routes

routes

Route handlers for the OpenAI-compatible API server.

Classes

Functions

chat_completions async

chat_completions(request_body: ChatCompletionRequest, request: Request)

Handle chat completion requests (streaming and non-streaming).

Source code in src/openjarvis/server/routes.py

@router.post("/v1/chat/completions")
async def chat_completions(request_body: ChatCompletionRequest, request: Request):
    """Handle chat completion requests (streaming and non-streaming)."""
    engine = request.app.state.engine
    agent = getattr(request.app.state, "agent", None)
    model = request_body.model
    use_server_agent = (
        agent is not None
        and not request_body.tools
        and (not request_body.stream or bool(getattr(agent, "_tools", None)))
    )

    # Inject memory context into messages before dispatching
    config = getattr(request.app.state, "config", None)
    memory_backend = getattr(request.app.state, "memory_backend", None)
    if (
        config is not None
        and config.agent.context_from_memory
        and request_body.messages
    ):
        try:
            from openjarvis.tools.storage.context import ContextConfig, inject_context

            memory_service = getattr(request.app.state, "memory_service", None)
            facts = memory_service.list_facts() if memory_service is not None else []

            # Extract query from the last user message
            query_text = ""
            for m in reversed(request_body.messages):
                if m.role == "user" and m.content:
                    query_text = m.content
                    break

            if query_text:
                messages = _to_messages(request_body.messages)
                # Direct engine paths need the server identity folded into
                # memory here so adapters receive one leading system message.
                # Agent paths build that same identity in BaseAgent; injecting
                # it at both layers duplicates the prompt around memory.
                if not use_server_agent:
                    messages = _ensure_identity_prompt(messages, config)
                ctx_cfg = ContextConfig(
                    top_k=config.memory.context_top_k,
                    min_score=config.memory.context_min_score,
                    max_context_tokens=config.memory.context_max_tokens,
                )
                enriched = inject_context(
                    query_text,
                    messages,
                    memory_backend,
                    config=ctx_cfg,
                    facts=facts,
                )
                # Rebuild after identity/context merging so downstream engine
                # adapters always receive exactly one system message.
                from openjarvis.server.models import ChatMessage

                new_msgs = []
                for msg in enriched:
                    new_msgs.append(
                        ChatMessage(
                            role=msg.role.value,
                            content=msg.content,
                            name=msg.name,
                            tool_calls=[
                                {
                                    "id": tool_call.id,
                                    "type": "function",
                                    "function": {
                                        "name": tool_call.name,
                                        "arguments": tool_call.arguments,
                                    },
                                }
                                for tool_call in (msg.tool_calls or [])
                            ]
                            or None,
                            tool_call_id=getattr(msg, "tool_call_id", None),
                        )
                    )
                request_body.messages = new_msgs
        except Exception:
            logging.getLogger("openjarvis.server").debug(
                "Memory context injection failed",
                exc_info=True,
            )

    # Run complexity analysis on the last user message
    complexity_info = None
    query_text_for_complexity = ""
    for m in reversed(request_body.messages):
        if m.role == "user" and m.content:
            query_text_for_complexity = m.content
            break
    if query_text_for_complexity:
        try:
            from openjarvis.learning.routing.complexity import (
                adjust_tokens_for_model,
                score_complexity,
            )

            cr = score_complexity(query_text_for_complexity)
            suggested = adjust_tokens_for_model(
                cr.suggested_max_tokens,
                model,
            )
            complexity_info = ComplexityInfo(
                score=cr.score,
                tier=cr.tier,
                suggested_max_tokens=suggested,
            )
            # Bump max_tokens when complexity suggests more than what
            # the client requested — never reduce below the request value.
            if suggested > request_body.max_tokens:
                request_body.max_tokens = suggested
        except Exception:
            logging.getLogger("openjarvis.server").debug(
                "Complexity analysis failed",
                exc_info=True,
            )

