{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-agentsmesh--agentsmesh","slug":"agentsmesh--agentsmesh","name":"AgentsMesh","type":"agent","url":"https://agentsmesh.ai","page_url":"https://unfragile.ai/agentsmesh--agentsmesh","categories":["ai-agents"],"tags":["agent-orchestration","agentsmesh","ai-agent","ai-agent-workforce-platform","ai-coding","aider","claude-code","codex-cli","gemini-cli","multi-agent","self-hosted"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-agentsmesh--agentsmesh__cap_0","uri":"capability://automation.workflow.multi.agent.pod.orchestration.with.isolated.execution.environments","name":"multi-agent pod orchestration with isolated execution environments","description":"AgentsMesh creates isolated AgentPods — each a containerized execution environment with a PTY terminal, Git worktree sandbox, and browser-accessible terminal view — managed via gRPC commands from the backend. Runners register with the backend using mTLS, receive lifecycle commands (spawn, terminate, execute), and maintain persistent connections for real-time state synchronization. Each Pod is a separate process boundary with its own filesystem sandbox and terminal session, enabling parallel multi-agent execution without cross-contamination.","intents":["Run multiple AI coding agents in parallel without blocking or interfering with each other","Give each agent an isolated filesystem and terminal to prevent state pollution","Scale from single-developer to team-wide agent execution without local machine constraints","Monitor and control individual agent terminal sessions from a web UI in real-time"],"best_for":["Teams scaling AI agent usage beyond single-developer local execution","Organizations needing isolated execution environments for compliance or security","Multi-agent workflows where agents must work on different codebases simultaneously"],"limitations":["Pod creation latency depends on Runner infrastructure — no sub-second spawning","Git worktree isolation adds filesystem overhead per Pod; not suitable for thousands of concurrent Pods per Runner","Terminal state is ephemeral unless explicitly persisted; no built-in session replay beyond current connection"],"requires":["Self-hosted Runner process (Go binary) with Docker or container runtime","Backend service with gRPC listener and mTLS PKI infrastructure","Git repository access from Runner environment","Network connectivity between Runner and Backend (gRPC over mTLS)"],"input_types":["gRPC commands (SpawnPod, TerminatePod, ExecuteCommand)","Git repository URLs and branch specifications","Terminal input streams (stdin)"],"output_types":["Terminal output streams (stdout, stderr)","Pod lifecycle events (created, running, terminated)","Real-time WebSocket events to frontend"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_1","uri":"capability://tool.use.integration.inter.agent.communication.via.channels.with.real.time.message.relay","name":"inter-agent communication via channels with real-time message relay","description":"Agents communicate asynchronously through Channels — named message queues managed by the backend and relayed to connected Runners via gRPC streaming. When an agent publishes a message to a Channel, the backend broadcasts it to all Runners with subscribed Pods, which deliver it to the agent's terminal or MCP interface. The Relay component handles session management and heartbeat-based connection health tracking, ensuring messages reach agents even if network conditions are unstable.","intents":["Enable agents to coordinate work by sharing status, results, or requests without direct terminal access","Build multi-agent workflows where one agent's output feeds another agent's input","Implement agent-to-agent negotiation or consensus patterns for complex tasks","Decouple agent communication from terminal I/O for cleaner message semantics"],"best_for":["Multi-agent teams working on interdependent tasks (e.g., one agent writes code, another reviews)","Workflows requiring agent-to-agent handoff without human intervention","Organizations building custom agent orchestration logic on top of AgentsMesh"],"limitations":["Channel messages are not persisted by default — no message history replay if agent reconnects","No built-in message ordering guarantees across multiple Runners; eventual consistency model","Channel names are application-defined; no schema validation or type safety for message payloads"],"requires":["Backend service with Channel management (ticket_service, mesh_service)","Runner with Relay client for Channel subscription","Network connectivity between Runner and Backend for gRPC streaming"],"input_types":["Channel name (string identifier)","Message payload (unstructured text or JSON)","Agent identifier (Pod ID or agent name)"],"output_types":["Broadcast