SDK Configuration Reference

Atlas SDK configs are the control tower for runtime orchestration. Every key is validated by a Pydantic schema (atlas-sdk/atlas/config/models.py), so mistakes surface before the adaptive dual-agent reasoning loop—your agent paired with a verifying teacher—spins up. Atlas uses LiteLLM as its primary adapter backend, making the system model-agnostic and compatible with 100+ LLM providers including OpenAI, Anthropic Claude, Google Gemini, XAI Grok, Azure OpenAI, AWS Bedrock, local models (Ollama, vLLM), and custom endpoints.

This page is a configuration reference. For adapter walkthroughs and orchestration concepts, see Bring Your Own Agent and How Orchestration Works.

Keep atlas.core.run(..., stream_progress=True) enabled while tuning configs—the live event stream mirrors exactly what persists to storage and makes it easy to spot misconfigured blocks.

Root Config Overview

Field	Type / Default	Required?	Why it matters
`agent`	Adapter union (`litellm` \| `http_api` \| `python` \| `openai`)	Yes	Connects the orchestrator to your underlying agent transport. Use `litellm` for all new projects.
`teacher`	`TeacherConfig`	Yes	Defines the verifying teacher persona, LLM, and feedback limits.
`rim`	`RIMConfig`	Yes	Configures the `RIM` ensemble that drives retries and adaptive feedback.
`student`	`StudentConfig` (token caps default to `2048`)	No	Controls your agent’s (student) prompts, tool usage, and token budgets.
`orchestration`	`OrchestrationConfig` (`max_retries=1`, `step_timeout_seconds=900`, `rim_guidance_tag="rim_feedback"`, `emit_intermediate_steps=true`)	No	Governs retries, timeouts, and telemetry emission.
`adaptive_teaching`	`AdaptiveTeachingConfig` (`enabled=true`)	No	Triage, probe, and lane-selection policy.
`storage`	`StorageConfig \| null` (default `null`)	No	Enables Postgres persistence for traces and learning memory.
`metadata`	`Dict[str, Any]` (default `{}`)	No	Free-form tags for analytics and logging.

Agent Block (`agent`)

This block wires the orchestrator to your agent. The schema is defined by AdapterConfig and its subclasses in atlas-sdk/atlas/config/models.py:67-176; extra keys are rejected.

Common fields

Parameter	Type / Default	Required?	Why adjust
`type`	Enum: `litellm`, `http_api`, `python`, `openai`	Yes	Selects which adapter subclass will validate the rest of the block. Use `litellm` for new projects.
`name`	`str`	Yes	Appears in telemetry and logs; use a descriptive identifier per deployment.
`system_prompt`	`str`	Yes	Baseline persona text passed to your agent (the student).
`tools`	`List[ToolDefinition]` (default `[]`)	No	Register JSON-schema tool signatures; validation ensures required keys exist.

HTTP adapter (`type: http_api`)

Parameter	Type / Default	Required?	Why adjust
`transport.base_url`	`str`	Yes	Base endpoint for your service.
`transport.headers`	`Dict[str, str]` (default `{}`)	No	Inject auth or custom headers.
`transport.timeout_seconds`	`float` (default `60.0`)	No	Increase when downstream APIs are slow.
`transport.retry.attempts`	`int` (default `1`, bounded `1..5`)	No	Add resilience for flaky endpoints.
`transport.retry.backoff_seconds`	`float` (default `1.0`)	No	Control backoff between retry attempts.
`payload_template`	`Dict[str, Any]` (default `{}`)	No	Provide a skeleton payload with placeholders the runtime will fill.
`result_path`	`Sequence[str] \| null`	No	Extract a nested field from the response JSON.

Python adapter (`type: python`)

Parameter	Type / Default	Required?	Why adjust
`import_path`	`str`	Yes	Python module or package that exposes your callable.
`attribute`	`str \| null`	No	Specify the function/class name when the module exports multiple callables.
`working_directory`	`str \| null`	No	Run relative imports against a specific path.
`allow_generator`	`bool` (default `false`)	No	Enable when the callable yields streaming results.
`llm`	`LLMParameters \| null`	No	Supply metadata when the callable proxies an LLM (e.g., for telemetry).

