Python Actions Reference#

Auto-generated summary | Last updated: 2026-01-25 | Git: 8eb000e

Total modules: 51 | Total actions: 276

This document lists all built-in actions available in the Python implementation of The Edge Agent.

For complete action documentation including parameters and examples, see the YAML Reference.

Quick Reference#

Module	Actions	Description
cache.*	4	Caching/memoization with LTM backend
core.*	7	HTTP, file operations, notifications, checkpoints
data.*	16	JSON/CSV parsing, transformation, validation
llm.*	4	LLM calls, streaming, retry, and tool calling
ltm.*	4	Long-term persistent key-value storage with FTS5
memory.*	3	Key-value storage with TTL
a2a.*	10	Inter-agent communication (send, receive, broadcast, delegate)
agent.*	5	Multi-agent collaboration (dispatch, parallel, sequential)
firestore.*	5	Firestore CRUD operations
graph.*	25	Graph database with Datalog and HNSW vectors
neo4j_gds.*	18	Neo4j GDS graph analytics algorithms
neo4j_trigger.*	11	Neo4j APOC trigger management
error.*	7	Error handling actions (is_retryable, clear, retry)
planning.*	4	Planning/decomposition primitives
ratelimit.*	1	Rate limiting with shared named limiters
reasoning.*	7	Reasoning techniques (CoT, ReAct, self-correct, decompose)
reflection.*	3	Self-reflection loop primitive
retry.*	1	General-purpose retry loop with correction
validation.*	2	Generic extraction validation with Prolog/probes
academic.*	3	Academic research via PubMed, ArXiv, CrossRef APIs
auth.*	2	Authentication verification (verify, get_user)
bmad.*	2	BMad story task extraction
catalog.*	10	Data catalog for tables, files, and snapshots
cloud_memory.*	5	Cloud storage with metadata management
code.*	2	Sandboxed Python code execution
context.*	1	Context assembly with relevance ranking
data_tabular.*	6	Tabular data operations
dspy.*	7	DSPy prompt optimization (cot, react, compile)
git.*	6	Git worktree actions (execution modes)
github.*	4	GitHub Issues integration
http_response.*	1	HTTP response for early termination
input_validation.*	2	Input schema validation
llamaextract.*	8	Document extraction via LlamaExtract
llamaindex.*	6	LlamaIndex RAG bridge (query, router, subquestion)
llm_local.*	6	Local LLM inference via llama-cpp-python
markdown.*	2	Markdown parsing with sections, variables, checklists
mem0.*	7	Mem0 universal memory integration
observability.*	7	Tracing spans and event logging
rag.*	4	Embedding creation, vector storage, semantic search
schema.*	1	Schema merge and manipulation
search.*	3	SQL and full-text search via QueryEngine
secrets.*	2	Secrets access via secrets.get and secrets.has
semtools.*	1	Semantic search using SemTools CLI
session.*	7	Session lifecycle with archive-based expiration
session_persistence.*	4	Session persistence (load, save, delete, exists)
storage.*	7	Cloud storage operations via fsspec (S3, GCS, Azure)
text.*	1	Text processing including citation insertion
textgrad.*	8	TextGrad learning actions
tools.*	5	Bridges to CrewAI, MCP, and LangChain tools
vector.*	8	Vector similarity search via VectorIndex
web.*	4	Web scraping, crawling, search via Firecrawl/Perplexity

Core Actions (P0)#

Core actions provide essential functionality for most workflows.

cache.*#

Module: cache_actions.py

Action	Description
`cache.wrap`	Wrap any action with automatic caching.
`cache.get`	Retrieve cached value by key without executing any action.
`cache.invalidate`	Invalidate (delete) cached entries by exact key or pattern.
`storage.hash`	Compute hash of file content from any URI.

core.*#

Module: core_actions.py

Action	Description
`http.get`	Make HTTP GET request.
`http.post`	Make HTTP POST request.
`file.write`	Write content to a file (local or remote via fsspec).
`file.read`	Read content from a file (local or remote via fsspec).
`notify`	Send a notification.
`checkpoint.save`	Save checkpoint to specified path.
`checkpoint.load`	Load checkpoint from specified path.