messages to all subscribed agents","Real-time WebSocket events for frontend Channel view","Message delivery confirmation via gRPC"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_10","uri":"capability://tool.use.integration.agent.type.abstraction.supporting.claude.code.codex.cli.gemini.cli.aider.and.custom.agents","name":"agent type abstraction supporting claude code, codex cli, gemini cli, aider, and custom agents","description":"AgentsMesh abstracts agent type as a configurable parameter when spawning a Pod. Supported agents include Claude Code, Codex CLI, Gemini CLI, and Aider, each with different CLI interfaces and capabilities. When a Pod is created, the Runner installs the specified agent binary and configures it with environment variables (API keys, model selection). The agent runs in the Pod's terminal, and AgentsMesh orchestrates its lifecycle without imposing constraints on the agent's internal behavior. Custom agents can be supported by providing a startup script or binary.","intents":["Use different AI coding agents (Claude, GPT, Gemini, Aider) in the same workflow","Switch between agents without changing orchestration logic","Support custom or proprietary coding agents via startup scripts","Leverage agent-specific features (e.g., Claude Code's artifact editing) within AgentsMesh"],"best_for":["Teams wanting to experiment with multiple AI coding agents","Organizations with custom agents that need orchestration","Workflows requiring agent-specific features (e.g., Claude Code artifacts)"],"limitations":["Agent installation and startup is agent-specific — no unified interface for all agents","No built-in agent capability discovery — must manually configure which agents support which features","Agent updates require manual Runner configuration or image rebuild","No standardized way to extract agent results (some agents output JSON, others plain text)"],"requires":["Agent binary or script available in Runner environment (Claude Code, Codex CLI, etc.)","API keys for the chosen agent's provider","Agent-specific configuration (model selection, parameters, etc.)"],"input_types":["Agent type (string identifier: 'claude-code', 'codex-cli', 'gemini-cli', 'aider')","Agent configuration (model, parameters, environment variables)"],"output_types":["Agent terminal output (varies by agent)","Agent-specific artifacts (e.g., Claude Code artifacts, file edits)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_11","uri":"capability://memory.knowledge.workspace.and.git.state.management.with.branch.tracking.and.commit.history","name":"workspace and git state management with branch tracking and commit history","description":"The Runner maintains workspace state for each Pod, including current Git branch, commit history, uncommitted changes, and file modifications. Agents can query workspace state via MCP tools or REST API to understand the current code context. The Runner tracks Git state by running git commands (git status, git log, git diff) and caching results. This enables agents to make informed decisions about which files to edit, which branches to work on, and whether changes are ready for commit.","intents":["Let agents understand the current code context (branch, uncommitted changes) before making edits","Enable agents to query commit history to understand code evolution","Track which files have been modified by agents for review or rollback","Support agents in making branch management decisions (create branch, switch branch, merge)"],"best_for":["Agents that need to understand code context before making changes","Workflows requiring agents to manage Git branches and commits","Teams wanting to track what code changes agents have made"],"limitations":["Workspace state is cached and may be stale if agents modify files outside of AgentsMesh","Git state queries (git log, git diff) can be slow for large repositories","No built-in conflict detection if multiple agents modify the same file","Commit history is read-only from agent perspective — agents cannot rewrite history"],"requires":["Git repository in Pod's worktree","Git binary available in Runner environment","Agent with MCP tools or REST API access to query workspace state"],"input_types":["Pod ID (to identify workspace)","Git query type (status, log, diff, branch list)"],"output_types":["Current branch name","List of uncommitted changes (modified files, staged changes)","Commit history (commits, authors, messages)","File diffs (changes between commits or working directory)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_12","uri":"capability://automation.workflow.auto.update.mechanism.for.runner.binary.with.zero.downtime.deployment","name":"auto-update mechanism for runner binary with zero-downtime deployment","description":"The Runner supports