hack/tools/scripts/llm-key-hunter/results/_cache/content/agenticenv_agent-sdk-go.json

{"repo": "agenticenv/agent-sdk-go", "files": {"examples/.env.defaults": {"a49086b5ab0a64a990d8b15a4d73aa01a0853462": {"t": 1785747082.5447714, "n": 4630, "h": "d42a712afc413925", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# LOG_ENABLE=false (default) uses NoopLogger. Set true + LOG_LEVEL for SDK stderr logs.\nLOG_ENABLE=false\n# error | warn | info | debug (used when LOG_ENABLE=true)\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=127.0.0.1\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini | deepseek | ollama. Set LLM_APIKEY in examples/.env.\n# For deepseek, also set LLM_BASEURL=https://api.deepseek.com in examples/.env (it overrides\n# the OpenAI default below) and LLM_MODEL=deepseek-chat (or deepseek-reasoner).\n# For ollama (local, no API key needed): set LLM_PROVIDER=ollama, LLM_MODEL=llama3.2 (or any\n# pulled model), and LLM_BASEURL=http://localhost:11434/v1 in examples/.env; leave LLM_APIKEY empty.\n# For Ollama Cloud: set LLM_PROVIDER=ollama, LLM_APIKEY=<key from ollama.com/settings/keys>\n# (or export OLLAMA_API_KEY instead), and LLM_MODEL=qwen3-coder:480b-cloud (or another \"-cloud\"\n# model); leave LLM_BASEURL unset — it defaults to https://ollama.com/v1 once LLM_APIKEY is set.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- Long-term memory (agent_with_memory/weaviate, agent_with_memory/pgvector) ---\n# MEMORY_STORE_MODE: always (run-end extract) or ondemand (save_memory tool). Default ondemand.\n# Task examples:local runs each backend in both modes (4 runs). Same infra as retriever examples.\nMEMORY_USER_ID=demo-user\nMEMORY_STORE_MODE=ondemand\nMEMORY_RECALL_ENABLED=true\nMEMORY_RECALL_LIMIT=10\nMEMORY_RECALL_MIN_SCORE=0.35\nWEAVIATE_MEMORY_CLASS=AgentMemory\nPGVECTOR_MEMORY_TABLE=agent_memories\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "6786e42eb86f3e820ae450d9fb7405ad659dcd22": {"t": 1785770311.33925, "n": 4630, "h": "d42a712afc413925", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# LOG_ENABLE=false (default) uses NoopLogger. Set true + LOG_LEVEL for SDK stderr logs.\nLOG_ENABLE=false\n# error | warn | info | debug (used when LOG_ENABLE=true)\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=127.0.0.1\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini | deepseek | ollama. Set LLM_APIKEY in examples/.env.\n# For deepseek, also set LLM_BASEURL=https://api.deepseek.com in examples/.env (it overrides\n# the OpenAI default below) and LLM_MODEL=deepseek-chat (or deepseek-reasoner).\n# For ollama (local, no API key needed): set LLM_PROVIDER=ollama, LLM_MODEL=llama3.2 (or any\n# pulled model), and LLM_BASEURL=http://localhost:11434/v1 in examples/.env; leave LLM_APIKEY empty.\n# For Ollama Cloud: set LLM_PROVIDER=ollama, LLM_APIKEY=<key from ollama.com/settings/keys>\n# (or export OLLAMA_API_KEY instead), and LLM_MODEL=qwen3-coder:480b-cloud (or another \"-cloud\"\n# model); leave LLM_BASEURL unset — it defaults to https://ollama.com/v1 once LLM_APIKEY is set.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- Long-term memory (agent_with_memory/weaviate, agent_with_memory/pgvector) ---\n# MEMORY_STORE_MODE: always (run-end extract) or ondemand (save_memory tool). Default ondemand.\n# Task examples:local runs each backend in both modes (4 runs). Same infra as retriever examples.