data.*#

Module: data_actions.py

Action	Description
`json.parse`	Parse a JSON string into a Python object.
`json_parse`	Parse a JSON string into a Python object.
`json.transform`	Transform data using JMESPath or JSONPath expressions.
`json_transform`	Transform data using JMESPath or JSONPath expressions.
`json.stringify`	Convert a Python object to a JSON string.
`json_stringify`	Convert a Python object to a JSON string.
`csv.parse`	Parse CSV data from text or file.
`csv_parse`	Parse CSV data from text or file.
`csv.stringify`	Convert a list of dicts or list of lists to a CSV string.
`csv_stringify`	Convert a list of dicts or list of lists to a CSV string.
`data.validate`	Validate data against a JSON Schema.
`data_validate`	Validate data against a JSON Schema.
`data.merge`	Merge multiple dictionaries/objects.
`data_merge`	Merge multiple dictionaries/objects.
`data.filter`	Filter list items using predicate expressions.
`data_filter`	Filter list items using predicate expressions.

llm.*#

Module: llm_actions.py

Action	Description
`llm.call`	Call a language model (supports OpenAI, Azure OpenAI, Ollama, LiteLLM, and Shell CLI)
`llm.stream`	Stream LLM responses token-by-token
`llm.retry`	DEPRECATED: Use llm.call with max_retries parameter instead
`llm.tools`	LLM call with tool/function calling support

LLM Provider Configuration#

The LLM actions support multiple providers: OpenAI, Azure OpenAI, Ollama, LiteLLM, and Shell CLI.

Provider Detection Priority:

Explicit provider parameter (highest priority)
Environment variable detection:
- OLLAMA_API_BASE → Ollama
- AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT → Azure OpenAI
Default → OpenAI

Environment Variables:

Variable	Provider	Description
`OPENAI_API_KEY`	OpenAI	OpenAI API key
`AZURE_OPENAI_API_KEY`	Azure	Azure OpenAI API key
`AZURE_OPENAI_ENDPOINT`	Azure	Azure endpoint URL
`AZURE_OPENAI_DEPLOYMENT`	Azure	Deployment name (optional)
`OLLAMA_API_BASE`	Ollama	Ollama API URL (default: `http://localhost:11434/v1`)

Ollama Example:

- name: ask_local_llm
  uses: llm.call
  with:
    provider: ollama
    model: llama3.2
    api_base: http://localhost:11434/v1
    messages:
      - role: user
        content: "{{ state.question }}"

LiteLLM Example:

- name: ask_claude
  uses: llm.call
  with:
    provider: litellm
    model: anthropic/claude-3-opus-20240229
    messages:
      - role: user
        content: "{{ state.question }}"

See YAML Reference for complete provider documentation.

ltm.*#

Module: ltm_actions.py

Action	Description
`ltm.store`	Store a key-value pair persistently with optional metadata.
`ltm.retrieve`	Retrieve a value from long-term memory by key.
`ltm.delete`	Delete a value from long-term memory by key.
`ltm.search`	Search across long-term memory using FTS5 and/or metadata filtering.

memory.*#

Module: memory_actions.py

Action	Description
`memory.store`	Store a key-value pair in memory with optional TTL.
`memory.retrieve`	Retrieve a value from memory by key.
`memory.summarize`	Summarize conversation history using LLM to fit token windows.

Integration Actions (P1)#

Integration actions connect TEA with external systems and enable multi-agent workflows.

a2a.*#

Module: a2a_actions.py

Action	Description
`a2a.send`	Send a message to a specific agent.
`a2a.receive`	Receive messages from agents.
`a2a.broadcast`	Broadcast message to all agents in namespace.
`a2a.delegate`	Delegate a task to another agent and wait for response.
`a2a.state.get`	Get a value from shared state.
`a2a.state.set`	Set a value in shared state.
`a2a.discover`	Discover available agents in namespace.
`a2a.register`	Register current agent for discovery and broadcasts.
`a2a.unregister`	Unregister current agent.
`a2a.heartbeat`	Send heartbeat to update last_seen timestamp.

agent.*#

Module: agent_actions.py

Action	Description
`agent.dispatch`	Dispatch a task to a single named agent.
`agent.parallel`	Dispatch same task to multiple agents in parallel.
`agent.sequential`	Chain multiple agents where output feeds into next agent’s input.
`agent.coordinate`	Coordinator pattern with leader agent dispatching to workers.
`agent.crewai_delegate`	Delegate to CrewAI for complex multi-agent workflows.