auto-update, where the backend can trigger a Runner to download and restart itself with a new binary version. The update process is designed for zero-downtime: existing Pods are allowed to complete, new Pod creation is paused during update, and the Runner restarts with the new binary. This enables platform updates without manual intervention or downtime for running agents.","intents":["Deploy Runner updates across the fleet without manual intervention","Fix bugs or add features to Runners without requiring operator action","Maintain platform consistency across multiple Runners with different versions","Enable zero-downtime updates for long-running agent workflows"],"best_for":["Teams managing multiple Runners and wanting automated updates","Organizations requiring frequent platform updates without downtime","Workflows with long-running agents that cannot tolerate Runner restarts"],"limitations":["Auto-update requires network access from Runner to binary repository (e.g., GitHub releases)","Update is not atomic — if Runner crashes during update, manual intervention may be needed","Existing Pods are not migrated to new Runner version; they continue on old version until termination","No rollback mechanism — if new version has bugs, manual downgrade is required"],"requires":["Runner with auto-update enabled (configuration flag)","Network access from Runner to binary repository","Backend capability to trigger updates (admin API endpoint)"],"input_types":["Update trigger from backend (version to update to)","Binary download URL (e.g., GitHub release)"],"output_types":["Update status (downloading, installing, restarting)","New Runner version confirmation"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_13","uri":"capability://automation.workflow.relay.based.session.management.for.runner.to.backend.communication.resilience","name":"relay-based session management for runner-to-backend communication resilience","description":"The Relay component manages Runner-to-Backend communication with session persistence and heartbeat-based health checking. When a Runner connects, the Relay establishes a session and monitors heartbeat messages. If the connection drops, the Relay maintains session state and allows the Runner to reconnect without losing context. This enables Runners to survive temporary network outages without losing Pod state or pending commands.","intents":["Maintain Runner connection state across temporary network outages","Prevent Pod state loss if Runner-Backend connection drops","Enable Runners to reconnect without manual intervention","Track Runner health and detect dead Runners for failover"],"best_for":["Deployments with unreliable network connectivity between Runner and Backend","Long-running agent workflows that must survive temporary outages","Teams wanting automatic failover and recovery without manual intervention"],"limitations":["Session state is in-memory on Backend; if Backend crashes, session state is lost","Heartbeat-based health checking has latency (typically 30-60 seconds to detect dead Runner)","No automatic failover to backup Runner — dead Pods are not automatically migrated","Session timeout is fixed (no configurable grace period)"],"requires":["Backend with Relay component enabled","Runner with Relay client configured","Network connectivity between Runner and Backend (gRPC)"],"input_types":["Heartbeat messages from Runner (periodic ping)","Session state (Pod list, pending commands)"],"output_types":["Session acknowledgment","Pending commands to execute","Health status (connected, disconnected, reconnecting)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_2","uri":"capability://tool.use.integration.agent.control.and.observation.via.bindings.cross.pod.terminal.access","name":"agent control and observation via bindings (cross-pod terminal access)","description":"Bindings allow one agent to observe and control another agent's terminal by establishing a read/write connection to a peer Pod's PTY. When Agent A creates a Binding to Agent B's Pod, Agent A gains terminal access to Agent B's session, enabling scenarios like one agent monitoring another's progress or taking over execution. Bindings are managed via MCP tools exposed by the Runner's MCP server, which translates tool invocations into gRPC commands to the backend's Runner Connection Manager.","intents":["Allow one agent to monitor another agent's terminal output in real-time without polling","Enable agent-to-agent handoff where one agent takes control of a peer's terminal session","Implement supervisor agents that observe and intervene in worker agent execution","Build debugging workflows where one agent inspects another agent's state"],"best_for":["Hierarchical agent workflows with supervisor/worker patterns","Multi-agent debugging