LiteLLM adapter (`type: litellm`)

Parameter	Type / Default	Required?	Why adjust
`llm.provider`	`str`	Yes	Choose from 100+ providers: `openai`, `anthropic`, `gemini`, `xai`, `azure-openai`, `bedrock`, etc.
`llm.model`	`str`	Yes	Choose the underlying chat model.
`llm.api_key_env`	`str`	Yes	Environment variable containing the API key.
`llm.api_base`	`str \| null`	No	Override the base URL for local models or custom endpoints.
`llm.temperature`	`float` (default `0.0`, range `0..2`)	Yes	Increase for more exploratory generations.
`llm.top_p`	`float \| null`	No	Apply nucleus sampling if desired.
`llm.max_output_tokens`	`int`	Yes	Cap response length.
`llm.timeout_seconds`	`float` (default `60.0`)	No	Widen for long-running completions.
`llm.retry.attempts`	`int` (default `1`, bounded `1..5`)	No	Increase for transient API failures.
`response_format`	`Dict[str, Any] \| null`	No	Request JSON schema enforcement when the provider supports it.

Using local models: The litellm adapter makes local model integration seamless.Ollama:

agent:
  type: litellm
  llm:
    provider: openai  # Ollama is OpenAI-compatible
    model: llama3.1
    api_base: http://localhost:11434
    api_key_env: DUMMY  # Ollama doesn't need auth
    temperature: 0.2
    max_output_tokens: 2048

vLLM:

agent:
  type: litellm
  llm:
    provider: openai
    model: meta-llama/Llama-3.1-8B-Instruct
    api_base: http://localhost:8000/v1
    api_key_env: DUMMY
    temperature: 0.2
    max_output_tokens: 2048

Both Ollama and vLLM are OpenAI-compatible, so use provider: openai with the correct api_base.

Provider Examples

OpenAI

llm:
  provider: openai
  model: gpt-4o-mini
  api_key_env: OPENAI_API_KEY
  temperature: 0.2
  max_output_tokens: 2048

Anthropic Claude

llm:
  provider: anthropic
  model: claude-sonnet-4-5
  api_key_env: ANTHROPIC_API_KEY
  temperature: 0.2
  max_output_tokens: 2048

Google Gemini

llm:
  provider: gemini
  model: gemini/gemini-2.5-flash
  api_key_env: GEMINI_API_KEY
  temperature: 0.2
  max_output_tokens: 2048

XAI Grok

llm:
  provider: xai
  model: xai/grok-4-fast
  api_key_env: XAI_API_KEY
  temperature: 0.2
  max_output_tokens: 2048

Azure OpenAI

llm:
  provider: azure-openai
  model: gpt-4o-mini
  api_key_env: AZURE_OPENAI_API_KEY
  api_base: https://your-resource.openai.azure.com
  temperature: 0.2
  max_output_tokens: 2048

AWS Bedrock

llm:
  provider: bedrock
  model: anthropic.claude-3-5-sonnet-20241022-v2:0
  api_key_env: AWS_ACCESS_KEY_ID
  # Also requires AWS_SECRET_ACCESS_KEY and AWS_REGION
  temperature: 0.2
  max_output_tokens: 2048

Student Block (`student`)

Guides the student agent’s prompts and token budgets. When prompts is omitted, the runtime builds defaults from the agent system_prompt.

Parameter	Type / Default	Required?	Why adjust
`prompts`	`StudentPrompts \| null`	No	Override planner/executor/synthesizer prompt templates explicitly.
`prompt_guidance`	`Dict[str, str]` (default `{}`)	No	Supply reusable chunks merged into prompts per run.
`max_plan_tokens`	`int` (default `2048`)	No	Raise when plans are truncated.
`max_step_tokens`	`int` (default `2048`)	No	Increase for verbose tool output.
`max_synthesis_tokens`	`int` (default `2048`)	No	Allow longer final answers.
`tool_choice`	Literal `auto` \| `required` (default `auto`)	No	Force tool invocation on every step when governance demands it.

Example override (add explicit prompts to your config):

student:
  prompts:
    planner: |
      {base_prompt}

      Break the user's task into a short numbered plan.
    executor: |
      {base_prompt}

      Execute the current plan step. Show the work that led to your answer.
    synthesizer: |
      {base_prompt}

      Summarize the important findings from every step and deliver the final answer.
  max_plan_tokens: 1024
  max_step_tokens: 1024
  max_synthesis_tokens: 1024
  tool_choice: auto

Teacher Block (`teacher`)

Defines the verifying teacher persona that validates plans, emits guidance, and certifies results.

