Manifests & Versioning

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What Is a Manifest?

Manifest v3
├── Tools: search, calculator, web_browser
├── Model: gpt-4o (temperature: 0.7)
├── Prompts: "You are a helpful research assistant..."
├── Subagents: fact-checker, summarizer
└── Skills: code-review, sql-optimizer

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How Auto-Detection Works

import decimalai

# Default — flag form auto-installs the integration
decimalai.init(api_key="your-api-key", langchain=True)

# Use LangChain as normal — everything is auto-detected

decimalai.init(api_key="your-api-key")
from decimalai.langchain import install
install(prompts={"system": "Your static template"})

# Default — flag form picks up Agent objects on first run
import decimalai
decimalai.init(api_key="your-api-key", openai_agents=True)

# Run your agent as usual; the SDK introspects it at trace time.

import decimalai
from agents import Agent
from decimalai.openai_agents import install

decimalai.init(api_key="your-api-key")
agent = Agent(name="research-agent", instructions="...", model="gpt-4o", tools=[...])
install(agent=agent)  # Full schema introspection

# Default — flag form
import decimalai
decimalai.init(api_key="your-api-key", otel=True)
# Or for CrewAI specifically: decimalai.init(api_key="...", crewai=True)

# Option 1: Explicit registration (highest fidelity)
decimalai.register_manifest(
    agent_name="my-agent",
    tools=[
        {"name": "search", "schema": {"type": "object", "properties": {"query": {"type": "string"}}}},
    ],
    prompts={"system": "You are a helpful research assistant."},
    models={"default": {"provider": "openai", "model": "gpt-4o", "temperature": 0.7}},
)

# Option 2: Auto-deduce from trace calls (zero config)
@decimalai.trace(agent_name="my-agent")
def run_agent(query: str):
    decimalai.log_llm_call(model="gpt-4o", ...)
    decimalai.log_tool_call(name="search", ...)

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Manifest Hashing

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Compatibility Scoring

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Repair: Fix Traces Mechanically

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What Repair Does

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Repair Flow

curl -X POST https://api.decimal.ai/api/v1/repair/preview \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"old_manifest_id": "...", "new_manifest_id": "...", "sample_size": 5}'

# Apply all rules
curl -X POST https://api.decimal.ai/api/v1/repair/apply \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"old_manifest_id": "...", "new_manifest_id": "..."}'

# Or apply selectively
curl -X POST https://api.decimal.ai/api/v1/repair/apply-selective \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"old_manifest_id": "...", "new_manifest_id": "...", "approved_rule_indices": [0, 2]}'

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Dashboard Quickstart

1. Click “Repair All” to preview repair rules (before/after examples for each affected trace)
2. Review the rules, then click “Apply Repairs” to execute
3. Or click “Repair & Build” for a combined flow: repair all eligible traces, then build a clean SFT dataset from keep + repaired traces — one click, end to end

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REST API

import httpx

base = "https://api.decimal.ai/api/v1"
headers = {"Authorization": "Bearer dai_sk_..."}

# Preview repair rules — returns the proposed transforms with sample diffs
rules = httpx.post(
    f"{base}/repair/preview",
    headers=headers,
    json={"old_manifest_id": "...", "new_manifest_id": "...", "sample_size": 5},
).json()

# Apply all repairs
result = httpx.post(
    f"{base}/repair/apply",
    headers=headers,
    json={"old_manifest_id": "...", "new_manifest_id": "..."},
).json()

# Apply selectively (approve specific rule indices only)
result = httpx.post(
    f"{base}/repair/apply-selective",
    headers=headers,
    json={
        "old_manifest_id": "...",
        "new_manifest_id": "...",
        "approved_rule_indices": [0, 2],
    },
).json()

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Compatibility Policies

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What Happens When a Manifest Changes

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The Manifest Timeline

• Version label (v1, v2, v3, or custom labels)
• Date registered
• Diff view — which surfaces changed between versions
• Trace count — how many traces were recorded under each version
• Compatibility summary — keep/repair/replay/drop counts

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When Manifests Are Registered

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Best Practices

1. Use static prompts. Dynamic content (RAG chunks, dates) in system prompts causes false drift. Pass dynamic content as user messages instead.
2. Use @decimalai.tool for full schema tracking. The decorator generates JSON schemas from Python type hints — enabling schema-level drift detection, not just name-level.
3. Start with the default policy. The balanced preset works for most teams. Switch to strict when you need training data purity guarantees.
4. Label important versions. Use version_label="v2.1-prod" when deploying significant changes so you can find them in the timeline.

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Next Steps