and troubleshooting scenarios","Teams implementing custom agent orchestration with cross-Pod visibility"],"limitations":["Bindings are unidirectional (A observes B) — no automatic bidirectional communication","Terminal control via Binding is sequential; simultaneous writes from multiple agents to same Pod cause undefined behavior","No access control per Binding — if Agent A can create a Binding, it has full terminal access to Agent B"],"requires":["Both Pods must be running on the same or connected Runners","Agent must invoke MCP tool (e.g., create_binding) with target Pod ID","Backend Runner Connection Manager must have both Pods registered"],"input_types":["Target Pod ID (string identifier)","Binding mode (read-only or read-write)","Terminal command or input stream"],"output_types":["Terminal output from bound Pod (stdout, stderr)","Binding lifecycle events (created, terminated)","Terminal input delivery confirmation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_3","uri":"capability://tool.use.integration.mcp.based.tool.exposure.for.agent.self.service.pod.and.binding.management","name":"mcp-based tool exposure for agent self-service pod and binding management","description":"The Runner exposes an MCP (Model Context Protocol) server that agents can invoke to autonomously spawn new Pods, create Bindings, and manage Channels without human intervention. Tools like create_pod, create_binding, and publish_to_channel are registered in the MCP server (runner/internal/mcp/http_server.go) and translated to gRPC commands sent to the backend. This enables agents to dynamically scale their own execution environment — e.g., an agent can spawn a new Pod for a subtask, bind to it for monitoring, and coordinate via Channels.","intents":["Allow agents to autonomously spawn new Pods for parallel subtask execution","Enable agents to discover and bind to peer Pods without manual configuration","Let agents publish results to Channels for consumption by other agents","Build self-scaling agent workflows where agents create resources on-demand"],"best_for":["Complex multi-agent workflows requiring dynamic resource allocation","Agents that need to parallelize work by spawning subtask Pods","Teams building custom agent orchestration logic using MCP tool invocations"],"limitations":["MCP tool invocations are synchronous — agent blocks until Pod creation completes","No quota or rate limiting on Pod creation; agents can exhaust Runner resources if not constrained","Tool schemas are defined in code (runner/internal/mcp/tools/types_*.go); no dynamic schema generation or validation"],"requires":["Agent must support MCP tool calling (Claude Code, Codex CLI, Gemini CLI, Aider)","Runner must have MCP server enabled and registered with backend","Agent must have permissions to create Pods (enforced at backend level via ticket/binding authorization)"],"input_types":["MCP tool invocation with parameters (Pod name, git repo, agent type, etc.)","Tool schema as JSON (defined in runner/internal/mcp/tools/types_*.go)"],"output_types":["Pod creation confirmation with Pod ID","Binding creation confirmation","Channel publish acknowledgment","Tool execution result (success/failure with details)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_4","uri":"capability://automation.workflow.ticket.based.agent.to.work.binding.with.kanban.board.and.webhook.integration","name":"ticket-based agent-to-work binding with kanban board and webhook integration","description":"AgentsMesh integrates a Ticket Service that binds agents to work items (tickets) on a Kanban board. Agents can query tickets, update status, and receive webhook notifications when external systems (GitHub, GitLab) create or update PRs/MRs. The backend's ticket_service maintains ticket state and enforces agent-to-ticket bindings, while the web frontend displays a Kanban view. Webhooks from external systems trigger ticket creation or status updates, enabling agents to react to external events without polling.","intents":["Bind agents to specific work items (tickets) to track progress and prevent duplicate work","Automatically create tickets from GitHub/GitLab PRs so agents can work on real external tasks","Update ticket status as agents complete work, creating a feedback loop to external systems","Enable agents to discover available work by querying the ticket board"],"best_for":["Teams integrating AI agents into existing GitHub/GitLab workflows","Organizations using Kanban boards for work tracking and wanting agent visibility","Workflows where agents should react to external PR/MR events automatically"],"limitations":["Ticket state is eventually consistent with external systems — no real-time bidirectional sync","Webhook integration requires manual setup per GitHub/GitLab repository","No built-in