{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47574045","slug":"phantom-open-source-ai-agent-on-its-own-vm-that-re","name":"Phantom – Open-source AI agent on its own VM that rewrites its config","type":"agent","url":"https://github.com/ghostwright/phantom","page_url":"https://unfragile.ai/phantom-open-source-ai-agent-on-its-own-vm-that-re","categories":["ai-agents"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47574045__cap_0","uri":"capability://planning.reasoning.self.modifying.agent.configuration.via.llm.driven.rewrites","name":"self-modifying agent configuration via llm-driven rewrites","description":"Phantom enables an AI agent running on an isolated VM to autonomously read, analyze, and rewrite its own configuration files based on task performance and learned patterns. The agent uses LLM reasoning to generate configuration changes (e.g., parameter tuning, prompt adjustments, tool enablement) and applies them directly to its runtime config, creating a feedback loop where the agent optimizes itself without human intervention. This is implemented via direct filesystem access within the VM sandbox and config serialization/deserialization that preserves schema integrity.","intents":["I want an AI agent that can self-optimize its behavior by modifying its own configuration based on task outcomes","I need an agent that learns from failures and adjusts its parameters without requiring manual redeployment","I want to observe how an agent evolves its own decision-making rules over time through self-modification"],"best_for":["researchers studying agent self-improvement and meta-learning","teams building adaptive AI systems that need to tune themselves in production","developers prototyping autonomous systems with minimal human oversight"],"limitations":["Configuration rewrites are not versioned by default — no built-in rollback mechanism if agent modifies itself into a broken state","LLM-driven config generation may produce syntactically valid but semantically incorrect configurations that degrade performance","No transaction semantics — concurrent config reads/writes from multiple agent instances can cause race conditions","Requires careful schema validation to prevent agent from writing configs that violate type constraints or break initialization"],"requires":["Isolated VM environment (KVM, VirtualBox, or cloud VM) with write access to config filesystem","LLM API access (OpenAI, Anthropic, or local model) for reasoning about config changes","Configuration file format with schema (JSON, YAML, or TOML) that agent can parse and regenerate","Agent runtime that supports hot-reloading of configuration without full restart"],"input_types":["configuration files (JSON/YAML/TOML)","task execution logs and performance metrics","agent reasoning traces and decision history"],"output_types":["modified configuration files","configuration change diffs","self-modification justification/reasoning"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47574045__cap_1","uri":"capability://automation.workflow.isolated.vm.based.agent.execution.with.filesystem.sandboxing","name":"isolated vm-based agent execution with filesystem sandboxing","description":"Phantom runs the AI agent on a dedicated virtual machine with controlled filesystem access, preventing the agent from modifying system files, accessing other VMs, or escaping the sandbox. The VM provides process isolation via hypervisor-level boundaries (KVM, Hyper-V, or similar), and the agent's filesystem is restricted to a designated config/data directory. This architecture uses standard VM image provisioning and network isolation to ensure the agent cannot compromise the host system or other workloads.","intents":["I want to run an AI agent that can modify its own code/config without risking the stability of my production system","I need to safely execute untrusted or experimental agent logic in isolation","I want to prevent a misbehaving agent from accessing sensitive files or other applications"],"best_for":["teams deploying autonomous agents in multi-tenant or shared infrastructure","researchers experimenting with agent self-modification in a controlled environment","organizations with strict security policies requiring process isolation for AI workloads"],"limitations":["VM overhead adds 500ms–2s startup latency compared to containerized agents","Filesystem sandboxing requires careful mount point configuration — overly restrictive mounts can break agent functionality","No built-in inter-VM communication — agents on separate VMs cannot directly share state without external message broker","VM resource allocation (CPU, RAM, disk) must be pre-configured; dynamic scaling requires orchestration layer"],"requires":["Hypervisor support (KVM on Linux, Hyper-V on Windows, or ESXi for enterprise)","VM image with agent runtime pre-installed (Python, Node.js, or language-specific runtime)","Network connectivity between host and VM (bridged or NAT networking)","Sufficient host resources: minimum 2 CPU cores and 2GB RAM per agent VM"],"input_types":["VM image (QCOW2, VHD, or OVA format)","agent code/scripts","configuration files"],"output_types":["agent execution logs","modified configuration/code files within VM","task completion status and results"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47574045__cap_2","uri":"capability://safety.moderation.agent.driven.configuration.schema.validation.and.type.checking","name":"agent-driven configuration schema validation and type checking","description":"Phantom validates configuration changes generated by the agent against a predefined schema before applying them, ensuring type safety and preventing the agent from writing malformed configs that would break initialization. The validation layer uses schema definitions (JSON Schema, Pydantic models, or similar) to enforce constraints on parameter types, ranges, and dependencies. When the agent generates a config rewrite, the system parses the proposed