firestore.*#

Module: firestore_actions.py

Action	Description
`firestore.get`
`firestore.set`
`firestore.query`
`firestore.delete`
`firestore.batch`

graph.*#

Module: graph_actions.py

Action	Description
`graph.store_entity`	Store an entity (node) in the graph database.
`graph.store_relation`	Store a relation (edge) between two entities.
`graph.query`	Execute a Cypher/Datalog/SQL-PGQ query or pattern match.
`graph.retrieve_context`	Retrieve relevant subgraph context.
`graph.delete_entity`	Delete an entity (node) from the graph database.
`graph.delete_relation`	Delete a relation (edge) from the graph database.
`graph.update_entity`	Update properties of an entity (node) in the graph database.
`graph.update_relation`	Update properties of a relation (edge) in the graph database.
`graph.add_labels`	Add labels to an entity (node) in the graph database.
`graph.remove_labels`	Remove labels from an entity (node) in the graph database.
`graph.store_entities_batch`	Bulk insert/update multiple entities in a single transaction.
`graph.store_relations_batch`	Bulk create/update multiple relations in a single transaction.
`graph.delete_entities_batch`	Delete multiple entities in a single transaction.
`graph.merge_entity`	Conditional upsert with ON CREATE / ON MATCH semantics.
`graph.merge_relation`	Conditional upsert of a relation with ON CREATE / ON MATCH semantics.
`graph.create`	Create a property graph from vertex and edge tables (DuckPGQ).
`graph.drop`	Drop a property graph (DuckPGQ).
`graph.algorithm`	Run a graph algorithm (DuckPGQ).
`graph.shortest_path`	Find shortest path between two entities (DuckPGQ).
`graph.list_graphs`	List all created property graphs (DuckPGQ).
`graph.vector_search`	Perform vector similarity search using Neo4j Vector Index.
`graph.create_vector_index`	Create a vector index in Neo4j for similarity search.
`graph.drop_vector_index`	Drop a vector index from Neo4j.
`graph.list_vector_indexes`	List all vector indexes in Neo4j.
`graph.check_vector_support`	Check if the Neo4j instance supports vector indexes.

neo4j_gds.*#

Module: neo4j_gds_actions.py

Action	Description
`neo4j.gds_check_available`	Check if Neo4j GDS library is available.
`neo4j.gds_version`	Get the installed Neo4j GDS library version.
`neo4j.gds_project_graph`	Create an in-memory graph projection for GDS algorithms.
`neo4j.gds_drop_graph`	Drop (remove) an in-memory graph projection.
`neo4j.gds_list_graphs`	List all active in-memory graph projections.
`neo4j.gds_estimate_memory`	Estimate memory requirements for a GDS algorithm.
`neo4j.gds_page_rank`	Run PageRank algorithm on a projected graph.
`neo4j.gds_betweenness`	Run Betweenness Centrality algorithm.
`neo4j.gds_degree`	Run Degree Centrality algorithm.
`neo4j.gds_closeness`	Run Closeness Centrality algorithm.
`neo4j.gds_louvain`	Run Louvain community detection algorithm.
`neo4j.gds_label_propagation`	Run Label Propagation community detection algorithm.
`neo4j.gds_wcc`	Run Weakly Connected Components algorithm.
`neo4j.gds_dijkstra`	Find shortest weighted path using Dijkstra’s algorithm.
`neo4j.gds_astar`	Find shortest path using A* algorithm with heuristic.
`neo4j.gds_all_shortest_paths`	Find shortest paths from source to all other nodes.
`neo4j.gds_node_similarity`	Compute Jaccard similarity between nodes based on shared neighbors.
`neo4j.gds_knn`	Run K-Nearest Neighbors algorithm on node properties.

neo4j_trigger.*#

Module: neo4j_trigger_actions.py

Action	Description
`neo4j.check_apoc`	Check if APOC library is installed and available.
`neo4j.get_apoc_version`	Get the installed APOC library version.
`neo4j.check_triggers`	Check if APOC triggers are enabled in Neo4j configuration.
`neo4j.register_trigger`	Register a database trigger using APOC.
`neo4j.unregister_trigger`	Remove a registered trigger.
`neo4j.list_triggers`	List all registered triggers.
`neo4j.pause_trigger`	Temporarily disable a trigger without removing it.
`neo4j.resume_trigger`	Re-enable a paused trigger.
`neo4j.register_callback`	Register a trigger that fires an HTTP webhook on graph changes.
`neo4j.register_state_update`	Register a trigger that writes to a state node for agent consumption.
`neo4j.cleanup_triggers`	Remove triggers by prefix, used for session/agent cleanup.