Parameter	Type / Default	Required?	Why adjust
`llm`	`LLMParameters`	Yes	Choose the verifying teacher model (often stronger than the student agent). Supports all LiteLLM providers; use `api_base` for local models.
`max_review_tokens`	`int \| null` (default `null`)	No	Cap plan-review responses.
`plan_cache_seconds`	`int` (default `300`)	No	Reuse approved plans for repeated task IDs.
`guidance_max_tokens`	`int \| null`	No	Limit per-step feedback length.
`validation_max_tokens`	`int \| null`	No	Cap the validation verdict.
`prompts`	`TeacherPrompts \| null`	No	Replace default reviewer prompts.
`prompt_guidance`	`Dict[str, str]` (default `{}`)	No	Inject reusable guidance fragments.

Orchestration Block (`orchestration`)

Controls retry semantics and telemetry.

Parameter	Type / Default	Required?	Why adjust
`max_retries`	`int` (default `1`, hard ceiling)	No	Set to `0` to disable retries entirely.
`step_timeout_seconds`	`float` (default `900.0`)	No	Lengthen for slow tools or external APIs.
`rim_guidance_tag`	`str` (default `"rim_feedback"`)	No	Change when your prompts expect a different insertion tag.
`emit_intermediate_steps`	`bool` (default `true`)	No	Toggle console/storage streaming of intermediate events.
`forced_mode`	`AdaptiveMode \| null`	No	Lock the runtime to `auto`, `paired`, or `coach` (useful for deterministic evaluation).

Example override (pairing with the prompts above):

orchestration:
  max_retries: 1
  step_timeout_seconds: 600
  rim_guidance_tag: rim_feedback
  emit_intermediate_steps: true

Reward System Block (RIM - Reward Integration Module)

The RIM (Reward Integration Module) evaluates each trajectory to decide whether to retry or accept the outcome. Configure the reward system using the rim block in your runtime config.

Parameter	Type / Default	Required?	Why adjust
`small_model`	`LLMParameters`	Yes	Fast path judge; keep lightweight for latency-sensitive checks. Supports all LiteLLM providers including local models.
`large_model`	`LLMParameters`	Yes	Escalation judge invoked on disagreement. Supports all LiteLLM providers including local models.
`active_judges`	`Dict[str, bool]` (default `{"process": true, "helpfulness": true}`)	No	Toggle built-in dimensions or add custom judges.
`variance_threshold`	`float` (default `0.15`)	No	Lower to escalate disagreements sooner.
`uncertainty_threshold`	`float` (default `0.3`)	No	Raise to reduce escalations on ambiguous scores.
`parallel_workers`	`int` (default `4`, range `1..32`)	No	Tune concurrency to match judge model throughput.
`judge_prompt`	`str \| null`	No	Provide a rubric that defines success for your domain.

Example of weighting judges:

rim:
  small_model:
    provider: gemini
    model: gemini/gemini-2.5-flash
    api_key_env: GEMINI_API_KEY
    max_output_tokens: 512
  large_model:
    provider: gemini
    model: gemini/gemini-2.5-flash
    api_key_env: GEMINI_API_KEY
    max_output_tokens: 768
  active_judges:
    process: true
    helpfulness: true
    accuracy: false
  variance_threshold: 0.15
  uncertainty_threshold: 0.3
  judge_prompt: 'Reward runs that stay on brief, respect APIs, and deliver correct answers.'

See Reward Design for guidance on composing ensembles and custom judges.

Adaptive Teaching Block (`adaptive_teaching`)

Configures triage, probing, and lane routing for the adaptive dual-agent pair—your agent plus the verifying teacher (atlas-sdk/atlas/config/models.py:185-227).

Parameter	Type / Default	Required?	Why adjust
`enabled`	`bool` (default `true`)	No	Disable to bypass adaptive routing entirely.
`certify_first_run`	`bool` (default `true`)	No	Force first-time personas through `paired` certification.
`mode_override`	Literal \| `null`	No	Pin execution to `auto`, `paired`, or `coach`.
`triage_adapter`	`str \| null`	No	Reference a custom dossier builder.
`default_tags`	`List[str]` (default `[]`)	No	Apply default metadata to persona memories.
`probe.llm`	`LLMParameters \| null`	No	Override the capability probe model.
`probe.thresholds`	`auto=0.85`, `paired=0.65`, `coach=0.35`	No	Adjust lane cut-offs; order must satisfy `auto ≥ paired ≥ coach`.
`probe.fallback_mode`	Literal (`"paired"` default)	No	Lane chosen when the probe cannot decide.
`probe.evidence_limit`	`int` (default `6`, range `1..32`)	No	Limit how many supporting reasons the probe collects.
`probe.timeout_seconds`	`float` (default `15.0`)	No	Extend for slower models.
`reward.type`	Literal `rim` (default) \| `python`	No	Switch to a custom reward objective.
`reward.import_path` / `attribute`	`str` / `str`	Required when `type="python"`	Point at your custom scorer.
`reward.focus_prompt`	`str \| null`	No	Give the reward model an extra steer for this deployment.

