results and logs","conversation history"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_4","uri":"capability://tool.use.integration.python.script.interface.for.programmatic.agent.access","name":"python script interface for programmatic agent access","description":"Exposes CodeAct agent functionality through a Python API, allowing developers to instantiate agents, send queries, and retrieve results programmatically. This interface abstracts away infrastructure details (execution engine, LLM service) and provides a simple function-call API for integrating agents into larger Python applications or scripts.","intents":["I want to embed CodeAct agents into my Python application without managing infrastructure","I need a simple API to send queries to agents and get results back","I want to script agent interactions for testing or batch processing"],"best_for":["Python developers building agent-powered applications","Researchers prototyping agent systems quickly","Automation scripts that need agent reasoning capabilities"],"limitations":["Python-only — no native support for other languages (requires HTTP wrapper)","Synchronous API blocks on execution — async support requires custom wrapping","No built-in error handling — developers must implement retry logic","Limited to local or network-accessible execution engines"],"requires":["Python 3.8+","CodeAct package installed","Access to LLM service (local or remote)","Access to code execution engine"],"input_types":["Python strings (queries)","Python dictionaries (context)","Python objects (state)"],"output_types":["Python strings (responses)","Python dictionaries (structured results)","Execution logs and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_5","uri":"capability://tool.use.integration.multi.backend.llm.service.abstraction","name":"multi-backend llm service abstraction","description":"Abstracts LLM inference across multiple backend options (vLLM for high-throughput serving, llama.cpp for local inference, cloud APIs) through a unified interface. The system can be configured to use different LLM backends depending on deployment context (laptop, server, Kubernetes cluster) without changing agent code. Supports CodeActAgent-Mistral-7b-v0.1 (32k context) and CodeActAgent-Llama-7b (4k context) variants.","intents":["I want to switch between local and cloud LLM inference without changing my agent code","I need to scale LLM serving from laptop development to production Kubernetes clusters","I want to use different model sizes depending on available resources"],"best_for":["Teams deploying agents across multiple environments (dev, staging, prod)","Researchers comparing different LLM backends","Organizations with varying compute resources across deployments"],"limitations":["vLLM backend requires GPU for reasonable throughput — CPU-only deployments fall back to llama.cpp with 10-50x slower inference","Model variants have different context windows (32k vs 4k) — long conversations may exceed limits on smaller models","Backend switching requires configuration changes — no automatic fallback if primary backend fails","LLM quality varies by backend — quantized models (llama.cpp) may produce lower-quality code"],"requires":["vLLM (for server/Kubernetes deployments)","llama.cpp (for laptop deployments)","GPU (recommended for vLLM, optional for llama.cpp)","Model weights for Mistral-7b or Llama-2-7b"],"input_types":["prompts (text)","conversation context","system instructions"],"output_types":["generated text (code or responses)","token counts","inference metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_6","uri":"capability://automation.workflow.docker.based.isolated.execution.with.per.conversation.containers","name":"docker-based isolated execution with per-conversation containers","description":"Deploys code execution in Docker containers where each conversation spawns a dedicated container with a clean Python environment. Containers are created on-demand, execute code, capture output, and are destroyed after the conversation ends. This approach provides strong isolation between conversations and prevents state leakage while maintaining simplicity compared to Kubernetes.","intents":["I want to run agent-generated code safely without risking my host system","I need complete isolation between different user conversations","I want a simple deployment option that doesn't require Kubernetes"],"best_for":["Small to medium-scale deployments (single server)","Development and testing environments","Teams prioritizing simplicity over horizontal scaling"],"limitations":["Container startup overhead adds 200-500ms per execution cycle","Single-server bottleneck — cannot scale beyond one machine's resources","Docker daemon must be running — adds operational complexity","No automatic cleanup if containers crash — requires monitoring and manual intervention","Storage overhead — each container requires disk space for base image and dependencies"],"requires":["Docker daemon installed and running","Docker image with Python 3.8+ and required dependencies","Sufficient disk space for container images and volumes","Linux kernel with cgroup support (or Docker Desktop on Mac/Windows)"],"input_types":["Python code strings","environment variables","mounted volumes (optional)"],"output_types":["stdout/stderr from container","exit codes","file outputs (if mounted)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_7","uri":"capability://automation.workflow.kubernetes.based.distributed.code.execution.with.pod.scaling","name":"kubernetes-based distributed code execution with pod scaling","description":"Deploys code execution across Kubernetes pods where each conversation or execution request spawns a pod in a Kubernetes cluster. Pods are managed by Kubernetes for resource allocation, scheduling, and automatic cleanup. This enables horizontal scaling across multiple nodes and automatic load balancing. Integrates with vLLM for distributed LLM serving on the same cluster.","intents":["I need to scale agent execution across multiple machines","I want automatic load balancing and resource management for