    if request_body.stream:
        # When the client passes `tools`, stream the model's raw
        # OpenAI-compat function-calling decision directly from the engine
        # (bypassing the agent) — the streaming mirror of the non-streaming
        # #454 fix. Routing client-supplied tools through a server-side agent
        # would execute the agent's different tool set and drop the raw tool
        # call the caller expects (#414).
        #
        # Without client-supplied tools, keep streaming requests on the
        # configured server agent so its server-side tool loop is available
        # to the desktop UI and other stream:true clients (#735). Fall back to
        # direct token streaming when no tool-bearing agent is configured.
        if request_body.tools:
            return await _handle_stream_tools(
                engine,
                model,
                request_body,
                complexity_info,
                app_config=config,
                bus=getattr(request.app.state, "bus", None),
                memory_service=getattr(request.app.state, "memory_service", None),
            )
        if use_server_agent:
            return await _handle_agent_stream(
                agent,
                model,
                request_body,
                complexity_info,
                trace_store=getattr(request.app.state, "trace_store", None),
                bus=getattr(request.app.state, "bus", None),
                memory_service=getattr(request.app.state, "memory_service", None),
            )
        return await _handle_stream(
            engine,
            model,
            request_body,
            complexity_info,
            trace_store=getattr(request.app.state, "trace_store", None),
            app_config=config,
            bus=getattr(request.app.state, "bus", None),
            memory_service=getattr(request.app.state, "memory_service", None),
        )

    # Non-streaming: use agent if available, otherwise direct engine call.
    #
    # EXCEPTION: when the client explicitly passed `tools`, they're asking
    # for raw OpenAI-compat function-calling — return the model's
    # tool_call decision verbatim. Routing through `_handle_agent` would
    # call `agent.run(input_text)`, which IGNORES `request_body.tools`,
    # runs the agent's own internal tool loop with its own (different)
    # tool spec, and returns only `result.content` — so the model's
    # tool_calls vanish and the user sees a generic acknowledgement
    # (e.g. "Understood. If you have another request...") that the
    # agent's re-prompted LLM produced. See #414.
    #
    # If a future caller needs agent orchestration WITH client-supplied
    # tools (e.g. injecting MCP tools through this endpoint and wanting
    # the agent to execute them), add an explicit opt-in header rather
    # than removing this guard — silent re-routing is what produced #414.
    # ``_handle_agent`` (sync ``agent.run()``) and ``_handle_direct`` (sync
    # ``engine.generate()``) both make blocking upstream calls; run them in a
    # worker thread so a slow/wedged non-streaming request can't stall the
    # event loop and every other concurrent request with it.
    if use_server_agent:
        response = await asyncio.to_thread(
            _handle_agent,
            agent,
            model,
            request_body,
            complexity_info,
            trace_store=getattr(request.app.state, "trace_store", None),
            bus=getattr(request.app.state, "bus", None),
        )
    else:
        bus = getattr(request.app.state, "bus", None)
        response = await asyncio.to_thread(
            _handle_direct,
            engine,
            model,
            request_body,
            bus=bus,
            complexity_info=complexity_info,
            app_config=config,
        )

    # Hand the completed exchange to the background memory service.
    _remember_exchange(
        getattr(request.app.state, "memory_service", None),
        query_text_for_complexity,
        response,
        bus=getattr(request.app.state, "bus", None),
        source="server.chat",
    )
    return response

list_models async

list_models(request: Request) -> ModelListResponse

List selectable engine models for the installed-model picker.

Direct cloud models live in the Cloud Models tab. Models advertised by a configured LiteLLM engine remain here because LiteLLM owns their routing and may use provider-qualified IDs that resemble OpenRouter IDs.

Source code in src/openjarvis/server/routes.py

@router.get("/v1/models")
async def list_models(request: Request) -> ModelListResponse:
    """List selectable engine models for the installed-model picker.

    Direct cloud models live in the Cloud Models tab. Models advertised by a
    configured LiteLLM engine remain here because LiteLLM owns their routing
    and may use provider-qualified IDs that resemble OpenRouter IDs.
    """
    from openjarvis.server.cloud_router import is_cloud_model, list_local_models

    # Prefer engine.list_models() so mock engines work in tests.
    # Filter out direct-cloud model IDs that may appear via MultiEngine, but
    # retain provider-qualified IDs owned by the configured LiteLLM engine.
    # Fall back to direct Ollama query only when the engine returns nothing.
    engine = request.app.state.engine
    all_ids = await asyncio.to_thread(engine.list_models)
    model_ids = [
        m
        for m in all_ids
        if not is_cloud_model(m) or _engine_key_for_model(engine, m) == "litellm"
    ]
    if not model_ids:
        model_ids = await list_local_models()