Reasoning Actions (P2)#

Reasoning actions provide advanced AI capabilities for planning, reflection, and error handling.

error.*#

Module: error_actions.py

Action	Description
`error.is_retryable`
`error.clear`
`error.get`
`error.has`
`error.type`
`error.retry`
`error.respond`

planning.*#

Module: planning_actions.py

Action	Description
`plan.decompose`	Decompose a goal into subtasks using LLM.
`plan.execute`	Execute plan subtasks respecting dependency order.
`plan.replan`	Re-plan from current state, preserving completed subtasks.
`plan.status`	Get current plan execution status.

ratelimit.*#

Module: ratelimit_actions.py

Action	Description
`ratelimit.wrap`	Wrap any action with rate limiting.

reasoning.*#

Module: reasoning_actions.py

Action	Description
`reason.cot`	Chain-of-Thought reasoning action.
`reason.react`	ReAct (Reason-Act) reasoning action.
`reason.self_correct`	Self-correction reasoning action.
`reason.decompose`	Problem decomposition reasoning action.
`reason.dspy.cot`	Chain-of-Thought using DSPy ChainOfThought module.
`reason.dspy.react`	ReAct using DSPy ReAct module with tool bridge.
`reason.dspy.compile`	Compile DSPy module with teleprompter for optimized prompts.

reflection.*#

Module: reflection_actions.py

Action	Description
`reflection.loop`	Execute a generate→evaluate→correct loop (AC: 1, 5, 6).
`reflection.evaluate`	Standalone evaluation action (AC: 7).
`reflection.correct`	Standalone correction action (AC: 8).

retry.*#

Module: retry_actions.py

Action	Description
`retry.loop`	Execute validation with retry loop (TEA-YAML-005).

validation.*#

Module: validation_actions.py

Action	Description
`validate.extraction`	Validate extracted entities and relationships (AC: 16-18).
`validate.generate_prompt`	Generate a schema-guided extraction prompt (AC: 23-27).

Utility Actions (P3)#

Utility actions provide specialized functionality for specific use cases.

academic.*#

Module: academic_actions.py

Action	Description
`academic.pubmed`	Search PubMed database for scientific articles via NCBI E-utilities
`academic.arxiv`	Search ArXiv preprint server for papers
`academic.crossref`	Query CrossRef API for DOI metadata or search by query string

academic.pubmed#

Search the PubMed database for scientific articles using NCBI E-utilities API.

Parameters:

Parameter	Type	Default	Description
`query`	string	required	Search query (PubMed query syntax)
`max_results`	int	5	Maximum results to return
`sort_by`	string	“relevance”	Sort order: “relevance” or “date”
`timeout`	int	30	Request timeout in seconds

Rate Limiting: 3 requests/second (10 req/s with NCBI_API_KEY)

academic.arxiv#

Search the ArXiv preprint server for research papers.

Parameters:

Parameter	Type	Default	Description
`query`	string	optional	Search query string
`arxiv_id`	string	optional	Direct paper lookup by ID
`max_results`	int	5	Maximum results to return
`sort_by`	string	“relevance”	Sort order: “relevance” or “date”

Rate Limiting: 1 request per 3 seconds (per ArXiv terms of service)

academic.crossref#

Query the CrossRef API for DOI metadata or search by query string.

Parameters:

Parameter	Type	Default	Description
`doi`	string	optional	DOI for direct lookup
`query`	string	optional	Search query string
`max_results`	int	5	Maximum results to return
`mailto`	string	optional	Email for polite pool access (50 req/s)

auth.*#

Module: auth_actions.py

Action	Description
`auth.verify`	Verify an authentication token
`auth.get_user`	Get full user profile by UID

auth.verify#

Verify an authentication token. Extracts token from headers if not provided directly.