conflict resolution if multiple agents bind to the same ticket","Ticket queries are simple filters (status, assignee) — no complex search or aggregation"],"requires":["Backend Ticket Service with database persistence","Webhook endpoint exposed by backend for GitHub/GitLab integration","GitHub/GitLab repository with webhook configured to point to AgentsMesh backend","Agent with MCP tools or REST API access to query/update tickets"],"input_types":["Webhook payload from GitHub/GitLab (PR/MR created, updated, closed)","Ticket query parameters (status, assignee, labels)","Ticket update payload (status, description, assignee)"],"output_types":["Ticket object with ID, title, status, assignee, linked PR/MR","Kanban board view (frontend)","Webhook delivery confirmation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_5","uri":"capability://automation.workflow.self.hosted.runner.registration.and.secure.backend.communication.via.mtls","name":"self-hosted runner registration and secure backend communication via mtls","description":"Runners are self-hosted Go binaries that register with the backend using mTLS (mutual TLS with certificate-based authentication). During registration, the Runner provides its identity (hostname, capabilities) and establishes a persistent gRPC connection to the backend. The backend's Runner Connection Manager (backend/internal/service/runner/connection_manager.go) maintains a registry of active Runners and routes commands (spawn Pod, execute command) to the appropriate Runner. All communication is encrypted and authenticated via PKI, ensuring only authorized Runners can connect.","intents":["Deploy AgentsMesh on-premise without sending code execution to cloud infrastructure","Ensure all AI agent execution stays within organizational network boundaries","Scale agent execution across multiple on-premise machines by deploying multiple Runners","Maintain full control over Runner infrastructure and resource allocation"],"best_for":["Organizations with strict data residency or security requirements","Teams running proprietary code that cannot leave internal networks","Enterprises needing to scale agent execution across multiple machines"],"limitations":["Runner registration requires PKI setup (certificate generation, distribution) — non-trivial operational overhead","No built-in Runner auto-scaling — requires manual deployment and registration of new Runners","Runner heartbeat is simple (periodic ping) — no sophisticated health checking or automatic failover","Backend must be network-accessible from all Runners; no support for Runner-initiated polling"],"requires":["Go runtime (Runner is Go binary)","mTLS certificates (CA cert, Runner cert, Runner key) — must be generated and distributed","Network connectivity from Runner to Backend (gRPC over mTLS, typically port 50051)","Backend service running with gRPC listener and mTLS configuration"],"input_types":["Runner binary with configuration (backend URL, mTLS certs, environment variables)","gRPC commands from backend (SpawnPod, TerminatePod, ExecuteCommand)"],"output_types":["Runner registration confirmation","Heartbeat acknowledgment","Command execution results (Pod created, command output, etc.)","Real-time event stream (Pod lifecycle, terminal output)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_6","uri":"capability://tool.use.integration.byok.bring.your.own.key.ai.provider.integration.without.api.proxying","name":"byok (bring your own key) ai provider integration without api proxying","description":"AgentsMesh does not proxy AI API calls. Users supply their own provider API keys (ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY) directly to the Runner process environment. Agents running in Pods access these keys from the Runner's environment and make direct API calls to the provider (Claude, GPT, Gemini). The platform orchestrates agent lifecycle and communication but never handles AI inference traffic, meaning AI costs appear on the user's own provider account and there are no per-token usage caps imposed by AgentsMesh.","intents":["Use multiple AI providers (Claude, GPT, Gemini) in the same workflow without vendor lock-in","Avoid paying markup on AI API calls — costs go directly to provider accounts","Maintain full control over API key management and rotation","Avoid sending code or prompts through AgentsMesh infrastructure"],"best_for":["Organizations with existing AI provider accounts and cost optimization concerns","Teams wanting to avoid vendor lock-in by using multiple AI providers","Enterprises with strict data governance requiring direct provider API calls"],"limitations":["Users must manage API keys and provider accounts independently — no centralized billing","No built-in cost tracking or usage analytics at the AgentsMesh level","API