Example of forcing coach mode:

adaptive_teaching:
  enabled: true
  mode_override: coach
  default_tags:
    - tenant:demo
    - domain:sre
  probe:
    thresholds:
      auto: 0.9
      paired: 0.7
      coach: 0.4
    fallback_mode: coach

Storage Block (`storage`)

Controls Postgres persistence (atlas-sdk/atlas/config/models.py:299-307). Omit the block or set storage: null for ephemeral runs.

Parameter	Type / Default	Required?	Why adjust
`database_url`	`str`	Yes (when block present)	Point at your managed or local Postgres instance.
`min_connections`	`int` (default `1`)	No	Increase for burstier workloads.
`max_connections`	`int` (default `5`)	No	Upper bound for connection pool size.
`statement_timeout_seconds`	`float` (default `30.0`)	No	Abort long-running queries sooner.

Tip: atlas init scaffolds a Docker Compose file with sensible defaults and exposes Postgres on localhost:5433.

Learning Block (`learning`)

Controls the runtime synthesizer that generates and applies student/teacher playbooks.

Parameter	Type / Default	Required?	Why adjust
`enabled`	`bool` (default `true`)	No	Disable to run without loading or updating playbooks.
`update_enabled`	`bool` (default `true`)	No	Freeze updates while keeping existing playbooks active.
`llm`	`LLMParameters \| null`	No	Override the synthesizer model; falls back to runtime defaults otherwise.
`prompts`	`LearningPrompts \| null`	No	Supply custom prompts for the synthesizer LLM.
`history_limit`	`int` (default `10`)	No	Cap historical sessions fed into each update.
`session_note_enabled`	`bool` (default `true`)	No	Persist per-session learning notes alongside the registry.
`apply_to_prompts`	`bool` (default `true`)	No	Toggle playbook injection into persona prompts and validation payloads.

Pair this section with Learning System Architecture for deeper context.

Runtime Safety Block (`runtime_safety`)

Defines production guardrails for drift detection and export review policies.

Parameter	Type / Default	Required?	Why adjust
`drift.enabled`	`bool` (default `true`)	No	Disable statistical drift alerts (rarely recommended).
`drift.window`	`int` (default `50`)	No	Increase for noisy telemetry; decrease for faster alerts.
`drift.z_threshold`	`float` (default `3.0`)	No	Lower to make alerts more sensitive.
`drift.min_baseline`	`int` (default `5`)	No	Require more samples before alerts fire.
`review.require_approval`	`bool` (default `true`)	No	Keep true in production to gate exports on human review.
`review.default_export_statuses`	`List[str]` (default `["approved"]`)	No	Adjust when automation needs additional review states.

See Runtime Safety & Review for operational guidance.

Metadata

Parameter	Type / Default	Required?	Why adjust
`metadata`	`Dict[str, Any]` (default `{}`)	No	Attach labels consumed by your monitoring stack.

Legacy configs may still include a prompt_rewrite block, but the runtime now rejects it (atlas-sdk/atlas/core/__init__.py raises a ValueError). Remove the block and rely on explicit student.prompts / teacher.prompts instead.

Cheat Sheet

Goal	Section to edit	Pointer
Swap to Anthropic or Gemini	`agent`	Use `type: litellm` with `provider: anthropic` or `provider: gemini`.
Use local models (Ollama/vLLM)	`agent`	Use `type: litellm`, `provider: openai`, and set `api_base` to your local server.
Tighten or loosen retries	`orchestration` + `rim`	Adjust `max_retries`, `variance_threshold`, and `uncertainty_threshold`.
Persist adaptive memories	`storage`	Add a Postgres URL or run `atlas init`.
Force a supervision lane	`adaptive_teaching`	Set `mode_override` to `auto`, `paired`, or `coach`.
Personalise prompts	`student.prompts` / `teacher.prompts`	Override templates or reuse `prompt_guidance`.
Enforce JSON output	`agent` (`response_format`)	Provide OpenAI-compatible schemas or swap to `http_api` with custom validation.
Freeze playbook updates	`learning.update_enabled`	Pause runtime learning while investigating regressions.
Require approvals for exports	`runtime_safety.review`	Keep `require_approval=true` and document review notes.