agent workloads","I need high availability and fault tolerance for production agent deployments"],"best_for":["Large-scale production deployments serving many concurrent users","Organizations with existing Kubernetes infrastructure","Teams requiring automatic scaling and high availability"],"limitations":["Kubernetes complexity — requires expertise in cluster management, networking, and storage","Pod startup latency (1-5 seconds) adds overhead compared to Docker","Network communication between pods adds latency — distributed tracing becomes necessary","Requires persistent storage for conversation history — adds complexity with PVCs or external databases","Cost overhead — Kubernetes clusters require minimum resource commitment"],"requires":["Kubernetes cluster (1.20+)","kubectl configured with cluster access","Container registry for pod images","Persistent storage (PVC or external database for MongoDB)","vLLM deployment on cluster (optional but recommended)"],"input_types":["Kubernetes pod specifications","Python code to execute","environment variables"],"output_types":["pod logs","execution results","pod status and metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_8","uri":"capability://automation.workflow.jupyter.kernel.based.local.code.execution","name":"jupyter kernel-based local code execution","description":"Executes code using persistent Jupyter kernels running locally or on a server. Code is sent to the kernel, executed in the same Python process, and results are captured. This approach maintains state between executions (variables, imports, definitions persist) and provides fast execution without containerization overhead. Suitable for development and research workflows.","intents":["I want fast code execution without containerization overhead for development","I need state to persist between code executions within a conversation","I want to use Jupyter-style interactive Python execution"],"best_for":["Local development and prototyping","Research environments where state persistence is valuable","Low-latency agent interactions on single machines"],"limitations":["No isolation between conversations — state leakage if kernels are reused","Kernel crashes require manual restart — no automatic recovery","Single-machine bottleneck — cannot scale beyond one server","Runaway code blocks the entire kernel — no timeout enforcement without external process management","Difficult to manage multiple concurrent conversations — requires kernel pooling logic"],"requires":["Jupyter kernel installed (via jupyter package)","Python 3.8+","jupyter_client library for kernel communication","Sufficient RAM for kernel process"],"input_types":["Python code strings","execution context from previous turns"],"output_types":["execution results","stdout/stderr","variable state (accessible to next execution)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__cap_9","uri":"capability://tool.use.integration.code.execution.api.for.external.integration","name":"code execution api for external integration","description":"Exposes code execution functionality through a REST or gRPC API, allowing external systems to submit code for execution and retrieve results. The API abstracts the underlying execution backend (Docker, Kubernetes, Jupyter) and provides a unified interface for code submission, result retrieval, and error handling. Enables integration with non-Python systems and microservice architectures.","intents":["I want to integrate CodeAct execution into a larger microservice architecture","I need to expose code execution to non-Python clients","I want to decouple code execution from the agent reasoning loop"],"best_for":["Microservice architectures with multiple components","Teams integrating CodeAct with non-Python systems","Scenarios requiring decoupled execution and reasoning"],"limitations":["Network latency adds overhead compared to in-process execution","API serialization/deserialization adds overhead for large code or results","Requires API authentication and authorization — adds security complexity","No built-in request queuing — high load may overwhelm API server","Debugging distributed failures becomes harder with network boundaries"],"requires":["HTTP server (Flask, FastAPI) or gRPC server","Code execution backend (Docker, Kubernetes, or Jupyter)","API documentation (OpenAPI/Swagger or protobuf)","Client libraries for API consumption"],"input_types":["HTTP POST requests with code payload","gRPC messages with code","execution context and environment variables"],"output_types":["JSON responses with execution results","gRPC messages with results","HTTP status codes and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codeact-agent__headline","uri":"capability://tool.use.integration.ai.agent.using.executable.python.code.for.dynamic.interactions","name":"ai agent using executable python code for dynamic interactions","description":"CodeAct Agent is a research-focused AI agent that leverages executable Python code as actions, enabling more flexible and powerful interactions compared to traditional JSON-based agents.","intents":["best AI agent for dynamic coding","AI agent using Python for automation","flexible AI agent for programming tasks","AI agent that executes code","best agent for multi-turn interactions"],"best_for":["developers needing dynamic coding capabilities"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LLM with strong Python code generation capability (Mistral-7b-v0.1 or Llama-2-7b minimum)","Isolated execution environment (Docker, Kubernetes, or local Jupyter kernel)","Docker daemon running (for Docker execution mode)","Kubernetes cluster (for K8s deployment)","Jupyter kernel (for local/server deployments)","Python 3.8+ in execution environment","Python exception handling (built-in)","sys.stdout/stderr capture mechanism","JSON serialization for result formatting"],"failure_modes":["Requires Python execution environment — cannot execute arbitrary compiled binaries or non-Python code natively","Code generation quality depends on LLM's Python proficiency — 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