changes, validates them against the schema, and either applies them or rejects them with detailed error messages that feed back into the agent's reasoning.","intents":["I want to prevent the agent from modifying its config in ways that would cause runtime errors or undefined behavior","I need to ensure that agent-generated configurations always satisfy type and constraint requirements","I want the agent to receive feedback when its config proposals are invalid so it can learn what changes are acceptable"],"best_for":["teams deploying self-modifying agents in production where config errors could cause outages","researchers studying how agents learn to respect schema constraints through feedback","developers building agent systems where configuration safety is a hard requirement"],"limitations":["Schema validation adds ~50–100ms latency per config rewrite (parsing, validation, serialization)","Complex nested schemas with conditional constraints can be difficult for LLMs to reason about correctly","Validation only catches type errors and constraint violations — it cannot detect semantic errors (e.g., setting learning_rate to a value that causes training divergence)","Schema changes require manual updates to the validation layer; no automatic schema evolution"],"requires":["Schema definition language (JSON Schema, Pydantic, or similar) with clear type and constraint specifications","Schema validation library (jsonschema, pydantic, or language-specific equivalent)","Configuration file format that can be parsed and validated (JSON, YAML, TOML)"],"input_types":["proposed configuration changes (as JSON/YAML/TOML or structured data)","schema definition (JSON Schema, Pydantic model, or similar)"],"output_types":["validation result (pass/fail)","detailed error messages for invalid configs","validated configuration ready for application"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47574045__cap_3","uri":"capability://planning.reasoning.agent.performance.monitoring.and.feedback.loop.for.self.optimization","name":"agent performance monitoring and feedback loop for self-optimization","description":"Phantom collects metrics on agent task performance (success rate, execution time, resource usage, error frequency) and feeds these metrics back to the agent as context for deciding what configuration changes to make. The monitoring layer tracks execution traces, logs, and outcome data, then synthesizes this into a performance summary that the agent can reason about. The agent uses this feedback to identify bottlenecks (e.g., 'my tool calls are timing out, I should increase timeout thresholds') and propose configuration adjustments that address observed problems.","intents":["I want the agent to analyze its own performance metrics and identify which configuration parameters are causing problems","I need the agent to automatically adjust its behavior based on observed task success/failure patterns","I want to understand what configuration changes the agent is making and why, based on performance data"],"best_for":["teams running agents in production who want autonomous performance tuning","researchers studying how agents learn to optimize their own behavior through feedback","developers building adaptive systems that need to tune themselves without manual intervention"],"limitations":["Feedback loop introduces latency — agent must complete multiple tasks before collecting enough data to identify patterns","Correlation vs causation: agent may incorrectly attribute performance changes to config modifications when external factors (e.g., API rate limits) are responsible","No built-in statistical significance testing — agent may over-fit to noise in performance metrics","Requires careful instrumentation to collect relevant metrics; missing metrics can lead to blind spots in agent reasoning"],"requires":["Metrics collection infrastructure (logging, tracing, or monitoring system)","Task execution framework that captures success/failure outcomes and execution time","Mechanism to aggregate metrics into summaries that the agent can reason about (e.g., 'success rate dropped 5% after last config change')"],"input_types":["task execution logs","performance metrics (success rate, latency, error counts)","agent configuration history"],"output_types":["performance summary/report","identified bottlenecks and problems","proposed configuration changes based on performance analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47574045__cap_4","uri":"capability://automation.workflow.configuration.change.history.tracking.and.diff.generation","name":"configuration change history tracking and diff generation","description":"Phantom maintains a versioned history of all configuration changes made by the agent, storing each version with a timestamp and optionally a diff showing what changed. When the agent modifies its config, the system generates a structured diff (e.g., JSON Patch, unified diff format) that captures the specific parameter changes. This history enables rollback to previous configurations, analysis of how the agent's configuration evolved over time, and debugging of configuration-related issues.","intents":["I want to see what configuration changes the agent has made and in what order","I need to rollback to a previous configuration if the agent's changes degraded performance","I want to analyze how the agent's configuration evolved and what patterns it discovered"],"best_for":["teams deploying self-modifying agents who need auditability and rollback capability","researchers studying agent learning and configuration evolution over time","operators managing production agents who need to understand and debug configuration changes"],"limitations":["History storage grows linearly with the number of config changes — no built-in pruning or archival","Diff generation adds ~10–50ms latency per config write (depending on config size)","No built-in branching or merging — if multiple agents modify the same