Validated Example (Quickstart)

This minimal config demonstrates the recommended litellm adapter with OpenAI models:

agent:
  type: litellm
  name: example-litellm-agent
  system_prompt: |
    You are an AI model acting as the Atlas Student. Follow instructions carefully and respond with JSON when asked.
  tools: []
  llm:
    provider: openai
    model: gpt-4o-mini
    api_key_env: OPENAI_API_KEY
    temperature: 0.2
    max_output_tokens: 2048

teacher:
  llm:
    provider: openai
    model: gpt-4o-mini
    api_key_env: OPENAI_API_KEY
    temperature: 0.1
    max_output_tokens: 2048

rim:
  small_model:
    provider: gemini
    model: gemini/gemini-2.5-flash
    api_key_env: GEMINI_API_KEY
    max_output_tokens: 8096
  large_model:
    provider: gemini
    model: gemini/gemini-2.5-flash
    api_key_env: GEMINI_API_KEY
    max_output_tokens: 8096
  judge_prompt: 'reward the agent for attending the issues mentioned in the task'
  variance_threshold: 0.15
  uncertainty_threshold: 0.3

storage:
  database_url: postgresql://atlas:atlas@localhost:5433/atlas
  min_connections: 1
  max_connections: 5
  statement_timeout_seconds: 30

Legacy configs: If you have existing configs using type: openai, they will continue to work but emit deprecation warnings. Migrate to type: litellm at your convenience.

Parameter Index (Alphabetical)

adaptive_teaching.default_tags – Tag sessions and learning updates with deployment metadata.
adaptive_teaching.mode_override – Force the runtime into a specific lane for deterministic evaluation.
agent.response_format – Request JSON-mode enforcement from OpenAI-compatible providers.
learning.apply_to_prompts – Enable/disable playbook injection into persona prompts.
learning.update_enabled – Gate persistence of new playbooks after each session.
orchestration.forced_mode – Hard-set the execution mode regardless of probe results.
runtime_safety.drift.z_threshold – Sensitivity of automatic drift alerts.
runtime_safety.review.default_export_statuses – Review states included when tooling omits filters.
storage.database_url – Connection string for the Postgres telemetry store.
student.tool_choice – Force tool invocation on each step when governance demands it.
teacher.plan_cache_seconds – Duration to reuse previously approved plans.

Bring Your Own Agent — Adapter-specific tutorials.
How Orchestration Works — Dual-agent control flow (student agent + verifying teacher) deep dive.
Reward Design — Building and tuning judge ensembles.

Getting Started

SDK Guides

Examples

Training

Core Concepts

Reference

Benchmarks

SDK Configuration Reference

Root Config Overview

Agent Block (`agent`)

Common fields

HTTP adapter (`type: http_api`)

Python adapter (`type: python`)

LiteLLM adapter (`type: litellm`)

Provider Examples

Student Block (`student`)

Teacher Block (`teacher`)

Orchestration Block (`orchestration`)

Reward System Block (RIM - Reward Integration Module)

Adaptive Teaching Block (`adaptive_teaching`)

Storage Block (`storage`)

Learning Block (`learning`)

Runtime Safety Block (`runtime_safety`)

Metadata

Cheat Sheet

Validated Example (Quickstart)

Parameter Index (Alphabetical)

Getting Started

SDK Guides

Examples

Training

Core Concepts

Reference

Benchmarks

​Root Config Overview

​Agent Block (agent)

​Common fields

​HTTP adapter (type: http_api)

​Python adapter (type: python)

​LiteLLM adapter (type: litellm)

​Provider Examples

​Student Block (student)

​Teacher Block (teacher)

​Orchestration Block (orchestration)

​Reward System Block (RIM - Reward Integration Module)

​Adaptive Teaching Block (adaptive_teaching)

​Storage Block (storage)

​Learning Block (learning)

​Runtime Safety Block (runtime_safety)

​Metadata

​Cheat Sheet

​Validated Example (Quickstart)

​Parameter Index (Alphabetical)

​Related Guides

Root Config Overview

Agent Block (`agent`)

Common fields

HTTP adapter (`type: http_api`)

Python adapter (`type: python`)

LiteLLM adapter (`type: litellm`)

Provider Examples

Student Block (`student`)

Teacher Block (`teacher`)

Orchestration Block (`orchestration`)

Reward System Block (RIM - Reward Integration Module)

Adaptive Teaching Block (`adaptive_teaching`)

Storage Block (`storage`)

Learning Block (`learning`)

Runtime Safety Block (`runtime_safety`)

Metadata

Cheat Sheet

Validated Example (Quickstart)

Parameter Index (Alphabetical)

Related Guides