key rotation requires Runner restart or environment variable update","No fallback or failover between providers if one API is unavailable"],"requires":["API keys for desired providers (Anthropic, OpenAI, Google) in Runner environment","Active accounts with each provider and sufficient API quota","Network connectivity from Runner to provider APIs (outbound HTTPS)"],"input_types":["Environment variables (ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, etc.)","Agent configuration specifying which provider to use"],"output_types":["Agent inference results (text, code, etc.) from chosen provider","API usage and costs appear on user's provider account"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_7","uri":"capability://automation.workflow.real.time.terminal.state.synchronization.via.websocket.and.event.bus","name":"real-time terminal state synchronization via websocket and event bus","description":"AgentsMesh synchronizes terminal state to the web frontend in real-time using WebSocket connections and an internal event bus. When a Pod's terminal produces output, the Runner emits an event (via gRPC) to the backend's event bus, which broadcasts it to all connected WebSocket clients viewing that Pod. The Relay component manages WebSocket session lifecycle and heartbeat, ensuring clients receive updates even if network conditions are unstable. Terminal state includes stdout, stderr, and PTY metadata (cursor position, window size).","intents":["Watch agent terminal output in real-time from a web browser without polling","Monitor multiple agent Pods simultaneously with live updates","Implement interactive terminal control (send input, resize window) from the web UI","Build dashboards showing real-time agent execution status across the team"],"best_for":["Teams wanting to monitor agent execution from a web UI without SSH/terminal access","Organizations building custom dashboards on top of AgentsMesh","Workflows requiring real-time visibility into multi-agent execution"],"limitations":["WebSocket connections are stateless — no automatic reconnection or message replay on disconnect","Terminal state is streamed as-is; no compression or delta encoding, so high-volume output can saturate bandwidth","Event bus is in-memory; no persistence of terminal history beyond current session","Multiple concurrent WebSocket clients viewing the same Pod may see slightly out-of-order events due to eventual consistency"],"requires":["WebSocket support in browser (all modern browsers)","Backend with event bus and WebSocket listener","Network connectivity from client to backend (WebSocket over HTTPS, typically port 443)"],"input_types":["Terminal output events from Runner (stdout, stderr, PTY metadata)","User input from web UI (keyboard input, window resize)"],"output_types":["Real-time terminal output in web UI","Terminal input delivery confirmation","Connection status (connected, disconnected, reconnecting)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_8","uri":"capability://automation.workflow.git.worktree.based.sandbox.isolation.per.pod.with.automatic.cleanup","name":"git worktree-based sandbox isolation per pod with automatic cleanup","description":"Each Pod gets its own Git worktree sandbox created from a specified repository and branch. The Runner creates a worktree (git worktree add) in an isolated directory, ensuring each Pod has a separate working directory with independent Git state. When a Pod terminates, the worktree is automatically cleaned up (git worktree remove). This isolation prevents agents from interfering with each other's code changes and enables agents to work on different branches or commits simultaneously without conflicts.","intents":["Isolate code changes between agents so one agent's edits don't affect another's","Allow agents to work on different branches or commits of the same repo simultaneously","Automatically clean up code changes when a Pod terminates without manual intervention","Enable agents to safely experiment with code without risking the main repository"],"best_for":["Multi-agent workflows where agents work on the same codebase but different features","Teams wanting to prevent accidental code conflicts between parallel agent execution","Workflows requiring isolated code sandboxes for each agent task"],"limitations":["Worktree creation adds latency to Pod startup (typically 100-500ms depending on repo size)","Large repositories with many worktrees can consume significant disk space","Worktree cleanup is not atomic — if Runner crashes, orphaned worktrees may remain","No built-in mechanism to merge or rebase changes from one worktree to another"],"requires":["Git repository accessible from Runner (local path or SSH/HTTPS URL)","Git binary available in Runner environment","Sufficient disk