    # Keep embed-only models out of the chat model picker. They still work for
    # memory/retrieval via the embedder path; putting them in /v1/models made
    # the UI auto-select nomic-embed-text and fail every generation with 400.
    model_ids = [m for m in model_ids if not is_embed_only_model(m)]

    return ModelListResponse(
        data=[
            ModelObject(
                id=mid,
                owned_by=(
                    "litellm"
                    if _engine_key_for_model(engine, mid) == "litellm"
                    else "openjarvis"
                ),
            )
            for mid in model_ids
        ],
    )

pull_model async

pull_model(request: Request)

Pull / download a model from the Ollama registry.

Source code in src/openjarvis/server/routes.py

@router.post("/v1/models/pull")
async def pull_model(request: Request):
    """Pull / download a model from the Ollama registry."""
    body = await request.json()
    model_name = body.get("model", "").strip()
    if not model_name:
        raise HTTPException(status_code=400, detail="'model' field is required")

    engine = request.app.state.engine
    engine_name = getattr(request.app.state, "engine_name", "")
    # Only Ollama supports pulling
    if engine_name != "ollama" and getattr(engine, "engine_id", "") != "ollama":
        raise HTTPException(
            status_code=501,
            detail="Model pulling is only supported with the Ollama engine",
        )

    import httpx as _httpx

    host = getattr(engine, "_host", "http://localhost:11434")
    try:
        async with _httpx.AsyncClient(base_url=host, timeout=600.0) as client:
            resp = await client.post(
                "/api/pull",
                json={"name": model_name, "stream": False},
            )
        resp.raise_for_status()
    except (_httpx.ConnectError, _httpx.TimeoutException) as exc:
        raise HTTPException(status_code=502, detail=f"Ollama unreachable: {exc}")
    except _httpx.HTTPStatusError as exc:
        raise HTTPException(
            status_code=exc.response.status_code,
            detail=f"Ollama error: {exc.response.text[:300]}",
        )

    return {"status": "ok", "model": model_name}

delete_model async

delete_model(model_name: str, request: Request)

Delete a model from Ollama.

Source code in src/openjarvis/server/routes.py

@router.delete("/v1/models/{model_name:path}")
async def delete_model(model_name: str, request: Request):
    """Delete a model from Ollama."""
    engine = request.app.state.engine
    engine_name = getattr(request.app.state, "engine_name", "")
    if engine_name != "ollama" and getattr(engine, "engine_id", "") != "ollama":
        raise HTTPException(status_code=501, detail="Only supported with Ollama engine")

    import httpx as _httpx

    host = getattr(engine, "_host", "http://localhost:11434")
    try:
        async with _httpx.AsyncClient(base_url=host, timeout=30.0) as client:
            resp = await client.request(
                "DELETE",
                "/api/delete",
                json={"name": model_name},
            )
        resp.raise_for_status()
    except (_httpx.ConnectError, _httpx.TimeoutException) as exc:
        raise HTTPException(status_code=502, detail=f"Ollama unreachable: {exc}")
    except _httpx.HTTPStatusError as exc:
        raise HTTPException(
            status_code=exc.response.status_code,
            detail=f"Ollama error: {exc.response.text[:300]}",
        )

    return {"status": "deleted", "model": model_name}

reload_cloud_engine async

reload_cloud_engine(request: Request)

Hot-reload cloud API keys and (re-)initialize the cloud engine.

Called by the desktop app immediately after the user saves a cloud API key so that cloud models become available without a full app restart.

Source code in src/openjarvis/server/routes.py

@router.post("/v1/cloud/reload")
async def reload_cloud_engine(request: Request):
    """Hot-reload cloud API keys and (re-)initialize the cloud engine.