- name: verify_token
  uses: auth.verify
  with:
    token: "{{ state.custom_token }}"  # Optional
    headers: "{{ state.request_headers }}"  # Optional
  output: auth_result

auth.get_user#

Get full user profile by UID from Firebase Authentication.

- name: get_profile
  uses: auth.get_user
  with:
    uid: "{{ state.__user__.uid }}"
  output: full_profile

bmad.*#

Module: bmad_actions.py

Action	Description
`bmad.parse_story`	Parse a BMad story file into structured data.
`bmad_parse_story`	Parse a BMad story file into structured data.

catalog.*#

Module: catalog_actions.py

Action	Description
`catalog.register_table`	Register a new table in the DuckLake catalog.
`catalog.get_table`	Get table metadata from the catalog.
`catalog.list_tables`	List tables in the catalog with optional filtering.
`catalog.track_file`	Track a Parquet or delta file in the catalog.
`catalog.get_file`	Get file metadata from the catalog.
`catalog.list_files`	List files for a table with optional filtering.
`catalog.create_snapshot`	Create a point-in-time snapshot for a table.
`catalog.get_latest_snapshot`	Get the most recent snapshot for a table.
`catalog.list_snapshots`	List snapshots for a table.
`catalog.get_changed_files`	Get files that changed since a snapshot.

cloud_memory.*#

Module: cloud_memory_actions.py

Action	Description
`memory.cloud_store`	Store an artifact in cloud storage with metadata and embedding.
`memory.cloud_retrieve`	Retrieve an artifact from cloud storage.
`memory.cloud_list`	List artifacts with filtering.
`memory.manifest_update`	Update metadata only (not file content).
`memory.manifest_search`	Search documents by anchors.

code.*#

Module: code_actions.py

Action	Description
`code.execute`	Execute Python code in a RestrictedPython sandbox.
`code.sandbox`	Manage persistent sandbox sessions for multi-step code execution.

context.*#

Module: context_actions.py

Action	Description
`context.assemble`	Assemble context from configured layers with relevance ranking.

data_tabular.*#

Module: data_tabular_actions.py

Action	Description
`data.create_table`	Register a new tabular table in the catalog.
`data.insert`	Insert rows into a tabular table.
`data.update`	Update rows matching WHERE clause.
`data.delete`	Delete rows matching WHERE clause.
`data.query`	Query tabular data with SQL.
`data.consolidate`	Full compaction: merge N Parquet files + inlined -> 1 Parquet file.

dspy.*#

Module: dspy_actions.py

Action	Description
`reason.dspy.cot`	Chain-of-Thought reasoning using DSPy ChainOfThought module.
`reason.dspy.react`	ReAct reasoning using DSPy ReAct module.
`reason.dspy.compile`	Compile a DSPy module with teleprompter for optimized prompts.
`reason.dspy.optimize`	Run optimization against a validation set.
`reason.dspy.list_compiled`	List all compiled DSPy modules.
`reason.dspy.export`	Export all compiled DSPy prompts for checkpoint persistence.
`reason.dspy.import`	Import compiled DSPy prompts from checkpoint persistence.

git.*#

Module: git_actions.py

Action	Description
`git.worktree_create`
`git.worktree_remove`
`git.worktree_merge`
`git.worktree_list`
`git.current_branch`
`git.status`

github.*#

Module: github_actions.py

Action	Description
`github.list_issues`	List issues from a GitHub repository.
`github.create_issue`	Create a new GitHub issue.
`github.update_issue`	Update an existing GitHub issue.
`github.search_issues`	Search GitHub issues using GitHub search syntax.

http_response.*#

Module: http_response_actions.py

Action	Description
`http.respond`	Synchronous version of http.respond.

input_validation.*#

Module: input_validation_actions.py

Action	Description
`validate.input`	Validate input data against a schema (AC10).
`validate.schema`	Create a reusable schema validator.