\nMEMORY_USER_ID=demo-user\nMEMORY_STORE_MODE=ondemand\nMEMORY_RECALL_ENABLED=true\nMEMORY_RECALL_LIMIT=10\nMEMORY_RECALL_MIN_SCORE=0.35\nWEAVIATE_MEMORY_CLASS=AgentMemory\nPGVECTOR_MEMORY_TABLE=agent_memories\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "8db9f9fb87d8e0111d8b421603347e49ffe7028f": {"t": 1785770313.0015423, "n": 4497, "h": "5e37bd6fb039da8d", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=127.0.0.1\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini | deepseek | ollama. Set LLM_APIKEY in examples/.env.\n# For deepseek, also set LLM_BASEURL=https://api.deepseek.com in examples/.env (it overrides\n# the OpenAI default below) and LLM_MODEL=deepseek-chat (or deepseek-reasoner).\n# For ollama (local, no API key needed): set LLM_PROVIDER=ollama, LLM_MODEL=llama3.2 (or any\n# pulled model), and LLM_BASEURL=http://localhost:11434/v1 in examples/.env; leave LLM_APIKEY empty.\n# For Ollama Cloud: set LLM_PROVIDER=ollama, LLM_APIKEY=<key from ollama.com/settings/keys>\n# (or export OLLAMA_API_KEY instead), and LLM_MODEL=qwen3-coder:480b-cloud (or another \"-cloud\"\n# model); leave LLM_BASEURL unset — it defaults to https://ollama.com/v1 once LLM_APIKEY is set.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- Long-term memory (agent_with_memory/weaviate, agent_with_memory/pgvector) ---\n# MEMORY_STORE_MODE: always (run-end extract) or ondemand (save_memory tool). Default ondemand.\n# Task examples:local runs each backend in both modes (4 runs). Same infra as retriever examples.\nMEMORY_USER_ID=demo-user\nMEMORY_STORE_MODE=ondemand\nMEMORY_RECALL_ENABLED=true\nMEMORY_RECALL_LIMIT=10\nMEMORY_RECALL_MIN_SCORE=0.35\nWEAVIATE_MEMORY_CLASS=AgentMemory\nPGVECTOR_MEMORY_TABLE=agent_memories\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "02d94b13fd817eaef892d09fa6d84d22c7128d76": {"t": 1785770315.7993648, "n": 4212, "h": "ebc9b624d355f28b", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=127.0.0.1\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini | deepseek | ollama. Set LLM_APIKEY in examples/.env.\n# For deepseek, also set LLM_BASEURL=https://api.deepseek.com in examples/.env (it overrides\n# the OpenAI default below) and LLM_MODEL=deepseek-chat (or deepseek-reasoner).\n# For ollama (local, no API key needed): set LLM_PROVIDER=ollama, LLM_MODEL=llama3.2 (or any\n# pulled model), and LLM_BASEURL=http://localhost:11434/v1 in examples/.env; leave LLM_APIKEY empty.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- Long-term memory (agent_with_memory/weaviate, agent_with_memory/pgvector) ---\n# MEMORY_STORE_MODE: always (run-end extract) or ondemand (save_memory tool). Default ondemand.\n# Task examples:local runs each backend in both modes (4 runs). Same infra as retriever examples.\nMEMORY_USER_ID=demo-user\nMEMORY_STORE_MODE=ondemand\nMEMORY_RECALL_ENABLED=true\nMEMORY_RECALL_LIMIT=10\nMEMORY_RECALL_MIN_SCORE=0.35\nWEAVIATE_MEMORY_CLASS=AgentMemory\nPGVECTOR_MEMORY_TABLE=agent_memories\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "c258f85ffcbfa5a651d7c66868bbf1d21f0e56af": {"t": 1785770317.3431773, "n": 4009, "h": "34f309040aeae905", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=127.0.0.1\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini | deepseek. Set LLM_APIKEY in examples/.env.