    Called by the desktop app immediately after the user saves a cloud API
    key so that cloud models become available without a full app restart.
    """
    import os

    submitted_keys: dict[str, str] | None = None
    try:
        body = await request.json()
        raw_keys = body.get("keys") if isinstance(body, dict) else None
        if isinstance(raw_keys, dict):
            submitted_keys = {
                str(k): str(v)
                for k, v in raw_keys.items()
                if str(k).endswith("_API_KEY")
            }
    except Exception:
        submitted_keys = None

    if submitted_keys is not None:
        for key, value in submitted_keys.items():
            if value:
                os.environ[key] = value
            else:
                os.environ.pop(key, None)
    else:
        # Compatibility fallback for non-desktop/manual configurations.
        keys_path = get_config_dir() / "cloud-keys.env"
        if keys_path.exists():
            for raw_line in keys_path.read_text().splitlines():
                line = raw_line.strip()
                if line and not line.startswith("#") and "=" in line:
                    k, v = line.split("=", 1)
                    os.environ[k.strip()] = v.strip()

    # Try to build a fresh CloudEngine.
    try:
        from openjarvis.engine.cloud import CloudEngine
        from openjarvis.engine.multi import MultiEngine

        cloud = CloudEngine()
        if not cloud.health():
            return {
                "status": "no_cloud",
                "message": "No cloud models available (check API keys)",
            }
    except Exception as exc:
        return {"status": "error", "message": str(exc)}

    # Locate the innermost engine, working through InstrumentedEngine layers.
    outer = request.app.state.engine
    inner = getattr(outer, "_inner", outer)

    if isinstance(inner, MultiEngine):
        # Replace or insert the cloud entry in the existing MultiEngine.
        new_engines = [(k, e) for k, e in inner._engines if k != "cloud"]
        new_engines.append(("cloud", cloud))
        inner._engines = new_engines
        inner._refresh_map()
    else:
        # Wrap the existing engine (which may be security-wrapped) with a new
        # MultiEngine that includes the cloud engine.
        engine_name = getattr(request.app.state, "engine_name", "local")
        new_multi = MultiEngine([(engine_name, inner), ("cloud", cloud)])
        if hasattr(outer, "_inner"):
            outer._inner = new_multi
        else:
            request.app.state.engine = new_multi
        request.app.state.engine_name = "multi"

    return {"status": "ok", "message": "Cloud engine reloaded"}

savings async

savings(request: Request)

Return savings summary compared to cloud providers.

Only includes telemetry from the current server session so that counters start at zero each time a new model + agent is launched.

Source code in src/openjarvis/server/routes.py

@router.get("/v1/savings")
async def savings(request: Request):
    """Return savings summary compared to cloud providers.

    Only includes telemetry from the current server session so that
    counters start at zero each time a new model + agent is launched.
    """
    from openjarvis.core.config import DEFAULT_CONFIG_DIR
    from openjarvis.server.savings import compute_savings, savings_to_dict
    from openjarvis.telemetry.aggregator import TelemetryAggregator

    db_path = DEFAULT_CONFIG_DIR / "telemetry.db"
    if not db_path.exists():
        empty = compute_savings(0, 0, 0)
        return savings_to_dict(empty)

    session_start = getattr(request.app.state, "session_start", None)

    agg = TelemetryAggregator(db_path)
    try:
        # current_methodology_only excludes pre-fix legacy rows from
        # the leaderboard's per-token efficiency numerator/denominator
        # — see the comment on _time_filter for the bimodal-Wh/token
        # background.
        summary = agg.summary(since=session_start, current_methodology_only=True)
        # Exclude cloud model tokens from savings — only local
        # inference counts toward cost savings.
        _cloud_prefixes = (
            "gpt-",
            "o1-",
            "o3-",
            "o4-",
            "claude-",
            "gemini-",
            "openrouter/",
        )
        local_models = [
            m
            for m in summary.per_model
            if not any(m.model_id.startswith(p) for p in _cloud_prefixes)
        ]
        result = compute_savings(
            prompt_tokens=sum(m.prompt_tokens for m in local_models),
            completion_tokens=sum(m.completion_tokens for m in local_models),
            total_calls=sum(m.call_count for m in local_models),
            session_start=session_start if session_start else 0.0,
            prompt_tokens_evaluated=sum(
                m.prompt_tokens_evaluated for m in local_models
            ),
        )
        return savings_to_dict(result)
    finally:
        agg.close()

reset_telemetry async

reset_telemetry()

Clear all stored telemetry records.

Useful after updating token-counting methodology — clears historical records that were computed under the old rules so that the savings dashboard and leaderboard submissions start fresh with corrected values.