llamaextract.*#

Module: llamaextract_actions.py

Action	Description
`llamaextract.extract`	Extract structured data from a document using LlamaExtract.
`llamaextract.upload_agent`	Create or update an extraction agent.
`llamaextract.list_agents`	List available extraction agents.
`llamaextract.get_agent`	Get extraction agent details.
`llamaextract.delete_agent`	Delete an extraction agent.
`llamaextract.submit_job`	Submit async extraction job to LlamaExtract.
`llamaextract.poll_status`	Poll job status from LlamaExtract.
`llamaextract.get_result`	Get extraction result for a completed job.

llamaindex.*#

Module: llamaindex_actions.py

Action	Description
`rag.llamaindex.query`	Execute a simple vector query against a LlamaIndex index.
`rag.llamaindex.router`	Execute a router query that selects the best engine for the query.
`rag.llamaindex.subquestion`	Execute a sub-question query that decomposes complex queries.
`rag.llamaindex.create_index`	Create a new LlamaIndex index from documents or a directory.
`rag.llamaindex.load_index`	Load a persisted LlamaIndex index.
`rag.llamaindex.add_documents`	Add documents to an existing LlamaIndex index.

llm_local.*#

Module: llm_local_actions.py

Action	Description
`llm.local.call`	LLM completion using local or API backend.
`llm.local.chat`	Chat completion using OpenAI-compatible format.
`llm.local.stream`	Streaming LLM generation with token-by-token output.
`llm.local.embed`	Generate text embeddings using local or API backend.
`llm.chat`	Chat completion using OpenAI-compatible format.
`llm.embed`	Generate text embeddings using local or API backend.

markdown.*#

Module: markdown_actions.py

Action	Description
`markdown.parse`	Parse Markdown content into a structured document.
`markdown_parse`	Parse Markdown content into a structured document.

mem0.*#

Module: mem0_actions.py

Action	Description
`memory.mem0.add`	Store messages with automatic fact extraction using Mem0.
`memory.mem0.search`	Search memories by semantic similarity using Mem0.
`memory.mem0.get_all`	Get all memories for a specified scope.
`memory.mem0.get`	Get a specific memory by its ID.
`memory.mem0.update`	Update an existing memory by ID.
`memory.mem0.delete`	Delete memories by ID or scope.
`memory.mem0.test`	Test Mem0 connection and configuration.

observability.*#

Module: observability_actions.py

Action	Description
`trace.start`	Start a new trace span.
`trace.log`	Log an event, metrics, or state snapshot to the current span.
`trace.end`	End the current trace span.
`opik.healthcheck`	Validate Opik connectivity and authentication (TEA-BUILTIN-005.3).
`obs.get_flow_log`	Get the complete flow log from ObservabilityContext (TEA-OBS-001.1).
`obs.log_event`	Log a custom event to the observability stream (TEA-OBS-001.1).
`obs.query_events`	Query events from the observability stream (TEA-OBS-001.1).

rag.*#

Module: rag_actions.py

Action	Description
`embedding.create`	Create embeddings from text.
`vector.store`	Store documents with embeddings in vector store.
`vector.query`	Query vector store for similar documents.
`vector.index_files`	Index files/directories into vector store (AC: 1-13).

schema.*#

Module: schema_actions.py

Action	Description
`schema.merge`	Deep merge multiple JSON Schemas with kubectl-style semantics.

search.*#

Module: search_actions.py

Action	Description
`memory.grep`	Execute grep-like search across agent memory.
`memory.sql_query`	Execute SQL query against agent_memory table with safety controls.
`memory.search_content`	Search for files by structured content field values.

secrets.*#

Module: secrets_actions.py

Action	Description
`secrets.get`	Get a secret value by key.
`secrets.has`	Check if a secret exists.

semtools.*#

Module: semtools_actions.py

Action	Description
`semtools.search`	Semantic search using SemTools CLI.

session.*#

Module: session_actions.py

Action	Description
`session.create`	Create a new session with expiration.
`session.end`	End session and archive its memory.
`session.archive`	Archive session with custom reason.
`session.restore`	Restore archived session.
`session.get`	Get session metadata.
`session.list`	List sessions with optional filtering.
`session.archive_expired`	Archive sessions that have exceeded their TTL.

session_persistence.*#

Module: session_persistence_actions.py

Action	Description
`session.load`	Load session data from the configured session backend.
`session.save`	Save current state to the session backend.
`session.delete`	Delete a session from the backend.
`session.exists`	Check if a session exists.