space for worktree copies (at least 2x repo size for concurrent Pods)"],"input_types":["Git repository URL or local path","Branch or commit to check out","Pod ID (used to name worktree)"],"output_types":["Worktree path (directory where agent can work)","Git state (current branch, commit, uncommitted changes)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-agentsmesh--agentsmesh__cap_9","uri":"capability://tool.use.integration.rest.api.for.programmatic.pod.ticket.and.channel.management","name":"rest api for programmatic pod, ticket, and channel management","description":"AgentsMesh exposes a REST API (backend/internal/api/rest/v1/) for programmatic control of Pods, Tickets, and Channels. Clients can create Pods, query ticket status, publish to Channels, and manage agent lifecycle via HTTP endpoints. The REST API is a thin wrapper over the backend's internal services (runner, ticket, mesh), translating HTTP requests to service method calls. Authentication is handled via API keys or JWT tokens, and responses are JSON-encoded.","intents":["Integrate AgentsMesh into external workflows or CI/CD pipelines via HTTP API","Build custom dashboards or tools that programmatically manage agents","Automate agent provisioning and task assignment from external systems","Query agent status and ticket progress without accessing the web UI"],"best_for":["Teams integrating AgentsMesh into existing CI/CD or automation platforms","Organizations building custom tools or dashboards on top of AgentsMesh","Workflows requiring programmatic agent lifecycle management"],"limitations":["REST API is synchronous — long-running operations (Pod creation) block until completion","No built-in pagination or filtering for large result sets (e.g., listing thousands of Pods)","API versioning is manual (v1 in URL); breaking changes require client updates","No OpenAPI/Swagger documentation (must infer from source code)"],"requires":["Backend service with REST API listener (typically port 8080)","API key or JWT token for authentication","HTTP client library (curl, requests, fetch, etc.)"],"input_types":["HTTP request with JSON payload (Pod creation, ticket update, Channel publish)","Query parameters (filters, pagination)"],"output_types":["JSON response with resource data (Pod ID, ticket status, etc.)","HTTP status codes (200 OK, 400 Bad Request, 404 Not Found, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Self-hosted Runner process (Go binary) with Docker or container runtime","Backend service with gRPC listener and mTLS PKI infrastructure","Git repository access from Runner environment","Network connectivity between Runner and Backend (gRPC over mTLS)","Backend service with Channel management (ticket_service, mesh_service)","Runner with Relay client for Channel subscription","Network connectivity between Runner and Backend for gRPC streaming","Agent binary or script available in Runner environment (Claude Code, Codex CLI, etc.)","API keys for the chosen agent's provider","Agent-specific configuration (model selection, parameters, etc.)"],"failure_modes":["Pod creation latency depends on Runner infrastructure — no sub-second spawning","Git worktree isolation adds filesystem overhead per Pod; not suitable for thousands of concurrent Pods per Runner","Terminal state is ephemeral unless explicitly persisted; no built-in session replay beyond current connection","Channel messages are not persisted by default — no message history replay if agent reconnects","No built-in message ordering guarantees across multiple Runners; eventual consistency model","Channel names are application-defined; no schema validation or type safety for message payloads","Agent installation and startup is agent-specific — no unified interface for all agents","No built-in agent capability discovery — must manually configure which agents support which features","Agent updates require manual Runner configuration or image rebuild","No standardized way to extract agent results (some agents output JSON, others plain text)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4898652921056387,"quality":0.5,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:34.540Z","last_commit":"2026-05-03T10:42:05Z"},"community":{"stars":1891,"forks":195,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agentsmesh--agentsmesh","compare_url":"https://unfragile.ai/compare?artifact=agentsmesh--agentsmesh"}},"signature":"q/gGjRlMAAqtWfWk3euu8/Q2CoemSXVZ5ggKIWyv7FN51RmUrjeytvVgFN2wYHe3qNtaP3GN0cR+0mg7jixrBg==","signedAt":"2026-06-22T03:52:28.265Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentsmesh--agentsmesh","artifact":"https://unfragile.ai/agentsmesh--agentsmesh","verify":"https://unfragile.ai/api/v1/verify?slug=agentsmesh--agentsmesh","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"}}