Environment Variables¶
All variables are set in compose.yaml per service. Agents read them via config/config.py.
Agent configuration¶
Variable |
Default |
Description |
|---|---|---|
|
|
LLM model name |
|
|
Ollama server URL |
|
|
Provider: ollama, openai, gemini, grok, anthropic, bedrock, azure |
|
— |
API key for cloud providers |
|
|
LLM temperature |
|
auto-assigned |
A2A protocol port |
|
auto-assigned |
HTTP/SSE port (if web interface enabled) |
|
|
DEBUG, INFO, WARNING, ERROR |
Semantic Layer¶
Variable |
Default |
Description |
|---|---|---|
|
|
Semantic Layer URL |
|
|
MCP server hostname |
|
|
MCP server port |
|
|
Transport: streamable-http, sse |
|
|
Weaviate URL |
|
|
Model for generating embeddings |
Security¶
Variable |
Default |
Description |
|---|---|---|
|
|
Guardian service URL |
|
|
OPA policy engine URL |
|
|
strict, permissive, or disabled |
|
|
Log all A2A validation attempts |
|
|
Block requests above this risk score |
|
|
Semantic Layer validation mode |
|
|
Require user email for MCP calls |
Artifact Store (MinIO)¶
Variable |
Default |
Description |
|---|---|---|
|
|
MinIO URL |
|
|
MinIO access key |
|
|
MinIO secret key |
|
|
Default bucket |
|
|
Upload execution logs to MinIO |
|
agent name |
Identifier in log bucket |
|
|
Bucket for logs |
Agent Memory (Redis AMS)¶
Note
Alpha. The Agent Memory Server is provisioned by the ABI Swarm scaffolding, which is under active development. These variables may change between releases.
The swarm scaffolding provisions a Redis 8 instance and a Redis Agent Memory Server (AMS) for system-wide short-term (working) and long-term memory. Agents reach it over the Docker network.
Variable |
Default |
Description |
|---|---|---|
|
|
AMS base URL (consumed by agents/ephemerals) |
|
— |
Session/context id used as the AMS |
AMS service configuration (set on the <project>-agent-memory service in compose.yaml):
Variable |
Default |
Description |
|---|---|---|
|
|
Redis 8 backing store |
|
|
Disable auth (local/dev only) |
|
|
Enable long-term (vector) memory |
|
|
LLM for summarization/extraction (via LiteLLM) |
|
|
Model for topic extraction / NER |
|
|
Model for complex tasks |
|
|
Embedding model (768 dims) |
|
|
Ollama endpoint for LiteLLM |
|
|
Vector dimensions (must match embedding model) |
Note: AMS requires Redis 8+ because it uses the
HSETEXcommand (introduced in Redis 8.0). Redis 8 also bundles RediSearch/RedisJSON in the core, so theredis:8image is sufficient — no separateredis-stackmodules needed.
Session Management¶
Note
Alpha. Framework-managed sessions are under active development. These variables may change between releases.
Sessions tie an opaque token to an internal context_id and its conversation
context. The backend is pluggable: memory (per-pod, dev) or redis (shared
state, LB/multi-pod safe). See Sessions & Multi-turn.
Variable |
Default |
Description |
|---|---|---|
|
|
|
|
|
Session/context lifetime in seconds |
|
falls back to |
Redis URL for the |
|
|
If |
Multi-pod? Use
SESSION_BACKEND=redis. Withmemory, each replica keeps its own sessions in RAM, so a request that lands on another pod loses the conversation. Redis shares the state across pods.
Ollama control¶
Variable |
Default |
Description |
|---|---|---|
|
|
Start local Ollama in container (distributed mode) |
|
|
Pull models on container start |
Agent card¶
Variable |
Default |
Description |
|---|---|---|
|
|
Path to agent card file |