\n# For deepseek, also set LLM_BASEURL=https://api.deepseek.com in examples/.env (it overrides\n# the OpenAI default below) and LLM_MODEL=deepseek-chat (or deepseek-reasoner).\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- Long-term memory (agent_with_memory/weaviate, agent_with_memory/pgvector) ---\n# MEMORY_STORE_MODE: always (run-end extract) or ondemand (save_memory tool). Default ondemand.\n# Task examples:local runs each backend in both modes (4 runs). Same infra as retriever examples.\nMEMORY_USER_ID=demo-user\nMEMORY_STORE_MODE=ondemand\nMEMORY_RECALL_ENABLED=true\nMEMORY_RECALL_LIMIT=10\nMEMORY_RECALL_MIN_SCORE=0.35\nWEAVIATE_MEMORY_CLASS=AgentMemory\nPGVECTOR_MEMORY_TABLE=agent_memories\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "55f103a8e8494f5dfb113e34f6f313482bf8590f": {"t": 1785770319.1467576, "n": 3825, "h": "200b608f20a2d5da", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=127.0.0.1\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini. Set LLM_APIKEY in examples/.env.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- Long-term memory (agent_with_memory/weaviate, agent_with_memory/pgvector) ---\n# MEMORY_STORE_MODE: always (run-end extract) or ondemand (save_memory tool). Default ondemand.\n# Task examples:local runs each backend in both modes (4 runs). Same infra as retriever examples.\nMEMORY_USER_ID=demo-user\nMEMORY_STORE_MODE=ondemand\nMEMORY_RECALL_ENABLED=true\nMEMORY_RECALL_LIMIT=10\nMEMORY_RECALL_MIN_SCORE=0.35\nWEAVIATE_MEMORY_CLASS=AgentMemory\nPGVECTOR_MEMORY_TABLE=agent_memories\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "c6653919dbed4bf81efe50ccc5ed23c1ab8846b0": {"t": 1785770320.7776446, "n": 3825, "h": "4a3095cadd8c4ae7", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=localhost\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini. Set LLM_APIKEY in examples/.env.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- Long-term memory (agent_with_memory/weaviate, agent_with_memory/pgvector) ---\n# MEMORY_STORE_MODE: always (run-end extract) or ondemand (save_memory tool). Default ondemand.\n# Task examples:local runs each backend in both modes (4 runs). Same infra as retriever examples.\nMEMORY_USER_ID=demo-user\nMEMORY_STORE_MODE=ondemand\nMEMORY_RECALL_ENABLED=true\nMEMORY_RECALL_LIMIT=10\nMEMORY_RECALL_MIN_SCORE=0.35\nWEAVIATE_MEMORY_CLASS=AgentMemory\nPGVECTOR_MEMORY_TABLE=agent_memories\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "d6b36bfa5ad0b83cd1b201a7bfe09742efd91e6f": {"t": 1785770322.0571933, "n": 3344, "h": "f0fb5fa6445dce86", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Example run output (examples/shared PrintRunFooters) ---\n# Set to true to print token usage and/or run telemetry after each agent run.\nSHOW_LLM_USAGE=false\nSHOW_TELEMETRY=false\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=localhost\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini. Set LLM_APIKEY in examples/.env.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "bb29ac950523d989f3e93cfa04c285e3ae1b8451": {"t": 1785770323.6198354, "n": 3160, "h": "738011aadeac167f", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Redis (agent_with_conversation; task infra:redis:up) ---\nREDIS_ADDR=localhost:6379\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=localhost\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini. Set LLM_APIKEY in examples/.env.