config, conflicts must be resolved manually","Rollback is not atomic — reverting to a previous config does not automatically revert any side effects (e.g., data written by the agent)"],"requires":["Persistent storage for configuration history (filesystem, database, or version control system)","Diff generation library (python-json-diff, deepdiff, or similar)","Mechanism to tag/label configuration versions with timestamps or semantic identifiers"],"input_types":["current configuration","proposed configuration changes","previous configuration versions"],"output_types":["configuration history (list of versions with timestamps)","diffs between configuration versions","rollback capability to previous versions"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47574045__cap_5","uri":"capability://planning.reasoning.multi.step.reasoning.with.configuration.impact.analysis","name":"multi-step reasoning with configuration impact analysis","description":"Phantom enables the agent to reason through multi-step decision chains where it analyzes the potential impact of configuration changes before applying them. The agent can query a simulation or impact model to predict how a proposed config change would affect task performance, then decide whether to apply the change. This uses chain-of-thought reasoning where the agent explicitly states its hypothesis (e.g., 'increasing timeout will reduce failures'), predicts the impact, and then evaluates whether the change is worth making.","intents":["I want the agent to think through the consequences of configuration changes before applying them","I need the agent to avoid making changes that would likely degrade performance","I want the agent to explain its reasoning for why it's making specific configuration adjustments"],"best_for":["teams deploying agents in production where configuration mistakes could cause outages","researchers studying agent reasoning and decision-making","developers building explainable AI systems where agent reasoning must be transparent"],"limitations":["Impact analysis requires a model or simulation of how config changes affect performance — building this model is non-trivial","Chain-of-thought reasoning adds latency (typically 1–5 seconds per decision) due to LLM token generation","Agent's predictions about impact may be inaccurate if the model is wrong or if the system behaves non-linearly","No guarantee that the agent will follow its own reasoning — it may generate a good analysis but then make a different decision"],"requires":["LLM with chain-of-thought capability (GPT-4, Claude, or similar)","Impact model or simulation that can predict how config changes affect performance","Structured prompt engineering to guide the agent through multi-step reasoning"],"input_types":["current configuration","performance metrics and task history","proposed configuration changes"],"output_types":["reasoning trace (step-by-step analysis)","predicted impact of configuration changes","decision to apply or reject changes"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Isolated VM environment (KVM, VirtualBox, or cloud VM) with write access to config filesystem","LLM API access (OpenAI, Anthropic, or local model) for reasoning about config changes","Configuration file format with schema (JSON, YAML, or TOML) that agent can parse and regenerate","Agent runtime that supports hot-reloading of configuration without full restart","Hypervisor support (KVM on Linux, Hyper-V on Windows, or ESXi for enterprise)","VM image with agent runtime pre-installed (Python, Node.js, or language-specific runtime)","Network connectivity between host and VM (bridged or NAT networking)","Sufficient host resources: minimum 2 CPU cores and 2GB RAM per agent VM","Schema definition language (JSON Schema, Pydantic, or similar) with clear type and constraint specifications","Schema validation library (jsonschema, pydantic, or language-specific equivalent)"],"failure_modes":["Configuration rewrites are not versioned by default — no built-in rollback mechanism if agent modifies itself into a broken state","LLM-driven config generation may produce syntactically valid but semantically incorrect configurations that degrade performance","No transaction semantics — concurrent config reads/writes from multiple agent instances can cause race conditions","Requires careful schema validation to prevent agent from writing configs that violate type constraints or break initialization","VM overhead adds 500ms–2s startup latency compared to containerized agents","Filesystem sandboxing requires careful mount point configuration — overly restrictive mounts can break agent functionality","No built-in inter-VM communication — agents on separate VMs cannot directly share state without external message broker","VM resource allocation (CPU, RAM, disk) must be pre-configured; dynamic scaling requires orchestration layer","Schema validation adds ~50–100ms latency per config rewrite (parsing, validation, serialization)","Complex nested schemas with conditional constraints can be difficult for LLMs to reason about correctly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.22,"ecosystem":0.46,"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":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:10:10.018Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=phantom-open-source-ai-agent-on-its-own-vm-that-re","compare_url":"https://unfragile.ai/compare?artifact=phantom-open-source-ai-agent-on-its-own-vm-that-re"}},"signature":"PuSEqrA+GqbvQLGYdXTesZGPHGovBV345VlSZngivF8woHv75Aq8DXFmtaSdwAckOODXGb1OPMOVezb902T4Bg==","signedAt":"2026-06-22T22:12:08.546Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phantom-open-source-ai-agent-on-its-own-vm-that-re","artifact":"https://unfragile.ai/phantom-open-source-ai-agent-on-its-own-vm-that-re","verify":"https://unfragile.ai/api/v1/verify?slug=phantom-open-source-ai-agent-on-its-own-vm-that-re","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"}}