storage.*#

Module: storage_actions.py

Action	Description
`storage.list`	List files/objects at the given path.
`storage.exists`	Check if a file/object exists.
`storage.delete`	Delete a file/object or directory.
`storage.copy`	Copy a file/object to another location.
`storage.info`	Get metadata/info about a file/object.
`storage.mkdir`	Create a directory/prefix.
`storage.native`	Execute a native filesystem operation not exposed by standard fsspec API.

text.*#

Module: text_actions.py

Action	Description
`text.insert_citations`	Insert citation markers using semantic embedding matching

text.insert_citations#

Insert citation markers into text using semantic embedding matching. Uses OpenAI embeddings to compute similarity between sentences and references, placing citations at the most semantically relevant positions.

Parameters:

Parameter	Type	Default	Description
`text`	string	required	Markdown text to process
`references`	list[str]	required	List of reference strings
`model`	string	“text-embedding-3-large”	OpenAI embedding model
`api_key`	string	None	OpenAI API key (uses env var if not provided)

Returns:

{
  "cited_text": "Text with [1] citation markers inserted.",
  "references_section": "## References\n\n1. Author. Title. 2020.",
  "citation_map": {"1": "Author. Title. 2020."},
  "text": "Full text with citations and References section"
}

Features:

Semantic matching via embeddings (not just keyword matching)
Citations placed at most relevant sentences
Conclusions and Abstract sections excluded from citation
References reordered by first occurrence
Markdown formatting preserved

textgrad.*#

Module: textgrad_actions.py

Action	Description
`learn.textgrad.variable`	Define an optimizable prompt variable (learn.textgrad.variable action).
`learn.textgrad.feedback`	Compute textual gradients from output evaluation (learn.textgrad.feedback action…
`learn.textgrad.optimize_prompt`	Optimize a prompt variable using TextGrad (learn.textgrad.optimize_prompt action…
`learn.textgrad.reflection_corrector`	Corrector for reflection.loop that uses TextGrad for prompt optimization (AC: 4)…
`textgrad.variable`	Define an optimizable prompt variable (learn.textgrad.variable action).
`textgrad.feedback`	Compute textual gradients from output evaluation (learn.textgrad.feedback action…
`textgrad.optimize_prompt`	Optimize a prompt variable using TextGrad (learn.textgrad.optimize_prompt action…
`textgrad.reflection_corrector`	Corrector for reflection.loop that uses TextGrad for prompt optimization (AC: 4)…

tools.*#

Module: tools_actions.py

Action	Description
`tools.crewai`	Execute a CrewAI tool.
`tools.mcp`	Execute a tool from an MCP server.
`tools.langchain`	Execute a LangChain tool.
`tools.discover`	Discover available tools from specified sources.
`tools.clear_cache`	Clear the tool discovery cache.

vector.*#

Module: vector_actions.py

Action	Description
`memory.vector_search`	Semantic search over agent memory using vector similarity.
`memory.vector_search_by_embedding`	Search using a pre-computed embedding vector.
`memory.vector_load_data`	Load vector data from a Parquet file or URL.
`memory.vector_build_index`	Build or rebuild the vector search index.
`memory.vector_stats`	Get statistics about the vector index.
`memory.embed`	Generate embedding for content.
`memory.embed_batch`	Generate embeddings for multiple content strings.
`memory.backfill_embeddings`	Backfill embeddings for documents missing them.

web.*#

Module: web_actions.py

Action	Description
`web.scrape`	Scrape a URL and extract LLM-ready content via Firecrawl API.
`web.crawl`	Crawl a website recursively via Firecrawl API.
`web.search`	Perform web search via Perplexity API.
`web.ai_scrape`	Extract structured data from a URL using ScrapeGraphAI.

Deprecated Actions#

The following actions have preferred alternatives:

Deprecated	Use Instead	Notes
`llm.retry`	`llm.call` with `max_retries`	Built-in retry support

Custom Actions#

imports:
  - path: ./my_actions.py
    actions:
      - my_custom_action

Your module must implement register_actions():

def register_actions(registry, engine):
    registry['custom.my_action'] = my_action_function

Source Location#

All action modules are in:

python/src/the_edge_agent/actions/

This document was auto-generated from the codebase. Run python scripts/extract_action_signatures.py to update.