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}, "73df5c58f6ce52eee1616bf37277ed1de3d1e5e5": {"t": 1785770325.0096173, "n": 3070, "h": "1b590af402f53687", "c": "# Committed defaults for examples. Loaded automatically by examples/config.go.\n# Optional overrides: create examples/.env (gitignored) for secrets and local changes.\n# Load order: .env.defaults → examples/.env → process environment (export / Task / CI).\n# Put LLM_APIKEY and EMBEDDING_OPENAI_APIKEY in examples/.env (never commit keys).\n\n# --- Logging ---\n# error | warn | info | debug\nLOG_LEVEL=error\n\n# --- Agent runtime ---\n# local = in-process (default). temporal = requires Temporal server (see temporal-setup.md).\nAGENT_RUNTIME=local\n\n# --- Temporal (when AGENT_RUNTIME=temporal) ---\n# TEMPORAL_TASKQUEUE is a base prefix; each example appends its suffix (e.g. agent-sdk-go-simple_agent).\nTEMPORAL_HOST=localhost\nTEMPORAL_PORT=7233\nTEMPORAL_NAMESPACE=default\nTEMPORAL_TASKQUEUE=agent-sdk-go\n\n# --- LLM (all examples) ---\n# Provider: openai | anthropic | gemini. Set LLM_APIKEY in examples/.env.\nLLM_PROVIDER=openai\nLLM_APIKEY=\nLLM_MODEL=gpt-4o\n# Used for OpenAI client and as embedding base URL fallback when EMBEDDING_OPENAI_BASEURL is unset.\nLLM_BASEURL=https://api.openai.com/v1\n\n# --- MCP (agent_with_mcp_config, agent_with_mcp_client) ---\n# Default: stdio + local filesystem sandbox (matches Task infra). Override in .env for streamable_http:\n#   MCP_TRANSPORT=streamable_http\n#   MCP_STREAMABLE_HTTP_URL=https://your-mcp-host/mcp\nMCP_TRANSPORT=stdio\nMCP_STDIO_COMMAND=npx\nMCP_STDIO_ARGS=[\"-y\",\"@modelcontextprotocol/server-filesystem\",\"./mcp-filesystem-sandbox\"]\n\n# --- A2A outbound (agent_with_a2a_config, agent_with_a2a_client) ---\n# Base URL of remote agent (no path). Default matches agent_with_a2a_server from task infra:a2a:up.\nA2A_URL=http://localhost:9999\n\n# --- A2A inbound server (agent_with_a2a_server) ---\nA2A_SERVER_HOST=localhost\nA2A_SERVER_PORT=9999\n\n# --- Retriever mode (weaviate and pgvector examples) ---\n# agentic | prefetch | hybrid\nRETRIEVER_MODE=agentic\n\n# --- Weaviate (agent_with_retriever/weaviate) ---\n# Run task infra:weaviate:up from examples/ first.\nWEAVIATE_HOST=localhost:8080\nWEAVIATE_SCHEME=http\nWEAVIATE_CLASS=Document\nWEAVIATE_RETRIEVER_NAME=weaviate-kb\nWEAVIATE_CONTENT_FIELD=content\nWEAVIATE_SOURCE_FIELD=source\n\n# --- pgvector (agent_with_retriever/pgvector) ---\n# Run task infra:pgvector:up from examples/ first.\nPGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable\nPGVECTOR_TABLE=documents\nPGVECTOR_RETRIEVER_NAME=pgvector-kb\nPGVECTOR_CONTENT_COL=content\nPGVECTOR_SOURCE_COL=source\nPGVECTOR_EMBEDDING_COL=embedding\nPGVECTOR_MIN_SCORE=0.35\n\n# --- OpenAI-compatible embeddings (pgvector client-side; Weaviate text2vec-openai in Docker) ---\n# Set EMBEDDING_OPENAI_APIKEY in examples/.env (separate from chat LLM_APIKEY for Anthropic/Gemini).\nEMBEDDING_OPENAI_APIKEY=\nEMBEDDING_OPENAI_MODEL=text-embedding-3-small\nEMBEDDING_OPENAI_BASEURL=https://api.openai.com/v1\n\n# --- OpenTelemetry (agent_with_observability; LGTM from task infra:lgtm:up) ---\n# Host:port without scheme. grpc uses port 4317; http often 4318.\nOTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317\nOTLP_PROTOCOL=grpc\nOTLP_INSECURE=true\n"}}}}