kollabor-ai

kollabor-ai is the model/provider layer for Kollabor.

It owns profile loading, provider creation, prompt rendering, context services, conversation/session helpers, token/cost accounting, and response parsing. The CLI, engine, and agent runtime should use this package instead of talking directly to provider SDKs.

Current Role

Normalize provider access across Anthropic, OpenAI, OpenAI Responses, Azure OpenAI, Gemini, OpenRouter, and custom OpenAI-compatible endpoints.
Load, validate, and resolve LLM profiles, including environment-variable and OAuth-backed credentials.
Render system prompts and <trender> prompt fragments.
Parse streaming text, thinking/reasoning blocks, and tool-call deltas.
Track conversation logs, session names, branch names, pricing, and context service metadata.

Architecture

Module	Responsibility
`api_communication_service.py`	high-level LLM request/streaming service
`providers/`	provider configs, adapters, registry, errors, transformers
`profile_manager.py`	profile model, config/env resolution, persistence
`profile_validator.py`	profile field validation and connection checks
`prompt_renderer.py`	dynamic prompt rendering and `<trender>` support
`system_prompt_builder.py`	assembled system prompt construction
`response_parser.py` / `response_processor.py`	response and tool-call parsing
`streaming_thinking_parser.py`	streamed thinking/reasoning extraction
`conversation_manager.py` / `conversation_logger.py`	history and raw logs
`context_service/`	context ledger, file tracking, hash utilities, hub bridge
`pricing_registry.py` / `cost_calculator.py`	model pricing and usage costs
`session_naming.py` / `session_parser.py`	session metadata helpers

Usage

from kollabor_ai import APICommunicationService, LLMProfile


class DictConfig:
    def __init__(self, values):
        self.values = values

    def get(self, key, default=None):
        return self.values.get(key, default)


profile = LLMProfile(
    name="default",
    provider="anthropic",
    model="claude-3-5-sonnet-20241022",
    api_key="${ANTHROPIC_API_KEY}",
)

api = APICommunicationService(
    config=DictConfig({"kollabor.llm.enable_streaming": True}),
    raw_conversations_dir=".kollab/raw",
    profile=profile,
)

await api.initialize()
text = await api.call_llm([{"role": "user", "content": "hello"}])

Known Gaps

ProviderRegistry currently caches singleton instances by provider type, so callers that need strict per-profile isolation must be careful until the registry is keyed by full provider configuration or sessions create their own providers.
LLMProfile.to_dict() includes resolved API keys when present; API layers must explicitly redact profile dictionaries before returning them to clients.
Provider behavior is still partly normalized by convention. Tool-call, thinking, usage, and stop-reason contracts need broader cross-provider tests.
Prompt rendering can execute dynamic includes; callers must sanitize user-controlled prompts before rendering.

Roadmap

Phase 1: Provider isolation and safety

Key provider instances by full provider config or create session-scoped providers for clients that need isolation.
Add a redacted profile view helper for API/UI use.
Expand provider conformance tests for streaming tool calls, thinking content, token usage, and error classification.

Phase 2: Contract cleanup

Make public service constructors and adapter boundaries easier to use outside the CLI orchestration layer.
Document the canonical message/tool-call schema expected by every provider.
Keep provider-specific transformers behind stable package APIs.

Phase 3: Context and cost maturity

Document the context-service ledger and hub bridge as first-class APIs.
Add pricing registry refresh/versioning guidance.
Add stronger diagnostics for context-window and max-token decisions.

Development

Targeted validation examples:

python -m py_compile packages/kollabor-ai/src/kollabor_ai/*.py
python -m pytest tests/unit/llm tests/unit/test_context_service_hub_bridge.py -q

Dependencies

pydantic >= 2.0
aiohttp >= 3.10
httpx >= 0.27
openai >= 1.0

License

MIT

Metadata

Release files for kollabor-ai 0.15.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for kollabor-ai 0.15.4
File	Size	Uploaded
kollabor_ai-0.15.4.tar.gz	229.1 kB	Oct 12, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for kollabor-ai 0.15.4
File	Interpreter	ABI	Platform	Reset
kollabor_ai-0.15.4-py3-none-any.whl 263.6 kB Oct 12, 2026	Python 3	none	any	Details

Total release size: 492.7 kB

Release files / kollabor_ai-0.15.4.tar.gz

Download URL	kollabor_ai-0.15.4.tar.gz
Size	229.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`5b043d2c5e8aa2f18cc90c5667d5343ab36b828ba2177bc855507e69e1943563`
BLAKE2b-256 checksum How to use checksums	`b6576a77d47c9006592e6528e6b2ddd25b5c60a87069577ba6a844d9f2486168`
Upload date	Oct 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.15`

Release files / kollabor_ai-0.15.4-py3-none-any.whl

Download URL	kollabor_ai-0.15.4-py3-none-any.whl
Size	263.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`5cd3fc33bf8647e00b8488b93130e3b9be4c604e0516f91aad20e914c74afa50`
BLAKE2b-256 checksum How to use checksums	`14db511981d4d15c753dc73ca76f1a89c389a20a4a5a9a81c1e231ae42729ee5`
Upload date	Oct 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.15`