Source code in src/openjarvis/server/routes.py

@router.post("/v1/telemetry/reset")
async def reset_telemetry():
    """Clear all stored telemetry records.

    Useful after updating token-counting methodology — clears
    historical records that were computed under the old rules so
    that the savings dashboard and leaderboard submissions start
    fresh with corrected values.
    """
    from openjarvis.core.config import DEFAULT_CONFIG_DIR
    from openjarvis.telemetry.aggregator import TelemetryAggregator

    db_path = DEFAULT_CONFIG_DIR / "telemetry.db"
    if not db_path.exists():
        return {"status": "ok", "records_cleared": 0}

    agg = TelemetryAggregator(db_path)
    try:
        count = agg.clear()
    finally:
        agg.close()
    return {"status": "ok", "records_cleared": count}

server_info async

server_info(request: Request)

Return server configuration: model, agent, engine.

Source code in src/openjarvis/server/routes.py

@router.get("/v1/info")
async def server_info(request: Request):
    """Return server configuration: model, agent, engine."""
    agent = getattr(request.app.state, "agent", None)
    agent_id = getattr(agent, "agent_id", None) if agent else None
    # Fall back to configured agent name if agent didn't instantiate
    if agent_id is None:
        agent_id = getattr(request.app.state, "agent_name", None)
    return {
        "model": getattr(request.app.state, "model", ""),
        "agent": agent_id,
        "engine": getattr(request.app.state, "engine_name", ""),
    }

health async

health(request: Request)

Health check endpoint.

Source code in src/openjarvis/server/routes.py

@router.get("/health")
async def health(request: Request):
    """Health check endpoint."""
    engine = request.app.state.engine
    healthy = engine.health()
    if not healthy:
        raise HTTPException(status_code=503, detail="Engine unhealthy")
    return {"status": "ok"}

list_channels async

list_channels(request: Request)

List available messaging channels.

Source code in src/openjarvis/server/routes.py

@router.get("/v1/channels")
async def list_channels(request: Request):
    """List available messaging channels."""
    bridge = getattr(request.app.state, "channel_bridge", None)
    if bridge is None:
        return {"channels": [], "message": "Channel bridge not configured"}
    channels = bridge.list_channels()
    return {"channels": channels, "status": bridge.status().value}

channel_send async

channel_send(request: Request)

Send a message to a channel.

Source code in src/openjarvis/server/routes.py

@router.post("/v1/channels/send")
async def channel_send(request: Request):
    """Send a message to a channel."""
    bridge = getattr(request.app.state, "channel_bridge", None)
    if bridge is None:
        raise HTTPException(status_code=503, detail="Channel bridge not configured")

    body = await request.json()
    channel_name = body.get("channel", "")
    content = body.get("content", "")
    conversation_id = body.get("conversation_id", "")

    if not channel_name or not content:
        raise HTTPException(
            status_code=400,
            detail="'channel' and 'content' are required",
        )

    ok = bridge.send(channel_name, content, conversation_id=conversation_id)
    if not ok:
        raise HTTPException(status_code=502, detail="Failed to send message")
    return {"status": "sent", "channel": channel_name}

channel_status async

channel_status(request: Request)

Return channel bridge connection status.

Source code in src/openjarvis/server/routes.py

@router.get("/v1/channels/status")
async def channel_status(request: Request):
    """Return channel bridge connection status."""
    bridge = getattr(request.app.state, "channel_bridge", None)
    if bridge is None:
        return {"status": "not_configured"}
    return {"status": bridge.status().value}

security_scan async

security_scan()

Run a read-only security environment audit and return findings.

Source code in src/openjarvis/server/routes.py

@router.get("/v1/security/scan")
async def security_scan():
    """Run a read-only security environment audit and return findings."""
    from openjarvis.cli.scan_cmd import PrivacyScanner

    scanner = PrivacyScanner()
    results = scanner.run_all()
    return {
        "has_warnings": any(r.status == "warn" for r in results),
        "has_failures": any(r.status == "fail" for r in results),
        "findings": [
            {
                "name": r.name,
                "status": r.status,
                "message": r.message,
                "platform": r.platform,
            }
            for r in results
        ],
    }